The genetics of quantitative traits: challenges and prospects

By T. Mackay, Eric A. Stone, Julien F. Ayroles. Nature reviews genetics, 2009

1,248 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/aa6627bbc3f18d9add7a4a3389e34e19371673d2

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In Genetics

Citations in the corpus

Progress and Promise of Genome-Wide Association Studies for Human Complex Trait Genetics (2011, background): “A systems genetics (see Table 1) approach is thus needed, in which large sets of genetic variants and/or genes are analyzed together, genetic data are integrated with external functional data types, and the results inform the biology of the complex trait directly (reviewed in Mackay et al. 2009).”
The genomics of local adaptation in trees: are we out of the woods yet? (2017, background): “…uncertainty regarding the exact number of loci underlying many adaptive traits, the effect size distribution of these loci, and how the number of underlying loci and effect distribution may change under various evolutionary regimes (Orr 2001; Slate 2005; Hansen 2006; Mackay et al. 2009).”
Climate change, adaptation, and phenotypic plasticity: the problem and the evidence (2014, background): “Given that the genetic underpinnings of most traits are still not known (Ellegren and Sheldon 2008; Mackay et al. 2009; Anderson et al. 2013) and that the accurate measurement of natural selection is difficult (Kingsolver et al.”
Characterization of a global germplasm collection and its potential utilization for analysis of complex quantitative traits in maize (2011, background): “Marker density is another determinant for increasing the power of association analysis (Mackay et al. 2009), especially for GWAS.”
The Drosophila melanogaster Genetic Reference Panel (2012, positive): “Model organism reference populations of inbred strains that can be shared among laboratories studying diverse phenotypes, and for which environmental conditions can be controlled and manipulated, greatly facilitate efforts to dissect the genetic architecture of quantitative traits 3,4 .”
Natural variation in genome architecture among 205 Drosophila melanogaster Genetic Reference Panel lines (2014, background): “…inferences about regions under purifying natural selection independent of functional analyses and highlighting the contribution of common and rare variants in protein coding as well as regulatory sequences to the genetic architecture of complex traits (Flint and Mackay, 2009; Mackay et al., 2009).”
Common disorders are quantitative traits (2009, background): “Identifying quantitative mechanisms Once multiple genes are found to be associated with a disorder, understanding the mechanisms by which each gene affects the disorder leads to quantitative traits being recognised at all levels of analysis: from gene expression profiles, to other '-omic' levels ”