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Wen Huang, Stephen Richards,
Mary Anna Carbone,
Dianhui Zhu,
Robert R H Anholt,
Julien F Ayroles,
Laura Duncan,
Katherine W Jordan,
Faye Lawrence,
Michael M Magwire, [......],
Shalini N Jhangiani,
Donna Muzny,
Fiona Ongeri,
Lora Perales,
Yuan-Qing Wu,
Yiqing Zhang,
Xiaoyan Zou,
Eric A Stone,
Richard A Gibbs,
Trudy F C Mackay
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ABSTRACT: Epistasis-nonlinear genetic interactions between polymorphic loci-is the genetic basis of canalization and speciation, and epistatic interactions can be used to infer genetic networks affecting quantitative traits. However, the role that epistasis plays in the genetic architecture of quantitative traits is controversial. Here, we compared the genetic architecture of three Drosophila life history traits in the sequenced inbred lines of the Drosophila melanogaster Genetic Reference Panel (DGRP) and a large outbred, advanced intercross population derived from 40 DGRP lines (Flyland). We assessed allele frequency changes between pools of individuals at the extremes of the distribution for each trait in the Flyland population by deep DNA sequencing. The genetic architecture of all traits was highly polygenic in both analyses. Surprisingly, none of the SNPs associated with the traits in Flyland replicated in the DGRP and vice versa. However, the majority of these SNPs participated in at least one epistatic interaction in the DGRP. Despite apparent additive effects at largely distinct loci in the two populations, the epistatic interactions perturbed common, biologically plausible, and highly connected genetic networks. Our analysis underscores the importance of epistasis as a principal factor that determines variation for quantitative traits and provides a means to uncover genetic networks affecting these traits. Knowledge of epistatic networks will contribute to our understanding of the genetic basis of evolutionarily and clinically important traits and enhance predictive ability at an individualized level in medicine and agriculture.
Proceedings of the National Academy of Sciences 09/2012; 109(39):15553-9. · 9.68 Impact Factor
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Ulrike Ober,
Julien F Ayroles,
Eric A Stone, Stephen Richards,
Dianhui Zhu,
Richard A Gibbs,
Christian Stricker,
Daniel Gianola,
Martin Schlather,
Trudy F C Mackay,
Henner Simianer
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ABSTRACT: Predicting organismal phenotypes from genotype data is important for plant and animal breeding, medicine, and evolutionary biology. Genomic-based phenotype prediction has been applied for single-nucleotide polymorphism (SNP) genotyping platforms, but not using complete genome sequences. Here, we report genomic prediction for starvation stress resistance and startle response in Drosophila melanogaster, using ∼2.5 million SNPs determined by sequencing the Drosophila Genetic Reference Panel population of inbred lines. We constructed a genomic relationship matrix from the SNP data and used it in a genomic best linear unbiased prediction (GBLUP) model. We assessed predictive ability as the correlation between predicted genetic values and observed phenotypes by cross-validation, and found a predictive ability of 0.239±0.008 (0.230±0.012) for starvation resistance (startle response). The predictive ability of BayesB, a Bayesian method with internal SNP selection, was not greater than GBLUP. Selection of the 5% SNPs with either the highest absolute effect or variance explained did not improve predictive ability. Predictive ability decreased only when fewer than 150,000 SNPs were used to construct the genomic relationship matrix. We hypothesize that predictive power in this population stems from the SNP-based modeling of the subtle relationship structure caused by long-range linkage disequilibrium and not from population structure or SNPs in linkage disequilibrium with causal variants. We discuss the implications of these results for genomic prediction in other organisms.
PLoS Genetics 05/2012; 8(5):e1002685. · 8.69 Impact Factor
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Trudy F C Mackay, Stephen Richards,
Eric A Stone,
Antonio Barbadilla,
Julien F Ayroles,
Dianhui Zhu,
Sònia Casillas,
Yi Han,
Michael M Magwire,
Julie M Cridland, [......],
Nehad Saada,
Lavanya Turlapati,
Kim C Worley,
Yuan-Qing Wu,
Akihiko Yamamoto,
Yiming Zhu,
Casey M Bergman,
Kevin R Thornton,
David Mittelman,
Richard A Gibbs
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ABSTRACT: A major challenge of biology is understanding the relationship between molecular genetic variation and variation in quantitative traits, including fitness. This relationship determines our ability to predict phenotypes from genotypes and to understand how evolutionary forces shape variation within and between species. Previous efforts to dissect the genotype-phenotype map were based on incomplete genotypic information. Here, we describe the Drosophila melanogaster Genetic Reference Panel (DGRP), a community resource for analysis of population genomics and quantitative traits. The DGRP consists of fully sequenced inbred lines derived from a natural population. Population genomic analyses reveal reduced polymorphism in centromeric autosomal regions and the X chromosome, evidence for positive and negative selection, and rapid evolution of the X chromosome. Many variants in novel genes, most at low frequency, are associated with quantitative traits and explain a large fraction of the phenotypic variance. The DGRP facilitates genotype-phenotype mapping using the power of Drosophila genetics.
Nature 02/2012; 482(7384):173-8. · 36.28 Impact Factor
