# Patent

## Title
Genotyping tool for improving the prognostic and clinical management of MS patients

## Abstract

The invention relates to methods of evaluating MS severity based on analysis of single nucleotide polymorphisms (SNPs) and to products and kits for use in such methods. The methods include a method of assessing a multiple sclerosis disease severity phenotype in a human subject having multiple sclerosis, by determining the genotype of the subject at one or more positions of single nucleotide polymorphism (SNP) selected from: rs2107538, rs1137933, rs1318, rs2069763, rs423904, rs876493, rs10243024, rs10259085, rs1042173, rs10492503, rs10492972, rs12047808, rs12202350, rs12861247, rs13353224, rs1350666, rs1555322, rs1611115, rs17641078, rs1805009, rs2028455, rs2032893, rs2049306, rs2066713, rs2074897, rs2076530, rs2187668, rs2213584, rs2227139, rs2234978, rs2239802, rs2395182, rs260461, rs28386840, rs3087456, rs3135388, rs3741981, rs3756450, rs3781202, rs3787283, rs3808585, rs4128767, rs4404254, rs4473631, rs4680534, rs6077690, rs6457594, rs6570426, rs659366, rs6917747, rs7208257, rs7528684, rs7577925, rs762550, rs7956189, rs7995215, rs8049651, rs8702, rs9808753 and rs987107, and/or a SNP in linkage disequilibrium with any one of said SNPs.

## Background
CROSS REFERENCE TO RELATED APPLICATIONS
This is the U.S. National Stage of International Application No. PCT/GB2010/000466, filed Mar. 12, 2010, which was published in English under PCT Article 21(2), which in turn claims the benefit of U.S. Provisional Application No. 61/210,124, filed Mar. 12, 2009, which is incorporated herein in its entirety.


FIELD OF THE INVENTION
The invention relates to methods and products, in particular microarrays, for in vitro genotyping of multiple sclerosis (MS) associated genetic variations and to methods for assessment of MS disease severity.
BACKGROUND OF THE INVENTION
Multiple Sclerosis is an autoimmune chronic inflammatory disease, characterized by a progressive demyelination of the central nervous system. While its origin still remains unknown, its multifactorial etiology is well known, consisting of a clear genetic component regulated by several environmental factors.
Clinical evolution of MS is very heterogeneous, and there are different phenotypes present. These range from a very severe form where patients worsen rapidly (known as primary progressive MS), to a more benign form, where the patient practically recovers completely after each disease relapse (known as relapsing remitting MS). Nowadays, disease diagnostics is clinically based, relying on three main points: clinical history, neurologic exploration and use of several techniques (Magnetic Resonance Imaging, analysis of cerebrospinal fluid and evoked potentials).
Currently there is no treatment that will cure MS. MS therapies aim at controlling symptoms and maintaining patient's quality of life. With such treatments, the number of relapses is controlled to a certain level, allowing partial prevention of consequences that may cause such relapses. The primary aims of therapy are returning function after an attack, preventing new attacks, and preventing disability. As with any medical treatment, medications used in the management of MS have several adverse effects. Disease-modifying treatments reduce the progression rate of the disease, but do not stop it. As multiple sclerosis progresses, the symptomatology tends to increase. The disease is associated with a variety of symptoms and functional deficits that result in a range of progressive impairments and disability.
Management of these deficits is therefore very important. Both drug therapy and neurorehabilitation have shown to ease the burden of some symptoms, though neither influences disease progression. As for any patient with neurologic deficits, a multidisciplinary approach is key to limiting and overcoming disability; however, there are particular difficulties in specifying a ‘core team’ because people with MS may need help from almost any health profession or service at some point. Similarly, for each symptom there are different treatment options. Treatments should therefore be individualized depending both on the patient and the physician.
SUMMARY OF THE INVENTION
Aspects of the invention relate to methods of analyzing a patient's genotype, for example through analysis of SNPs, optionally combined with clinical-environmental data, for prognosis and treatment management of MS patients, leading to personalized medicine. Accordingly, in a first aspect the present invention provides a method of assessing a MS disease severity phenotype in a human subject having or suspected of having MS, the method comprising determining the genotype of the subject at one or more positions of single nucleotide polymorphism (SNP) selected from those listed in Table 10 and/or a SNP in linkage disequilibrium with any one of said SNPs. The SNPs may be as disclosed in the NCBI dbSNP build 131, Homo sapiens genome build 37.1 and/or NCBI dbSNP build 129, Homo sapiens build 36.3. The presence of one or more “risk alleles” as identified in Table 10 at one or more of the SNPs indicates that the subject has a higher probability of having a greater severity of MS. In some cases, the method of this and other aspects of the invention comprises determining that the subject does have at least one risk allele at at least one of said SNPs. In other cases, the subject may be determined to be free from said risk alleles at at least one of said SNPs. In some cases, the method of this and other aspects of the invention, the presence of:

    the TT genotype at rs2107538;
    the GG genotype at rs1137933;
    the AA genotype at rs1318;
    the GG genotype at rs2069763;
    the CC genotype at rs423904;
    the AA genotype at rs876493;
    the GG genotype at rs10243024;
    the GG genotype at rs10259085;
    the AA genotype at rs1042173;
    the TT genotype at rs10492503;
    the GG genotype at rs10492972;
    the GG genotype at rs12047808;
    the AA genotype at rs12202350;
    the GG genotype at rs12861247;
    the AA genotype at rs13353224;
    the GG genotype at rs1350666;
    the AA genotype at rs1555322;
    the AA genotype at rs1611115;
    the GG genotype at rs17641078;
    the GG genotype at rs1805009;
    the GG genotype at rs2028455;
    the AA genotype at rs2032893;
    the AA genotype at rs2049306;
    the AA genotype at rs2066713;
    the AA genotype at rs2074897;
    the GG genotype at rs2076530;
    the AA genotype at rs2187668;
    the AA genotype at rs2213584;
    the CC genotype at rs2227139;
    the TT genotype at rs2234978;
    the GG genotype at rs2239802;
    the GG genotype at rs2395182;
    the AA genotype at rs260461;
    the AA genotype at rs28386840;
    the GG genotype at rs3087456;
    the AA genotype at rs3135388;
    the AA genotype at rs3741981;
    the AA genotype at rs3756450;
    the CT genotype at rs3781202;
    the AA genotype at rs3787283;
    the AA genotype at rs3808585;
    the GG genotype at rs4128767;
    the GG genotype at rs4404254;
    the CC genotype at rs4473631;
    the AA genotype at rs4680534;
    the TT genotype at rs6077690;
    the AA genotype at rs6457594;
    the TT genotype at rs6570426;
    the CC genotype at rs659366;
    the GG genotype at rs6917747;
    the AA genotype at rs7208257;
    the GG genotype at rs7528684;
    the AA genotype at rs7577925;
    the AA genotype at rs762550;
    the GG genotype at rs7956189;
    the GG genotype at rs7995215;
    the AA genotype at rs8049651;
    the GG genotype at rs8702;
    the GG genotype at rs9808753; and/or
    the AA genotype at rs987107 is indicative of the subject having, or having a high probability of having, a more severe multiple sclerosis disease phenotype.


In some cases, a more severe multiple sclerosis disease phenotype may be a phenotype selected from: a multiple sclerosis severity score (MSSS) of 2.5 or greater; an increase in size and/or distribution of T2 brain lesions; an increased number of focal lesions in the spinal cord; an increased T2 lesion load in the brain; and the presence of diffuse abnormalities in the spinal cord. Optionally, the method of this and other aspects of the invention may comprise determining the genotype of the subject at 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 35, 40, 45, 50, 55 or more of said SNPs. Optionally, the method of the invention further comprises the measurement of at least one clinical variable, such as a clinical variable is selected from: age of the subject at onset of multiple sclerosis, gender of the subject and type of multiple sclerosis at onset of multiple sclerosis. The method of the invention may, in some cases, comprise determining the genotype of the subject at a specific combination or sub-set of SNPs selected from those listed in Table 10, such as the first 2, first 3, first 4, first 5 or first 6. Accordingly, in some cases, the method of the invention comprises determining the genotype of the subject at: at least rs2107538, rs1137933 and rs1318; at least rs2107538, rs1137933, rs1318, rs2069763, rs423904 and rs876493. In some cases, the method of the invention comprises determining the genotype of the subject at substantially all of the SNPs listed in Table 10. In some cases, the method of the invention comprises determining the genotype of the subject at only the SNPs listed in Table 10 and/or only SNPs in linkage disequilibrium with one or more of the SNPs listed in Table 10.
In certain cases, the method of the invention comprises determining the genotype of the subject at a sub-set of SNPs of those listed in Table 10, which sub-set is indicative of a particular MS disease severity phenotype. Methods for assessing particular MS disease severity phenotypes, such as a multiple sclerosis severity score (MSSS) of 2.5 or greater; an increase in size and/or distribution of T2 brain lesions; an increased number of focal lesions in the spinal cord; an increased T2 lesion load in the brain; and the presence of diffuse abnormalities in the spinal cord, may be combined to yield assessment of multiple specific MS disease severity phenotypes or performed independently.
In particular, the method of the invention may be for assessing multiple sclerosis severity score (MSSS), such as whether or not the subject has an MSSS score of 2.5 or greater, wherein the method comprises determining the genotype of the subject at at least 2 of the following positions of SNP: rs423904, rs876493, rs1137933, rs1318, rs2069763, rs2107538, rs3756450, rs12047808, rs10259085, rs1042173, rs6077690, rs1611115, rs4473631, rs2032893, rs2066713, rs260461, rs3787283, rs6917747, rs2049306, rs12861247, rs4404254, rs4680534, rs17641078, rs2187668, rs7528684, rs7577925, rs1805009, rs3741981, rs12202350, rs28386840, rs2028455, rs10492503, rs8049651, rs13353224, rs1555322, rs10243024 and rs6570426, wherein the presence of one or more of the risk alleles shown in Table 10 at one or more of said SNPs is indicative of having an MSSS score of 2.5 or greater. For example, the method may comprise determining the genotype of the subject at at least the following positions of SNP: rs2107538, rs1137933, rs1318, rs2069763, rs423904 and rs876493. Methods for assessing multiple sclerosis severity score (MSSS) of a subject may advantageously combine genotyping SNPs as specified above with determining at least 1, 2 or 3 clinical variables selected from: age of the subject at onset of multiple sclerosis, gender of the subject and type of multiple sclerosis at onset of multiple sclerosis. Thus, the method of the invention may comprise assessment of MSSS score utilising a model which combines the SNPs and clinical variables shown in Table 3, Table 3B and/or Table 3C, optionally employing the respective coefficient for each SNP and/or clinical variable shown in column “B” of said table or tables
In certain cases, the method of this and other aspects of the invention may be for assessing the probability of increased size and/or distribution of T2 brain lesions in the subject, wherein the method comprises determining the genotype of the subject at at least 2, 3 or 4 of the following positions of SNP: rs2213584, rs2227139, rs2076530 rs876493, rs9808753, rs2074897, rs762550, rs2234978, rs3781202.
In certain cases, the method of this and other aspects of the invention may be for assessing the probability of increased T2 lesion load in the brain, wherein the method comprises determining the genotype of the subject at at least 1, 2, 3 or 4 of the following positions of SNP: rs2107538, rs12861247, rs2074897 and rs7995215, such as determining the genotype of the subject at: rs12861247, rs2074897 and rs7995215.
In certain cases, the method of this and other aspects of the invention may be for assessing an increased number of focal lesions in the spinal cord, wherein the method comprises determining the genotype of the subject at at least 1, 2, 3 or 4 of the following positions of SNP: rs3135388, rs2395182, rs2239802, rs2227139, rs2213584, rs3087456, rs10492972, rs12202350, rs8049651, rs8702 and rs987107, such as determining the genotype of the subject at: rs3135388, rs3087456 and rs2227139.
In certain cases, the method of this and other aspects of the invention may be for assessing the presence of diffuse abnormalities in the spinal cord, wherein the method comprises determining the genotype of the subject at at least 1, 2, 3 or 4 of the following positions of SNP: rs1350666, rs3808585, rs4128767, rs6457594, rs7208257 and rs7956189.
The method in accordance with this and other aspects of the invention may, in some cases, be carried out in vitro using a nucleic acid-containing sample that has been obtained from the subject. In some cases the genotype of the subject at said one or more positions of SNP may be determined indirectly by determining the genotype of the subject at a position of SNP that is in linkage disequilibrium with said one or more positions of SNP, while in some cases the genotype of the subject at said one or more positions of SNP may be determined directly by identifying one or both alleles at said one or more positions of SNP.
In accordance with the method of this and other aspects of the invention, determining the genotype of the subject at said one or more positions of SNP may comprise:

    
    
        (i) extracting and/or amplifying DNA from a sample that has been obtained from the subject;
        (ii) contacting the DNA with an array comprising a plurality of probes suitable for determining the identity of at least one allele at a position of SNP as listed in Table 10, for example using one or more probes selected from those listed in Table 7. In some cases, the array may be a DNA array, a DNA microarray or a bead array.
    
    


In accordance with the method of this and other aspects of the invention the method may comprise amplifying DNA from a sample that has been obtained from the subject, wherein said amplifying comprises contacting the DNA with at least one forward primer as listed in Table 8 and at least one reverse primer as listed in Table 9.
In a further aspect, the present invention provides an array of probes for use in a method according to the invention, wherein the array comprises:

    
    
        at least 5, 10, 15, 20, 50 or more nucleic acid probes suitable for determining the identity of at least one allele at a position of SNP as listed in Table 10; and
        a solid support on which said probes are immobilised,

wherein said probes comprise at least 50%, at least 60%, at least 70%, at least 80%, at least 90%, at least 95%, at least 99% of the total number of nucleic acid probes in the array, or essentially all of the nucleic acid probes in the array. The probes suitable for determining the identity of at least one allele at a position of SNP may be selected from the probes listed in Table 7.

    
    


In a further aspect, the present invention provides methods of evaluating disease severity in a patient having multiple sclerosis, including obtaining a DNA sample from the patient, and determining the presence or absence of two or more single nucleotide polymorphisms (SNPs) associated with severity of the disease, wherein the presence of two or more SNPs associated with severity of the disease indicates a likelihood of increased disease severity. In some embodiments the two or more SNPs associated with the disease comprise SNPs in PNMT, IL1R, CCL5, IL2, PITPNC1 or NOS2A. In certain embodiments the two or more SNPs are selected from those listed in Table 10. In certain embodiments, the two or more SNPs associated with the disease are selected from the group consisting of 2073 Intron2 C/T (rs423904), rs876493, rs1137933, rs1318, rs2069763 and rs2107538. In certain embodiments the two or more SNPs associated with the disease are selected from the group consisting of rs3135388, rs2395182, rs2239802, rs2227139, rs2213584, rs3087456 and rs2107538.
The presence or absence of two or more SNPs associated with severity of the disease can be determined by any method known in the art such as a gene chip, bead array, RFLP analysis, and/or sequencing. In some embodiments the two or more SNPs associated with the disease comprise SNPs in PNMT, IL1R, CCL5, IL2, PITPNC1 or NOS2A. In certain embodiments the two or more SNPs associated with the disease are selected from the group consisting of, rs876493, rs1137933, rs1318, rs2069763 and rs2107538. In certain embodiments the two or more SNPs associated with the disease are selected from the group consisting of rs1137933, rs1318, rs2069764 and rs2107538.
Aspects of the invention relate to SNPs associated with increased T2 lesion load in the brain. In some embodiments the SNP is associated with an increased number of focal spinal cord abnormalities. In some embodiments the two or more SNPs are in linkage disequilibrium. In certain embodiments the two or more SNPs are in linkage disequilibrium with SNPs selected from the group consisting of rs2239802, rs2213584, rs3135388, 2213584 rs2227139, rs1137933, rs1318, rs2069764 and rs2107538.
In some embodiments methods described herein further include the measurement of one or more clinical variables such as age of onset, gender, and/or type of onset of disease. In some embodiments disease severity is based on an MS severity scale such as the Multiple Sclerosis Severity Score (MSSS) test, the Kurtzke Expanded Disability Status Scale (EDSS), or the Multiple Sclerosis Functional Composite (MSFC) measure.
In some embodiments the presence or absence of at least 6 SNPs is determined. In certain embodiments the two or more SNPs are selected from the group consisting of 2073 Intron2 C/T (rs423904), rs876493, rs1137933, rs1318, rs2069763, rs2107538, rs3135388, rs2395182, rs2239802, rs2227139, rs2213584 and rs3087456. In certain embodiments at least one of the SNPs is in linkage disequilibrium with a SNP selected from the group consisting of 2073 Intron2 C/T (rs423904), rs876493, rs1137933, rs1318, rs2069763, rs2107538, rs3135388, rs2395182, rs2239802, rs2227139, rs2213584 and rs3087456. In some embodiments methods described herein include use of one or more probe sets listed in Table 7. In some embodiments methods described herein include at least one forward primer from Table 8 and one reverse primer from Table 9.
Aspects of the invention relate to methods of designing a treatment regimen for a patient having multiple sclerosis, including obtaining a DNA sample from the patient, determining the presence or absence of two or more single nucleotide polymorphisms (SNPs) associated with severity of the disease, wherein the presence of two or more SNPs associated with severity of the disease indicates a likelihood of increased disease severity, and designing the treatment regimen based on the presence or absence of the SNPs associated with the disease. In some embodiments the treatment regimen comprises early or elevated doses of glatiramer acetate, vitamin D, interferon beta-1a or -1b, natalizumab, mitoxantrone, and/or corticosteroids.
Aspects of the invention relate to methods of treating a patient having a prognosis of increased disease severity, comprising early or elevated doses of glatiramer acetate, vitamin D, interferon beta-1 a or -1 b, natalizumab, mitoxantrone, and/or corticosteroids.
Aspects of the invention relate to methods of identifying SNPs associated with severity of symptoms in multiple sclerosis, including obtaining a DNA sample from a patient having multiple sclerosis, identifying SNPs in the DNA, wherein the SNPs comprise two or more of the SNPs listed in Table 1, performing an MRI on the patient to determine spatial distribution of T2 brain lesions, T2 lesion load, presence of diffuse abnormalities and/or number of spinal cord lesions, comparing identified SNPs with the spatial distribution of T2 brain lesions, T2 lesion load, presence of diffuse abnormalities and/or number of spinal cord lesions, and identifying the SNPs that correlate with spatial distribution of T2 brain lesion, T2 lesion load, presence of diffuse abnormalities and/or number of spinal cord lesions, wherein the SNPs that correlate with spatial distribution of T2 brain lesions, T2 lesion load, presence of diffuse abnormalities and/or number of spinal cord lesions, are SNPs associated with severity of symptoms in multiple sclerosis. In some embodiments at least one of the SNPs is in linkage disequilibrium with a SNP listed in Table 1. In some embodiments identifying SNPs associated with severity of symptoms in multiple sclerosis further comprises consideration of clinical data.
Aspects of the invention relate to methods of evaluating disease severity, as measured using the Multiple Sclerosis Severity Score (MSSS) test, the Kurtzke Expanded Disability Status Scale (EDSS), and/or the Multiple Sclerosis Functional Composite measure (MSFC), in a patient having multiple sclerosis, the method including obtaining a DNA sample from the patient, and determining the presence or absence of two or more single nucleotide polymorphisms (SNPs), wherein said SNPs comprise two or more of the SNPs listed in Table 1, and wherein the presence of said two or more SNPs indicates a likelihood of increased disease severity. In some embodiments evaluating disease severity further comprises consideration of clinical data. In some embodiments at least one of the SNPs is in linkage disequilibrium with a SNP listed in Table 1.
Aspects of the invention relate to methods of evaluating the severity of spinal cord lesions in a patient having multiple sclerosis, the method including obtaining a DNA sample from the patient, and determining the presence or absence of two or more single nucleotide polymorphisms (SNPs) associated with spinal cord lesions, wherein the presence of two or more SNPs associated with spinal cord lesions indicates a likelihood of increased disease severity. In some embodiments the two or more SNPs are selected from the group consisting of rs3135388, rs2395182, rs2239802, rs2227139, rs2213584 and rs3087456. In certain embodiments one of the SNPs is rs3135388. In some embodiments the two or more SNPs are selected from the group consisting of 2073 Intron2 C/T (rs423904), rs876493, rs1137933, rs1318, rs2069763, rs2107538, rs3135388, rs2395182, rs2239802, rs2227139, rs2213584 and rs3087456. In certain embodiments at least one of the SNPs is in linkage disequilibrium with a SNP selected from the group consisting of 2073 Intron2 C/T (rs423904), rs876493, rs1137933, rs1318, rs2069763, rs2107538, rs3135388, rs2395182, rs2239802, rs2227139, rs2213584 and rs3087456.
Aspects of the invention relate to method of prognosing the likelihood of T2 lesions and/or T2 lesion load in a patient having multiple sclerosis, the method including obtaining a DNA sample from the patient, and determining the presence or absence of SNP rs2107538, wherein the presence of SNP rs2107538 indicates a likelihood of T2 lesions and/or T2 lesion load in the patient.
Aspects of the invention relate to methods where determining the presence or absence of SNPs includes (a) providing, for each genetic variation to be genotyped, at least 2 oligonucleotide probe pairs, wherein: (i) one pair consists of probes 1 and 2, and the other pair consists of probes 3 and 4; (ii) one probe in each pair is capable of hybridising to genetic variation A and the other probe in each pair is capable of hybridising to genetic variation B; (iii) each probe is provided in replicates; and (iv) the probe replicates are each coupled to a solid support; (c) amplifying and detectably labelling the target DNA; (d) contacting the target DNA with the probes under conditions which allow hybridisation to occur, thereby forming detectably labeled nucleic acid-probe hybridisation complexes, (e) determining the intensity of detectable label at each probe replica position, thereby obtaining a raw intensity value; (f) optionally amending the raw intensity value to take account of background noise, thereby obtaining a clean intensity value for each replica; and (g) applying a suitable algorithm to the intensity data from (e) or (f), thereby determining the genotype with respect to each genetic variation, wherein application of the algorithm comprises calculating an average intensity value from the intensity values for each of the replicas of each probe and wherein the algorithm uses three Fisher linear functions that characterize each of the three possible genotypes AA, AB or BB for the genetic variation.
Aspects of the invention relate to kits for evaluating severity of disease in a subject having multiple sclerosis, the kit including: (i) at least one set of probes listed in table 7; optionally (ii) instruction for genotyping analysis as described in claim H1; and optionally (iii) instructions for determining the severity MS phenotype from the outcomes. Aspects of the invention relate to PCR amplification kits comprising at least one pair of PCR primers from tables 8 and 9, a thermostable polymerase, dNTPs, a suitable buffer, and optionally instructions for use.
Further aspects of the invention relate to a computational method of deriving a probability function for use in determining an MS severity phenotype in a subject, including applying a probability function such as stepwise multiple logistic regression analysis to outcome data and phenotype data obtained from a suitable study population of individuals, wherein each individual is of known clinically determined phenotype with respect to the Multiple Sclerosis severity phenotype, thereby deriving a probability function which produces a statistically significant separation between individuals of different phenotype in the population; wherein: (i) the phenotype data comprises the known clinically determined phenotype of each individual; (ii) the outcomes data for each individual comprises the genotype of the individual at each SNP in a set of SNPs; and wherein: (a) the probability function is for distinguishing or differentially diagnosing MS severity phenotype, and the set of SNPs is selected from the set of MS severity phenotype discriminating SNPs in Table 3; (b) the probability function is for prognosing MS disease severity phenotype and the set of SNPs is selected from the set of MS disease severity discriminating SNPs in Table 3; and/or (c) the probability function is for prognosing MS disease severity phenotype and the set of SNPs is selected from the set of MS disease severity discriminating SNPs in Table 10.
The present invention includes the combination of the aspects and preferred features described except where such a combination is clearly impermissible or is stated to be expressly avoided. These and further aspects and embodiments of the invention are described in further detail below and with reference to the accompanying examples and figures.



BRIEF DESCRIPTION OF THE DRAWINGS
FIG. 1 is a graph showing a ROC (receiver operating characteristic) curve obtained for the model MSSS<2.5 versus≧2.5, showing the relationship between sensitivity (y-axis) and percentage (x-axis).
FIG. 2 depicts MRI data maps showing mean lesion frequency map of the patient sample (n=208). Lesion frequency across the patient sample is shown for every voxel on axial and sagittal slices. The colour bar indicates lesion frequency; voxels with a lesion frequency<1% are not shown; peak frequency was 32%.
FIG. 3 depicts MRI data maps showing clusterwise (t=2) associations of lesion presence with genotype, on a background of the common brain image. The cluster colour bar indicates clusterwise p-value, with the range indicated by the colour bar; only clusters with p<0.05 are shown. A: rs2213584 (HLA-DRA gene); B: rs2227139 (HLA-DRA gene); C: rs2076530 (BTNL2 gene); D: rs876493 (PNMT gene).
FIG. 4 is a graph showing the mean number of focal spinal cord lesions in patients who carry HLA-DRB1*1501 (measured as presence of A-allele of rs3135388). Difference between carriers and non-carriers p<0.001, Maim Whitney U test. Error bars show 95% confidence interval of mean.
FIG. 5 is a graph showing a ROC (receiver operating characteristic) curve obtained for the model MSSS<2.5 versus≧2.5, showing the relationship between sensitivity (y-axis) and percentage (x-axis), as further described in Table 3B.
FIG. 6 is a graph showing a ROC (receiver operating characteristic) curve obtained for the model MSSS<2.5 versus≧2.5, showing the relationship between sensitivity (y-axis) and percentage (x-axis), as further described in Table 3B.
FIG. 7 is a graph showing a ROC (receiver operating characteristic) curve obtained for the model MSSS<2.5 versus≧2.5, showing the relationship between sensitivity (y-axis) and percentage (x-axis), as further described in Table 3C.



The Sequence Listing is submitted as an ASCII text file in the form of the file named Sequence_Listing.txt, which was created on Sep. 9, 2011, and is 173,767 bytes, which is incorporated by reference herein.
DETAILED DESCRIPTION
Multiple Sclerosis (MS) is a multifocal inflammatory demyelinating disease of the central nervous system (CNS), characterized by inflammation, demyelination and axonal loss resulting in a highly variable clinical presentation. Most patients suffer from relapsing-remitting (RR) MS, experiencing waves of inflammation leading to alternating periods of disability (relapses) and stable disease (remissions). The RRMS phase usually leads to progressive and irreversible disability (the secondary progressive [SP] phase). For a subset of patients, the disease is progressive from onset (primary progressive [PP] MS). Treatment decisions are based on the occurrence of relapses, and the development of white matter lesions visible on MRI. Brain lesion volume and distribution however are highly variable among MS patients, and correlate only moderately with disability. As treatment guidelines would strongly benefit from a better understanding of this variability, the present invention is drawn to methods of genetic screening and predicting severity of disease using genetic information that correlates with increased numbers of lesions in the brain, optic nerve, or spinal cord.
Aspects of the invention relate at least in part to the surprising discovery that MS severity can be associated (e.g., statistically) with one or more genetic markers. As used herein, a genetic marker refers to a DNA sequence that has a known location on a chromosome. Several non-limiting examples of classes of genetic markers include RFLP (restriction fragment length polymorphism), AFLP (amplified fragment length polymorphism), RAPD (random amplification of polymorphic DNA), VNTR (variable number tandem repeat), microsatellite polymorphism, SNP (single nucleotide polymorphism), STR (short tandem repeat), and SFP (single feature polymorphism).
In some embodiments, genetic markers associated with the invention are SNPs. As used herein a SNP or “single nucleotide polymorphism” refers to a specific site in the genome where there is a difference in DNA base between individuals. In some embodiments the SNP is located in a coding region of a gene. In other embodiments the SNP is located in a noncoding region of a gene. In still other embodiments the SNP is located in an intergenic region. It should be appreciated that SNPs exhibit variability in different populations. In some embodiments, a SNP associated with the invention may occur at higher frequencies in some ethnic populations than in others. In some embodiments, SNPs associated with the invention are SNPs that are linked to MS. In certain embodiments a SNP associated with the invention is a SNP associated with a gene that is linked to MS. A SNP that is linked to MS may be identified experimentally. In other embodiments a SNP that is linked to MS may be identified through accessing a database containing information regarding SNPs. Several non-limiting examples of databases from which information on SNPs or genes that are associated with human disease can be retrieved include: NCBI resources, The SNP Consortium LTD, NCBI dbSNP database, International HapMap Project, 1000 Genomes Project, Glovar Variation Browser, SNPStats, PharmGKB, GEN-SniP, and SNPedia. In some embodiments, SNPs associated with the invention comprise two or more of the SNPs listed in Table 1 and/or Table 10. In some embodiments 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30 or more than 30 SNPs are evaluated in a patient sample. In some embodiments, multiple SNPs are evaluated simultaneously while in other embodiments SNPS are evaluated separately.
SNPs are identified herein using the rs identifier numbers in accordance with the NCBI dbSNP database, which is publically available at: http://www.ncbi.nlm.nih.gov/projects/SNP/. As used herein, rs numbers refer to the dbSNP build 129, Homo sapiens build 36.3 available from 14 Apr. 2008 and/or dbSNP build 131, Homo sapiens build 37.1 available from 2 Feb. 2010. Except where indicated otherwise, the rs identifiers are identical for dbSNP build 129, Homo sapiens build 36.3 and dbSNP build 131, Homo sapiens build 37.1.
In some embodiments, SNPs in linkage disequilibrium with the SNPs associated with the invention are useful for obtaining similar results. As used herein, linkage disequilibrium refers to the non-random association of SNPs at two or more loci. Techniques for the measurement of linkage disequilibrium are known in the art. As two SNPs are in linkage disequilibrium if they are inherited together, the information they provide is correlated to a certain extent. SNPs in linkage disequilibrium with the SNPs included in the models can be obtained from databases such as HapMap or other related databases, from experimental setups run in laboratories or from computer-aided in-silico experiments. Determining the genotype of a subject at a position of SNP as specified herein, e.g. as specified by NCBI dbSNP rs identifier, may comprise directly genotyping, e.g. by determining the identity of the nucleotide of each allele at the locus of SNP, and/or indirectly genotyping, e.g. by determining the identity of each allele at one or more loci that are in linkage disequilibrium with the SNP in question and which allow one to infer the identity of each allele at the locus of SNP in question with a substantial degree of confidence. In some cases, indirect genotyping may comprise determining the identity of each allele at one or more loci that are in sufficiently high linkage disequilibrium with the SNP in question so as to allow one to infer the identity of each allele at the locus of SNP in question with a probability of at least 90%, at least 95% or at least 99% certainty.
As used herein MS or multiple sclerosis refers to a progressive neurodegenerative disease involving demyelination of nerve cells. Several non-limiting classifications of MS include: relapsing-remitting (RRMS) (typically characterized by partial or total recovery after attacks (also called exacerbations, relapses, or flares)), secondary progressive MS (SPMS) (generally characterized by fewer relapses, with an increase in disability and symptoms), and primary progressive MS (PPMS) (generally characterized by progression of symptoms and disability without remission).
Some non-limiting examples of symptoms of MS include: fatigue (also referred to as MS lassitude), muscle fatigue, paresthesias, difficulty in walking and/or balance problems, abnormal sensations such as numbness, prickling, or “pins and needles”, pain, bladder dysfunction, bowel dysfunction, changes in cognitive function (including problems with memory, attention, concentration, judgment, and problem-solving), dizziness and vertigo, emotional problems (e.g., depression), sexual dysfunction, and vision problems. In some embodiments, symptoms of MS can include partial or complete paralysis (such as blurred or double vision, red-green color distortion, or blindness in one eye), headache, hearing loss, itching, seizures, spasticity, speech and swallowing disorders, and tremors. In some embodiments, clinical symptoms of MS can include increased CD4:CD8 cell ratio compared to normal, decreased number of CD14+ cells compared to normal, increased expression of HLA-DR on CD14+ cells compared to normal CD14+ cells, increased levels of activated monocytes or macrophages compared to normal, the presence of proliferating macrophages, and decreased serum IgG and/or IgM compared to normal, where “normal” as used in this context refers to a subject who does not have MS.
Previous studies have explored patterns of spatial lesion distribution in MS patients. Without wishing to be bound by any theory, one potential factor underlying differences in lesion burden and spatial lesion distribution among MS patients may be found in pathological and immunological heterogeneity: studies on spatial lesion distribution throughout the brain demonstrated differences in lesion distribution across disease types and across lesion types. These findings of distinct lesion distributions across patient subgroups and lesion types suggest that different subtypes of pathology exist in MS based at least in part on different immunological mechanisms. For example, periventricular predilection of MS lesions may be caused at least in part by differences in the vasculature compared to other regions, making this location vulnerable to pathological changes. Without wishing to be bound by any theory, enhanced lesions in peripheral as opposed to central brain regions may be caused, at least in part, by central lesions developing from progressive gliosis and peripheral lesions being more inflammatory. As lesions in different locations may have different immunological backgrounds, they may warrant different treatment mechanisms. Results described herein suggest that differences in immunological backgrounds of lesion formation among MS patients may be driven by genetic predisposition.
Aspects of the invention relate to a large-scale study investigating the genetic influences on different phenotypes of MS (disease severity, subtype, MRI characteristics, response to treatment). Described herein is an investigation into the correlation between genetic background and spatial lesion distribution in a large cohort of MS patients using a variety of SNPs.
Aspects of the invention relate to evaluating the severity of MS in a patient. One symptom associated with MS is the presence of demyelination (lesions or plaques) in the brain and/or spinal cord of a patient. It should be appreciated that regions of demyelination may be detected through any means known to one of ordinary skill in the art. In some embodiments, lesions are detected through MRI. In some embodiments treatment decisions regarding a patient with MS, are based on the occurrence of relapses and the development of white matter lesions visible on MRI. Brain lesion volume and distribution, however, are highly variable among MS patients and correlate only moderately with disability. Treatment guidelines would benefit from a better understanding of this variability. Differences in genetic background may lead to different lesion distribution, which in turn may lead to a different clinical expression of the disease. Thus, the correlations revealed herein, between the presence of specific genetic markers and the presence of lesions offer important applications for screening of patients who have or are at risk of MS, diagnostic and prognostics for MS patients, as well as development of appropriate therapeutic approaches. As used herein, the term disease severity refers to the evaluation of a patient's disability using the tests listed above or other similar tests known in the art. An assessment of disease severity in some embodiments includes determining rapidity of development of disability, disease duration, rate of progression or relapse of symptoms, and symptoms such as changes in sensation, fatigue, pain, muscle weakness and/or spasm, problems in speech, visual problems, difficulty in moving, difficulties with coordination and balance, bladder and bowel difficulties and cognitive impairment.
Several methods have been established for assessing the severity of MS based on analysis of clinical factors such as those in Table 2, below. Non-limiting examples of tests used to assess the severity of MS include the Kurtzke Expanded Disability Status Scale (EDSS), the Multiple Sclerosis Functional Composite measure (MSFC), and the Multiple Sclerosis Severity Score (MSSS). The MSSS test relates scores on the Expanded Disability Status Scale (EDSS) to the distribution of disability in patients with comparable disease durations. Effectively the MSSS assigns to each EDSS its median decile score within this distribution. For example, an MSSS of 5.0 indicates the disease is progressing at the median rate. A patient whose MSSS is 9.0 is a fast progressor, progressing faster than 90% of patients. A patient whose MSSS is 1.0 is a slow progressor, progressing faster than just 10% of patients. In some embodiments, based on the MSSS test a patient may be assigned a median docile score of 1, 2, 3, 4, 5, 6, 7, 8, 9, or 9.5, including any intermediate values. In some embodiments, based on a test such as the MSSS test, MS patients are allocated into severe and benign subgroups. In some embodiments, MS patients are classified into different categories of severity based on a test such as MSSS. In some embodiments MS patient may be classified as relapsing-remitting (RRMS), secondary progressive MS (SPMS), and primary progressive MS (PPMS).
The invention in one aspect presents a model for assessing the strength of the disability, or the severity of the form of MS, according to the MSSS scale, using SNP analysis, thus allowing differential treatment management for a given patient. Results described herein generate a model from the analysis of 605 MS patients and 700 MS patients (see Example section). In some aspects, the invention evaluates differences between patients that have an MSSS score of less than 2.5 versus patients that have an MSSS score 2.5 or greater. Aspects of the invention relate to using genetic markers that are correlated to certain degrees of MS severity as predictive of MS severity and as indicators of recommended therapeutic approaches. In some embodiments, methods described herein relate to screening a patient for one or more risk factors associated with MS. In some embodiments the presence of two or more of the SNPs described herein indicate a more severe form of MS.
The invention in one aspect relates to correlating specific SNPs or combinations of SNPs with the presence and/or severity of lesions in the brain and/or spinal cord. The SNPs or combinations of SNPs that are correlated to the presence and/or severity of lesions in the brain and/or spinal cord can be used as predictive, diagnostic or prognostic indicators of the presence and/or severity of lesions in the brain and/or spinal cord. The detection of such SNPs, indicating the presence of lesions in the brain and/or spinal cord may in some embodiments be used as an indicator of the severity of MS.
Aspects of the invention relate to determining the presence of SNPs through obtaining a patient DNA sample and evaluating the patient sample for the presence of two or more SNPs. It should be appreciated that a patient DNA sample can be extracted, and a SNP can be detected in the sample, through any means known to one of ordinary skill in art. Some non-limiting examples of known techniques include detection via restriction fragment length polymorphism (RFLP) analysis, planar microarrays, bead arrays, sequencing, single strand conformation polymorphism analysis (SSCP), chemical cleavage of mismatch (CCM), and denaturing high performance liquid chromatography (DHPLC).
In some embodiments, a SNP is detected through PCR amplification and sequencing of the DNA region comprising the SNP. In some embodiments SNPs are detected using microarrays. Microarrays for detection of genetic polymorphisms, changes or mutations (in general, genetic variations) such as a SNP in a DNA sequence, comprise a solid surface, typically glass, on which a high number of genetic sequences are deposited (the probes), complementary to the genetic variations to be studied. Using standard robotic printers to apply probes to the array a high density of individual probe features can be obtained, for example probe densities of 600 features per cm2 or more can be typically achieved. The positioning of probes on an array is precisely controlled by the printing device (robot, inkjet printer, photolithographic mask etc) and probes are aligned in a grid. The organisation of probes on the array facilitates the subsequent identification of specific probe-target interactions. Additionally it is common, but not necessary, to divide the array features into smaller sectors, also grid-shaped, that are subsequently referred to as sub-arrays. Sub-arrays typically comprise 32 individual probe features although lower (e.g. 16) or higher (e.g. 64 or more) features can comprise each subarray.
In some embodiments, detection of genetic variation such as the presence of a SNP involves hybridization to sequences which specifically recognize the normal and the mutant allele in a fragment of DNA derived from a test sample. Typically, the fragment has been amplified, e.g. by using the polymerase chain reaction (PCR), and labelled e.g. with a fluorescent molecule. A laser can be used to detect bound labelled fragments on the chip and thus an individual who is homozygous for the normal allele can be specifically distinguished from heterozygous individuals (in the case of autosomal dominant conditions then these individuals are referred to as carriers) or those who are homozygous for the mutant allele. In some embodiments, the amplification reaction and/or extension reaction is carried out on the microarray or bead itself.
In some embodiments, methods described herein may involve hybridization. For differential hybridization based methods there are a number of methods for analysing hybridization data for genotyping:
Increase in hybridization level: The hybridization levels of probes complementary to the normal and mutant alleles are compared.
Decrease in hybridization level: Differences in the sequence between a control sample and a test sample can be identified by a decrease in the hybridization level of the totally complementary oligonucleotides with a reference sequence. A loss approximating 100% is produced in mutant homozygous individuals while there is only an approximately 50% loss in heterozygotes. In Microarrays for examining all the bases of a sequence of “n” nucleotides (“oligonucleotide”) of length in both strands, a minimum of “2n” oligonucleotides that overlap with the previous oligonucleotide in all the sequence except in the nucleotide are necessary. Typically the size of the oligonucleotides is about 25 nucleotides. However it should be appreciated that the oligonucleotide can be any length that is appropriate as would be understood by one of ordinary skill in the art. The increased number of oligonucleotides used to reconstruct the sequence reduces errors derived from fluctuation of the hybridization level. However, the exact change in sequence cannot be identified with this method; in some embodiments this method is combined with sequencing to identify the mutation.
Where amplification or extension is carried out on the microarray or bead itself, three methods are presented by way of example:
In the Minisequencing strategy, a mutation specific primer is fixed on the slide and after an extension reaction with fluorescent dideoxynucleotides, the image of the Microarray is captured with a scanner.
In the Primer extension strategy, two oligonucleotides are designed for detection of the wild type and mutant sequences respectively. The extension reaction is subsequently carried out with one fluorescently labelled nucleotide and the remaining nucleotides unlabelled. In either case the starting material can be either an RNA sample or a DNA product amplified by PCR.
In the Tag arrays strategy, an extension reaction is carried out in solution with specific primers, which carry a determined 5′ sequence or “tag”. The use of Microarrays with oligonucleotides complementary to these sequences or “tags” allows the capture of the resultant products of the extension. Examples of this include the high density Microarray “Flex-flex” (Affymetrix).
For cost-effective genetic diagnosis, in some embodiments, the need for amplification and purification reactions presents disadvantages for the on-chip or on-bead extension/amplification methods compared to the differential hybridization based methods. However the techniques may still be used to detect and diagnose conditions according to the invention.
Typically, Microarray or bead analysis is carried out using differential hybridization techniques. However, differential hybridization does not produce as high specificity or sensitivity as methods associated with amplification on glass slides. For this reason the development of mathematical algorithms, which increase specificity and sensitivity of the hybridization methodology, are needed (Cutler D J, Zwick M E, Carrasquillo M N, Yohn C T, Tobi K P, Kashuk C, Mathews D J, Shah N, Eichler E E, Warrington J A, Chakravarti A. Genome Research; 11:1913-1925 (2001). Methods of genotyping using microarrays and beads are known in the art.
Some non-limiting examples of genotyping and data analysis can be found in co-pending patent application U.S. Ser. No. 11/813,646 (WO 2006/075254), which is hereby incorporated by reference. In some embodiments the genotypes are determined as follows: The signal from the probes which detect the different genetic variations is determined with a scanner. The scanner software executes a function to subtract the local background noise from the absolute signal intensity value obtained for each probe. Next, the replicates for each of the 4 probes that are used to characterize each genetic variation are grouped. The average intensity value for each of 4 probes is calculated using the average collated from the replicates in order to identify abnormal values (outliers) that can be excluded from further consideration. Once the average intensity value for each of the probes is known then two ratios are calculated (ratio 1 and ratio 2):



  
    
      Ratio
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      1
    
    =
    
      
        Average
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        for
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        probe
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        1
      
      
        
          Average
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          intensity
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          for
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          probe
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          1
        
        +
        
          Average
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          intensity
          ⁢
          
            
          
          ⁢
          for
          ⁢
          
            
          
          ⁢
          probe
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          ⁢
          2
        
      
    
  




  
    
      Ratio
      ⁢
      
        
      
      ⁢
      2
    
    =
    
      
        Average
        ⁢
        
          
        
        ⁢
        intensity
        ⁢
        
          
        
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        for
        ⁢
        
          
        
        ⁢
        probe
        ⁢
        
          
        
        ⁢
        3
      
      
        
          Average
          ⁢
          
            
          
          ⁢
          intensity
          ⁢
          
            
          
          ⁢
          for
          ⁢
          
            
          
          ⁢
          probe
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          3
        
        +
        
          Average
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          intensity
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          probe
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wherein probe 1 detects (is capable of specifically hybridising to) genetic variation A (e.g. a normal allele), probe 2 detects (is capable of specifically hybridising to) genetic variation B (e.g. a mutant allele), probe 3 detects (is capable of specifically hybridising to) genetic variation A (e.g. a normal allele) and probe 4 detects (is capable of specifically hybridising to) genetic variation B (e.g. a mutant allele).

These ratios are substituted in three Fisher linear functions which characterize each one of the three possible genotypes:
















AA
Function 1



AB
Function 2



BB
Function 3










The function which presents the highest absolute value determines the genotype of the patient.
The Fisher linear functions are obtained by analyzing 3 subjects for each of the three possible genotypes of the genetic variation (AA, AB, BB). With the results, ratios 1 and 2 are calculated for the SNPs analyzed and for the 3 subjects. These ratios are classification variables for the three groups to create the linear functions, with which the discriminatory capacity of the two pairs of designed probes is evaluated. If the discriminatory capacity is not 100%, the probes are redesigned. New subjects characterized for each of the three genotypes make up new ratios 1 and 2 to perfect the linear functions and in short, to improve the discriminatory capacity of the algorithm based on these three functions.
When using a fluorescent laser, to obtain reliable results it is preferable that ratios 1 and 2 are within the range of the ratios used to build the groups.
Again when a fluorescent scanner is used in the experiment, for a complete hybridization to be considered reliable preferably the ratio of probe fluorescence intensity to background noise of all the beads DNA array probes is above 15. Likewise, the average of all the ratios is preferably above 0.6 and the negative control is preferably less than or equal to 3 times the background noise.
In summary, four probes are presented in the hybridization analysis for detection of each mutation. Two of the probes detect one genetic variation (A) and the other two the other genetic variation (B). The examined base is located in the central position of the probes.
A subject homozygous for the genetic variation A will not show genetic variation B. Consequently, the probes which detect genetic variation B will show a hybridization signal significantly less than that shown by variation A and vice versa. In this case the ratios 1 and 2 will show 1 and the subjects will be assigned as homozygous AA by the software analysis.
On the other hand, a heterozygous subject for the determined genetic variation shows both the genetic variations. Therefore, the probes which detect them show an equivalent hybridization signal. The ratios 1 and 2 will show 0.5 and the subject will be assigned as heterozygous AB the software analysis as described.
In one aspect of the invention, DNA polymorphisms are selected based on their association with the etiology of MS, such as those shown in Table 1 below:










TABLE 1







Gene
RefSNP accession I.D.









ACCN1
rs28936



ACE
rs4343



ADAMTS14
rs4747075



ADAMTS14
rs7081273



ADAMTS14
rs4746060



ALK
rs7577363



ANKRD15
rs10975200



Apo I/Fas
rs1800682



Apo I/Fas
rs3781202



Apo I/Fas
rs2234978



BDNF
rs6265



BTNL2
rs2076530



C10orf27
rs2254174



C10orf27
rs12221473



C10orf27
rs12221474



C10orf27
rs2791196



CACNG4
rs4790896



CBLB
rs12487066



CCL11
rs17735961



CCL14
rs854682



CCL17
rs223828



CCL2
rs1024610



CCL22
rs4359426



CCL23
rs1003645



CCL23
rs854655



CCL5
rs2107538



CCL5
rs2280788



CCR5
rs333



CD14
rs2569190



CD226
rs763361



CD24
rs8734



CD58
rs12044852



CNTF
rs1800169



CRYAB
rs14133



CRYAB
rs762550



CRYAB
rs2234702



CTLA4
rs231775



CTLA4
rs5742909



CTSS
rs1136774



CTSS
rs3754212



CXLCL10
rs3921



CXLCL10
rs8878



DBC1
rs10984447



DRB1
rs3135388



EBF
rs1368297



EVI5
rs10735781



EVI5
rs6680578



FAM69A
rs11164838



FAM69A
rs7536563



GABBRA1
rs1805057



GLO1
rs2736654



GR
rs6189



GR
rs6190



HELZ
rs2363846



HFE
rs1800562



HLA
rs2395166



HLA
rs2213584



HLA
rs2227139



HLA
rs3135388



HLA
rs9268458



HLA
rs6457594



HLA-DRA
rs2395182



HLA-DRA
rs2239802



ICOS
rs4404254



ICOS
rs10932036



ICOS
rs4675379



IFI30
rs11554159



IFNAR
rs1012334



IFNAR1
rs2257167



IFNG
rs1861494



IFNG
rs2069727



IFNG
rs2430561



IFNG
rs3181034



IFNG
rs7954499



IFNGR2
rs9808753



IKBL
rs3130062



IL10
rs1800871



IL10
rs1800872



IL10
rs1800896



IL1A
rs1800587



IL1B
rs1799916



IL1B
rs1143627



IL1B
rs1143634



IL1RN
2073 Intron2 C/T (rs423904)



IL1RN
rs419598



IL2
rs2069763



IL2
rs2069762



IL23R
rs7517847



IL23R
rs11209026



IL2RA
rs12722489



IL2RA
rs2104282



IL4R
rs1801275



IL5RA
rs2290608



IL7R
rs11567685



IL7R
rs7718919



IL7R
rs11567686



IL7R
rs6897932



IL7R
rs3194051



IL7R
rs987106



IL7R
rs987107



IL7R
rs11567685



IL7R
rs7718919



IL7R
rs11567686



IL8
rs4073



IRF1
rs2070721



IRF5
rs3807306



IRF5
rs4728142



IRF5
5 bp insertion-deletion polymorphism located




in the promoter and first intron of the IRF5




gene



IRF-5
rs10954213



IRF-5
rs2004640



IRF-5
rs2280714



IRF-5
rs3757385



ITGA4
rs1449263



KCNH7
rs2068330



KIAA0350
rs6498169



KLC1
rs8702



KLRB1
rs4763655



LAG3
rs1922452



LAG3
rs870849



LAG3
rs951818



LAG3
rs19922452



LMP7
rs2071543



MBP
rs470929



MC1R
rs1805009



MC1R
rs1805006



MEFV
rs28940577



MGC33887
rs987931



MHC2TA
rs4774



MHC2TA
rs3087456



MOG
rs2857766



MOG
rs3130250



MOG
rs3130253



MxA
rs2071430



NDUFA7
rs2288414



NDUFA7
rs561



NDUFS5
rs2889683



NDUFS5
rs6981



NDUFS7
rs2074897



NOS2A
rs1137933



NOS2A
rs2779248



NOTCH4
rs367398



NR4A2
rs1405735



OAS1
rs10774071



OAS1
rs3741981 (rs1131454 in version. 37.1)



PD-1
rs11568821



PDE4B
rs1321172



PITPNC1
rs1318



PITPNC1
rs2365403



PNMT
rs876493



PON
rs854560



PPARG
rs1801282



PRKCA
rs7220007



PRKCA
rs887797



PRKCA
rs2078153



PRKCA
rs3890137



PTPN22
rs2476601



PTPRC
rs17612648



PTPRC
rs4915154



PVRL2
rs394221



RPL5
rs6604026



SELE
rs1805193



SELE
rs5361



SPARCL1
rs1049544



Spp1
rs1126616



Spp1
rs1126772



Spp1
rs2853744



Spp1
rs9138



Spp1
rs4754



STAT1
rs1547550



STAT1
rs2066802



TAC1
rs2072100



TAC1
rs7793277



TGFB1
rs1800469



TGFB1
rs1800470



TGFB1
rs1800471



TGFB1
rs1982073



TNF-alpha
rs1800629



TRAIL
rs1131568



TRIF (TICAM1)
rs1046673



TRIF (TICAM1)
rs2292151



UCP2
rs659366



VDR
rs10735810



VDR
rs1544410



VDR
rs731236










Each individual in the study population is tested to determine an outcome for each of the discriminating variables for the particular phenotype. This provides a number of outcomes for each individual. Testing, e.g. genotyping, may be carried out by any of the methods described herein, e.g. by microarray analysis as described herein. Testing is typically ex vivo, carried out on a suitable sample obtained from an individual.
Multiple genotype-phenotype associations may then be analysed using stepwise multivariate logistic regression analysis, using as the dependent variable the clinically determined MS phenotype and as independent variables the outcomes of the informative variables. The goodness of fit of the models obtained may be evaluated using Hosmer-Lemeshow statistics and their accuracy assessed by calculating the area under the curve (AUC) of the Receiver Operating Characteristic curve (ROC) with 95% confidence intervals (see, e.g. (Janssens A C J W et al., 2006)).
Mean probability function values for each of the alternative phenotypes in the population can be compared using a t test. In general the probability functions are able to distinguish between the different phenotypes in the study population in a statistically significant way, for example, at p≦0.05 in a t-test. Thus the probability functions produce a statistically significant separation between individuals of different phenotype in the population.
In some embodiments, the presence of two or more genetic markers in a sample from an MS patient is compared to the presence of two or more genetic markers in a control sample. In some embodiments a control sample is a sample from an individual who does not have MS. In other embodiments a control sample is a sample from an individual who has MS. In certain embodiments, a control sample is a sample from an individual who has MS of a specified classification or degree of severity. It will be understood that the interpretation of a comparison between a test sample and a control sample will depend on the nature of both samples. One possible measurement of the level of expression of genetic markers in a sample is the absolute number of genetic markers identified in a sample. Another measurement of the level of expression of genetic markers in a sample is a measurement of the specific combination of genetic markers in a sample.
In some embodiments, a control value may be a predetermined value, which can take a variety of forms. It can be a single cut-off value, such as a median or mean. It can be established based upon comparative groups, such as in groups not having MS, or groups have specified classifications or levels of severity of MS. For example, in some embodiments, a control sample that is taken from an individual who does not have MS, may be considered to exhibit control or normal patterns of expression of genetic markers for MS. In some embodiments where severity of MS is being assessed, a control sample that is taken from an individual that has a specified classification or level of severity of MS, such as a mild form of MS, may be considered to exhibit a normal or control pattern of expression of markers for MS. In some embodiments a control sample will be from an individual who is of the same ethnic background, gender, age, MS classification and/or MS disease duration as the individual who is being screened and/or diagnosed.
Based at least in part on results of correlations and methods discussed herein, predetermined values can be arranged. For example, test samples and subjects from which the samples were extracted can be divided into groups such as low-severity, medium-severity, and high-severity groups based on the presence of two or more genetic markers that are correlated to MS severity. In some embodiments the classification of a sample and subject into a group can be used to aid or assist in screening, diagnosis, prognosis or development of a treatment strategy for a given subject.
Described herein are correlations between the presence of specific genetic markers and the severity of symptoms of MS in a patient. Such correlations and methods for detecting such correlations have widespread applications for MS patients. In some embodiments methods described herein are used to screen patients who have or are at risk of having MS. In some embodiments, evaluation of the presence of two or more SNPs in a patient will be used to assist in the diagnosis or to indicate or evaluate the severity of MS in the patient. In some embodiments, genetic information obtained from methods described herein will be combined with other clinical data to assess the severity of MS in a given patient.
In some embodiments, the identification of two or more SNPs in a DNA sample from an MS patient will be used to initiate or change a treatment regimen for the patient. For example, in some embodiments, detection of two or more SNPs that are associated with increased severity of MS may cause a physician to change the treatment strategy of an MS patient in order to target a more severe form of the disease, or advise a patient that they may benefit from a change in treatment strategy. In some embodiments, detection of two or more SNPs that are associated with increased severity of MS may cause a physician to monitor an MS patient more closely or rigorously. In some embodiments, detection of two or more SNPs that are associated with increased severity of MS may cause a physician to recommend or advise that a patient undergo genetic counseling.
In some aspects of the invention measurement of clinical variables comprises part of the severity prediction model along with the genetic variables in Table 1, above. Some non-limiting examples are age at onset, gender of patient studied, and type of onset of the disease (e.g. progressive or relapsing) (see Table 2). Age at onset refers to the age in years at which the patient was diagnosed with MS. In the present models this measure has been treated as a continuous variable, which is included in the logistic regression function of the models. Thus an outcome for this variable is age of patient when diagnosed for MS.
Gender refers to the gender of the patient diagnosed with MS. In the present models this measure has been treated as a categorical variable, with levels “male” and “female”, which is included in the logistic regression function of the models. Thus an outcome for this variable is gender of patient diagnosed with MS. If the gender is male, this is coded as (1), and if the gender is female, this is coded as (0).
Type of onset refers to the type of onset of disease, progressive or relapsing, for the studied patient diagnosed with MS. In the present models this measure has been treated as a categorical variable, which is included in the logistic regression function of the models. Thus an outcome for this variable is age of patient when diagnosed for MS. If the type of onset is progressive, this is coded as (1), and if the type of onset is relapsing, this is coded as (0).







TABLE 2







Clinical Variables










Variable
Variable Type







Age at onset (Age_at_onset)
Continuous variable



Gender
Categorical variable



Onset type (Onsettype_cod)
Categorical variable










In embodiments comprising methods of evaluation of MS severity in a patient, the method typically comprises determining or obtaining for the subject, an outcome for each of the variables listed in Table 2. In some embodiments, use of the results of the measurements of these variables, along with the variables in Table 1, allows prognosis of MS severity phenotype in a Dutch population with an LR+ of 8.4. Details for the calculation of a probability function using these variables are given in Table 3.
Preferably the number and combination of variables such as SNPs used to construct a model for predicting a phenotype according to the invention, is such that the model allows prediction to be made with an LR+ value of at least 1.5, such as at least 2, 3, 4, 5, 6, 7, 8, 9, or 10. Calculation of LR+ values is described herein.
Once an outcome is determined for each of the variables for prediction of a given phenotype, these outcomes are used in or inserted in a suitable probability function (for prediction of that phenotype), as described herein and a probability function value is calculated. Outcomes may be codified for use in the probability function and calculation of the probability function value. The probability function value is then compared with probability function values obtained for a population of individuals of known (clinically determined) phenotype. The risk of the subject having or developing the particular phenotype is thereby determined.
The sensitivity, specificity, and positive likelihood ratio (LR+=sensitivity/(1−specificity)) may be computed by means of ROC curves. Preferably the model has an LR+ value of at least 1.5, for example, at least 2, 3, 4, 5, 6, 7, 8, 9 or 10.
Also within the scope of the invention are kits and instructions for their use. In some embodiments kits associated with the invention are kits for identifying two or more SNPs within a patient sample. In some embodiments a kit may contain primers for amplifying a specific genetic locus. In some embodiments, a kit may contain a probe for hybridizing to a specific SNP. A kit of the invention can include a description of use of the contents of the kit for participation in any biological or chemical mechanism disclosed herein. A kit can include instructions for use of the kit components alone or in combination with other methods or compositions for assisting in screening or diagnosing a sample and/or determining a treatment strategy for MS.
The kits described herein may also contain one or more containers, which may contain a composition and other ingredients as previously described. The kits also may contain instructions for mixing, diluting, and/or administering or applying the compositions of the invention in some cases. The kits also can include other containers with one or more solvents, surfactants, preservative and/or diluents (e.g., normal saline (0.9% NaCl), or 5% dextrose) as well as containers for mixing, diluting or administering the components in a sample or to a subject in need of such treatment.
The compositions of the kit may be provided as any suitable form, for example, as liquid solutions or as dried powders. When the composition (e.g., a primer) provided is a dry powder, the composition may be reconstituted by the addition of a suitable solvent, which may also be provided. In embodiments where liquid forms of the composition are used, the liquid form may be concentrated or ready to use. The solvent will depend on the composition and the mode of use or administration. Suitable solvents for drug compositions are well known, for example as previously described, and are available in the literature. The solvent will depend on the composition and the mode of use or administration.
As used herein, the term “subject” refers to a human or non-human mammal or animal. Non-human mammals include livestock animals, companion animals, laboratory animals, and non-human primates. Non-human subjects also specifically include, without limitation, chickens, horses, cows, pigs, goats, dogs, cats, guinea pigs, hamsters, mink, and rabbits. In some embodiments of the invention, a subject is a patient. As used herein, a “patient” refers to a subject who is under the care of a physician or other health care worker, including someone who has consulted with, received advice from or received a prescription or other recommendation from a physician or other health care worker. A patient is typically a subject having or at risk of having MS.
The term “treatment” or “treating” is intended to relate to prophylaxis, amelioration, prevention and/or cure of a condition (e.g., MS). Treatment after a condition (e.g., MS) has started aims to reduce, ameliorate or altogether eliminate the condition, and/or its associated symptoms, or prevent it from becoming worse. Treatment of subjects before a condition (e.g., MS) aims to reduce the risk of developing the condition and/or lessen its severity if the condition does develop. As used herein, the term “prevent” refers to the prophylactic treatment of a subject who is at risk of developing a condition (e.g., MS) resulting in a decrease in the probability that the subject will develop the disorder, and to the inhibition of further development of an already established disorder.
Treatment for MS varies with the stage of the disease and the clinical presentation of the patient. In general it is advantageous to begin treatment early in the course of the disease. Goals for treatment may include slowing the progression of the disease, reducing the number of the attacks, and improving recovery from attacks. Corticosteroids such as Methylprednisolone (Solu-Medrol®, Medrol, Depo-Medrol), and Prednisone (Deltasone®, Liquid Pred, Orasone, Prednicen-M) are used to treat exacerbations of MS. In some embodiments Methylpredisone is given intravenously for 2-7 days, followed by a course of Prednisone. Corticosteroids may be used only for very severe attacks, as the effects vary and there are numerous reported side effects.
In some embodiments an MS patient is treated with therapies that can modify the course of the disease. Certain immune modulatory therapies are thought to slow the progression of MS by tempering the immune system's attack on the central nervous system. Some non-limiting examples include Interferon beta-1a, Interferon beta-1b, and Glatiramer acetate. Some examples of Interferon beta-1a include Avonex® and Rebif®. Avonex® is typically administered by intramuscular injection once weekly, whereas Rebif® is typically administered subcutaneously 3 times per week, at a dose of 22 or 44 mcg. Interferon beta-1b, e.g. Betaseron, is in some embodiments given by subcutaneous injection ever other day. Patients treated with interferon may experience fewer relapses or faster recovery from attacks, and an overall slowing of the progression of the disease. Glatiramer acetate, e.g. Copaxone®, is a synthetic amino acid that modifies actions of the immune system that may affect the progression of MS. It has been shown to reduce the frequency of exacerbations and the level of disability. In some embodiments this medication is given subcutaneously daily.
Other immune modulatory therapies include Natalizumab (Tysabri®), a monoclonal antibody against VLA-4, Mitoxantrone (Novantrone®), a chemotherapy drug. Natalizumab is administered via monthly intravenous injections and has been shown to reduce the frequency of clinical relapses and delay the progression of physical disability. Mitoxantrone is used for reducing neurologic disability and/or the frequency of clinical relapses. In some embodiments vitamin D is used as a treatment.
Other treatments for relief from complications of the disease are aimed at specific to symptoms, such as muscle spasticity, weakness, eye problems, fatigue, emotional outbursts, pain, bladder dysfunction, constipation, sexual dysfunction, and tremors.
EXAMPLES
In multiple sclerosis (MS), the total volume of spinal and brain lesions and their spatial distribution are highly variable. Elucidating this variability may contribute to understanding clinical heterogeneity in MS.
Materials and Methods
Study Participants:
Patients were selected retrospectively from natural history studies conducted at the
MS Center at the VU University Medical Center in Amsterdam. Patients were selected for the availability of DNA material, as well as spinal cord and brain MRI, which fulfilled certain standardization requirements and were performed less than two years apart. The study was carried out with the approval of the Medical Ethical Committee of the VUmc and informed consent was obtained from all participants. Patients, all diagnosed with MS ascertained by Poser or McDonald criteria (Poser et al., Ann. Neurol 1983; 3:227-231; Polman C H et al., Ann. Neurol. 2005; 6:840-846). For the patients included in the analysis, clinical data were collected including age, gender, type of disease onset, age at onset, disease course and duration of disease. Disability status was determined for all subjects by using Kurtzke's Expanded Disability Status Scale (EDSS) and when available Multiple Sclerosis Functional Composite scale (MSFC).
Selection of SNPs:
Polymorphisms were selected based on published involvement in MS pathogenesis, prognosis and response to treatment. The polymorphisms were confirmed and associated to an identifier by using dbsnp database (www.ncbi.nlm.nih.gov/SNP). Nucleotide sequences for the design of allele-specific probes and PCR primers where retrieved in the SNPper database (http://snpper.chip.org/bio). Sequence specific probes and primers were designed by using the software Primer3 freely available at http://frodo.wi.mit.edu/. Some non-limiting examples of probes and primers useful in the instant invention can be found in Tables 7-9.
If a polymorphism was not present in the database, position and sequences were established by performing a blast search (http://www.ncbi.nlm.nih.gov.catalog.llu.edu/BLAST/) using the data available in the literature.
Genotyping
Genomic DNA was isolated from anti-coagulated blood with DNAzo1 reagent (Molecular Research Center, Inc., Cincinnati, Ohio).
Genotyping was carried out using a newly developed low-density DNA microarray based on allele-specific probes. The design, fabrication, validation and analysis of the arrays were performed following the procedure described by Tejedor et al. (2005), Clin. Chem., Vol. 51(7), pp. 1137-1144, with minor modifications.
Brain MRI
Scans were acquired either on 1.0 Tesla or 1.5 Tesla (Siemens) scanners with standard head coils, using standard 2D conventional or fast spin-echo PD- and T2-weighted images (TR: 2200-3000 ms, TE: 20-30 & 80-100 ms) with a slice thicknesses of 3-5 mm, a maximum gap between slices of 0.5 mm, and an in-plane resolution of 1×1 mm2. Lesions were identified by an expert reader and then outlined on the corresponding PD image using home-developed semi-automated seed-growing software based on a local thresholding technique. Lesion areas were multiplied with the interslice distance to obtain total T2 brain lesion volume for each patient.
Spinal Cord MRI:
Spinal cord scanning included a cardiac-triggered sagittal PD and T2-weighted dual-echo spin echo sequence with a slice-thickness of 3 mm covering the whole spinal cord (TR: 2500-3000 ms, TE: 20-30 & 80-100 ms) with an in plane resolution of 1×1 mm. From this sequence the number of focal abnormalities and the presence of diffuse abnormalities were scored by one experienced reader (CL). Diffuse abnormalities were defined as areas with poorly delineated areas of increased signal intensity compared to signal intensity of spinal CSF on intermediate-weighted images.
Statistical Methods for MRI Data:
First the association between the brain parameter (T2 lesion load) and spinal cord parameters (number of focal lesions, presence of diffuse abnormalities) were tested per SNP and per clinical variable. The non-parametric Kruskal-Wallis test and ChiSquare test were used appropriately, applying the False Discovery Rate (FDR) according to Benjamini and Hochberg (Benjamini, Y, 1995, J. R. Stat. Soc. B Met 289) to correct for multiple testing. The corrected number represents the expected proportion false discoveries for a given p-value cut-off. The cut-off point after FDR correction of p<0.05 was used. Pearson's correlation coefficient was used to test the correlations between two scaled variables. All analyses were performed using SPSS (version 15; SPSS Inc., Chicago, Ill., USA).
Statistical Methods for Regression and Association Analysis:
First the association between MS severity score, the brain parameter (T2 lesion load) and spinal cord parameters (number of focal lesions, presence of diffuse abnormalities) were tested per SNP and per clinical variable and statistically significant associations between particular genotypes and particular phenotypes are identified. Methods for determining statistical significance are known in the art. Models were created by means of multivariate logistic and/or linear regression, for categorical or continuous dependent variables respectively, with clinically determined disease phenotypes as dependent variables and the SNPs and clinical variables as independent variables or regressors. To evaluate the impact of the regressors included in the prognosis of the analysed phenotypes, the sensitivity, specificity and positive likelihood ratio (LR+=sensitivity/(1−specificity)) were computed by means of Receiver Operating Characteristic curves. In the case of multiple linear regression, the impact of the regressors the corrected R square was computed. All analyses were performed using the Statistical Package for the Social Sciences (SPSS) version 15 and HelixTree (Golden Helix, Inc., Bozeman, Mont.).
Example 1
Identification of Polymorphisms Associated with Increased MSSS Score
The invention presents a model for predicting the probability of having a stronger disability, as measured by the MSSS scale, thus allowing differential treatment management for a given patient. This model was obtained from the analysis of 605 MS patients. The invention evaluates differences between patients that have an MSSS score of less than 2.5 versus patients that have an MSSS score of 2.5 or greater.
Table 3 (shown below) shows the six SNPs (rs876493, rs1137933, rs1318, rs2069763, rs2107538 and 2073 Intron2 C/T (rs423904)) with the associated genotypes and the three clinical variables (age at onset, gender and onset type) and the associated levels, together with their significance (Sig.), the coefficients in the model (B) and their odds ratios (OR) with lower and upper bound confidence intervals (I.C 95.0% for OR) used to compute the model for the prediction of the MSSS<2.5 versus≧2.5 phenotype. This model provides the probability to develop a severe form of MS.







TABLE 3







Regression Analysis









I.C. 95% for OR














Genotype/



Lower



Variable name
Variable level
Sig.
B
OR
bound
Upper bound
















IL1RN 2073 Intron2
CC vs CT/TT
0.064
−0.469
0.625
0.381
1.028


C/T (rs423904)


PNMT rs876493
AA/AG vs GG
0.025
−0.65
0.522
0.295
0.923


Age_at_onset

0.004
0.048
1.049
1.016
1.083


gender
0 = female vs
0.017
0.684
1.982
1.127
3.485



1 = male


Onsettype_cod
0 = relapsing
0.021
1.466
4.331
1.244
15.082



vs 1 = progressive


NOS2A rs1137933
GG vs AG/AA
0.005
−0.715
0.489
0.298
0.803


PITPNC1 rs1318
AA vs AG/GG
0.002
−0.775
0.461
0.28
0.759


IL2 rs2069763
GG vs GT/TT
0.001
−0.922
0.398
0.23
0.688


CCL5 rs2107538
CC vs CT/TT
0.023
0.649
1.914
1.092
3.355









FIG. 1 shows a ROC (receiver operating characteristic) curve obtained for the model MSSS<2.5 versus≧2.5 that allows the estimation of its discriminatory power. The ROC curve was calculated in order to maximize the specificity, thus reducing at the same time the “false” positive rate. A specificity of 95.3% with a sensibility of 39.7% is the cut-off for this model regarding the phenotype MSSS<2.5 versus≧2.5. This model shows a positive likelihood ratio (LR+) value of 8.4.
Additional MS patients have been recruited increasing the MS cohort to 700 MS patients. In a first stage of analysis, feature selection was employed to identify the most important and predictive features in the model to be analyzed. This approach of variable filtering is based on the marginal association between each variable (SNP or clinical variable) and phenotype, as variables are typically filtered on the basis of a p-value cut-off from a univariate analysis. For the selection of variables, HelixTree® software (Golden Helix, Inc., Bozeman, Mont., USA) was used to calculate allelic association between different groups. In Table 3A, SNPs associated with MSSS score at a significance level of p<0.1 set as the decision threshold are shown.







TABLE 3A







Table showing the 37 SNPs associated with MSSS score at the selected


significance level











Rs-number SNP
Gene
Sig. p-value















rs3756450
LOC728594
0.00436



rs12047808
C1orf125
0.00883



rs10259085
C1GALT1
0.00949



rs1042173
SLC6A4
0.01142



rs1318
PITPNC1
0.01426



rs6077690
SNAP25
0.02478



rs1611115
DBH
0.02577



rs2107538
CCL5
0.03258



rs4473631
MORF4
0.03348



rs2032893
SLC1A3
0.03470



rs2066713
SLC6A4
0.03744



rs260461
ZNF544
0.03924



rs3787283
SNAP25
0.03976



rs1137933
NOS2A
0.04094



rs6917747
IGF2R
0.04710



rs2049306
CSMD1
0.04909



rs12861247
STS
0.05177



rs4404254
ICOS
0.05585



rs4680534
IL12A
0.05729



rs17641078
DMRT2
0.05833



rs2187668
HLA-DQA1
0.06045



rs7528684
FCRL3
0.06099



rs876493
PNMT
0.06135



rs7577925
FLJ34870
0.06232



rs1805009
MC1R
0.06375



rs423904
IL1RN
0.06449



rs3741981
OAS1
0.06993



(rs1131454 in



version. 37.1)



rs2069763
IL2
0.07750



rs12202350
IGF2R
0.07981



rs28386840
SLC6A2
0.08145



rs2028455
LOC647094
0.08244



rs10492503
GPC5
0.08486



rs8049651
GRIN2A
0.08826



rs13353224
DSEL
0.08906



rs1555322
MMP24
0.09161



rs10243024
MET
0.09398



rs6570426
LOC729293
0.09635










A Multivariate prognostic model was then constructed for dichotomous MSSS with the cut-off point of 2.5 using logistic regression model, using SPSS version 15.0 (SPSS Inc. Headquarters, Chicago, Ill., USA) and R packages Design (Harrell, 2001) and Stats (R Development Core Team, 2008). The model was developed including information for the clinical variables available.

85% of the cohort was selected at random as exploratory cohort (n=595) and the 15% of the cohort as replication cohort (n=105). The model obtained with the exploratory cohort (Table 3B) included the same variables as the one obtained from the analysis of 605 MS patients (Table 3). The model showed in Table 3B was validated in the replication cohort (AUC exploratory cohort=0.743 (0.691-0.796) (FIG. 5) versus AUC replication cohort=0.787 (0.667-0906) (FIG. 6), differences between both ROC curves not statistically significant).







TABLE 3B







Regression Analysis









I.C. 95% for OR













Variable name
Genotype/Variable level
Sig.
B
OR
Lower bound
Upper bound
















IL1RN 2073 Intron2 C/T (rs423904)
CC vs CT/TT
0.056
−0.482
0.618
0.377
1.012


PNMT rs876493
AA/AG vs GG
0.071
−0.531
0.588
0.331
1.046


Age_at_onset

0.069
0.028
1.029
0.998
1.061


gender
0 = female vs 1 = male
0.01
0.743
2.102
1.194
3.703


Onsettype_cod
0 = relapsing vs 1 = progressive
0.006
1.71
5.529
1.616
18.917


NOS2A rs1137933
GG vs AG/AA
0.018
−0.593
0.553
0.339
0.901


PITPNC1 rs1318
AA vs AG/GG
0.026
−0.561
0.571
0.349
0.934


IL2 rs2069763
GG vs GT/TT
0.009
−0.709
0.492
0.288
0.839


CCL5 rs2107538
CC vs CT/TT
0.031
0.606
1.832
1.058
3.173









The model obtained from the analysis of the 700 MS patients is showed in Table 3C. The model includes the same variables that the obtained from the analysis of 605 MS patients (Table 3) and from the analysis of the exploratory cohort or 595 MS patients (Table 3B).







TABLE 3C







Regression Analysis









I.C. 95% for OR













Variable name
Genotype/Variable level
Sig.
B
OR
Lower bound
Upper bound
















IL1RN 2073 Intron2 C/T (rs423904)
CC vs CT/TT
.084
−.404
.668
.422
1.056


PNMT rs876493
AA/AG vs GG
.053
−.533
.587
.342
1.006


Age_at_onset

.015
.036
1.036
1.007
1.066


gender
0 = female vs 1 = male
.017
.634
1.884
1.119
3.173


Onsettype_cod
0 = relapsing vs 1 = progressive
.005
1.758
5.801
1.714
19.638


NOS2A rs1137933
GG vs AG/AA
.005
−.649
.522
.331
.824


PITPNC1 rs1318
AA vs AG/GG
.025
−.527
.590
.373
.935


IL2 rs2069763
GG vs GT/TT
.001
−.818
.441
.267
.730


CCL5 rs2107538
CC vs CT/TT
.015
.643
1.902
1.132
3.196









The ROC curve area obtained for the model MSSS≧2.5 vs MSSS<2.5 analysing the 700 MS patients is 0.749 (95% CI 0.700-0.797) (FIG. 7). A specificity of 95% with a sensitivity of 32% is the cut-off for this model. The model shows a positive likelihood ratio (LR+) value of 6.2.

Example 2
Identification of SNPs Associated with T2 Brain Lesions
In order to determine whether certain SNPs are associated with increasing size and distribution of T2 brain lesions, analysis was performed on a group of 208 MS patients with MRI data collected. The MRI data show spatial distribution of T2 brain lesions. FIG. 2 shows lesion frequency across the patient sample.
FIG. 3 shows maps of clusterwise (t=2) associations of lesion presence with genotype, on a background of the common brain image. The cluster colour bar indicates clusterwise p-value, with the range indicated by the colour bar; only clusters with p<0.05 are shown. These data have been correlated to genotype data. The results show significant associations for four SNPs to brain lesions. A: rs2213584 (HLA-DRA gene); B: rs2227139 (HLA-DRA gene); C: rs2076530 (BTNL2 gene); D: rs876493 (PNMT gene).
Example 2A
Identification of SNPs Associated with T2 Brain Lesions
Further investigation was carried out essentially as described in Example 2. Additionally, lesions were manually outlined on Magnetic Resonance Imaging scans and binary lesion masks were produced and registered to a common space. Using Randomise software, the lesion masks were related to genotype using a voxelwise nonparametric General Linear Model approach, followed by clusterwise analysis. The results show significant associations for eight SNPs to brain lesions: rs9808753 (IFNGR2 gene), rs2074897 (NDUFS7 gene), rs762550 (CRYAB gene), rs2076530 (BTNL2 gene), rs2234978 (FAS gene), rs3781202 (FAS gene), rs2107538 (CCL5 gene), rs659366 (UCP2 gene).
Example 3
Identification of SNPs Associated with MS Severity Phenotypes
Patient Characteristics:
One hundred and fifty patients were included in the analysis. The patient group reflects a representative MS population, with approximately 35% men and 20% primary progressive MS patients (see Table 4). The majority of patients (132/150) demonstrated abnormalities on the spinal cord MRI scan, while all patients had abnormalities on the brain MRI scan.







TABLE 4







Patient characteristics












All
RR
SP
PP















Total n
150
88
32
30















Gender (n; % M)
55
(36.7%)
26
(29.5%)
17
(53.1%)
12
(40%)











Age at MRI (mean)
41.4
36.1
46.5
51.2















Disease duration mean (range)
7.1
(0.0-33.0)
4.36
(0.0-32.0)
12.8
(2.0-33.0)
9.2
(0.0-28.0)











EDSS (median)
3.5
2.0
5.5
4.0


T2 lesionload (ml) (mean)
7.7
4.9
16.2
7.0


Number of focal lesions in the spinal
3.4
3.3
4.5
2.8


cord (mean)


Percentage of patients with diffuse
13.3
10.2
18.8
16.7


abnormalities (%)









Genotyping:

In total 80 SNPs in 44 genes were selected on the MSchip. Twelve SNPs were excluded from further analysis: five were monomorphic and seven SNPs had a minor allele frequency below five percent (see Table). Hardy Weinberg equilibrium was calculated for all SNPs. Values are noted in table 5.







TABLE 5







Results analysis of the correlation SNPs and MRI parameters.











Clinical/MRI






parameter


Uncorrected p-
FDR-


correlated
Rs-number

value Kruskal
corrected


with SNPs:
SNP
Gene
Wallis test:
p-value














Number of
rs3135388
MHC II
0.00082
0.03


focal
rs2395182*
MHC II
0.00107
0.03


lesion in the
rs2239802*
MHC II
0.00122
0.03


spinal cord
rs2227139**
MHC II
0.00169
0.03



rs2213584**
MHC II
0.00330
0.05



rs3087456
MHC II
0.00900
0.10




TransActivator


T2 lesion
rs2107538
CCL5
0.001
0.07


load in the


brain





* and ** LD values still need to be calculated.






Correlation Between Clinical Parameters and MRI Features:

The EDSS showed a mild correlation with the number of focal lesions in the spinal cord (p=0.043, r=0.165, Pearson correlation), with the number of segments involved (p=0.006, r=0.224, Pearson correlation) and a moderate correlation with T2 lesion load in the brain (p<0.001, r=0.395). A weak correlation was present between the number of focal spinal cord lesions and T2 lesion load in the brain (p=0.063, r=0.152).
Disease duration was found to be related to number of segments of the spinal cord involved (p=0.017, r=0.195).
The T2 lesion load in the brain was closely related to the PASAT score (p=0.000, r=−0.581) and 9 Hole Peg Test of the dominant hand (p=0.001, r=0.306).
Correlation Between Lesion Load in the Brain and Genotypes:
In the univariate analysis on T2 lesion load in the brain and the MS-chip, the only ‘trend’ correlation was rs2107538 (CCL5) (see Table 5).
Correlation Between Spinal Cord Abnormalities and Genotypes:
Several HLA SNPs were found to be related to the number of focal spinal cord abnormalities (see Table 5). The most significant is SNP rs3135388. Carriership of the A-allele (surrogate marker for HLA-DRB1*1501) was associated with a significantly higher number of lesions in the spinal cord (FIG. 4).
When corrected for multiple testing, five SNPs within the MHC region (rs3135388, rs2395182, rs2239802, rs2227139 and rs2213584), remained significant and one SNP within the MHC-2TA gene (Major Histocompatibility Complex Class II Transactivator) showed a trend towards a correlation. The five HLA SNPs are in high linkage disequilibrium.
A linear model has been developed using multiple linear regression to predict the number of focal lesions in the spinal cord. This method uses three of these SNPs rs3135388, rs3087456, and rs2227139. A model including the combination of these three SNPs improves the use of one single SNP (rs3135388) for prediction of number of focal lesions in the spinal cord. Corrected Rsquared for model using only one SNP=0.064. Corrected Rsquared for model using combination of three SNPs=0.112. The combination of these three SNPs or any SNP in linkage disequilibrium with any of these three SNPs improves prediction of number of focal lesions in the spinal cord over the use of one single SNP.
No interactions between the SNPs and the clinical variables were present. No association was observed between the presence of diffuse abnormalities and the evaluated SNPs.
Example 4
Identification of Additional SNPs Associated with MRI Parameters: Number of Focal Lesion in the Spinal Cord, T2 Lesion Load in the Brain and Presence of Diffuse Abnormalities
In order to determine whether certain additional SNPs are associated with MRI parameters, a similar analysis to Example 3 was performed using different SNPs on one hundred and fifty patients. Results of the correlation of additional SNPs and MRI parameters are shown in table 5A.
In our study cohort of 150 MS patients with MRI data, MRI data are significantly correlated with MS severity given by MSSS (p=0.023). It can thus be assumed that identification of SNPs associated with MRI parameters allows inferring MS severity.







TABLE 5A







Results of analysis of the correlation of additional SNPs and MRI


parameters.












Clinical/






MRI


Uncorrected



parameter


p-value



correlated
Rs-number

Kruskal



with SNPs:
SNP
Gene
Wallis test:
















Number of
rs10492972
KIF1B
0.0063



focal lesion in
rs12202350
IGF2R
0.005



the
rs8049651
GRIN2A
0.0023



spinal cord
rs8702
KLC1
5.00E−04



T2 lesion load
rs987107
IL7R
0.0091



in the brain
rs12861247
STS
0.005




rs2074897
NDUFS7
0.006




rs7995215
GPC6
0.006



Presence of
rs1350666
EREG
0.008



diffuse
rs3808585
ADRA1A
0.003



abnormalities
rs4128767
IL16
0.006




rs6457594
MHC II
0.005




rs7208257
ARRB2
0.006




rs7956189
NTF3
0.008

















TABLE 6







SNPs included in the analyses; HWE = Hardy-Weinberg Equilibrium in


our sample; MAF = minor allele frequency in our sample















Poly-




Gene
rs-nr
Chromosome
morphism
HWE*
MAF















ADAMTS14
rs4747075
10q22
A/G
7.74*
0.30


ADAMTS14
rs7081273
10q22
C/G
1.2
0.34


ADAMTS14
rs4746060
10q22
C/T
1.05
0.08


Apo I/Fas
rs1800682
10q23
C/T
0.02
0.47


Apo I/Fas
rs3781202
10q23
C/T
7.41*
0.40


Apo I/Fas
rs2234978
10q23
C/T
0.43
0.31


BTNL2
rs2076530
6p21.3
A/G
29.78*
0.26


CACNG4
rs4790896
17q24
A/G
0.36
0.41


CCR5
rs333
3p21
−/+
0.02
0.11


CD24
rs8734
6q21
C
NA
0.00**


CNTF
rs1800169
11q12
A/G
0.80
0.12


CRYAB
rs14133
11q21-q23
C/G
0.08
0.27


CRYAB
rs762550
11q21-q23
A/G
0.14
0.42


CRYAB
rs2234702
11q21-q23
C
NA
0.00**


CTLA4
rs231775
2q33
A/G
1.03
0.37


CTLA4
rs5742909
2q33
C/T
0.45
0.09


EBF
rs1368297
5q34
A/T
0.06
0.38


GABBRA1
rs1805057
6p22
C
NA
0.00**


HELZ
rs2363846
17q24
C/T
2.23
0.48


HLA
rs2395166
6p21.3
C/T
3.46
0.47


HLA
rs2213584
6p21.3
A/G
3.61
0.40


HLA
rs2227139
6p21.3
C/T
2.89
0.40


HLA
rs3135388
6p21.3
A/G
0.97
0.33


HLA
rs9268458
6p21.3
A/C
1.29
0.20


HLA
rs6457594
6p21.3
A/G
35.65*
0.40


HLA-DRA
rs2395182
6p21.3
G/T
1.04
0.38


HLA-DRA
rs2239802
6p21.3
C/G
1.34
0.38


IFNAR1
rs2257167
21q22
C/G
0.00
0.08


IFNGR2
rs9808753
21q22
A/G
0.00
0.14


IKBL
rs3130062
6p21.3.
C/T
1.14
0.18


IL-10
rs1800896
1q32
A/G
0.56
0.46


IL1B
rs1799916
2q14
A
NA
0.00**


IL1B
rs1143627
2q14
A/G
5.32*
0.34


IL-1B
rs1143634
2q14
C/T
0.01
0.23


IL-1RN
rs419598
2q12-q14
C/T
0.53
0.31


IL-1RN
2073
2q12-q14
C/T
0.72
0.30



Intron2 C/T



(rs423904)


IL-2
rs2069763
4q26
G/T
0.75
0.36


IL-2
rs2069762
4q26
G/T
0.31
0.27


IL-4R
rs1801275
16p12
A/G
0.37
0.20


IL7R
rs11567685
5p13
C/T
0.68
0.25


IL7R
rs7718919
5p13
G/T
0.22
0.13


IL7R
rs11567686
5p13
A/G
1.44
0.34


MC1R
rs1805009
16q24
C/G
0.02
0.01**


MC1R
rs1805006
16q24
A/C
0.00
0.00**


MEFV
rs28940577
16p13.3
A
NA
0.00**


MGC33887
rs987931
17q24
G/T
0.39
0.32


MHC2TA
rs3087456
16p13
A/G
0.13
0.26


MOG
rs3130250
6p22
A/G
0.01
0.19


MOG
rs3130253
6p22
A/G
0.80
0.12


NDUFA7
rs2288414
19p13.2
C/G
7.90*
0.03**


NDUFA7
rs561
19p13.2
A/G
0.04
0.21


NDUFS5
rs2889683
1p34.2
C/T
2.63
0.31


NDUFS5
rs6981
1p34.2
A/G
105.96*
0.04**


NDUFS7
rs2074897
19p13.3
A/G
6.21*
0.47


NOS2A
rs1137933
17q11.2
A/G
0.49
0.25


NOS2A
rs2779248
17q11.2
C/T
0.00
0.39


NOTCH4
rs367398
6p21.3
A/G
0
0.16


PD-1
rs11568821
2q37
G/A
6.24*
0.11


PITPNC1
rs1318
17q24
A/G
0.01
0.21


PITPNC1
rs2365403
17q24
C/G
0.55
0.18


PNMT
rs876493
17q11-q23
A/G
0.70
0.39


PRKCA
rs7220007
17q24
A/G
0.10
0.49


PRKCA
rs887797
17q24
C/T
0.50
0.30


PRKCA
rs2078153
17q24
C/G
0.91
0.23


PRKCA
rs3890137
17q24
A/G
0.44
0.37


PTPN22
rs2476601
1p13
A/G
2.29
0.11


PTPRC
rs17612648
1q31
C/G
0.11
0.03**


PTPRC
rs4915154
1q31
A/G
0.00
0.00**


CCL5
rs2280788
17q11.2-q12
C/G
0.06
0.02**


CCL5
rs2107538
17q11.2-q12
C/T
0.00
0.18


Spp1
rs1126616
4q21
C/T
0.01
0.23


Spp1
rs1126772
4q21
A/G
0.23
0.18


Spp1
rs2853744
4q21
G/T
0.48
0.05


Spp1
rs9138
4q21
A/C
0.03
0.24


Spp1
rs4754
4q21
C/T
0.07
0.24


TNF-alpha
rs1800629
6p21.3
A/G
2.02
0.17


TRAIL
rs1131568
3q26
C/T
1.53
0.32


UCP2
rs659366
11q13
C/T
0.15
0.37


VDR
rs1544410
12q13
A/G
1.27
0.48


VDR
rs731236
12q13
A/G
0.39
0.48





*ChiSquare value. A value >3.84 indicates deviation from Hardy-Weinberg Equilibrium (p < 0.05).


**Excluded due to minor allele frequency <0.05)













TABLE 6A







Additional SNPs included in the analyses










Gene
rs-nr







KIF1B
rs10492972



IGF2R
rs12202350



GRIN2A
rs8049651



KLC1
rs8702



IL7R
rs987107



STS
rs12861247



GPC6
rs7995215



EREG
rs1350666



ADRA1A
rs3808585



IL16
rs4128767



ARRB2
rs7208257



NTF3
rs7956189



IL12A
rs4680534



SLC6A4
rs1042173

















TABLE 7







Examples of Probes Used in SNP Analysis  












Gene



Oligonucleotide sequence
SEQ


Symbol
Gene Name
rs ID
SNP
(5′ > 3′)
ID NO:















EBF1
Early B-cell Factor 1
rs1368297
intron 7 
TAAAGTTAGTC A GTTCTATGCTT
1





(271,440) A/T
TAAAGTTAGTC T GTTCTATGCTT
2






AAGCATAGAAC T GACTAACTTTA
3






AAGCATAGAAC A GACTAACTTTA
4


RANTES/
chemokine (C-C motif)
rs2280788
−28C/G
GGGATGCCCCT C AACTGGCCCTA
5


CCL5
ligand 5


GGGATGCCCCT G AACTGGCCCTA
6






TAGGGCCAGTT G AGGGGCATCCC
7






TAGGGCCAGTT C AGGGGCATCCC
8


RANTES/
chemokine (C-C motif)
rs2107538
−403G/A
AGGGAAAGGAG G TAAGATCTGTA
9


CCL5
ligand 5


AGGGAAAGGAG A TAAGATCTGTA
10






TACAGATCTTA C CTCCTTTCCCT
11






TACAGATCTTA T CTCCTTTCCCT
12


TGFB1
transforming growth 
rs17851976
L10P G869A
GTAGCAGCAGC G GCAGCAGCCGC
13



factor,beta 1


GTAGCAGCAGC A GCAGCAGCCGC
14






GCGGCTGCTGC C GCTGCTGCTAC
15






GCGGCTGCTGC T GCTGCTGCTAC
16


UPC2
uncoupling protein 2
rs659366
−866G/A
GGGGTAACTGA C GCGTGAACAGC
17






GGGGTAACTGA T GCGTGAACAGC
18






GCTGTTCACGC G TCAGTTACCCC
19






GCTGTTCACGC A TCAGTTACCCC
20


IKBL
inhibitory kappaB-like
rs3130062
C224R; 738T/C
CAGAGGGATCC C GTCGACCCCCA
21






CAGAGGGATCC T GTCGACCCCCA
22






TGGGGGTCGAC G GGATCCCTCTG
23






TGGGGGTCGAC A GGATCCCTCTG
24


Apo I/Fas
tumor necrosis factor 
rs1800682
−671A/G
GTCCATTCCAG A AACGTCTGTGA
25


(CD 95)
receptor superfamily


GTCCATTCCAG G AACGTCTGTGA
26






TCACAGACGTT T CTGGAATGGAC
27






TCACAGACGTT C CTGGAATGGAC
28


Apo I/Fas
tumor necrosis factor 
rs3781202
A/T (735)G/C
ATAAAATTTTC C TAGCAAATAAA
29


(CD 95)
receptor superfamily

intron 4
ATAAAATTTTC T TAGCAAATAAA
30






TTTATTTGCTA G GAAAATTTTAT
31






TTTATTTGCTA A GAAAATTTTAT
32


IL2
interleukin 2
rs2069763
114G/T
GAGCATTTACT G CTGGATTTACA
33






GAGCATTTACT T CTGGATTTACA
34






TGTAAATCCAG C AGTAAATGCTC
35






TGTAAATCCAG A AGTAAATGCTC
36


IL2
interleukin 2
rs2069762
−385A/C
TTTTCTTTGTC A TAAAACTACAC
37






TTTTCTTTGTC C TAAAACTACAC
38






TTCAGTGTAGTTTTA T
39






GACAAAGAAAATTTT







TTCAGTGTAGTTTTA G
40






GACAAAGAAAATTTT



IL10
interleukin 10
rs1800896
−1082G/A
GCTTCTTTGGGAAGGGGAAGTAGGG
41






GCTTCTTTGGGAGGGGGAAGTAGGG
42






CCCTACTTCCCCTTCCCAAAGAAGC
43






CCCTACTTCCCCCTCCCAAAGAAGC
44


IL4R
interleukin 4 receptor
rs1801275
Q551R
CAGTGGCTATC G GGAGTTTGTAC
45






CAGTGGCTATC A GGAGTTTGTAC
46






TACAAACTCC C GATAGCCACT
47






TACAAACTCC T GATAGCCACT
48


PTPRC
protein tyrosine
rs17612648
C77G
GCATTCTCACC C GCAAGCACCTT
49



phosphatase, receptor


GCATTCTCACC G GCAAGCACCTT
50



type, C


AAGGTGCTTGC G GGTGAGAATGC
51






AAGGTGCTTGC C GGTGAGAATGC
52


PTPRC
protein tyrosine 
rs4915154
A138G
TCACAGCGAAC G CCTCAGGTCTG
53



phosphatase,receptor 


TCACAGCGAAC A CCTCAGGTCTG
54



type, C


CAGACCTGAGG C GTTCGCTGTGA
55






CAGACCTGAGG T GTTCGCTGTGA
56


PD-
programmed cell death 1
rs11568821
G7146A
AGCCCACCTGC G GTCTCCGGGGG
57


1/PDCD1



AGCCCACCTGC A GTCTCCGGGGG
58






CCCCCGGAGAC C GCAGGTGGGCT
59






CCCCCGGAGAC T GCAGGTGGGCT
60


CRYAB
crystallin, alpha B
rs14133
−C249G
TGAAACAAGAC C ATGACAAGTCA
61






TGAAACAAGAC G ATGACAAGTCA
62






TGACTTGTCAT G GTCTTGTTTCA
63






TGACTTGTCAT C GTCTTGTTTCA
64


CRYAB
crystallin, alpha B
rs762550
−A652G
GAGCCACATAGAACGAAAGATGC
65






GAGCCACATAGGACGAAAGATGC
66






GCATCTTTCGTTCTATGTGGCTC
67






CATCTTTCGT C CTATGTGGCT
68


CRYAB
crystallin, alpha B
rs2234702
−C650G
GCCACATAGAA C GAAAGATGCAA
69






GCCACATAGAA G GAAAGATGCAA
70






TTGCATCTTTC G TTCTATGTGGC
71






TTGCATCTTTC C TTCTATGTGGC
72


NDUFS5
NADH dehydrogenase
rs2889683
−5649T/C
ACAACAGCAGA A ATAATAATCAA
73



(ubiquinone) Fe—S


ACAACAGCAGA G ATAATAATCAA
74



protein 5


TTGATTATTAT T TCTGCTGTTGT
75






TTGATTATTAT C TCTGCTGTTGT
76


NDUFS5
NADH dehydrogenase
rs6981
3′ UTR 5789
CAGCTGCTGAT A TCTGGAGGCTG
77



(ubiquinone) Fe—S

A/G
CAGCTGCTGAT G TCTGGAGGCTG
78



protein 5


CAGCCTCCAGA T ATCAGCAGCTG
79






CAGCCTCCAGA C ATCAGCAGCTG
80


NDUFS7
NADH dehydrogenase
rs2074897
intron 6 
GCCCTGATGGC A CTTATCAAAAG
81



(ubiquinone) Fe—S

(6 + 71) A/G
GCCCTGATGGC G CTTATCAAAAG
82



protein 7


CTTTTGATAAG T GCCATCAGGGC
83






CTTTTGATAAG C GCCATCAGGGC
84


NDUFA7
NADH dehydrogenase
rs2288414
intron 2 
ATGTCAGCCCT C CGTTTCAGGGG
85



(ubiquinone) 1 alpha

(2 + 89) C/G
ATGTCAGCCCT G CGTTTCAGGGG
86






CCCCTGAAACG G AGGGCTGACAT
87






CCCCTGAAACG C AGGGCTGACAT
88


NDUFA7
NADH dehydrogenase
rs561
9825 A/G
CCACCTCTTTAT A GGAGGAGCTGGA
89



(ubiquinone) 1 alpha


CCACCTCTTTAT G GGAGGAGCTGGA
90






CCAGCTCCTCC T ATAAAGAGGTG
91






CCAGCTCCTCC C ATAAAGAGGTG
92


ADAMTS14
ADAM metallopeptidase with
rs4747075
intron 2 
CCCAGATGATG A CATTCGCCTTC
93



thrombospondin type 1

16860 A/G
CCCAGATGATG G CATTCGCCTTC
94






GAAGGCGAATG T CATCATCTGGG
95






GAAGGCGAATG C CATCATCTGGG
96


ADAMTS14
ADAM metallopeptidase with
rs7081273
intron 2 
CATTTGGCAAA C GTAGGCTGGTC
97



thrombospondin type 1

24479 C/G
CATTTGGCAAA G GTAGGCTGGTC
98






GACCAGCCTAC G TTTGCCAAATG
99






GACCAGCCTAC C TTTGCCAAATG
100


ADAMTS14
ADAM metallopeptidase with
rs4746060
intron 4 
GCACATCTATA C TGGGTCATCTT
101



thrombospondin type 1

44225 C/T
GCACATCTATA T TGGGTCATCTT
102






AAGATGACCCA G TATAGATGTGC
103






AAGATGACCCA A TATAGATGTGC
104


NFKBIA
nuclear factor of kappa
rs11569591
−708ins8
GCGTGGGGGGG T GGGGGCGAAGC
105



light polypeptide gene 


GGGTGGGGGGG A GGGGGCGAAGC
106



enhancer in B-cells 


GCTTCGCCCCC A CCCCCCCACGC
107



inhibitor, alpha


GCTTCGCCCCC T CCCCCCCACCC
108


NFKBIA
nuclear factor of kappa
rs11569591
−708ins8
CGTGGGGGGG T GGGGGCGAAG
109



light polypeptide gene 


GGTGGGGGGG A GGGGGCGAAG
110



enhancer in B-cells 


CTTCGCCCCC A CCCCCCCACG
111



inhibitor, alpha


CTTCGCCCCC T CCCCCCCACC
112


NFKBIA
nuclear factor of kappa
rs11569591
−708ins8
TGCGTGGGGGGG T GGGGGCGAAGCT
113



light polypeptide gene 


GGGGTGGGGGGG A GGGGGCGAAGCT
114



enhancer in B-cells 


AGCTTCGCCCCC A CCCCCCCACGCA
115



inhibitor, alpha


AGCTTCGCCCCC T CCCCCCCACCCC
116


SPP1
secreted phosphoprotein 1
rs28357094
−66[G/T]
GACACAATCTC G CCGCCTCCCTG
117






GACACAATCTC T CCGCCTCCCTG
118






CAGGGAGGCGG C GAGATTGTGTC
119






CAGGGAGGCGG A GAGATTGTGTC
120


HLA-
major histocompatibility
rs367398
−25 A/G 
CTCCAAGCCCC A GTCCCTGTCCC
121


DR*1501
complex, class II, DR

(NOTCH4)
CTCCAAGCCCC G GTCCCTGTCCC
122






GGGACAGGGAC T GGGGCTTGGAG
123






GGGACAGGGAC C GGGGCTTGGAG
124


HLA-
major histocompatibility
rs1800629
−308G > A
TGAGGGGCATG A GGACGGGGTTC
125


DR*1501
complex, class II, DR

(TNF-alpha)
TGAGGGGCATG G GGACGGGGTTC
126






_AACCCCGTCC T CATGCCCCTC—
127






_AACCCCGTCC C CATGCCCCTC—
128


IL7R
interleukin 7 receptor
rs11567685
−504T/C
GCATTTGCCTGCAGTCCTAGCTA
129






GCATTTGCCTGTAGTCCTAGCTA
130






TAGCTAGGACTGCAGGCAAATGC
131






TAGCTAGGACTACAGGCAAATGC
132


IL7R
interleukin 7 receptor
rs7718919
−1085G/T
CACAAATGGGT G AGGCTGTATTC
133






CACAAATGGGT T AGGCTGTATTC
134






GAATACAGCCT C ACCCATTTGTG
135






GAATACAGCCT A ACCCATTTGTG
136


IL7R
interleukin 7 receptor
rs11567686
−449A/G
CCTGGGAGGTG A AAATTGCAGTG
137






CCTGGGAGGTG G AAATTGCAGTG
138






CACTGCAATTT T CACCTCCCAGG
139






CACTGCAATTT C CACCTCCCAGG
140


IFNAR1
interferon (alpha, beta
rs2257167
V168L 
ACATATAGCTTA C TTATCTGGAAAA
141



and omega) receptor 1

(G18417C)
ACATATAGCTTA G TTATCTGGAAAA
142






TTTTCCAGATAA G TAAGCTATATGT
143






TTTTCCAGATAA C TAAGCTATATGT
144


IFNAR2
interferon (alpha, beta
rs7279064
F10V 
ATGCCTTCATC G TCAGATCACTT
145



and omega) receptor 2

(11876T > G)
ATGCCTTCATC T TCAGATCACTT
146






AAGTGATCTGA C GATGAAGGCAT
147






AAGTGATCTGA A GATGAAGGCAT
148


IL1B
interleukin 1, beta
rs1799916
−511 A/C
AAGAGAATCCC A GAGCAGCCTGT
149



proprotein


AAGAGAATCCC C GAGCAGCCTGT
150






ACAGGCTGCTC T GGGATTCTCTT
151






ACAGGCTGCTC G GGGATTCTCTT
152


IFNGR2
interferon gamma receptor
rs9808753
Q64R
TGTTGTCTACC A AGTGCAGTTTA
153



2 (interferon gamma


TGTTGTCTACC G AGTGCAGTTTA
154



transducer 1)


TAAACTGCACT T GGTAGACAACA
155






TAAACTGCACT C GGTAGACAACA
156


Apo I/Fas
tumor necrosis factor
rs2234978
E7(74) C > T
GAATCTCCAAC C TTAAATCCTGT
157


(CD 95)
receptor superfamily


GAATCTCCAAC T TTAAATCCTGT
158






ACAGGATTTAA G GTTGGAGATTC
159






ACAGGATTTAA A GTTGGAGATTC
160


CD24
CD24 antigen precursor
rs8734
V57A 
CACCACCAAGG T GGCTGGTGGTG
161





(226T > C)
CACCACCAAGG C GGCTGGTGGTG
162






CACCACCAGCC A CCTTGGTGGTG
163






CACCACCAGCC G CCTTGGTGGTG
164


MEFV
Mediterranean fever 
rs28940577
M694V
GGGTGGTGATA A TGATGAAGGAA
165



protein


GGGTGGTGATA G TGATGAAGGAA
166






TTCCTTCATCA T TATCACCACCC
167






TTCCTTCATCA C TATCACCACCC
168


CTLA4
cytotoxic T-lymphocyte-
rs231775
+49A/G
TGAACCTGGCT A CCAGGACCTGG
169



associated antigen 4


TGAACCTGGCT G CCAGGACCTGG
170






CCAGGTCCTGG T AGCCAGGTTCA
171






CCAGGTCCTGG C AGCCAGGTTCA
172


CNTF
ciliary neurotrophic factor
rs1800169
intron 1 
CCTGTATCCTC A GCCAGGTGAAG
173





(2-7) A/G
CCTGTATCCTC G GCCAGGTGAAG
174






CTTCACCTGGC T GAGGATACAGG
175






CTTCACCTGGC C GAGGATACAGG
176


MHC2TA
class II, major
rs3087456
−168A/G
TTCAGAGGTGT A GGGAGGGCTTA
177



histocompatibility 


TTCAGAGGTGT G GGGAGGGCTTA
178



complex, transactivator


TAAGCCCTCCC T ACACCTCTGAA
179






TAAGCCCTCCC C ACACCTCTGAA
180


VDR
vitamin D receptor
rs1544410
33062 A/G 
GACAGGCCTGC A CATTCCCAATA
181





Intron
GACAGGCCTGC G CATTCCCAATA
182






ATTGGGAATG T GCAGGCCTGT
183






TTGGGAATG C GCAGGCCTG
184


PRKCA
protein kinase C, alpha
rs7220007
intron 3 
CCCCTGCTGGC A GATTGTTGCTA
185





264550 A/G
CCCCTGCTGGC G GATTGTTGCTA
186






TAGCAACAATC T GCCAGCAGGGG
187






TAGCAACAATC C GCCAGCAGGGG
188


PRKCA
protein kinase C, alpha
rs887797
intron 3 
GTCTTTTTAATA G CTGTAGACATCT
189





280475 C/T
GTCTTTTTAATA A CTGTAGACATCT
190






GTCTTTTTAATA G CTGTAGACATCT
191






GTCTTTTTAATA A CTGTAGACATCT
192


PRKCA
protein kinase C, alpha
rs2078153
intron 3 
AGTTACAGGGA C AAGAAGCCTTT
193





252845 C/G
AGTTACAGGGA G AAGAAGCCTTT
194






AAAGGCTTCTT G TCCCTGTAACT
195






AAAGGCTTCTT C TCCCTGTAACT
196


CTLA4
cytotoxic T-lymphocyte-
rs5742909
−318C/T
ATCCAGATCCT C AAAGTGAACAT
197



associated protein 4


ATCCAGATCCT T AAAGTGAACAT
198






ATGTTCACTTT G AGGATCTGGAT
199






ATGTTCACTTT A AGGATCTGGAT
200


MGC33887
coiled-coil domain 
rs987931
intron 21 
GCAGCAGTTT G CCCTGTGAGT
201



containing 46

413506 G/T
GCAGCAGTTT T CCCTGTGAGT
202






ACTCACAGGG C AAACTGCTGC
203






ACTCACAGGG A AAACTGCTGC
204


CACNG4
calcium channel, voltage-
rs4790896
intron 1 
GACTCCGATGA A GTTTGAGCAGA
205



dependent, gamma

15546 C/T
GACTCCGATGA G GTTTGAGCAGA
206



subunit 4


TCTGCTCAAAC T TCATCGGAGTC
207






TCTGCTCAAAC C TCATCGGAGTC
208


HELZ
helicase with zinc finger
rs2363846
intron 18 
TCAATAATAAA C ATCATCTGACC
209





68091 C/T
TCAATAATAAA T ATCATCTGACC
210






GGTCAGATGAT G TTTATTATTGA
211






GGTCAGATGAT A TTTATTATTGA
212


PITPNC1
phosphatidylinositol
rs1318
C/T
TGGGTGGTGTA A ATATTCCTTTA
213



transfer protein,


TGGGTGGTGTA G ATATTCCTTTA
214



cytoplasmic 1


GCTAAAGGAATAT T 
215






TACACCACCCACC







GCTAAAGGAATAT C 
216






TACACCACCCACC



PITPNC1
phosphatidylinositol
rs2365403
C/G
ACTGACTTTCT C TGCCTAATGTA
217



transfer protein,


ACTGACTTTCT G TGCCTAATGTA
218



cytoplasmic 1


TACATTAGGCA G AGAAAGTCAGT
219






TACATTAGGCA C AGAAAGTCAGT
220


MC1R
melanocortin 1 receptor
rs1805009
294 D/H
ATGCCATCATC C ACCCCCTCATC
221






ATGCCATCATC G ACCCCCTCATC
222






GATGAGGGGGT G GATGATGGCAT
223






GATGAGGGGGT C GATGATGGCAT
224


MC1R
melanocortin 1 receptor
rs1805006
84 Asp/Glu
GCCTTGTCGGA A CTGCTGGTGAG
225






GCCTTGTCGGA C CTGCTGGTGAG
226






CTCACCAGCAG T TCCGACAAGGC
227






CTCACCAGCAG G TCCGACAAGGC
228


PRKCA
protein kinase C, alpha
rs1010544
intron 8 
TAAAAAGGTGC A TGTATCTGTGT
229





388476 C/T
TAAAAAGGTGC G TGTATCTGTGT
230






ACACAGATACA T GCACCTTTTTA
231






ACACAGATACA C GCACCTTTTTA
232


PRKCA
protein kinase C, alpha
rs3890137
intron 8 
GGCTGGCTTT A CCACAGACTG
233





427857 A/G
TGGCTGGCTTT G CCACAGACTGT
234






CAGTCTGTGG T AAAGCCAGCC
235






ACAGTCTGTGG C AAAGCCAGCCA
236


BTNL2
butyrophilin-like 2
rs2076530
11084C/T
TGAAGGTGGTA A GTAAGAATTCT
237


(DRb1*15)



TGAAGGTGGTA G GTAAGAATTCT
238






AGAATTCTTAC T TACCACCTTCA
239






AGAATTCTTAC C TACCACCTTCA
240


PNMT
phenylethanolamine
rs876493
−184G/A
CACTCACCTCC A GTGTGTCTGCA
241



N-methyltransferase


CACTCACCTCC G GTGTGTCTGCA
242






CACTCACCTCC A GTGTGTCTGCA
243






CACTCACCTCC G GTGTGTCTGCA
244


PNMT
phenylethanolamine
rs3764351
−390G/A
ATGGCTGCGGG A GGCTGGAGAAG
245



N-methyltransferase


ATGGCTGCGGG G GGCTGGAGAAG
246






CTTCTCCAGCC T CCCGCAGCCAT
247






CTTCTCCAGCC C CCCGCAGCCAT
248


TRAIL/
tumor necrosis factor
rs9880164
1595C/T
GCTAATTTTTG C ACTTTCAGTAG
249


TNFSF10
(ligand) superfamily,
(rs1131568

GCTAATTTTTG T ACTTTCAGTAG
250



member 10
in v. 37.1)

CTACTGAAAGT G CAAAAATTAGC
251






CTACTGAAAGT A CAAAAATTAGC
252


PTPN22
protein tyrosine 
rs2476601
1858C/T: 
TTCAGGTGTCC A TACAGGAAGTG
253



phosphatase,non-receptor 

(620 W/R)
TTCAGGTGTCC G TACAGGAAGTG
254



type 22


CACTTCCTGTA T GGACACCTGAA
255






CACTTCCTGTA C GGACACCTGAA
256


MOG
myelin oligodendrocyte
rs3130250
15G/A [S5S]
GCAAGCTTATC A AGACCCTCTCT
257



glycoprotein


GCAAGCTTATC G AGACCCTCTCT
258






AGAGAGGGTCT T GATAAGCTTGC
259






AGAGAGGGTCT C GATAAGCTTGC
260


MOG
myelin oligodendrocyte
rs3130253
520G/A 
CTGTTGGCCTC A TCTTCCTCTGC
261



glycoprotein

[V145I]
CTGTTGGCCTC G TCTTCCTCTGC
262






GCAGAGGAAGA T GAGGCCAACAG
263






GCAGAGGAAGA C GAGGCCAACAG
264


SPP1
secreted phosphoprotein 1
rs9138
1286 A/C
ATTTATGTAGA A GCAAACAAAAT
265






ATTTATGTAGA C GCAAACAAAAT
266






ATTTTGTTTGC T TCTACATAAAT
267






ATTTTGTTTGC G TCTACATAAAT
268


SPP1
secreted phosphoprotein 1
rs4754
282T/C
GAAGATGATGA C GACCATGTGGA
269






GAAGATGATGA T GACCATGTGGA
270






TCCACATGGTC G TCATCATCTTC
271






TCCACATGGTC A TCATCATCTTC
272


SPP1
secreted phosphoprotein 1
rs1126616
750C/T
AAGCGGAAAGC C AATGATGAGAG
273






AAGCGGAAAGC T AATGATGAGAG
274






CTCATCATT G GCTTTCCGC
275






CTCATCATT A GCTTTCCGC
276


SPP1
secreted phosphoprotein 1
rs1126772
1083A/G
TGGAAATAACT A ATGTGTTTGAT
277






TGGAAATAACT G ATGTGTTTGAT
278






ATCAAACACAT T AGTTATTTCCA
279






ATCAAACACAT C AGTTATTTCCA
280


HLA-DRA
major histocompatibility
rs2395182
G/T
AGATGCCTATT G TATTACCGAGA
281



complex, class II,


AGATGCCTATT T TATTACCGAGA
282



DR alpha


TCTCGGTAATA C AATAGGCATCT
283






TCTCGGTAATA A AATAGGCATCT
284


HLA
major histocompatibility
rs2395166
C/T
ATAAGGTGAAA C AGAAACAGATC
285



complex


ATAAGGTGAAA T AGAAACAGATC
286






GATCTGTTTCT G TTTCACCTTAT
287






GATCTGTTTCT A TTTCACCTTAT
288


HLA
major histocompatibility
rs2213584
A/G
TGAGCAAAGAG A TTGGACACTGA
289



complex


TGAGCAAAGAG G TTGGACACTGA
290






TCAGTGTCCAA T CTCTTTGCTCA
291






TCAGTGTCCAA C CTCTTTGCTCA
292


HLA
major histocompatibility
rs2227139
C/T
CAACAGTTCAT C GTGTTTCAAAT
293



complex


CAACAGTTCAT T GTGTTTCAAAT
294






ATATTTGAAACTC G 
295






ATGAACTGTTGCT







ATATTTGAAACTC A 
296






ATGAACTGTTGCT



IL1RN
interleukin 1 receptor
rs419598
2018 T/C
CCAACTAGTTGCTGGATACTTGCAA
297



antagonist


CCAACTAGTTGCCGGATACTTGCAA
298






TTGCAAGTATCCAGCAACTAGTTGG
299






TTGCAAGTATCCGGCAACTAGTTGG
300


IL1RN
interleukin 1 receptor
2073 
2073 C/T 
TGCCAGGAAAG C CAATGTATGTG
301



antagonist
intron2 C/T
Intron2
TTGCCAGGAAAG T CAATGTATGTGG
302




(rs423904)

CCACATACATTG G CTTTCCTGGCAA
303






CCACATACATTG A CTTTCCTGGCAA
304


NOS2A
nitric oxide synthase 2A
rs1137933
exon 10 C/T, 
TAGCGCTGGAC A TCACAGAAGTC
305



isoform 1

D346D
TAGCGCTGGAC G TCACAGAAGTC
306






GACTTCTGTGA T GTCCAGCGCTA
307






GACTTCTGTGA C GTCCAGCGCTA
308


GABBRA1
gamma-aminobutyric acid
rs1805057
G1465A 
ACCAGAACGGC C GCCTCCTCCAG
309



(GABA) B receptor 1

(489 G/S)
ACCAGAACGGC T GCCTCCTCCAG
310






CTGGAGGAGGC G GCCGTTCTGGT
311






CTGGAGGAGGC A GCCGTTCTGGT
312


VDR
vitamin D receptor
rs731236
Taq 1
TGGATGGCCTC A ATCAGCGCGGC
313






TGGATGGCCTC G ATCAGCGCGGC
314






GCCGCGCTGAT T GAGGCCATCCA
315






GCCGCGCTGAT C GAGGCCATCCA
316


NOS2A
nitric oxide synthase 2A
rs2779248
−277 A/G
GGCTGCTAAGA C AGAGGCACCAC
317



isoform 1


GGCTGCTAAGA T AGAGGCACCAC
318






GTGGTGCCTCT G TCTTAGCAGCC
319






GTGGTGCCTCT A TCTTAGCAGCC
320


IL1B
interleukin 1, beta
rs1143627
−31 Tata
CTTTTGAAAGC T ATAAAAACAGC
321






CTTTTGAAAGC C ATAAAAACAGC
322






CTTTTGAAAGC T ATAAAAACAGC
323






CTTTTGAAAGC C ATAAAAACAGC
324


HLA-DRA
major histocompatibility
rs2239802
intron 4 
CCAGATGATAC C AATGTCTGATT
325



complex, class II,

4118 C/G
CCAGATGATAC G AATGTCTGATT
326



DR alpha


AATCAGACATT G GTATCATCTGG
327






AATCAGACATT C GTATCATCTGG
328


IL1B
interleukin 1, beta
rs1143634
+3953-4
CCTATCTTCTT C GACACATGGGA
329






CCTATCTTCTT T GACACATGGGA
330






TCCCATGTGTC G AAGAAGATAGG
331






TCCCATGTGTC A AAGAAGATAGG
332


SPP1
secreted phosphoprotein 1
rs2853744
−616G/T
GCAGTCATCCT G CTCTCAGTCAG
333






GCAGTCATCCT T CTCTCAGTCAG
334






CTGACTGAGAG C AGGATGACTGC
335






CTGACTGAGAG A AGGATGACTGC
336


CCR5
chemokine (C-C motif)
rs333
CCR5*D32
TTTTCCATACAGTCAGTATCAAT
337



receptor 5


TTTTCCATACATTAAAGATAGTC
338






ATTGATACTGACTGTATGGAAAA
339






GACTATCTTTAATGTATGGAAAA
340


HLA-DRA
major histocompatibility
rs3135388
3′ UTR 
CCTAAAGTGGG A TTGGTTTGTTG
341



complex, class II,

5323 C/T
CCTAAAGTGGG G TTGGTTTGTTG
342



DR alpha


CAACAAACCAA T CCCACTTTAGG
343






CAACAAACCAA C CCCACTTTAGG
344


HLA
major histocompatibility
rs9268458
A/C
AAAGTGCTCGG A TGTTGGGATTA
345



complex


AAAGTGCTCGG C TGTTGGGATTA
346






TAATCCCAACA T CCGAGCACTTT
347






TAATCCCAACA G CCGAGCACTTT
348


HLA
major histocompatibility
rs6457594
A/G
TCCACACATAC A GGTTTGTCACT
349



complex


TCCACACATAC G GGTTTGTCACT
350






AGTGACAAACC T GTATGTGTGGA
351






AGTGACAAACC C GTATGTGTGGA
352


HLA
major histocompatibility
rs7451962
A/G
GGCAGGAATTC A GAATCCCTCAT
353



complex


GGCAGGAATTC G GAATCCCTCAT
354






ATGAGGGATTC T GAATTCCTGCC
355






ATGAGGGATTC C GAATTCCTGCC
356


HLA
major histocompatibility
rs7451962
A/G
GGGCAGGAATTC A GAATCCCTCATC
357



complex


GGGCAGGAATTC G GAATCCCTCATC
358






GATGAGGGATTC T GAATTCCTGCCC
359






GATGAGGGATTC C GAATTCCTGCCC
360


HLA
major histocompatibility
rs7451962
A/G
GCAGGAATTC A GAATCCCTCA
361



complex


GCAGGAATTC G GAATCCCTCA
362






TGAGGGATTC T GAATTCCTGC
363






TGAGGGATTC C GAATTCCTGC
364


PNMT
phenylethanolamine
rs3764351
−390G/A
ATGGCTGCGGG A GGCTGGAGAAG
365



N-methyltransferase


ATGGCTGCGGG G GGCTGGAGAAG
366






TTCTCCAGCC T CCCGCAGCCA
367






TTCTCCAGCC C CCCGCAGCCA
368


KIF1B
kinesin family member 18
rs10492972
C/T
CGCTACAATTCT C CTGGTCAGGTTT
369






CGCTACAATTCT T CTGGTCAGGTTT
370






AAACCTGACCAG G AGAATTGTAGCG
371






AAACCTGACCAG A AGAATTGTAGCG
372


IGF2R
Immunoglobulin G Fc
rs12202350
C/T
GATAACTTCACA C AGATTGAAATGT
373



Receptor II


GATAACTTCACA T AGATTGAAATGT
374






ACATTTCAATCT G TGTGAAGTTATC
375






ACATTTCAATCT A TGTGAAGTTATC
376


GRIN2A
glutamate receptor,
rs8049651
C/T
ACACGTCTCGGT C AGGGGGTCTATG
377



ionotropic, N-methyl


ACACGTCTCGGT T AGGGGGTCTATG
378



D-aspartate 2A


CATAGACCCCCT G ACCGAGACGTGT
379






CATAGACCCCCT A ACCGAGACGTGT
380


KLC1
kinesin light chain 1
rs8702
C/G
ACATGCCTTGCT C TAAGGCTTAGTT
381






ACATGCCTTGCT G TAAGGCTTAGTT
382






AACTAAGCCTTA G AGCAAGGCATGT
383






AACTAAGCCTTA C AGCAAGGCATGT
384


IL7R
interleukin 7 receptor
rs987107
C/T
TCTCTTTACTGA C AGCAACTCTGGC
385






TCTCTTTACTGA T AGCAACTCTGGC
386






GCCAGAGTTGCT G TCAGTAAAGAGA
387






GCCAGAGTTGCT A TCAGTAAAGAGA
388


STS
STS steroid sulfatase,
rs12861247
A/G
CAGGGAGGAATG A ACCTGGATTCCT
389



isozyme S


CAGGGAGGAATG G ACCTGGATTCCT
390






AGGAATCCAGGT T CATTCCTCCCTG
391






AGGAATCCAGGT C CATTCCTCCCTG
392


GPC6
glypican 6
rs7995215
A/G
TGCACACTTCAG A ATGTTTGGCACC
393






TGCACACTTCAG G ATGTTTGGCACC
394






GGTGCCAAACAT T CTGAAGTGTGCA
395






GGTGCCAAACAT C CTGAAGTGTGCA
396


EREG
epiregulin
rs1350666
C/T
TGGCTATTGTTT C ATTGCATTCACT
397






TGGCTATTGTTT T ATTGCATTCACT
398






AGTGAATGCAAT G AAACAATAGCCA
399






AGTGAATGCAAT A AAACAATAGCCA
400


ADRA1A
adrenergic, alpha-1A-,
rs3808585
C/T
GGGGTAGAGGGG C CGGTATAAAACC
401



receptor


GGGGTAGAGGGG T CGGTATAAAACC
402






GGTTTTATACCG G CCCCTCTACCCC
403






GGTTTTATACCG A CCCCTCTACCCC
404


IL16
interleukin 16
rs4128767
C/T
GCTGTACCATAG C TTTTCTGAGAAA
405






GCTGTACCATAG T TTTTCTGAGAAA
406






TTTCTCAGAAAA G CTATGGTACAGC
407






TTTCTCAGAAAA A CTATGGTACAGC
408


ARRB2
arrestin, beta 2
rs7208257
C/T
TGAAGTCTTCTC C TTCCTCCGCCAC
409






TGAAGTCTTCTC T TTCCTCCGCCAC
410






GTGGCGGAGGAA G GAGAAGACTTCA
411






GTGGCGGAGGAA A GAGAAGACTTCA
412


NTF3
neurotrophin-3
rs7956189
A/G
TAAGTAAGTGGC A GAGTGAAGATTG
413






TAAGTAAGTGGC G GAGTGAAGATTG
414






CAATCTTCACTC T GCCACTTACTTA
415






CAATCTTCACTC C GCCACTTACTTA
416


IL12A
interleukin-12 subunit 
rs4680534
C/T
ATCTATGTGTGT C TGTACATGAATA
417



alpha


ATCTATGTGTGT T TGTACATGAATA
418






TATTCATGTACA G ACACACATAGAT
419






TATTCATGTACA A ACACACATAGAT
420


SLC6A4
solute carrier family 6,
rs1042173
G/T
GAGTAGCATATA G AATTTTATTGCT
421



member 4


GAGTAGCATATA T AATTTTATTGCT
422






AGCAATAAAATT C TATATGCTACTC
423






AGCAATAAAATT A TATATGCTACTC
424


FLJ34870
FLJ34870
rs7577925
A/G
TCCTTGACTGTT A GACACCAAGGAG
425






TCCTTGACTGTT G GACACCAAGGAG
426






CTCCTTGGTGTC T AACAGTCAAGGA
427






CTCCTTGGTGTC C AACAGTCAAGGA
428


FCRL3
Fc receptor-like 3
rs7528684
nearGene-5′ 
ATGTACAGATCA A GGACTTCCCGTA
429





A/G
ATGTACAGATCA G GGACTTCCCGTA
430






TACGGGAAGTCC T TGATCTGTACAT
431






TACGGGAAGTCC C TGATCTGTACAT
432


IGF2R
insulin-like growth 
rs6917747
A/G
CTGGGAGAGACT A GCTCACACAGCT
433



factor 2 receptor


CTGGGAGAGACT G GCTCACACAGCT
434






AGCTGTGTGAGC T AGTCTCTCCCAG
435






AGCTGTGTGAGC C AGTCTCTCCCAG
436


LOC729293
LOC729293
rs6570426
A/T
CCCTTCCAAATA A CCAATCATACAC
437






CCCTTCCAAATA T CCAATCATACAC
438






GTGTATGATTGG T TATTTGGAAGGG
439






GTGTATGATTGG A TATTTGGAAGGG
440


SNAP25
synaptosomal-associated
rs6077690
A/T
CACTTTGGAAAA A ATTCTGACTACA
441



protein, 25 kDa


CACTTTGGAAAA T ATTCTGACTACA
442






TGTAGTCAGAAT T TTTTCCAAAGTG
443






TGTAGTCAGAAT A TTTTCCAAAGTG
444


MORF4
mortality factor 4
rs4473631
A/C
CAGAGGACAATT A TCTTGGAAAGCA
445






CAGAGGACAATT C TCTTGGAAAGCA
446






TGCTTTCCAAGA T AATTGTCCTCTG
447






TGCTTTCCAAGA G AATTGTCCTCTG
448


SNAP25
synaptosomal-associated
rs3787283
C/T
AATTCCAGAAAA C GAATGATTCCCA
449



protein, 25 kDa


AATTCCAGAAAA T GAATGATTCCCA
450






TGGGAATCATTC G TTTTCTGGAATT
451






TGGGAATCATTC A TTTTCTGGAATT
452


LOC728594
hypothetical protein
rs3756450
C/T
CCACAATGATAA C AAAGCCGACTTG
453



LOC728594


CCACAATGATAA T AAAGCCGACTTG
454






CAAGTCGGCTTT G TTATCATTGTGG
455






CAAGTCGGCTTT A TTATCATTGTGG
456


SLC6A2
solute carrier family 6
rs28386840
A/T
GGGCTGAGCACC A GTTTCCCCAGCA
457



member 2


GGGCTGAGCACC T GTTTCCCCAGCA
458






TGCTGGGGAAAC T GGTGCTCAGCCC
459






TGCTGGGGAAAC A GGTGCTCAGCCC
460


ZNF544
zinc finger protein 544
rs260461
A/G
ATCAATGTCACT A GATCAAAATCAA
461






ATCAATGTCACT G GATCAAAATCAA
462






TTGATTTTGATC T AGTGACATTGAT
463






TTGATTTTGATC C AGTGACATTGAT
464


MHC II/
major histocompatibility
rs2187668
A/G
AGCTGAGAGTAA G TGAGGACCATGT
465


HLA-
complex, class II,


AGCTGAGAGTAA A TGAGGACCATGT
466


DQA1
DQ alpha 1


ACATGGTCCTCA C TTACTCTCAGCT
467






ACATGGTCCTCA T TTACTCTCAGCT
468


SLC6A4
solute carrier family 6,
rs2066713
C/T
GCATTTCCCTTC C GTAGACCCTCTG
469



member 4


GCATTTCCCTTC T GTAGACCCTCTG
470






CAGAGGGTCTAC G GAAGGGAAATGC
471






CAGAGGGTCTAC A GAAGGGAAATGC
472


CSMD1
CUB and Sushi multiple
rs2049306
A/C
GTTCTGAAAGCA A ACATTTAAATAT
473



domains 1


GTTCTGAAAGCA C ACATTTAAATAT
474






ATATTTAAATGT T TGCTTTCAGAAC
475






ATATTTAAATGT G TGCTTTCAGAAC
476


SLC1A3
solute carrier family 1
rs2032893
A/G
ATAAATAAATAT A CAGAAGCATTGG
477



member 3


ATAAATAAATAT G CAGAAGCATTGG
478






CCAATGCTTCTG T ATATTTATTTAT
479






CCAATGCTTCTG C ATATTTATTTAT
480


LOC647094
LOC647094
rs2028455
A/G
ACATGCCTGCCT A GAATGATTACTT
481






ACATGCCTGCCT G GAATGATTACTT
482






AAGTAATCATTC T AGGCAGGCATGT
483






AAGTAATCATTC C AGGCAGGCATGT
484


DMRT2
doublesex and mab-3 related
rs17641078
C/G
AAGATCAGCAAA C AAAACACCAGGC
485



transcription factor 2


AAGATCAGCAAA G AAAACACCAGGC
486






GCCTGGTGTTTT G TTTGCTGATCTT
487






GCCTGGTGTTTT C TTTGCTGATCTT
488


DBH
dopamine beta-hydroxylase
rs1611115
C/T
TCAGTCTACTTG C GGGAGAGGACAG
489



(dopamine beta-


TCAGTCTACTTG T GGGAGAGGACAG
490



monooxygenase)


CTGTCCTCTCCC G CAAGTAGACTGA
491






CTGTCCTCTCCC A CAAGTAGACTGA
492


MMP24
MMP24 matrix
rs1555322
A/G
CACGCACTTCAC A TGTATCTTATTC
493



metallopeptidase 24


CACGCACTTCAC G TGTATCTTATTC
494






GAATAAGATACA T GTGAAGTGCGTG
495






GAATAAGATACA C GTGAAGTGCGTG
496


DSEL
DSEL
rs13353224
A/G
ATCAGAGTTAAT A AACTTCCCTATT
497






ATCAGAGTTAAT G AACTTCCCTATT
498






AATAGGGAAGTT T ATTAACTCTGAT
499






AATAGGGAAGTT C ATTAACTCTGAT
500


C1orf125
chromosome 1 open reading
rs12047808
A/G
AATGAGAGGGGT A ACACACATTATG
501



frame 125


AATGAGAGGGGT G ACACACATTATG
502






CATAATGTGTGT T ACCCCTCTCATT
503






CATAATGTGTGT C ACCCCTCTCATT
504


GPC5
glypican 5
rs10492503
A/T
TGGATAACTGCT A CAATTATAGTTT
505






TGGATAACTGCT T CAATTATAGTTT
506






AAACTATAATTG T AGCAGTTATCCA
507






AAACTATAATTG A AGCAGTTATCCA
508


C1GALT1
core 1 synthase,
rs10259085
C/T
TAAAAACAATTA C GTAACACCAAGA
509



glycoprotein-N-


TAAAAACAATTA T GTAACACCAAGA
510



acetylgalactosamine 3-beta-


TCTTGGTGTTAC G TAATTGTTTTTA
511



galactosyltransferase, 1


TCTTGGTGTTAC A TAATTGTTTTTA
512


MET
met proto-oncogene
rs10243024
A/G
TATTTTTACTCC A AATACTGTTTCA
513



(hepatocyte growth


TATTTTTACTCC G AATACTGTTTCA
514



factor receptor)


TGAAACAGTATT T GGAGTAAAAATA
515






TGAAACAGTATT C GGAGTAAAAATA
516


ICOS
inducible T-cell co-
rs4404254
C/T
TTACAAGTTTAG C TCTTTTTGTAGA
517



stimulator


TTACAAGTTTAG T TCTTTTTGTAGA
518






TCTACAAAAAGA G CTAAACTTGTAA
519






TCTACAAAAAGA A CTAAACTTGTAA
520


OAS1
2′,5′-oligoadenylate
rs3741981/
A/G
CAGTTGACTGGC A GCTATAAACCTA
521



synthetase 1
rs1131454

CAGTTGACTGGC G GCTATAAACCTA
522






TAGGTTTATAGC T GCCAGTCAACTG
523






TAGGTTTATAGC C GCCAGTCAACTG
524
















TABLE 8







Examples of Forward Primers Used in SNP Analysis











SNP #
Gene Symbol
rs ID
Forward Primers (sequence 5′ > 3′)
SEQ ID NO:














1
EBF1
rs1368297
CCAAATCTTGGTTTTCAGTGC
525


2
RANTES/CCL5
rs2280788
TATGATACCGGCCAATGCTT
526


3
RANTES/CCL5
rs2107538
CACCTCCTTTGGGGACTGTA
527


4
TGFB1
rs17851976
TCGATAGTCTTGCAGGTGGA
528


6
UPC2
rs659366
TTCGCCTTTAATTGGCTGAC
529


7
IKBL
rs3130062
TGAGTCCTTCTCAGCCTGGT
530


8
Apo I/Fas (CD 95)
rs1800682
CCTATGGCGCAACATCTGTA
531


9
Apo I/Fas (CD 95)
rs3781202
CCAATGCCTACCTAGCCTGT
532


10
IL2
rs2069763
GCATTGCACTAAGTCTTGCAC
533


11
IL2
rs2069762
ACCCCCAAAGACTGACTGAA
534


12
IL10
rs1800896
ATGGAGGCTGGATAGGAGGT
535


13
IL4R
rs1801275
CAACCTGAGCCAGAAACCTG
536


14
PTPRC
rs17612648
ATGCCCAGTGTTCCACTTTC
537


15
PTPRC
rs4915154
GCAGATGTCCCAGGAGAGAG
538


16
PD-1/PDCD1
rs11568821
TATAGCCAGGACCCCACCTC
539


17
CRYAB
rs14133
TGCTTGGGATTCCTGACTCT
540


18
CRYAB
rs762550
GCACCCAATTCCTAAAGCAC
541


19
CRYAB
rs2234702
GCACCCAATTCCTAAAGCAC
542


20
NDUFS5
rs2889683
TTGCTCAACTTTAGTTTTTCAGTCA
543


21
NDUFS5
rs6981
GCAGCGGGATAAGCTGATAA
544


22
NDUFS7
rs2074897
GGTCTCCAGGGACAGACGTA
545


24
NDUFA7
rs2288414
CGCTGAGCACTGCAAATCTA
546


25
NDUFA7
rs561
CCAAGGAGGCAAAGTAGTCG
547


26
ADAMTS14
rs4747075
TCCATTGTGGGGATTTTTGT
548


27
ADAMTS14
rs7081273
GCCTTGGAAGGAGAAAGGAG
549


28
ADAMTS14
rs4746060
CTGGGGAGGTGCTATGGAT
550


29
NFKBIA
rs11569591
AGGCTTTTCACTCCTCCAAA
551


29
NFKBIA
rs11569591
AGGCTTTTCACTCCTCCAAA
552


29
NFKBIA
rs11569591
AGGCTTTTCACTCCTCCAAA
553


30
SPP1
rs28357094
TGTGTGTGTGCGTTTTTGTTT
554


31
HLA-DR*1501
rs367398
TGAGACACATAGCAGCAGCA
555


32
HLA-DR*1501
rs1800629
GCCCCTCCCAGTTCTAGTTC
556


34
IL7R
rs11567685
GCAGGCAGATCACTTGAGGT
557


35
IL7R
rs7718919
GCTCTGCCATTGTTGCATAA
558


36
IL7R
rs11567686
CCGTCTCCACTGAAAACACA
559


37
IFNAR1
rs2257167
GCTCAGATTGGTCCTCCAGA
560


38
IFNAR2
rs7279064
TCTTGTCTTTGCTCCCATTTTT
561


39
IL1B
rs1799916
GGCAGAGAGACAGAGAGACTCC
562


40
IFNGR2
rs9808753
TGTACAACGCAGAGCAGGTC
563


41
Apo I/Fas (CD 95)
rs2234978
TGCAGAAAGCACAGAAAGGA
564


42
CD24
rs8734
ACCCACGCAGATTTATTCCA
565


43
MEFV
rs28940577
TTGGAGACAAGACAGCATGG
566


44
CTLA4
rs231775
GGATCCTGAAAGGTTTTGCTC
567


45
CNTF
rs1800169
GACACTGGGGTGATGACAGA
568


46
MHC2TA
rs3087456
AGGTTCCCCCAACAGACTTT
569


47
VDR
rs1544410
CCTCACTGCCCTTAGCTCTG
570


48
PRKCA
rs7220007
AGCTGAGTGTTGTGCAGTGG
571


49
PRKCA
rs887797
AACCCCTGCATTTCAGAATTT
572


50
PRKCA
rs2078153
AAACAACTCCACCCAGGTTC
573


51
CTLA4
rs5742909
TGGTTAAGGATGCCCAGAAG
574


52
MGC33887
rs987931
CTTCGATAAATAGTGCTGGGAAA
575


53
CACNG4
rs4790896
CTTAATCGGAAAGCTGTGTCG
576


54
HELZ
rs2363846
GGAAAACACCAACACTCTCCA
577


55
PITPNC1
rs1318
TCAGTTGCAAAGCTACGATGA
578


56
PITPNC1
rs2365403
ACGCCTTTGGAACAACAATC
579


57
MC1R
rs1805009
AACCTCTTTCTCGCCCTCAT
580


58
MC1R
rs1805006
TGCACTCACCCATGTACTGC
581


59
PRKCA
rs1010544
ACCAGCTTGCAGTCTCTGCT
582


60
PRKCA
rs3890137
AGCCAGGAGACCTGAGACTG
583


61
BTNL2 (DRb1*15)
rs2076530
TACTCAGTGCCAGACCTTCG
584


62
PNMT
rs876493
TAAAGATTGTGGGGGTGAGG
585


63
PNMT
rs3764351
AAAGGGCCTAATTCCCCAGT
586


64
TRAIL/TNFSF10
rs9880164
ACTACAGGCATGTGCCAACA
587




(rs1131568 in v. 37.1)




65
PTPN22
rs2476601
TGCCCATCCCACACTTTATT
588


66
MOG
rs3130250
TCTGTCCCCAGGAACAGTAGA
589


67
MOG
rs3130253
ATGCTGAGTGTTGGGGATTC
590


68
SPP1
rs9138
GCTTCATGGAAACTCCCTGT
591


69
SPP1
rs4754
AGACCCTTCCAAGTAAGTCCAA
592


70
SPP1
rs1126616
AGAGTGCTGAAACCCACAGC
593


71
SPP1
rs1126772
GAACATGAAATGCTTCTTTCTCAG
594


72
HLA-DRA
rs2395182
GACTGGCCTTACCCATTCTG
595


73
HLA
rs2395166
CGCTTTCCATAGAAACCTTGG
596


74
HLA
rs2213584
CATTGCAGGATTTACATATCAACA
597


75
HLA
rs2227139
CAGCCAAGATGAAACCCAAG
598


76
IL1RN
rs419598
ACAAGTTCTGGGGGACACAG
599


77
IL1RN
2073 Intron2 C/T
ACAAGTTCTGGGGGACACAG
600




(rs423904)




78
NOS2A
rs1137933
CAGAGTGATAGCGGCGAGT
601


79
GABBRA1
rs1805057
TGGTCGGTAATGGTCTGGTT
602


80
VDR
rs731236
AGGTCGGCTAGCTTCTGGAT
603


81
NOS2A
rs2779248
CTCTGTGTGGTGCCTCTTCA
604


82
IL1B
rs1143627
CAGTTTCTCCCTCGCTGTTT
605


83
HLA-DRA
rs2239802
TGATCAAGGTGCCCGTCTAT
606


84
IL1B
rs1143634
ATGCTCAGGTGTCCTCCAAG
607


85
SPP1
rs2853744
ACACAGCGGAATTCAGAACC
608


87
CCR5
rs333
CGTCTCTCCCAGGAATCATC
609


88
HLA-DRA
rs3135388
CATTTGGGCTTGGTCTCATT
610


89
HLA
rs9268458
AATGGGGCCTCACTATGTTG
611


90
HLA
rs6457594
TGAATTCTGGGGGCTTACTG
612


91
HLA
rs7451962
GCCAGCTCAGTGAGGTCAGTA
613


92
HLA
rs7451962
GCCAGCTCAGTGAGGTCAGTA
614


93
HLA
rs7451962
GCCAGCTCAGTGAGGTCAGTA
615


94
PNMT
rs3764351
AAAGGGCCTAATTCCCCAGT
616


95
KIF1B
rs10492972
TGACCTCACATTGGCTATTGG
617


96
IGF2R
rs12202350
ATAGGCATAAGCCACCATGC
618


97
GRIN2A
rs8049651
AGCATTCCTGCCACTCACTT
619


98
KLC1
rs8702
AGAAAAGCAGAATGCCCAAA
620


99
IL7R
rs987107
ACCTCTGGGAAAAAGCCCTA
621


100
STS
rs12861247
TAAACAAGGAAGGGCACTGG
622


101
GPC6
rs7995215
CAGCAGTGTCCATGAGAATCA
623


102
EREG
rs1350666
TTGGGGGCTATTTAAGTTCA
624


103
ADRA1A
rs3808585
CTCGGGCAAAGACTCTTGTT
625


104
IL16
rs4128767
ATGATCACACCACTGCATCC
626


105
ARRB2
rs7208257
CAGCGTCTCCAGCCTCTTAG
627


106
NTF3
rs7956189
AATCCTTTGAGGGAGCCAGT
628


107
IL12A
rs4680534
TCAGGTTTTCCTCCTACTTCAAA
629


108
SLC6A4
rs1042173
AAACTGCGTAGGAGAGAACAGG
630


109
FLJ34870
rs7577925
TGGGAGCAAAGTGAAAGTCA
631


110
FCRL3
rs7528684
TCACACAGCCTTTGGTTCTG
632


111
IGF2R
rs6917747
TTCCTGGTGGTGGTTTTCTC
633


112
LOC729293
rs6570426
CATTTCTGGAACTGCCTTGG
634


113
SNAP25
rs6077690
CCTCCTCCATTCCTTCACAA
635


114
MORF4
rs4473631
TCATATGCCTGGCAGTTTACA
636


115
SNAP25
rs3787283
AGGGCTGCTACCAGCATAAA
637


116
LOC728594
rs3756450
TTGGAGACAGCAGTCAGTGG
638


117
SLC6A2
rs28386840
GCGGCCTTCATGGATAAATA
639


118
ZNF544
rs260461
GAGGCCACAAGTCCAAAATC
640


119
MHC II/HLA-DQA1
rs2187668
CTTAGCCACATGCCCATTTT
641


120
SLC6A4
rs2066713
CTTCTGAGATGGACCGCATT
642


121
CSMD1
rs2049306
TTGCCACTAGTTCTGAAAGCA
643


122
SLC1A3
rs2032893
ATCCCTATCAGGGGCAGACT
644


123
LOC647094
rs2028455
GCATAATGCCACAGGACCTT
645


124
DMRT2
rs17641078
GCCTCACACTCCTGAGATCC
646


125
DBH
rs1611115
ACAGGAGGGAAAAGGAAGGA
647


126
MMP24
rs1555322
CAACAGCTGCCATTCTGTGT
648


127
DSEL
rs13353224
TGGGGGTGCTAAGACAGTTT
649


128
C1orf125
rs12047808
GGCAAATCAAATCCAGCAGT
650


129
GPC5
rs10492503
GCGGAAGATTGGATAACTGC
651


130
C1GALT1
rs10259085
AGTCATAAGGCCGGAGTCCT
652


131
MET
rs10243024
AGCGATTTCTGGAAGCATGT
653


132
ICOS
rs4404254
CCCGGAATTGAAAGCAAAT
654


133
OAS1
rs3741981/rs1131454
GGATCAGGAATGGACCTCAA
655
















TABLE 9







Examples of Reverse Primers Used in SNP Analysis











SNP #
Gene Symbol
rs ID
Reverse Primers (sequence 5′ > 3′)
SEQ ID NO:














1
EBF1
rs1368297
CTGCCCAGTGCTTTTCATTT
656


 


2
RANTES/CCL5
rs2280788
GAGGGCAGTAGCAATGAGGA
657


 


3
RANTES/CCL5
rs2107538
GGAGTGGCAGTTAGGACAGG
658


 


4
TGFB1
rs17851976
ACCACACCAGCCCTGTTC
659


 


6
UPC2
rs659366
AGTCCCTTCTGCTGGTGAAA
660


 


7
IKBL
rs3130062
CTCTCACGCAGCTCTTCCTC
661


 


8
Apo I/Fas (CD 95)
rs1800682
AGTTGGGGAGGTCTTGAAGG
662


 


9
Apo I/Fas (CD 95)
rs3781202
AAGGGCCTTGTCTTTTAGGC
663


 


10
IL2
rs2069763
TCCTGGTGAGTTTGGGATTC
664


 


11
IL2
rs2069762
TCTTGCTCTTGTCCACCACA
665


 


12
IL10
rs1800896
CTTCCCCAGGTAGAGCAACA
666


 


13
IL4R
rs1801275
CCACATTTCTCTGGGGACAC
667


 


14
PTPRC
rs17612648
CTTTTGTGTGCCAACCTGTG
668


 


15
PTPRC
rs4915154
AACTGAAGACACTACTAGAGCAGCA
669


 


16
PD-1/PDCD1
rs11568821
AGGCAGGCACACACATGG
670


 


17
CRYAB
rs14133
GACTTGTGATCCGGGATTTG
671


 


18
CRYAB
rs762550
GGTCAACATGTCAGCACCAG
672


 


19
CRYAB
rs2234702
GGTCAACATGTCAGCACCAG
673


 


20
NDUFS5
rs2889683
AGTGGCAGACCATCCACATC
674


 


21
NDUFS5
rs6981
CTTTGACAAGGAGGTTTGTCG
675


 


22
NDUFS7
rs2074897
AGGAATCGTTCTGGGGAGAG
676


 


24
NDUFA7
rs2288414
GCTCTGTCCTTTCTCCACCA
677


 


25
NDUFA7
rs561
AGAAAGTCCCTGTGGGTGTG
678


 


26
ADAMTS14
rs4747075
CTGGCTTCTCTGGGAGGAAT
679


 


27
ADAMTS14
rs7081273
GCTTGGCTCTCAGGAGACAG
680


 


28
ADAMTS14
rs4746060
GCTTCAAAGTGCTCAAATGGT
681


 


29
NFKBIA
rs11569591
AAGGACGCACTGTGGTTAGG
682


 


29
NFKBIA
rs11569591
AAGGACGCACTGTGGTTAGG
683


 


29
NFKBIA
rs11569591
AAGGACGCACTGTGGTTAGG
684


 


30
SPP1
rs28357094
CCAAGCCCTCCCAGAATTTA
685


 


31
HLA-DR*1501
rs367398
CAGGAAACAGCTCAGACGTG
686


 


32
HLA-DR*1501
rs1800629
AAAGTTGGGGACACACAAGC
687


 


34
IL7R
rs11567685
GCCCAGGCTGGAGTACAATA
688


 


35
IL7R
rs7718919
CACACCACAGTAGGCATTCAA
689


 


36
IL7R
rs11567686
GCCCAGGCTGGAGTACAATA
690


 


37
IFNAR1
rs2257167
TTCGCCTAATTTTTCTCTCACA
691


 


38
IFNAR2
rs7279064
GACTTCCTGCCAGTGCTCTC
692


 


39
IL1B
rs1799916
AAACAGCGAGGGAGAAACTG
693


 


40
IFNGR2
rs9808753
TGTTTCCCACGGGTTTGATA
694


 


41
Apo I/Fas (CD 95)
rs2234978
CTGGGCTATGGAGCAAGACT
695


 


42
CD24
rs8734
ACCACGAAGAGACTGGCTGT
696


 


43
MEFV
rs28940577
GCTTGGGAGGCTCCTTTATT
697


 


44
CTLA4
rs231775
CCTCCTCCATCTTCATGCTC
698


 


45
CNTF
rs1800169
GCCAACAAAACATGGAAGGT
699


 


46
MHC2TA
rs3087456
CAAGCTAAGCCAACATGCAA
700


 


47
VDR
rs1544410
CAGGAATGTTGAGCCCAGTT
701


 


48
PRKCA
rs7220007
GCATAGCCTCGGAGACAGAC
702


 


49
PRKCA
rs887797
TCCCGGGTATATGATCTCCA
703


 


50
PRKCA
rs2078153
TCACCTAAGGACAGTCTAAAATTGC
704


 


51
CTLA4
rs5742909
AGCCGTGGGTTTAGCTGTTA
705


 


52
MGC33887
rs987931
GCTTGGAAGTTGCCATTCAT
706


 


53
CACNG4
rs4790896
AGCTTGCCACAGGACAGTTT
707


 


54
HELZ
rs2363846
TTGAGTTGTTGCAGCAGAGATT
708


 


55
PITPNC1
rs1318
TGCCTTTTGATGACTGGGTTA
709


 


56
PITPNC1
rs2365403
AGCAGGGAAGCACTTGAAGA
710


 


57
MC1R
rs1805009
GGTCACACAGGAACCAGACC
711


 


58
MC1R
rs1805006
TGCAGGTGATCACGTCAATG
712


 


59
PRKCA
rs1010544
CCCCAAACCCTGACTTTCAT
713


 


60
PRKCA
rs3890137
TACTGATTGAGCCCCCTTGT
714


 


61
BTNL2 (DRb1*15)
rs2076530
TTAAAGTGGCAGGAGCAGGT
715


 


62
PNMT
rs876493
CCCATTCATCCATCTCCCTTA
716


 


63
PNMT
rs3764351
CCTCACCCCCACAATCTTTA
717


 


64
TRAIL/TNFSF10
rs9880164(rs1131568
CGAGATCAAGAGATCAAGACCA
718




in v. 37.1)




 


65
PTPN22
rs2476601
TGGATAGCAACTGCTCCAAG
719


 


66
MOG
rs3130250
GCTGGAAGACACTTGGAGGA
720


 


67
MOG
rs3130253
TCCAAGAAGCCAGCTCATTT
721


 


68
SPP1
rs9138
CACACCACAAAAAGATAATCACAA
722


 


69
SPP1
rs4754
CATCAGACTGGTGAGAATCATC
723


 


70
SPP1
rs1126616
ATTCACGGCTGACTTTGGAA
724


 


71
SPP1
rs1126772
TGAACATAGACATAACCCTGAAGC
725


 


72
HLA-DRA
rs2395182
TCCACTCAAAGACACATCTTCAA
726


 


73
HLA
rs2395166
TGTGTCAGGCAATGAGGCTA
727


 


74
HLA
rs2213584
GGCATCTGAGACTATGTCTAACAGAA
728


 


75
HLA
rs2227139
GGGTTGGGGAGAAAGATATGA
729


 


76
IL1RN
rs419598
ATTGCACCTAGGGTTTGTGC
730


 


77
IL1RN
2073 Intron2
ATTGCACCTAGGGTTTGTGC
731




C/T (rs423904)




 


78
NOS2A
rs1137933
CCCTTCAATGGCTGGTACAT
732


 


79
GABBRA1
rs1805057
TGGCCTATGATGCCATCTG
733


 


80
VDR
rs731236
CTGAGAGCTCCTGTGCCTTC
734


 


81
NOS2A
rs2779248
CAGCTTCCTGGACTCCTGTC
735


 


82
IL1B
rs1143627
TTTGCTACTCCTTGCCCTTC
736


 


83
HLA-DRA
rs2239802
TGTAAGGCACATGGAGGTGA
737


 


84
IL1B
rs1143634
GTGATCGTACAGGTGCATCG
738


 


85
SPP1
rs2853744
GCTTGTTACTTAGACAAATGGCACT
739


 


87
CCR5
rs333
TGTAGGGAGCCCAGAAGAGA
740


 


88
HLA-DRA
rs3135388
TCCATACCTTGGGGTTTCAG
741


 


89
HLA
rs9268458
TGCAGGGTTTTGATACATGG
742


 


90
HLA
rs6457594
ATTTCTCCTCCACCCTCTGC
743


 


91
HLA
rs7451962
GAACGGTCCTCTCACTTCTCA
744


 


92
HLA
rs7451962
GAACGGTCCTCTCACTTCTCA
745


 


93
HLA
rs7451962
GAACGGTCCTCTCACTTCTCA
746


 


94
PNMT
rs3764351
CCTCACCCCCACAATCTTTA
747


 


95
KIF1B
rs10492972
CACATTGGAATTTGGGAAGAA
748


 


96
IGF2R
rs12202350
AGGTGAGGGGCTGAAGAAGT
749


 


97
GRIN2A
rs8049651
GTCCTTCTCCGACTGTGAGC
750


 


98
KLC1
rs8702
CATGACGGTGACCTGTTGAC
751


 


99
IL7R
rs987107
CCCCACTTCCACCAAAATTA
752


 


100
STS
rs12861247
GGATTGGCTGAACATTTTGG
753


 


101
GPC6
rs7995215
AATGGGTGGGGGTGTTATTT
754


 


102
EREG
rs1350666
GACTGAGTGCAATGCCAAAA
755


 


103
ADRA1A
rs3808585
CGCTTTTTCCACCAGGTTT
756


 


104
IL16
rs4128767
CTGGGCTCTGCTTGTTTCTC
757


 


105
ARRB2
rs7208257
AGCTGTTCCTCCCGTACCTT
758


 


106
NTF3
rs7956189
AGACTAGTGCCGAGGGTTCA
759


 


107
IL12A
rs4680534
TCGTGCAAAATCAAGGTTCA
760


 


108
SLC6A4
rs1042173
CAAGCTTGCATGGACACACT
761


 


109
FLJ34870
rs7577925
ATCTTGGCATCTCCTTGGTG
762


 


110
FCRL3
rs7528684
TGAGAAGGGCTTTGGCTTTA
763


 


111
IGF2R
rs6917747
CCCTAAGAAAGGTGCCATGA
764


 


112
LOC729293
rs6570426
AAATGGTGCTGGGAAAACTG
765


 


113
SNAP25
rs6077690
GAATAGGGGGAAAGGGGTTT
766


 


114
MORF4
rs4473631
CTTGAAGGATGCTTTCCAAGA
767


 


115
SNAP25
rs3787283
AGTTTGGTTTCCCCACACTG
768


 


116
LOC728594
rs3756450
TTTGCCCTAAATGCCAAGTC
769


 


117
SLC6A2
rs28386840
AGGGAAGGAAACCAGGAGAA
770


 


118
ZNF544
rs260461
GGAGAAAGGCAGAGGGAGAT
771


 


119
MHC II/HLA-DQA1
rs2187668
TCTCCGGTGGTAGATCTTGG
772


 


120
SLC6A4
rs2066713
TCCTGACCTCACATGATCCA
773


 


121
CSMD1
rs2049306
TTCACTTCGACCAGGATATTCA
774


 


122
SLC1A3
rs2032893
TCGGGCATTCACAATGTTTA
775


 


123
LOC647094
rs2028455
AATCAGTGCTGCTGCTTGTG
776


 


124
DMRT2
rs17641078
TCAGGACCCGATTTGTCAGT
777


 


125
DBH
rs1611115
ACAGGACCTTTGCCATCATC
778


 


126
MMP24
rs1555322
GATCCTGAGGGTGGAACTGA
779


 


127
DSEL
rs13353224
CATGAGGCTGGGAGTTAGGA
780


 


128
C1orf125
rs12047808
GGCAGGCAATACACACACAC
781


 


129
GPC5
rs10492503
CATCCCATGGATTTGTAGCC
782


 


130
C1GALT1
rs10259085
GCAAGGCATCTATCCTGGAG
783


 


131
MET
rs10243024
GATGGGTCCCCATTTTTCTT
784


 


132
ICOS
rs4404254
GCTCTACCCCATGAGAATGC
785


 


133
OAS1
rs3741981/rs1131454
GGAGAACTCGCCCTCTTTCT
786









Table 10 shows SNPs and associated risk alleles for MS disease severity. Presence of one or more risk alleles as indicated in Table 10 at the specified SNPs is associated with a higher probability that the subject has a greater severity of MS disease phenotype, for example: a multiple sclerosis severity score (MSSS) of 2.5 or greater; an increase in size and/or distribution of T2 brain lesions; an increased number of focal lesions in the spinal cord; an increased T2 lesion load in the brain; and/or the presence of diffuse abnormalities in the spinal cord.












TABLE 10







Marker
RS
Risk allele









PGK_317
rs2107538
T



PGK_309
rs1137933
G



PGK_324
rs1318
A



PGK_066
rs2069763
G



PGK_027
rs423904
C



PGK_321
rs876493
A



PGK_169
rs10243024
G



PGK_156
rs10259085
G



PGK_310
rs1042173
A



PGK_268
rs10492503
T



KIF1B
rs10492972
G



PGK_014
rs12047808
G



PGK_154
rs12202350
A



PGK_377
rs12861247
G



PGK_332
rs13353224
A



PGK_059
rs1350666
G



PGK_358
rs1555322
A



PGK_202
rs1611115
A



PGK_186
rs17641078
G



PGK_302
rs1805009
G



PGK_328
rs2028455
G



PGK_097
rs2032893
A



PGK_176
rs2049306
A



PGK_312
rs2066713
A



NDUFS7
rs2074897
A



BTNL2
rs2076530
G



PGK_134
rs2187668
A



MHC II
rs2213584
A



MHC II
rs2227139
C



FAS
rs2234978
T



MHC II
rs2239802
G



MHC II
rs2395182
G



PGK_350
rs260461
A



PGK_289
rs28386840
A



MHC2TA
rs3087456
G



MHC II
rs3135388
A



PGK_256
rs3741981 in NCBI db
A




SNP build 129;




Homo sapiens build




36.3




(rs1131454 in NCBI db




SNP build 131;




Homo sapiens build




37.1)



PGK_086
rs3756450
A



FAS
rs3781202
CT





heterozygosity



PGK_355
rs3787283
A



PGK_181
rs3808585
A



PGK_280
rs4128767
G



PGK_036
rs4404254
G



PGK_070
rs4473631
C



PGK_051
rs4680534
A



PGK_352
rs6077690
T



HLA_M9001
rs6457594
A



PGK_150
rs6570426
T



UCP2
rs659366
C



PGK_155
rs6917747
G



PGK_304
rs7208257
A



PGK_011
rs7528684
G



PGK_030
rs7577925
A



CRYAB
rs762550
A



PGK_234
rs7956189
G



GPC6
rs7995215
G



PGK_285
rs8049651
A



KLC1
rs8702
G



IFNGR2
rs9808753
G



IL7R
rs987107
A










EQUIVALENTS
The foregoing written specification is considered to be sufficient to enable one skilled in the art to practice the invention. The present invention is not to be limited in scope by examples provided, since the examples are intended as a single illustration of one aspect of the invention and other functionally equivalent embodiments are within the scope of the invention. Various modifications of the invention in addition to those shown and described herein will become apparent to those skilled in the art from the foregoing description and fall within the scope of the appended claims. The advantages and objects of the invention are not necessarily encompassed by each embodiment of the invention.
All references, including patent documents, disclosed herein are incorporated by reference in their entirety, particularly for the disclosure referenced herein.

## Claims

1. A method of treating a human subject having multiple sclerosis (MS), the method comprising:
obtaining a DNA sample from the subject;
detecting in the DNA sample the presence of at least one T allele at single nucleotide polymorphism (SNP) rs2107538, at least one G allele at SNP rs1137933, and at least one A allele at SNP rs1318;
correlating the presence of said at least one T allele at SNP rs2107538, at least one G allele at SNP rs1137933, and at least one A allele at SNP rs1318 with an increased MS disease severity, wherein the increased MS disease severity is measured by multiple sclerosis severity score or increased size and/or distribution of T2 brain lesions; and
administering an MS therapeutic comprising interferon-beta or glatiramer acetate to the subject when at least one T allele at SNP rs2107538, at least one G allele at SNP rs1137933, and at least one A allele at SNP rs1318 is present.

2. The method according to claim 1, wherein the method comprises:
detecting the presence of the TT genotype at SNP rs2107538, the GG genotype at SNP rs1137933 and the AA genotype at SNP rs1318; and
correlating the presence of said TT genotype at SNP rs2107538, GG genotype at SNP rs1137933 and AA genotype at SNP rs1318 with the increased MS disease severity.

3. The method according to claim 1, wherein said increased MS disease severity is a multiple sclerosis severity score (MSSS) of 2.5 or greater.

4. The method according to claim 1, wherein the method further comprises the measurement of at least one clinical variable.

5. The method according to claim 4, wherein the at least one clinical variable is selected from: age of the subject at onset of multiple sclerosis, gender of the subject and type of multiple sclerosis at onset of multiple sclerosis.

6. The method according to claim 1, wherein detecting in the DNA sample the presence of at least one T allele at SNP rs2107538, at least one G allele at SNP rs1137933, and at least one A allele at SNP rs1318 comprises:
(i) extracting and/or amplifying DNA from the sample that has been obtained from the subject; and
(ii) contacting the DNA with an array comprising at least one probe suitable for determining the identity of at least one allele at each of SNP rs2107538, SNP rs1137933, and SNP rs1318.

7. The method according to claim 6, wherein the array is a DNA array, a DNA microarray or a bead array.

8. The method according to claim 6, wherein:
said at least one probe for determining the identity of at least one allele at SNP rs2107538 is selected from the group consisting of AGGGAAAGGAGGTAAGATCTGTA (SEQ ID NO: 9), AGGGAAAGGAGATAAGATCTGTA (SEQ ID NO: 10), TACAGATCTTACCTCCTTTCCCT (SEQ ID NO: 11) and TACAGATCTTATCTCCTTTCCCT (SEQ ID NO: 12);
said at least one probe for determining the identity of at least one allele at SNP rs1137933 is selected from the group consisting of TAGCGCTGGACATCACAGAAGTC (SEQ ID NO: 305), TAGCGCTGGACGTCACAGAAGTC (SEQ ID NO: 306), GACTTCTGTGATGTCCAGCGCTA (SEQ ID NO: 307) and GACTTCTGTGACGTCCAGCGCTA (SEQ ID NO: 308); and
said at least one probe for determining the identity of at least one allele at SNP rs1318 is selected from the group consisting of TGGGTGGTGTAAATATTCCTTTA (SEQ ID NO: 213), TGGGTGGTGTAGATATTCCTTTA (SEQ ID NO: 214), GCTAAAGGAATATTTACACCACCCACC (SEQ ID NO: 215), and GCTAAAGGAATATCTACACCACCCACC (SEQ ID NO: 216).

9. The method according to claim 1, wherein the method comprises amplifying DNA from a sample that has been obtained from the subject, and wherein said amplifying comprises contacting the DNA with at least one forward primer selected from CACCTCCTTTGGGGACTGTA (SEQ ID NO: 527), CAGAGTGATAGCGGCGAGT (SEQ ID NO: 601) and TCAGTTGCAAAGCTACGATGA (SEQ ID NO: 578).

10. The method according to claim 1, wherein the method comprises amplifying DNA from a sample that has been obtained from the subject, and wherein said amplifying comprises contacting the DNA with at least one reverse primer selected from GGAGTGGCAGTTAGGACAGG (SEQ ID NO: 658), CCCTTCAATGGCTGGTACAT (SEQ ID NO: 732) and TGCCTTTTGATGACTGGGTTA (SEQ ID NO: 709).