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Mapping the genomic architecture of adaptive traits with interspecific introgressive origin: a coalescent-based approach

Overview of attention for article published in BMC Genomics, January 2016
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Title
Mapping the genomic architecture of adaptive traits with interspecific introgressive origin: a coalescent-based approach
Published in
BMC Genomics, January 2016
DOI 10.1186/s12864-015-2298-2
Pubmed ID
Authors

Hussein A. Hejase, Kevin J. Liu

Abstract

Recent studies of eukaryotes including human and Neandertal, mice, and butterflies have highlighted the major role that interspecific introgression has played in adaptive trait evolution. A common question arises in each case: what is the genomic architecture of the introgressed traits? One common approach that can be used to address this question is association mapping, which looks for genotypic markers that have significant statistical association with a trait. It is well understood that sample relatedness can be a confounding factor in association mapping studies if not properly accounted for. Introgression and other evolutionary processes (e.g., incomplete lineage sorting) typically introduce variation among local genealogies, which can also differ from global sample structure measured across all genomic loci. In contrast, state-of-the-art association mapping methods assume fixed sample relatedness across the genome, which can lead to spurious inference. We therefore propose a new association mapping method called Coal-Map, which uses coalescent-based models to capture local genealogical variation alongside global sample structure. Using simulated and empirical data reflecting a range of evolutionary scenarios, we compare the performance of Coal-Map against EIGENSTRAT, a leading association mapping method in terms of its popularity, power, and type I error control. Our empirical data makes use of hundreds of mouse genomes for which adaptive interspecific introgression has recently been described. We found that Coal-Map's performance is comparable or better than EIGENSTRAT in terms of statistical power and false positive rate. Coal-Map's performance advantage was greatest on model conditions that most closely resembled empirically observed scenarios of adaptive introgression. These conditions had: (1) causal SNPs contained in one or a few introgressed genomic loci and (2) varying rates of gene flow - from high rates to very low rates where incomplete lineage sorting dominated as a primary cause of local genealogical variation.

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 28 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 28 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 6 21%
Student > Doctoral Student 3 11%
Student > Bachelor 3 11%
Other 3 11%
Researcher 2 7%
Other 6 21%
Unknown 5 18%
Readers by discipline Count As %
Agricultural and Biological Sciences 15 54%
Biochemistry, Genetics and Molecular Biology 6 21%
Nursing and Health Professions 1 4%
Environmental Science 1 4%
Unknown 5 18%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 1. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 20 April 2016.
All research outputs
#18,453,763
of 22,865,319 outputs
Outputs from BMC Genomics
#8,188
of 10,663 outputs
Outputs of similar age
#285,439
of 395,007 outputs
Outputs of similar age from BMC Genomics
#216
of 243 outputs
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