Variant Association Tests Research Articles

Whole Genome Sequence Association Analysis and Diabetes-centric Functional Annotation of Type 2 Diabetes

Our understanding of the genetic basis of T2D has been significantly advanced by identification of T2D loci through analysis of common variation; however, evidence suggests a role for rare regulatory variation influencing T2D. We leveraged the largest available whole genome sequence (WGS) data from NHLBI’s Trans-Omics for Precision Medicine (TOPMed) initiative and diabetes-centric genomic function data to the interpretation of results. We performed single-variant pooled-ancestry association analyses with (>30x deep sequence) WGS in 13,200 individuals (2,601 T2D and 10,599 controls) from 6 studies. The association analyses were performed in the Analysis Commons hosted on DNANexus. We demonstrated the utility of the OASIS (Omics Analysis, Search and Information System) platform and integrating diabetes-specific functional annotation in a recently discovered locus (GIP-IGF2BP1) to fine-map to the likely causal variants. Our results identified common (minor allele frequency [MAF] > 0.05) variant associations (P < 5 × 10-8) at a known locus with T2D: TCF7L2 (rs7903146, P = 3.8 × 10-12). We also identified a potentially novel association on Chr15q21 with 27 rare variants (MAF = 0.2%, P =1.3 × 10-8, r2=1) spanning a 7.5 MB region. In the Old Order Amish study, 53 individuals carry the haplotype tagged by these 27 variants; 17% of carriers are diagnosed with T2D compared to 3% of the non-carriers. Interrogation of the GIP-IGF2BP1 locus, utilizing diabetes-specific annotation and association evidence of both common and rare non-coding variants, within the OASIS browser implicated nearby genes in the region as being the likely biologic candidates. In conclusion, preliminary results suggest WGS analysis can discover novel loci for complex traits. Work is ongoing to refine annotation for rare variant association tests and perform functional fine-mapping across the genome. We soon will extend our analysis into the next release of WGS data from TOPMed (N∼69,000 individuals). Disclosure J. Wessel: Employee; Spouse/Partner; Eli Lilly and Company. J.A. Brody: None. A. Manning: None.

Next-generation DNA sequencing to identify novel genetic risk factors for cerebral vein thrombosis

BackgroundCerebral vein thrombosis (CVT) is a rare, life-threatening disease affecting one adult per 100,000 per year. Genetic risk factors are deficiencies of the natural anticoagulant proteins antithrombin, protein C, protein S or single nucleotide polymorphisms such as factor V Leiden and prothrombin 20210A. In 20% of patients, the cause of CVT remains unknown. AimTo identify novel genetic risk factors for CVT using targeted next-generation DNA sequencing (NGS). MethodsWe investigated 171 CVT patients and 298 healthy controls. Patients were selected using the following criteria: objective diagnosis of CVT, no active cancer. We performed targeted NGS analysis of the protein-coding regions of 734 candidate genes related to hemostasis and inflammation, 150 ancestry informative markers and 28 thrombosis-associated variants. ResultsWe identified 3723 common and low frequency variants with minor allele frequency (MAF) >1% in 590 genes. Single variant association testing using logistic regression analysis identified rs8176719 insertion/deletion (indel) variant in the ABO gene associated with CVT (age and sex adjusted OR 2.03; 95% CI 1.52–2.73; P = 2.07 × 10−6; Bonferroni P = 0.008). In addition, we identified 8839 rare variants (MAF ≤ 1%) in 723 genes. Gene-based association analysis of these rare variants using a burden test revealed only a tentative association of non-coding variants located in the F8 locus with CVT. ConclusionTargeted NGS identified a common indel variant rs8176719 in the ABO gene. Gene-based tests of association failed to reveal genomic loci with a cumulative burden of rare variants associated with CVT.

Whole-Exome Sequencing in Age-Related Macular Degeneration Identifies Rare Variants in COL8A1, a Component of Bruch’s Membrane

PurposeGenome-wide association studies and targeted sequencing studies of candidate genes have identified common and rare variants that are associated with age-related macular degeneration (AMD). Whole-exome sequencing (WES) studies allow a more comprehensive analysis of rare coding variants across all genes of the genome and will contribute to a better understanding of the underlying disease mechanisms. To date, the number of WES studies in AMD case-control cohorts remains scarce and sample sizes are limited. To scrutinize the role of rare protein-altering variants in AMD cause, we performed the largest WES study in AMD to date in a large European cohort consisting of 1125 AMD patients and 1361 control participants.DesignGenome-wide case-control association study of WES data.ParticipantsOne thousand one hundred twenty-five AMD patients and 1361 control participants.MethodsA single variant association test of WES data was performed to detect variants that are associated individually with AMD. The cumulative effect of multiple rare variants with 1 gene was analyzed using a gene-based CMC burden test. Immunohistochemistry was performed to determine the localization of the Col8a1 protein in mouse eyes.Main Outcome MeasuresGenetic variants associated with AMD.ResultsWe detected significantly more rare protein-altering variants in the COL8A1 gene in patients (22/2250 alleles [1.0%]) than in control participants (11/2722 alleles [0.4%]; P = 7.07×10–5). The association of rare variants in the COL8A1 gene is independent of the common intergenic variant (rs140647181) near the COL8A1 gene previously associated with AMD. We demonstrated that the Col8a1 protein localizes at Bruch’s membrane.ConclusionsThis study supported a role for protein-altering variants in the COL8A1 gene in AMD pathogenesis. We demonstrated the presence of Col8a1 in Bruch’s membrane, further supporting the role of COL8A1 variants in AMD pathogenesis. Protein-altering variants in COL8A1 may alter the integrity of Bruch’s membrane, contributing to the accumulation of drusen and the development of AMD.

Identification of seven novel loci associated with amino acid levels using single-variant and gene-based tests in 8545 Finnish men from the METSIM study

Comprehensive metabolite profiling captures many highly heritable traits, including amino acid levels, which are potentially sensitive biomarkers for disease pathogenesis. To better understand the contribution of genetic variation to amino acid levels, we performed single variant and gene-based tests of association between nine serum amino acids (alanine, glutamine, glycine, histidine, isoleucine, leucine, phenylalanine, tyrosine, and valine) and 16.6 million genotyped and imputed variants in 8545 non-diabetic Finnish men from the METabolic Syndrome In Men (METSIM) study with replication in Northern Finland Birth Cohort (NFBC1966). We identified five novel loci associated with amino acid levels (P = < 5×10-8): LOC157273/PPP1R3B with glycine (rs9987289, P = 2.3×10-26); ZFHX3 (chr16:73326579, minor allele frequency (MAF) = 0.42%, P = 3.6×10-9), LIPC (rs10468017, P = 1.5×10-8), and WWOX (rs9937914, P = 3.8×10-8) with alanine; and TRIB1 with tyrosine (rs28601761, P = 8×10-9). Gene-based tests identified two novel genes harboring missense variants of MAF <1% that show aggregate association with amino acid levels: PYCR1 with glycine (Pgene = 1.5×10-6) and BCAT2 with valine (Pgene = 7.4×10-7); neither gene was implicated by single variant association tests. These findings are among the first applications of gene-based tests to identify new loci for amino acid levels. In addition to the seven novel gene associations, we identified five independent signals at established amino acid loci, including two rare variant signals at GLDC (rs138640017, MAF=0.95%, Pconditional = 5.8×10-40) with glycine levels and HAL (rs141635447, MAF = 0.46%, Pconditional = 9.4×10-11) with histidine levels. Examination of all single variant association results in our data revealed a strong inverse relationship between effect size and MAF (Ptrend<0.001). These novel signals provide further insight into the molecular mechanisms of amino acid metabolism and potentially, their perturbations in disease.

Whole-exome sequencing and gene-based rare variant association tests suggest that PLA2G4E might be a risk gene for panic disorder

Panic disorder (PD) is characterized by recurrent and unexpected panic attacks, subsequent anticipatory anxiety, and phobic avoidance. Recent epidemiological and genetic studies have revealed that genetic factors contribute to the pathogenesis of PD. We performed whole-exome sequencing on one Japanese family, including multiple patients with panic disorder, which identified seven rare protein-altering variants. We then screened these genes in a Japanese PD case–control group (384 sporadic PD patients and 571 controls), resulting in the detection of three novel single nucleotide variants as potential candidates for PD (chr15: 42631993, T>C in GANC; chr15: 42342861, G>T in PLA2G4E; chr20: 3641457, G>C in GFRA4). Statistical analyses of these three genes showed that PLA2G4E yielded the lowest p value in gene-based rare variant association tests by Efficient and Parallelizable Association Container Toolbox algorithms; however, the p value did not reach the significance threshold in the Japanese. Likewise, in a German case–control study (96 sporadic PD patients and 96 controls), PLA2G4E showed the lowest p value but again did not reach the significance threshold. In conclusion, we failed to find any significant variants or genes responsible for the development of PD. Nonetheless, our results still leave open the possibility that rare protein-altering variants in PLA2G4E contribute to the risk of PD, considering the function of this gene.

Deleterious genetic variants in ciliopathy genes increase risk of ritodrine-induced cardiac and pulmonary side effects

BackgroundRitodrine is a commonly used tocolytic to prevent preterm labour. However, it can cause unexpected serious adverse reactions, such as pulmonary oedema, pulmonary congestion, and tachycardia. It is unknown whether such adverse reactions are associated with pharmacogenomic variants in patients.MethodsWhole-exome sequencing of 13 subjects with serious ritodrine-induced cardiac and pulmonary side-effects was performed to identify causal genes and variants. The deleterious impact of nonsynonymous substitutions for all genes was computed and compared between cases (n = 13) and controls (n = 30). The significant genes were annotated with Gene Ontology (GO), and the associated disease terms were categorised into four functional classes for functional enrichment tests. To assess the impact of distributed rare variants in cases with side effects, we carried out rare variant association tests with a minor allele frequency ≤ 1% using the burden test, the sequence Kernel association test (SKAT), and optimised SKAT.ResultsWe identified 28 genes that showed significantly lower gene-wise deleteriousness scores in cases than in controls. Three of the identified genes—CYP1A1, CYP8B1, and SERPINA7—are pharmacokinetic genes. The significantly identified genes were categorized into four functional classes: ion binding, ATP binding, Ca2+-related, and ciliopathies-related. These four classes were significantly enriched with ciliary genes according to SYSCILIA Gold Standard genes (P < 0.01), thus representing ciliary genes. Furthermore, SKAT showed a marginal trend toward significance after Bonferroni correction with Joubert Syndrome ciliopathy genes (P = 0.05). With respect to the pharmacokinetic genes, rs1048943 (CYP1A1) and rs1804495 (SERPINA7) showed a significantly higher frequency in cases than controls, as determined by Fisher’s exact test (P < 0.05 and P < 0.01, respectively).ConclusionsRitodrine-induced cardiac and pulmonary side effects may be associated with deleterious genetic variants in ciliary and pharmacokinetic genes.

Abstract 148: Exome Sequence Study on Extreme MRI Markers of Cerebral Small Vessel Disease

We performed an exome sequence association study on extreme MRI-markers of cerebral small vessel disease (extensive-CSVD versus minimal-CSVD) in participants from the 3C-Dijon French population-based cohort study, focusing on genes harbouring causal mutations for Mendelian forms of CSVD ( NOTCH3 , HTRA1 , COL4A1 , COL4A2 , TREX1 and CTSA ). The study population comprised 514 participants aged 73.30±4.24 years (59.24% women). The extensive-CSVD group comprised 261 persons in the upper extreme distribution of WMHV residuals adjusted for age, gender, and intracranial volume, prioritizing participants with lacunar brain infarcts within the top quartile of WMHV residuals. The minimal-CSVD group comprised 253 participants without MRI-defined brain infarct who were in the lower extreme distribution of WMHV residuals. These participants were whole-exome sequenced with average depth coverage of 100X. We screened for the presence of known pathogenic genotypes in our cohort, and performed single variant association tests and gene-based burden tests. We identified one individual with extensive-CSVD carrying a heterozygote genotype in HTRA1 previously described as a CARASIL causing mutation in Asians, and not previously reported in European families with the dominant HTRA1 phenotype. We also identified significant association of a common intronic variant, rs2293871, in HTRA1 with extensive-CSVD (p=8.209 х 10 -5 ) that remained significant after correction for multiple testing and accounting for regional linkage disequilibrium. Furthermore, the burden of rare and low frequency protein-modifying alleles in NOTCH3 was significantly associated with increased risk of extensive-CSVD (p=9.990 х 10 -3 ). In conclusion, we describe a novel strategy to identify genetic determinants of CSVD using population cohorts of elderly based on an extreme composite phenotype. We report one clinically important case carrying a heterozygote genotype at the CARASIL causing mutation. We show significant association of an intronic common variant in HTRA1 gene and the burden of rare and low frequency protein damaging variants in NOTCH3 gene with extensive-CSVD. These associations suggest some shared mechanisms between monogenic and multifactorial CSVD.

Integrated rare variant-based risk gene prioritization in disease case-control sequencing studies.

Rare variants of major effect play an important role in human complex diseases and can be discovered by sequencing-based genome-wide association studies. Here, we introduce an integrated approach that combines the rare variant association test with gene network and phenotype information to identify risk genes implicated by rare variants for human complex diseases. Our data integration method follows a 'discovery-driven' strategy without relying on prior knowledge about the disease and thus maintains the unbiased character of genome-wide association studies. Simulations reveal that our method can outperform a widely-used rare variant association test method by 2 to 3 times. In a case study of a small disease cohort, we uncovered putative risk genes and the corresponding rare variants that may act as genetic modifiers of congenital heart disease in 22q11.2 deletion syndrome patients. These variants were missed by a conventional approach that relied on the rare variant association test alone.

Comprehensive investigation of cytokine- and immune-related gene variants in HBV-associated hepatocellular carcinoma patients.

Host genotype may be closely related to the different outcomes of Hepatitis B virus (HBV) infection. To identify the association of variants and HBV infection, we comprehensively investigated the cytokine- and immune-related gene mutations in patients with HBV associated hepatocellular carcinoma (HBV-HCC). Fifty-three HBV-HCC patients, 53 self-healing cases (SH) with HBV infection history and 53 healthy controls (HCs) were recruited, the whole exon region of 404 genes were sequenced at >900× depth. Comprehensive variants and gene levels were compared between HCC and HC, and HCC and SH. Thirty-nine variants (adjusted P<0.0001, Fisher’s exact test) and 11 genes (adjusted P<0.0001, optimal unified approach for rare variant association test (SKAT-O) gene level test) were strongly associated with HBV-HCC. Thirty-four variants were from eight human leukocyte antigen (HLA) genes that were previously reported to be associated with HBV-HCC. The novelties of our study are: five variants (rs579876, rs579877, rs368692979, NM_145007:c.*131_*130delTG, NM_139165:exon5:c.623-2->TT) from three genes (REAT1E, NOD-like receptor (NLR) protein 11 (NLRP11), hydroxy-carboxylic acid receptor 2 (HCAR2)) were found strongly associated with HBV-HCC. We found 39 different variants in 11 genes that were significantly related to HBV-HCC. Five of them were new findings. Our data implied that chronic hepatitis B patients who carry these variants are at a high risk of developing HCC.

Gene-based genetic association test with adaptive optimal weights.

It is well known that using proper weights for genetic variants is crucial in enhancing the power of gene- or pathway-based association tests. To increase the power, we propose a general approach that adaptively selects weights among a class of weight families and apply it to the popular sequencing kernel association test. Through comprehensive simulation studies, we demonstrate that the proposed method can substantially increase power under some conditions. Applications to real data are also presented. This general approach can be extended to all current set-based rare variant association tests whose performances depend on variant's weight assignment.

Locus and gene-based GWAS meta-analysis identifies new diabetic nephropathy genes.

Objective Assimilation of SNPs Interacting in Synchrony (OASIS) is a locus-based clustering algorithm recently described that can potentially address false positives and negatives in genome-wide association studies (GWAS) of complex disorders. Diabetic nephropathy (DN) is incompletely understood due to a paucity of genes identified despite several GWAS. OASIS was applied to three DN dbGAP GWAS datasets (4725 subjects; 1.06 million SNPs). OASIS identified 19 DN genes which were verified using single variant replication in a standard association study and gene-based analysis using GATES. CARS and FRMD3 were confirmed as DN genes, and five known diabetes-associated genes, viz. NLRP3, INPPL1, PIK3C2G, NRXN3, and TBC1D4, not previously identified using these datasets were discovered. Furthermore, three additional novel DN genes were found which replicated in two sets of analysis, viz. NTN1, EBF2, and DNAH11. Hence, composite analysis with OASIS, gene-based, and single variant association testing can be universally applied to existing GWAS datasets for the identification of new genes.

Pathway-based discovery of genetic interactions in breast cancer.

Breast cancer is the second largest cause of cancer death among U.S. women and the leading cause of cancer death among women worldwide. Genome-wide association studies (GWAS) have identified several genetic variants associated with susceptibility to breast cancer, but these still explain less than half of the estimated genetic contribution to the disease. Combinations of variants (i.e. genetic interactions) may play an important role in breast cancer susceptibility. However, due to a lack of statistical power, the current tests for genetic interactions from GWAS data mainly leverage prior knowledge to focus on small sets of genes or SNPs that are known to have an association with breast cancer. Thus, many genetic interactions, particularly among novel variants, remain understudied. Reverse-genetic interaction screens in model organisms have shown that genetic interactions frequently cluster into highly structured motifs, where members of the same pathway share similar patterns of genetic interactions. Based on this key observation, we recently developed a method called BridGE to search for such structured motifs in genetic networks derived from GWAS studies and identify pathway-level genetic interactions in human populations. We applied BridGE to six independent breast cancer cohorts and identified significant pathway-level interactions in five cohorts. Joint analysis across all five cohorts revealed a high confidence consensus set of genetic interactions with support in multiple cohorts. The discovered interactions implicated the glutathione conjugation, vitamin D receptor, purine metabolism, mitotic prometaphase, and steroid hormone biosynthesis pathways as major modifiers of breast cancer risk. Notably, while many of the pathways identified by BridGE show clear relevance to breast cancer, variants in these pathways had not been previously discovered by traditional single variant association tests, or single pathway enrichment analysis that does not consider SNP-SNP interactions.

Region-based association tests for sequencing data on survival traits.

Family-based designs enriched with affected subjects and disease associated variants can increase statistical power for identifying functional rare variants. However, few rare variant analysis approaches are available for time-to-event traits in family designs and none of them applicable to the X chromosome. We developed novel pedigree-based burden and kernel association tests for time-to-event outcomes with right censoring for pedigree data, referred to FamRATS (family-based rare variant association tests for survival traits). Cox proportional hazard models were employed to relate a time-to-event trait with rare variants with flexibility to encompass all ranges and collapsing of multiple variants. In addition, the robustness of violating proportional hazard assumptions was investigated for the proposed and four current existing tests, including the conventional population-based Cox proportional model and the burden, kernel, and sum of squares statistic (SSQ) tests for family data. The proposed tests can be applied to large-scale whole-genome sequencing data. They are appropriate for the practical use under a wide range of misspecified Cox models, as well as for population-based, pedigree-based, or hybrid designs. In our extensive simulation study and data example, we showed that the proposed kernel test is the most powerful and robust choice among the proposed burden test and the existing four rare variant survival association tests. When applied to the Diabetes Heart Study, the proposed tests found exome variants of the JAK1 gene on chromosome 1 showed the most significant association with age at onset of type 2 diabetes from the exome-wide analysis.

A Zoom-Focus algorithm (ZFA) to locate the optimal testing region for rare variant association tests

Increasing amounts of whole exome or genome sequencing data present the challenge of analysing rare variants with extremely small minor allele frequencies. Various statistical tests have been proposed, which are specifically configured to increase power for rare variants by conducting the test within a certain bin, such as a gene or a pathway. However, a gene may contain from several to thousands of markers, and not all of them are related to the phenotype. Combining functional and non-functional variants in an arbitrary genomic region could impair the testing power. We propose a Zoom-Focus algorithm (ZFA) to locate the optimal testing region within a given genomic region. It can be applied as a wrapper function in existing rare variant association tests to increase testing power. The algorithm consists of two steps. In the first step, Zooming, a given genomic region is partitioned by an order of two, and the best partition is located. In the second step, Focusing, the boundaries of the zoomed region are refined. Simulation studies showed that ZFA substantially increased the statistical power of rare variants' tests, including the SKAT, SKAT-O, burden test and the W-test. The algorithm was applied on real exome sequencing data of hypertensive disorder, and identified biologically relevant genetic markers to metabolic disorders that were undetectable by a gene-based method. The proposed algorithm is an efficient and powerful tool to enhance the power of association study for whole exome or genome sequencing data. The ZFA software is available at: http://www2.ccrb.cuhk.edu.hk/statgene/software.html. maggiew@cuhk.edu.hk or bzee@cuhk.edu.hk. Supplementary data are available at Bioinformatics online.

Abstract P104: Genetic Factors Influence Glycated Hemoglobin, Fasting Glucose, and Fasting Insulin Levels in the Population Architecture Using Genomics and Epidemiology Consortium

Elevated levels of fasting glucose and fasting insulin precede the development type 2 diabetes (T2D). While T2D and cardiovascular disease share common risk factors, dysregulation of glucose metabolism is of particular concern because it can result in end-organ damage including the heart, nerves, kidneys, and eyes. The prevalence of diabetes is higher in American minority populations. Through genome-wide association studies, approximately 60 loci have been identified for fasting glucose, fasting insulin, and glycated hemoglobin. To better understand the genetic basis of glucose dysregulation, the Population Architecture using Genomics and Epidemiology (PAGE) Consortium genotyped 12,801 Hispanic/Latinos, 5,696 African Americans, 1,405 Native Hawaiians, 398 Native Americans, and 1,727 Asians without diabetes using the approximately 1.3 million variants on the Multi-Ethnic Global Array (MEGA), which is enriched for coding variation and ancestral diverse content. Through ancestry-combined single variant association testing with fasting glucose, fasting insulin, and glycated hemoglobin (HbA1c), we identified a novel potential association between decreased HbA1c and the minor A allele of rs11887523 ( p = 4.51x10 -9 , MFSD2B p.A60T, minor allele frequency (MAF) 7% in African Americans, MAF 2% in Hispanics). Other associations with HbA1c included hemoglobin genes, further highlighting the importance of considering variation in hemoglobin and red-blood cell lifespan when using HbA1c as a measure of glucose control. We also replicated several known associations including between fasting glucose and variants near G6PC2, GCKR, MTNR1B, and FOXA2 ; and between fasting insulin and the GRB14/COBLL1 locus. Further analyses including imputing the data out to 1000 Genomes phase3 and gene-based tests of rare variants are underway. These will increase power to detect associations with ancestry-specific rare variants.

Whole exome sequencing in the Framingham Heart Study identifies rare variation in HYAL2 that influences platelet aggregation.

Inhibition of platelet reactivity is a common therapeutic strategy in secondary prevention of cardiovascular disease. Genetic and environmental factors influence inter-individual variation in platelet reactivity. Identifying genes that contribute to platelet reactivity can reveal new biological mechanisms and possible therapeutic targets. Here, we examined rare coding variation to identify genes associated with platelet reactivity in a population-based cohort. To do so, we performed whole exome sequencing in the Framingham Heart Study and conducted single variant and gene-based association tests against platelet reactivity to collagen, adenosine diphosphate (ADP), and epinephrine agonists in up to 1,211 individuals. Single variant tests revealed no significant associations (p<1.44×10-7), though we observed a suggestive association with previously implicated MRVI1 (rs11042902, p = 1.95×10-7). Using gene-based association tests of rare and low-frequency variants, we found significant associations of HYAL2 with increased ADP-induced aggregation (p = 1.07×10-7) and GSTZ1 with increased epinephrine-induced aggregation (p = 1.62×10-6). HYAL2 also showed suggestive associations with epinephrine-induced aggregation (p = 2.64×10-5). The rare variants in the HYAL2 gene-based association included a missense variant (N357S) at a known N-glycosylation site and a nonsense variant (Q406*) that removes a glycophosphatidylinositol (GPI) anchor from the resulting protein. These variants suggest that improper membrane trafficking of HYAL2 influences platelet reactivity. We also observed suggestive associations of AR (p = 7.39×10-6) and MAPRE1 (p = 7.26×10-6) with ADP-induced reactivity. Our study demonstrates that gene-based tests and other grouping strategies of rare variants are powerful approaches to detect associations in population-based analyses of complex traits not detected by single variant tests and possible new genetic influences on platelet reactivity.

Rare variant association test with multiple phenotypes.

Although genome-wide association studies (GWAS) have now discovered thousands of genetic variants associated with common traits, such variants cannot explain the large degree of "missing heritability," likely due to rare variants. The advent of next generation sequencing technology has allowed rare variant detection and association with common traits, often by investigating specific genomic regions for rare variant effects on a trait. Although multiple correlated phenotypes are often concurrently observed in GWAS, most studies analyze only single phenotypes, which may lessen statistical power. To increase power, multivariate analyses, which consider correlations between multiple phenotypes, can be used. However, few existing multivariant analyses can identify rare variants for assessing multiple phenotypes. Here, we propose Multivariate Association Analysis using Score Statistics (MAAUSS), to identify rare variants associated with multiple phenotypes, based on the widely used sequence kernel association test (SKAT) for a single phenotype. We applied MAAUSS to whole exome sequencing (WES) data from a Korean population of 1,058 subjects to discover genes associated with multiple traits of liver function. We then assessed validation of those genes by a replication study, using an independent dataset of 3,445 individuals. Notably, we detected the gene ZNF620 among five significant genes. We then performed a simulation study to compare MAAUSS's performance with existing methods. Overall, MAAUSS successfully conserved type 1 error rates and in many cases had a higher power than the existing methods. This study illustrates a feasible and straightforward approach for identifying rare variants correlated with multiple phenotypes, with likely relevance to missing heritability.

Prioritization of family member sequencing for the detection of rare variants.

BackgroundThe advent of affordable sequencing has enabled researchers to discover many variants contributing to disease, including rare variants. There are methods for determining the most informative individuals for sequencing, but the application of these methods is more complex when working with families. Sets of large families can be beneficial in finding rare variants, but it may be unfeasible to sequence all members of these family sets.MethodsUsing simulated data from the Genetic Analysis Workshop 19, we apply multiple regression to identify cases and controls. To find the best controls for each case, we used kinship coefficients to match within families. Selected cases and controls were analyzed for rare variants, collapsed by gene, associated with hypertension using the family-based rare variant association test (FARVAT).ResultsThe gene with the strongest simulated effect, MAP4, did not meet the Bonferroni corrected significance threshold. However, analysis of cases and controls using our selection method substantially improved the significance of MAP4, despite the reduction in sample size.ConclusionsTaking the additional steps to select the optimal cases and controls from large family data sets can help ensure that only informative individuals are included in analysis and may improve the ability to detect rare variants.

Comparison of multiple single-nucleotide variant association tests in a meta-analysis of Genetic Analysis Workshop 19 family and unrelated data

BackgroundMeta-analysis has been widely used in genetic association studies to increase sample size and to improve power, both in the context of single-variant analysis, as well as for gene-based tests. Meta-analysis approaches for haplotype analysis have not been extensively developed and used, and have not been compared with other ways of jointly analysing multiple genetic variants.MethodsWe propose a novel meta-analysis approach for a gene-based haplotype association test, and compare it with an existing meta-analysis approach of the sequence kernel association test (SKAT), using the unrelated samples and family samples of the Genetic Analysis Workshop 19 data sets. We performed association tests with diastolic blood pressure and restricted our analyses to all variants in exonic regions on all odd chromosomes.ResultsMeta-analysis of haplotype results and SKAT identified different genes. The most significantly associated gene identified by SKAT was the ALCAM gene on chromosome 3 with a p value of 7.0 × 10− 5. Two of the most associated genes identified by the haplotype method were FPGT (p = 6.7 × 10− 8) on chromosome 1 and SPARC (p = 3.3 × 10− 7) on chromosome 5. Both genes were previously implicated in blood pressure regulation and hypertension.ConclusionWe compared two meta-analysis approaches to jointly analyze multiple variants: SKAT and haplotype tests. The difference in observed results may be because the haplotype method considered all observed haplotypes, whereas SKAT weighted variants inversely to their minor allele frequency, masking the effects of common variants. The two approaches identified different top genes, and appear to be complementary.

Comparing family-based rare variant association tests for dichotomous phenotypes.

BackgroundIt has been repeatedly stressed that family-based samples suffer less from genetic heterogeneity and that association analyses with family-based samples are expected to be powerful for detecting susceptibility loci for rare disease. Various approaches for rare-variant analysis with family-based samples have been proposed.MethodsIn this report, performances of the existing methods were compared with the simulated data set provided as part of Genetic Analysis Workshop 19 (GAW19). We considered the rare variant transmission disequilibrium test (RV-TDT), generalized estimating equations-based kernel association (GEE-KM) test, an extended combined multivariate and collapsing test for pedigree data (known as Pedigree Combined Multivariate and Collapsing [PedCMC]), gene-level kernel and burden association tests with disease status for pedigree data (PedGene), and the family-based rare variant association test (FARVAT).ResultsThe results show that PedGene and FARVAT are usually the most efficient, and the optimal test statistic provided by FARVAT is robust under different disease models. Furthermore, FARVAT was implemented with C++, which is more computationally faster than other methods.ConclusionsConsidering both statistical and computational efficiency, we conclude that FARVAT is a good choice for rare-variant analysis with extended families.