Framework for identifying quantitative trait loci in association studies using structural equation modeling

In this article, we suggest a framework for identifying quantitative trait loci (QTL) in association studies using structural equation modeling. Two tests to detect QTLs and estimate the proportion of variance they explain are discussed. The first test assumes that there is no population admixture and only requires that the subjects are genotyped. The second one is a TDT-like test that cannot give false-positive results due to population admixture but requires that the parents of the subjects are genotyped as well and that subjects have at least one heterozygous parent. Power calculations showed that with the first test, 100 subjects were generally sufficient to detect a locus that explained 10% and less than 1,000 subject to detect a locus that explained 1% of the total variance. To obtain the same power, the TDT-like test required an initial sample that was on average 1.7 times larger. Calculations showed that the first test was quite robust against population admixture and that the power of tests to detect admixture was good. This suggested that in the extreme and very specific conditions in which population admixture may cause false-positive findings, admixture can often be detected.