Genomic analysis of inbreeding depression in the wild.
Genomic analysis of inbreeding depression in the wild.
批准号:
423718723
负责人:
Martin Stoffel
金额:
$0.0万
依托单位国家:
德国
项目类别:
Research Fellowships
财政年份:
2019
资助国家:
德国
项目状态:
未结题
起止时间:
2018-12-31 至 --
中文摘要
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英文摘要
The detrimental consequences of inbreeding have fascinated scientists since Darwin. One and a half centuries later, we know that inbreeding depression is ubiquitous in diploid organisms and evidence for its critical impacts on a broad range of traits, individual fitness and population viability comes from all over the animal and plant kingdoms. Despite the enormous significance of inbreeding depression, little is known about its most fundamental features: How many regions of the genome contribute to inbreeding depression? What are the underlying genes and alleles and how strong are their deleterious effects? How strongly is inbreeding depression selected against over the generations? Is inbreeding depression stronger in earlier or in later life-stages? These questions can only be addressed in wild populations, where fitness can be measured under the pressures of natural environments, but until recently we lacked the genomic tools to answer them. Here, I propose the most in-depth genomic investigation of inbreeding depression in a wild population to date. In one of the best-phenotyped free-living study populations in the world, the Soay sheep of St Kilda, I will use high density SNP data for selected pedigree members in combination with population-wide 50k SNP chip data to impute more than 450,000 SNP genotypes throughout the genomes of thousands of study individuals. Using this dataset, I will quantify regions in the genome which are identical-by-descent (IBD) to (1) investigate whether most inbreeding depression resides in long stretches of IBD derived from recent inbreeding, (2) quantify the abundance of embryonic lethal alleles in the population, (3) locate the genetic variants causing inbreeding depression across the genome and quantify their effect sizes using a combination of established and novel methods and (4) follow-up large effect loci using whole-genome-sequencing data for a deeper investigation of the underlying genes and the mode of selection involved.
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