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PDF file, 204K, Quantile-quantile plots of test comparison for genotype frequencies in cases vs controls.

It is important to identify the patients at highest risk of fractures. A recent large-scale meta-analysis identified 63 autosomal single-nucleotide polymorphisms (SNPs) associated with bone mineral density (BMD), of which 16 were also associated with fracture risk. Based on these findings, two genetic risk scores (GRS63 and GRS16) were developed. Our aim was to determine the clinical usefulness of these GRSs for the prediction of BMD, BMD change, and fracture risk in elderly subjects. We studied two male (Osteoporotic Fractures in Men Study [MrOS] US, MrOS Sweden) and one female (Study of Osteoporotic Fractures [SOF]) large prospective cohorts of older subjects, looking at BMD, BMD change, and radiographically and/or medically confirmed incident fractures (8067 subjects, 2185 incident nonvertebral or vertebral fractures). GRS63 was associated with BMD (≅3% of the variation explained) but not with BMD change. Both GRS63 and GRS16 were associated with fractures. After BMD adjustment, the effect sizes for these associations were substantially reduced. Similar results were found using an unweighted GRS63 and an unweighted GRS16 compared with those found using the corresponding weighted risk scores. Only minor improvements in C-statistics (AUC) for fractures were found when the GRSs were added to a base model (age, weight, and height), and no significant improvements in C-statistics were found when they were added to a model further adjusted for BMD. Net reclassification improvements with the addition of the GRSs to a base model were modest and substantially attenuated in BMD-adjusted models. GRS63 is associated with BMD, but not BMD change, suggesting that the genetic determinants of BMD differ from those of BMD change. When BMD is known, the clinical utility of the two GRSs for fracture prediction is limited in elderly subjects.

Objectives: While genetic determinants of low density lipoprotein (LDL) cholesterol levels are well characterised in the general population, they are understudied in rheumatoid arthritis (RA). Our objective was to determine the association of established LDL and RA genetic alleles with LDL levels in RA cases compared with non-RA controls. Methods: Using data from electronic medical records, we linked validated RA cases and non-RA controls to discarded blood samples. For each individual, we extracted data on: first LDL measurement, age, gender and year of LDL measurement. We genotyped subjects for 11 LDL and 44 non-HLA RA alleles, and calculated RA and LDL genetic risk scores (GRS). We tested the association between each GRS and LDL level using multivariate linear regression models adjusted for age, gender, year of LDL measurement and RA status. Results: Among 567 RA cases and 979 controls, 80% were female and mean age at the first LDL measurement was 55 years. RA cases had significantly lower mean LDL levels than controls (117.2 vs 125.6 mg/dl, respectively, p, National Institutes of Health (U.S.) (Grant U54-LM008748)

Objective— Low high-density lipoprotein cholesterol (HDL-C) is associated with cardiometabolic pathologies. In this study, we investigate the biological pathways and individual genes behind low HDL-C by integrating results from 3 high-throughput data sources: adipose tissue transcriptomics, HDL lipidomics, and dense marker genotypes from Finnish individuals with low or high HDL-C (n=450). Approach and Results— In the pathway analysis of genetic data, we demonstrate that genetic variants within inflammatory pathways were enriched among low HDL-C associated single-nucleotide polymorphisms, and the expression of these pathways upregulated in the adipose tissue of low HDL-C subjects. The lipidomic analysis highlighted the change in HDL particle quality toward putatively more inflammatory and less vasoprotective state in subjects with low HDL-C, as evidenced by their decreased antioxidative plasmalogen contents. We show that the focal point of these inflammatory pathways seems to be the HLA region with its low HDL-associated alleles also associating with more abundant local transcript levels in adipose tissue, increased plasma vascular cell adhesion molecule 1 (VCAM1) levels, and decreased HDL particle plasmalogen contents, markers of adipose tissue inflammation, vascular inflammation, and HDL antioxidative potential, respectively. In a population-based look-up of the inflammatory pathway single-nucleotide polymorphisms in a large Finnish cohorts (n=11 211), no association of the HLA region was detected for HDL-C as quantitative trait, but with extreme HDL-C phenotypes, implying the presence of low or high HDL genes in addition to the population-genomewide association studies–identified HDL genes. Conclusions— Our study highlights the role of inflammation with a genetic component in subjects with low HDL-C and identifies novel cis -expression quantitative trait loci ( cis -eQTL) variants in HLA region to be associated with low HDL-C.

Obsessive-compulsive disorder (OCD) is a common, debilitating neuropsychiatric illness with complex genetic etiology. The International OCD Foundation Genetics Collaborative (IOCDF-GC) is a multi-national collaboration established to discover the genetic variation predisposing to OCD. A set of individuals affected with DSM-IV OCD, a subset of their parents, and unselected controls, were genotyped with several different Illumina SNP microarrays. After extensive data cleaning, 1465 cases, 5557 ancestry-matched controls and 400 complete trios remained, with a common set of 469 410 autosomal and 9657 X-chromosome single nucleotide polymorphisms (SNPs). Ancestry-stratified case-control association analyses were conducted for three genetically-defined subpopulations and combined in two meta-analyses, with and without the trio-based analysis. In the case-control analysis, the lowest two P-values were located within DLGAP1 (P = 2.49 x 10(-6) and P = 3.44 x 10(-6)), a member of the neuronal postsynaptic density complex. In the trio analysis, rs6131295, near BTBD3, exceeded the genome-wide significance threshold with a P-value = 3.84 x 10(-8). However, when trios were meta-analyzed with the case-control samples, the P-value for this variant was 3.62 x 10(-5), losing genome-wide significance. Although no SNPs were identified to be associated with OCD at a genome-wide significant level in the combined trio-case-control sample, a significant enrichment of methylation QTLs (P < 0.001) and frontal lobe expression quantitative trait loci (eQTLs) (P = 0.001) was observed within the top-ranked SNPs (P < 0.01) from the trio-case-control analysis, suggesting these top signals may have a broad role in gene expression in the brain, and possibly in the etiology of OCD.

Tourette's syndrome (TS) is a developmental disorder that has one of the highest familial recurrence rates among neuropsychiatric diseases with complex inheritance. However, the identification of definitive TS susceptibility genes remains elusive. Here, we report the first genome-wide association study (GWAS) of TS in 1285 cases and 4964 ancestry-matched controls of European ancestry, including two European-derived population isolates, Ashkenazi Jews from North America and Israel and French Canadians from Quebec, Canada. In a primary meta-analysis of GWAS data from these European ancestry samples, no markers achieved a genome-wide threshold of significance (P

Clonal mosaicism for large chromosomal anomalies (duplications, deletions and uniparental disomy) was detected using SNP microarray data from over 50,000 subjects recruited for genome-wide association studies. This detection method requires a relatively high frequency of cells (>5–10%) with the same abnormal karyotype (presumably of clonal origin) in the presence of normal cells. The frequency of detectable clonal mosaicism in peripheral blood is low (

Genome-wide association studies (GWAS) are a useful approach in the study of the genetic components of complex phenotypes. Aside from large cohorts, GWAS have generally been limited to the study of one or a few diseases or traits. The emergence of biobanks linked to electronic medical records (EMRs) allows the efficient re-use of genetic data to yield meaningful genotype-phenotype associations for multiple phenotypes or traits. Phase I of the electronic MEdical Records and GEnomics (eMERGE-I) Network is a National Human Genome Research Institute (NHGRI)-supported consortium composed of five sites to perform various genetic association studies using DNA repositories and EMR systems. Each eMERGE site has developed EMR-based algorithms to comprise a core set of fourteen phenotypes for extraction of study samples from each site’s DNA repository. Each eMERGE site selected samples for a specific phenotype, and these samples were genotyped at either the Broad Institute or at the Center for Inherited Disease Research (CIDR) using the Illumina Infinium BeadChip technology. In all, approximately 17,000 samples from across the five sites were genotyped. A unified quality control (QC) pipeline was developed by the eMERGE Genomics Working Group and used to ensure thorough cleaning of the data. This process includes examination of sample quality, marker quality, and various batch effects. Upon completion of the genotyping and QC analyses for each site’s primary study, the eMERGE Coordinating Center merged the datasets from all five sites. This larger merged dataset re-entered the established eMERGE QC pipeline. Based on lessons learned during the process, additional analyses and QC checkpoints were added to the pipeline to ensure proper merging. Here we explore the challenges associated with combining datasets from different genotyping centers and describe the expansion to the eMERGE QC pipeline for merged datasets. These additional steps will be useful as the eMERGE project expands to include additional sites in eMERGE-II and also serve as a starting point for investigators merging multiple genotype data sets accessible through the National Center for Biotechnology Information (NCBI) in the database of Genotypes and Phenotypes (dbGaP). Our experience demonstrates that merging multiple datasets after additional QC can be an efficient use of genotype data despite new challenges that appear in the process.

Genotyping experiments are widely used in clinical and basic research laboratories to identify associations between genetic variations and normal/abnormal phenotypes. Genotyping assay techniques vary from single genomic regions that are interrogated using PCR reactions to high throughput assays examining genome-wide sequence and structural variation. The resulting genotype data may include millions of markers of thousands of individuals, requiring various statistical, modeling or other data analysis methodologies to interpret the results. To date, there are no standards for reporting genotyping experiments. Here we present the Minimum Information about a Genotyping Experiment (MIGen) standard, defining the minimum information required for reporting genotyping experiments. MIGen standard covers experimental design, subject description, genotyping procedure, quality control and data analysis. MIGen is a registered project under MIBBI (Minimum Information for Biological and Biomedical Investigations) and is being developed by an interdisciplinary group of experts in basic biomedical science, clinical science, biostatistics and bioinformatics. To accommodate the wide variety of techniques and methodologies applied in current and future genotyping experiment, MIGen leverages foundational concepts from the Ontology for Biomedical Investigations (OBI) for the description of the various types of planned processes and implements a hierarchical document structure. The adoption of MIGen by the research community will facilitate consistent genotyping data interpretation and independent data validation. MIGen can also serve as a framework for the development of data models for capturing and storing genotyping results and experiment metadata in a structured way, to facilitate the exchange of metadata.

White blood cell count (WBC) is unique among identified inflammatory predictors of chronic disease in that it is routinely measured in asymptomatic patients in the course of routine patient care. We led a genome-wide association analysis to identify variants associated with WBC levels in 13,923 subjects in the electronic Medical Records and Genomics (eMERGE) Network. We identified two regions of interest that were each unique to subjects of genetically determined ancestry to the African continent (AA) or to the European continent (EA). WBC varies among different ancestry groups. Despite being ancestry specific, these regions were identifiable in the combined analysis. In AA subjects, the region surrounding the Duffy antigen/chemokine receptor gene (DARC) on 1q21 exhibited significant association (p value = 6.71e–55). These results validate the previously reported association between WBC and of the regulatory variant rs2814778 in the promoter region, which causes the Duffy negative phenotype (Fy−/−). A second missense variant (rs12075) is responsible for the two principal antigens, Fya and Fyb of the Duffy blood group system. The two variants, consisting of four alleles, act in concert to produce five antigens and subsequent phenotypes. We were able to identify the marginal and novel interaction effects of these two variants on WBC. In the EA subjects, we identified significantly associated SNPs tagging three separate genes in the 17q21 region: (1) GSDMA, (2) MED24, and (3) PSMD3. Variants in this region have been reported to be associated with WBC, neutrophil count, and inflammatory diseases including asthma and Crohn’s disease.

Genome-wide scans of nucleotide variation in human subjects are providing an increasing number of replicated associations with complex disease traits. Most of the variants detected have small effects and, collectively, they account for a small fraction of the total genetic variance. Very large sample sizes are required to identify and validate findings. In this situation, even small sources of systematic or random error can cause spurious results or obscure real effects. The need for careful attention to data quality has been appreciated for some time in this field, and a number of strategies for quality control and quality assurance (QC/QA) have been developed. Here we extend these methods and describe a system of QC/QA for genotypic data in genome-wide association studies. This system includes some new approaches that (1) combine analysis of allelic probe intensities and called genotypes to distinguish gender misidentification from sex chromosome aberrations, (2) detect autosomal chromosome aberrations that may affect genotype calling accuracy, (3) infer DNA sample quality from relatedness and allelic intensities, (4) use duplicate concordance to infer SNP quality, (5) detect genotyping artifacts from dependence of Hardy-Weinberg equilibrium (HWE) test p-values on allelic frequency, and (6) demonstrate sensitivity of principal components analysis (PCA) to SNP selection. The methods are illustrated with examples from the ‘Gene Environment Association Studies’ (GENEVA) program. The results suggest several recommendations for QC/QA in the design and execution of genome-wide association studies.

It is unclear if buccal cell samples contain sufficient human DNA with adequately sized fragments for high throughput genetic bioassays. Yet buccal cell sample collection is an attractive alternative to gathering blood samples for genetic epidemiologists engaged in large-scale genetic biomarker studies. We assessed the genotyping efficiency (GE) and genotyping concordance (GC) of buccal cell DNA samples compared to corresponding blood DNA samples, from 32 Nurses' Health Study (NHS) participants using the Illumina Infinium 660W-Quad platform. We also assessed how GE and GC accuracy varied as a function of DNA concentration using serial dilutions of buccal DNA samples. Finally we determined the nature and genomic distribution of discordant genotypes in buccal DNA samples. The mean GE of undiluted buccal cell DNA samples was high (99.32%), as was the GC between the paired buccal and blood samples (99.29%). GC between the dilutions versus the undiluted buccal DNA was also very high (>97%), though both GE and GC notably declined at DNA concentrations less than 5 ng/μl. Most (>95%) genotype determinations in buccal cell samples were of the “missing call” variety (as opposed to the “alternative genotype call” variety) across the spectrum of buccal DNA concentrations studied. Finally, for buccal DNA concentration above 1.7 ng/ul, discordant genotyping calls did not cluster in any particular chromosome. Buccal cell-derived DNA represents a viable alternative to blood DNA for genotyping on a high-density platform.

Plasma soluble leptin receptor (sOB-R) levels were inversely associated with diabetes risk factors, including adiposity and insulin resistance, and highly correlated with the expression levels of leptin receptor, which is ubiquitously expressed in most tissues. We conducted a genome-wide association study of sOB-R in 1504 women of European ancestry from the Nurses’ Health Study. The initial scan yielded 26 single nucleotide polymorphisms (SNPs) significantly associated with sOB-R levels (P < 5 3 10 28 ); all mapping to the leptin receptor gene (LEPR). Analysis of imputed genotypes on autosomal chromosomes revealed an additional 106 SNPs in and adjacent to this gene that reached genome-wide significance level. Of these 132 SNPs (including two non-synonymous SNPs, rs1137100 and rs1137101), rs2767485, rs1751492 and rs4655555 remained associated with sOB-R levels at the 0.05 level (P 5 9.1 3 10 29 , 0.0105 and 0.0267, respectively) after adjustment for other univariately associated SNPs in a forward selection procedure. Significant associations with these SNPs were replicated in an independent sample of young males (n 5 875) residing in Cyprus (P < 1 3 10 24 ). These data provide novel evidence revealing the role of polymorphisms in LEPR in modulating plasma levels of sOB-R and may further our understanding of the complex relationships among leptin, leptin receptor and diabetes-related traits.

A genome-wide association study was carried out in 1020 case subjects with recurrent early-onset major depressive disorder (MDD) (onset before age 31) and 1636 control subjects screened to exclude lifetime MDD. Subjects were genotyped with the Affymetrix 6.0 platform. After extensive quality control procedures, 671 424 autosomal single nucleotide polymorphisms (SNPs) and 25 068 X chromosome SNPs with minor allele frequency greater than 1% were available for analysis. An additional 1 892 186 HapMap II SNPs were analyzed based on imputed genotypic data. Single-SNP logistic regression trend tests were computed, with correction for ancestry-informative principal component scores. No genome-wide significant evidence for association was observed, assuming that nominal P5 × 10(-8) approximates a 5% genome-wide significance threshold. The strongest evidence for association was observed on chromosome 18q22.1 (rs17077540, P=1.83 × 10(-7)) in a region that has produced some evidence for linkage to bipolar-I or -II disorder in several studies, within an mRNA detected in human brain tissue (BC053410) and approximately 75 kb upstream of DSEL. Comparing these results with those of a meta-analysis of three MDD GWAS data sets reported in a companion article, we note that among the strongest signals observed in the GenRED sample, the meta-analysis provided the greatest support (although not at a genome-wide significant level) for association of MDD to SNPs within SP4, a brain-specific transcription factor. Larger samples will be required to confirm the hypothesis of association between MDD (and particularly the recurrent early-onset subtype) and common SNPs.

Genetic risk for multiple sclerosis (MS) is thought to involve both common and rare risk alleles. Recent GWAS and subsequent meta-analysis have established the critical role of the HLA locus and identified new common variants associated to MS. These variants have small odds ratios (ORs) and explain only a fraction of the genetic risk. To expose potentially rare, high-impact alleles, we conducted a GWAS of 68 distantly related cases and 136 controls from a high-risk internal isolate of Finland with increased prevalence and familial occurrence of MS. The top 27 loci with p10(-4) were tested in 711 cases and 1029 controls from Finland, and the top two findings were validated in 3859 cases and 9110 controls from more heterogeneous populations. SNP (rs744166) within the STAT3 gene was associated to MS (p = 2.75 x 10(-10), OR 0.87, confidence interval 0.83-0.91). The protective haplotype for MS in STAT3 is a risk allele for Crohn disease, implying that STAT3 represents a shared risk locus for at least two autoimmune diseases. This study also demonstrates the potential of special isolated populations in search for variants contributing to complex traits.

Genome-wide association studies (GWAS) have emerged as powerful means for identifying genetic loci related to complex diseases. However, the role of environment and its potential to interact with key loci has not been adequately addressed in most GWAS. Networks of collaborative studies involving different study populations and multiple phenotypes provide a powerful approach for addressing the challenges in analysis and interpretation shared across studies. The Gene, Environment Association Studies (GENEVA) consortium was initiated to: identify genetic variants related to complex diseases; identify variations in gene-trait associations related to environmental exposures; and ensure rapid sharing of data through the database of Genotypes and Phenotypes. GENEVA consists of several academic institutions, including a coordinating center, two genotyping centers and 14 independently designed studies of various phenotypes, as well as several Institutes and Centers of the National Institutes of Health led by the National Human Genome Research Institute. Minimum detectable effect sizes include relative risks ranging from 1.24 to 1.57 and proportions of variance explained ranging from 0.0097 to 0.02. Given the large number of research participants (N > 80,000), an important feature of GENEVA is harmonization of common variables, which allow analyses of additional traits. Environmental exposure information available from most studies also enables testing of gene-environment interactions. Facilitated by its sizeable infrastructure for promoting collaboration, GENEVA has established a unified framework for genotyping, data quality control, analysis and interpretation. By maximizing knowledge obtained through collaborative GWAS incorporating environmental exposure information, GENEVA aims to enhance our understanding of disease etiology, potentially identifying opportunities for intervention.

OBJECTIVE— To use genome-wide fixed marker arrays and improved analytical tools to detect genetic associations with type 2 diabetes in a carefully phenotyped human sample. RESEARCH DESIGN AND METHODS— A total of 1,087 Framingham Heart Study (FHS) family members were genotyped on the Affymetrix 100K single nucleotide polymorphism (SNP) array and examined for association with incident diabetes and six diabetes-related quantitative traits. Quality control filters yielded 66,543 SNPs for association testing. We used two complementary SNP selection strategies (a “lowest P value” strategy and a “multiple related trait” strategy) to prioritize 763 SNPs for replication. We genotyped a subset of 150 SNPs in a nonoverlapping sample of 1,465 FHS unrelated subjects and examined all 763 SNPs for in silico replication in three other 100K and one 500K genome-wide association (GWA) datasets. RESULTS— We replicated associations of 13 SNPs with one or more traits in the FHS unrelated sample (16 expected under the null); none of them showed convincing in silico replication in 100K scans. Seventy-eight SNPs were nominally associated with diabetes in one other 100K GWA scan, and two (rs2863389 and rs7935082) in more than one. Twenty-five SNPs showed promising associations with diabetes-related traits in 500K GWA data; one of them (rs952635) replicated in FHS. Five previously reported associations were confirmed in our initial dataset. CONCLUSIONS— The FHS 100K GWA resource is useful for follow-up of genetic associations with diabetes-related quantitative traits. Discovery of new diabetes genes will require larger samples and a denser array combined with well-powered replication strategies.

Analyses of genome-wide association study (GWAS) data have revealed that detectable genetic mosaicism involving large (>2 Mb) structural autosomal alterations occurs in a fraction of individuals. We present results for a set of 24,849 genotyped individuals (total GWAS set II [TGSII]) in whom 341 large autosomal abnormalities were observed in 168 (0.68%) individuals. Merging data from the new TGSII set with data from two prior reports (the Gene-Environment Association Studies and the total GWAS set I) generated a large dataset of 127,179 individuals; we then conducted a meta-analysis to investigate the patterns of detectable autosomal mosaicism (n = 1,315 events in 925 [0.73%] individuals). Restricting to events >2 Mb in size, we observed an increase in event frequency as event size decreased. The combined results underscore that the rate of detectable mosaicism increases with age (p value = 5.5 × 10(-31)) and is higher in men (p value = 0.002) but lower in participants of African ancestry (p value = 0.003). In a subset of 47 individuals from whom serial samples were collected up to 6 years apart, complex changes were noted over time and showed an overall increase in the proportion of mosaic cells as age increased. Our large combined sample allowed for a unique ability to characterize detectable genetic mosaicism involving large structural events and strengthens the emerging evidence of non-random erosion of the genome in the aging population. Some individuals, studies, and centers received individual support. The grant numbers are: Addiction (U01HG004422, NIAAA: U10AA008401, NCI: P01CA089392, NIDA: R01DA013423, R01DA019963); Alpha-Tocopherol, Beta-Carotene Cancer Prevention Study (U.S. Public Health Service contracts: N01-CN-45165, N01-RC-45035, N01-RC-37004, NCI contract: HHSN261201000006C); Birth weight (U01HG004415); Blood clotting (R37 HL 039693); Broad Center for Genotyping and Analysis (U01HG04424); Cancer Prevention Study-II (American Cancer Society); Center for Inherited Disease Research (U01HG004438, HHSN268200782096C); Cleft lip/palate (NIDCR: U01DE018993 and R01DE016148, NIH contract: HHSN268200782096C); Dental Caries (NIDCR:U01DE018903 and R01DE014899, NIH CIDR contract: HHSN268200-782096C); Endometrial cancer (R01 CA134958); Fudan Lung Cancer Study (Ministry of Health (201002007); Ministry of Science and Technology (2011BAI09B00); National S&T Major Special Project (2011ZX09102-010-01); China National High-Tech Research and Development Program (2012AA02A517, 2012AA02A518); National Science Foundation of China (30890034); National Basic Research Program (2012CB944600); Scientific and Technological Support Plans from Jiangsu Province (BE2010715)); Gene-Environment Association Studies (Coordinating Center :U01 HG004446, Manuscript preparation: P01-GM099568); Genes and Environment in Lung Cancer, Singapore Study (National Medical Research Council Singapore grant (NMRC/0897/2004, NMRC/1075/2006); Agency for Science, Technology and Research (A*STAR) of Singapore); Genetic Epidemiological Study of Lung Adenocarcinoma (National Research Program on Genomic Medicine in Taiwan (DOH98-TD-G-111-015); National Research Program for Biopharmaceuticals in Taiwan (DOH 100-TD-PB-111-TM013); National Science Council,Taiwan (NSC 100-2319-B-400-001)); Glaucoma (NHGRI: U01HG004728, NEI: R01EY015473, NEI: R01EY015872, Harvard Medical School Distinguished Ophthalmology Scholar Award: Louis Pasquale); Guangdong Study (Foundation of Guangdong Science and Technology Department (2006B60101010, 2007A032000002, 2011A030400010); Guangzhou Science and Information Technology Bureau (2011Y2-00014); Chinese Lung Cancer Research Foundation; National Natural Science Foundation of China (81101549); Natural Science Foundation of Guangdong Province (S2011010000792)); Health Professionals Follow-up Study (UM1 CA167552, R01 HL35464); Hong Kong Study (General Research Fund of Research Grant Council, Hong Kong (781511M)); Intramural Research Program of the Division of Cancer Epidemiology and Genetics, National Cancer Institute, NIH; Intramural Research Program of the NIH, National Library of Medicine; Intramural Research Program of the National Institute for Occupational Safety and Health; Japanese Female Lung Cancer Collaborative Study (Grants-in-Aid from the Ministry of Health, Labor, and Welfare for Research on Applying Health Technology and for the 3rd-term Comprehensive 10-year Strategy for Cancer Control; National Cancer Center Research and Development Fund; Grant-in-Aid for Scientific Research on Priority Areas and on Innovative Area from the Ministry of Education, Science, Sports, Culture and Technology of Japan; NCI (R01-CA121210)); Lung cancer (Z01CP010200); Lung health (U01HG004738); Ministry of Health (201002007); Ministry of Science and Technology (2011BAI09B00); Melanoma (NCI R29CA70334, R01CA100264, P50CA093459); NLCS (China National High-Tech Research and Development Program Grant (2009AA022705); Priority Academic Program Development of Jiangsu Higher Education Institution; National Key Basic Research Program Grant (2011CB503805)); Nurses’ Health Study (P01 CA87969, R01 CA49449); Nurses’ Health Study II (UM1 CA176726, R01, 67262); OpPancreatic cancer (Mayo Clinic SPORE in Pancreatic Cancer: P50CA102701); Prematurity (U01HG004423); Prostate cancer (U01HG004726, NCI: CA63464, CA54281, CA1326792, RC2 CA148085); Shanghai Women’s Health Cohort Study (National Institutes of Health (R37 CA70867); National Cancer Institute intramural research program; NCI Intramural Research Program contract (N02 CP1101066)); Shenyang Lung Cancer Study (National Nature Science Foundation of China (81102194); Liaoning Provincial Department of Education (LS2010168); China Medical Board (00726)); Singapore Chinese Health Study (NIH grants: NCI R01 CA55069, R35 CA53890, R01 CA80205, and R01 CA144034); South Korea Multi-Center Lung Cancer Study (National Research Foundation of Korea (NRF) grant funded by the Korea government (MEST) (2011-0016106); National R&D Program for Cancer Control, Ministry of Health &Welfare, Republic of Korea (0720550-2); (A010250)); Tianjin Lung Cancer Study (Program for Changjiang Scholars and Innovative Research Team in University (PCSIRT); China (IRT1076), Tianjin Cancer Institute and Hospital, National Foundation for Cancer Research US); Venous thromboembolism (U01HG004735); Wuhan lung cancer study (National Key Basic Research and Development Program (2011CB503800)) and Yunnan Lung Cancer Study (Intramural program of U.S. National Institutes of Health; National Cancer Institute). Additionally, K.C.B. was supported in part by the Mary Beryl Patch Turnbull Scholar Program. The GENEVA consortium thanks the participants and the staff of all GENEVA studies for their important contributions. Support for the GENEVA genome-wide association studies was provided through the NIH Genes, Environment and Health Initiative (GEI).

Herpes zoster, commonly referred to as shingles, is caused by the varicella zoster virus (VZV). VZV initially manifests as chicken pox, most commonly in childhood, can remain asymptomatically latent in nerve tissues for many years and often re-emerges as shingles. Although reactivation may be related to immune suppression, aging and female sex, most inter-individual variability in re-emergence risk has not been explained to date. We performed a genome-wide association analyses in 22,981 participants (2280 shingles cases) from the electronic Medical Records and Genomics Network. Using Cox survival and logistic regression, we identified a genomic region in the combined and European ancestry groups that has an age of onset effect reaching genome-wide significance (P>1.0 × 10(-8)). This region tags the non-coding gene HCP5 (HLA Complex P5) in the major histocompatibility complex. This gene is an endogenous retrovirus and likely influences viral activity through regulatory functions. Variants in this genetic region are known to be associated with delay in development of AIDS in people infected by HIV. Our study provides further suggestion that this region may have a critical role in viral suppression and could potentially harbor a clinically actionable variant for the shingles vaccine.

Background Asthma is a chronic respiratory disease that often originates in early childhood. Although candidate gene studies have identified many potential asthma susceptibility genes in adult populations, few have studied associations with immune phenotypes in the first year that might be early clinical markers of asthma. Objective The aim of this study was to assess the contribution of genetic variation to cytokine response profiles and atopic phenotypes in the first year of life in the Childhood Origin of Asthma cohort. Methods Two hundred seven European American children participating in the Childhood Origin of Asthma study were genotyped for 61 single nucleotide polymorphisms in 35 genes involved in immune regulation. We examined the relationship between these single nucleotide polymorphisms and PHA-induced cytokine (IL-5, IL-10, IL-13, and IFN-γ) response profiles at birth and at year 1, respiratory syncytial virus–induced wheezing and atopic dermatitis in the first year of life, and total IgE levels, peripheral blood eosinophil counts, and allergic sensitization at age 1 year. The data were analyzed by using censored regression for quantitative measurements and logistic regression for qualitative phenotypes. Results The 237Gly allele of the high-affinity IgE receptor β chain ( FCER1B ) and a silent substitution in the nitric oxide synthase ( NOS)2A gene were associated with reduced IL-13 responses in cord blood ( P = .0025 and P = .0062, respectively). A significant gene-gene interaction between FCER1B 237Gly and NOS2A D346D was detected, with individuals carrying the minor allele for both polymorphisms having the lowest cord blood IL-13 levels. Furthermore, the IL13 110Gln allele showed an association with increased IgE levels at year 1 ( P = .0026), and the colony-stimulating factor 2 ( CSF2 ) 117Thr allele showed an association with a greater increase in IL-5 responses during the first year ( P = .0092). The TGF-β1 ( TGFB1 ) –509T allele was associated with respiratory syncytial virus–related wheezing in the first year ( P = .0005). None of the polymorphisms included in this study were associated with atopic dermatitis during the first year or a positive RAST result at 1 year of age. Conclusion These data suggest that variations in genes involved in immune regulation are associated with biologic and clinical phenotypes in the first year of life that might increase the risk for the subsequent development of childhood asthma.

We have investigated the association of multiple sclerosis (MS) with polymorphisms in the IL4R gene in 332 single-case MS families. IL4R encodes a subunit of the interleukin-4 receptor, a molecule important for T-cell development and differentiation, and is a gene shown to be associated with immune-related diseases such as asthma and type I diabetes. By genotyping two promoter and eight coding IL4R SNPs and identifying haplotypes (complex alleles) in the MS families, stratified for HLA genotype, we have observed evidence of the association of the IL4R gene to MS. In particular, we have identified a specific susceptibility haplotype, and observe that the risk is conferred primarily to individuals not carrying the high MS-risk HLA DR2 (DRB1(*)1501-DQB1(*)0602) haplotype (nominal P=0.009). These findings suggest a potentially important role for the IL4R gene in predisposition to MS, and provide further evidence of its relevance as a candidate gene for immune-related diseases.

Type 1 diabetes is an autoimmune disease with a Th1 phenotype in which insulin-producing β-cells in the pancreas are destroyed. The T-cell–specific transcription factor TCF7 activates genes involved in immune regulation and is a candidate locus for genetic susceptibility to type 1 diabetes. A nonsynonymous single nucleotide polymorphism (SNP) (Pro to Thr) in the TCF7 gene, C883A, was examined in samples from 282 Caucasian multiplex type 1 diabetic families. HLA-DRB1 and -DQB1 genotypes were previously determined for these samples, allowing data stratification based on HLA-associated risk. The transmission disequilibrium test showed significant overtransmission of the A allele from fathers (64.1%, P < 0.007) and nonsignificant overtransmission (57.4%, P < 0.06) of the A allele to patients who do not carry the highest-risk HLA-DR3/DR4 genotype. Elliptical sib pair analysis showed significant associations of the A allele with type 1 diabetes in paternal transmissions (P < 0.03), transmissions to male children (P < 0.04), and in the non-DR3/DR4 group (P < 0.04). These data also suggest that TCF7 C883A may affect age of disease onset. Analysis of genotype data from surrounding SNPs suggests that this TCF7 polymorphism may itself represent a risk factor for type 1 diabetes.

It is important to identify the patients at highest risk of fractures. A recent large-scale meta-analysis identified 63 autosomal single-nucleotide polymorphisms (SNPs) associated with bone mineral density (BMD), of which 16 were also associated with fracture risk. Based on these findings, two genetic risk scores (GRS63 and GRS16) were developed. Our aim was to determine the clinical usefulness of these GRSs for the prediction of BMD, BMD change, and fracture risk in elderly subjects. We studied two male (Osteoporotic Fractures in Men Study [MrOS] US, MrOS Sweden) and one female (Study of Osteoporotic Fractures [SOF]) large prospective cohorts of older subjects, looking at BMD, BMD change, and radiographically and/or medically confirmed incident fractures (8067 subjects, 2185 incident nonvertebral or vertebral fractures). GRS63 was associated with BMD (≅3% of the variation explained) but not with BMD change. Both GRS63 and GRS16 were associated with fractures. After BMD adjustment, the effect sizes for these associations were substantially reduced. Similar results were found using an unweighted GRS63 and an unweighted GRS16 compared with those found using the corresponding weighted risk scores. Only minor improvements in C-statistics (AUC) for fractures were found when the GRSs were added to a base model (age, weight, and height), and no significant improvements in C-statistics were found when they were added to a model further adjusted for BMD. Net reclassification improvements with the addition of the GRSs to a base model were modest and substantially attenuated in BMD-adjusted models. GRS63 is associated with BMD, but not BMD change, suggesting that the genetic determinants of BMD differ from those of BMD change. When BMD is known, the clinical utility of the two GRSs for fracture prediction is limited in elderly subjects.

Maternal metabolism during pregnancy impacts the developing fetus, affecting offspring birth weight and adiposity. This has important implications for metabolic health later in life (e.g., offspring of mothers with pre-existing or gestational diabetes mellitus have an increased risk of metabolic disorders in childhood). To identify genetic loci associated with measures of maternal metabolism obtained during an oral glucose tolerance test at ∼28 weeks’ gestation, we performed a genome-wide association study of 4,437 pregnant mothers of European (n = 1,367), Thai (n = 1,178), Afro-Caribbean (n = 1,075), and Hispanic (n = 817) ancestry, along with replication of top signals in three additional European ancestry cohorts. In addition to identifying associations with genes previously implicated with measures of glucose metabolism in nonpregnant populations, we identified two novel genome-wide significant associations: 2-h plasma glucose and HKDC1, and fasting C-peptide and BACE2. These results suggest that the genetic architecture underlying glucose metabolism may differ, in part, in pregnancy.

Newborns characterized as large and small for gestational age are at risk for increased mortality and morbidity during the first year of life as well as for obesity and dysglycemia as children and adults. The intrauterine environment and fetal genes contribute to the fetal size at birth. To define the genetic architecture underlying the newborn size, we performed a genome-wide association study (GWAS) in 4281 newborns in four ethnic groups from the Hyperglycemia and Adverse Pregnancy Outcome Study. We tested for association with newborn anthropometric traits (birth length, head circumference, birth weight, percent fat mass and sum of skinfolds) and newborn metabolic traits (cord glucose and C-peptide) under three models. Model 1 adjusted for field center, ancestry, neonatal gender, gestational age at delivery, parity, maternal age at oral glucose tolerance test (OGTT); Model 2 adjusted for Model 1 covariates, maternal body mass index (BMI) at OGTT, maternal height at OGTT, maternal mean arterial pressure at OGTT, maternal smoking and drinking; Model 3 adjusted for Model 2 covariates, maternal glucose and C-peptide at OGTT. Strong evidence for association was observed with measures of newborn adiposity (sum of skinfolds model 3 Z-score 7.356, P = 1.90×10⁻¹³, and to a lesser degree fat mass and birth weight) and a region on Chr3q25.31 mapping between CCNL and LEKR1. These findings were replicated in an independent cohort of 2296 newborns. This region has previously been shown to be associated with birth weight in Europeans. The current study suggests that association of this locus with birth weight is secondary to an effect on fat as opposed to lean body mass.

With white blood cell count emerging as an important risk factor for chronic inflammatory diseases, genetic associations of differential leukocyte types, specifically monocyte count, are providing novel candidate genes and pathways to further investigate. Circulating monocytes play a critical role in vascular diseases such as in the formation of atherosclerotic plaque. We performed a joint and ancestry-stratified genome-wide association analyses to identify variants specifically associated with monocyte count in 11 014 subjects in the electronic Medical Records and Genomics Network. In the joint and European ancestry samples, we identified novel associations in the chromosome 16 interferon regulatory factor 8 (IRF8) gene (P-value = 2.78×10(−16), β = −0.22). Other monocyte associations include novel missense variants in the chemokine-binding protein 2 (CCBP2) gene (P-value = 1.88×10(−7), β = 0.30) and a region of replication found in ribophorin I (RPN1) (P-value = 2.63×10(−16), β = −0.23) on chromosome 3. The CCBP2 and RPN1 region is located near GATA binding protein2 gene that has been previously shown to be associated with coronary heart disease. On chromosome 9, we found a novel association in the prostaglandin reductase 1 gene (P-value = 2.29×10(−7), β = 0.16), which is downstream from lysophosphatidic acid receptor 1. This region has previously been shown to be associated with monocyte count. We also replicated monocyte associations of genome-wide significance (P-value = 5.68×10(−17), β = −0.23) at the integrin, alpha 4 gene on chromosome 2. The novel IRF8 results and further replications provide supporting evidence of genetic regions associated with monocyte count.

The plasma glycoprotein von Willebrand factor (VWF) exhibits fivefold antigen level variation across the normal human population determined by both genetic and environmental factors. Low levels of VWF are associated with bleeding and elevated levels with increased risk for thrombosis, myocardial infarction, and stroke. To identify additional genetic determinants of VWF antigen levels and to minimize the impact of age and illness-related environmental factors, we performed genome-wide association analysis in two young and healthy cohorts ( n = 1,152 and n = 2,310) and identified signals at ABO ( P < 7.9E-139) and VWF ( P < 5.5E-16), consistent with previous reports. Additionally, linkage analysis based on sibling structure within the cohorts, identified significant signals at chromosome 2q12–2p13 (LOD score 5.3) and at the ABO locus on chromosome 9q34 (LOD score 2.9) that explained 19.2% and 24.5% of the variance in VWF levels, respectively. Given its strong effect, the linkage region on chromosome 2 could harbor a potentially important determinant of bleeding and thrombosis risk. The absence of a chromosome 2 association signal in this or previous association studies suggests a causative gene harboring many genetic variants that are individually rare, but in aggregate common. These results raise the possibility that similar loci could explain a significant portion of the “missing heritability” for other complex genetic traits.

Sex hormone-binding globulin (SHBG) is a glycoprotein responsible for the transport and biologic availability of sex steroid hormones, primarily testosterone and estradiol. SHBG has been associated with chronic diseases including type 2 diabetes (T2D) and with hormone-sensitive cancers such as breast and prostate cancer. We performed a genome-wide association study (GWAS) meta-analysis of 21,791 individuals from 10 epidemiologic studies and validated these findings in 7,046 individuals in an additional six studies. We identified twelve genomic regions (SNPs) associated with circulating SHBG concentrations. Loci near the identified SNPs included SHBG (rs12150660, 17p13.1, p = 1.8×10−106), PRMT6 (rs17496332, 1p13.3, p = 1.4×10−11), GCKR (rs780093, 2p23.3, p = 2.2×10−16), ZBTB10 (rs440837, 8q21.13, p = 3.4×10−09), JMJD1C (rs7910927, 10q21.3, p = 6.1×10−35), SLCO1B1 (rs4149056, 12p12.1, p = 1.9×10−08), NR2F2 (rs8023580, 15q26.2, p = 8.3×10−12), ZNF652 (rs2411984, 17q21.32, p = 3.5×10−14), TDGF3 (rs1573036, Xq22.3, p = 4.1×10−14), LHCGR (rs10454142, 2p16.3, p = 1.3×10−07), BAIAP2L1 (rs3779195, 7q21.3, p = 2.7×10−08), and UGT2B15 (rs293428, 4q13.2, p = 5.5×10−06). These genes encompass multiple biologic pathways, including hepatic function, lipid metabolism, carbohydrate metabolism and T2D, androgen and estrogen receptor function, epigenetic effects, and the biology of sex steroid hormone-responsive cancers including breast and prostate cancer. We found evidence of sex-differentiated genetic influences on SHBG. In a sex-specific GWAS, the loci 4q13.2-UGT2B15 was significant in men only (men p = 2.5×10−08, women p = 0.66, heterogeneity p = 0.003). Additionally, three loci showed strong sex-differentiated effects: 17p13.1-SHBG and Xq22.3-TDGF3 were stronger in men, whereas 8q21.12-ZBTB10 was stronger in women. Conditional analyses identified additional signals at the SHBG gene that together almost double the proportion of variance explained at the locus. Using an independent study of 1,129 individuals, all SNPs identified in the overall or sex-differentiated or conditional analyses explained ∼15.6% and ∼8.4% of the genetic variation of SHBG concentrations in men and women, respectively. The evidence for sex-differentiated effects and allelic heterogeneity highlight the importance of considering these features when estimating complex trait variance., Author Summary Sex hormone-binding globulin (SHBG) is the key protein responsible for binding and transporting the sex steroid hormones, testosterone and estradiol, in the circulatory system. SHBG regulates their bioavailability and therefore their effects in the body. SHBG has been linked to chronic diseases including type 2 diabetes and to hormone-sensitive cancers such as breast and prostate cancer. SHBG concentrations are approximately 50% heritable in family studies, suggesting SHBG concentrations are under significant genetic control; yet, little is known about the specific genes that influence SHBG. We conducted a large study of the association of SHBG concentrations with markers in the human genome in ∼22,000 white men and women to determine which loci influence SHBG concentrations. Genes near the identified genomic markers in addition to the SHBG protein coding gene included PRMT6, GCKR, ZBTB10, JMJD1C, SLCO1B1, NR2F2, ZNF652, TDGF3, LHCGR, BAIAP2L1, and UGT2B15. These genes represent a wide range of biologic pathways that may relate to SHBG function and sex steroid hormone biology, including liver function, lipid metabolism, carbohydrate metabolism and type 2 diabetes, and the development and progression of sex steroid hormone-responsive cancers.

This work was supported in part by the following National Institutes of Health grants: GENEVA Coordinating Center (U01 HG004446); GARNET Coordinating Center (U01 HG005157); Center for Inherited Disease Research (U01HG004438, NIH contract numbers HHSN268200782096C and HHSN268201100011I); and Broad Center for Genotyping and Analysis (U01HG04424).

Return of individual genetic results to research participants, including participants in archives and biorepositories, is receiving increased attention. However, few groups have deliberated on specific results or weighed deliberations against relevant local contextual factors.The Electronic Medical Records and Genomics (eMERGE) Network, which includes five biorepositories conducting genome-wide association studies, convened a return of results oversight committee to identify potentially returnable results. Network-wide deliberations were then brought to local constituencies for final decision making.Defining results that should be considered for return required input from clinicians with relevant expertise and much deliberation. The return of results oversight committee identified two sex chromosomal anomalies, Klinefelter syndrome and Turner syndrome, as well as homozygosity for factor V Leiden, as findings that could warrant reporting. Views about returning findings of HFE gene mutations associated with hemochromatosis were mixed due to low penetrance. Review of electronic medical records suggested that most participants with detected abnormalities were unaware of these findings. Local considerations relevant to return varied and, to date, four sites have elected not to return findings (return was not possible at one site).The eMERGE experience reveals the complexity of return of results decision making and provides a potential deliberative model for adoption in other collaborative contexts.

BACKGROUND: Recent genome-wide association studies (GWAS) have identified a number of novel SNPs associated with blood pressure and hypertension-related traits although studies were very large (approaching 80K participants) and effect sizes were modest with per allele effect sizes as small variants as a single millimeter of mercury difference in blood pressure. However, because hypertension is the most potent modifiable risk factor for ischemic stroke, lowering blood pressure by a few mm Hg can translate into measurable decrease in stroke risk. METHODS: We investigated 22 hypertension (HTN) associated SNPs in a case-control analysis of ischemic stroke in multiple stroke studies including the Siblings With Ischemic Stroke Study (SWISS), the Ischemic Stroke Genetics Study (ISGS), the Bio-Repository of DNA in Stroke (BRAINS), the Vitamin Intervention for Stroke Prevention (VISP) trial, and the Women's Health Initiative (WHI). SWISS/ISGS/BRAINS were analyzed together and included subtype information. Genotyping was funded through the individual studies, the Genomics and Randomized Trials Network (GARNET) consortium, and the SHARe (SNP Health Association Resource). All studies had both raw and imputed GWAS data. Analyses included the following phenotypes: all ischemic stroke, stroke subtypes, and age-at-stroke-onset and were stratified by race. Analyses were minimally adjusted for age, sex and principal components 1 & 2. Alpha was set at 0.05. RESULTS: Eleven of the SNPs from the HTN GWAS were associated with ischemic stroke in European ancestry from one or more of the study populations. Two SNPs on chromosome 12 and one on chromosome 21 were associated in multiple European ancestry cohorts. No SNP was associated in all studies. A SNP previously associated with HTN in African ancestry populations was associated with stroke in African-Americans. Analysis of subtypes found associations with small vessel disease and cardioembolic stroke but not large artery disease. The case-only age-at-stroke-onset analysis also found associations for the two SNPs on chromosome 12. CONCLUSION: These data suggest that candidate SNPs from a HTN GWAS may be associated with ischemic stroke and warrant further investigation. Racial differences were observed. Replication in populations from the other SiGN (Stroke Genetics Network), GENEVA (Gene Environment Association Studies) consortium, and the ISGC (International Stroke Genetics Consortium) studies are underway.

Genome-wide association studies have identified numerous genetic loci for spirometic measures of pulmonary function, forced expiratory volume in one second (FEV1), and its ratio to forced vital capacity (FEV1/FVC). Given that cigarette smoking adversely affects pulmonary function, we conducted genome-wide joint meta-analyses (JMA) of single nucleotide polymorphism (SNP) and SNP-by-smoking (ever-smoking or pack-years) associations on FEV1 and FEV1/FVC across 19 studies (total N = 50,047). We identified three novel loci not previously associated with pulmonary function. SNPs in or near DNER (smallest P JMA = 5.00×10−11), HLA-DQB1 and HLA-DQA2 (smallest P JMA = 4.35×10−9), and KCNJ2 and SOX9 (smallest P JMA = 1.28×10−8) were associated with FEV1/FVC or FEV1 in meta-analysis models including SNP main effects, smoking main effects, and SNP-by-smoking (ever-smoking or pack-years) interaction. The HLA region has been widely implicated for autoimmune and lung phenotypes, unlike the other novel loci, which have not been widely implicated. We evaluated DNER, KCNJ2, and SOX9 and found them to be expressed in human lung tissue. DNER and SOX9 further showed evidence of differential expression in human airway epithelium in smokers compared to non-smokers. Our findings demonstrated that joint testing of SNP and SNP-by-environment interaction identified novel loci associated with complex traits that are missed when considering only the genetic main effects., Author Summary Measures of pulmonary function provide important clinical tools for evaluating lung disease and its progression. Genome-wide association studies have identified numerous genetic risk factors for pulmonary function but have not considered interaction with cigarette smoking, which has consistently been shown to adversely impact pulmonary function. In over 50,000 study participants of European descent, we applied a recently developed joint meta-analysis method to simultaneously test associations of gene and gene-by-smoking interactions in relation to two major clinical measures of pulmonary function. Using this joint method to incorporate genetic main effects plus gene-by-smoking interaction, we identified three novel gene regions not previously related to pulmonary function: (1) DNER, (2) HLA-DQB1 and HLA-DQA2, and (3) KCNJ2 and SOX9. Expression analyses in human lung tissue from ours or prior studies indicate that these regions contain genes that are plausibly involved in pulmonary function. This work highlights the utility of employing novel methods for incorporating environmental interaction in genome-wide association studies to identify novel genetic regions.

Diabetic kidney disease, or diabetic nephropathy (DN), is a major complication of diabetes and the leading cause of end-stage renal disease (ESRD) that requires dialysis treatment or kidney transplantation. In addition to the decrease in the quality of life, DN accounts for a large proportion of the excess mortality associated with type 1 diabetes (T1D). Whereas the degree of glycemia plays a pivotal role in DN, a subset of individuals with poorly controlled T1D do not develop DN. Furthermore, strong familial aggregation supports genetic susceptibility to DN. However, the genes and the molecular mechanisms behind the disease remain poorly understood, and current therapeutic strategies rarely result in reversal of DN. In the GEnetics of Nephropathy: an International Effort (GENIE) consortium, we have undertaken a meta-analysis of genome-wide association studies (GWAS) of T1D DN comprising ∼2.4 million single nucleotide polymorphisms (SNPs) imputed in 6,691 individuals. After additional genotyping of 41 top ranked SNPs representing 24 independent signals in 5,873 individuals, combined meta-analysis revealed association of two SNPs with ESRD: rs7583877 in the AFF3 gene (P = 1.2×10−8) and an intergenic SNP on chromosome 15q26 between the genes RGMA and MCTP2, rs12437854 (P = 2.0×10−9). Functional data suggest that AFF3 influences renal tubule fibrosis via the transforming growth factor-beta (TGF-β1) pathway. The strongest association with DN as a primary phenotype was seen for an intronic SNP in the ERBB4 gene (rs7588550, P = 2.1×10−7), a gene with type 2 diabetes DN differential expression and in the same intron as a variant with cis-eQTL expression of ERBB4. All these detected associations represent new signals in the pathogenesis of DN., Author Summary The global prevalence of diabetes has reached epidemic proportions, constituting a major health care problem worldwide. Diabetic kidney disease, or diabetic nephropathy (DN)—the major long term microvascular complication of diabetes—is associated with excess mortality among patients with type 1 diabetes. Even though DN has been shown to cluster in families, the underlying genetic and molecular pathways remain poorly defined. We have undertaken the largest genome-wide association study and meta-analysis to date on DN and on its most severe form of kidney disease, end-stage renal disease (ESRD). We identified new loci significantly associated with diabetic ESRD: AFF3 and an intergenic locus on chromosome 15q26 residing between RGMA and MCTP2. Our functional analyses suggest that AFF3 influences renal tubule fibrosis, a pathological hallmark of severe DN. Another locus in ERBB4 was suggestively associated with DN and resides in the same intronic region as a variant affecting the expression of ERBB4. Subsequent pathway analysis of the genes co-expressed with ERBB4 indicated involvement of fibrosis.

Optic nerve degeneration caused by glaucoma is a leading cause of blindness worldwide. Patients affected by the normal-pressure form of glaucoma are more likely to harbor risk alleles for glaucoma-related optic nerve disease. We have performed a meta-analysis of two independent genome-wide association studies for primary open angle glaucoma (POAG) followed by a normal-pressure glaucoma (NPG, defined by intraocular pressure (IOP) less than 22 mmHg) subgroup analysis. The single-nucleotide polymorphisms that showed the most significant associations were tested for association with a second form of glaucoma, exfoliation-syndrome glaucoma. The overall meta-analysis of the GLAUGEN and NEIGHBOR dataset results (3,146 cases and 3,487 controls) identified significant associations between two loci and POAG: the CDKN2BAS region on 9p21 (rs2157719 [G], OR = 0.69 [95%CI 0.63–0.75], p = 1.86×10−18), and the SIX1/SIX6 region on chromosome 14q23 (rs10483727 [A], OR = 1.32 [95%CI 1.21–1.43], p = 3.87×10−11). In sub-group analysis two loci were significantly associated with NPG: 9p21 containing the CDKN2BAS gene (rs2157719 [G], OR = 0.58 [95% CI 0.50–0.67], p = 1.17×10−12) and a probable regulatory region on 8q22 (rs284489 [G], OR = 0.62 [95% CI 0.53–0.72], p = 8.88×10−10). Both NPG loci were also nominally associated with a second type of glaucoma, exfoliation syndrome glaucoma (rs2157719 [G], OR = 0.59 [95% CI 0.41–0.87], p = 0.004 and rs284489 [G], OR = 0.76 [95% CI 0.54–1.06], p = 0.021), suggesting that these loci might contribute more generally to optic nerve degeneration in glaucoma. Because both loci influence transforming growth factor beta (TGF-beta) signaling, we performed a genomic pathway analysis that showed an association between the TGF-beta pathway and NPG (permuted p = 0.009). These results suggest that neuro-protective therapies targeting TGF-beta signaling could be effective for multiple forms of glaucoma., Author Summary Loss of vision from glaucoma, a common cause of blindness worldwide, is due to irreversible damage to the optic nerve. Current therapies cannot prevent glaucoma-related optic nerve disease and very little is known about the underlying responsible molecular events. Glaucoma patients affected by the “normal-pressure” subtype of glaucoma (NPG) have increased susceptibility to optic nerve degeneration. Although NPG has a high heritability, common predisposing genetic variants have not been identified. Therefore, we performed a meta-analysis of two independent genome-wide association studies for a common form of glaucoma, primary open angle glaucoma (POAG), followed by NPG subgroup analysis. We found that SNPs in the CDKN2BAS gene region on 9p21 and a highly conserved region with a probable regulatory function on 8q22 were associated with NPG and with optic nerve disease in a second type of glaucoma, exfoliation glaucoma. Both genomic regions are predicted to influence TGF-beta activity, and using whole-genome data we showed that the TGF-beta signaling pathway overall is associated with NPG. These results reveal new insights into the molecular pathogenesis of optic nerve disease in glaucoma and are an important step toward the development of preventative and protective therapies.

Genome-wide association studies (GWAS) are a useful approach in the study of the genetic components of complex phenotypes. Aside from large cohorts, GWAS have generally been limited to the study of one or a few diseases or traits. The emergence of biobanks linked to electronic medical records (EMRs) allows the efficient re-use of genetic data to yield meaningful genotype-phenotype associations for multiple phenotypes or traits. Phase I of the electronic MEdical Records and GEnomics (eMERGE-I) Network is a National Human Genome Research Institute (NHGRI)-supported consortium composed of five sites to perform various genetic association studies using DNA repositories and EMR systems. Each eMERGE site has developed EMR-based algorithms to comprise a core set of fourteen phenotypes for extraction of study samples from each site’s DNA repository. Each eMERGE site selected samples for a specific phenotype, and these samples were genotyped at either the Broad Institute or at the Center for Inherited Disease Research (CIDR) using the Illumina Infinium BeadChip technology. In all, approximately 17,000 samples from across the five sites were genotyped. A unified quality control (QC) pipeline was developed by the eMERGE Genomics Working Group and used to ensure thorough cleaning of the data. This process includes examination of sample quality, marker quality, and various batch effects. Upon completion of the genotyping and QC analyses for each site’s primary study, the eMERGE Coordinating Center merged the datasets from all five sites. This larger merged dataset re-entered the established eMERGE QC pipeline. Based on lessons learned during the process, additional analyses and QC checkpoints were added to the pipeline to ensure proper merging. Here we explore the challenges associated with combining datasets from different genotyping centers and describe the expansion to the eMERGE QC pipeline for merged datasets. These additional steps will be useful as the eMERGE project expands to include additional sites in eMERGE-II and also serve as a starting point for investigators merging multiple genotype data sets accessible through the National Center for Biotechnology Information (NCBI) in the database of Genotypes and Phenotypes (dbGaP). Our experience demonstrates that merging multiple datasets after additional QC can be an efficient use of genotype data despite new challenges that appear in the process.

Primary open-angle glaucoma (POAG) is a genetically complex common disease characterized by progressive optic nerve degeneration that results in irreversible blindness. Recently, a genome-wide association study (GWAS) for POAG in an Icelandic population identified significant associations with single nucleotide polymorphisms (SNPs) between the CAV1 and CAV2 genes on chromosome 7q31. In this study, we confirm that the identified SNPs are associated with POAG in our Caucasian US population and that specific haplotypes located in the CAV1/CAV2 intergenic region are associated with the disease. We also present data suggesting that associations with several CAV1/CAV2 SNPs are significant mostly in women.

Genome-wide association studies (GWAS) are being conducted at an unprecedented rate in population-based cohorts and have increased our understanding of the pathophysiology of complex disease. The recent application of GWAS to clinic-based cohorts has also yielded genetic predictors of clinical outcomes. Regardless of context, the practical utility of this information will ultimately depend upon the quality of the original data. Quality control (QC) procedures for GWAS are computationally intensive, operationally challenging, and constantly evolving. With each new dataset, new realities are discovered about GWAS data and best practices continue to be developed. The Genomics Workgroup of the National Human Genome Research Institute (NHGRI) funded electronic Medical Records and Genomics (eMERGE) network has invested considerable effort in developing strategies for QC of these data. The lessons learned by this group will be valuable for other investigators dealing with large scale genomic datasets. Here we enumerate some of the challenges in QC of GWAS data and describe the approaches that the eMERGE network is using for quality assurance in GWAS data, thereby minimizing potential bias and error in GWAS results. In this protocol we discuss common issues associated with QC of GWAS data, including data file formats, software packages for data manipulation and analysis, sex chromosome anomalies, sample identity, sample relatedness, population substructure, batch effects, and marker quality. We propose best practices and discuss areas of ongoing and future research.

A low level of HDL-C is the most common plasma lipid abnormality observed in men with established coronary heart disease (CHD). To identify allelic variants associated with susceptibility to low HDL-C and CHD, we examined 60 candidate genes with key roles in HDL metabolism, insulin resistance, and inflammation using samples from the Veterans Affairs HDL Intervention Trial (VA-HIT; cases, n = 699) and the Framingham Offspring Study (FOS; controls, n = 705). VA-HIT was designed to examine the benefits of HDL-raising with gemfibrozil in men with low HDL-C (≤40 mg/dl) and established CHD. After adjustment for multiple testing within each gene, single-nucleotide polymorphisms (SNP) significantly associated with case status were identified in the genes encoding LIPC (rs4775065, P < 0.0001); CETP (rs5882, P = 0.0002); RXRA (rs11185660, P = 0.0021); ABCA1 (rs2249891, P = 0.0126); ABCC6 (rs150468, P = 0.0206; rs212077, P = 0.0443); CUBN (rs7893395, P = 0.0246); APOA2 (rs3813627, P = 0.0324); SELP (rs732314, P = 0.0376); and APOC4 (rs10413089, P = 0.0425). Included among the novel findings of this study are the identification of susceptibility alleles for low HDL-C/CHD risk in the genes encoding CUBN and RXRA, and the observation that genetic variation in SELP may influence CHD risk through its effects on HDL.

BackgroundIn Northern European descended populations, genetic susceptibility for multiple sclerosis (MS) is associated with alleles of the human leukocyte antigen (HLA) Class II gene DRB1. Whether other major histocompatibility complex (MHC) genes contribute to MS susceptibility is controversial.Methodology/principal findingsA case control analysis was performed using 958 single nucleotide polymorphisms (SNPs) spanning the MHC assayed in two independent datasets. The discovery dataset consisted of 1,018 cases and 1,795 controls and the replication dataset was composed of 1,343 cases and 1,379 controls. The most significantly MS-associated SNP in the discovery dataset was rs3135391, a Class II SNP known to tag the HLA-DRB1*15:01 allele, the primary MS susceptibility allele in the MHC (O.R. = 3.04, p < 1 x 10(-78)). To control for the effects of the HLA-DRB1*15:01 haplotype, case control analysis was performed adjusting for this HLA-DRB1*15:01 tagging SNP. After correction for multiple comparisons (false discovery rate = .05) 52 SNPs in the Class I, II and III regions were significantly associated with MS susceptibility in both datasets using the Cochran Armitage trend test. The discovery and replication datasets were merged and subjects carrying the HLA-DRB1*15:01 tagging SNP were excluded. Association tests showed that 48 of the 52 replicated SNPs retained significant associations with MS susceptibility independently of the HLA-DRB1*15:01 as defined by the tagging SNP. 20 Class I SNPs were associated with MS susceptibility with p-values < or = 1 x 10(-8). The most significantly associated SNP was rs4959039, a SNP in the downstream un-translated region of the non-classical HLA-G gene (Odds ratio 1.59, 95% CI 1.40, 1.81, p = 8.45 x 10(-13)) and is in linkage disequilibrium with several nearby SNPs. Logistic regression modeling showed that this SNP's contribution to MS susceptibility was independent of the Class II and Class III SNPs identified in this screen.ConclusionsA MHC Class I locus contributes to MS susceptibility independently of the HLA-DRB1*15:01 haplotype.

To identify type 2 diabetes (T2D) susceptibility loci, we conducted genome-wide association (GWA) scans in nested case–control samples from two prospective cohort studies, including 2591 patients and 3052 controls of European ancestry. Validation was performed in 11 independent GWA studies of 10 870 cases and 73 735 controls. We identified significantly associated variants near RBMS1 and ITGB6 genes at 2q24, best-represented by SNP rs7593730 (combined OR = 0.90, 95% CI = 0.86–0.93; P = 3.7 × 10−8). The frequency of the risk-lowering allele T is 0.23. Variants in this region were nominally related to lower fasting glucose and HOMA-IR in the MAGIC consortium (P < 0.05). These data suggest that the 2q24 locus may influence the T2D risk by affecting glucose metabolism and insulin resistance.

Objective— Interleukin-18 (IL-18) is a proinflammatory cytokine involved in the processes of innate and acquired immunities and associated with cardiovascular disease and type 2 diabetes. We sought to identify the common genetic variants associated with IL-18 levels. Methods and Results— We performed a 2-stage genome-wide association study among women of European ancestry from the Nurses’ Health Study (NHS) and Women’s Genome Health Study (WGHS). IL-18 levels were measured by ELISA. In the discovery stage (NHS, n=1523), 7 single-nucleotide polymorphisms (SNPs) at the IL18 -BCO2 locus were associated with IL-18 concentrations at the 1×10 −5 significance level. The strongest association was found for SNP rs2115763 in the BCO2 gene ( P =6.31×10 −8 ). In silico replication in WGHS (435 women) confirmed these findings. The combined analysis of the 2 studies indicated that SNPs rs2115763, rs1834481, and rs7106524 reached a genome-wide significance level ( P −8 ). Forward selection analysis indicated that SNPs rs2115763 and rs1834481 were independently associated with IL-18 levels ( P =0.0002 and 0.0006, respectively). The 2 SNPs together explained 2.9% of variation of plasma IL-18 levels. Conclusion— This study identified several novel variants at the IL18 -BCO2 locus associated with IL-18 levels.

Blood soluble E-selectin (sE-selectin) levels have been related to various conditions such as type 2 diabetes. We performed a genome-wide association study among women of European ancestry from the Nurses' Health Study, and identified genome-wide significant associations between a cluster of markers at the ABO locus (9q34) and plasma sE-selectin concentration. The strongest association was with rs651007, which explained approximately 9.71% of the variation in sE-selectin concentrations. SNP rs651007 was also nominally associated with soluble intracellular cell adhesion molecule-1 (sICAM-1) (P = 0.026) and TNF-R2 levels (P = 0.018), independent of sE-selectin. In addition, the genetic-inferred ABO blood group genotypes were associated with sE-selectin concentrations (P = 3.55 x 10(-47)). Moreover, we found that the genetic-inferred blood group B was associated with a decreased risk (OR = 0.44, 0.27-0.70) of type 2 diabetes compared with blood group O, adjusting for sE-selectin, sICAM-1, TNF-R2 and other covariates. Our findings indicate that the genetic variants at ABO locus affect plasma sE-selectin levels and diabetes risk. The genetic associations with diabetes risk were independent of sE-selectin levels.

The Type I Diabetes Genetics Consortium (T1DGC) Rapid Response Workshop was established to evaluate published candidate gene associations in a large collection of affected sib-pair (ASP) families. We report on our quality control (QC) and preliminary family-based association analyses. A random sample of blind duplicates was analyzed for QC. Quality checks, including examination of plate-panel yield, marker yield, Hardy–Weinberg equilibrium, mismatch error rate, Mendelian error rate, and allele distribution across plates, were performed. Genotypes from 2324 families within nine cohorts were obtained from a panel of 21 candidate genes, including 384 single-nucleotide polymorphisms on two genotyping platforms performed at the Broad Institute Center for Genotyping and Analysis (Cambridge, MA, USA). The T1DGC Rapid Response project, following rigorous QC procedures, resulted in a 2297 family, 9688 genotyped individual database on a single-candidate gene panel. The available data include 9005 individuals with genotype data from both platforms and 683 individuals genotyped (276 in Illumina; 407 in Sequenom) on only one platform.

The human MHC represents the strongest susceptibility locus for autoimmune diseases. However, the identification of the true predisposing gene(s) has been handicapped by the strong linkage disequilibrium across the region. Furthermore, most studies to date have been limited to the examination of a subset of the HLA and non-HLA genes with a marker density and sample size insufficient for mapping all independent association signals. We genotyped a panel of 1,472 SNPs to capture the common genomic variation across the 3.44 megabase (Mb) classic MHC region in 10,576 DNA samples derived from patients with systemic lupus erythematosus, Crohn's disease, ulcerative colitis, rheumatoid arthritis, myasthenia gravis, selective IgA deficiency, multiple sclerosis, and appropriate control samples. We identified the primary association signals for each disease and performed conditional regression to identify independent secondary signals. The data demonstrate that MHC associations with autoimmune diseases result from complex, multilocus effects that span the entire region.

Schizophrenia, a devastating psychiatric disorder, has a prevalence of 0.5–1%, with high heritability (80–85%) and complex transmission.1 Recent studies implicate rare, large, high-penetrance copy number variants (CNVs) in some cases2, but it is not known what genes or biological mechanisms underlie susceptibility. Here we show that schizophrenia is significantly associated with single nucleotide polymorphisms (SNPs) in the extended Major Histocompatibility Complex (MHC) region on chromosome 6. We carried out a genome-wide association study (GWAS) of common SNPs in the Molecular Genetics of Schizophrenia (MGS) case-control sample, and then a meta-analysis of data from the MGS, International Schizophrenia Consortium (ISC) and SGENE datasets. No MGS finding achieved genome-wide statistical significance. In the meta-analysis of European-ancestry subjects (8,008 cases, 19,077 controls), significant association with schizophrenia was observed in a region of linkage disequilibrium on chromosome 6p22.1 (P = 9.54 × 10−9). This region includes a histone gene cluster and several immunity-related genes, possibly implicating etiologic mechanisms involving chromatin modification, transcriptional regulation, auto-immunity and/or infection. These results demonstrate that common schizophrenia susceptibility alleles can be detected. The characterization of these signals will suggest important directions for research on susceptibility mechanisms.