Generalization of Rare Variant Association Tests for Longitudinal Family Studies

Chien, Li‐Chu; Hsu, Fang‐Chi; Bowden, Donald W.; Chiu, Yen‐Feng

doi:10.1002/gepi.21951

Cited by 6 publications

(17 citation statements)

References 41 publications

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“…Although longitudinal genetic data analyses have been reported previously, only a few of these reports focused on rare variants 13–16 . The kernel machine method based on the LM framework 15 was extended to rare variants in longitudinal data for a family-based study, but only applicable to continuous traits.…”

Section: Discussionmentioning

confidence: 99%

“…The SKAT method proposed by Chien et al . 16 was built under the GEE framework and was applicable to longitudinal binary data. However, the SKAT approach fits genes one at a time, and can be computationally expensive 15 .…”

Section: Discussionmentioning

confidence: 99%

“…Very few methods have been developed or extended to detect rare variants associated with longitudinal disease traits 13–16 . Yan et al .…”

Section: Introductionmentioning

confidence: 99%

“…Chien et al . 16 extended burden test and kernel association tests to genetic longitudinal studies in the generalized estimating equations (GEE) framework. Wu et al .…”

Section: Introductionmentioning

confidence: 99%

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Longitudinal data analysis for rare variants detection with penalized quadratic inference function

Cao

Zhi

Huang

et al. 2017

Sci Rep

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Longitudinal genetic data provide more information regarding genetic effects over time compared with cross-sectional data. Coupled with next-generation sequencing technologies, it becomes reality to identify important genes containing both rare and common variants in a longitudinal design. In this work, we adopted a weighted sum statistic (WSS) to collapse multiple variants in a gene region to form a gene score. When multiple genes in a pathway were considered together, a penalized longitudinal model under the quadratic inference function (QIF) framework was applied for efficient gene selection. We evaluated the estimation accuracy and model selection performance under different model settings, then applied the method to a real dataset from the Genetic Analysis Workshop 18 (GAW18). Compared with the unpenalized QIF method, the penalized QIF (pQIF) method achieved better estimation accuracy and higher selection efficiency. The pQIF remained optimal even when the working correlation structure was mis-specified. The real data analysis identified one important gene, angiotensin II receptor type 1 (AGTR1), in the Ca2+/AT-IIR/α-AR signaling pathway. The estimated effect implied that AGTR1 may have a protective effect for hypertension. Our pQIF method provides a general tool for longitudinal sequencing studies involving large numbers of genetic variants.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

“…Very few methods have been developed or extended to detect rare variants associated with longitudinal disease traits 13–16 . Yan et al .…”

Section: Introductionmentioning

confidence: 99%

“…Chien et al . 16 extended burden test and kernel association tests to genetic longitudinal studies in the generalized estimating equations (GEE) framework. Wu et al .…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Longitudinal data analysis for rare variants detection with penalized quadratic inference function

Cao

Zhi

Huang

et al. 2017

Sci Rep

View full text Add to dashboard Cite

show abstract

“…For a large sample and under the null hypothesis,

Q_{k, BT} / {Var}_{0} false(\sum_{l = 1}^{p} w_{k l} Z_{k l} false)

approximately follows a Χ 2 distribution with 1 df , where

{Var}_{0} false(\sum_{l = 1}^{p} w_{k l} Z_{k l} false)

is the null variance of

\sum_{l = 1}^{p} w_{k l} Z_{k l}

(Chien et al, ).…”

Section: Methodsmentioning

confidence: 99%

General retrospective mega‐analysis framework for rare variant association tests

Chien

Chiu

2018

Genetic Epidemiology

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Here, we describe a retrospective mega-analysis framework for gene- or region-based multimarker rare variant association tests. Our proposed mega-analysis association tests allow investigators to combine longitudinal and cross-sectional family- and/or population-based studies. This framework can be applied to a continuous, categorical, or survival trait. In addition to autosomal variants, the tests can be applied to conduct mega-analyses on X-chromosome variants. Tests were built on study-specific region- or gene-level quasiscore statistics and, therefore, do not require estimates of effects of individual rare variants. We used the generalized estimating equation approach to account for complex multiple correlation structures between family members, repeated measurements, and genetic markers. While accounting for multilevel correlations and heterogeneity across studies, the test statistics were computationally efficient and feasible for large-scale sequencing studies. The retrospective aspect of association tests helps alleviate bias due to phenotype-related sampling and type I errors due to misspecification of phenotypic distribution. We evaluated our developed mega-analysis methods through comprehensive simulations with varying sample sizes, covariates, population stratification structures, and study designs across multiple studies. To illustrate application of the proposed framework, we conducted a mega-association analysis combining a longitudinal family study and a cross-sectional case-control study from Genetic Analysis Workshop 19.

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Longitudinal SNP‐set association analysis of quantitative phenotypes

Wang

Zhang

et al. 2016

Genetic Epidemiology

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Many genetic epidemiological studies collect repeated measurements over time. This design not only provides a more accurate assessment of disease condition, but allows us to explore the genetic influence on disease development and progression. Thus, it is of great interest to study the longitudinal contribution of genes to disease susceptibility. Most association testing methods for longitudinal phenotypes are developed for single variant, and may have limited power to detect association, especially for variants with low minor allele frequency. We propose Longitudinal SNP-set/Sequence Kernel Association Test (LSKAT), a robust, mixed-effects method for association testing of rare and common variants with longitudinal quantitative phenotypes. LSKAT uses several random effects to account for the within-subject correlation in longitudinal data, and allows for adjustment for both static and time-varying covariates. We also present a longitudinal-trait burden test (LBT), where we test association between the trait and the burden score in linear mixed models. In simulation studies, we demonstrate that LBT achieves high power when variants are almost all deleterious or all protective, while LSKAT performs well in a wide range of genetic models. By making full use of trait values from repeated measures, LSKAT is more powerful than several tests applied to a single measurement or average over all time points. Moreover, LSKAT is robust to misspecification of the covariance structure. We apply the LSKAT and LBT methods to detect association with longitudinally-measured body mass index in the Framingham Heart Study, where we are able to replicate association with a circadian gene NR1D2.

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Generalization of Rare Variant Association Tests for Longitudinal Family Studies

Cited by 6 publications

References 41 publications

Longitudinal data analysis for rare variants detection with penalized quadratic inference function

Longitudinal data analysis for rare variants detection with penalized quadratic inference function

General retrospective mega‐analysis framework for rare variant association tests

Longitudinal SNP‐set association analysis of quantitative phenotypes

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