Penalized Likelihood Regression Approach for Quantitative Trait Loci Mapping From Samples with Related Individuals

Identifying Quantitative Trait Loci (QTL) by association mapping is critical for understanding the genetic architecture of complex traits or diseases. Many statistical methods have been developed to locate genes and estimate the effects of these genes that are responsible for quantitative traits. Penalized maximum likelihood method is one of the powerful statistical tools for QTL mapping, especially in dealing with the problem of p >n, where p is the number of genetic effects and n is the sample size. Most methods derived from it are limited to analyzing single trait from samples with independent individuals. Genetic inheritable complex diseases usually affect family members and are expressed by multiple correlated traits. The purpose of this study is to develop a statistical method (penalized likelihood regression approach) to target QTL from samples in a general setting, that is, arbitrary related individuals, for both single and multiple traits. Simulation studies show that the proposed method has great performance in detecting QTL in both single- and two-trait scenarios with related and unrelated individuals.