SNP Set Association Testing for Survival Outcomes in the Presence of Intrafamilial Correlation

Leclerc, Martin; Simard, Jacques; Lakhal‐Chaieb, Lajmi

doi:10.1002/gepi.21914

Cited by 6 publications

(7 citation statements)

References 33 publications

(89 reference statements)

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“…Family-based study designs allow the characterization of gene mutation effect on the disease risk by considering related individuals. A few methods have been developed for testing sets of genetic variants in family studies but only few approaches were proposed in the context of right-censored time-to-event data [21].…”

Section: Resultsmentioning

confidence: 99%

sim1000G: a user-friendly genetic variant simulator in R for unrelated individuals and family-based designs

Dimitromanolakis

Król

et al. 2019

BMC Bioinformatics

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BackgroundSimulation of genetic variants data is frequently required for the evaluation of statistical methods in the fields of human and animal genetics. Although a number of high-quality genetic simulators have been developed, many of them require advanced knowledge in population genetics or in computation to be used effectively. In addition, generating simulated data in the context of family-based studies demands sophisticated methods and advanced computer programming.ResultsTo address these issues, we propose a new user-friendly and integrated R package, sim1000G, which simulates variants in genomic regions among unrelated individuals or among families. The only input needed is a raw phased Variant Call Format (VCF) file. Haplotypes are extracted to compute linkage disequilibrium (LD) in the simulated genomic regions and for the generation of new genotype data among unrelated individuals. The covariance across variants is used to preserve the LD structure of the original population. Pedigrees of arbitrary sizes are generated by modeling recombination events with sim1000G. To illustrate the application of sim1000G, various scenarios are presented assuming unrelated individuals from a single population or two distinct populations, or alternatively for three-generation pedigree data. Sim1000G can capture allele frequency diversity, short and long-range linkage disequilibrium (LD) patterns and subtle population differences in LD structure without the need of any tuning parameters.ConclusionSim1000G fills a gap in the vast area of genetic variants simulators by its simplicity and independence from external tools. Currently, it is one of the few simulation packages completely integrated into R and able to simulate multiple genetic variants among unrelated individuals and within families. Its implementation will facilitate the application and development of computational methods for association studies with both rare and common variants.Electronic supplementary materialThe online version of this article (10.1186/s12859-019-2611-1) contains supplementary material, which is available to authorized users.

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Section: Resultsmentioning

confidence: 99%

sim1000G: a user-friendly genetic variant simulator in R for unrelated individuals and family-based designs

Dimitromanolakis

Król

et al. 2019

BMC Bioinformatics

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“…The off‐diagonal entry

(j, k)

V^{(i)}

captures the polygenic heritability between individuals

j

and

k

in the family

i

. In general,

V_{j k}^{(i)}

involves a polygenic heritability parameter and the kinship coefficient between these individuals (Leclerc et al ., ; Lakhal‐Chaieb et al ., ). In this work, we consider two models for

V^{(i)}

, which we describe in Sections and .…”

Section: Notation and Modelsmentioning

confidence: 99%

Testing the heritability and parent‐of‐origin hypotheses for ages at onset of psoriatic arthritis under biased sampling

2019

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The heritability and parent‐of‐origin effect hypotheses for chronic diseases can be evaluated by estimating and conducting inference about the parameters that measure the within‐family dependences in disease onset times. We model the within‐family associations in these times using a Gaussian copula whose correlation matrix accommodates the different pairwise family relationships. We derive score‐type statistics to test the heritability and parent‐of‐origin effect hypotheses when the families selection protocol induces a sampling bias. We illustrate the use of the developed methods through an application to a motivating family study in Psoriatic arthritis and provide strong evidence of excessive paternal transmission of risk.

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“…There are limited gene‐based approaches to analyze censored data from studies of either familial or cryptically related individuals. To our knowledge, Leclerc () can analyze related samples but it targets on regions containing a small number of variants while Chein, Bowden, and Chiu () does not release related software.…”

Section: Introductionmentioning

confidence: 99%

Gene‐based association analysis of survival traits via functional regression‐based mixed effect cox models for related samples

Chiu

Zhang

Wang

et al. 2019

Genetic Epidemiology

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The importance to integrate survival analysis into genetics and genomics is widely recognized, but only a small number of statisticians have produced relevant work toward this study direction. For unrelated population data, functional regression (FR) models have been developed to test for association between a quantitative/dichotomous/survival trait and genetic variants in a gene region. In major gene association analysis, these models have higher power than sequence kernel association tests. In this paper, we extend this approach to analyze censored traits for family data or related samples using FR based mixed effect Cox models (FamCoxME). The FamCoxME model effect of major gene as fixed mean via functional data analysis techniques, the local gene or polygene variations or both as random, and the correlation of pedigree members by kinship coefficients or genetic relationship matrix or both. The association between the censored trait and the major gene is tested by likelihood ratio tests (FamCoxME FR LRT). Simulation results indicate that the LRT control the type I error rates accurately/conservatively and have good power levels when both local gene or polygene variations are modeled. The proposed methods were applied to analyze a breast cancer data set from the Consortium of Investigators of Modifiers of BRCA1 and BRCA2 (CIMBA). The FamCoxME provides a new tool for gene-based analysis of family-based studies or related samples. K E Y W O R D Sassociation study, common variants, complex diseases, functional data analysis, mixed effect Cox models, rare variants

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SNP Set Association Testing for Survival Outcomes in the Presence of Intrafamilial Correlation

Cited by 6 publications

References 33 publications

sim1000G: a user-friendly genetic variant simulator in R for unrelated individuals and family-based designs

sim1000G: a user-friendly genetic variant simulator in R for unrelated individuals and family-based designs

Testing the heritability and parent‐of‐origin hypotheses for ages at onset of psoriatic arthritis under biased sampling

Gene‐based association analysis of survival traits via functional regression‐based mixed effect cox models for related samples

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