Nonparametric Tests of Association of Multiple Genes with Human Disease

Schaid, Daniel J.; McDonnell, Shannon K.; Hebbring, Scott J.; Cunningham, Julie M.; Thibodeau, Stephen N.

doi:10.1086/429838

Cited by 120 publications

(190 citation statements)

References 21 publications

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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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Section: Discussionmentioning

confidence: 99%

A generalized association test based on U statistics

Wei

2017

Bioinformatics

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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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“…Wessel and Schork (2006) and Zapala and Schork (2012) introduced the method to applications in genetics and genomics. For a single trait, it is closely related to kernel methods (Schaid et al 2005;Pan 2011). Suppose d ij represents the distance between subjects i and j; let A ¼ ða ij Þ ¼ ð21=2 d 2 ij Þ and G be its centered version.…”

Section: Appendix B: Spu(22) and Mdmrmentioning

confidence: 99%

Powerful and Adaptive Testing for Multi-trait and Multi-SNP Associations with GWAS and Sequencing Data

Kim¹,

Zhang²,

Pan

2016

Genetics

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Testing for genetic association with multiple traits has become increasingly important, not only because of its potential to boost statistical power, but also for its direct relevance to applications. For example, there is accumulating evidence showing that some complex neurodegenerative and psychiatric diseases like Alzheimer's disease are due to disrupted brain networks, for which it would be natural to identify genetic variants associated with a disrupted brain network, represented as a set of multiple traits, one for each of multiple brain regions of interest. In spite of its promise, testing for multivariate trait associations is challenging: if not appropriately used, its power can be much lower than testing on each univariate trait separately (with a proper control for multiple testing). Furthermore, differing from most existing methods for single-SNP-multiple-trait associations, we consider SNP set-based association testing to decipher complicated joint effects of multiple SNPs on multiple traits. Because the power of a test critically depends on several unknown factors such as the proportions of associated SNPs and of traits, we propose a highly adaptive test at both the SNP and trait levels, giving higher weights to those likely associated SNPs and traits, to yield high power across a wide spectrum of situations. We illuminate relationships among the proposed and some existing tests, showing that the proposed test covers several existing tests as special cases. We compare the performance of the new test with that of several existing tests, using both simulated and real data. The methods were applied to structural magnetic resonance imaging data drawn from the Alzheimer's Disease Neuroimaging Initiative to identify genes associated with gray matter atrophy in the human brain default mode network (DMN). For genomewide association studies (GWAS), genes AMOTL1 on chromosome 11 and APOE on chromosome 19 were discovered by the new test to be significantly associated with the DMN. Notably, gene AMOTL1 was not detected by single SNP-based analyses. To our knowledge, AMOTL1 has not been highlighted in other Alzheimer's disease studies before, although it was indicated to be related to cognitive impairment. The proposed method is also applicable to rare variants in sequencing data and can be extended to pathway analysis.KEYWORDS adaptive association test; ADNI; default mode network; gene-based test; imaging genetics; multiple traits A LZHEIMER'S disease (AD) (MIM 104300) is the most common neurodegenerative disease, and every 67 sec, someone in the United States develops AD (Alzheimer's Association 2015a). Currently there is no cure for AD, and most cases are diagnosed in the late stage of the disease. It is projected that the number of Americans of age 65 years and older with AD will increase from 5.1 million in 2015 to 13.5 million in 2050, a growth from an estimated 11% of the U.S. senior population in 2015 to 16% in 2050, costing .$1.1 trillion in 2050 (Alzheimer's Association 2015b). To advance our...

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Nonparametric Tests of Association of Multiple Genes with Human Disease

Cited by 120 publications

References 21 publications

A generalized association test based on U statistics

A generalized association test based on U statistics

A Powerful Nonparametric Statistical Framework for Family-Based Association Analyses

Powerful and Adaptive Testing for Multi-trait and Multi-SNP Associations with GWAS and Sequencing Data

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