Roffler Gretchen H, Amish Stephen J, Smith Seth, Cosart Ted, Kardos Marty, Schwartz Michael K, Luikart Gordon
Alaska Science Center, U.S. Geological Survey, 4210 University Drive, Anchorage, AK, 99508, USA.
Wildlife Biology Program, Department of Ecosystem Sciences and Conservation, College of Forestry and Conservation, University of Montana, Missoula, MT, 59812, USA.
Mol Ecol Resour. 2016 Sep;16(5):1147-64. doi: 10.1111/1755-0998.12560. Epub 2016 Jul 18.
Identification of genes underlying genomic signatures of natural selection is key to understanding adaptation to local conditions. We used targeted resequencing to identify SNP markers in 5321 candidate adaptive genes associated with known immunological, metabolic and growth functions in ovids and other ungulates. We selectively targeted 8161 exons in protein-coding and nearby 5' and 3' untranslated regions of chosen candidate genes. Targeted sequences were taken from bighorn sheep (Ovis canadensis) exon capture data and directly from the domestic sheep genome (Ovis aries v. 3; oviAri3). The bighorn sheep sequences used in the Dall's sheep (Ovis dalli dalli) exon capture aligned to 2350 genes on the oviAri3 genome with an average of 2 exons each. We developed a microfluidic qPCR-based SNP chip to genotype 476 Dall's sheep from locations across their range and test for patterns of selection. Using multiple corroborating approaches (lositan and bayescan), we detected 28 SNP loci potentially under selection. We additionally identified candidate loci significantly associated with latitude, longitude, precipitation and temperature, suggesting local environmental adaptation. The three methods demonstrated consistent support for natural selection on nine genes with immune and disease-regulating functions (e.g. Ovar-DRA, APC, BATF2, MAGEB18), cell regulation signalling pathways (e.g. KRIT1, PI3K, ORRC3), and respiratory health (CYSLTR1). Characterizing adaptive allele distributions from novel genetic techniques will facilitate investigation of the influence of environmental variation on local adaptation of a northern alpine ungulate throughout its range. This research demonstrated the utility of exon capture for gene-targeted SNP discovery and subsequent SNP chip genotyping using low-quality samples in a nonmodel species.
识别自然选择基因组特征背后的基因是理解对当地环境适应性的关键。我们使用靶向重测序来识别5321个候选适应性基因中的单核苷酸多态性（SNP）标记，这些基因与绵羊和其他有蹄类动物已知的免疫、代谢和生长功能相关。我们选择性地靶向了所选候选基因的蛋白质编码区以及附近的5'和3'非翻译区中的8161个外显子。靶向序列取自大角羊（加拿大盘羊）外显子捕获数据，并直接取自家羊基因组（绵羊参考基因组版本3；oviAri3）。用于白令山羊（多尔氏绵羊）外显子捕获的大角羊序列与oviAri3基因组上的2350个基因对齐，每个基因平均有2个外显子。我们开发了一种基于微流控定量聚合酶链反应（qPCR）的SNP芯片，对来自其分布范围内各地的476只多尔氏绵羊进行基因分型，并测试选择模式。使用多种确证方法（Lositan和Bayescan），我们检测到28个可能正在经历选择的SNP位点。我们还确定了与纬度、经度、降水量和温度显著相关的候选位点，表明存在局部环境适应性。这三种方法一致支持对九个具有免疫和疾病调节功能（如卵巢主要组织相容性复合体II类分子DRα（Ovar-DRA）、抗原呈递细胞（APC）、BATF2、黑色素瘤相关抗原家族B18（MAGEB18））、细胞调节信号通路（如KRIT1、磷脂酰肌醇-3-激酶（PI3K）、ORRC3）和呼吸健康（半胱氨酰白三烯受体1（CYSLTR1））的基因进行自然选择。利用新的基因技术表征适应性等位基因分布，将有助于研究环境变化对一种北方高山有蹄类动物在其整个分布范围内局部适应性的影响。这项研究证明了外显子捕获在非模式物种中利用低质量样本进行基因靶向SNP发现以及后续SNP芯片基因分型的实用性。
