SNP Set Research Articles

A genome-wide association study of energy intake and expenditure.

Excessive energy intake or insufficient energy expenditure, which result in energy imbalance, contribute to the development of obesity. Obesity-related genes, such as FTO, are associated with energy traits. No genome-wide association studies (GWAS) have been conducted to detect the genetic associations with energy-related traits, including energy intake and energy expenditure, among European-ancestry populations. In this study, we conducted a genome-wide study using pooled GWAS including 12,030 European-ancestry women and 6,743 European-ancestry men to identify genetic variants associated with these two energy traits. We observed a statistically significant genome-wide SNP heritability for energy intake of 6.05% (95%CI = (1.76, 10.34), P = 0.006); the SNP heritability for expenditure was not statistically significantly greater than zero. We discovered three SNPs on chromosome 12q13 near gene ANKRD33 that were genome-wide significantly associated with increased total energy intake among all men. We also identified signals on region 2q22 that were associated with energy expenditure among lean people. Body mass index related SNPs were found to be significantly associated with energy intake and expenditure through SNP set analyses. Larger GWAS studies of total energy traits are warranted to explore the genetic basis of energy intake, including possible differences between men and women, and the association between total energy intake and other downstream phenotypes, such as diabetes and chronic diseases.

Clines on the seashore: The genomic architecture underlying rapid divergence in the face of gene flow.

Adaptive divergence and speciation may happen despite opposition by gene flow. Identifying the genomic basis underlying divergence with gene flow is a major task in evolutionary genomics. Most approaches (e.g., outlier scans) focus on genomic regions of high differentiation. However, not all genomic architectures potentially underlying divergence are expected to show extreme differentiation. Here, we develop an approach that combines hybrid zone analysis (i.e., focuses on spatial patterns of allele frequency change) with system‐specific simulations to identify loci inconsistent with neutral evolution. We apply this to a genome‐wide SNP set from an ideally suited study organism, the intertidal snail Littorina saxatilis, which shows primary divergence between ecotypes associated with different shore habitats. We detect many SNPs with clinal patterns, most of which are consistent with neutrality. Among non‐neutral SNPs, most are located within three large putative inversions differentiating ecotypes. Many non‐neutral SNPs show relatively low levels of differentiation. We discuss potential reasons for this pattern, including loose linkage to selected variants, polygenic adaptation and a component of balancing selection within populations (which may be expected for inversions). Our work is in line with theory predicting a role for inversions in divergence, and emphasizes that genomic regions contributing to divergence may not always be accessible with methods purely based on allele frequency differences. These conclusions call for approaches that take spatial patterns of allele frequency change into account in other systems.

A system-based analysis of the genetic determinism of udder conformation and health phenotypes across three French dairy cattle breeds.

Using GWAS to identify candidate genes associated with cattle morphology traits at a functional level is challenging. The main difficulty of identifying candidate genes and gene interactions associated with such complex traits is the long-range linkage disequilibrium (LD) phenomenon reported widely in dairy cattle. Systems biology approaches, such as combining the Association Weight Matrix (AWM) with a Partial Correlation in an Information Theory (PCIT) algorithm, can assist in overcoming this LD. Used in a multi-breed and multi-phenotype context, the AWM-PCIT could aid in identifying udder traits candidate genes and gene networks with regulatory and functional significance. This study aims to use the AWM-PCIT algorithm as a post-GWAS analysis tool with the goal of identifying candidate genes underlying udder morphology. We used data from 78,440 dairy cows from three breeds and with own phenotypes for five udder morphology traits, five production traits, somatic cell score and clinical mastitis. Cows were genotyped with medium (50k) or low-density (7 to 10k) chips and imputed to 50k. We performed a within breed and trait GWAS. The GWAS showed 9,830 significant SNP across the genome (p < 0.05). Five thousand and ten SNP did not map a gene, and 4,820 SNP were within 10-kb of a gene. After accounting for 1SNP:1gene, 3,651 SNP were within 10-kb of a gene (set1), and 2,673 significant SNP were further than 10-kb of a gene (set2). The two SNP sets formed 6,324 SNP matrix, which was fitted in an AWM-PCIT considering udder depth/ development as the key trait resulting in 1,013 genes associated with udder morphology, mastitis and production phenotypes. The AWM-PCIT detected ten potential candidate genes for udder related traits: ESR1, FGF2, FGFR2, GLI2, IQGAP3, PGR, PRLR, RREB1, BTRC, and TGFBR2.

POLARIS: Polygenic LD-adjusted risk score approach for set-based analysis of GWAS data.

ABSTRACTPolygenic risk scores (PRSs) are a method to summarize the additive trait variance captured by a set of SNPs, and can increase the power of set‐based analyses by leveraging public genome‐wide association study (GWAS) datasets. PRS aims to assess the genetic liability to some phenotype on the basis of polygenic risk for the same or different phenotype estimated from independent data. We propose the application of PRSs as a set‐based method with an additional component of adjustment for linkage disequilibrium (LD), with potential extension of the PRS approach to analyze biologically meaningful SNP sets. We call this method POLARIS: POlygenic Ld‐Adjusted RIsk Score. POLARIS identifies the LD structure of SNPs using spectral decomposition of the SNP correlation matrix and replaces the individuals' SNP allele counts with LD‐adjusted dosages. Using a raw genotype dataset together with SNP effect sizes from a second independent dataset, POLARIS can be used for set‐based analysis. MAGMA is an alternative set‐based approach employing principal component analysis to account for LD between markers in a raw genotype dataset. We used simulations, both with simple constructed and real LD‐structure, to compare the power of these methods. POLARIS shows more power than MAGMA applied to the raw genotype dataset only, but less or comparable power to combined analysis of both datasets. POLARIS has the advantages that it produces a risk score per person per set using all available SNPs, and aims to increase power by leveraging the effect sizes from the discovery set in a self‐contained test of association in the test dataset.

Kernel machine SNP set analysis provides new insight into the association between obesity and polymorphisms located on the chromosomal 16q.12.2 region: Tehran Lipid and Glucose Study

IntroductionObesity is a serious health problem that leads to low quality of life and early mortality. To the purpose of prevention and gene therapy for such a worldwide disease, genome wide association study is a powerful tool for finding SNPs associated with increased risk of obesity. To conduct an association analysis, kernel machine regression is a generalized regression method, has an advantage of considering the epistasis effects as well as the correlation between individuals due to unknown factors. Materials and methodsIn this study, information of the people who participated in Tehran cardio-metabolic genetic study was used. They were genotyped for the chromosomal region, evaluation 986 variations located at 16q12.2; build 38hg. Kernel machine regression and single SNP analysis were used to assess the association between obesity and SNPs genotyped data. ResultsWe found that associated SNP sets with obesity, were almost in the FTO (P = 0.01), AIKTIP (P = 0.02) and MMP2 (P = 0.02) genes. Moreover, two SNPs, i.e., rs10521296 and rs11647470, showed significant association with obesity using kernel regression (P = 0.02). ConclusionIn conclusion, significant sets were randomly distributed throughout the region with more density around the FTO, AIKTIP and MMP2 genes.Furthermore, two intergenic SNPs showed significant association after using kernel machine regression. Therefore, more studies have to be conducted to assess their functionality or precise mechanism.

Identifying and exploiting trait-relevant tissues with multiple functional annotations in genome-wide association studies.

Genome-wide association studies (GWASs) have identified many disease associated loci, the majority of which have unknown biological functions. Understanding the mechanism underlying trait associations requires identifying trait-relevant tissues and investigating associations in a trait-specific fashion. Here, we extend the widely used linear mixed model to incorporate multiple SNP functional annotations from omics studies with GWAS summary statistics to facilitate the identification of trait-relevant tissues, with which to further construct powerful association tests. Specifically, we rely on a generalized estimating equation based algorithm for parameter inference, a mixture modeling framework for trait-tissue relevance classification, and a weighted sequence kernel association test constructed based on the identified trait-relevant tissues for powerful association analysis. We refer to our analytic procedure as the Scalable Multiple Annotation integration for trait-Relevant Tissue identification and usage (SMART). With extensive simulations, we show how our method can make use of multiple complementary annotations to improve the accuracy for identifying trait-relevant tissues. In addition, our procedure allows us to make use of the inferred trait-relevant tissues, for the first time, to construct more powerful SNP set tests. We apply our method for an in-depth analysis of 43 traits from 28 GWASs using tissue-specific annotations in 105 tissues derived from ENCODE and Roadmap. Our results reveal new trait-tissue relevance, pinpoint important annotations that are informative of trait-tissue relationship, and illustrate how we can use the inferred trait-relevant tissues to construct more powerful association tests in the Wellcome trust case control consortium study.

A Universal Droplet Digital PCR Approach for Monitoring of Graft Health After Transplantation Using a Preselected SNP Set.

Transplanted organs release cell-free DNA into the bloodstream of the recipient. This graft-derived cell-free DNA (GcfDNA) is a sensitive biomarker for organ health, since higher GcfDNA levels are indicative of increased cell-death in the graft. This protocol describes a method to measure relative GcfDNA concentrations by ddPCR assays. The method uses a set of preselected SNP assays from which the informative SNPs for each recipient-donor combination are selected in a straightforward two-step procedure that requires only one blood draw. Sampling of donor tissue and separate genotyping is not required, rendering the technique applicable also to patients, whose transplantation was not recent. In these patients there will be mostly no access to donor DNA anymore.

Development and Evaluation of a Barley 50k iSelect SNP Array.

High-throughput genotyping arrays continue to be an attractive, cost-effective alternative to sequencing based approaches. We have developed a new 50k Illumina Infinium iSelect genotyping array for barley, a cereal crop species of major international importance. The majority of SNPs on the array have been extracted from variants called in exome capture data of a wide range of European barley germplasm. We used the recently published barley pseudomolecule assembly to map the exome capture data, which allowed us to generate markers with accurate physical positions and detailed gene annotation. Markers from an existing and widely used barley 9k Infinium iSelect array were carried over onto the 50k chip for backward compatibility. The array design featured 49,267 SNP markers that converted into 44,040 working assays, of which 43,461 were scorable in GenomeStudio. Of the working assays, 6,251 are from the 9k iSelect platform. We validated the SNPs by comparing the genotype calls from the new array to legacy datasets. Rates of agreement averaged 98.1 and 93.9% respectively for the legacy 9k iSelect SNP set (Comadran et al., 2012) and the exome capture SNPs. To test the utility of the 50k chip for genetic mapping, we genotyped a segregating population derived from a Golden Promise × Morex cross (Liu et al., 2014) and mapped over 14,000 SNPs to genetic positions which showed a near exact correspondence to their known physical positions. Manual adjustment of the cluster files used by the interpreting software for genotype scoring improved results substantially, but migration of cluster files between sites led to a deterioration of results, suggesting that local adjustment of cluster files is required on a site-per-site basis. Information relating to the markers on the chip is available online at https://ics.hutton.ac.uk/50k.

Design and validation of a 90K SNP genotyping assay for the water buffalo (Bubalus bubalis).

BackgroundThe availability of the bovine genome sequence and SNP panels has improved various genomic analyses, from exploring genetic diversity to aiding genetic selection. However, few of the SNP on the bovine chips are polymorphic in buffalo, therefore a panel of single nucleotide DNA markers exclusive for buffalo was necessary for molecular genetic analyses and to develop genomic selection approaches for water buffalo. The creation of a 90K SNP panel for river buffalo and testing in a genome wide association study for milk production is described here.MethodsThe genomes of 73 buffaloes of 4 different breeds were sequenced and aligned against the bovine genome, which facilitated the identification of 22 million of sequence variants among the buffalo genomes. Based on frequencies of variants within and among buffalo breeds, and their distribution across the genome, inferred from the bovine genome sequence, 90,000 putative single nucleotide polymorphisms were selected to create an Axiom® Buffalo Genotyping Array 90K.ResultsThis 90K “SNP-Chip” was tested in several river buffalo populations and found to have ∼70% high quality and polymorphic SNPs. Of the 90K SNPs about 24K were also found to be polymorphic in swamp buffalo. The SNP chip was used to investigate the structure of buffalo populations, and could distinguish buffalo from different farms. A Genome Wide Association Study identified genomic regions on 5 chromosomes putatively involved in milk production.ConclusionThe 90K buffalo SNP chip described here is suitable for the analysis of the genomes of river buffalo breeds, and could be used for genetic diversity studies and potentially as a starting point for genome-assisted selection programmes. This SNP Chip could also be used to analyse swamp buffalo, but many loci are not informative and creation of a revised SNP set specific for swamp buffalo would be advised.

Predicting bull fertility using genomic data and biological information

The genomic prediction of unobserved genetic values or future phenotypes for complex traits has revolutionized agriculture and human medicine. Fertility traits are undoubtedly complex traits of great economic importance to the dairy industry. Although genomic prediction for improved cow fertility has received much attention, bull fertility largely has been ignored. The first aim of this study was to investigate the feasibility of genomic prediction of sire conception rate (SCR) in US Holstein dairy cattle. Standard genomic prediction often ignores any available information about functional features of the genome, although it is believed that such information can yield more accurate and more persistent predictions. Hence, the second objective was to incorporate prior biological information into predictive models and evaluate their performance. The analyses included the use of kernel-based models fitting either all single nucleotide polymorphisms (SNP; 55K) or only markers with presumed functional roles, such as SNP linked to Gene Ontology or Medical Subject Heading terms related to male fertility, or SNP significantly associated with SCR. Both single- and multikernel models were evaluated using linear and Gaussian kernels. Predictive ability was evaluated in 5-fold cross-validation. The entire set of SNP exhibited predictive correlations around 0.35. Neither Gene Ontology nor Medical Subject Heading gene sets achieved predictive abilities higher than their counterparts using random sets of SNP. Notably, kernel models fitting significant SNP achieved the best performance with increases in accuracy up to 5% compared with the standard whole-genome approach. Models fitting Gaussian kernels outperformed their counterparts fitting linear kernels irrespective of the set of SNP. Overall, our findings suggest that genomic prediction of bull fertility is feasible in dairy cattle. This provides potential for accurate genome-guided decisions, such as early culling of bull calves with low SCR predictions. In addition, exploiting nonlinear effects through the use of Gaussian kernels together with the incorporation of relevant markers seems to be a promising alternative to the standard approach. The inclusion of gene set results into prediction models deserves further research.

IntroMap: A Pipeline and Set of Diagnostic DiploidArachisSNPs as a Tool for Mapping Alien Introgressions inArachis hypogaea

ABSTRACTFor crops with a narrow cultivated genetic base, incorporating beneficial alleles from related species through alien introgression widens the genetic base and provides key resistances to disease and abiotic stresses. Fine mapping of these introgressions can increase the efficiency of marker-assisted selection for breeding programs. To facilitate high resolution fine mapping of alien introgressions, we developed an automated pipeline, IntroMap. This pipeline was developed with accessibility and utility in mind, and does not present novel mapping algorithms. Using five diploid wild Arachis species, we identified diagnostic SNP sets for introgression mapping in Arachis hypogaea, cultivated peanut. IntroMap has applicability in all crops where alien introgression is used to bring in beneficial alleles from related species, so the pipeline includes an option to generate new diagnostic SNPs from any species/accession of interest for use in the pipeline. These user generated resources will be included for distribution with IntroMap to increase the SNP resources for all users. We demonstrate the efficacy of IntroMap by fine mapping three alien introgressions in an elite peanut breeding line with superior disease resistance. IntroMap works well even at low coverage, recovering at 2x coverage almost 50% of the diagnostic SNPs found at 10x coverage. The true benefit of IntroMap is the availability and generation of shared public resources, specifically for Arachis spp. IntroMap is freely distributed at https://sourceforge.net/projects/intromap/.

Abstract 2591: A guide to filtering TARGET Complete Genomics germline variants

Abstract With the public release of TARGET’s pediatric cancer data, researchers are eager to investigate and integrate variants from both tumor and germline sequencing data, but caution should be used when interpreting TARGET variant calls from Complete Genomics data, as they unlike the Illumina based calls for which most of our databases and tools are customized. Here, we use germline samples from 55 neuroblastoma cases with both whole genome Complete Genomics variants and whole exome Illumina variants from GATK, Pindel, Platypus, and bam2mpg to investigate differences between germline variant calls from Complete Genomics and Illumina platforms. Our study of ~160,000 single nucleotide polymorphisms (SNPS) and ~19,000 insertions or deletions (indels) reveals that while SNP sets largely agree between Complete Genomics and Illumina, Complete Genomics data have many indels not present in Illumina calls, making it difficult to screen Complete Genomics indels against popular Illumina based variant databases like those provided by the Exome Aggregation Consortium and the National Heart, Lung, and Blood Institute Exome Sequencing Project. Further probing of Complete Genomics indels uncovered inconsistent indel formatting resulting a loss of recurrent variants, and miscalculated read depths in earlier versions of the Complete Genomics platform. To solve these issues and make Complete Genomics variant calls comparable to Illumina calls, we introduce a Snakemake pipeline that reformats Complete Genomics variant files and creates variant features for use in decision tree or random forest classifiers trained to recognize Complete Genomics variants that will be present in Illumina variant call sets. We determine that the most important features for locating Complete Genomics variants likely to be found in Illumina germline variant call sets are the presence of reads from both directions, average variant allele fraction across the cohort, a variant call in the matched tumor, the variant fraction of reads, and read depth. Using ten fold cross validation with our decision tree, we obtain &amp;gt;95% recall and &amp;gt;98% precision for SNPs, and &amp;gt;90% recall and &amp;gt;85% precision for indels. We advocate the use of our pipeline and filtering scheme for making Complete Genomics variant calls comparable to Illumina based calls. Citation Format: James P. Evans, Rajesh Patidar, Zalman Vaksman, Sivasish Sindiri, Douglas R. Stewart, Javed Khan, Jun S. Wei, Sharon J. Diskin. A guide to filtering TARGET Complete Genomics germline variants [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2017; 2017 Apr 1-5; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2017;77(13 Suppl):Abstract nr 2591. doi:10.1158/1538-7445.AM2017-2591

Abstract 1323: Genome-wide association study of energy intake and its relationship to prostate cancer

Abstract Background/Objectives: Previous studies suggested that high energy intake may increase the risk of prostate cancer. However, the mechanisms remain unclear. No genome-wide association studies (GWAS) have been conducted to detect the genetic variations in total energy intake. We aimed to identify genetic variants associated with total energy intake in men and women, and to determine the association between previously identified prostate cancer susceptibility loci and total energy intake. Methods: We conducted a genome-wide study using combined GWAS data from 12,031 European-ancestry women and 6,743 European-ancestry men from the Nurses’ Health Study, Nurses’ Health Study II, Health Professionals Follow-up Study, and Physician’s Health Study. Total energy intake was measured through validated food-frequency questionnaires. Missing genotypes were imputed using MACH with 1000 Genomes Project ALL Phase I Integrated Release Version 3 Haplotypes as the reference panel. We stratified by sex and adjusted for age, weight, height, physical activity and three principal components accounting for subpopulation structure. Meta-analysis based on p-values across sex was implemented. SNP set analyses, including a weighted fixed-effects (Mendelian randomization, MR) and an unweighted random-effects approach (METASOFT), were used to test the association between prostate cancer risk loci and total energy intake using summary statistics of total energy intake from the combined GWAS and the summary statistics for genome-wide significant SNPs (NSNPs = 104) from a GWAS of prostate cancer in 43,303 cases and 43,737 controls [Al Olama (2014) Nat Genet]. Results: Three SNPs near gene ANKRD33 were genome-wide significantly (P &amp;lt; 5.0×10-8) associated with the increase in total energy intake among men (rs10876214 [P = 5.81×10-10], rs9669605 [P = 1.46×10-8], and rs10783478 [P = 2.42×10-8]). No significant genome-wide association was found in women or the meta-analysis combining men and women. SNP set analyses did not find a statistically significant association between established prostate cancer SNPs and energy intake (PMR = 0.11 and PMETASOFT = 0.09, respectively). Conclusions: We identified three significant SNPs near gene ANKRD33 associated with increased total energy intake among men. Genetic alleles related to risk of prostate cancer were not associated with total energy intake. Larger GWAS studies of total energy intake are warranted to explore the genetic basis of energy intake, including possible differences between men and women, and the association between total energy intake, prostate cancer, and other cancers and chronic diseases. Citation Format: Lai Jiang, Peter Kraft, Kathryn M. Wilson. Genome-wide association study of energy intake and its relationship to prostate cancer [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2017; 2017 Apr 1-5; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2017;77(13 Suppl):Abstract nr 1323. doi:10.1158/1538-7445.AM2017-1323

Genomic-polygenic and polygenic predictions for nine ultrasound and carcass traits in Angus-Brahman multibreed cattle using three sets of genotypes

The objectives of this study were to estimate variance components, genetic parameters, EBV, accuracies, and rankings for nine ultrasound and carcass traits in a multibreed Angus-Brahman population using three genomic-polygenic models and one polygenic model (PM). The genomic-polygenic models used the complete GeneSeek GPF250k SNP set (GPM), top 5% SNP (GPMR1), and 5% SNP evenly spread across the genome (GPMR2). Yearling ultrasound traits were weight (UW), ribeye area (UREA), backfat (UFAT), and percent intramuscular fat (UPIMF). Carcass traits were slaughter age (SLA), hot carcass weight (HCW), ribeye area (REA), backfat thickness (FAT), and marbling score (MAR). The 9-trait GPM, GPMR1, GPMR2, and PM contained fixed contemporary group, age of calf (ultrasound traits only), sex of calf, and direct heterosis effects, and random animal and residual effects. Variance components and genetic parameters were computed using AIREMLF90. Comparable heritabilities were obtained with GPM and PM for UW (GPM: 0.54 ± 0.05; PM: 0.51 ± 0.05), UREA (GPM: 0.36 ± 0.03; PM: 0.34 ± 0.03), UFAT (GPM: 0.12 ± 0.02; PM: 0.11 ± 0.02), UMPIMF (GPM: 0.34 ± 0.03; PM: 0.30 ± 0.03), SLA (GPM: 0.59 ± 0.07, PM: 0.61 ± 0.06), HCW (GPM: 0.58 ± 0.06, PM: 0.52 ± 0.07), REA (GPM: 0.48 ± 0.04, PM: 0.45 ± 0.05), FAT (GPM: 0.41 ± 0.05, PM: 0.30 ± 0.05), and MAR (GPM: 0.56 ± 0.07, PM: 0.51 ± 0.08). Additive genetic correlations between pairs of ultrasound and carcass traits were all between −0.31 and 0.81. The highest positive additive genetic correlations were between UW and UREA, UW and HCW, UW and REA, UREA and HCW, UREA and REA, UFAT and FAT, and between HCW and REA. The largest negative additive genetic correlations were between UREA and UPIMF, UFAT and SLA, UFAT and HCW, UPIMF and REA, and between REA and MAR. High similarity existed among predicted EBV and accuracies from GPM, GPMR1, and GPMR2 as well as high-rank correlations for sires, dams, and progenies. This indicated that the two reduced genotype sets were appropriate alternatives to the complete GPF250k set for genomic-polygenic evaluation and selection in this multibreed Angus-Brahman population. High EBV variability existed among animals of all Angus and Brahman percentages and no specific breed composition was overwhelmingly better or worse for any of the nine traits. This indicated that optimization of genetic progress through selection in multibreed Angus-Brahman populations should be based solely on genetic merit regardless of breed composition.

Comparison and confirmation of SNP-bud burst associations in European beech populations in Germany

European beech (Fagus sylvatica L.) is one of the most important forest tree species in Central Europe. Climate change scenarios predict an increase in annual mean temperatures that may cause earlier bud burst in spring, potentially leading to an increased late frost-risk. Despite the ecologic and economic importance of beech, knowledge about the molecular basis of bud burst is still scarce in this species. Here, an association analysis was used to detect SNPs that are significantly associated with beech bud burst. A translocation experiment was established with progenies of six different beech populations from three widely separated regions in Northeast, Central, and Southwest Germany. In total, 600 individuals of the translocation experiment were genotyped using a set of 46 SNPs located in bud burst candidate genes. The association analysis revealed seven SNPs significantly associated with bud burst, each SNP explaining only a few percent of the observed phenotypic variation. Since the same SNP set was used in a previous association analysis with European beech, we were able to compare and confirm significant associations between SNPs and bud burst in distinct beech populations growing in different environments.

Weighted pseudolikelihood for SNP set analysis with multiple secondary outcomes in case-control genetic association studies.

We propose a weighted pseudolikelihood method for analyzing the association of a SNP set, example, SNPs in a gene or a genetic pathway or network, with multiple secondary phenotypes in case-control genetic association studies. To boost analysis power, we assume that the SNP-specific effects are shared across all secondary phenotypes using a scaled mean model. We estimate regression parameters using Inverse Probability Weighted (IPW) estimating equations obtained from the weighted pseudolikelihood, which accounts for case-control sampling to prevent potential ascertainment bias. To test the effect of a SNP set, we propose a weighted variance component pseudo-score test. We also propose a penalized IPW pseudolikelihood method for selecting a subset of SNPs that are associated with the multiple secondary phenotypes. We show that the proposed variable selection procedure has the oracle properties and is robust to misspecification of the correlation structure among secondary phenotypes. We select the tuning parameter using a weighted Bayesian Information-like Criterion (wBIC). We evaluate the finite sample performance of the proposed methods via simulations, and illustrate the methods by the analysis of the multiple secondary smoking behavior outcomes in a lung cancer case-control genetic association study.

Evaluating Sequence-Based Genomic Prediction with an Efficient New Simulator.

The vast amount of sequence data generated to analyze complex traits is posing new challenges in terms of the analysis and interpretation of the results. Although simulation is a fundamental tool to investigate the reliability of genomic analyses and to optimize experimental design, existing software cannot realistically simulate complete genomes. To remedy this, we have developed a new strategy (Sequence-Based Virtual Breeding, SBVB) that uses real sequence data and simulates new offspring genomes and phenotypes in a very efficient and flexible manner. Using this tool, we studied the efficiency of full sequence in genomic prediction compared to SNP arrays. We used real porcine sequences from three breeds as founder genomes of a 2500-animal pedigree and two genetic architectures: "neutral" and "selective." In the neutral architecture, frequencies and allele effects were sampled independently whereas, in the selective case, SNPs were sites putatively under selection after domestication and a negative correlation between effect and frequency was induced. We compared the effectiveness of different genotyping strategies for genomic selection, including the use of full sequence commercial arrays or randomly chosen SNP sets in both outbred and crossbred experimental designs. We found that accuracy increases using sequence instead of commercial chips but modestly, perhaps by ≤ 4%. This result was robust to extreme genetic architectures. We conclude that full sequence is unlikely to offset commercial arrays for predicting genetic value when the number of loci is relatively large and the prior given to each SNP is uniform. Using sequence to improve selection thus requires optimized prior information and, likely, increased population sizes. The code and manual for SBVB are available at https://github.com/mperezenciso/sbvb0.

The Generalized Higher Criticism for Testing SNP-Set Effects in Genetic Association Studies

ABSTRACTIt is of substantial interest to study the effects of genes, genetic pathways, and networks on the risk of complex diseases. These genetic constructs each contain multiple SNPs, which are often correlated and function jointly, and might be large in number. However, only a sparse subset of SNPs in a genetic construct is generally associated with the disease of interest. In this article, we propose the generalized higher criticism (GHC) to test for the association between an SNP set and a disease outcome. The higher criticism is a test traditionally used in high-dimensional signal detection settings when marginal test statistics are independent and the number of parameters is very large. However, these assumptions do not always hold in genetic association studies, due to linkage disequilibrium among SNPs and the finite number of SNPs in an SNP set in each genetic construct. The proposed GHC overcomes the limitations of the higher criticism by allowing for arbitrary correlation structures among the SNPs in an SNP-set, while performing accurate analytic p-value calculations for any finite number of SNPs in the SNP-set. We obtain the detection boundary of the GHC test. We compared empirically using simulations the power of the GHC method with existing SNP-set tests over a range of genetic regions with varied correlation structures and signal sparsity. We apply the proposed methods to analyze the CGEM breast cancer genome-wide association study. Supplementary materials for this article are available online.

Genome-Wide Association Study of Panic Disorder

BackgroundPanic disorder (PD) is one of the most common anxiety disorders with a lifetime prevalence of about 4%. The disorder is characterized by recurrent episodes of abrupt intense fear accompanied by additional physiological or cognitive symptoms. Although PD shows moderate heritability estimates of 30-54%, the specific genetic variants contributing to PD are largely unknown, with few and somewhat inconsistent loci reported to date. To address the challenge of underpowered individual studies, we conducted the largest genome-wide association study of PD to date comprising more than 8,700 individuals. MethodsIn our study we generated genome-wide SNP data from > 2,000 clinically well-characterized patients with PD and > 6,700 ethnically matched controls. The samples originate from five different European countries (Denmark, Estonia, Finland, Germany and Sweden). Standard GWAS quality control procedures were performed on each dataset individually. Imputation was performed using the 1000 Genomes Project reference panel. Only SNPs present in all datasets were kept for the meta-analysis using METASOFT. ResultsIn the single-marker analysis we identified several SNPs with PD association on the significance level of p < 10-05. Furthermore, using the recently published LD Score regression method on our GWAS data we estimated for the first time the PD heritability. In addition, we examined all previously reported genome-wide significant SNPs for bipolar disorder and schizophrenia in our PD GWAS results. Thus, we can estimate the degree of shared genetic risk factors between PD and bipolar disorder or schizophrenia. Finally, we performed pathway analyses using SNP sets with different PD association thresholds. DiscussionIn this collaborative study with sample sizes being larger than any other PD GWAS published to date, we identified PD associated loci across different European samples. LD Score regression analysis of our GWAS data provides robust evidence for a polygenic contribution of common SNPs to PD heritability. Replication of our top findings as well as further pathway and eQTL analyses are currently underway and will be presented.

M58 - Cross-Tissue EQTL Enrichment of Associations In Schizophrenia

Background In the era of Big Data, genome-wide association studies (GWASs) have been the main instrument to better our understanding of the genetics behind human traits. The schizophrenia GWAS by the psychiatric genomics consortium (PGC) identified many loci plausibly involved in the etiology of the disorder [Ripke et al., 2014]. However, the picture appears to be far from complete [Lichtenstein, 2009; Polderman, 2015; Gejman, 2010; Lee, 2012]. Judging by the number of non-coding variants identified by GWASs of schizophrenia and other complex traits, many causal variants are likely to affect the regulation of the genes involved rather than severely disrupt their function. Expression quantitative trait loci (eQTLs) are genomic loci that contribute to variation in levels of mRNA. It is known [Bacanu, 2014] that many eQTLs are associated with schizophrenia, as well as other phenotypes [Nicolae, 2010]. Methods Given known pleiotropy between schizophrenia and other traits, we looked for association enrichment of eQTLs in tissues not obviously relevant to psychiatric disorders (blood, adipose, LCL, and skin [Buil, 2015]). In order to consolidate any evidence of tissue-specificity, we further looked for differential enrichment in different ENCODE functional annotation categories. We used summary p-value statistics from schizophrenia as well as body mass index, Crohn's disease, height, systolic blood pressure and type II diabetes GWASs. We matched the eQTLs to SNP sets with similar minor allele frequencies (MAF) and distances from their respective transcription start sites (TSS), and no evidence of association with expression levels of any genes. We further subdivided eQTLs into proximal and distal eQTLs, according to their position relative to the TSS or to their affiliation to promoters, and we annotated them according to their ENCODE designation. We visualized the enrichment in fold enrichment plots, screened it using Fisher tests and assessed its extent by regressing the squared association z-score against SNP affiliations and LD-score. Results Significant enrichment was seen in adipose (beta=0.24, p=2.2E-06), LCL (beta=0.27, p=4.0E-08) and skin (beta=0.23, p=2.7E-06) tissues. The enrichment was as widespread as that observed in another highly polygenic trait such as height, and more widespread than in traits such as body mass index (adipose and skin) or blood pressure (adipose). The schizophrenia association extent does not appear to depend on the eQTLs' distance from the TSS nor from their explicit affiliation to specific functional elements, as defined by ENCODE, suggesting that risk alleles for schizophrenia could frequently act in many different tissue contexts. Discussion Our results are in line with the notion that schizophrenia is a disorder involving many tissues and known pleiotropy with cardiovascular and immune diseases back our findings. The statistical power of the underlying GWAS does play a role in the detection of association enrichment of the sort we sought. Polygenicity, however, is also a major factor, as exemplified by the results for height, body mass index and blood pressure. In summary, we find that looking for functional correlates of schizophrenia risk loci in non-obvious tissues is productive, and this could be because of either pleiotropy or increased effectiveness of variants that work in many different environmental contexts. This suggests the utility of large, single tissue eQTL experiments in the study of schizophrenia in addition to smaller, multiple tissue approaches.