Bayesian group Lasso for nonparametric varying-coefficient models with application to functional genome-wide association studies

Li, Jiahan; Wang, Zhong; Li, Runze; Wu, Rongling

doi:10.1214/15-aoas808

Cited by 59 publications

(98 citation statements)

References 48 publications

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“…This assumption can be relaxed by modeling the effects as more flexible functions of age, e.g., polynomial functions and nonparametric functions, which is known as the varying-coefficient models (Hastie and Tibshirani, 1993; Fan and Zhang, 1999, 2008). Varying-coefficient models have been used to study the time-dependent genetic effects and gene-environment interactions (Gong and Zou, 2012; Wu and Cui, 2013; Li et al, 2015). Compared to L2R2, these studies only modeled univariate response variables.…”

Section: Discussionmentioning

confidence: 99%

Bayesian longitudinal low-rank regression models for imaging genetic data from longitudinal studies

Khondker²,

Ibrahim³

et al. 2017

NeuroImage

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To perform a joint analysis of multivariate neuroimaging phenotypes and candidate genetic markers obtained from longitudinal studies, we develop a Bayesian longitudinal low-rank regression (L2R2) model. The L2R2 model integrates three key methodologies: a low-rank matrix for approximating the high-dimensional regression coefficient matrices corresponding to the genetic main effects and their interactions with time, penalized splines for characterizing the overall time effect, and a sparse factor analysis model coupled with random effects for capturing within-subject spatio-temporal correlations of longitudinal phenotypes. Posterior computation proceeds via an efficient Markov chainMonte Carlo algorithm. Simulations show that the L2R2 model outperforms several other competing methods. We apply the L2R2 model to investigate the effect of single nucleotide polymorphisms (SNPs) on the top 10 and top 40 previously reported Alzheimer disease-associated genes. We also identify associations between the interactions of these SNPs with patient age and the tissue volumes of 93 regions of interest from patients’ brain images obtained from the Alzheimer’s Disease Neuroimaging Initiative.

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Section: Discussionmentioning

confidence: 99%

Bayesian longitudinal low-rank regression models for imaging genetic data from longitudinal studies

Khondker²,

Ibrahim³

et al. 2017

NeuroImage

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“…In addition, the LSKAT and LBT methods assume constant genetic effects for each variants. It would be possible to extend the method to allow for time‐varying genetic effects, as the relative influence from genetic and environmental factors on a trait of interest can fluctuate over time (Li, Wang, Li, & Wu, ). A simple approach would be to replace the genetic parameter

γ

with

{bold-italicγ}_{j}

in Model (1) such that the genetic effects could vary at different time points.…”

Section: Discussionmentioning

confidence: 99%

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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“…More recently, Li et al . () has incorporated variable selection into f GWAS, allowing geneticists to chart a global picture of genetic control over developmental processes of any complex traits.…”

Section: Discussionmentioning

confidence: 99%

Computational identification of genes modulating stem height–diameter allometry

Jiang

Zhu

et al. 2016

Plant Biotechnology Journal

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SummaryThe developmental variation in stem height with respect to stem diameter is related to a broad range of ecological and evolutionary phenomena in trees, but the underlying genetic basis of this variation remains elusive. We implement a dynamic statistical model, functional mapping, to formulate a general procedure for the computational identification of quantitative trait loci (QTLs) that control stem height–diameter allometry during development. Functional mapping integrates the biological principles underlying trait formation and development into the association analysis of DNA genotype and endpoint phenotype, thus providing an incentive for understanding the mechanistic interplay between genes and development. Built on the basic tenet of functional mapping, we explore two core ecological scenarios of how stem height and stem diameter covary in response to environmental stimuli: (i) trees pioneer sunlit space by allocating more growth to stem height than diameter and (ii) trees maintain their competitive advantage through an inverse pattern. The model is equipped to characterize ‘pioneering’ QTLs (pi QTLs) and ‘maintaining’ QTLs (mi QTLs) which modulate these two ecological scenarios, respectively. In a practical application to a mapping population of full‐sib hybrids derived from two Populus species, the model has well proven its versatility by identifying several pi QTLs that promote height growth at a cost of diameter growth and several mi QTLs that benefit radial growth at a cost of height growth. Judicious application of functional mapping may lead to improved strategies for studying the genetic control of the formation mechanisms underlying trade‐offs among quantities of assimilates allocated to different growth parts.

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Bayesian group Lasso for nonparametric varying-coefficient models with application to functional genome-wide association studies

Cited by 59 publications

References 48 publications

Bayesian longitudinal low-rank regression models for imaging genetic data from longitudinal studies

Bayesian longitudinal low-rank regression models for imaging genetic data from longitudinal studies

Longitudinal SNP‐set association analysis of quantitative phenotypes

Computational identification of genes modulating stem height–diameter allometry

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