bioRxivGenomicsPreprint
Population genomics reveals extensive inbreeding and purging of mutational load in wild Amur tigers
Tianming LaniD, Haimeng Li, Le ZhangiD, Minhui ShiiD, Boyang LiuiD, Liangyu CuiiD, Nicolás DussexiD, Qing WangiD, Yue MaiD, Dan Liu, Weiyao Kong, Jiangang WangiD, Haorong LuiD, Shaofang Zhang, Jieyao Yu, Xinyu WangiD, Yuxin WuiD, Xiaotong NiuiD, Jiale FaniD, Yue Zhao, Love DaléniD, Guangshun JiangiD, Huan LiuiD, Yanchun XuiD
Abstract
The inbreeding is a big threat for the persistence of genetic diversity in small and isolated populations of endangered species. The homozygous genome could exacerbate inbreeding depression by introducing homozygous deleterious alleles in the population. However, purging of inbreeding loads as they become homozygotes in small populations could alleviate the depression. The Amur tiger (Panthera tigris altaica) is typically exists in small population living in forests in Northeast Asia and is among the most endangered animals on the planet with great symbolic significance of conservation. By comparing with captive individuals, we revealed substantially higher and more extensive inbreeding in the wild Amur tiger population (FROH=0.51) than in captive Amur tigers (FROH=0.26). We further found much less mutational loads in wild populations when compared with captive Amur tigers. However, the frequency of loss of function and deleterious nonsynonymous mutations inside ROH regions are much lower than that in non-ROH regions in both wild and captive Amur tigers, indicating the purging may had occurred in both populations but much effective in the wild population. In addition, we found the average frequency of deleterious alleles was much lower than that of neutral alleles in the wild population, indicating that the purifying selection contributed to the purging of mutational loads in the wild Amur tigers. These findings provide valuable genome-wide evidence to support the making of future conservation plans of wild Amur tigers.
- assay: whole-genome sequencing
- research focus: variant calling
- research focus: variant effect prediction
- research focus: GWAS / association mapping
- approach: statistical model
- Citations
- 2
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