Dataset Information

Fast genetic mapping of complex traits in C. elegans using millions of individuals in bulk.

ABSTRACT: Genetic studies of complex traits in animals have been hindered by the need to generate, maintain, and phenotype large panels of recombinant lines. We developed a new method, C. elegans eXtreme Quantitative Trait Locus (ceX-QTL) mapping, that overcomes this obstacle via bulk selection on millions of unique recombinant individuals. We use ceX-QTL to map a drug resistance locus with high resolution. We also map differences in gene expression in live worms and discovered that mutations in the co-chaperone sti-1 upregulate the transcription of HSP-90. Lastly, we use ceX-QTL to map loci that influence fitness genome-wide confirming previously reported causal variants and uncovering new fitness loci. ceX-QTL is fast, powerful and cost-effective, and will accelerate the study of complex traits in animals.

SUBMITTER: Burga A

PROVIDER: S-EPMC6582151 | biostudies-literature | 2019 Jun

REPOSITORIES: biostudies-literature

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Fast genetic mapping of complex traits in C. elegans using millions of individuals in bulk.

Burga Alejandro A Ben-David Eyal E Lemus Vergara Tzitziki T Boocock James J Boocock James J Kruglyak Leonid L

Nature communications 20190618 1

Genetic studies of complex traits in animals have been hindered by the need to generate, maintain, and phenotype large panels of recombinant lines. We developed a new method, C. elegans eXtreme Quantitative Trait Locus (ceX-QTL) mapping, that overcomes this obstacle via bulk selection on millions of unique recombinant individuals. We use ceX-QTL to map a drug resistance locus with high resolution. We also map differences in gene expression in live worms and discovered that mutations in the co-ch ...[more]

PMID: 31213597

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Project description:BackgroundGenome-wide association studies can have limited power to identify QTL, partly due to the stringent correction for multiple testing and low linkage-disequilibrium between SNPs and QTL. Regional Heritability Mapping (RHM) has been advanced as an alternative approach to capture underlying genetic effects. In this study, RHM was used to identify loci underlying variation in the 16(th) QTLMAS workshop simulated traits.MethodsThe method was implemented by fitting a mixed model where a genomic region and the overall genetic background were added as random effects. Heritabilities for the genetic regional effects were estimated, and the presence of a QTL in the region was tested using a likelihood ratio test (LRT). Several region sizes were considered (100, 50 and 20 adjacent SNPs). Bonferroni correction was used to calculate the LRT thresholds for genome-wide (p < 0.05) and suggestive (i.e., one false positive per genome scan) significance.ResultsGenomic heritabilities (0.31, 0.32 and 0.48, respectively) and genetic correlations (0.80, -0.42 and 0.19, between trait-pairs 1&2, 1&3 and 2&3) were similar to the simulated ones. RHM identified 7 QTL (4 at genome-wide and 3 at suggestive level) for Trait1; 4 (2 genome-wide and 2 suggestive) for Trait2; and 7 (6 genome-wide and 1 suggestive) for Trait3. Only one of the identified suggestive QTL was a false-positive. The position of these QTL tended to coincide with the position where the largest QTL (or several of them) were simulated. Several signals were detected for the simulated QTL with smaller effect. A combined analysis including all significant regions showed that they explain more than half of the total genetic variance of the traits. However, this might be overestimated, due to Beavis effect. All QTL affecting traits 1&2 and 2&3 had positive correlations, following the trend of the overall correlation of both trait-pairs. All but one QTL affecting traits 1&3 were negatively correlated, in agreement with the simulated situation. Moreover, RHM identified extra loci that were not found by association and linkage analysis, highlighting the improved power of this approach.ConclusionsRHM identified the largest QTL among the simulated ones, with some signals for the ones with small effect. Moreover, RHM performed better than association and linkage analysis, in terms of both power and resolution.

Dataset Information

Fast genetic mapping of complex traits in C. elegans using millions of individuals in bulk.

Publications

Fast genetic mapping of complex traits in C. elegans using millions of individuals in bulk.

Similar Datasets

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