Genome-Wide Association Study Provides Insights Into the Genetic Architecture of Bone Size and Mass in Chickens
Total Page：16
File Type：pdf, Size：1020Kb
Genome Genome-wide association study provides insights into the genetic architecture of bone size and mass in chickens Journal: Genome Manuscript ID gen-2019-0022.R2 Manuscript Type: Article Date Submitted by the 20-Nov-2019 Author: Complete List of Authors: GUO, Jun; Jiangsu Institute of Poultry Science, layer breeding Qu, Liang; Jiangsu Institute of Poultry Science DOU, Taocun; Jiangsu Institute of Poultry Science, layer breeding Shen, Manman; Jiangsu Institute of Poultry Science Hu, Yuping;Draft Jiangsu Institute of Poultry Science Ma, Meng; Jiangsu Institute of Poultry Science WANG, Kehua; Jiangsu Institute of Poultry Science, layer breeding Bone length, Dominance effect, Genome-wide association study, Keyword: Heritability, Linear mixed model Is the invited manuscript for consideration in a Special Not applicable (regular submission) Issue? : https://mc06.manuscriptcentral.com/genome-pubs Page 1 of 29 Genome 1 Genome-wide association study provides insights into the genetic 2 architecture of bone size and mass in chickens 3 4 Jun Guo, Liang Qu, Tao-Cun Dou, Man-Man Shen, Yu-Ping Hu, Meng Ma and Ke-Hua Wang* 5 6 Jiangsu Institute of Poultry Science, Key Laboratory for Poultry Genetics and Breeding of Jiangsu 7 province, Yangzhou, Jiangsu, 225125, China 8 9 *Corresponding author:Kehua Wang 10 Jiangsu Institute of Poultry Science at Yangzhou, China. 11 Mailing address: Draft 12 No. 58 Cangjie Road, 225125, Yangzhou , China 13 Tel: +86(514) 85599012 14 Fax: +86(514)85599035 15 Phone number:13805276606 16 Email:[email protected] 17 1 https://mc06.manuscriptcentral.com/genome-pubs Genome Page 2 of 29 18 19 Abstract: Bone size is an important trait for chickens due to its association with osteoporosis in 20 layers and meat production in broilers. Here, we employed high density genotyping platforms to 21 detect candidate genes for bone traits. Estimates of the narrow heritabilities ranged from 0.37 ± 22 0.04 for shank length to 0.59 ± 0.04 for tibia length. The dominance heritability was 0.12±0.04 23 for shank length. Using a linear mixed model approach, we identified a promising locus within 24 NCAPG on chromosome 4, which was associated with tibia length and mass, femur length and 25 area and shank length. In addition, three other loci were associated with bone size or mass at a 26 Bonferroni-corrected genome-wide significance threshold of 1%. One region on chicken 27 chromosome 1 between 168.38 and 171.82 Mb, harboredHTR2A, LPAR6, CAB39L, and TRPC4. 28 A second region that accounted for 2.2%Draft of the phenotypic variance was located around WNT9A 29 on chromosome 2, where allele substitution was predicted to be associated with tibia length. Four 30 candidate genes identified on chromosome 27 comprising SPOP, NGFR, GIP, and HOXB3 were 31 associated with tibia length and mass, femur length and area, and shank length. Genome 32 partitioning analysis indicated that the variance explained by each chromosome was proportional to 33 its length. 34 Keywords: Bone length; Dominance effect; Genome-wide association study; Heritability; Linear 35 mixed model 36 2 https://mc06.manuscriptcentral.com/genome-pubs Page 3 of 29 Genome 37 Introduction 38 Bone growth is of importance to poultry production as skeletal problems are associated with 39 economic losses and welfare issue(Bradshaw et al. 2002; Kapell et al. 2012). Long bone 40 distortions are a common disease in broiler production, although the causes of these deformities 41 are multifactorial diseases. In Europe, about 44 billion broiler chickens with leg disorders are 42 sacrificed annually (Turner et al. 2003). Osteoporosis leads to the unbalanced bone resorption and 43 the loss of structural bone, and it is a major health problem in layers. Osteoporosis accounts for 44 20% to 35% of all mortalities during the egg laying cycle in caged hens (Anderson 2002; 45 Whitehead and Fleming 2000). Poultry selection breeding systems have traditionally focused on 46 the improvement of economic traits, but the selection on welfare traits such as bone-related traits 47 has been observed in recent years (Kapell et al. 2012; Whitehead 2004). 48 In general, bone length and mass are regardedDraft as important parameters for evaluating bone growth 49 in chickens and other species (Tsudzuki et al. 2007; Gao et al. 2010). Numerous factors can affect 50 bone length and mass, and most probably have genetic origins. González-Cerón et al. (2015) 51 reported that the heritabilities of tibia length and mass in a broiler populationwere0.54 ± 0.07 and 52 0.31 ± 0.06, respectively. Ragognetti et al. (2015) found that the heritabilities of tibia length and 53 mass in a F2 populationwere0.23 ± 0.08 and 0.23 ± 0.07, respectively. Abdellatif (1989) reported 54 that the heritability of shank length was 0.58 ± 0.28. Tsudzuki et al. (2007) estimated that shank 55 length had a high heritability of 0.37 in a crossbred population. Bone length and mass are highly 56 genetically determined, but the genetic architecture that underlies these traits is still poorly 57 defined. 58 The aim of the present study was to elucidate the genetic architecture of bone traits in the chicken 59 using a genome-wide association study (GWAS) approach. To do this we used an F2 population 60 obtained from White Leghorn and Dongxiang Blue-shelled chickens, and identified single 61 nucleotide polymorphisms (SNPs) related to bone growth. We investigated whether the bone 3 https://mc06.manuscriptcentral.com/genome-pubs Genome Page 4 of 29 62 traits were determined by common variants and estimated the genetic parameters for the bone 63 traits. 64 65 Material and methods 66 Ethics statement 67 All procedures involving animals were in compliance with the guidelines for the care and use of 68 experimental animals established by the Ministry of Agriculture of China. The ethics committee 69 of Jiangsu Poultry Science Institute specifically approved the present study. 70 Study design 71 The F2 population was generated from a reciprocal cross between an indigenous breed (Dongxiang Blue-shelled 72 chickens) and commercial layer(White Leghorn). Dongxiang Blue-shelled chickens were introduced from 73 Jiangxi province into an experimental farm Draftat Jiangsu Institute of Poultry Science in 1998. White Leghorn 74 chickens were kindly provided by Shanghai Poultry Breeding Company. Six White Leghorn cocks were mated 75 with 133 Dongxiang hens and six Dongxiang cocks were mated with 80 White Leghorn hens, and the F1 76 generations comprised of 1,029 birds (White Leghorn cocks × Dongxiang hens)and 552 birds (Dongxiang cocks 77 × White Leghorn hens).The F2 generations were produced by the F1 individuals within the respective crosses to 78 obtain 1,856 cockerels and 1,893 pullets. All of the F2 birds were measured to determine their bone traits. The 79 laying mash contained 16.16% crude protein, 10.64 MJ·kg−1 metabolizable energy, 3.4% calcium, and 0.52% 80 total phosphorus. 81 Trait measurement 82 The shank length was quantified using a measuring tape at 508 days old as the distance from the 83 hock joint to the tarsometatarsus. The F2 chickens were then sacrificed to obtain other bone 84 measurements. The right femur and tibiae were dissected from the carcass. Muscle and connective 85 tissue were carefully removed from the bone with a scalpel. The tibia and femur lengths were 86 measured with Vernier calipers. The femur area was determined by dual energy X-ray 87 absorptiometry using the Discovery DXA system (Hologic, Inc. Bedford, MA, USA). 88 Genotyping 4 https://mc06.manuscriptcentral.com/genome-pubs Page 5 of 29 Genome 89 DNA was extracted from the whole blood according to a standard protocol(Moore and Dowhan 90 2002). Genotyping analyses of 1,534 samples from the F2 generation were conducted using an 91 Affymetrix Axiom 600K Chicken Genotyping Array (Affymetrix, Inc., Santa Clara, CA, USA). 92 More details of the genotyping array were described by Kranis et al. (2013). The quality of the 93 array data was evaluated with Affymetrix Power Tools and PLINK software (Purcell et al. 2007). 94 SNPs were excluded if they had a minor-allele frequency (MAF) <1%. SNPs that deviated from 95 the Hardy–Weinberg equilibrium (P value < 1e−6) were removed. SNPs on the sex chromosome 96 were removed. Samples with call rates <95% were removed. Phasing analyses were performed 97 with Beagle software (version 4.0) (Browning and Browning 2007). Finally, 435,867 autosomal 98 SNPs and 1,512 samples passed the quality control procedure. 99 Association analysis 100 Before the association test, an independentDraft SNP set was established using PLINK with a window 101 size of 25 SNPs, a step of five SNPs, and an r2 threshold of 0.2. The principal components (PCs) 102 were then obtained using the linkage equilibrium SNPs. The top five PCs were assigned as 103 covariates in the linear mixed model. In the present study, the effective number of independent 104 tests was 59,308, and thus, the genome-wide suggestive and significant P-values were 1.69e−5 and 105 8.43e−7, respectively. We searched for the candidate genes closest to the associated SNPs in 106 GeneCards (http://www.genecards.org/), Ensembl (http://asia.ensembl.org, version 89 ), and 107 NCBI (http://www.ncbi.nlm.nih.gov) database. The positions of interesting SNPs were obtained 108 from Ensembl version 89 and NCBI Gallus_gallus-5.0. 109 Conditional analysis was conducted to examine the potential associated SNPs that might be 110 masked by a strong signal. Briefly, the initial screen involved testing with the strongest SNP 111 covariate. Association analysis conditioning was then implemented based on the selected SNP(s) 112 to iteratively search for the top SNPs one by one using a stepwise model selection procedure until 113 no SNP had a conditional P-value that passed the significance level.