U-statistics in genetic association studies

Genotype-phenotype (G-P) analyses for complex morphological traits typically utilize simple, predetermined anatomical measures or features derived via unsupervised dimension reduction techniques (e.g. principal component analysis (PCA) or eigen-shapes). Despite the popularity of these approaches, they do not necessarily reveal axes of phenotypic variation that are genetically relevant. Therefore, we introduce a framework to optimize phenotyping for G-P analyses, such as genome-wide association studies (GWAS) of common variants or rare variant association studies (RVAS) of rare variants. Our strategy is two-fold: (i) we construct a multidimensional feature space spanning a wide range of phenotypic variation, and (ii) within this feature space, we use an optimization algorithm to search for directions or feature combinations that are genetically enriched. To test our approach, we examine human facial shape in the context of GWAS and RVAS. In GWAS, we optimize for phenotypes exhibiting high heritability, estimated from either family data or genomic relatedness measured in unrelated individuals. In RVAS, we optimize for the skewness of phenotype distributions, aiming to detect commingled distributions that suggest single or few genomic loci with major effects. We compare our approach with eigen-shapes as baseline in GWAS involving 8246 individuals of European ancestry and in gene-based tests of rare variants with a subset of 1906 individuals. After applying linkage disequilibrium score regression to our GWAS results, heritability-enriched phenotypes yielded the highest SNP heritability, followed by eigen-shapes, while commingling-based traits displayed the lowest SNP heritability. Heritability-enriched phenotypes also exhibited higher discovery rates, identifying the same number of independent genomic loci as eigen-shapes with a smaller effective number of traits. For RVAS, commingling-based traits resulted in more genes passing the exome-wide significance threshold than eigen-shapes, while heritability-enriched phenotypes lead to only a few associations. Overall, our results demonstrate that optimized phenotyping allows for the extraction of genetically relevant traits that can specifically enhance discovery efforts of common and rare variants, as evidenced by their increased power in facial GWAS and RVAS.

Genetic Association Studies Identify Unanticipated Gene Pathways Influencing Sepsis Outcome

Genome-wide associations studies (GWAS) have been very successful in identifying common genetic variation associated to numerous complex diseases [1]. However, most of the identified common genetic variants appear to confer modest risk and few causal alleles have been identified [2]. Furthermore, these associations account for a small portion of the total heritability of inherited disease variation [1]. This has led to the reexamination of the contribution of environment, gene-gene and gene-environment interactions, and rare genetic variants in complex diseases [1, 3, 4]. There is strong evidence that rare variants play an important role in complex disease etiology and may have larger genetic effects than common variants [2]. Currently, much of what we know regarding the contribution of rare genetic variants to disease risk is based on a limited number of phenotypes and candidate genes. However, rapid advancement of second generation sequencing technologies will invariably lead to widespread association studies comparing whole exome and eventually whole genome sequencing of cases and controls. A tremendous challenge for enabling these "next generation" medical genomic studies is developing statistical approaches for correlating rare genetic variants with disease outcome. The analysis of rare variants is challenging since methods used for common variants are woefully underpowered. Therefore, methods that can deal with genetic heterogeneity at the trait-associated locus have been developed to analyze rare variants. These methods instead analyzing individual variants analyze variants within a region/gene as a group and usually rely on collapsing. They can be applied to both in cases vs. controls and quantitative trait studies are needed. The paper of Bansal et al. in this volume describes the application of a number of statistical methods for testing associations between rare variants in two genes to obesity. The authors considered the relative merits of the different methods as well as important implementation details, such as the leveraging of genomic annotations and determining p-values. Knowledge of haplotypes can increase the power of GWAS studies and also highlight associations that are impossible to detect without haplotype phase (e.g. loss of heterozygosity). Even more complicated phase-dependent interactions of variants in linkage equilibrium have also been suggested as possible causes of missing heritability. In their work, Hallsorsson et al. formulate algorithmic strategies for haplotype phasing by multi-assembly of shared haplotypes from next-generation sequencing data. These methods would allow testing haplotypes harboring rare variants for association and potentially increase their explanatory power. Since single SNP tests are often underpowered in rare variant association analysis, Zeggini and Asimit propose a locus-based method that has high power in the presence of rare variants and that incorporate base quality scores available for sequencing data. Their results suggest that this multi-marker approach may be best suited for smaller regions, or after some filtering to reduce the number of SNPs that are jointly tested to reduce loss of power due to multiple-testing adjustments. Finally, the paper of Zhou et al., presents a penalized regression framework for association testing on sequence data, in the presence of both common and rare variants. This method also introduces the use of weights to incorporate available biological information on the variants. Although these tactics improve both false positive and false negative rates, they represent an incremental development and there is still significant room for improvement. With the development of sequencing technologies and methods to detect complex trait rare variant associations many new and exciting discovery are imminent. The analysis of rare variants is still in its infancy and the next few years promises to produce many new methods to meet the special demands of analyzing this type of data. Note from Publisher: This article contains the abstract and references.

Most current genetic association studies, including genome-wide association studies, look for the single nucleotide polymorphisms (SNPs) with a relatively large minor allele frequency (MAF) (e.g. >5%) in the search for genetic loci underlying the susceptibility for complex diseases. The strategy of focusing on common SNPs in genetic association studies is very effective under the common-disease-common-variant (CDCV) hypothesis, which claims that common diseases are caused by common variants that have relatively small to moderate effects. Although the CDCV hypothesis has become the dogma guiding the conduct of association studies over the past decade, growing evidence from recent empirical data and simulations suggests that the causal genetic polymorphisms, including SNPs and copy number variants (CNVs), for common diseases have a wide spectrum of MAFs, ranging from rare to common. Unlike the analysis for common genetic variants, statistical approaches for the analysis of rare variants receive very little attention. Methods developed for common variants usually rely on their asymptotic properties, which can be inaccurate for the study of the rare variants with limited sample size. Although Fisher’s exact test can be used for such a scenario, it is usually conservative and thus its usefulness is diminished to some extent. Here we propose two novel approaches for the analysis of rare genetic variants. Simulation studies and two real examples demonstrate the advantages of the proposed methods over the existing methods.

Genome-wide association studies (GWAS) and rare variant association studies (RVAS) are applied across many areas of complex disease to analyze variation in whole genomes of thousands of unrelated patients. These approaches are able to identify variants and/or biological pathways which are associated with disease status and, in contrast to traditional linkage studies or candidate gene approaches, do so without requiring multigenerational affected families, prior hypotheses, or known genes of interest. However, the novel associations identified by these methods typically have lower effect sizes than those found in classical family studies. In the motor neuron disease amyotrophic lateral sclerosis (ALS), GWAS, and RVAS have been used to identify multiple disease-associated genes but have not yet resulted in novel therapeutic interventions. There is significant urgency within the ALS community to identify additional genetic markers of disease to uncover novel biological mechanisms, stratify genetic subgroups of disease, and drive drug development. Given the widespread and increasing application of genetic association studies of complex disease, it is important to recognize the strengths and limitations of these approaches. Here, we review ALS gene discovery via GWAS and RVAS.

The quest for genes associated with different phenotypes and diseases is a central theme of research in human genetics. Several human phenotypes/diseases including height, myopia, diabetes, and others show complex pattern of inheritance where many genes together with environmental factors influence the intensity of a phenotype or disease. The genome-wide association study (GWAS) has been successful in mapping loci associated to variety of human traits and diseases. GWAS test for association of single nucleotide polymorphism (SNP) markers with a phenotype of interest at the genome-wide scale, this hence requires a severe multiple testing correction for statistical significance (p-value < 5 × 10-8). A single marker association approach has limited power in finding disease susceptibility genes especially in situations where the risk architecture of a gene is defined by many SNPs rather than one or two. This issue can be addressed through gene-based association approaches where the combined (or mean) statistics of a number of markers in a gene is tested for association with the trait of interest. Gene-based association approaches are established as a complementary approach to GWAS. It is well established that genes work in concert as molecular networks and cellular pathways to perform biological processes. Individual tests for association of a gene (or a SNP in a gene) with phenotype may have limited power in identifying interacting genes with small effects. SNP or gene-based tests are limited in how much they can reveal of the biological mechanism behind phenotypic variation. Pathway-based association approaches, where gene-based tests statistics are combined into gene sets and tested for association, have been developed to overcome these limitations. Each gene set represents a biological pathway or a process, based on prior biological knowledge. Various techniques have been proposed to perform pathway-based association tests on GWAS data, including ALIGATOR, MAGENTA, and INRICH. These packages show several limitations, which can be addressed by the development of a new pathway-based association approach. The aim of this thesis is to demonstrate the analytical approaches (GWAS and post-GWAS) to identify genes and pathways associated with complex ophthalmology traits and report methods for gene- and pathways-based analysis of GWAS summary data. Chapter 1 introduces the basic principles and strategies involved in GWAS analysis. Further, it extensively reviews the popular methods available for gene-based and pathway-based association tests. Chapter 2 describes an initial GWAS on corneal curvature, a phenotype of interest to diseases such as myopia and keratoconus, in Australians and further meta-analysis of two cohorts comprising of 1788 Australian twins and their families and 1013 individuals from a birth cohort from Western Australia. It also reports on replication in Australians of Northern European ancestry of the previously reported association of SNPs in PDGFRA and FRAP1 genes with corneal curvature in an Asian population. Chapter 3 is a demonstration of GWAS and pathway-based analysis. It reports the first GWAS and meta-analysis for corneal astigmatism in two Australian cohort studies, total sample size ~2,700. This chapter describes the application of Pathway-VEGAS, an extension of the gene-based analysis tool Versatile Gene-based Association Study (VEGAS) program for pathway-based analysis. Chapter 4 reports the successful application of VEGAS and Pathway-VEGAS to GWAS meta-analysis data. As part of the international glaucoma genetics consortium (IGGC), I participated in genetic analysis of the vertical cup disc ratio (VCDR) phenotype. This manuscript describes the meta-analysis of GWAS on a discovery sample of 21,094 individuals from ten cohorts of northern European ancestry and a replication sample of 6,784 individuals from four Asian cohorts. Gene-based and pathway-based tests were performed using the VEGAS and Pathway-VEGAS programs separately for northern European ancestry samples and Asian ancestry samples, and respective results were meta-analysed. This chapter demonstrates the advantages of using different approaches such as single marker GWAS, gene-based association approach and pathway based association approach. Chapter 5 presents the VEGAS2 software. VEGAS is one of the most popular gene-based association software. VEGAS however has some limitations such as the inability to perform a gene-based tests on the X chromosome, dependence on HapMap2 data to model the correlation between SNPs and inflexibility in the selection of gene boundary. The VEGAS2 software is an extension of VEGAS, redeveloped and upgraded to overcome these limitations. Chapter 6 presents the VEGAS2Pathway approach for pathway analysis of GWAS summary data. This chapter describes the shortcomings of many popular pathway-based association approaches, and how VEGAS2-Pathway overcomes these limitations. Further it demonstrates the application of VEGAS2Pathway on the endometriosis GWAS summary data. The last chapter provides the contributions this thesis makes to the literature and discusses its implications, limitations and future directions. In conclusion, my thesis provides the methodological and analytical approaches to further our knowledge in human genetics.

See related article, pages 825–832. With a swiftly moving, highly technical field, it helps to have a reliable guide. In this issue of Stroke, Lanktree et al1 provide a timely overview of genomic analysis applied to stroke.1 Readers find a clear and concise synopsis of single-nucleotide polymorphisms, copy number variations, listings of the strengths and limitations of genome-wide association studies (GWAS), and a distillation of the findings from GWAS performed in 6 cohorts (5 of which were ischemic stroke cohorts). The review also covers a topic not usually found in clinical reviews, namely techniques to visually display quantitative information. Excellence in statistical graphics should, among other things, avoid distorting what the data have to say, present many numbers in a small space, and make large data sets coherent.2 The Manhattan and quantile–quantile plots are excellent examples of statistical graphics that have become invaluable for interpreting GWAS results. Along with visualization comes interpretation of data in the context of GWAS. Clinical investigators are well aware of the problem of multiple testing from such settings as interim and subgroup analyses in clinical trials, which can lead to wildly spurious conclusions, such as concluding that aspirin only helped individuals of certain astrological signs in the second International Study of Infarct Survival.3 GWAS simply escalates the problem of multiple testing by orders of magnitude. Because single-nucleotide polymorphism-based GWAS test hundreds of thousands …

IL28B single nucleotide polymorphisms in the treatment of hepatitis C

Background. Genome-wide association studies have succeeded in detecting novel common variants which associate with complex diseases. As a result of the fast changes in next generation sequencing technology, a large number of sequencing data are generated, which offers great opportunities to identify rare variants that could explain a larger proportion of missing heritability. Many effective and powerful methods are proposed, although they are usually limited to continuous, dichotomous or ordinal traits. Notice that traits having nominal categorical features are commonly observed in complex diseases, especially in mental disorders, which motivates the incorporation of the characteristics of the categorical trait into association studies with rare and common variants.Methods. We construct two simple and intuitive nonparametric tests, MIT and aMIT, based on mutual information for detecting association between genetic variants in a gene or region and a categorical trait. MIT and aMIT can gauge the difference among the distributions of rare and common variants across a region given every categorical trait value. If there is little association between variants and a categorical trait, MIT or aMIT approximately equals zero. The larger the difference in distributions, the greater values MIT and aMIT have. Therefore, MIT and aMIT have the potential for detecting functional variants.Results.We checked the validity of proposed statistics and compared them to the existing ones through extensive simulation studies with varied combinations of the numbers of variants of rare causal, rare non-causal, common causal, and common non-causal, deleterious and protective, various minor allele frequencies and different levels of linkage disequilibrium. The results show our methods have higher statistical power than conventional ones, including the likelihood based score test, in most cases: (1) there are multiple genetic variants in a gene or region; (2) both protective and deleterious variants are present; (3) there exist rare and common variants; and (4) more than half of the variants are neutral. The proposed tests are applied to the data from Collaborative Studies on Genetics of Alcoholism, and a competent performance is exhibited therein.Discussion. As a complementary to the existing methods mainly focusing on quantitative traits, this study provides the nonparametric tests MIT and aMIT for detecting variants associated with categorical trait. Furthermore, we plan to investigate the association between rare variants and multiple categorical traits.

For decades, the heritable contribution of cancer has been the subject of intense study, first pursued in families with multiple affected individuals and more recently investigated in unrelated population-based studies. For example, family studies have yielded important genes for breast cancer (BRCA1 and BRCA2), colon cancer (MSH2 and MLH1), and families with a spectrum of less common cancers (TP53). The pursuit of candidate genes yielded only a handful of conclusive associations, such as NAT2 and GSTM1 in bladder cancer or alcohol dehydrogenase genes (ADH1B and ADH7) in aerodigestive cancers. New genotyping technologies together with a comprehensive map of human haplotypes (International HapMap Project) have enabled investigators to scan across the genome in a sufficiently large set of cases and controls without a set of prior hypotheses in search of common susceptibility alleles with low effect sizes. Genome-wide association studies (GWAS) have emerged as an important tool for discovering regions of the genome using hundreds of thousands of common single nucleotide polymorphism (SNP) markers. This ‘agnostic’ approach has conclusively discovered more than 125 regions for nearly two dozen types of cancer. At least eight regions have been associated with more than one distinct cancer type. For instance, the region flanking the MYC oncogene on 8q24 harbors at least five independent loci associated with prostate cancer as well as loci associated with cancers of the breast, colon, bladder, ovaries, and chronic lymphocytic leukemia. On 5p15.33, there are at least seven cancers, namely, basal cell carcinoma, bladder, brain, lung (adenocarcinoma), melanoma, pancreas, and testicular that map to the TERT-CLPTM1L locus. This locus is notable for the telomerase gene (TERT) in which rare mutations have been associated with dyskeratosis congenital (an inherited bone marrow failure syndrome), idiopathic pulmonary fibrosis, acute myelogenous leukemia, and chronic lymphocytic leukemia. The number of variants identified by GWAS per cancer varies greatly, suggesting different underlying genetic architectures, namely genetic contributions to common and uncommon cancers. Of the 125 regions associated with cancer risk, none of the regions have been conclusively associated with prognosis. In prostate cancer, there are at least 35 distinct loci harboring common susceptibility alleles identified by GWAS yet not a single one clearly distinguishes between aggressive and nonaggressive disease. This observation suggests that there could be regions that distinctly contribute to cancer risk and cancer outcomes (e.g., aggressive or metastatic disease). For lung cancer, a disease strongly driven by exposure to tobacco products, so far, only three or four regions have been conclusively established. Of these regions, the signal on chromosome 15 could be related to smoking. For nearly all common SNP markers (namely those with a minor allele frequency of greater than 5%), the estimated effect sizes are below 1.5. A notable exception is for testicular cancer in which variants on chromosome 12 near the KITLG gene have estimated effect sizes of greater than 2.5 for heterozygotes; this is not surprising since the hereditary component of testicular cancer is very strong. Overall, each region confers a small contribution to the risk for cancer, which suggests that it is daunting to consider any single SNP as a clinical test. GWAS regions are undergoing fine mapping to nominate the optimal candidates for functional studies designed to explain the direct association. Nearly all GWAS signals have mapped to noncoding regions, underscoring the contribution of regulatory mechanisms in neighboring genes (e.g., FGFR2 and breast cancer, MSMB/NCOA4 and prostate cancer). The plausibility of the underlying association can form the foundation for developing new strategies to intervene or diagnose cancer at an earlier stage by providing new insights into biological mechanisms that become targets. The application of GWAS to therapeutic outcomes and toxicities is beginning to discover new regions worthy of follow-up studies. However, for pharmacogenomics to eventually have an impact in the clinic, there will have to be a transition from discovery to characterization of the regions that ultimately yields targets for therapy and chemoprevention. Since we are still early in the discovery of common genetic variants associated with risk for specific cancers, it is not surprising that the utility of applying common SNPs to disease prediction is premature. Common genetic variants represent only a proportion of genetic variants that contribute to disease risk; uncommon, rare and copy number variants will undoubtedly contribute to risk in both familial and unrelated settings, and their relative contributions are expected to vary by cancer site. The addition of common SNP markers to classical epidemiological risk factors for breast cancer has minimally shifted the receiver operator curve (ROC) from 50% to approximately 60%. For prostate cancer, a disease in which epidemiological studies have identified age, ethnic background, and family history as the only conclusive risk factors, it is not surprising that more than 30 common SNPs shift the ROC curve to roughly 65%. Discovery of additional genetic variants should improve the risk prediction but each will be limited by the overall heredity of the cancer. New approaches towards applying common genetic variants could be beneficial in the reclassification of individuals based on genetic risk profiles or alternatively, the identification of subsets of individuals at high risk who might undergo a diagnostic or therapeutic intervention. In conclusion, the fruits of GWAS have important implications for investigating the etiology of different cancers, particularly as it relates to gene-environment and gene-gene interactions. The complex genomic architecture of disease susceptibility will require further discovery of uncommon and rare variants using next-generation sequence technologies. It will be critical to assess the applicability of genetic tests in specific clinical settings, such as when to perform screening tests with calculable risks (e.g., biopsies or chemoprevention) before incorporating SNPs into clinical practice. To incorporate the fruits of current genomic observations, new studies will need to be designed to validate the utility of known genetic variants in assessing risk for cancer as well as its outcomes. Citation Format: {Authors}. {Abstract title} [abstract]. In: Proceedings of the 102nd Annual Meeting of the American Association for Cancer Research; 2011 Apr 2-6; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2011;71(8 Suppl):Abstract nr PL01-02. doi:10.1158/1538-7445.AM2011-PL01-02

In human genome research, genetic association studies of rare variants have been widely studied since the advent of high-throughput DNA sequencing platforms. However, detection of outcome-related rare variants still remains a statistically challenging problem because the number of observed genetic mutations is extremely rare. Recently, a power set-based statistical selection procedure has been proposed to locate both risk and protective rare variants within the outcome-related genes or genetic regions. Although it can perform an individual selection of rare variants, the procedure has a limitation that it cannot measure the certainty of selected rare variants. In this article, we propose a selection probability of individual rare variants, where selection frequencies of rare variants are computed based on bootstrap resampling. Therefore, it can quantify the certainty of both selected and unselected rare variants. Also, a new selection approach using a threshold of selection probability is introduced and compared with some existing selection procedures from extensive simulation studies and real sequencing data analysis. We have demonstrated that the proposed approach outperforms the existing methods in terms of a selection power.

Genome-wide association studies (GWAS) have identified hundreds of common variants associated with alcohol consumption. In contrast, genetic studies of alcohol consumption that use rare variants are still in their early stages. No prior studies of alcohol consumption have examined whether common and rare variants implicate the same genes and molecular networks, leaving open the possibility that the two approaches might identify distinct biology. To address this knowledge gap, we used publicly available alcohol consumption GWAS summary statistics (GSCAN, N = 666,978) and whole exome sequencing data (Genebass, N = 393,099) to identify a set of common and rare variants for alcohol consumption. We used gene-based analysis to implicate genes from common and rare variant analyses, which we then propagated onto a shared molecular network using a network colocalization procedure. Gene-based analysis of each dataset implicated 294 (common variants) and 35 (rare variants) genes, including ethanol metabolizing genes ADH1B and ADH1C, which were identified by both analyses, and ANKRD12, GIGYF1, KIF21B, and STK31, which were identified in only the rare variant analysis, but have been associated with other neuropsychiatric traits. Network colocalization revealed significant network overlap between the genes identified via common and rare variants. The shared network identified gene families that function in alcohol metabolism, including ADH, ALDH, CYP, and UGT. Seventy-one of the genes in the shared network were previously implicated in neuropsychiatric or substance use disorders but not alcohol-related behaviors (e.g. EXOC2, EPM2A, and CACNG4). Differential gene expression analysis showed enrichment in the liver and several brain regions. Genes implicated by network colocalization identify shared biology relevant to alcohol consumption, which also underlie neuropsychiatric traits and substance use disorders that are comorbid with alcohol use, providing a more holistic understanding of two disparate sources of genetic information.

Both common and rare variants contribute to the genetic architecture of pulmonary fibrosis. Genome-wide association studies have identified common variants, or those with a minor allele frequency of >5%, that...

In humans, most of the genetic variation is rare and often population-specific. Whereas the role of rare genetic variants in familial monogenic diseases is firmly established, we are only now...

Mendelian randomization (MR) has become an important technique for establishing causal relationships between risk factors and health outcomes. By using genetic variants as instrumental variables, it can mitigate bias due to confounding and reverse causation in observational studies. Current MR analyses have predominantly used common genetic variants as instruments, which represent only part of the genetic architecture of complex traits. Rare variants, which can have larger effect sizes and provide unique biological insights, have been understudied due to statistical and methodological challenges. We introduce MR-common and annotation-informed rare variants (MR-CARV), a novel framework integrating common and rare genetic variants in two-sample MR. This method leverages comprehensive genetic data made available by high-throughput sequencing technologies and large-scale consortia. Rare variants are aggregated into functional categories, such as gene-coding, gene-noncoding, and nongene regions, by leveraging variant annotations and biological impact as weights. The effects of rare variant sets are then estimated with STAARpipeline and combined with the estimated effects of common variants by the existing MR methods. Simulation studies demonstrate that MR-CARV maintains robust type I error and achieves higher statistical power, with up to a 66.3% relative increase compared with existing methods only based on common variants. Consistent with these findings, application to real data on high-density lipoprotein cholesterol (HDL-C) and preeclampsia showed that MR-CARV [inverse variance weighted (IVW)] yielded a more precise and statistically significant effect estimate (–0.020, SE = 0.0102, P =.0470) than IVW using only common variants (–0.023, SE = 0.0123, P =.0659).