Center for Quantitative Genetics and Genomics, Aarhus University, 8830, Tjele, Denmark.
SEGES Danish Pig Research Centre, Agro Food Park 15, 8200, Aarhus N, Denmark.
BMC Genomics. 2022 Feb 15;23(1):133. doi: 10.1186/s12864-022-08373-3.
Imputation from genotyping array to whole-genome sequence variants using resequencing of representative reference populations enhances our ability to map genetic factors affecting complex phenotypes in livestock species. The accumulation of knowledge about gene function in human and laboratory animals can provide substantial advantage for genomic research in livestock species.
In this study, 201,388 pigs from three commercial Danish breeds genotyped with low to medium (8.5k to 70k) SNP arrays were imputed to whole genome sequence variants using a two-step approach. Both imputation steps achieved high accuracies, and in total this yielded 26,447,434 markers on 18 autosomes. The average estimated imputation accuracy of markers with minor allele frequency ≥ 0.05 was 0.94. To overcome the memory consumption of running genome-wide association study (GWAS) for each breed, we performed within-breed subpopulation GWAS then within-breed meta-analysis for average daily weight gain (ADG), followed by a multi-breed meta-analysis of GWAS summary statistics. We identified 15 quantitative trait loci (QTL). Our post-GWAS analysis strategy to prioritize of candidate genes including information like gene ontology, mammalian phenotype database, differential expression gene analysis of high and low feed efficiency pig and human GWAS catalog for height, obesity, and body mass index, we proposed MRAP2, LEPROT, PMAIP1, ENSSSCG00000036234, BMP2, ELFN1, LIG4 and FAM155A as the candidate genes with biological support for ADG in pigs.
Our post-GWAS analysis strategy helped to identify candidate genes not just by distance to the lead SNP but also by multiple sources of biological evidence. Besides, the identified QTL overlap with genes which are known for their association with human growth-related traits. The GWAS with this large data set showed the power to map the genetic factors associated with ADG in pigs and have added to our understanding of the genetics of growth across mammalian species.
使用代表性参考群体的重测序对基因分型阵列到全基因组序列变体进行推断,提高了我们在畜牧物种中映射影响复杂表型的遗传因素的能力。人类和实验室动物中基因功能知识的积累可为畜牧物种的基因组研究提供实质性优势。
在这项研究中,使用两步法,对三种丹麦商业品种的 201388 头猪进行了低至中等(8.5k 至 70k)SNP 阵列的基因分型,然后对全基因组序列变体进行了推断。这两个推断步骤都达到了很高的准确性,总共在 18 条常染色体上产生了 26447434 个标记。具有次要等位基因频率≥0.05的标记的平均估计推断准确性为 0.94。为了克服为每个品种运行全基因组关联研究(GWAS)的内存消耗,我们在每个品种内进行了亚种群 GWAS,然后对平均日增重(ADG)进行了品种内荟萃分析,然后对 GWAS 汇总统计数据进行了多品种荟萃分析。我们确定了 15 个数量性状位点(QTL)。我们的 GWAS 后分析策略是对候选基因进行优先级排序,包括基因本体论、哺乳动物表型数据库、高和低饲料效率猪和人类 GWAS 目录的差异表达基因分析,以及身高、肥胖和体重指数,我们提出了 MRAP2、LEPROT、PMAIP1、ENSSSCG00000036234、BMP2、ELFN1、LIG4 和 FAM155A 作为候选基因,这些基因在猪中具有 ADG 的生物学支持。
我们的 GWAS 后分析策略不仅通过与 lead SNP 的距离,而且通过多种来源的生物学证据来识别候选基因。此外,鉴定的 QTL 与已知与人类生长相关性状相关的基因重叠。使用这个大数据集进行的 GWAS 显示了映射与猪 ADG 相关的遗传因素的能力,并增加了我们对跨哺乳动物物种生长遗传学的理解。