Due to the complexity of genotype-phenotype relationships, simultaneous analyses of genomic associations with multiple traits will be more powerful and informative than a series of univariate analyses. However, in most cases, studies of genotypephenotype relationships have been analyzed only one trait at a time. Here, we report the results of a fully integrated multivariate genome-wide association analysis of the shape of the Drosophila melanogaster wing in the Drosophila Genetic Reference Panel. Genotypic effects on wing shape were highly correlated between two different laboratories. We found 2396 significant SNPs using a 5% false discovery rate cutoff in the multivariate analyses, but just four significant SNPs in univariate analyses of scores on the first 20 principal component axes. One quarter of these initially significant SNPs retain their effects in regularized models that take into account population structure and linkage disequilibrium. A key advantage of multivariate analysis is that the direction of the estimated phenotypic effect is much more informative than a univariate one. We exploit this fact to show that the effects of knockdowns of genes implicated in the initial screen were on average more similar than expected under a null model. A subset of SNP effects were replicable in an unrelated panel of inbred lines. Association studies that take a phenomic approach, considering many traits simultaneously, are an important complement to the power of genomics.
For a given gene, different mutations influence organismal phenotypes to varying degrees. However, the expressivity of these variants not only depends on the DNA lesion associated with the mutation, but also on factors including the genetic background and rearing environment. The degree to which these factors influence related alleles, genes, or pathways similarly, and whether similar developmental mechanisms underlie variation in the expressivity of a single allele across conditions and among alleles is poorly understood. Besides their fundamental biological significance, these questions have important implications for the interpretation of functional genetic analyses, for example, if these factors alter the ordering of allelic series or patterns of complementation. We examined the impact of genetic background and rearing environment for a series of mutations spanning the range of phenotypic effects for both the scalloped and vestigial genes, which influence wing development in Drosophila melanogaster. Genetic background and rearing environment influenced the phenotypic outcome of mutations, including intra-genic interactions, particularly for mutations of moderate expressivity. We examined whether cellular correlates (such as cell proliferation during development) of these phenotypic effects matched the observed phenotypic outcome. While cell proliferation decreased with mutations of increasingly severe effects, surprisingly it did not co-vary strongly with the degree of background dependence. We discuss these findings and propose a phenomenological model to aid in understanding the biology of genes, and how this influences our interpretation of allelic effects in genetic analysis.
42For a given gene, different mutations influence organismal phenotypes to varying 43 degrees. However, the expressivity of these variants not only depends on the DNA lesion 44 associated with the mutation, but also on factors including the genetic background and 45 rearing environment. The degree to which these factors influence related alleles, genes, or 46 pathways similarly, and whether similar developmental mechanisms underlie variation in the 47 expressivity of a single allele across conditions and variation across alleles is poorly 48 understood. Besides their fundamental biological significance, these questions have 49 important implications for the interpretation of functional genetic analyses, for example, if 50 these factors alter the ordering of allelic series or patterns of complementation. We 51 examined the impact of genetic background and rearing environment for a series of 52 mutations spanning the range of phenotypic effects for both the scalloped and vestigial 53 genes, which influence wing development in Drosophila melanogaster. Genetic background 54 and rearing environment influenced the phenotypic outcome of mutations, including intra-55 genic interactions, particularly for mutations of moderate expressivity. We examined whether 56 cellular correlates (such as cell proliferation during development) of these phenotypic effects 57 matched the observed phenotypic outcome. While cell proliferation decreased with 58 mutations of increasingly severe effects, surprisingly it did not co-vary strongly with the 59 degree of background dependence. We discuss these findings and propose a 60 phenomenological model to aid in understanding the biology of genes, and how this 61 influences our interpretation of allelic effects in genetic analysis. 62 63 Author Summary 64 Different mutations in a gene, or in genes with related functions, can have effects of 65 varying severity. Studying sets of mutations and analyzing how they interact are essential 66 components of a geneticist's toolkit. However, the effects caused by a mutation depend not 67 only on the mutation itself, but on additional genetic variation throughout an organism's 68 genome and on the environment that organism has experienced. Therefore, identifying how 69 the genomic and environmental context alter the expression of mutations is critical for 70 making reliable inferences about how genes function. Yet studies on this context 71 dependence have largely been limited to single mutations in single genes. We examined 72 how the genomic and environmental context influence the expression of multiple mutations 73 in two related genes affecting the fruit fly wing. Our results show that the genetic and 74 3 environmental context generally affect the expression of related mutations in similar ways. 75 However, the interactions between two different mutations in a single gene sometimes 76 depended strongly on context. In addition, cell proliferation in the developing wing and adult 77 wing size were not affected by the genetic and environmental context...
Due to the complexity of genotype-phenotype relationships, simultaneous analyses of genomic associations with multiple traits will be more powerful and more informative than a series of univariate analyses. In most cases, however, studies of genotype-phenotype relationships have analyzed only one trait at a time, even as the rapid advances in molecular tools have expanded our view of the genotype to include whole genomes. Here, we report the results of a fully integrated multivariate genome-wide association analysis of the shape of the Drosophila melanogaster wing in the Drosophila Genetic Reference Panel. Genotypic effects on wing shape were highly correlated between two different labs. We found 2,396 significant SNPs using a 5%FDR cutoff in the multivariate analyses, but just 4 significant SNPs in univariate analyses of scores on the first 20 principal component axes. A key advantage of multivariate analysis is that the direction of the estimated phenotypic effect is much more informative than a univariate one.Exploiting this feature, we show that the directions of effects were on average replicable in an unrelated panel of inbred lines. Effects of knockdowns of genes implicated in the initial screen were on average more similar than expected under a null model. Association studies that take a phenomic approach in considering many traits simultaneously are an important complement to the power of genomics. Multivariate analyses of such data are more powerful, more informative, and allow the unbiased study of pleiotropy.Forward genetic analyses are generally built on a single measurable quantity, such as size, color, or the presence/absence of a distinctive organismal feature. The rise of phenomics, with its . CC-BY-NC-ND 4.0 International license peer-reviewed) is the author/funder. It is made available under a The copyright holder for this preprint (which was not . http://dx.doi.org/10.1101/108308 doi: bioRxiv preprint first posted online Feb. 14, 2017; P i t c h e r s e t a l .-3 emphasis on high-throughput measurement of high-dimensional traits, is beginning to allow us to address the genetics of more complex traits that no single measurement can capture (Houle 2010; Houle et al. 2010). For instance, any one measurement of the wing of a fly, such as the length, incompletely captures wing size and shape Houle and Fierst 2013;Pitchers et al. 2013).Despite the growing enthusiasm for a more comprehensive approach to the phenotype, the vast majority of genome-wide association studies (GWAS) that include more than one trait have undertaken multiple univariate analyses for each site, rather than a single multivariate analysis (e.g., Teslovich et al. 2010;Battle et al. 2014). Statisticians have long appreciated the value of genuinely multivariate approaches to association studies (Lange et al. 2003;Shriner 2012), and this has led to a recent flowering of multivariate methods and software (O'Reilly et al. 2012;Stephens 2013; van der Sluis et al. 2013;Scutari et al. 2014;Zhou and Stephens 2014;Schaid et al. 2016). ...
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