Department of Animal Science, University of São Paulo, Piracicaba, SP, 13418-900, Brazil.
University of São Paulo (USP) / College of Animal Science and Food Engineering (FZEA), Pirassununga, São Paulo, Brazil.
BMC Genet. 2019 Nov 6;20(1):83. doi: 10.1186/s12863-019-0783-3.
Feed efficiency and growth rate have been targets for selection to improve chicken production. The incorporation of genomic tools may help to accelerate selection. We genotyped 529 individuals using a high-density SNP chip (600 K, Affymetrix®) to estimate genomic heritability of performance traits and to identify genomic regions and their positional candidate genes associated with performance traits in a Brazilian F Chicken Resource population. Regions exhibiting selection signatures and a SNP dataset from resequencing were integrated with the genomic regions identified using the chip to refine the list of positional candidate genes and identify potential causative mutations.
Feed intake (FI), feed conversion ratio (FC), feed efficiency (FE) and weight gain (WG) exhibited low genomic heritability values (i.e. from 0.0002 to 0.13), while body weight at hatch (BW1), 35 days-of-age (BW35), and 41 days-of-age (BW41) exhibited high genomic heritability values (i.e. from 0.60 to 0.73) in this F population. Twenty unique 1-Mb genomic windows were associated with BW1, BW35 or BW41, located on GGA1-4, 6-7, 10, 14, 24, 27 and 28. Thirty-eight positional candidate genes were identified within these windows, and three of them overlapped with selection signature regions. Thirteen predicted deleterious and three high impact sequence SNPs in these QTL regions were annotated in 11 positional candidate genes related to osteogenesis, skeletal muscle development, growth, energy metabolism and lipid metabolism, which may be associated with body weight in chickens.
The use of a high-density SNP array to identify QTL which were integrated with whole genome sequence signatures of selection allowed the identification of candidate genes and candidate causal variants. One novel QTL was detected providing additional information to understand the genetic architecture of body weight traits. We identified QTL for body weight traits, which were also associated with fatness in the same population. Our findings form a basis for further functional studies to elucidate the role of specific genes in regulating body weight and fat deposition in chickens, generating useful information for poultry breeding programs.
饲料效率和生长速度一直是选择的目标，以提高鸡的生产性能。基因组工具的应用可能有助于加速选择。我们对 529 个个体进行了基因分型，使用高密度 SNP 芯片（600K，Affymetrix®）来估计性能性状的基因组遗传力，并鉴定与巴西 F 鸡资源群体性能性状相关的基因组区域及其位置候选基因。表现出选择特征的区域和重测序的 SNP 数据集与使用芯片鉴定的基因组区域整合在一起，以细化位置候选基因列表并鉴定潜在的因果突变。
采食量（FI）、饲料转化率（FC）、饲料效率（FE）和体重增加（WG）表现出低的基因组遗传力值（即 0.0002 至 0.13），而孵化时体重（BW1）、35 日龄体重（BW35）和 41 日龄体重（BW41）在这个 F 群体中表现出高的基因组遗传力值（即 0.60 至 0.73）。20 个独特的 1-Mb 基因组窗口与 BW1、BW35 或 BW41 相关，位于 GGA1-4、6-7、10、14、24、27 和 28 上。在这些窗口内鉴定出 38 个位置候选基因，其中 3 个与选择特征区域重叠。在这些 QTL 区域内注释了 11 个与成骨、骨骼肌发育、生长、能量代谢和脂质代谢相关的位置候选基因中发现了 13 个预测的有害和 3 个高影响序列 SNP，这可能与鸡的体重有关。
使用高密度 SNP 阵列来识别与全基因组选择信号整合的 QTL，允许鉴定候选基因和候选因果变异。在同一群体中检测到一个新的 QTL，提供了更多关于体重性状遗传结构的信息。我们鉴定了与体重性状相关的 QTL，这些 QTL也与脂肪含量相关。我们的研究结果为进一步的功能研究提供了基础，以阐明特定基因在调节鸡体重和脂肪沉积中的作用，为家禽育种计划生成有用的信息。
