Genetic structure of six cattle populations revealed by transcriptomewide SNPs and gene expression
Wei Wang, Sichuan Animal Science Academy, Chengdu, People's Republic of China | Huai Wang, Sichuan Animal Science Academy, Chengdu, People's Republic of China | Hui Tang, Sichuan Animal Science Academy, Chengdu, People's Republic of China | Jia Gan, Xuanhan Animal Breeding and Improvement Station, Sichuan, People's Republic of China | Changgeng Shi, Xuanhan Animal Breeding and Improvement Station, Sichuan, People's Republic of China | Qing Lu, Sichuan Yangping Cow Breeding Farm, Sichuan, People's Republic of China | Donghui Fang, Sichuan Animal Science Academy, Chengdu, People's Republic of China | Jun Yi, Sichuan Animal Science Academy, Chengdu, People's Republic of China | Maozhong Fu, Sichuan Animal Science Academy, Chengdu, People's Republic of China
There are abundant cattle breeds/populations in China, and the systematic discovery of genomic variants is essential for performing the marker assisted selection and conservation of genetic resources. In the present study, we employed whole transcriptome sequencing (RNA-Seq) technology for revealing genetic structure among six Chinese cattle populations according to transcriptome-wide SNPs and gene expression. A total of 68,094 variants consisting of 61,754 SNPs and 6340 InDels were detected and widely distributed among all chromosomes, by which the clear patterns of population structures were revealed. We also found the significantly differential density of variant distribution among genes. Additionally, we totally assembled 15,992 genes and detected obvious differences on the expression profiles among populations. In contrast to genomic variants, the measure of gene expression levels failed to support the expected population structure. Here, we provided a global landscape on the differential expression genes among these cattle populations.
Показать больше [+] Меньше [-]Ключевые слова АГРОВОК
Библиографическая информация
Эту запись предоставил Korea Agricultural Science Digital Library