Dataset Information

Comprehensive Analyses of 723 Transcriptomes Enhance GWAS Biological Interpretation and Genomic Prediction for Complex Traits in Cattle

ABSTRACT: Comprehensive Analyses of 723 Transcriptomes Enhance GWAS Biological Interpretation and Genomic Prediction for Complex Traits in Cattle

PROVIDER: PRJNA526257 | ENA |

REPOSITORIES: ENA

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			Action	DRS
	SRR8703140.fastq.gz	Fastqsanger.gz
	SRR8703150.fastq.gz	Fastqsanger.gz
	SRR8703160.fastq.gz	Fastqsanger.gz
	SRR8703170.fastq.gz	Fastqsanger.gz
	SRR8703180.fastq.gz	Fastqsanger.gz

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Project description:Human genome-wide association studies (GWASs) are revealing the genetic architecture of anthropomorphic and biomedical traits, i.e., the frequencies and effect sizes of variants that contribute to heritable variation in a trait. To interpret these findings, we need to understand how genetic architecture is shaped by basic population genetics processes-notably, by mutation, natural selection, and genetic drift. Because many quantitative traits are subject to stabilizing selection and because genetic variation that affects one trait often affects many others, we model the genetic architecture of a focal trait that arises under stabilizing selection in a multidimensional trait space. We solve the model for the phenotypic distribution and allelic dynamics at steady state and derive robust, closed-form solutions for summary statistics of the genetic architecture. Our results provide a simple interpretation for missing heritability and why it varies among traits. They predict that the distribution of variances contributed by loci identified in GWASs is well approximated by a simple functional form that depends on a single parameter: the expected contribution to genetic variance of a strongly selected site affecting the trait. We test this prediction against the results of GWASs for height and body mass index (BMI) and find that it fits the data well, allowing us to make inferences about the degree of pleiotropy and mutational target size for these traits. Our findings help to explain why the GWAS for height explains more of the heritable variance than the similarly sized GWAS for BMI and to predict the increase in explained heritability with study sample size. Considering the demographic history of European populations, in which these GWASs were performed, we further find that most of the associations they identified likely involve mutations that arose shortly before or during the Out-of-Africa bottleneck at sites with selection coefficients around s = 10-3.

Dataset Information

Comprehensive Analyses of 723 Transcriptomes Enhance GWAS Biological Interpretation and Genomic Prediction for Complex Traits in Cattle

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