Charting the genotype–phenotype map: lessons from the Drosophila melanogaster Genetic Reference Panel

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Trudy F.C. Mackay, Wen Huang

Published Online: Aug 22 2017

DOI: 10.1002/wdev.289

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Understanding the genetic architecture (causal molecular variants, their effects, and frequencies) of quantitative traits is important for precision agriculture and medicine and predicting adaptive evolution, but is challenging in most species. The Drosophila melanogaster Genetic Reference Panel (DGRP) is a collection of 205 inbred strains with whole genome sequences derived from a single wild population in Raleigh, NC, USA. The large amount of quantitative genetic variation, lack of population structure, and rapid local decay of linkage disequilibrium in the DGRP and outbred populations derived from DGRP lines present a favorable scenario for performing genome‐wide association (GWA) mapping analyses to identify candidate causal genes, polymorphisms, and pathways affecting quantitative traits. The many GWA studies utilizing the DGRP have revealed substantial natural genetic variation for all reported traits, little evidence for variants with large effects but enrichment for variants with low P‐values, and a tendency for lower frequency variants to have larger effects than more common variants. The variants detected in the GWA analyses rarely overlap those discovered using mutagenesis, and often are the first functional annotations of computationally predicted genes. Variants implicated in GWA analyses typically have sex‐specific and genetic background‐specific (epistatic) effects, as well as pleiotropic effects on other quantitative traits. Studies in the DGRP reveal substantial genetic control of environmental variation. Taking account of genetic architecture can greatly improve genomic prediction in the DGRP. These features of the genetic architecture of quantitative traits are likely to apply to other species, including humans. WIREs Dev Biol 2018, 7:e289. doi: 10.1002/wdev.289 This article is categorized under: Invertebrate Organogenesis > Flies

Genetic correlation and pleiotropy in the Drosophila melanogaster Genetic Reference Panel. (a) Hierarchical clustering of line means across 61 published traits in females (Table 3). The distance between each pair of traits is measured as 1 − |Spearman's rank correlation|. Line means were normal quantile transformed and adjusted for Wolbachia and five major inversions before subsequent analyses. The names of the traits are annotated in (b). (b) Estimated allele effects of variants associated with starvation resistance on all traits. Each column on the heatmap represents a trait. The order of the traits follows (a) and is annotated only in (b). Starvation resistance is marked with an asterisk. (c) Box plots of pleiotropic effects of DNA variants across eight different annotation classes. The class ‘peptide sequence’ indicates variants that create peptide sequence truncation or elongation by eliminating or introducing start and stop codons and frameshifts. When calculating pleiotropic effects, we arbitrarily select traits that are representative of a cluster from the same study. The selected traits are colored in red in (b). (d) Box plots of pleiotropic effects of DNA variants according to minor allele frequency. (e) Associations between 317 variants in the highly pleiotropic gene mmd are represented as −log 10(P‐value) for each of the 61 traits. A total of 11 traits contained at least one variant with P < 0.001. For each of these traits, the strongest association is marked with a colored number on top of the peak, where 1 = olfactory behavior (benzaldehyde), 2 = chill coma recovery, 3 = developmental time (standard diet), 4 = olfactory behavior (ethyl acetate), 5 = susceptibility to fungus Ma549 (LT50), 6 = phototaxis (4 day old), 7 = alcohol sensitivity (1st elution), 8 = night sleep, 9 = bacterial load, 10 = percent dead post intestinal pathogen colonization, 11 = recombination rate (y–v interval).

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Power to detect quantitative trait loci in mapping populations of individual and replicated genotypes. Power is calculated by assuming a 1df test. Under the alternative hypothesis, for a DNA variant explaining a² amount of genetic variation for a trait of heritability H², the test statistic is distributed as a noncentral with noncentrality parameter , where n is the number of lines and r is the number of replicate individuals of the same genotype. Using a P‐value threshold of α = 10^{− 5}, the power for the association test is . Power to detect association at different effect sizes under two scenarios where H² = 0.5 and r = 1 (individual) and 50 (line means) respectively is plotted.

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