Mapping main, epistatic and sex-specific QTL for body composition in a chicken population divergently selected for low or high growth rate

<p>Abstract</p> <p>Background</p> <p>Delineating 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 F₂reciprocal intercross between two chicken lines divergently selected for high or low growth rate.</p> <p>Results</p> <p>We 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 [<it>Pectoralis (P) major </it>and <it>P. minor</it>], 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 <it>P. major </it>QTL and sex-antagonistic <it>P. minor </it>and abdominal fatness QTL. Also, several QTL on different chromosomes interact with each other to affect body composition and abdominal fatness.</p> <p>Conclusions</p> <p>The 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.</p

Digital Quantification of Human Eye Color Highlights Genetic Association of Three New Loci

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

The Molecular Genetic Architecture of Self-Employment

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 (σg2/σP2= 25%, h2= 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<10-5were 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

The Molecular Genetic Architecture of Self-Employment

Peer reviewe

Q–Q plots of the self-employment discovery meta-analyses.

<p>Q–Q plot of the self-employment discovery meta-analysis for (A) pooled males and females, (B) males only, and (C) females only. The grey shaded areas in the Q–Q plots represent the 95% confidence bands around the <i>p</i>-values.</p

Variance in the tendency to engage in self-employment explained by all autosomal SNPs in a combined sample of RS-I and STR for pooled males and females, males only, and females only.

<p>The genetic relationships were estimated from 301,115 directly genotyped autosomal SNPs that were available in both studies. All analyses controlled for age, study, and the first 10 principal components of the genetic similarity matrix of the combined sample of RS-I and STR. In the pooled sample we also controlled for sex. The results did not change markedly when 4 or 20 principal components were included; <i>σ_g</i>²/<i>σ_P</i>²: proportion of phenotypic variance explained by the variance of the total additive genetic effects of the 301,115 autosomal SNPs; s.e.: standard error; <i>p</i>-value: <i>p</i>-value from a likelihood ratio (LR) test assuming that the LR is distributed as a 50∶50 mixture of zero and <i>χ</i>₁².</p

Descriptive statistics of the sixteen discovery studies and the replication study.

<p>AGES: Age, Gene/Environment Susceptibility–Reykjavik Study; ASPS: Austrian Stroke Prevention Study; ERF: Erasmus Rucphen Family study; GHS: Gutenberg Health Study; H2000: Health 2000; HBCS: Helsinki Birth Cohort Study; HRS: Health and Retirement Study; KORA S4: Cooperative Health Research in the Region of Augsburg; NFBC1966: Northern Finland Birth Cohort 1966; NTR1: Netherlands Twin Register Cohort 1; NTR2: Netherlands Twin Register Cohort 2; RS-I: Rotterdam Study Baseline; RS-II: Rotterdam Study Extension of Baseline; RS-III: Rotterdam Study Young; SardiNIA: SardiNIA Study of Aging; SHIP: Study of Health in Pomerania; THISEAS: The Hellenic study of Interactions between SNPs & Eating in Atherosclerosis Susceptibility; TwinsUK: the UK Adult Twin Registry; YFS: the Cardiovascular Risk in Young Finns Study; STR: Swedish Twin Registry; Cases: number of participants that were at least once self-employed; Controls: number of participants that were not, and ideally never, self-employed; SD: standard deviation.</p>a<p>The number of male participants was insufficient for a male stratified analysis.</p

Top SNPs (<i>p</i><1×10⁻⁵) from the self-employment discovery meta-analyses for pooled males and females, males only, and females only.

<p>Chr.: chromosome; Pos.: position; EAF: average effect allele frequency; In the column “direction”, the studies are in the following order: 1. AGES, 2. ASPS, 3. ERF, 4. GHS, 5. H2000, 6. HBCS, 7. HRS, 8. KORA, 9. NFBC1966, 10. NTR1, 11. NTR2, 12. RS-I, 13. RS-II, 14. RS-III, 15. SardINIA, 16. SHIP, 17. THISEAS, 18. TwinsUK (pooled and female sample)/YFS (male sample), 19. YFS (pooled and female sample); A question mark indicates that the SNP was not tested in that specific study; For SNPs that were located close together in the same region, only the most significant SNP is included in the table. The last column shows the number of neighboring SNPs that exceed the threshold for suggestive SNPs.</p

Manhattan plots of the self-employment discovery meta-analyses.

<p>Manhattan plot of the self-employment discovery meta-analysis for (A) pooled males and females, (B) males only, and (C) females only. SNPs are plotted on the <i>x</i>-axis according to their position on each chromosome against association with self-employment on the <i>y</i>-axis (shown as −log10 <i>p</i>-value). The solid line indicates the threshold for genome-wide significance (<i>p</i><5×10⁻⁸) and the dashed line the threshold for suggestive SNPs (<i>p</i><1×10⁻⁵).</p