Patent Application: US-201414776277-A

Abstract:
a method for predicting the severity or progression of oa in a human subject , comprising : determining the identity of at least one allele at each of at least 4 positions of single nucleotide polymorphism selected from the group consisting of : rs2206593 , rs10465850 , rs780094 , rs1374281 , rs1143634 , rs2073508 , rs2243250 , rs4720262 , rs917760 , rs7838918 , rs12009 , rs730720 , rs874692 , rs893953 , rs1799750 , rs10845493 , rs11054704 , rs7986347 , rs1802536 , rs10519263 , rs7342880 , rs16947882 and rs10413815 , and one or more snps in linkage disequilibrium at a level of at least r 2 ≧ 0 . 8 therewith , as well as products , in particular systems and kits for use in such a method .

Description:
as used herein , positions of single nucleotide polymorphism ( snp ) are identified by rs number , said rs number denoting the database entry in the ncbi dbsnp build 137 , homo sapiens genome build 37 . 3 , updated 26 jun . 2012 . the entire contents of each rs number entry identified herein , including flanking sequence , is expressly incorporated herein by reference . this study was approved by the clinical research ethical committee of the involved hospitals . data were collected on patients at departments of rheumatology , orthopaedics , rehabilitation and primary care at 31 spanish hospitals and primary care centers . the study population consisted of 219 knee osteoarthritis ( koa ) patients fulfilling the following eligibility criteria : patients who had a clinical and radiological diagnosis of primary koa patients who at koa diagnosis moment were ≧ 40 years old patients who at koa diagnosis moment had a radiographic kellgren - lawrence grade 2 or 3 . patients with a follow - up since diagnosis of : a ) 8 years or less if had reached a kl grade of 4 or / and have undergone an arthroplasty ( bad prognosis ). minimum follow - up of 2 years . 87 out of 219 recruited koa patients were classified into the bad prognosis group . b ) 8 years or more if had not reached a kl grade of 4 and neither have undergone and arthroplasty ( good prognosis ). 132 out of 219 recruited koa patients were classified into the good prognosis group . patients with two x - rays : one of the beginning and the other of the end of the follow up period . patients who provided saliva or blood sample . patients who provided a written informed consent . patients with koa secondary to fractures or to metabolic , endocrine or other rheumatic diseases . patients not able to understand and cooperate with the requirements of the study protocol . besides , an external population was recruited . the external population was composed of 62 koa patients , 37 out of 62 with bad radiographic koa prognosis and 25 out 62 with good radiographic koa prognosis . the study was done in accordance with the helsinki declaration and european medicines agency recommendations . the two x - rays per recruited koa patient ( at the beginning and at the end of the follow - up ) were evaluated by the same evaluator in order to avoid bias in the classification of the x - rays into the kellgren - lawrence grades . we followed a candidate gene strategy . to establish the list of candidate genes , we selected genes implicated in the molecular processes involved in oa ( cartilage degradation , inflammation , extracellular matrix metabolism and bone remodeling ), in genes known to be associated with oa , and in genes known to be associated to oa comorbidities ( diabetes type 2 , metabolic syndrome , hypercholesterolemia ) with the available information up to jun . 21 , 2011 . we selected 2 or 3 snps per gene and if there was no snp described inside the gene we selected snps in the flanking regions . we used dbsnp ( http :// www . ncbi . nlm . nih . gov / projects / snp ) database for snps selection . dna was extracted from blood or saliva using the qiaamp dna blood mini kit from ( qiagen , hilden , del .) and quantified with a nanodrop nd - 1000 spectrophotometer ( nanodrop technologies , wilmington , del .). 768 snps were genotyped using a iilumina golden gate assay ( illumina inc ., san diego , calif .) ( fan et al . in cold spring harb symp quant biol . 68 : 69 - 78 ( 2003 )), and 6 snps were genotyped using the kaspar chemistry ( kbioscience , hertfordshire , uk ). statistical analyses were performed by using the spss v15 . 0 ( spss , chicago , ill ., usa ), the helixtree ( golden helix , bozeman , mont ., usa ) and the analyse - it ( analyse - it software , ltd ., uk ) softwares . univariate analysis using the chi - square and student unpaired t tests was done to identify associations between baseline clinical variables ( cvs ) or genetic polymorphisms ( snps ) and radiographic koa prognosis . only snps conforming to hardy - weinberg expectations in each group were included . for each snp all inheritance models were explored . to limit the overall false - positive rate variables ( snps and cvs ) were filtered before modeling ( steyerberg e ). individual association p values were used to rank snps , and only snps with an association of p & lt ; 0 . 05 ( chi - squared single value permutation test , n = 1000 permutations ) in the allelic association test and genotypic association test were included on multivariate analysis . individual association p values were also used to rank cvs ( gender , age at koa diagnosis , body mass index , presence of other affected joints by oa , contralateral joint oa , etc . . . . ), and only cvs with an association of p & lt ; 0 . 05 ( chi - squared test or student unpaired t test or non - parametric mann - whitney test ). multivariate analysis or predictive models were done using forward rv logistic regression . radiographic koa progression was considered the dependent variable , and baseline cvs and snps were included as predictors . each snp was included , considering the inheritance model significantly associated with the phenotype . the p values to enter and remove cutoffs were 0 . 05 and 0 . 1 , respectively ( steyerberg e ). accuracy was assessed by the roc curve auc . to measure the impact of the snps and variables included in the models of the analyzed phenotype , the sensitivity ( s ), specificity ( sp ) and positive likelihood ratio [ lr += sensitivity /( 1_specificity )] were computed by means of the roc curves . models were externally validated by using an external population composed of 62 koa patients ( 25 out of 62 koa patients with good radiographic koa prognosis and 37 out of 62 koa patients with bad radiographic koa prognosis ). a z test to compare two independent samples was used to analyse if the observed differences between auc - rocs ( initial population versus external population ) were statistically significant . snps with poor genotype cloud clustering or & lt ; 90 % ( 18 and 6 snps , respectively ) and those which were not in hardy - weinberg equilibrium in the population ( p & lt ; 0 . 0001 ) ( 3 snps ) were excluded . monomorphic snps were also excluded ( 33 snps ). we also excluded samples with an individual genotyping call - rate & lt ; 90 % ( 6 samples were excluded ). therefore , a total of 714 snps and 281 samples ( 219 samples of the initial population and 62 samples of the external population ) verified the quality control criteria . we found a total of 23 snps significantly associated to radiographic koa prognosis at the allelic and genotypic level ( single value ( sv ) permutation allele and genotype test ( 1000 permutations ), p & lt ; 0 . 05 ) in the comparison bad prognosis versus good prognosis . the 23 snps associated to radiographic koa prognosis are displayed in the table 1 . we found that 2 of the 23 associated snps to radiographic koa prognosis were in strong linkage disequilibrium ( r 2 ≧ 0 . 8 ), and therefore these snps are not independent variables . the linked snps are located in the chromosome 15 , rs1802536 and rs10519263 . statistical results of allele and genotype comparisons of the 23 snps are given in table 1 . in the table 1 it is specified if the risk allele corresponds to the top or bot strand of the dna following ilumina &# 39 ; s nomenclature for dna strand identification . the simplest case of determining strand designations occurs when one of the possible variations of the snp is an adenine ( a ), and the remaining variation is either a cytosine ( c ) or guanine ( g ). in this instance , the sequence for this snp is designated top . similar to the rules of reverse complementarity , when one of the possible variations of the snp is a thymine ( t ), and the remaining variation is either a c or a g , the sequence for this snp is designated bot . if the snp is an [ a / t ] or a [ c / g ], then the above rules do not apply . illumina employs a ‘ sequence walking ’ technique to designate strand for [ a / t ] and [ c / g ] snps . for this sequence walking method , the actual snp is considered to be position ‘ n ’. the sequences immediately before and after the snp are ‘ n − 1 ’ and ‘ n + 1 ’, respectively . similarly , two base pairs before the snp is ‘ n − 2 ’ and two base pairs after the snp ‘ n + 2 ’, etc . using this method , sequence walking continues until an unambiguous pairing ( a / g , a / c , t / c , or t / g .) is present . to designate strand , when the a or t in the first unambiguous pair is on the 5 ′ side of the snp , then the sequence is designated top . when the a or t in the first unambiguous pair is on the 3 ′ side of the snp , then the sequence is designated bot . the codification of the snps considering the more significant inheritance model is shown in the table 1 . besides , the or ( 95 % ci ) is included . multivariate analysis or predictive models were done using forward rv logistic regression . radiographic koa progression was considered the dependent variable , and snps were included as predictors . each snp was included , considering the more significant inheritance model ( table 1 ). the accuracy of the predictive models was evaluated by means of the area under the curve ( auc ) of a receiver operating characteristic ( roc ) curve . the area under the roc curve ( auc - roc ) is a measure of discrimination ; a model with a high area under the roc curve suggests that the model is able to accurately predict the value of an observation &# 39 ; s response ( the radiographic koa progression in our example ). hosmer and lemeshow provide general rules for interpreting auc values . paraphrasing their rules gives the general guidelines below ( hosmer d w , and lemeshow s ): therefore , we found predictive models or combinations of snps with at least an acceptable discrimination for their use in the radiographic , therefore at least with an auc - roc ≧ 0 . 70 (≧ 70 %). combinations of at least 4 snps from the list of the 23 associated snps to radiographic koa prognosis allow to reach auc - rocs ≧ 0 . 70 ( 70 %). we present herein , as non - limiting examples , 15 examples ( table 2 ). the 23 associated snps to radiographic koa prognosis are represented the number of times indicated in the table 3 . therefore , each one of the 23 snps are included at least once in the 15 examples of predictive models shown in the table 2 . the results showed in table 2 demonstrate that at least 4 snps from the list of the 23 associated snps ( table 1 ) to radiographic koa prognosis reach an auc - roc ≧ 0 . 70 . the table 4 includes fifteen non limiting examples of predictive models including more than 4 snps , exactly 5 snps , to demonstrate that more than 4 snps from the list of the 23 associated snps to radiographic koa prognosis also reach an auc - roc ≧ 0 . 70 . combinations of 4 snps different from the ones included in the table 1 do not reach the auc - roc ≧ 0 . 70 . the table 5 includes 24 snps different from the 23 associated snps to radiographic koa prognosis which are not associated to radiographic koa prognosis neither at the allelic level nor at the genotypic level ( single value ( sv ) permutation allele and genotype test ( 1000 permutations )). the table 6 includes six non limiting examples of predictive models combining 4 snps ( included in the table 5 ) different from the 23 associated snps to radiographic koa prognosis . table 7 includes 10 snps different from the 23 associated snps to radiographic koa prognosis which are not associated to radiographic koa prognosis both at the allelic level and at the genotypic level . these 10 snps are only associated to radiographic koa prognosis at the genotypic level . these examples ( table 6 and table 8 ) prove that combinations of 4 snps different from the list of the 23 associated snps ( table 1 ) do not reach the auc - roc ≧ 0 . 70 , neither combinations of 4 snps not associated to radiographic koa prognosis ( neither at the allelic level nor at the genotypic level , table 5 ) nor combinations of 4 snps associated only at the genotypic level ( table 7 ). predictive models including less than 4 snps from the list of the 23 associated snps ( table 1 ) to radiographic koa prognosis do not reach the auc - roc ≧ 0 . 70 . the table 9 includes the four possible predictive models combining 3 snps pear each one of the fifteen non limiting examples shown in table 2 . predictive models including 4 snps , 3 snps from the list of the 23 associated snps ( table 1 ) to radiographic koa prognosis and 1 snp different from the list of the 23 associated snps , do not reach the auc - roc ≧ 0 . 70 . the table 10 includes the four possible predictive models combining 3 snps from the list of the 23 associated snps and 1 snp from the table 5 which includes snps different from the mentioned list ( snps not associated to radiographic koa prognosis neither at allelic level nor at genotypic level ) per each one of the fifteen non limiting examples shown in table 2 . the table 11 includes three examples combining 3 snps from the list of the 23 associated snps and 1 snp from the table 7 which includes snps different from the mentioned list ( snps associated to radiographic koa prognosis at the genotypic level , and not associated at the allelic level ). based on the results showed in tables 2 - 11 , we can conclude that a combination of at least 4 snps from the list of the 23 associated snps ( table 1 ) to radiographic koa progression allow predictive models with an acceptable auc - roc following hosmer and lemeshow criteria ( hosmer d w , and lemeshow 5 ) ( auc - roc ≧ 0 . 70 ). both predictive models with 3 snps from the 23 associated snps and predictive models with 3 snps from the 23 associated snps plus 1 snp out of the mentioned snp list ( table 5 or table 7 ) showed auc - rocs & lt ; 0 . 70 , both if the 1 additional snp out of the list is not associated to radiographic koa progression neither at the allelic level nor genotypic level and if the 1 additional snp is only associated at the genotypic level and not at the allelic level . predictive models with more than 4 snps ( 5 snps ) from the list of the 23 associated snps ( table 1 ) also showed auc - roc ≧ 0 . 70 . these results are summarized in the fig1 . multivariate analysis or predictive models were done using forward rv logistic regression . radiographic koa progression was considered the dependent variable , and snps were included as predictors . each snp was included , considering the inheritance model significantly associated with the phenotype . the 23 associated snps ( table 1 ) to radiographic koa progression were included as independent variables . we present herein , as non - limiting examples , a predictive model with an excellent accuracy for radiographic koa progression which combines 8 snps ( auc - roc over 80 %, excellent discrimination following the hosmer and lemeshow &# 39 ; s general rules for interpreting auc - roc values ( hosmer d w , and lemeshow s ) ( table 12 and fig2 ). the predictive model shows an auc - roc of 0 . 782 ± 0 . 031 ( auc - roc ± std . error ), with cut - off points which maximise the sensitivity and specificity of 75 . 9 % and 69 . 7 % respectively . the sensitivity and specificity values at different cut - off points of positive likelihood ratio ( lr +) are shown in table 13 . univariate analysis identified the association between the baseline cv age at koa diagnosis and the radiographic koa prognosis . multivariate analysis or predictive models were done using forward rv logistic regression . radiographic koa progression was considered the dependent variable , and snps and baseline cvs were included as predictors . each snp was included , considering the inheritance model significantly associated with the phenotype . the age at koa diagnosis was codified as & gt ; 60 years old versus ≦ 60 years old . the 23 associated snps ( table 1 ) to radiographic koa progression were included as independent variables . we present herein , as non - limiting examples , a predictive model with an excellent accuracy for radiographic koa progression which combines 8 snps and 1 cv ( auc - roc over 80 %, excellent discrimination following the hosmer and lemeshow &# 39 ; s general rules for interpreting auc - roc values ( hosmer d w , and lemeshow s ) ( table 14 and fig3 ). the predictive model shows an auc - roc of 0 . 820 ± 0 . 028 ( auc - roc ± std . error ), with cut - off points which maximise the sensitivity and specificity of 73 . 6 % and 73 . 5 % respectively . the sensitivity and specificity values at different cut - off points of positive likelihood ratio ( lr +) are shown in table 15 . both predictive models ( table 12 and table 14 ) were validated in an external koa population composed of 62 koa patients , 37 out of 62 with bad radiographic koa prognosis and 25 out 62 with good radiographic koa prognosis . both models were externally validated . the auc - rocs of the model shown in the table 12 in the initial population ( n = 219 ) used to generate the model and in the external population ( n = 62 ) were 0 . 782 ± 0 . 031 ( area ± std . error ) and 0 . 735 ± 0 . 067 ( area ± std . error ), respectively . there were not statistical differences between these auc - rocs ( p - value = 0 . 5244 ). therefore we can conclude that the predictive model created in an initial population was replicated in an external population . the auc - rocs of the model shown in the table 14 in the initial population ( n = 219 ) used to generate the model and in the external population ( n = 62 ) were 0 . 820 ± 0 . 028 ( area ± std . error ) and 0 . 726 ± 0 . 066 ( area ± std . error ), respectively . there were not statistical differences between these auc - rocs ( p - value = 0 . 1898 ). therefore we can conclude that the predictive model created in an initial population was replicated in an external population . 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 for all purposes , particularly for the disclosure referenced herein . arcogen ( 2012 ). identification of new susceptibility loci for osteoarthritis ( arcogen ): a genome - wide association study . lancet , 380 , 815 - 823 . attur , m ., wang , h . y ., kraus , v . b ., bukowski , j . f ., aziz , n ., krasnokutsky , s ., samuels , j ., greenberg , j ., mcdaniel , g ., abramson , s . b . & amp ; 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( 2011 ). the gdf5 rs143383 polymorphism is associated with osteoarthritis of the knee with genome - wide statistical significance . ann . rheum . dis ., 70 , 873 - 875 . hosmer d w , and lemeshow s . applied logistic regression . 2nd ed . ( 2000 ). john wiley & amp ; sons , inc . pp . 156 - 164 steyerberg e . clinical prediction models . a practical approach to development , validation , and updating . new york : springer 2009 .