Associating Multivariate Quantitative Phenotypes with Genetic Variants in Family Samples with a Novel Kernel Machine Regression Method.
The recent development of sequencing technology allows identification of association between the whole spectrum of genetic variants and complex diseases. Over the past few years, a number of association tests for rare variants have been developed. Jointly testing for association between genetic variants and multiple correlated phenotypes may increase the power to detect causal genes in family-based studies, but familial correlation needs to be appropriately handled to avoid an inflated type I error rate. Here we propose a novel approach for multivariate family data using kernel machine regression (denoted as MF-KM) that is based on a linear mixed-model framework and can be applied to a large range of studies with different types of traits. In our simulation studies, the usual kernel machine test has inflated type I error rates when applied directly to familial data, while our proposed MF-KM method preserves the expected type I error rates. Moreover, the MF-KM method has increased power compared to methods that either analyze each phenotype separately while considering family structure or use only unrelated founders from the families. Finally, we illustrate our proposed methodology by analyzing whole-genome genotyping data from a lung function study.
Division of Pulmonary Medicine, Allergy and Immunology, Department of Pediatrics, Children's Hospital of Pittsburgh, University of Pittsburgh Medical Center, University of Pittsburgh, Pennsylvania 15224.
Qi
Q
Yan
Departments of Human Genetics and Biostatistics, Graduate School of Public Health, University of Pittsburgh, Pennsylvania 15261.
Daniel E
DE
Weeks
Division of Pulmonary Medicine, Allergy and Immunology, Department of Pediatrics, Children's Hospital of Pittsburgh, University of Pittsburgh Medical Center, University of Pittsburgh, Pennsylvania 15224 Departments of Human Genetics and Biostatistics, Graduate School of Public Health, University of Pittsburgh, Pennsylvania 15261.
Juan C
JC
Celedón
Department of Biostatistics, University of Alabama at Birmingham, Alabama 35294.
Hemant K
HK
Tiwari
Departments of Molecular Physiology and Biophysics and Neurology, Vanderbilt University Medical Center, Nashville, Tennessee 37232.
Bingshan
B
Li
Analytics of Metrics Central, Global QARAC Headquarters, ConvaTec, Inc., Greensboro, North Carolina 27409.
Xiaojing
X
Wang
Institute of Epidemiology and Preventive Medicine, College of Public Health, National Taiwan University, Taipei, Taiwan.
Wan-Yu
WY
Lin
Department of Biostatistics and Bioinformatics, Tulane University, New Orleans, Louisiana 70112.
Xiang-Yang
XY
Lou
Department of Public Health Sciences, University of Chicago, Illinois 60637.
Guimin
G
Gao
Division of Pulmonary Medicine, Allergy and Immunology, Department of Pediatrics, Children's Hospital of Pittsburgh, University of Pittsburgh Medical Center, University of Pittsburgh, Pennsylvania 15224 Departments of Human Genetics and Biostatistics, Graduate School of Public Health, University of Pittsburgh, Pennsylvania 15261 nliu@uab.edu wei.chen@chp.edu.
Wei
W
Chen
Department of Biostatistics, University of Alabama at Birmingham, Alabama 35294 nliu@uab.edu wei.chen@chp.edu.
Nianjun
N
Liu
D000328:Adult; D000465:Algorithms; D002648:Child; D003198:Computer Simulation; D005260:Female; D056726:Genetic Association Studies / Q000379:methods*; D020022:Genetic Predisposition to Disease; D014644:Genetic Variation; D015894:Genome, Human; D006801:Humans; D008168:Lung / Q000503:physiopathology; D008297:Male; D008957:Models, Genetic; D015233:Models, Statistical; D015999:Multivariate Analysis; D010641:Phenotype; D029424:Pulmonary Disease, Chronic Obstructive / Q000235:genetics* / Q000503:physiopathology; D040641:Quantitative Trait Loci
family samples; kernel function; linear mixed model; multivariate traits; rare variants