Comparison of Methods to Account for Relatedness in Genome-Wide Association Studies with Family-Based Data

Eu-ahsunthornwattana, Jakris; Miller, E.N.; Fakiola, Michaela; Jerônimo, Selma M. B.; Blackwell, Jenefer M.; Cordell, Heather J.

doi:10.1371/journal.pgen.1004445

Cited by 111 publications

(126 citation statements)

References 46 publications

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“…An interesting comparison can be made with the linear mixed model (LMM) which has recently gained popularity in GWAS studies (Eu-ahsunthornwattana et al, 2014), where a kinship matrix, estimated from either pedigree or genome sequences, is used to structure the covariance matrix for genetic random effects. LMM deals with univariate response so it can only look into the kinship matrix for structured unexplained variance, while for neuroimaging data the richness of the structural information allows further decomposition of the observed signals.…”

Section: Discussionmentioning

confidence: 99%

“…However, in modern neuroimaging-genetic studies, a genome wide association study (GWAS) is often performed, which means testing the association with the imaging phenotypes for a colossal number of candidate genes/SNPs (typically anywhere from thousands to millions). Estimating the complete model for each candidates incurs a heavy computational burden, a practice often to be avoided even in conventional univariate GWAS studies (Eu-ahsunthornwattana et al, 2014). Also an accurate yet efficient statistical testing procedure is required to assign the significance level to the observed association.…”

Section: Methodsmentioning

confidence: 99%

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Generalized reduced rank latent factor regression for high dimensional tensor fields, and neuroimaging-genetic applications

et al. 2017

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We propose a generalized reduced rank latent factor regression model (GRRLF) for the analysis of tensor field responses and high dimensional covariates. The model is motivated by the need from imaging-genetic studies to identify genetic variants that are associated with brain imaging phenotypes, often in the form of high dimensional tensor fields. GRRLF identifies from the structure in the data the effective dimensionality of the data, and then jointly performs dimension reduction of the covariates, dynamic identification of latent factors, and nonparametric estimation of both covariate and latent response fields. After accounting for the latent and covariate effects, GRLLF performs a nonparametric test on the remaining factor of interest. GRRLF provides a better factorization of the signals compared with common solutions, and is less susceptible to * Corresponding author at: Centre for Computational Systems Biology and School of Mathematical Sciences, Fudan University, Shanghai, PR China. jianfeng64@gmail.com (J. Feng). 1 Data used in preparation of this article were obtained from the Alzheimers Disease Neuroimaging Initiative (ADNI) database (adni.loni.usc.edu). As such, the investigators within the ADNI contributed to the design and implementation of ADNI and/or provided data but did not participate in analysis or writing of this report. A complete listing of ADNI investigators can be found at: http://adni.loni.usc.edu/wp-content/uploads/how_to_apply/ADNI_Acknowledgement_List.pdfThe authors report no conflict of interest. HHS Public Access Author Manuscript Author ManuscriptAuthor ManuscriptAuthor Manuscript overfitting because it exploits the effective dimensionality. The generality and the flexibility of GRRLF also allow various statistical models to be handled in a unified framework and solutions can be efficiently computed. Within the field of neuroimaging, it improves the sensitivity for weak signals and is a promising alternative to existing approaches. The operation of the framework is demonstrated with both synthetic datasets and a real-world neuroimaging example in which the effects of a set of genes on the structure of the brain at the voxel level were measured, and the results compared favorably with those from existing approaches.

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Section: Discussionmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Generalized reduced rank latent factor regression for high dimensional tensor fields, and neuroimaging-genetic applications

et al. 2017

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“…Several options are available for such tests, using data from related individuals. Typically either linear mixed-effects models (LMMs) 12 or generalized estimating equations (GEE methods) 13 are used. For LMMs, the correlation among individuals in the same family is accounted for by adding to Equation (1) a random effect with variance-covariance matrix proportional to the relevant kinship matrix.…”

Section: Subjects and Methods Methodsmentioning

confidence: 99%

Genome-wide gene–environment interactions on quantitative traits using family data

et al. 2015

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Gene-environment interactions may provide a mechanism for targeting interventions to those individuals who would gain the most benefit from them. Searching for interactions agnostically on a genome-wide scale requires large sample sizes, often achieved through collaboration among multiple studies in a consortium. Family studies can contribute to consortia, but to do so they must account for correlation within families by using specialized analytic methods. In this paper, we investigate the performance of methods that account for within-family correlation, in the context of gene-environment interactions with binary exposures and quantitative outcomes. We simulate both cross-sectional and longitudinal measurements, and analyze the simulated data taking family structure into account, via generalized estimating equations (GEE) and linear mixed-effects models. With sufficient exposure prevalence and correct model specification, all methods perform well. However, when models are misspecified, mixed modeling approaches have seriously inflated type I error rates. GEE methods with robust variance estimates are less sensitive to model misspecification; however, when exposures are infrequent, GEE methods require modifications to preserve type I error rate. We illustrate the practical use of these methods by evaluating gene-drug interactions on fasting glucose levels in data from the Framingham Heart Study, a cohort that includes related individuals.

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“…In studies of related individuals, the appropriate heritability parameter is the heritability explained by all genetic variants under an additive model (narrowsense heritability), h 2 . 8,9,11 In studies of unrelated individuals, the appropriate heritability parameter is the heritability explained by genotyped SNPs under an additive model (SNP-heritability), h g 2 , 3-5 which is generally smaller than h…”

Section: Estimation Of Narrow-sense Heritabilitymentioning

confidence: 99%

“…We also simulated multiplex families by taking a random sample of 10 individuals from a family of 31 individuals simulated over four generations (1,2,4,8,16 individuals at generations 0, 1, 2, 3, 4, respectively; two children per individual at each generation). The 31 individuals were simulated using forward simulations.…”

Section: Simulated Genotypes and Simulated Phenotypesmentioning

confidence: 99%

Mixed Model Association with Family-Biased Case-Control Ascertainment

Hayeck

Zaitlen

Loh

et al. 2016

Preprint

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Mixed models have become the tool of choice for genetic association studies; however, standard mixed model methods may be poorly calibrated or underpowered under family sampling bias and/or case-control ascertainment. Previously, we introduced a liability threshold-based mixed model association statistic (LTMLM) to address case-control ascertainment in unrelated samples. Here, we consider family-biased case-control ascertainment, where case and control subjects are ascertained non-randomly with respect to family relatedness. Previous work has shown that this type of ascertainment can severely bias heritability estimates; we show here that it also impacts mixed model association statistics. We introduce a family-based association statistic (LT-Fam) that is robust to this problem. Similar to LTMLM, LT-Fam is computed from posterior mean liabilities (PML) under a liability threshold model; however, LT-Fam uses published narrow-sense heritability estimates to avoid the problem of biased heritability estimation, enabling correct calibration. In simulations with family-biased case-control ascertainment, LT-Fam was correctly calibrated (average c 2 ¼1.00-1.02 for null SNPs), whereas the Armitage trend test (ATT), standard mixed model association (MLM), and case-control retrospective association test (CARAT) were mis-calibrated (e.g., average c 2 ¼ 0.50-1.22 for MLM, 0.89-2.65 for CARAT). LT-Fam also attained higher power than other methods in some settings. In 1,259 type 2 diabetes-affected case subjects and 5,765 control subjects from the CARe cohort, downsampled to induce family-biased ascertainment, LT-Fam was correctly calibrated whereas ATT, MLM, and CARAT were again mis-calibrated. Our results highlight the importance of modeling family sampling bias in case-control datasets with related samples.

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Comparison of Methods to Account for Relatedness in Genome-Wide Association Studies with Family-Based Data

Cited by 111 publications

References 46 publications

Generalized reduced rank latent factor regression for high dimensional tensor fields, and neuroimaging-genetic applications

Generalized reduced rank latent factor regression for high dimensional tensor fields, and neuroimaging-genetic applications

Genome-wide gene–environment interactions on quantitative traits using family data

Mixed Model Association with Family-Biased Case-Control Ascertainment

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