BACKGROUND 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 F2 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. RESULTS 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 F2 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. CONCLUSIONS 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.

中文翻译：

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通过综合方法揭示与饲料效率和体重性状有关的基因组关联。

背景技术饲料效率和生长速率已成为选择以提高鸡肉产量的目标。整合基因组工具可能有助于加速选择。我们使用高密度SNP芯片（600 K，Affymetrix®）对529个个体进行了基因分型，以评估性能特征的基因组遗传力，并确定巴西F2鸡资源种群中与性能特征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来自重测序的SNP数据集与使用芯片鉴定的基因组区域整合在一起，以完善位置候选基因的列表并鉴定潜在的致病突变。结果采食量（FI），饲料转化率（FC），饲料效率（FE）和增重（WG）表现出较低的基因组遗传度值（即0.0002至0）。13），而在该F2种群中，孵化时的体重（BW1），35天（BW35）和41天（BW41）表现出较高的基因组遗传力值（即0.60至0.73）。20个独特的1-Mb基因组窗口与位于GGA1-4、6-7、10、14、24、27和28上的BW1，BW35或BW41相关。在这些窗口中鉴定出38个位置候选基因，其中三个它们与选择签名区域重叠。在这些QTL区域中的13个预测的有害和3个高影响序列SNPs在与成骨，骨骼肌发育，生长，能量代谢和脂质代谢有关的11个位置候选基因中进行了注释，这些基因可能与鸡的体重有关。结论使用高密度SNP阵列鉴定QTL，并与选择的全基因组序列签名整合在一起，可以鉴定候选基因和候选因果变体。检测到一种新颖的QTL，可提供更多信息来了解体重性状的遗传结构。我们针对体重特征确定了QTL，这些特征也与同一人群中的肥胖有关。我们的发现为进一步的功能研究奠定了基础，以阐明特定基因在调节鸡体重和脂肪沉积中的作用，从而为家禽育种计划提供有用的信息。检测到一种新颖的QTL，可提供更多信息来了解体重性状的遗传结构。我们针对体重特征确定了QTL，这些特征也与同一人群中的肥胖有关。我们的发现为进一步的功能研究奠定了基础，以阐明特定基因在调节鸡体重和脂肪沉积中的作用，从而为家禽育种计划提供有用的信息。检测到一种新颖的QTL，可提供更多信息来了解体重性状的遗传结构。我们针对体重特征确定了QTL，这些特征也与同一人群中的肥胖有关。我们的发现为进一步的功能研究奠定了基础，以阐明特定基因在调节鸡体重和脂肪沉积中的作用，从而为家禽育种计划提供有用的信息。