Genome-wide gene–gene interaction analysis for next-generation sequencing

Zhao, Jinying; Zhu, Yun; Xiong, Momiao

doi:10.1038/ejhg.2015.147

Cited by 16 publications

(17 citation statements)

References 37 publications

(41 reference statements)

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“…Fixed effect FR models are based on traditional theory of population genetics [Fisher ]. The FR models treat the contribution of genetic variants as an unknown function of physical position [Fan et al., , , , ; Luo et al., , , ; Vsevolozhskaya et al., ; Wang et al., ; Zhang et al., ; Zhao et al., ]. For quantitative traits, functional linear models lead to both F ‐distributed and χ 2 ‐distributed test statistics, which are almost always more powerful than SKAT and SKAT‐O [Fan et al., , ; Luo et al., ; Wang et al., ].…”

Section: Introductionmentioning

confidence: 99%

Gene-Based Association Analysis for Censored Traits Via Fixed Effect Functional Regressions

et al. 2016

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Summary Genetic studies of survival outcomes have been proposed and conducted recently, but statistical methods for identifying genetic variants that affect disease progression are rarely developed. Motivated by our ongoing real studies, we develop here Cox proportional hazard models using functional regression (FR) to perform gene-based association analysis of survival traits while adjusting for covariates. The proposed Cox models are fixed effect models where the genetic effects of multiple genetic variants are assumed to be fixed. We introduce likelihood ratio test (LRT) statistics to test for associations between the survival traits and multiple genetic variants in a genetic region. Extensive simulation studies demonstrate that the proposed Cox RF LRT statistics have well-controlled type I error rates. To evaluate power, we compare the Cox FR LRT with the previously developed burden test (BT) in a Cox model and sequence kernel association test (SKAT) which is based on mixed effect Cox models. The Cox FR LRT statistics have higher power than or similar power as Cox SKAT LRT except when 50%/50% causal variants had negative/positive effects and all causal variants are rare. In addition, the Cox FR LRT statistics have higher power than Cox BT LRT. The models and related test statistics can be useful in the whole genome and whole exome association studies. An age-related macular degeneration dataset was analyzed as an example.

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Section: Introductionmentioning

confidence: 99%

Gene-Based Association Analysis for Censored Traits Via Fixed Effect Functional Regressions

et al. 2016

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“…For rare variants, a problem of the traditional additive models is that it is hard to estimate the effects of all genetic variants because the number of genetic terms in the regression model can be large, and the related tests can be less powerful. The fixed-effect FR models are based on population genetics theory and model the contribution of genetic variants as an unknown function of physical position [Fan et al, 2013[Fan et al, , 2014[Fan et al, , 2016a[Fan et al, , 2016bLuo et al, 2011Luo et al, , 2012Luo et al, , 2013Vsevolozhskaya et al, 2014;Wang et al, 2015;Zhang et al, 2014;Zhao et al, 2016]. The fixed-effect FR models can analyze rare variants or common variants or a combination of the two.…”

Section: Introductionmentioning

confidence: 99%

A Comparison Study of Fixed and Mixed Effect Models for Gene Level Association Studies of Complex Traits

Fan

Chiu

Jung

et al. 2016

Genetic Epidemiology

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In this paper, extensive simulations are performed to compare two statistical methods to analyze multiple correlated quantitative phenotypes: (1) approximate F-distributed tests of multivariate functional linear models (MFLM) and additive models of multivariate analysis of variance (MANOVA), and (2) Gene Association with Multiple Traits (GAMuT) for association testing of high-dimensional genotype data. It is shown that approximate F-distributed tests of MFLM and MANOVA have higher power and are more appropriate for major gene association analysis (i.e., scenarios in which some genetic variants have relatively large effects on the phenotypes); GAMuT has higher power and is more appropriate for analyzing polygenic effects (i.e., effects from a large number of genetic variants each of which contributes a small amount to the phenotypes). MFLM and MANOVA are very flexible and can be used to perform association analysis for (i) rare variants, (ii) common variants, and (iii) a combination of rare and common variants. Although GAMuT was designed to analyze rare variants, it can be applied to analyze a combination of rare and common variants and it performs well when (1) the number of genetic variants is large and (2) each variant contributes a small amount to the phenotypes (i.e., polygenes). MFLM and MANOVA are fixed effect models that perform well for major gene association analysis. GAMuT can be viewed as an extension of sequence kernel association tests (SKAT). Both GAMuT and SKAT are more appropriate for analyzing polygenic effects and they perform well not only in the rare variant case, but also in the case of a combination of rare and common variants. Data analyses of European cohorts and the Trinity Students Study are presented to compare the performance of the two methods.

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“…There is increasing evidence to suggest that DI plays an important role in the genetic 285 architecture of many conditions. The three previously reported approaches searching for gene 286 x gene interactions in the general context of rare variant association studies are based on case-287 control designs (26)(27)(28). Moreover, these tests were assessed in limited simulation studies 288 involving short genomic sequences of less than 500 variants (n=1) or only 20 variants (n=2), 289 and were not based on WES-based simulated data.…”

Section: Discussionmentioning

confidence: 99%

A genome-wide case-only test for the detection of digenic inheritance in human exomes

Kerner

Bouaziz

Cobat

et al. 2020

Preprint

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Whole-exome sequencing (WES) has facilitated the discovery of genetic lesions underlying monogenic disorders. Incomplete penetrance and variable expressivity suggest a contribution of additional genetic lesions to clinical manifestations and outcome. Some monogenic disorders may therefore actually be digenic. However, only a few digenic disorders have been reported, all discovered by candidate gene approaches applied to at least one locus. We propose here a novel two-locus genome-wide test for detecting digenic inheritance in WES data. This approach uses the gene as the unit of analysis and tests all pairs of genes to detect pairwise gene x gene interactions underlying disease. It is a case-only method, which has several advantages over classic case-control tests, in particular by avoiding recruitment and bias of controls. Our simulation studies based on real WES data identified two major sources of type I error inflation in this case-only test: linkage disequilibrium and population stratification. Both were corrected by specific procedures. Moreover, our case-only approach is more powerful than the corresponding case-control test for detecting digenic interactions in various population stratification scenarios. Finally, we validated our unbiased, genome-wide approach by successfully identifying a previously reported digenic lesion in patients with craniosynostosis. Our case-only test is a powerful and timely tool for detecting digenic inheritance in WES data from patients.Significance statement. Despite a growing number of reports of rare disorders not fully explained by monogenic lesions, digenic inheritance has been reported for only 54 diseases to date. The very few existing methods for detecting gene x gene interactions from nextgeneration sequencing data were generally studied in rare-variant association studies with limited simulation analyses for short genomic regions, under a case-control design. We describe the first case-only approach designed specifically to search for digenic inheritance, which avoids recruitment and bias related to controls. We show, through both extensive 4 simulation studies on real WES datasets and application to a real example of craniosynostosis, that our method is robust and powerful for the genome-wide identification of digenic lesions. 5

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Genome-wide gene–gene interaction analysis for next-generation sequencing

Cited by 16 publications

References 37 publications

Gene-Based Association Analysis for Censored Traits Via Fixed Effect Functional Regressions

Gene-Based Association Analysis for Censored Traits Via Fixed Effect Functional Regressions

A Comparison Study of Fixed and Mixed Effect Models for Gene Level Association Studies of Complex Traits

A genome-wide case-only test for the detection of digenic inheritance in human exomes

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