STOPGAP: a database for systematic target opportunity assessment by genetic association predictions.

SUMMARY: We developed the STOPGAP (Systematic Target OPportunity assessment by Genetic Association Predictions) database, an extensive catalog of human genetic associations mapped to effector gene candidates. STOPGAP draws on a variety of publicly available GWAS associations, linkage disequilibrium (LD) measures, functional genomic and variant annotation sources. Algorithms were developed to merge the association data, partition associations into non-overlapping LD clusters, map variants to genes and produce a variant-to-gene score used to rank the relative confidence among potential effector genes. This database can be used for a multitude of investigations into the genes and genetic mechanisms underlying inter-individual variation in human traits, as well as supporting drug discovery applications. AVAILABILITY AND IMPLEMENTATION: Shell, R, Perl and Python scripts and STOPGAP R data files (version 2.5.1 at publication) are available at https://github.com/StatGenPRD/STOPGAP . Some of the most useful STOPGAP fields can be queried through an R Shiny web application at http://stopgapwebapp.com . CONTACT: matthew.r.nelson@gsk.com. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Exome Genotyping Identifies Pleiotropic Variants Associated with Red Blood Cell Traits.

Red blood cell (RBC) traits are important heritable clinical biomarkers and modifiers of disease severity. To identify coding genetic variants associated with these traits, we conducted meta-analyses of seven RBC phenotypes in 130,273 multi-ethnic individuals from studies genotyped on an exome array. After conditional analyses and replication in 27,480 independent individuals, we identified 16 new RBC variants. We found low-frequency missense variants in MAP1A (rs55707100, minor allele frequency [MAF] = 3.3%, p = 2 × 10(-10) for hemoglobin [HGB]) and HNF4A (rs1800961, MAF = 2.4%, p < 3 × 10(-8) for hematocrit [HCT] and HGB). In African Americans, we identified a nonsense variant in CD36 associated with higher RBC distribution width (rs3211938, MAF = 8.7%, p = 7 × 10(-11)) and showed that it is associated with lower CD36 expression and strong allelic imbalance in ex vivo differentiated human erythroblasts. We also identified a rare missense variant in ALAS2 (rs201062903, MAF = 0.2%) associated with lower mean corpuscular volume and mean corpuscular hemoglobin (p < 8 × 10(-9)). Mendelian mutations in ALAS2 are a cause of sideroblastic anemia and erythropoietic protoporphyria. Gene-based testing highlighted three rare missense variants in PKLR, a gene mutated in Mendelian non-spherocytic hemolytic anemia, associated with HGB and HCT (SKAT p < 8 × 10(-7)). These rare, low-frequency, and common RBC variants showed pleiotropy, being also associated with platelet, white blood cell, and lipid traits. Our association results and functional annotation suggest the involvement of new genes in human erythropoiesis. We also confirm that rare and low-frequency variants play a role in the architecture of complex human traits, although their phenotypic effect is generally smaller than originally anticipated.

Platelet-Related Variants Identified by Exomechip Meta-analysis in 157,293 Individuals.

Platelet production, maintenance, and clearance are tightly controlled processes indicative of platelets' important roles in hemostasis and thrombosis. Platelets are common targets for primary and secondary prevention of several conditions. They are monitored clinically by complete blood counts, specifically with measurements of platelet count (PLT) and mean platelet volume (MPV). Identifying genetic effects on PLT and MPV can provide mechanistic insights into platelet biology and their role in disease. Therefore, we formed the Blood Cell Consortium (BCX) to perform a large-scale meta-analysis of Exomechip association results for PLT and MPV in 157,293 and 57,617 individuals, respectively. Using the low-frequency/rare coding variant-enriched Exomechip genotyping array, we sought to identify genetic variants associated with PLT and MPV. In addition to confirming 47 known PLT and 20 known MPV associations, we identified 32 PLT and 18 MPV associations not previously observed in the literature across the allele frequency spectrum, including rare large effect (FCER1A), low-frequency (IQGAP2, MAP1A, LY75), and common (ZMIZ2, SMG6, PEAR1, ARFGAP3/PACSIN2) variants. Several variants associated with PLT/MPV (PEAR1, MRVI1, PTGES3) were also associated with platelet reactivity. In concurrent BCX analyses, there was overlap of platelet-associated variants with red (MAP1A, TMPRSS6, ZMIZ2) and white (PEAR1, ZMIZ2, LY75) blood cell traits, suggesting common regulatory pathways with shared genetic architecture among these hematopoietic lineages. Our large-scale Exomechip analyses identified previously undocumented associations with platelet traits and further indicate that several complex quantitative hematological, lipid, and cardiovascular traits share genetic factors.

Deep resequencing unveils genetic architecture of ADIPOQ and identifies a novel low-frequency variant strongly associated with adiponectin variation.

Increased adiponectin levels have been shown to be associated with a lower risk of type 2 diabetes. To understand the relations between genetic variation at the adiponectin-encoding gene, ADIPOQ, and adiponectin levels, and subsequently its role in disease, we conducted a deep resequencing experiment of ADIPOQ in 14,002 subjects, including 12,514 Europeans, 594 African Americans, and 567 Indian Asians. We identified 296 single nucleotide polymorphisms (SNPs), including 30 amino acid changes, and carried out association analyses in a subset of 3,665 subjects from two independent studies. We confirmed multiple genome-wide association study findings and identified a novel association between a low-frequency SNP (rs17366653) and adiponectin levels (P = 2.2E-17). We show that seven SNPs exert independent effects on adiponectin levels. Together, they explained 6% of adiponectin variation in our samples. We subsequently assessed association between these SNPs and type 2 diabetes in the Genetics of Diabetes Audit and Research in Tayside Scotland (GO-DARTS) study, comprised of 5,145 case and 6,374 control subjects. No evidence of association with type 2 diabetes was found, but we were also unable to exclude the possibility of substantial effects (e.g., odds ratio 95% CI for rs7366653 [0.91-1.58]). Further investigation by large-scale and well-powered Mendelian randomization studies is warranted.

Performance of genotype imputation for rare variants identified in exons and flanking regions of genes.

Genotype imputation has the potential to assess human genetic variation at a lower cost than assaying the variants using laboratory techniques. The performance of imputation for rare variants has not been comprehensively studied. We utilized 8865 human samples with high depth resequencing data for the exons and flanking regions of 202 genes and Genome-Wide Association Study (GWAS) data to characterize the performance of genotype imputation for rare variants. We evaluated reference sets ranging from 100 to 3713 subjects for imputing into samples typed for the Affymetrix (500K and 6.0) and Illumina 550K GWAS panels. The proportion of variants that could be well imputed (true r(2)>0.7) with a reference panel of 3713 individuals was: 31% (Illumina 550K) or 25% (Affymetrix 500K) with MAF (Minor Allele Frequency) less than or equal 0.001, 48% or 35% with 0.0010.05. The performance for common SNPs (MAF>0.05) within exons and flanking regions is comparable to imputation of more uniformly distributed SNPs. The performance for rare SNPs (0.01

A comparison of internal validation techniques for multifactor dimensionality reduction.

BACKGROUND: It is hypothesized that common, complex diseases may be due to complex interactions between genetic and environmental factors, which are difficult to detect in high-dimensional data using traditional statistical approaches. Multifactor Dimensionality Reduction (MDR) is the most commonly used data-mining method to detect epistatic interactions. In all data-mining methods, it is important to consider internal validation procedures to obtain prediction estimates to prevent model over-fitting and reduce potential false positive findings. Currently, MDR utilizes cross-validation for internal validation. In this study, we incorporate the use of a three-way split (3WS) of the data in combination with a post-hoc pruning procedure as an alternative to cross-validation for internal model validation to reduce computation time without impairing performance. We compare the power to detect true disease causing loci using MDR with both 5- and 10-fold cross-validation to MDR with 3WS for a range of single-locus and epistatic disease models. Additionally, we analyze a dataset in HIV immunogenetics to demonstrate the results of the two strategies on real data. RESULTS: MDR with 3WS is computationally approximately five times faster than 5-fold cross-validation. The power to find the exact true disease loci without detecting false positive loci is higher with 5-fold cross-validation than with 3WS before pruning. However, the power to find the true disease causing loci in addition to false positive loci is equivalent to the 3WS. With the incorporation of a pruning procedure after the 3WS, the power of the 3WS approach to detect only the exact disease loci is equivalent to that of MDR with cross-validation. In the real data application, the cross-validation and 3WS analyses indicate the same two-locus model. CONCLUSIONS: Our results reveal that the performance of the two internal validation methods is equivalent with the use of pruning procedures. The specific pruning procedure should be chosen understanding the trade-off between identifying all relevant genetic effects but including false positives and missing important genetic factors. This implies 3WS may be a powerful and computationally efficient approach to screen for epistatic effects, and could be used to identify candidate interactions in large-scale genetic studies.

Candidate single-nucleotide polymorphisms from a genomewide association study of Alzheimer disease.

OBJECTIVE: To identify single-nucleotide polymorphisms (SNPs) associated with risk and age at onset of Alzheimer disease (AD) in a genomewide association study of 469 438 SNPs. DESIGN: Case-control study with replication. SETTING: Memory referral clinics in Canada and the United Kingdom. PARTICIPANTS: The hypothesis-generating data set consisted of 753 individuals with AD by National Institute of Neurological and Communicative Diseases and Stroke/Alzheimer's Disease and Related Disorders Association criteria recruited from 9 memory referral clinics in Canada and 736 ethnically matched control subjects; control subjects were recruited from nonbiological relatives, friends, or spouses of the patients and did not exhibit cognitive impairment by history or cognitive testing. The follow-up data set consisted of 418 AD cases and 249 nondemented control cases from the United Kingdom Medical Research Council Genetic Resource for Late-Onset AD recruited from clinics at Cardiff University, Cardiff, Wales, and King's College London, London, England. MAIN OUTCOME MEASURES: Odds ratios and 95% confidence intervals for association of SNPs with AD by logistic regression adjusted for age, sex, education, study site, and French Canadian ancestry (for the Canadian data set). Hazard ratios and 95% confidence intervals from Cox proportional hazards regression for age at onset with similar covariate adjustments. RESULTS: Unadjusted, SNP RS4420638 within APOC1 was strongly associated with AD due entirely to linkage disequilibrium with APOE. In the multivariable adjusted analyses, 3 SNPs within the top 120 by P value in the logistic analysis and 1 in the Cox analysis of the Canadian data set provided additional evidence for association at P< .05 within the United Kingdom Medical Research Council data set: RS7019241 (GOLPH2), RS10868366 (GOLPH2), RS9886784 (chromosome 9), and RS10519262 (intergenic between ATP8B4 and SLC27A2). CONCLUSIONS: Our genomewide association analysis again identified the APOE linkage disequilibrium region as the strongest genetic risk factor for AD. This could be a consequence of the coevolution of more than 1 susceptibility allele, such as APOC1, in this region. We also provide new evidence for additional candidate genetic risk factors for AD that can be tested in further studies.