Genotype imputation

Genotype imputation with thousands of genomes

Genotype imputation is a statistical technique that is often used to increase the power and resolution of genetic association studies. Imputation methods work by using haplotype patterns in a reference panel to predict unobserved genotypes in a study dataset, and a number of approaches have been proposed for choosing subsets of reference haplotypes that will maximize accuracy in a given study population. These panel selection strategies become harder to apply and interpret as sequencing efforts like the 1000 Genomes Project produce larger and more diverse reference sets, which led us to develop an alternative framework. Our approach is built around a new approximation that uses local sequence similarity to choose a custom reference panel for each study haplotype in each region of the genome. This approximation makes it computationally efficient to use all available reference haplotypes, which allows us to bypass the panel selection step and to improve accuracy at low-frequency variants by capturing unexpected allele sharing among populations. Using data from HapMap 3, we show that our framework produces accurate results in a wide range of human populations. We also use data from the Malaria Genetic Epidemiology Network (MalariaGEN) to provide recommendations for imputation-based studies in Africa. We demonstrate that our approximation improves efficiency in large, sequence-based reference panels, and we discuss general computational strategies for modern reference datasets. Genome-wide association studies will soon be able to harness the power of thousands of reference genomes, and our work provides a practical way for investigators to use this rich information. New methodology from this study is implemented in the IMPUTE2 software package.

Genetic associations in the vitamin D receptor and colorectal cancer in African Americans and Caucasians

Low vitamin D levels are associated with an increased incidence of colorectal cancer (CRC) and higher mortality from the disease. In the US, African Americans (AAs) have the highest CRC incidence and mortality and the lowest levels of vitamin D. Single nucleotide polymorphisms (SNPs) in the vitamin D receptor (VDR) gene have been previously associated with CRC, but few studies have included AAs. We studied 795 AA CRC cases and 985 AA controls from Chicago and North Carolina as well as 1324 Caucasian cases and 990 Caucasian controls from Chicago and Spain. We genotyped 54 tagSNPs in VDR (46586959 to 46521297 Mb) and tested for association adjusting for West African ancestry, age, gender, and multiple testing. Untyped markers were imputed using MACH1.0. We analyzed associations by gender and anatomic location in the whole study group as well as by vitamin D intake in the North Carolina AA group. In the joint analysis, none of the SNPs tested was significantly associated with CRC. For four previously tested restriction fragment length polymorphisms, only one (referred to as ApaI), tagged by the SNP rs79628898, had a nominally significant p-value in AAs; none of these polymorphisms were associated with CRC in Caucasians. In the North Carolina AAs, for whom we had vitamin D intake data, we found a significant association between an intronic SNP rs11574041 and vitamin D intake, which is evidence for a VDR gene-environment interaction in AAs. In summary, using a systematic tagSNP approach, we have not found evidence for significant associations between VDR and CRC in AAs or Caucasians.

SNPpy--database management for SNP data from genome wide association studies

Background

We describe SNPpy, a hybrid script database system using the Python SQLAlchemy library coupled with the PostgreSQL database to manage genotype data from Genome-Wide Association Studies (GWAS). This system makes it possible to merge study data with HapMap data and merge across studies for meta-analyses, including data filtering based on the values of phenotype and Single-Nucleotide Polymorphism (SNP) data. SNPpy and its dependencies are open source software.

Results

The current version of SNPpy offers utility functions to import genotype and annotation data from two commercial platforms. We use these to import data from two GWAS studies and the HapMap Project. We then export these individual datasets to standard data format files that can be imported into statistical software for downstream analyses.

Conclusions

By leveraging the power of relational databases, SNPpy offers integrated management and manipulation of genotype and phenotype data from GWAS studies. The analysis of these studies requires merging across GWAS datasets as well as patient and marker selection. To this end, SNPpy enables the user to filter the data and output the results as standardized GWAS file formats. It does low level and flexible data validation, including validation of patient data. SNPpy is a practical and extensible solution for investigators who seek to deploy central management of their GWAS data.

The genetic association between personality and major depression or bipolar disorder. A polygenic score analysis using genome-wide association data

The relationship between major depressive disorder (MDD) and bipolar disorder (BD) remains controversial. Previous research has reported differences and similarities in risk factors for MDD and BD, such as predisposing personality traits. For example, high neuroticism is related to both disorders, whereas openness to experience is specific for BD. This study examined the genetic association between personality and MDD and BD by applying polygenic scores for neuroticism, extraversion, openness to experience, agreeableness and conscientiousness to both disorders. Polygenic scores reflect the weighted sum of multiple single-nucleotide polymorphism alleles associated with the trait for an individual and were based on a meta-analysis of genome-wide association studies for personality traits including 13,835 subjects. Polygenic scores were tested for MDD in the combined Genetic Association Information Network (GAIN-MDD) and MDD2000+ samples (N=8921) and for BD in the combined Systematic Treatment Enhancement Program for Bipolar Disorder and Wellcome Trust Case-Control Consortium samples (N=6329) using logistic regression analyses. At the phenotypic level, personality dimensions were associated with MDD and BD. Polygenic neuroticism scores were significantly positively associated with MDD, whereas polygenic extraversion scores were significantly positively associated with BD. The explained variance of MDD and BD, ∼0.1%, was highly comparable to the variance explained by the polygenic personality scores in the corresponding personality traits themselves (between 0.1 and 0.4%). This indicates that the proportions of variance explained in mood disorders are at the upper limit of what could have been expected. This study suggests shared genetic risk factors for neuroticism and MDD on the one hand and for extraversion and BD on the other.

Genomics of ADME gene expression: mapping expression quantitative trait loci relevant for absorption, distribution, metabolism and excretion of drugs in human liver

Expression quantitative trait loci (eQTL) analysis is a powerful approach toward identifying genetic loci associated with quantitative changes in gene expression. We applied genome-wide association analysis to a data set of >300 000 single-nucleotide polymorphisms and >48 000 mRNA expression phenotypes obtained by Illumina microarray profiling of 149 human surgical liver samples obtained from Caucasian donors with detailed medical documentation. Of 1226 significant associations, only 200 were validated when comparing with a previously published similar study. Potential explanations for low replication rate include differences in microarray platforms, statistical modeling, covariates considered and origin and collection procedures of tissues. Focused analysis revealed a subset of 95 associations related to absorption, distribution, metabolism and excretion of drugs. Of these, 21 were true replications and 74 were newly discovered associations in enzymes, transporters, transcriptional regulators and other genes. This study extends our knowledge about the genetics of inter-individual variability of gene expression with particular emphasis on pharmacogenomics.

A genome-wide meta-analysis of six type 1 diabetes cohorts identifies multiple associated loci

Diabetes impacts approximately 200 million people worldwide, of whom approximately 10% are affected by type 1 diabetes (T1D). The application of genome-wide association studies (GWAS) has robustly revealed dozens of genetic contributors to the pathogenesis of T1D, with the most recent meta-analysis identifying in excess of 40 loci. To identify additional genetic loci for T1D susceptibility, we examined associations in the largest meta-analysis to date between the disease and ∼2.54 million SNPs in a combined cohort of 9,934 cases and 16,956 controls. Targeted follow-up of 53 SNPs in 1,120 affected trios uncovered three new loci associated with T1D that reached genome-wide significance. The most significantly associated SNP (rs539514, P = 5.66×10⁻¹¹) resides in an intronic region of the LMO7 (LIM domain only 7) gene on 13q22. The second most significantly associated SNP (rs478222, P = 3.50×10⁻⁹) resides in an intronic region of the EFR3B (protein EFR3 homolog B) gene on 2p23; however, the region of linkage disequilibrium is approximately 800 kb and harbors additional multiple genes, including NCOA1, C2orf79, CENPO, ADCY3, DNAJC27, POMC, and DNMT3A. The third most significantly associated SNP (rs924043, P = 8.06×10⁻⁹) lies in an intergenic region on 6q27, where the region of association is approximately 900 kb and harbors multiple genes including WDR27, C6orf120, PHF10, TCTE3, C6orf208, LOC154449, DLL1, FAM120B, PSMB1, TBP, and PCD2. These latest associated regions add to the growing repertoire of gene networks predisposing to T1D.

Despite the fact that there is clearly a large genetic component to type 1 diabetes (T1D), uncovering the genes contributing to this disease has proven challenging. However, in the past three years there has been relatively major progress in this regard, with advances in genetic screening technologies allowing investigators to scan the genome for variants conferring risk for disease without prior hypotheses. Such genome-wide association studies have revealed multiple regions of the genome to be robustly and consistently associated with T1D. More recent findings have been a consequence of combining of multiple datasets from independent investigators in meta-analyses, which have more power to pick up additional variants contributing to the trait. In the current study, we describe the largest meta-analysis of T1D genome-wide genotyped datasets to date, which combines six large studies. As a consequence, we have uncovered three new signals residing at the chromosomal locations 13q22, 2p23, and 6q27, which went on to be replicated in independent sample sets. These latest associated regions add to the growing repertoire of gene networks predisposing to T1D.

CHEK2, MGMT, SULT1E1 and SULT1A1 polymorphisms and endometrial cancer risk

Several single nucleotide polymorphisms (SNPs) in candidate genes of DNA repair and hormone pathways have been reported to be associated with endometrial cancer risk. We sought to confirm these associations in two endometrial cancer case-control sample sets and used additional data from an existing genome-wide association study to prioritize an additional SNP for further study. Five SNPs from the CHEK2, MGMT, SULT1E1 and SULT1A1 genes, genotyped in a total of 1597 cases and 1507 controls from two case-control studies, the Australian National Endometrial Cancer Study and the Polish Endometrial Cancer Study, were assessed for association with endometrial cancer risk using logistic regression analysis. Imputed data was drawn for CHEK2 rs8135424 for 666 cases from the Study of Epidemiology and Risk factors in Cancer Heredity study and 5190 controls from the Wellcome Trust Case Control Consortium. We observed no association between SNPs in the MGMT, SULT1E1 and SULT1A1 genes and endometrial cancer risk. The A allele of the rs8135424 CHEK2 SNP was associated with decreased risk of endometrial cancer (adjusted per-allele OR 0.83; 95%CI 0.70-0.98; p = .03) however this finding was opposite to that previously published. Imputed data for CHEK2 rs8135424 supported the direction of effect reported in this study (OR 0.85; 95% CI 0.65-1.10). Previously reported endometrial cancer risk associations with SNPs from in genes involved in estrogen metabolism and DNA repair were not replicated in our larger study population. This study highlights the need for replication of candidate gene SNP studies using large sample groups, to confirm risk associations and better prioritize downstream studies to assess the causal relationship between genetic variants and cancer risk. Our findings suggest that the CHEK2 SNP rs8135424 be prioritized for further study as a genetic factor associated with risk of endometrial cancer.

Significant linkage at chromosome 19q for otitis media with effusion and/or recurrent otitis media (COME/ROM)

Background

In previous analyses, we identified a region of chromosome 19 as harboring a susceptibility locus for chronic otitis media with effusion and/or recurrent otitis media (COME/ROM). Our aim was to further localize the linkage signal and ultimately identify the causative variant or variants. We followed up our previous linkage scan with dense SNP genotyping across in a 5 Mb region. A total of 607 individuals from 139 families, including 159 affected sib pairs and 62 second-degree affected relative pairs, were genotyped at 1,091 SNPs. We carried out a nonparametric linkage analysis, modeling marker-to-marker linkage disequilibrium.

Results

The maximum log of the odds (LOD) score increased to 3.75 (P = 1.6 × 10^-5) at position 63.4 Mb, with a LOD-1 support interval between 61.6 Mb and 63.8 Mb, providing significant evidence of linkage between this region and COME/ROM. The support interval contains over 90 known genes, including several genes involved in the inflammasome protein complex, a key regulator of the innate immune response to harmful exogenous or endogenous stimuli. Parametric linkage analysis suggests that for a sib of an affected individual, the recurrence risk of COME/ROM due to this linkage region is twice the recurrence risk in the population. We examined potential associations between the SNPs genotyped in this region and COME/ROM, however none provided evidence for association.

Conclusion

This study has refined the 19q region of linkage with COME/ROM, and association results suggest that the linkage signal may be due to rare variants.

Genome-wide association study identifies a susceptibility locus for thoracic aortic aneurysms and aortic dissections spanning FBN1 at 15q21.1

Although thoracic aortic aneurysms and dissections (TAAD) can be inherited as a single-gene disorder, the genetic predisposition in the majority of affected people is poorly understood. In a multistage genome-wide association study (GWAS), we compared 765 individuals who had sporadic TAAD (STAAD) with 874 controls and identified common SNPs at a 15q21.1 locus that were associated with STAAD, with odds ratios of 1.6-1.8 that achieved genome-wide significance. We followed up 107 SNPs associated with STAAD with P < 1 × 10(-5) in the region, in two separate STAAD cohorts. The associated SNPs fall into a large region of linkage disequilibrium encompassing FBN1, which encodes fibrillin-1. FBN1 mutations cause Marfan syndrome, whose major cardiovascular complication is TAAD. This study shows that common genetic variants at 15q21.1 that probably act via FBN1 are associated with STAAD, suggesting a common pathogenesis of aortic disease in Marfan syndrome and STAAD.

A statistical framework for SNP calling, mutation discovery, association mapping and population genetical parameter estimation from sequencing data

MOTIVATION

Most existing methods for DNA sequence analysis rely on accurate sequences or genotypes. However, in applications of the next-generation sequencing (NGS), accurate genotypes may not be easily obtained (e.g. multi-sample low-coverage sequencing or somatic mutation discovery). These applications press for the development of new methods for analyzing sequence data with uncertainty.

RESULTS

We present a statistical framework for calling SNPs, discovering somatic mutations, inferring population genetical parameters and performing association tests directly based on sequencing data without explicit genotyping or linkage-based imputation. On real data, we demonstrate that our method achieves comparable accuracy to alternative methods for estimating site allele count, for inferring allele frequency spectrum and for association mapping. We also highlight the necessity of using symmetric datasets for finding somatic mutations and confirm that for discovering rare events, mismapping is frequently the leading source of errors.

AVAILABILITY

http://samtools.sourceforge.net.

CONTACT

hengli@broadinstitute.org.

Viral genotype-specific role of PNPLA3, PPARG, MTTP, and IL28B in hepatitis C virus-associated steatosis

BACKGROUND & AIMS

Steatosis is a prominent feature of hepatitis C, especially in patients infected with genotype 3. The analysis of genetic polymorphisms influencing steatosis in chronic hepatitis C has been limited by the studies' small sample size, and important single nucleotide polymorphisms (SNPs), such as those in the patatin-like phospholipase family 3 protein (PNPLA3), were never evaluated.

METHODS

We analyzed the role of SNPs, from 19 systematically selected candidate genes, on steatosis in 626 Caucasian hepatitis C virus (HCV) infected patients. SNPs were extracted from a genome-wide association-generated dataset. Associations of alleles with the presence and/or different severity of steatosis were evaluated by univariate and multivariate logistic regression, accounting for all relevant covariates.

RESULTS

The risk of steatosis was increased by carriage of I148M in PNPLA3, but only in patients with HCV genotypes non-3 (odds ratio [OR]=1.9, 95% confidence interval [CI]=1.6-2.3, p<0.001) and similar, albeit weaker associations were found for SNPs in peroxisome proliferator-activated receptor-γ (PPARG) and interleukin-28B (IL28B). Carriage of a SNP in the microsomal triglyceride transfer protein (MTTP) increased the risk of steatosis, but only in patients with HCV genotype 3 (rs1800803, OR=3.4, 95% CI=2.4-4.9, p=0.001).

CONCLUSIONS

The rs738409 SNP in PNPLA3 is associated with an increased risk of steatosis in patients infected with HCV genotypes non-3. Host genes affect steatosis depending on the infecting HCV genotype, suggesting their interaction with viral factors.

A genome-wide association study identifies a region at chromosome 12 as a potential susceptibility locus for restenosis after percutaneous coronary intervention

Percutaneous coronary intervention (PCI) has become an effective therapy to treat obstructive coronary artery diseases (CAD). However, one of the major drawbacks of PCI is the occurrence of restenosis in 5-25% of all initially treated patients. Restenosis is defined as the re-narrowing of the lumen of the blood vessel, resulting in renewed symptoms and the need for repeated intervention. To identify genetic variants that are associated with restenosis, a genome-wide association study (GWAS) was conducted in 295 patients who developed restenosis (cases) and 571 who did not (controls) from the GENetic Determinants of Restenosis (GENDER) study. Analysis of ~550 000 single nucleotide polymorphisms (SNPs) in GENDER was followed by a replication phase in three independent case-control populations (533 cases and 3067 controls). A potential susceptibility locus for restenosis at chromosome 12, including rs10861032 (P(combined) = 1.11 × 10(-7)) and rs9804922 (P(combined) = 1.45 × 10(-6)), was identified in the GWAS and replication phase. In addition, both SNPs were also associated with coronary events (rs10861032, P(additive) = 0.005; rs9804922, P(additive) = 0.023) in a trial based cohort set of elderly patients with (enhanced risk of) CAD (PROSPER) and all-cause mortality in PROSPER (rs10861032, P(additive) = 0.007; rs9804922, P(additive) = 0.013) and GENDER (rs10861032, P(additive) = 0.005; rs9804922, P(additive) = 0.023). Further analysis suggests that this locus could be involved in regulatory functions.

Fine-mapping of breast cancer susceptibility loci characterizes genetic risk in African Americans

Genome-wide association studies (GWAS) have revealed 19 common genetic variants that are associated with breast cancer risk. Testing of the index signals found through GWAS and fine-mapping of each locus in diverse populations will be necessary for characterizing the role of these risk regions in contributing to inherited susceptibility. In this large study of breast cancer in African-American women (3016 cases and 2745 controls), we tested the 19 known risk variants identified by GWAS and replicated associations (P < 0.05) with only 4 variants. Through fine-mapping, we identified markers in four regions that better capture the association with breast cancer risk in African Americans as defined by the index signal (2q35, 5q11, 10q26 and 19p13). We also identified statistically significant associations with markers in four separate regions (8q24, 10q22, 11q13 and 16q12) that are independent of the index signals and may represent putative novel risk variants. In aggregate, the more informative markers found in the study enhance the association of these risk regions with breast cancer in African Americans [per allele odds ratio (OR) = 1.18, P = 2.8 × 10(-24) versus OR = 1.04, P = 6.1 × 10(-5)]. In this detailed analysis of the known breast cancer risk loci, we have validated and improved upon markers of risk that better characterize their association with breast cancer in women of African ancestry.

Associations between gene polymorphisms in fatty acid metabolism pathway and preterm delivery in a US urban black population

There is increasing evidence suggesting that higher intakes of fish or n-3 polyunsaturated fatty acids supplements may decrease the risk of preterm delivery (PTD). We hypothesized that genetic variants of the enzymes critical to fatty acids biosynthesis and metabolism may be associated with PTD. We genotyped 231 potentially functional single nucleotide polymorphisms (SNPs) and tagSNPs in 9 genes (FADS1, FADS2, PTGS1, PTGS2, ALOX5, ALOX5AP, PTGES, PTGES2, and PTGES3) among 1,110 black mothers, including 542 mothers who delivered preterm (<37 weeks gestation) and 568 mothers who delivered full-term babies (≥37 weeks gestation) at Boston Medical Center. After excluding SNPs that are in complete linkage disequilibrium or have lower minor allele frequency (<1%) or call rate (<90%), we examined the association of 206 SNPs with PTD using multiple logistic regression models. We also imputed 190 HapMap SNPs via program MACH and examined their associations with PTD. Finally, we explored gene-level and pathway-level associations with PTD using the adaptive rank truncated product (ARTP) methods. A total of 21 SNPs were associated with PTD (p value ranging from 0.003 to 0.05), including 3 imputed SNPs. Gene-level ARTP statistics indicated that the gene PTGES2 was significantly associated with PTD with a gene-based p value equal to 0.01. No pathway-based association was found. In this large and comprehensive candidate gene study, we found a modest association of genes in fatty acid metabolism pathway with PTD. Further investigation of these gene polymorphisms jointly with fatty acid measures and other genetic factors would help better understand the pathogenesis of PTD.

Genome-wide association studies of antidepressant outcome: a brief review

Genome-wide association studies (GWAS) of antidepressant treatment outcome have been at the forefront of psychiatric pharmacogenetics. Such studies may ultimately help match medications with patients, maximizing efficacy while minimizing adverse effects. The hypothesis-free approach of the GWAS has the advantage of interrogating genes that otherwise would have not been considered as candidates due to our limited understanding of their function, and may also uncover important regulatory variation within the large regions of the genome that do not contain protein-coding genes. Three independent samples have so far been studied using a genome-wide approach: The Sequenced Treatment Alternatives to Relieve Depression sample (STAR*D) (n=1953), the Munich Antidepressant Response Signature (MARS) sample (n=339) and the Genome-based Therapeutic Drugs for Depression (GENDEP) sample (n=706). None of the studies reported results that achieved genome-wide significance, suggesting that larger samples and better outcome measures will be needed. This review discusses the published GWAS studies, their strengths, limitations, and possible future directions.

A genome-wide association study identifies two new risk loci for Graves' disease

Graves' disease is a common autoimmune disorder characterized by thyroid stimulating hormone receptor autoantibodies (TRAb) and hyperthyroidism. To investigate the genetic architecture of Graves' disease, we conducted a genome-wide association study in 1,536 individuals with Graves' disease (cases) and 1,516 controls. We further evaluated a group of associated SNPs in a second set of 3,994 cases and 3,510 controls. We confirmed four previously reported loci (in the major histocompatibility complex, TSHR, CTLA4 and FCRL3) and identified two new susceptibility loci (the RNASET2-FGFR1OP-CCR6 region at 6q27 (P(combined) = 6.85 × 10(-10) for rs9355610) and an intergenic region at 4p14 (P(combined) = 1.08 × 10(-13) for rs6832151)). These newly associated SNPs were correlated with the expression levels of RNASET2 at 6q27, of CHRNA9 and of a previously uncharacterized gene at 4p14, respectively. Moreover, we identified strong associations of TSHR and major histocompatibility complex class II variants with persistently TRAb-positive Graves' disease.

Common minor histocompatibility antigen discovery based upon patient clinical outcomes and genomic data

Background

Minor histocompatibility antigens (mHA) mediate much of the graft vs. leukemia (GvL) effect and graft vs. host disease (GvHD) in patients who undergo allogeneic stem cell transplantation (SCT) [1], [2], [3], [4]. Therapeutic decision making and treatments [5] based upon mHAs will require the evaluation of multiple candidate mHAs and the selection of those with the potential to have the greatest impact on clinical outcomes. We hypothesized that common, immunodominant mHAs, which are presented by HLA-A, B, and C molecules, can mediate clinically significant GvL and/or GvHD, and that these mHAs can be identified through association of genomic data with clinical outcomes.

Methodology/Principal Findings

Because most mHAs result from donor/recipient cSNP disparities, we genotyped 57 myeloid leukemia patients and their donors at 13,917 cSNPs [6]. We correlated the frequency of genetically predicted mHA disparities with clinical evidence of an immune response and then computationally screened all peptides mapping to the highly associated cSNPs for their ability to bind to HLA molecules. As proof-of-concept, we analyzed one predicted antigen, T4A, whose mHA mismatch trended towards improved overall and disease free survival in our cohort. T4A mHA mismatches occurred at the maximum theoretical frequency for any given SCT. T4A-specific CD8+ T lymphocytes (CTLs) were detected in 3 of 4 evaluable post-transplant patients predicted to have a T4A mismatch.

Conclusions/Significance

Our method is the first to combine clinical outcomes data with genomics and bioinformatics methods to predict and confirm a mHA. Refinement of this method should enable the discovery of clinically relevant mHAs in the majority of transplant patients and possibly lead to novel immunotherapeutics [5].

Kernel machine SNP-set analysis for censored survival outcomes in genome-wide association studies

In this article, we develop a powerful test for identifying single nucleotide polymorphism (SNP)-sets that are predictive of survival with data from genome-wide association studies. We first group typed SNPs into SNP-sets based on genomic features and then apply a score test to assess the overall effect of each SNP-set on the survival outcome through a kernel machine Cox regression framework. This approach uses genetic information from all SNPs in the SNP-set simultaneously and accounts for linkage disequilibrium (LD), leading to a powerful test with reduced degrees of freedom when the typed SNPs are in LD with each other. This type of test also has the advantage of capturing the potentially nonlinear effects of the SNPs, SNP-SNP interactions (epistasis), and the joint effects of multiple causal variants. By simulating SNP data based on the LD structure of real genes from the HapMap project, we demonstrate that our proposed test is more powerful than the standard single SNP minimum P-value-based test for association studies with censored survival outcomes. We illustrate the proposed test with a real data application.

Novel locus FER is associated with serum HMW adiponectin levels

OBJECTIVE

High molecular weight (HMW) adiponectin is a predominant isoform of circulating adiponectin and has been related to type 2 diabetes. Previous linkage studies suggest that different genetic components might be involved in determining HMW and total adiponectin levels.

RESEARCH DESIGN AND METHODS

We performed a genome-wide association study (GWAS) of serum HMW adiponectin levels in individuals of European ancestry drawn from the Nurses' Health Study (NHS) (N = 1,591). The single nucleotide polymorphisms (SNPs) identified in the GWAS analysis were replicated in an independent cohort of Europeans (N = 626). We examined the associations of the identified variations with diabetes risk and metabolic syndrome.

RESULTS

We identified a novel locus near the FER gene (5q21) at a genome-wide significance level, best represented by SNP rs10447248 (P = 4.69 × 10(-8)). We also confirmed that variations near the adiponectin-encoding ADIPOQ locus (3q27) were related to serum HMW adiponectin levels. In addition, we found that FER SNP rs10447248 was related to HDL cholesterol levels (P = 0.009); ADIPOQ variation was associated with fasting glucose (P = 0.04), HDL cholesterol (P = 0.04), and a metabolic syndrome score (P = 0.002).

CONCLUSIONS

Our results suggest that different loci may be involved in regulation of circulating HMW adiponectin levels and provide novel insight into the mechanisms that affect HMW adiponectin homeostasis.

Genome-wide association and meta-analysis in populations from Starr County, Texas, and Mexico City identify type 2 diabetes susceptibility loci and enrichment for expression quantitative trait loci in top signals

AIMS/HYPOTHESIS

We conducted genome-wide association studies (GWASs) and expression quantitative trait loci (eQTL) analyses to identify and characterise risk loci for type 2 diabetes in Mexican-Americans from Starr County, TX, USA.

METHOD

Using 1.8 million directly interrogated and imputed genotypes in 837 unrelated type 2 diabetes cases and 436 normoglycaemic controls, we conducted Armitage trend tests. To improve power in this population with high disease rates, we also performed ordinal regression including an intermediate class with impaired fasting glucose and/or glucose tolerance. These analyses were followed by meta-analysis with a study of 967 type 2 diabetes cases and 343 normoglycaemic controls from Mexico City, Mexico.

RESULT

The top signals (unadjusted p value <1 × 10(-5)) included 49 single nucleotide polymorphisms (SNPs) in eight gene regions (PER3, PARD3B, EPHA4, TOMM7, PTPRD, HNT [also known as RREB1], LOC729993 and IL34) and six intergenic regions. Among these was a missense polymorphism (rs10462020; Gly639Val) in the clock gene PER3, a system recently implicated in diabetes. We also report a second signal (minimum p value 1.52 × 10(-6)) within PTPRD, independent of the previously implicated SNP, in a population of Han Chinese. Top meta-analysis signals included known regions HNF1A and KCNQ1. Annotation of top association signals in both studies revealed a marked excess of trans-acting eQTL in both adipose and muscle tissues.

CONCLUSIONS/INTERPRETATION

In the largest study of type 2 diabetes in Mexican populations to date, we identified modest associations of novel and previously reported SNPs. In addition, in our top signals we report significant excess of SNPs that predict transcript levels in muscle and adipose tissues.

Fine mapping of five loci associated with low-density lipoprotein cholesterol detects variants that double the explained heritability

Complex trait genome-wide association studies (GWAS) provide an efficient strategy for evaluating large numbers of common variants in large numbers of individuals and for identifying trait-associated variants. Nevertheless, GWAS often leave much of the trait heritability unexplained. We hypothesized that some of this unexplained heritability might be due to common and rare variants that reside in GWAS identified loci but lack appropriate proxies in modern genotyping arrays. To assess this hypothesis, we re-examined 7 genes (APOE, APOC1, APOC2, SORT1, LDLR, APOB, and PCSK9) in 5 loci associated with low-density lipoprotein cholesterol (LDL-C) in multiple GWAS. For each gene, we first catalogued genetic variation by re-sequencing 256 Sardinian individuals with extreme LDL-C values. Next, we genotyped variants identified by us and by the 1000 Genomes Project (totaling 3,277 SNPs) in 5,524 volunteers. We found that in one locus (PCSK9) the GWAS signal could be explained by a previously described low-frequency variant and that in three loci (PCSK9, APOE, and LDLR) there were additional variants independently associated with LDL-C, including a novel and rare LDLR variant that seems specific to Sardinians. Overall, this more detailed assessment of SNP variation in these loci increased estimates of the heritability of LDL-C accounted for by these genes from 3.1% to 6.5%. All association signals and the heritability estimates were successfully confirmed in a sample of ∼10,000 Finnish and Norwegian individuals. Our results thus suggest that focusing on variants accessible via GWAS can lead to clear underestimates of the trait heritability explained by a set of loci. Further, our results suggest that, as prelude to large-scale sequencing efforts, targeted re-sequencing efforts paired with large-scale genotyping will increase estimates of complex trait heritability explained by known loci.

Despite the striking success of genome-wide association studies in identifying genetic loci associated with common complex traits and diseases, much of the heritable risk for these traits and diseases remains unexplained. A higher resolution investigation of the genome through sequencing studies is expected to clarify the sources of this missing heritability. As a preview of what we might learn in these more detailed assessments of genetic variation, we used sequencing to identify potentially interesting variants in seven genes associated with low-density lipoprotein cholesterol (LDL-C) in 256 Sardinian individuals with extreme LDL-C levels, followed by large scale genotyping in 5,524 individuals, to examine newly discovered and previously described variants. We found that a combination of common and rare variants in these loci contributes to variation in LDL-C levels, and also that the initial estimate of the heritability explained by these loci doubled. Importantly, our results include a Sardinian-specific rare variant, highlighting the need for sequencing studies in isolated populations. Our results provide insights about what extensive whole-genome sequencing efforts are likely to reveal for the understanding of the genetic architecture of complex traits.

Population-genetic comparison of the Sorbian isolate population in Germany with the German KORA population using genome-wide SNP arrays

Background

The Sorbs are an ethnic minority in Germany with putative genetic isolation, making the population interesting for disease mapping. A sample of N = 977 Sorbs is currently analysed in several genome-wide meta-analyses. Since genetic differences between populations are a major confounding factor in genetic meta-analyses, we compare the Sorbs with the German outbred population of the KORA F3 study (N = 1644) and other publically available European HapMap populations by population genetic means. We also aim to separate effects of over-sampling of families in the Sorbs sample from effects of genetic isolation and compare the power of genetic association studies between the samples.

Results

The degree of relatedness was significantly higher in the Sorbs. Principal components analysis revealed a west to east clustering of KORA individuals born in Germany, KORA individuals born in Poland or Czech Republic, Half-Sorbs (less than four Sorbian grandparents) and Full-Sorbs. The Sorbs cluster is nearest to the cluster of KORA individuals born in Poland. The number of rare SNPs is significantly higher in the Sorbs sample. FST between KORA and Sorbs is an order of magnitude higher than between different regions in Germany. Compared to the other populations, Sorbs show a higher proportion of individuals with runs of homozygosity between 2.5 Mb and 5 Mb. Linkage disequilibrium (LD) at longer range is also slightly increased but this has no effect on the power of association studies.

Oversampling of families in the Sorbs sample causes detectable bias regarding higher FST values and higher LD but the effect is an order of magnitude smaller than the observed differences between KORA and Sorbs. Relatedness in the Sorbs also influenced the power of uncorrected association analyses.

Conclusions

Sorbs show signs of genetic isolation which cannot be explained by over-sampling of relatives, but the effects are moderate in size. The Slavonic origin of the Sorbs is still genetically detectable.

Regarding LD structure, a clear advantage for genome-wide association studies cannot be deduced. The significant amount of cryptic relatedness in the Sorbs sample results in inflated variances of Beta-estimators which should be considered in genetic association analyses.

Comparing strategies to fine-map the association of common SNPs at chromosome 9p21 with type 2 diabetes and myocardial infarction

Noncoding variants at human chromosome 9p21 near CDKN2A and CDKN2B are associated with type 2 diabetes, myocardial infarction, aneurysm, vertical cup disc ratio and at least five cancers. Here we compare approaches to more comprehensively assess genetic variation in the region. We carried out targeted sequencing at high coverage in 47 individuals and compared the results to pilot data from the 1000 Genomes Project. We imputed variants into type 2 diabetes and myocardial infarction cohorts directly from targeted sequencing, from a genotyped reference panel derived from sequencing and from 1000 Genomes Project low-coverage data. Polymorphisms with frequency >5% were captured well by all strategies. Imputation of intermediate-frequency polymorphisms required a higher density of tag SNPs in disease samples than is available on first-generation genome-wide association study (GWAS) arrays. Our association analyses identified more comprehensive sets of variants showing equivalent statistical association with type 2 diabetes or myocardial infarction, but did not identify stronger associations than the original GWAS signals.

Comparing strategies to fine-map the association of common SNPs at chromosome 9p21 with type 2 diabetes and myocardial infarction

Noncoding variants at human chromosome 9p21 near CDKN2A and CDKN2B are associated with type 2 diabetes, myocardial infarction, aneurysm, vertical cup disc ratio and at least five cancers. Here we compare approaches to more comprehensively assess genetic variation in the region. We carried out targeted sequencing at high coverage in 47 individuals and compared the results to pilot data from the 1000 Genomes Project. We imputed variants into type 2 diabetes and myocardial infarction cohorts directly from targeted sequencing, from a genotyped reference panel derived from sequencing and from 1000 Genomes Project low-coverage data. Polymorphisms with frequency >5% were captured well by all strategies. Imputation of intermediate-frequency polymorphisms required a higher density of tag SNPs in disease samples than is available on first-generation genome-wide association study (GWAS) arrays. Our association analyses identified more comprehensive sets of variants showing equivalent statistical association with type 2 diabetes or myocardial infarction, but did not identify stronger associations than the original GWAS signals.

Association mapping

Association mapping seeks to identify marker alleles present at significantly different frequencies in cases carrying a particular disease or trait compared with controls. Genome-wide association studies are increasingly replacing candidate gene-based association studies for complex diseases, where a number of loci are likely to contribute to disease risk and the effect size of each particular risk allele is typically modest or low. Good study design is essential to the success of an association study, and factors such as the heritability of the disease under investigation, the choice of controls, statistical power, multiple testing and whether the association can be replicated need to be considered before beginning. Likewise, thorough quality control of the genotype data needs to be undertaken prior to running any association analyses. Finally, it should be kept in mind that a significant genetic association is not proof positive that a particular genetic locus causes a disease, but rather an important first step in discovering the genetic variants underlying a complex disease.

Genome-wide association study identifies HLA-DP as a susceptibility gene for pediatric asthma in Asian populations

Asthma is a complex phenotype influenced by genetic and environmental factors. We conducted a genome-wide association study (GWAS) with 938 Japanese pediatric asthma patients and 2,376 controls. Single-nucleotide polymorphisms (SNPs) showing strong associations (P<1×10⁻⁸) in GWAS were further genotyped in an independent Japanese samples (818 cases and 1,032 controls) and in Korean samples (835 cases and 421 controls). SNP rs987870, located between HLA-DPA1 and HLA-DPB1, was consistently associated with pediatric asthma in 3 independent populations (P_combined = 2.3×10⁻¹⁰, odds ratio [OR] = 1.40). HLA-DP allele analysis showed that DPA1*0201 and DPB1*0901, which were in strong linkage disequilibrium, were strongly associated with pediatric asthma (DPA1*0201: P = 5.5×10⁻¹⁰, OR = 1.52, and DPB1*0901: P = 2.0×10⁻⁷, OR = 1.49). Our findings show that genetic variants in the HLA-DP locus are associated with the risk of pediatric asthma in Asian populations.

Asthma is the most common chronic disorder in children, and asthma exacerbation is an important cause of childhood morbidity and hospitalization. Here, taking advantage of recent technological advances in human genetics, we performed a genome-wide association study and follow-up validation studies to identify genetic variants for asthma. By examining 6,428 Asians, we found rs987870 and HLA-DPA1*0201/DPB1*0901 were associated with pediatric asthma. The association signal was stretched in the region of HLA-DPB2, collagen, type XI, alpha 2 (COL11A2), and Retinoid X receptor beta (RXRB), but strong linkage disequilibrium in this region made it difficult to specifically identify causative variants. Interestingly, the SNP (or the HLA-DP allele) associated with pediatric asthma (Th-2 type immune diseases) in the present study confers protection against Th-1 type immune diseases, such as type 1 diabetes and rheumatoid arthritis. Therefore, the association results obtained in the present study could partially explain the inverse relationship between asthma and Th-1 type immune diseases and may lead to better understanding of Th-1/Th-2 immune diseases.

Complement receptor 1 gene variants are associated with erythrocyte sedimentation rate

The erythrocyte sedimentation rate (ESR), a commonly performed test of the acute phase response, is the rate at which erythrocytes sediment in vitro in 1 hr. The molecular basis of erythrocyte sedimentation is unknown. To identify genetic variants associated with ESR, we carried out a genome-wide association study of 7607 patients in the Electronic Medical Records and Genomics (eMERGE) network. The discovery cohort consisted of 1979 individuals from the Mayo Clinic, and the replication cohort consisted of 5628 individuals from the remaining four eMERGE sites. A nonsynonymous SNP, rs6691117 (Val→IIe), in the complement receptor 1 gene (CR1) was associated with ESR (discovery cohort p = 7 × 10(-12), replication cohort p = 3 × 10(-14), combined cohort p = 9 × 10(-24)). We imputed 61 SNPs in CR1, and a "possibly damaging" SNP (rs2274567, His→Arg) in linkage disequilibrium (r(2) = 0.74) with rs6691117 was also associated with ESR (discovery p = 5 × 10(-11), replication p = 7 × 10(-17), and combined cohort p = 2 × 10(-25)). The two nonsynonymous SNPs in CR1 are near the C3b/C4b binding site, suggesting a possible mechanism by which the variants may influence ESR. In conclusion, genetic variation in CR1, which encodes a protein that clears complement-tagged inflammatory particles from the circulation, influences interindividual variation in ESR, highlighting an association between the innate immunity pathway and erythrocyte interactions.

Interaction between ERAP1 and HLA-B27 in ankylosing spondylitis implicates peptide handling in the mechanism for HLA-B27 in disease susceptibility

Ankylosing spondylitis is a common form of inflammatory arthritis predominantly affecting the spine and pelvis that occurs in approximately 5 out of 1,000 adults of European descent. Here we report the identification of three variants in the RUNX3, LTBR-TNFRSF1A and IL12B regions convincingly associated with ankylosing spondylitis (P < 5 × 10(-8) in the combined discovery and replication datasets) and a further four loci at PTGER4, TBKBP1, ANTXR2 and CARD9 that show strong association across all our datasets (P < 5 × 10(-6) overall, with support in each of the three datasets studied). We also show that polymorphisms of ERAP1, which encodes an endoplasmic reticulum aminopeptidase involved in peptide trimming before HLA class I presentation, only affect ankylosing spondylitis risk in HLA-B27-positive individuals. These findings provide strong evidence that HLA-B27 operates in ankylosing spondylitis through a mechanism involving aberrant processing of antigenic peptides.

Artifact due to differential error when cases and controls are imputed from different platforms

Including previously genotyped controls in a genome-wide association study can provide cost-savings, but can also create design biases. When cases and controls are genotyped on different platforms, the imputation needed to provide genome-wide coverage will introduce differential measurement error and may lead to false positives. We compared genotype frequencies of two healthy control groups from the Nurses' Health Study genotyped on different platforms [Affymetrix 6.0 (n = 1,672) and Illumina HumanHap550 (n = 1,038)]. Using standard imputation quality filters, we observed 9,841 single-nucleotide polymorphisms (SNPs) out of 2,347,809 (0.4%) significant at the 5 × 10(-8) level. We explored three methods for controlling for this Type I error inflation. One method was to remove platform effects using principal components; another was to restrict to SNPs of highest quality imputation; and a third was to genotype some controls alongside cases to exclude SNPs that are statistical artifact. The first method could not reduce the Type I error rate; the other two could dramatically reduce the error rate, although both required that a portion of SNPs be excluded from analysis. Ideally, the biases we describe would be eliminated at the design stage, by genotyping sufficient numbers of cases and controls on each platform. Researchers using imputation to combine samples genotyped on different platforms with severely unbalanced case-control ratios should be aware of the potential for inflated Type I error rates and apply appropriate quality filters. Every SNP found with genome-wide significance should be validated on another platform to verify that its significance is not an artifact of study design.

Evaluating the influence of quality control decisions and software algorithms on SNP calling for the affymetrix 6.0 SNP array platform

OBJECTIVE

Our goal was to evaluate the influence of quality control (QC) decisions using two genotype calling algorithms, CRLMM and Birdseed, designed for the Affymetrix SNP Array 6.0.

METHODS

Various QC options were tried using the two algorithms and comparisons were made on subject and call rate and on association results using two data sets.

RESULTS

For Birdseed, we recommend using the contrast QC instead of QC call rate for sample QC. For CRLMM, we recommend using the signal-to-noise rate ≥4 for sample QC and a posterior probability of 90% for genotype accuracy. For both algorithms, we recommend calling the genotype separately for each plate, and dropping SNPs with a lower call rate (<95%) before evaluating samples with lower call rates. To investigate whether the genotype calls from the two algorithms impacted the genome-wide association results, we performed association analysis using data from the GENOA cohort; we observed that the number of significant SNPs were similar using either CRLMM or Birdseed.

CONCLUSIONS

Using our suggested workflow both algorithms performed similarly; however, fewer samples were removed and CRLMM took half the time to run our 854 study samples (4.2 h) compared to Birdseed (8.4 h).

Genetic polymorphisms of matrix metalloproteinase 3 in primary sclerosing cholangitis

BACKGROUND

The damaging cholestasis inherent to primary sclerosing cholangitis (PSC) results from bile duct stricturing because of progressive fibrosis. The matrix metalloproteinase 3 (MMP3) degrades a wide range of matrix components and is expressed by activated liver stellate cells, and so is a candidate for involvement with the fibrotic processes underlying PSC. Moreover, the MMP3 gene harbours polymorphisms associated with variation in its activity directly impacting clinical phenotypes.

AIMS

We aimed to examine the influence of MMP3 polymorphisms on PSC risk and progression.

METHODS

Nine single nucleotide polymorphisms (SNPs) tagging the common genetic variation of MMP3 were genotyped in 266 PSC patients and 407 controls. SNPs and inferred haplotypes were assessed for PSC association by logistic regression and score tests. The effect of SNPs on survival to liver transplant or death was analysed using Cox regression, and Kaplan-Meier curves were constructed.

RESULTS

No association of PSC with individual SNPs or haplotypes of MMP3 was detected. However, progression to death or liver transplant was significantly associated with homozygosity for minor alleles of rs522616, rs650108 and rs683878, particularly among PSC patients with concurrent ulcerative colitis (UC) (strongest in redundant SNPs rs650108/rs683878, hazard ratio=3.23, 95% confidence interval 1.45-7.25, P=0.004).

CONCLUSIONS

Genetic variation in MMP3 influences PSC progression, possibly in the context of coexisting UC. While the functional variants and specific mechanisms remain unknown, this finding implicates the turnover of the extracellular matrix as an important and variable component of PSC pathogenesis. Efforts to understand this process could form the basis for developing effective treatments, which are currently lacking for PSC.

Ex-Vivo Modeling for Heritability Assessment and Genetic Mapping in Pharmacogenomics

The investigation of genetic factors that determine differential drug response is a key goal of pharmacogenomics (PGX), and relies on the often-untested assumption that differential response is heritable. While limitations in traditional study design often prohibit heritability (h²) estimates in PGX, new approaches may allow such estimates. We demonstrate an ex vivo model system to determine the h² of drug-induced cell killing and performed genome-wide analysis for gene mapping. The cytotoxic effect of 29 diverse chemotherapeutic agents on lymphoblastoid cell lines (LCLs) derived from family- and population-based cohorts was investigated. We used a high throughput format to determine cytotoxicity of the drugs on LCLs and developed a new evolutionary computation approach to fit response curves for each individual. Variance components analysis determined the h² for each drug response and a wide range of values was observed across drugs. Genome-wide analysis was performed using new analytical approaches. These results lay the groundwork for future studies to uncover genes influencing chemotherapeutic response and demonstrate a new computational framework for performing such analysis.

Genome-wide association of bipolar disorder suggests an enrichment of replicable associations in regions near genes

Although a highly heritable and disabling disease, bipolar disorder's (BD) genetic variants have been challenging to identify. We present new genotype data for 1,190 cases and 401 controls and perform a genome-wide association study including additional samples for a total of 2,191 cases and 1,434 controls. We do not detect genome-wide significant associations for individual loci; however, across all SNPs, we show an association between the power to detect effects calculated from a previous genome-wide association study and evidence for replication (P = 1.5×10⁻⁷). To demonstrate that this result is not likely to be a false positive, we analyze replication rates in a large meta-analysis of height and show that, in a large enough study, associations replicate as a function of power, approaching a linear relationship. Within BD, SNPs near exons exhibit a greater probability of replication, supporting an enrichment of reproducible associations near functional regions of genes. These results indicate that there is likely common genetic variation associated with BD near exons (±10 kb) that could be identified in larger studies and, further, provide a framework for assessing the potential for replication when combining results from multiple studies.

Bipolar disorder (BD) is a highly heritable disease that has been difficult to characterize genetically. We have genotyped 1,190 BD cases and 401 controls to find regions of the genome associated with BD. After combining these data with previously existing genotyped samples, we did not find any genome-wide significant associations. However, when we used an additional study to prioritize loci for replication and meta-analysis purposes, we found that we were more likely to see an association in our sample with variants for which we had the highest power. We quantified this effect using logistic regression and saw a strong association between power to detect an effect based on an initial study's results and replication P-value in a second study (P = 1.5×10⁻⁷), supporting the presence of shared genetic risk factors across the studies. Moreover, this association was stronger when we restricted analysis to SNPs near coding regions, and it was further enriched when SNPs had the same direction of effect in both studies. This result supports the presence of genetic factors underlying BD near exons whose collective effect results in a detectable signal and provides a framework for assessing the potential for replication when combining results from multiple studies.

Identification of nine novel loci associated with white blood cell subtypes in a Japanese population

White blood cells (WBCs) mediate immune systems and consist of various subtypes with distinct roles. Elucidation of the mechanism that regulates the counts of the WBC subtypes would provide useful insights into both the etiology of the immune system and disease pathogenesis. In this study, we report results of genome-wide association studies (GWAS) and a replication study for the counts of the 5 main WBC subtypes (neutrophils, lymphocytes, monocytes, basophils, and eosinophils) using 14,792 Japanese subjects enrolled in the BioBank Japan Project. We identified 12 significantly associated loci that satisfied the genome-wide significance threshold of P<5.0×10⁻⁸, of which 9 loci were novel (the CDK6 locus for the neutrophil count; the ITGA4, MLZE, STXBP6 loci, and the MHC region for the monocyte count; the SLC45A3-NUCKS1, GATA2, NAALAD2, ERG loci for the basophil count). We further evaluated associations in the identified loci using 15,600 subjects from Caucasian populations. These WBC subtype-related loci demonstrated a variety of patterns of pleiotropic associations within the WBC subtypes, or with total WBC count, platelet count, or red blood cell-related traits (n = 30,454), which suggests unique and common functional roles of these loci in the processes of hematopoiesis. This study should contribute to the understanding of the genetic backgrounds of the WBC subtypes and hematological traits.

White blood cells (WBCs) are blood cells that mediate immune systems and defend the body against foreign microorganisms. It is well known that WBCs consist of various subtypes of cells with distinct roles, although the genetic background of each of the WBC subtypes has yet to be examined. In this study, we report genome-wide association studies (GWAS) for the 5 main WBC subtypes (neutrophils, lymphocytes, monocytes, basophils, and eosinophils) using 14,792 Japanese subjects. We identified 12 significantly associated genetic loci, and 9 of them were novel. Evaluation of the associations of these identified loci in cohorts of Caucasian populations demonstrated both ethnically common and divergent genetic backgrounds of the WBC subtypes. These loci also indicated a variety of patterns of pleiotropic associations within the hematological traits, including the other WBC subtypes, total WBC count, platelet count, or red blood cell-related traits, which suggests unique and common functional roles of these loci in the processes of hematopoiesis.

Sequence imputation of HPV16 genomes for genetic association studies

BACKGROUND

Human Papillomavirus type 16 (HPV16) causes over half of all cervical cancer and some HPV16 variants are more oncogenic than others. The genetic basis for the extraordinary oncogenic properties of HPV16 compared to other HPVs is unknown. In addition, we neither know which nucleotides vary across and within HPV types and lineages, nor which of the single nucleotide polymorphisms (SNPs) determine oncogenicity.

METHODS

A reference set of 62 HPV16 complete genome sequences was established and used to examine patterns of evolutionary relatedness amongst variants using a pairwise identity heatmap and HPV16 phylogeny. A BLAST-based algorithm was developed to impute complete genome data from partial sequence information using the reference database. To interrogate the oncogenic risk of determined and imputed HPV16 SNPs, odds-ratios for each SNP were calculated in a case-control viral genome-wide association study (VWAS) using biopsy confirmed high-grade cervix neoplasia and self-limited HPV16 infections from Guanacaste, Costa Rica.

RESULTS

HPV16 variants display evolutionarily stable lineages that contain conserved diagnostic SNPs. The imputation algorithm indicated that an average of 97.5±1.03% of SNPs could be accurately imputed. The VWAS revealed specific HPV16 viral SNPs associated with variant lineages and elevated odds ratios; however, individual causal SNPs could not be distinguished with certainty due to the nature of HPV evolution.

CONCLUSIONS

Conserved and lineage-specific SNPs can be imputed with a high degree of accuracy from limited viral polymorphic data due to the lack of recombination and the stochastic mechanism of variation accumulation in the HPV genome. However, to determine the role of novel variants or non-lineage-specific SNPs by VWAS will require direct sequence analysis. The investigation of patterns of genetic variation and the identification of diagnostic SNPs for lineages of HPV16 variants provides a valuable resource for future studies of HPV16 pathogenicity.

Implication of next-generation sequencing on association studies

Background

Next-generation sequencing technologies can effectively detect the entire spectrum of genomic variation and provide a powerful tool for systematic exploration of the universe of common, low frequency and rare variants in the entire genome. However, the current paradigm for genome-wide association studies (GWAS) is to catalogue and genotype common variants (5% < MAF). The methods and study design for testing the association of low frequency (0.5% < MAF ≤ 5%) and rare variation (MAF ≤ 0.5%) have not been thoroughly investigated. The 1000 Genomes Project represents one such endeavour to characterize the human genetic variation pattern at the MAF = 1% level as a foundation for association studies. In this report, we explore different strategies and study designs for the near future GWAS in the post-era, based on both low coverage pilot data and exon pilot data in 1000 Genomes Project.

Results

We investigated the linkage disequilibrium (LD) pattern among common and low frequency SNPs and its implication for association studies. We found that the LD between low frequency alleles and low frequency alleles, and low frequency alleles and common alleles are much weaker than the LD between common and common alleles. We examined various tagging designs with and without statistical imputation approaches and compare their power against de novo resequencing in mapping causal variants under various disease models. We used the low coverage pilot data which contain ~14 M SNPs as a hypothetical genotype-array platform (Pilot 14 M) to interrogate its impact on the selection of tag SNPs, mapping coverage and power of association tests. We found that even after imputation we still observed 45.4% of low frequency SNPs which were untaggable and only 67.7% of the low frequency variation was covered by the Pilot 14 M array.

Conclusions

This suggested GWAS based on SNP arrays would be ill-suited for association studies of low frequency variation.

Genome-wide association study of smoking behaviours in patients with COPD

Background Cigarette smoking is a major risk factor for chronic obstructive pulmonary disease (COPD) and COPD severity. Previous genome-wide association studies (GWAS) have identified numerous single nucleotide polymorphisms (SNPs) associated with the number of cigarettes smoked per day (CPD) and a dopamine beta-hydroxylase (DBH) locus associated with smoking cessation in multiple populations. Objective To identify SNPs associated with lifetime average and current CPD, age at smoking initiation, and smoking cessation in patients with COPD. Methods GWAS were conducted in four independent cohorts encompassing 3441 ever-smoking patients with COPD (Global Initiative for Obstructive Lung Disease stage II or higher). Untyped SNPs were imputed using the HapMap (phase II) panel. Results from all cohorts were meta-analysed. Results Several SNPs near the HLA region on chromosome 6p21 and in an intergenic region on chromosome 2q21 showed associations with age at smoking initiation, both with the lowest p=2×10(-7). No SNPs were associated with lifetime average CPD, current CPD or smoking cessation with p<10(-6). Nominally significant associations with candidate SNPs within cholinergic receptors, nicotinic, alpha 3/5 (CHRNA3/CHRNA5; eg, p=0.00011 for SNP rs1051730) and cytochrome P450, family 2, subfamily A, polypeptide 6 (CYP2A6; eg, p=2.78×10(-5) for a non-synonymous SNP rs1801272) regions were observed for lifetime average CPD, however only CYP2A6 showed evidence of significant association with current CPD. A candidate SNP (rs3025343) in DBH was significantly (p=0.015) associated with smoking cessation. Conclusion The authors identified two candidate regions associated with age at smoking initiation in patients with COPD. Associations of CHRNA3/CHRNA5 and CYP2A6 loci with CPD and DBH with smoking cessation are also likely of importance in the smoking behaviours of patients with COPD.

A gene-centric association scan for Coagulation Factor VII levels in European and African Americans: the Candidate Gene Association Resource (CARe) Consortium

Polymorphisms in several distinct genomic regions, including the F7 gene, were recently associated with factor VII (FVII) levels in European Americans (EAs). The genetic determinants of FVII in African Americans (AAs) are unknown. We used a 50,000 single nucleotide polymorphism (SNP) gene-centric array having dense coverage of over 2,000 candidate genes for cardiovascular disease (CVD) pathways in a community-based sample of 16,324 EA and 3898 AA participants from the Candidate Gene Association Resource (CARe) consortium. Our aim was the discovery of new genomic loci and more detailed characterization of existing loci associated with FVII levels. In EAs, we identified three new loci associated with FVII, of which APOA5 on chromosome 11q23 and HNF4A on chromosome 20q12-13 were replicated in a sample of 4289 participants from the Whitehall II study. We confirmed four previously reported FVII-associated loci (GCKR, MS4A6A, F7 and PROCR) in CARe EA samples. In AAs, the F7 and PROCR regions were significantly associated with FVII. Several of the FVII-associated regions are known to be associated with lipids and other cardiovascular-related traits. At the F7 locus, there was evidence of at least five independently associated SNPs in EAs and three independent signals in AAs. Though the variance in FVII explained by the existing loci is substantial (20% in EA and 10% in AA), larger sample sizes and investigation of lower frequency variants may be required to identify additional FVII-associated loci in EAs and AAs and further clarify the relationship between FVII and other CVD risk factors.

Genome-wide association study reveals three susceptibility loci for common migraine in the general population

Migraine is a common, heterogeneous and heritable neurological disorder. Its pathophysiology is incompletely understood, and its genetic influences at the population level are unknown. In a population-based genome-wide analysis including 5,122 migraineurs and 18,108 non-migraineurs, rs2651899 (1p36.32, PRDM16), rs10166942 (2q37.1, TRPM8) and rs11172113 (12q13.3, LRP1) were among the top seven associations (P < 5 × 10(-6)) with migraine. These SNPs were significant in a meta-analysis among three replication cohorts and met genome-wide significance in a meta-analysis combining the discovery and replication cohorts (rs2651899, odds ratio (OR) = 1.11, P = 3.8 × 10(-9); rs10166942, OR = 0.85, P = 5.5 × 10(-12); and rs11172113, OR = 0.90, P = 4.3 × 10(-9)). The associations at rs2651899 and rs10166942 were specific for migraine compared with non-migraine headache. None of the three SNP associations was preferential for migraine with aura or without aura, nor were any associations specific for migraine features. TRPM8 has been the focus of neuropathic pain models, whereas LRP1 modulates neuronal glutamate signaling, plausibly linking both genes to migraine pathophysiology.

Genome-wide association study identifies two loci strongly affecting transferrin glycosylation

Polysaccharide sidechains attached to proteins play important roles in cell-cell and receptor-ligand interactions. Variation in the carbohydrate component has been extensively studied for the iron transport protein transferrin, because serum levels of the transferrin isoforms asialotransferrin + disialotransferrin (carbohydrate-deficient transferrin, CDT) are used as biomarkers of excessive alcohol intake. We conducted a genome-wide association study to assess whether genetic factors affect CDT concentration in serum. CDT was measured in three population-based studies: one in Switzerland (CoLaus study, n = 5181) and two in Australia (n = 1509, n = 775). The first cohort was used as the discovery panel and the latter ones served as replication. Genome-wide single-nucleotide polymorphism (SNP) typing data were used to identify loci with significant associations with CDT as a percentage of total transferrin (CDT%). The top three SNPs in the discovery panel (rs2749097 near PGM1 on chromosome 1, and missense polymorphisms rs1049296, rs1799899 in TF on chromosome 3) were successfully replicated , yielding genome-wide significant combined association with CDT% (P = 1.9 × 10(-9), 4 × 10(-39), 5.5 × 10(-43), respectively) and explain 5.8% of the variation in CDT%. These allelic effects are postulated to be caused by variation in availability of glucose-1-phosphate as a precursor of the glycan (PGM1), and variation in transferrin (TF) structure.

The association of genome-wide significant spirometric loci with chronic obstructive pulmonary disease susceptibility

Two recent metaanalyses of genome-wide association studies conducted by the CHARGE and SpiroMeta consortia identified novel loci yielding evidence of association at or near genome-wide significance (GWS) with FEV(1) and FEV(1)/FVC. We hypothesized that a subset of these markers would also be associated with chronic obstructive pulmonary disease (COPD) susceptibility. Thirty-two single-nucleotide polymorphisms (SNPs) in or near 17 genes in 11 previously identified GWS spirometric genomic regions were tested for association with COPD status in four COPD case-control study samples (NETT/NAS, the Norway case-control study, ECLIPSE, and the first 1,000 subjects in COPDGene; total sample size, 3,456 cases and 1,906 controls). In addition to testing the 32 spirometric GWS SNPs, we tested a dense panel of imputed HapMap2 SNP markers from the 17 genes located near the 32 GWS SNPs and in a set of 21 well studied COPD candidate genes. Of the previously identified GWS spirometric genomic regions, three loci harbored SNPs associated with COPD susceptibility at a 5% false discovery rate: the 4q24 locus including FLJ20184/INTS12/GSTCD/NPNT, the 6p21 locus including AGER and PPT2, and the 5q33 locus including ADAM19. In conclusion, markers previously associated at or near GWS with spirometric measures were tested for association with COPD status in data from four COPD case-control studies, and three loci showed evidence of association with COPD susceptibility at a 5% false discovery rate.

A latent model for prioritization of SNPs for functional studies

One difficult question facing researchers is how to prioritize SNPs detected from genetic association studies for functional studies. Often a list of the top M SNPs is determined based on solely the p-value from an association analysis, where M is determined by financial/time constraints. For many studies of complex diseases, multiple analyses have been completed and integrating these multiple sets of results may be difficult. One may also wish to incorporate biological knowledge, such as whether the SNP is in the exon of a gene or a regulatory region, into the selection of markers to follow-up. In this manuscript, we propose a Bayesian latent variable model (BLVM) for incorporating “features” about a SNP to estimate a latent “quality score”, with SNPs prioritized based on the posterior probability distribution of the rankings of these quality scores. We illustrate the method using data from an ovarian cancer genome-wide association study (GWAS). In addition to the application of the BLVM to the ovarian GWAS, we applied the BLVM to simulated data which mimics the setting involving the prioritization of markers across multiple GWAS for related diseases/traits. The top ranked SNP by BLVM for the ovarian GWAS, ranked 2^nd and 7^th based on p-values from analyses of all invasive and invasive serous cases. The top SNP based on serous case analysis p-value (which ranked 197^th for invasive case analysis), was ranked 8^th based on the posterior probability of being in the top 5 markers (0.13). In summary, the application of the BLVM allows for the systematic integration of multiple SNP “features” for the prioritization of loci for fine-mapping or functional studies, taking into account the uncertainty in ranking.

Polymorphisms in stromal genes and susceptibility to serous epithelial ovarian cancer: a report from the Ovarian Cancer Association Consortium

Alterations in stromal tissue components can inhibit or promote epithelial tumorigenesis. Decorin (DCN) and lumican (LUM) show reduced stromal expression in serous epithelial ovarian cancer (sEOC). We hypothesized that common variants in these genes associate with risk. Associations with sEOC among Caucasians were estimated with odds ratios (OR) among 397 cases and 920 controls in two U.S.-based studies (discovery set), 436 cases and 1,098 controls in Australia (replication set 1) and a consortium of 15 studies comprising 1,668 cases and 4,249 controls (replication set 2). The discovery set and replication set 1 (833 cases and 2,013 controls) showed statistically homogeneous (P(heterogeneity)≥0.48) decreased risks of sEOC at four variants: DCN rs3138165, rs13312816 and rs516115, and LUM rs17018765 (OR = 0.6 to 0.9; P(trend) = 0.001 to 0.03). Results from replication set 2 were statistically homogeneous (P(heterogeneity)≥0.13) and associated with increased risks at DCN rs3138165 and rs13312816, and LUM rs17018765: all ORs = 1.2; P(trend)≤0.02. The ORs at the four variants were statistically heterogeneous across all 18 studies (P(heterogeneity)≤0.03), which precluded combining. In post-hoc analyses, interactions were observed between each variant and recruitment period (P(interaction)≤0.003), age at diagnosis (P(interaction) = 0.04), and year of diagnosis (P(interaction) = 0.05) in the five studies with available information (1,044 cases, 2,469 controls). We conclude that variants in DCN and LUM are not directly associated with sEOC, and that confirmation of possible effect modification of the variants by non-genetic factors is required.

Characterizing genetic risk at known prostate cancer susceptibility loci in African Americans

GWAS of prostate cancer have been remarkably successful in revealing common genetic variants and novel biological pathways that are linked with its etiology. A more complete understanding of inherited susceptibility to prostate cancer in the general population will come from continuing such discovery efforts and from testing known risk alleles in diverse racial and ethnic groups. In this large study of prostate cancer in African American men (3,425 prostate cancer cases and 3,290 controls), we tested 49 risk variants located in 28 genomic regions identified through GWAS in men of European and Asian descent, and we replicated associations (at p≤0.05) with roughly half of these markers. Through fine-mapping, we identified nearby markers in many regions that better define associations in African Americans. At 8q24, we found 9 variants (p≤6×10(-4)) that best capture risk of prostate cancer in African Americans, many of which are more common in men of African than European descent. The markers found to be associated with risk at each locus improved risk modeling in African Americans (per allele OR = 1.17) over the alleles reported in the original GWAS (OR = 1.08). In summary, in this detailed analysis of the prostate cancer risk loci reported from GWAS, we have validated and improved upon markers of risk in some regions that better define the association with prostate cancer in African Americans. Our findings with variants at 8q24 also reinforce the importance of this region as a major risk locus for prostate cancer in men of African ancestry.

ParaHaplo 3.0: A program package for imputation and a haplotype-based whole-genome association study using hybrid parallel computing

BACKGROUND

Use of missing genotype imputations and haplotype reconstructions are valuable in genome-wide association studies (GWASs). By modeling the patterns of linkage disequilibrium in a reference panel, genotypes not directly measured in the study samples can be imputed and used for GWASs. Since millions of single nucleotide polymorphisms need to be imputed in a GWAS, faster methods for genotype imputation and haplotype reconstruction are required.

RESULTS

We developed a program package for parallel computation of genotype imputation and haplotype reconstruction. Our program package, ParaHaplo 3.0, is intended for use in workstation clusters using the Intel Message Passing Interface. We compared the performance of ParaHaplo 3.0 on the Japanese in Tokyo, Japan and Han Chinese in Beijing, and Chinese in the HapMap dataset. A parallel version of ParaHaplo 3.0 can conduct genotype imputation 20 times faster than a non-parallel version of ParaHaplo.

CONCLUSIONS

ParaHaplo 3.0 is an invaluable tool for conducting haplotype-based GWASs. The need for faster genotype imputation and haplotype reconstruction using parallel computing will become increasingly important as the data sizes of such projects continue to increase. ParaHaplo executable binaries and program sources are available at http://en.sourceforge.jp/projects/parallelgwas/releases/.

Genome-wide association study of prostate cancer in men of African ancestry identifies a susceptibility locus at 17q21

In search of common risk alleles for prostate cancer that could contribute to high rates of the disease in men of African ancestry, we conducted a genome-wide association study (GWAS), with 1,047,986 single nucleotide polymorphism (SNP) markers examined in 3,425 African American prostate cancer cases and 3,290 African American male controls. The most significant 17 novel associations in stage 1 were followed-up in 1,844 cases and 3,269 controls of African ancestry. We identified a novel risk variant on chromosome 17q21 (rs7210100; odds ratio per allele=1.51; p=3.4×10⁻¹³). The frequency of the risk allele is ~5% in men of African descent while it is rare in other populations (<1%). Further studies are needed to investigate the biological contribution of this allele to prostate cancer risk. These findings emphasize the importance of conducting GWAS in diverse populations.

Genome-wide association studies: results from the first few years and potential implications for clinical medicine

Most common diseases and quantitative traits are heritable: determined in part by genetic variation within the population. The inheritance is typically polygenic in that combined effects of variants in numerous genes, plus nongenetic factors, determine outcome. The genes influencing common disease and quantitative traits remained largely unknown until the implementation in 2006 of genome-wide association (GWA) studies that comprehensively surveyed common genetic variation (frequency>5%). By 2010, GWA studies identified>1,000 genetic variants for polygenic traits. Typically, these variants together account for a modest fraction (10%-30%) of heritability, but they have highlighted genes in both known and new biological pathways and genes of unknown function. This initial effort prefigures new studies aimed at rarer variation and decades of functional work to decipher newly glimpsed biology. The greatest impact of GWA studies may not be in predictive medicine but rather in the development over the next decades of therapies based on novel biological insights.