BACKGROUND
prolactin, a hormone mostly secreted from the lactotroph cells within the anterior pituitary gland  <cit>  and expressed by the prl gene, plays an important role in milk lactation for pregnant women  <cit> , helps regulate the menstrual cycle, and also affects reproduction, metabolism, homeostasis, tissue growth, osmoregulation, immunoregulation, and behavior  <cit> .
prolactin levels are regulated in a short-loop feedback mechanism by prolactin inhibitory factors , dopamine being an important example  <cit> .
this feedback system changes during pregnancy, and prolactinomas, hypothyroidism, medications, stress, exercise, herbs, and certain foods can also affect prolactin levels  <cit> .
prolactin has also been shown to suppress apoptosis, and increase survival and function of cells, including t-lymphocytes  <cit> .
cerebrospinal fluid  and plasma separated by the blood–brain barrier and levels of expression in these biological fluids are often independent, suggesting the genes are regulated independently across tissues on either side of the blood–brain barrier  <cit> .
currently, little is known about genetic markers that affect prolactin expression in plasma or csf.
in this study we conducted a genome-wide association study of prolactin levels in the csf and in the plasma of individuals from two datasets, looking for snps that are associated with prolactin levels in both csf and plasma.
further research of the variants we identified will help researchers further understand how prolactin is regulated across multiple tissues in the human body and how it affects human health.
methods
subjects and data description
csf and plasma samples were collected from the knight-alzheimer’s disease research center at washington university school of medicine  and from the alzheimer’s disease neuroimaging initiative .
in this study, we used  <dig> csf and  <dig> plasma samples from adni, and  <dig> csf and  <dig> plasma samples from knight adrc.
the majority of the samples were controls, although 7 % of knight adrc samples were alzheimer’s disease cases, and 15 % of adni samples were ad cases.
levels for  <dig> biomarkers were measured for each sample using the human discoverymap panel v <dig>  and a luminex  <dig> platform  <cit>  and the samples were genotyped using the illumina  <dig> or the omniexpress chip.
a description of the collection methods and the knight adrc samples has been previously published  <cit>  and the adni samples were collected as part of the adni biomarker study  <cit> , and were obtained from the adni database .
all samples were of european descent, and varied in age from  <dig> to 91 years, with an average age of 76 years, for the adni samples, and varied in age from  <dig> to 91 years, with an average age of 73 years, for the knight adrc samples.
all individuals whose data were included in this study were explicitly consented, following appropriate institutional review board policies.
snp imputation
snps were imputed as previously described  <cit> .
beagle was used to impute snps from the data from the  <dig> genomes project .
imputed snps with the following criteria were removed:  an r <dig> of  <dig>  or lower,  a minor allele frequency  lower than  <dig>   out of hardy-weinberg equilibrium ,  a call rate lower than 95 %, or  a gprobs score lower than  <dig> .
exactly  <dig> , <dig> snps passed the qc process.
data cleaning and analysis
we conducted analyses using plink  <cit> , a whole genome association analysis toolset.
we excluded snps that exceeded thresholds for hardy-weinberg equilibrium  <cit>  , missing genotype rate , and minor allele frequency  on the knight adrc and adni datasets.
then, we excluded individuals with a missing genotype rate greater than 2 % .
with the cleaned data, we conducted a linear regression for all remaining snps, within each data set, to test for an association with prolactin levels, adjusting for age, gender, and the first two principle components generated using eigensoft  <cit> .
we then performed a meta-analysis across adni and knight adrc for csf and another meta-analysis across adni and knight adrc for plasma, each accounting for sample size, p-values, and direction of effect using the default metal  <cit>  settings.
we retained all snps that had a meta-analysis p-value less than 5 × 10− <dig> and that had the same direction of effect in both the knight adrc and adni datasets, in both resulting meta-analysis files.
we then looked for snps that were replicated in both the significant csf and plasma meta-analysis resulting files.
we searched for these snps in the nhgri catalog of published genome-wide association studies  <cit> .
for known disease associations.
we then used regulomedb  <cit>  and functional annotations from wannovar  <cit>  to identify snps that are biologically likely to modify gene function or expression.
regulomedb scores range from “1a” to “6”.
lower scores indicate stronger evidence that the snp affects gene regulation based on both empirical data, such as chip-seq, and whether the snp is within a known transcription factor binding motif.
we generated regional association plots using snap  <cit>  for regions of interest and explored whether any genes of interest are part of the same pathway or regulatory network using pathwaycommons  <cit> .
for snps where linkage disequilibrium data is unknown in snap, we modified the snap source code to plot all snps in the region regardless of linkage disequilibrium status and omit r <dig> values.
by default, snap only plots snps with a known r <dig> greater than  <dig>  we also generated q-q plots in r to check for evidence of inflation of p-values.
RESULTS
we identified  <dig> snps associated with prolactin levels in plasma and  <dig> snps associated with prolactin levels in csf , none of which are located in or around the prl gene.
significant snps were spread across  <dig> chromosomes for the csf results and across  <dig> different chromosomes for the plasma results.
there are several hits on chromosome  <dig>  but all are more than  <dig> million base pairs away from where the prl gene is located.
there were  <dig> snps in common between the plasma and csf results ,  <dig> of which were on chromosome  <dig>  approximately  <dig> million base pairs away from the prl gene.
regulomedb scores for the  <dig> snps ranged from  <dig> to  <dig> and mafs ranged from  <dig>  to  <dig> .
none of the  <dig> snps were found in the nhgri catalog of published genome-wide association studies.
the q-q plots demonstrated no evidence of inflation .
according to pathwaycommons, prl, sulf <dig>  and trib <dig> are all regulated by some of the same transcription factors  including pbx <dig>  xbp <dig>  tcf <dig>  lef <dig>  vsx <dig>  pitx <dig>  and lhx <dig>  there were no other known relationships among the genes identified in this study.table  <dig> significant snps were scattered across chromosomes  <dig>   <dig>   <dig>   <dig> and  <dig>  with the majority of the snps being on chromosome  <dig>  these  <dig> snps were all significant in both the blood plasma and csf.
information on the snps includes chromosome, reference and alternate allele, minor allele frequency, predicted function, the gene the snp is found in or near, regulomedb score, and the meta-analysis p-values for plasma and csf
sulf1
trib2
tob2p1
znf192p <dig> tob2p1
znf192p <dig> tob2p1
gimap7
phb,loc101927207
tob2p1
tob2p1
zscan9
phb,loc101927207
tns3
fig.
<dig> pathwaycommons output showing the gene that codes for prolactin along with the major players sulf <dig> and trib <dig>  our significant snps, rs <dig> and rs <dig>  fall in sulf <dig> and trib <dig>  respectively.
this means that although none of the snps fall directly in or near prl, they could still be affecting the prolactin pathway because they are regulated by some of the same transcription factors
discussion
twelve snps were significantly associated with prolactin levels in both plasma and csf,  <dig> are located on chromosome  <dig> and the remaining  <dig> snps are scattered across chromosomes  <dig>   <dig>   <dig>  and  <dig>  the  <dig> snps on chromosome  <dig> cluster in and around zscan <dig>  tob2p <dig>  and znf192p <dig>  according to annovar, though visualizing the snps’ locations in the ncbi viewer shows that  <dig> of the  <dig> snps fall within a zscan <dig> intron for one specific transcript  as well as within tob2p1—a pseudogene that falls within the same intronic region of zscan <dig>  snp rs <dig> is in the 5′utr region of zscan <dig>  snps rs <dig> and rs <dig> are between znf192p <dig> and tob2p <dig>  according to annovar.
znf192p <dig> is also a pseudogene that is proximal to zscan <dig>  in short, all  <dig> snps on chromosome  <dig> are located in or around zscan <dig> and zscan <dig>  both of which are protein-coding genes, while  <dig> of the  <dig> fall directly within a pseudogene .
of the significant snps on chromosome  <dig>  rs <dig> was most significantly correlated with prolactin levels in plasma  while rs <dig> was most significantly correlated with prolactin levels in csf .fig.
<dig> regional association plot generated using snap showing rs <dig> has the strongest association with prolactin plasma levels of the snps found in this region of chromosome  <dig>  we identified several snps associated with prolactin levels in the plasma and plotted association p-values in the region.
we omitted r <dig> values in this plot because snap does not have linkage disequilibrium data for this snp
fig.
<dig> regional association plot generated using snap showing rs <dig> has the strongest association with prolactin csf levels of the snps found in this region of chromosome  <dig>  we identified several snps associated with prolactin levels in the csf and plotted association p-values in the region.
we omitted r <dig> values in this plot because snap does not have linkage disequilibrium data for this snp
the remaining  <dig> snps are located on chromosomes  <dig>   <dig>   <dig>  and  <dig>  where  <dig> of the snps are intergenic,  <dig> are intronic, and one is located in a 3′utr region .
snp rs <dig> is an intronic snp within the sulf <dig> gene on chromosome  <dig> and was most significantly associated with prolactin levels in plasma out of the  <dig> found in common between the two fluids.
it was also one of most significantly associated with prolactin levels in csf.
snps rs <dig> and rs <dig> are intronic snps within the trib <dig>  and tns <dig>  genes, respectively.
snps rs <dig> and rs <dig> are intergenic snps that are both located on chromosome  <dig> between the gene phb and a non-coding rna loc <dig>  the next closest protein-coding gene is ngfr.
snp rs <dig> is located in the 3′utr region of gimap <dig> on chromosome  <dig>  while rs <dig>  and rs <dig>  are both located on chromosome  <dig>  they are distant from each other on opposite arms of the chromosome, suggesting their associations with prolactin levels are independent of each other.
while there is no direct evidence that any of these markers directly impact prolactin expression, it appears that prl, sulf <dig>  and trib <dig> in that they are all regulated by common transcription factors, including pbx <dig>  xbp <dig>  tcf <dig>  lef <dig>  vsx <dig>  pitx <dig>  and lhx <dig>  it is possible that these genes and variants are involved in prl regulation through more complex biological relationships.
this may be significant because genes regulated by the same transcription factor are often active in the same tissues at the same time  <cit> .
CONCLUSIONS
in summary, we have identified significant and replicable association between several genetic variants in both plasma and csf levels of prolactin.
these results provide a foundation for a better understanding of prolactin regulation, and in turn the host of phenotypes in which prolactin plays a role, including lactation, immunoregulation, apoptosis and t-lymphocyte function .
future work on these associated markers will provide meaningful insights into these phenotypes.
abbreviations
pif, prolactin inhibitory factors; adni, alzheimer’s disease neuroimaging initiative; csf, cerebrospinal fluid; eqtl, expression quantitative trait locus; knight adrc, knight-alzheimer’s disease research center at washington university school of medicine; snp, single nucleotide polymorphism; utrs, untranslated regions.
additional files
additional file 1: file contains a table of snps significantly associated with prolactin levels in blood plasma by meta-analysis.
additional file 2: file contains a table of snps significantly associated with prolactin levels in csf by meta-analysis.
additional file 3: file contains a q-q plot of the plasma data used in this study.
additional file 4: file contains a q-q plot of the csf data used in this study.

 prolactin, a hormone mostly secreted from the lactotroph cells within the anterior pituitary gland and expressed by the prl gene, plays an important role in milk lactation for pregnant women, helps regulate the menstrual cycle, and also affects reproduction, metabolism, homeostasis, tissue growth, osmoregulation, immunoregulation, and behavior. prolactin levels are regulated in a short-loop feedback mechanism by prolactin inhibitory factors, dopamine being an important example. this feedback system changes during pregnancy, and prolactinomas, hypothyroidism, medications, stress, exercise, herbs, and certain foods can also affect prolactin levels. prolactin has also been shown to suppress apoptosis, and increase survival and function of cells, including t-lymphocytes. cerebrospinal fluid and plasma separated by the blood–brain barrier and levels of expression in these biological fluids are often independent, suggesting the genes are regulated independently across tissues on either side of the blood–brain barrier. currently, little is known about genetic markers that affect prolactin expression in plasma or csf. in this study we conducted a genome-wide association study of prolactin levels in the csf and in the plasma of individuals from two datasets, looking for snps that are associated with prolactin levels in both csf and plasma. further research of the variants we identified will help researchers further understand how prolactin is regulated across multiple tissues in the human body and how it affects human health. methods subjects and data description csf and plasma samples were collected from the knight-alzheimer’s disease research center at washington university school of medicine and from the alzheimer’s disease neuroimaging initiative. in this study, we used csf and plasma samples from adni, and csf and plasma samples from knight adrc. the majority of the samples were controls, although 7 % of knight adrc samples were alzheimer’s disease cases, and 15 % of adni samples were ad cases. levels for biomarkers were measured for each sample using the human discoverymap panel v and a luminex platform and the samples were genotyped using the illumina or the omniexpress chip. a description of the collection methods and the knight adrc samples has been previously published and the adni samples were collected as part of the adni biomarker study, and were obtained from the adni database. all samples were of european descent, and varied in age from to 91 years, with an average age of 76 years, for the adni samples, and varied in age from to 91 years, with an average age of 73 years, for the knight adrc samples. all individuals whose data were included in this study were explicitly consented, following appropriate institutional review board policies. snp imputation snps were imputed as previously described. beagle was used to impute snps from the data from the genomes project. imputed snps with the following criteria were removed: an r of or lower, a minor allele frequency lower than out of hardy-weinberg equilibrium, a call rate lower than 95 %, or a gprobs score lower than. exactly, snps passed the qc process. data cleaning and analysis we conducted analyses using plink, a whole genome association analysis toolset. we excluded snps that exceeded thresholds for hardy-weinberg equilibrium, missing genotype rate, and minor allele frequency on the knight adrc and adni datasets. then, we excluded individuals with a missing genotype rate greater than 2 %. with the cleaned data, we conducted a linear regression for all remaining snps, within each data set, to test for an association with prolactin levels, adjusting for age, gender, and the first two principle components generated using eigensoft. we then performed a meta-analysis across adni and knight adrc for csf and another meta-analysis across adni and knight adrc for plasma, each accounting for sample size, p-values, and direction of effect using the default metal settings. we retained all snps that had a meta-analysis p-value less than 5 × 10− and that had the same direction of effect in both the knight adrc and adni datasets, in both resulting meta-analysis files. we then looked for snps that were replicated in both the significant csf and plasma meta-analysis resulting files. we searched for these snps in the nhgri catalog of published genome-wide association studies. for known disease associations. we then used regulomedb and functional annotations from wannovar to identify snps that are biologically likely to modify gene function or expression. regulomedb scores range from “1a” to “6”. lower scores indicate stronger evidence that the snp affects gene regulation based on both empirical data, such as chip-seq, and whether the snp is within a known transcription factor binding motif. we generated regional association plots using snap for regions of interest and explored whether any genes of interest are part of the same pathway or regulatory network using pathwaycommons. for snps where linkage disequilibrium data is unknown in snap, we modified the snap source code to plot all snps in the region regardless of linkage disequilibrium status and omit r values. by default, snap only plots snps with a known r greater than we also generated q-q plots in r to check for evidence of inflation of p-values.  we identified snps associated with prolactin levels in plasma and snps associated with prolactin levels in csf, none of which are located in or around the prl gene. significant snps were spread across chromosomes for the csf results and across different chromosomes for the plasma results. there are several hits on chromosome but all are more than million base pairs away from where the prl gene is located. there were snps in common between the plasma and csf results, of which were on chromosome approximately million base pairs away from the prl gene. regulomedb scores for the snps ranged from to and mafs ranged from to. none of the snps were found in the nhgri catalog of published genome-wide association studies. the q-q plots demonstrated no evidence of inflation. according to pathwaycommons, prl, sulf and trib are all regulated by some of the same transcription factors including pbx xbp tcf lef vsx pitx and lhx there were no other known relationships among the genes identified in this study.table significant snps were scattered across chromosomes and with the majority of the snps being on chromosome these snps were all significant in both the blood plasma and csf. information on the snps includes chromosome, reference and alternate allele, minor allele frequency, predicted function, the gene the snp is found in or near, regulomedb score, and the meta-analysis p-values for plasma and csf sulf1 trib2 tob2p1 znf192p tob2p1 znf192p tob2p1 gimap7 phb,loc101927207 tob2p1 tob2p1 zscan9 phb,loc101927207 tns3 fig. pathwaycommons output showing the gene that codes for prolactin along with the major players sulf and trib our significant snps, rs and rs fall in sulf and trib respectively. this means that although none of the snps fall directly in or near prl, they could still be affecting the prolactin pathway because they are regulated by some of the same transcription factors discussion twelve snps were significantly associated with prolactin levels in both plasma and csf, are located on chromosome and the remaining snps are scattered across chromosomes and the snps on chromosome cluster in and around zscan tob2p and znf192p according to annovar, though visualizing the snps’ locations in the ncbi viewer shows that of the snps fall within a zscan intron for one specific transcript as well as within tob2p1—a pseudogene that falls within the same intronic region of zscan snp rs is in the 5′utr region of zscan snps rs and rs are between znf192p and tob2p according to annovar. znf192p is also a pseudogene that is proximal to zscan in short, all snps on chromosome are located in or around zscan and zscan both of which are protein-coding genes, while of the fall directly within a pseudogene. of the significant snps on chromosome rs was most significantly correlated with prolactin levels in plasma while rs was most significantly correlated with prolactin levels in csf.fig. regional association plot generated using snap showing rs has the strongest association with prolactin plasma levels of the snps found in this region of chromosome we identified several snps associated with prolactin levels in the plasma and plotted association p-values in the region. we omitted r values in this plot because snap does not have linkage disequilibrium data for this snp fig. regional association plot generated using snap showing rs has the strongest association with prolactin csf levels of the snps found in this region of chromosome we identified several snps associated with prolactin levels in the csf and plotted association p-values in the region. we omitted r values in this plot because snap does not have linkage disequilibrium data for this snp the remaining snps are located on chromosomes and where of the snps are intergenic, are intronic, and one is located in a 3′utr region. snp rs is an intronic snp within the sulf gene on chromosome and was most significantly associated with prolactin levels in plasma out of the found in common between the two fluids. it was also one of most significantly associated with prolactin levels in csf. snps rs and rs are intronic snps within the trib and tns genes, respectively. snps rs and rs are intergenic snps that are both located on chromosome between the gene phb and a non-coding rna loc the next closest protein-coding gene is ngfr. snp rs is located in the 3′utr region of gimap on chromosome while rs and rs are both located on chromosome they are distant from each other on opposite arms of the chromosome, suggesting their associations with prolactin levels are independent of each other. while there is no direct evidence that any of these markers directly impact prolactin expression, it appears that prl, sulf and trib in that they are all regulated by common transcription factors, including pbx xbp tcf lef vsx pitx and lhx it is possible that these genes and variants are involved in prl regulation through more complex biological relationships. this may be significant because genes regulated by the same transcription factor are often active in the same tissues at the same time.  in summary, we have identified significant and replicable association between several genetic variants in both plasma and csf levels of prolactin. these results provide a foundation for a better understanding of prolactin regulation, and in turn the host of phenotypes in which prolactin plays a role, including lactation, immunoregulation, apoptosis and t-lymphocyte function. future work on these associated markers will provide meaningful insights into these phenotypes. abbreviations pif, prolactin inhibitory factors; adni, alzheimer’s disease neuroimaging initiative; csf, cerebrospinal fluid; eqtl, expression quantitative trait locus; knight adrc, knight-alzheimer’s disease research center at washington university school of medicine; snp, single nucleotide polymorphism; utrs, untranslated regions. additional files additional file 1: file contains a table of snps significantly associated with prolactin levels in blood plasma by meta-analysis. additional file 2: file contains a table of snps significantly associated with prolactin levels in csf by meta-analysis. additional file 3: file contains a q-q plot of the plasma data used in this study. additional file 4: file contains a q-q plot of the csf data used in this study. 
prolactin is also found in multiple fluids throughout the body, including plasma and cerebrospinal fluid. in this study, we measured prolactin levels in both plasma and csf, and performed a genome-wide association study. we identified snps associated with increased prolactin levels in both biological fluids. prolactin is a polypeptide hormone secreted by the anterior pituitary gland that plays an essential role in lactation, tissue growth, and suppressing apoptosis to increase cell survival.
 prolactin is a polypeptide hormone secreted by the anterior pituitary gland that plays an essential role in lactation, tissue growth, and suppressing apoptosis to increase cell survival. prolactin serves as a key player in many life-critical processes, including immune system and reproduction. prolactin is also found in multiple fluids throughout the body, including plasma and cerebrospinal fluid. methods in this study, we measured prolactin levels in both plasma and csf, and performed a genome-wide association study. we then performed meta-analyses using metal with a significance threshold of p < 5 × 10− and removed snps where the direction of the effect was different between the two datasets.  we identified snps associated with increased prolactin levels in both biological fluids.  our efforts will help researchers understand how prolactin is regulated in both csf and plasma, which could be beneficial in research for the immune system and reproduction. electronic supplementary material the online version of this article contains supplementary material, which is available to authorized users. keywords associationgeneticscsfplasmaprolactin12th annual biotechnology and bioinformatics symposium provo, ut, usa 10- december http://biotconf.org/issue-copyright-statement© the author 2016 