On a semiparametric test to detect associations between quantitative traits and candidate genes using unrelated individuals

• Published in: Genetic epidemiology Vol. 24; no. 1; pp. 44 - 56
• Main Authors: Zhang, Shuanglin, Zhu, Xiaofeng, Zhao, Hongyu
• Format: Journal Article
• Language: English
• Published: New York Wiley Subscription Services, Inc., A Wiley Company 01.01.2003; Wiley-Liss
• Subjects: Analysis of Variance; Bias; Biological and medical sciences; coalescent model; Factor Analysis, Statistical; Fundamental and applied biological sciences. Psychology; General aspects; General aspects. Genetic counseling; Genetic Markers - genetics; Genetic Variation - genetics; Genetics, Population; Genotype; Humans; Least-Squares Analysis; Linear Models; Mathematics in biology. Statistical analysis. Models. Metrology. Data processing in biology (general aspects); Medical genetics; Medical sciences; Models, Genetic; partial linear model; Pedigree; population genetics; population stratification; Quantitative Trait, Heritable; quantitative traits; Regression Analysis; semi-parametric model; smoothing method; Statistics, Nonparametric; Human; quantitative traits; population genetics; semi-parametric model; Quantitative character; partial linear model; Statistical association; Semiparametric method; coalescent model; smoothing method; Statistical test; Population stratification; Candidate gene; Robustness; Detection
• ISSN: 0741-0395; 1098-2272
• DOI: 10.1002/gepi.10196

Although genetic association studies using unrelated individuals may be subject to bias caused by population stratification, alternative methods that are robust to population stratification such as family‐based association designs may be less powerful. Recently, various statistical methods robust to population stratification were proposed for association studies, using unrelated individuals to identify associations between candidate markers and traits of interest (both qualitative and quantitative). Here, we propose a semiparametric test for association (SPTA). SPTA controls for population stratification through a set of genomic markers by first deriving a genetic background variable for each sampled individual through his/her genotypes at a series of independent markers, and then modeling the relationship between trait values, genotypic scores at the candidate marker, and genetic background variables through a semiparametric model. We assume that the exact form of relationship between the trait value and the genetic background variable is unknown and estimated through smoothing techniques. We evaluate the performance of SPTA through simulations both with discrete subpopulation models and with continuous admixture population models. The simulation results suggest that our procedure has a correct type I error rate in the presence of population stratification and is more powerful than statistical association tests for family‐based association designs in all the cases considered. Moreover, SPTA is more powerful than the Quantitative Similarity‐Based Association Test (QSAT) developed by us under continuous admixture populations, and the number of independent markers needed by SPTA to control for population stratification is substantially fewer than that required by QSAT. Genet Epidemiol 24:44–56, 2003. © 2003 Wiley‐Liss, Inc.