A comprehensive SNP and indel imputability database

Motivation: Genotype imputation has become an indispensible step in genome-wide association studies (GWAS). Imputation accuracy, directly influencing downstream analysis, has shown to be improved using re-sequencing-based reference panels; however, this comes at the cost of high computational burden due to the huge number of potentially imputable markers (tens of millions) discovered through sequencing a large number of individuals. Therefore, there is an increasing need for access to imputation quality information without actually conducting imputation. To facilitate this process, we have established a publicly available SNP and indel imputability database, aiming to provide direct access to imputation accuracy information for markers identified by the 1000 Genomes Project across four major populations and covering multiple GWAS genotyping platforms

Genome-Wide Association Study of Anthropometric Traits and Evidence of Interactions With Age and Study Year in Filipino Women

Increased values of multiple adiposity-related anthropometric traits are important risk factors for many common complex diseases. We performed a genome-wide association (GWA) study for four quantitative traits related to body size and adiposity (body mass index [BMI], weight, waist circumference, and height) in a cohort of 1,792 adult Filipino women from the Cebu Longitudinal Health and Nutrition Survey. This is the first GWA study of anthropometric traits in Filipinos, a population experiencing a rapid transition into a more obesogenic environment. In addition to identifying suggestive evidence of additional SNP association signals (P < 10−5), we replicated (P < 0.05, same direction of additive effect) associations previously reported in European populations of both BMI and weight with MC4R and FTO, of BMI with BDNF, and of height with EFEMP1, ZBTB38, and NPPC, but none with waist circumference. We also replicated loci reported in Japanese or Korean populations as associated with BMI (OTOL1) and height (HIST1H1PS2, C14orf145, GPC5). A difference in local linkage disequilibrium between European and Asian populations suggests a narrowed association region for BDNF, while still including a proposed functional non-synonymous amino acid substitution variant (rs6265, Val66Met). Finally, we observed significant evidence (P < 0.0042) for age-by-genotype interactions influencing BMI for rs17782313 (MC4R) and rs9939609 (FTO), and for a study year-by-genotype interaction for rs4923461 (BDNF). Our results show that several genetic risk factors are associated with anthropometric traits in Filipinos and provide further insight into the effects of BDNF, FTO, and MC4R on BMI

Genetic association with lipids in Filipinos: waist circumference modifies an APOA5 effect on triglyceride levels

Blood levels of lipoprotein cholesterol and triglycerides (TGs) are highly heritable and are major risk factors for cardiovascular disease (CVD). Approximately 100 lipid-associated loci have been identified in populations of European ancestry. We performed a genome-wide association study of lipid traits in 1,782 Filipino women from the Cebu Longitudinal Health and Nutrition Survey, and tested for evidence of interactions with waist circumference. We conducted additional association and interaction analyses in 1,719 of their young adult offspring. Genome-wide significant associations (P < 5 × 10−8) were detected at APOE for low density lipoprotein cholesterol and total cholesterol, and at APOA5 for TGs. Suggestive associations (P < 10−6) were detected at GCKR for TGs, and at CETP and TOM1 for high density lipoprotein cholesterol. Our data also supported the existence of allelic heterogeneity at APOA5, CETP, LIPC, and APOE. The secondary signal (Gly185Cys) at APOA5 exhibited a single nucleotide polymorphism (SNP)-by-waist circumference interaction affecting TGs (Pinteraction = 1.6 × 10−4), manifested by stronger SNP effects as waist circumference increased. These findings provide the first evidence that central obesity may accentuate the effect of the TG-increasing allele of the APOA5 signal, emphasizing that CVD risk could be reduced by central obesity control

Expression of SMARCD1 interacts with age in association with asthma control on inhaled corticosteroid therapy.

BackgroundGlobal gene expression levels are known to be highly dependent upon gross demographic features including age, yet identification of age-related genomic indicators has yet to be comprehensively undertaken in a disease and treatment-specific context.MethodsWe used gene expression data from CD4+ lymphocytes in the Asthma BioRepository for Integrative Genomic Exploration (Asthma BRIDGE), an open-access collection of subjects participating in genetic studies of asthma with available gene expression data. Replication population participants were Puerto Rico islanders recruited as part of the ongoing Genes environments &amp; Admixture in Latino Americans (GALA II), who provided nasal brushings for transcript sequencing. The main outcome measure was chronic asthma control as derived by questionnaires. Genomic associations were performed using regression of chronic asthma control score on gene expression with age in years as a covariate, including a multiplicative interaction term for gene expression times age.ResultsThe SMARCD1 gene (SWI/SNF-related matrix-associated actin-dependent regulator of chromatin subfamily D member 1) interacted with age to influence chronic asthma control on inhaled corticosteroids, with a doubling of expression leading to an increase of 1.3 units of chronic asthma control per year (95% CI [0.86, 1.74], p = 6 × 10- 9), suggesting worsening asthma control with increasing age. This result replicated in GALA II (p = 3.8 × 10- 8). Cellular assays confirmed the role of SMARCD1 in glucocorticoid response in airway epithelial cells.ConclusionFocusing on age-dependent factors may help identify novel indicators of asthma medication response. Age appears to modulate the effect of SMARCD1 on asthma control with inhaled corticosteroids

Genome-wide association study for adiponectin levels in Filipino women identifies CDH13 and a novel uncommon haplotype at KNG1–ADIPOQ

Adiponectin is an adipocyte-secreted protein involved in a variety of metabolic processes, including glucose regulation and fatty acid catabolism. We conducted a genome-wide association study to investigate the genetic loci associated with plasma adiponectin in 1776 unrelated Filipino women from the Cebu Longitudinal Health and Nutrition Survey (CLHNS). Our strongest signal for adiponectin mapped to the gene CDH13 (rs3865188, P ≤ 7.2 × 10−16), which encodes a receptor for high-molecular-weight forms of adiponectin. Strong association was also detected near the ADIPOQ gene (rs864265, P = 3.8 × 10−9) and at a novel signal 100 kb upstream near KNG1 (rs11924390, P = 7.6 × 10−7). All three signals were also observed in 1774 young adult CLHNS offspring and in combined analysis including all 3550 mothers and offspring samples (all P ≤ 1.6 × 10−9). An uncommon haplotype of rs11924390 and rs864265 (haplotype frequency = 0.050) was strongly associated with lower adiponectin compared with the most common C–G haplotype in both CLHNS mothers (P = 1.8 × 10−25) and offspring (P = 8.7 × 10−32). Comprehensive imputation of 2653 SNPs in a 2 Mb region using as reference combined CHB, JPT and CEU haplotypes from the 1000 Genomes Project revealed no variants that perfectly tagged this haplotype. Our findings provide the first genome-wide significant evidence of association with plasma adiponectin at the CDH13 locus and identify a novel uncommon KNG1–ADIPOQ haplotype strongly associated with adiponectin levels in Filipinos

Population-specific coding variant underlies genome-wide association with adiponectin level

Adiponectin is a protein hormone that can affect major metabolic processes including glucose regulation and fat metabolism. Our previous genome-wide association (GWA) study of circulating plasma adiponectin levels in Filipino women from the Cebu Longitudinal Health and Nutrition Survey (CLHNS) detected a 100 kb two-SNP haplotype at KNG1–ADIPOQ associated with reduced adiponectin (frequency = 0.050, P = 1.8 × 10−25). Subsequent genotyping of CLHNS young adult offspring detected an uncommon variant [minor allele frequency (MAF) = 0.025] located ∼800 kb from ADIPOQ that showed strong association with lower adiponectin levels (P = 2.7 × 10−15, n = 1695) and tagged a subset of KNG1–ADIPOQ haplotype carriers with even lower adiponectin levels. Sequencing of the ADIPOQ-coding region detected variant R221S (MAF = 0.015, P = 2.9 × 10−69), which explained 17.1% of the variance in adiponectin levels and largely accounted for the initial GWA signal in Filipinos. R221S was not present in 12 514 Europeans with previously sequenced exons. To explore the mechanism of this substitution, we re-measured adiponectin level in 20 R221S offspring carriers and 20 non-carriers using two alternative antibodies and determined that the presence of R221S resulted in artificially low quantification of adiponectin level using the original immunoassay. These data provide an example of an uncommon variant responsible for a GWA signal and demonstrate that genetic associations with phenotypes measured by antibody-based quantification methods can be affected by uncommon coding SNPs residing in the antibody target region

Across-cohort QC analyses of GWAS summary statistics from complex traits.

Genome-wide association studies (GWASs) have been successful in discovering SNP trait associations for many quantitative traits and common diseases. Typically, the effect sizes of SNP alleles are very small and this requires large genome-wide association meta-analyses (GWAMAs) to maximize statistical power. A trend towards ever-larger GWAMA is likely to continue, yet dealing with summary statistics from hundreds of cohorts increases logistical and quality control problems, including unknown sample overlap, and these can lead to both false positive and false negative findings. In this study, we propose four metrics and visualization tools for GWAMA, using summary statistics from cohort-level GWASs. We propose methods to examine the concordance between demographic information, and summary statistics and methods to investigate sample overlap. (I) We use the population genetics Fst statistic to verify the genetic origin of each cohort and their geographic location, and demonstrate using GWAMA data from the GIANT Consortium that geographic locations of cohorts can be recovered and outlier cohorts can be detected. (II) We conduct principal component analysis based on reported allele frequencies, and are able to recover the ancestral information for each cohort. (III) We propose a new statistic that uses the reported allelic effect sizes and their standard errors to identify significant sample overlap or heterogeneity between pairs of cohorts. (IV) To quantify unknown sample overlap across all pairs of cohorts, we propose a method that uses randomly generated genetic predictors that does not require the sharing of individual-level genotype data and does not breach individual privacy

The metabolomics of asthma control: a promising link between genetics and disease

Short-acting β agonists (e.g., albuterol) are the most commonly used medications for asthma, a disease that affects over 300 million people in the world. Metabolomic profiling of asthmatics taking β agonists presents a new and promising resource for identifying the molecular determinants of asthma control. The objective is to identify novel genetic and biochemical predictors of asthma control using an integrative “omics” approach. We generated lipidomic data by liquid chromatography tandem mass spectrometry (LC-MS), ­ using plasma samples from 20 individuals with asthma. The outcome of interest was a binary indicator of asthma control defined by the use of albuterol inhalers in the preceding week. We integrated metabolomic data with genome-wide genotype, gene expression, and methylation data of this cohort to identify genomic and molecular indicators of asthma control. A Conditional Gaussian Bayesian Network (CGBN) was generated using the strongest predictors from each of these analyses. Integrative and metabolic pathway over-representation analyses (ORA) identified enrichment of known biological pathways within the strongest molecular determinants. Of the 64 metabolites measured, 32 had known identities. The CGBN model based on four SNPs (rs9522789, rs7147228, rs2701423, rs759582) and two metabolites—monoHETE_0863 and sphingosine-1-phosphate (S1P) could predict asthma control with an AUC of 95%. Integrative ORA identified 17 significantly enriched pathways related to cellular immune response, interferon signaling, and cytokine-related signaling, for which arachidonic acid, PGE2 and S1P, in addition to six genes (CHN1, PRKCE, GNA12, OASL, OAS1, and IFIT3) appeared to drive the pathway results. Of these predictors, S1P, GNA12, and PRKCE were enriched in the results from integrative and metabolic ORAs. Through an integrative analysis of metabolomic, genomic, and methylation data from a small cohort of asthmatics, we implicate altered metabolic pathways, related to sphingolipid metabolism, in asthma control. These results provide insight into the pathophysiology of asthma control

Quality control and conduct of genome-wide association meta-analyses

Rigorous organization and quality control (QC) are necessary to facilitate successful genome-wide association meta-analyses (GWAMAs) of statistics aggregated across multiple genome-wide association studies. This protocol provides guidelines for [1] organizational aspects of GWAMAs, and for [2] QC at the study file level, the meta-level across studies, and the meta-analysis output level. Real–world examples highlight issues experienced and solutions developed by the GIANT Consortium that has conducted meta-analyses including data from 125 studies comprising more than 330,000 individuals. We provide a general protocol for conducting GWAMAs and carrying out QC to minimize errors and to guarantee maximum use of the data. We also include details for use of a powerful and flexible software package called EasyQC. For consortia of comparable size to the GIANT consortium, the present protocol takes a minimum of about 10 months to complete

Biological, clinical and population relevance of 95 loci for blood lipids.

Plasma concentrations of total cholesterol, low-density lipoprotein cholesterol, high-density lipoprotein cholesterol and triglycerides are among the most important risk factors for coronary artery disease (CAD) and are targets for therapeutic intervention. We screened the genome for common variants associated with plasma lipids in &gt;100,000 individuals of European ancestry. Here we report 95 significantly associated loci (P &lt; 5 x 10(-8)), with 59 showing genome-wide significant association with lipid traits for the first time. The newly reported associations include single nucleotide polymorphisms (SNPs) near known lipid regulators (for example, CYP7A1, NPC1L1 and SCARB1) as well as in scores of loci not previously implicated in lipoprotein metabolism. The 95 loci contribute not only to normal variation in lipid traits but also to extreme lipid phenotypes and have an impact on lipid traits in three non-European populations (East Asians, South Asians and African Americans). Our results identify several novel loci associated with plasma lipids that are also associated with CAD. Finally, we validated three of the novel genes-GALNT2, PPP1R3B and TTC39B-with experiments in mouse models. Taken together, our findings provide the foundation to develop a broader biological understanding of lipoprotein metabolism and to identify new therapeutic opportunities for the prevention of CAD