Distribution of lod scores in oligogenic linkage analysis

Challenges and prospects in genome-wide quantitative trait loci mapping of standing genetic variation in natural populations

A considerable challenge in evolutionary genetics is to understand the genetic mechanisms that facilitate or impede evolutionary adaptation in natural populations. For this, we must understand the genetic loci contributing to trait variation and the selective forces acting on them. The decreased costs and increased feasibility of obtaining genotypic data on a large number of individuals have greatly facilitated gene mapping in natural populations, particularly because organisms whose genetics have been historically difficult to study are now within reach. Here we review the methods available to evolutionary ecologists interested in dissecting the genetic basis of traits in natural populations. Our focus lies on standing genetic variation in outbred populations. We present an overview of the current state of research in the field, covering studies on both plants and animals. We also draw attention to particular challenges associated with the discovery of quantitative trait loci and discuss parallels to studies on crops, livestock, and humans. Finally, we point to some likely future developments in genetic mapping studies.

Sex-specific QTL effects on variation in paraoxonase 1 (PON1) activity in Mexican Americans

Paraoxonase 1 (PON1), a high-density lipoprotein-associated enzyme known to protect against cellular damage from toxic agents, may also have antioxidant properties. PON1 activity levels have been reported to differ by sex in human and animal studies with females exhibiting higher basal levels. We measured PON1 activity frozen serum for 1,406 individuals in over 40 extended pedigrees from the San Antonio Family Heart Study (SAFHS). We used a maximum likelihood-based, variance decomposition approach implemented in SOLAR to test for genotype-by-sex (G x S) interaction on variation in PON1 activity and to determine if any of the four PON1 quantitative trait loci (QTL) previously reported by us for this population might account for sex differences in PON1 activity levels. The residual additive genetic correlation (rho(G) = 0.82) between males and females is significantly different from 1 (P = 0.009), suggesting that some of the genes that influence PON1 activity act differently in females and males or, possibly, that a different combination of genes influences this trait in each sex. In addition to the QTL at or near the PON structural locus on 7q21-22, three other potential QTLs were evaluated for sex-specific effects: one each on chromosomes 12, 17 and 19. The QTL on chromosome 17 (LOD = 2.32, P = 0.0003; flanked by microsatellite marker loci D17S974 and D17S969) shows a significant (P = 0.005) sex-specific effect on PON1 activity; accounting for 6% of the additive genetic variance in males and 20% in females. This study represents the first formal statistical genetic test for G x S interactions on normal quantitative variation in PON1 activity in humans.

Identification of QTLs for serum lipid levels in a female sib-pair cohort: a novel application to improve the power of two-locus linkage analysis

Using a novel approach for a two-locus model that provides a greatly increased power to detect multiple quantitative trait loci (QTLs) in simulated data, we identified in a sample of 961 female sib-pairs, three genome-wide significant QTLs for apolipoprotein A1 on chromosomes 8p21.1-q13.1 (LOD score 3.71), 9q21.32-33.1 (LOD score 3.28) and 10p15.1-p13 (LOD score 5.51), two for lipoprotein (a) on chromosomes 6q25.2-q27 (LOD score 10.18) and 21q21.1-q21.3 (LOD score 4.57) and two for triglycerides on chromosomes 4q28.3-32.1 (LOD score 3.71) and 5q23.1-q32 (LOD score 3.60). The two-locus ordered-subset analysis has led to the confirmation of known and likely identification of novel regions linked to serum lipid levels that would have otherwise been missed and deserves wider application in linkage analyses of quantitative traits. Given the relative lack of power for the sample sizes commonly used in human genetics linkage studies, minor QTL effects often go undetected and those that are detected will be upwardly biased. We show through simulation that the discrepancy between the real and estimated QTL-effects is often likely to generate an unpredictable source of false-negative errors, using multi-locus models, reducing the power to detect multiple QTLs through oligogenic linkage analysis. The successful simultaneous modelling of the identified QTLs in a multi-locus context helps to eliminate false positives and increases the power to detect linkages, adding compelling evidence that they are likely to be reliable QTLs for these lipid traits.