Previous studies have successfully identified genetic variants in several genes associated with human iris (eye) color; however, they all used simplified categorical trait information. Here, we quantified continuous eye color variation into hue and saturation values using high-resolution digital full-eye photographs and conducted a genome-wide association study on 5,951 Dutch Europeans from the Rotterdam Study. Three new regions, 1q42.3, 17q25.3, and 21q22.13, were highlighted meeting the criterion for genome-wide statistically significant association. The latter two loci were replicated in 2,261 individuals from the UK and in 1,282 from Australia. The LYST gene at 1q42.3 and the DSCR9 gene at 21q22.13 serve as promising functional candidates. A model for predicting quantitative eye colors explained over 50% of trait variance in the Rotterdam Study. Over all our data exemplify that fine phenotyping is a useful strategy for finding genes involved in human complex traits.

BackgroundDelineating the genetic basis of body composition is important to agriculture and medicine. In addition, the incorporation of gene-gene interactions in the statistical model provides further insight into the genetic factors that underlie body composition traits. We used Bayesian model selection to comprehensively map main, epistatic and sex-specific QTL in an F2 reciprocal intercross between two chicken lines divergently selected for high or low growth rate.ResultsWe identified 17 QTL with main effects across 13 chromosomes and several sex-specific and sex-antagonistic QTL for breast meat yield, thigh + drumstick yield and abdominal fatness. Different sets of QTL were found for both breast muscles [Pectoralis (P) major and P. minor], which suggests that they could be controlled by different regulatory mechanisms. Significant interactions of QTL by sex allowed detection of sex-specific and sex-antagonistic QTL for body composition and abdominal fat. We found several female-specific P. major QTL and sex-antagonistic P. minor and abdominal fatness QTL. Also, several QTL on different chromosomes interact with each other to affect body composition and abdominal fatness.ConclusionsThe detection of main effects, epistasis and sex-dimorphic QTL suggest complex genetic regulation of somatic growth. An understanding of such regulatory mechanisms is key to mapping specific genes that underlie QTL controlling somatic growth in an avian model.

Economic variables such as income, education, and occupation are known to affect mortality and morbidity, such as cardiovascular disease, and have also been shown to be partly heritable. However, very little is known about which genes influence economic variables, although these genes may have both a direct and an indirect effect on health. We report results from the first large-scale collaboration that studies the molecular genetic architecture of an economic variable–entrepreneurship–that was operationalized using self-employment, a widely-available proxy. Our results suggest that common SNPs when considered jointly explain about half of the narrow-sense heritability of self-employment estimated in twin data (σg 2/σP 2 = 25%, h 2 = 55%). However, a meta-analysis of genome-wide association studies across sixteen studies comprising 50,627 participants did not identify genome-wide significant SNPs. 58 SNPs with p<10−5 were tested in a replication sample (n = 3,271), but none replicated. Furthermore, a gene-based test shows that none of the genes that were previously suggested in the literature to influence entrepreneurship reveal significant associations. Finally, SNP-based genetic scores that use results from the meta-analysis capture less than 0.2% of the variance in self-employment in an independent sample (p≥0.039). Our results are consistent with a highly polygenic molecular genetic architecture of self-employment, with many genetic variants of small effect. Although self-employment is a multi-faceted, heavily environmentally influenced, and biologically distal trait, our results are similar to those for other genetically complex and biologically more proximate outcomes, such as height, intelligence, personality, and several diseases.

An F(2) population (695 individuals) was established from broiler chickens divergently selected for either high (HG) or low (LG) growth, and used to localize QTL for developmental changes in body weight (BW), shank length (SL9) and shank diameter (SD9) at 9 weeks. QTL mapping revealed three genome-wide QTL on chromosomes (GGA) 2, 4 and 26 and three suggestive QTL on GGA 1, 3 and 5. Most of the BW QTL individually explained 2-5% of the phenotypic variance. The BW QTL on GGA2 explained about 7% of BW from 3 to 7 weeks of age, while that on GGA4 explained 15% of BW from 5 to 9 weeks. The BW QTL on GGA2 and GGA4 could be associated with early and late growth respectively. The GGA4 QTL also had the largest effect on SL9 and SD9 and explained 7% and 10% of their phenotypic variances respectively. However, when SL9 and SD9 were corrected with BW9, a shank length percent QTL was identified on GGA2. We identified novel QTL and also confirmed previously identified loci in other chicken populations. As the foundation population was established from commercial broiler strains, it is possible that QTL identified in this study could still be segregating in commercial strains.