Detecting genetic interactions for quantitative traits with U-statistics

Li, Ming; Ye, Chengyin; Fu, Wenjiang; Elston, Robert C.; Lu, Qing

doi:10.1002/gepi.20594

Cited by 23 publications

(27 citation statements)

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“…Several formations of the U-statistic were recently adopted in population-based association studies for detecting genetic variants underlying complex human diseases (Schaid et al 2005;Wei et al 2008;Li et al 2011). These U-statistic-based methods have shown great promise, especially when underlying phenotype distributions and modes of inheritance are unknown (Li 2012).…”

Section: Discussionmentioning

confidence: 99%

A Powerful Nonparametric Statistical Framework for Family-Based Association Analyses

Schaid

et al. 2015

Genetics

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Family-based study design is commonly used in genetic research. It has many ideal features, including being robust to population stratification (PS). With the advance of high-throughput technologies and ever-decreasing genotyping cost, it has become common for family studies to examine a large number of variants for their associations with disease phenotypes. The yield from the analysis of these family-based genetic data can be enhanced by adopting computationally efficient and powerful statistical methods. We propose a general framework of a family-based U-statistic, referred to as family-U, for family-based association studies. Unlike existing parametricbased methods, the proposed method makes no assumption of the underlying disease models and can be applied to various phenotypes (e.g., binary and quantitative phenotypes) and pedigree structures (e.g., nuclear families and extended pedigrees). By using only withinfamily information, it can offer robust protection against PS. In the absence of PS, it can also utilize additional information (i.e., betweenfamily information) for power improvement. Through simulations, we demonstrated that family-U attained higher power over a commonly used method, family-based association tests, under various disease scenarios. We further illustrated the new method with an application to large-scale family data from the Framingham Heart Study. By utilizing additional information (i.e., between-family information), family-U confirmed a previous association of CHRNA5 with nicotine dependence.KEYWORDS population stratification; pedigree structure; within-family information; between-family information; nicotine dependence B OTH population-based and family-based designs have been commonly used in genetic association studies. Case-control study is a typical population-based design, by which unrelated cases are recruited and compared with healthy controls. When cases and controls come from different ethnicity backgrounds, they could have distinct ancestry distribution, leading to false-positive association results due to population stratification (PS) (Knowler et al. 1988;Freedman et al. 2004). Many statistical methods have been proposed to address the issue of PS (Devlin and Roeder 1999;Pritchard and Rosenberg 1999; Pritchard et al. 2000a,b;Satten et al. 2001;Chen et al. 2003;Price et al. 2006;Bauchet et al. 2007). However, these methods can infer only the average effect of PS based on a large number of genetic variants, but not the locus-specific PS. Population stratification related to a particular locus may vary and deviate from the average effect. In the presence of locus-specific PS, these approaches may overadjust or underadjust the PS effect, leading to either low power or inflated type I error (Marchini et al. 2004;Qin and Zhu 2012). Unlike population-based studies, family-based studies offer robust protection against PS (Weinberg et al. 1998;Cardon and Palmer 2003;Weinberg 2003). In a typical family-based association study, the alleles transmitted to affected individu...

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Section: Discussionmentioning

confidence: 99%

A Powerful Nonparametric Statistical Framework for Family-Based Association Analyses

Schaid

et al. 2015

Genetics

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“…In recent years, U-statistic-based methods became popular in genetic data analysis, and have shown their robustness and flexibility for analyzing genetic data (Schaid et al, 2005;Li et al, 2011;Wei and Lu, 2015;Wei et al, 2016). GSU is a general framework of association analysis and is based on similarity measurements and U statistics.…”

Section: Discussionmentioning

confidence: 99%

A generalized association test based on U statistics

Wei

2017

Bioinformatics

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Summary:Motivation: Second generation sequencing technologies are being increasingly used for genetic association studies, where the main research interest is to identify sets of genetic variants that contribute to various phenotype. The phenotype can be univariate disease status, multivariate responses and even high-dimensional outcomes. Considering the genotype and phenotype as two complex objects, this also poses a general statistical problem of testing association between complex objects. Results: We here proposed a similarity-based test, generalized similarity U (GSU), that can test the association between complex objects. We first studied the theoretical properties of the test in a general setting and then focused on the application of the test to sequencing association studies. Based on theoretical analysis, we proposed to use Laplacian kernel based similarity for GSU to boost power and enhance robustness. Through simulation, we found that GSU did have advantages over existing methods in terms of power and robustness. We further performed a whole genome sequencing (WGS) scan for Alzherimer's Disease Neuroimaging Initiative (ADNI) data, identifying three genes, APOE, APOC1 and TOMM40, associated with imaging phenotype. Availability: We developed a C++ package for analysis of whole genome sequencing data using GSU. The source codes can be downloaded at https://github.com/changshuaiwei/gsu. Contact:

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“…Simply clustering all the individuals into just two risk groups could lead to low power of the approach, while treating each p -locus genotype as a separate risk group will lead to inflated Type 1 error [Lu et al, 2010]. Based on the idea of forward selection [Li et al, 2011; Ye et al, 2011], we introduce a computationally feasible and efficient forward selection algorithm to cluster individuals into an optimal number of risk groups, i.e., the number that best represents the underlying risks. We assume diallelic loci, though in principle (but not with the same ease of computation), the method can be extended to any number of alleles.…”

Section: Lrmw Testmentioning

confidence: 99%

A Likelihood Ratio‐Based Mann‐Whitney Approach Finds Novel Replicable Joint Gene Action for Type 2 Diabetes

Wei

et al. 2012

Genetic Epidemiology

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The potential importance of the joint action of genes, whether modeled with or without a statistical interaction term, has long been recognized. However, identifying such action has been a great challenge, especially when millions of genetic markers are involved. We propose a likelihood ratio-based Mann-Whitney test to search for joint gene action either among candidate genes or genome-wide. It extends the traditional univariate Mann-Whitney test to assess the joint association of genotypes at multiple loci with disease, allowing for high-order statistical interactions. Because only one overall significance test is conducted for the entire analysis, it avoids the issue of multiple testing. Moreover, the approach adopts a computationally efficient algorithm, making a genome-wide search feasible in a reasonable amount of time on a high performance personal computer. We evaluated the approach using both theoretical and real data. By applying the approach to 40 type 2 diabetes (T2D) susceptibility single-nucleotide polymorphisms (SNPs), we identified a four-locus model strongly associated with T2D in the Wellcome Trust (WT) study (permutation P-value < 0.001), and replicated the same finding in the Nurses’ Health Study/Health Professionals Follow-Up Study (NHS/HPFS) (P-value = 3.03 × 10–11). We also conducted a genome-wide search on 385,598 SNPs in the WT study. The analysis took approximately 55 hr on a personal computer, identifying the same first two loci, but overall a different set of four SNPs, jointly associated with T2D (P-value = 1.29 × 10–5). The nominal significance of this same association reached 4.01 × 10–6 in the NHS/HPFS.

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Detecting genetic interactions for quantitative traits with U-statistics

Cited by 23 publications

References 50 publications

A Powerful Nonparametric Statistical Framework for Family-Based Association Analyses

A Powerful Nonparametric Statistical Framework for Family-Based Association Analyses

A generalized association test based on U statistics

A Likelihood Ratio‐Based Mann‐Whitney Approach Finds Novel Replicable Joint Gene Action for Type 2 Diabetes

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