https://orcid.org/0000-0002-1607-3437
Figures
Abstract
The underlying genetic mechanisms affecting turkey growth traits have not been widely investigated. Genome-wide association studies (GWAS) is a powerful approach to identify candidate regions associated with complex phenotypes and diseases in livestock. In the present study, we performed GWAS to identify regions associated with 18-week body weight in a turkey population. The data included body weight observations for 24,989 female turkeys genotyped based on a 65K SNP panel. The analysis was carried out using a univariate mixed linear model with hatch-week-year and the 2 top principal components fitted as fixed effects and the accumulated polygenic effect of all markers captured by the genomic relationship matrix as random. Thirty-three significant markers were observed on 1, 2, 3, 4, 7 and 12 chromosomes, while 26 showed strong linkage disequilibrium extending up to 410 kb. These significant markers were mapped to 37 genes, of which 13 were novel. Interestingly, many of the investigated genes are known to be involved in growth and body weight. For instance, genes AKR1D1, PARP12, BOC, NCOA1, ADCY3 and CHCHD7 regulate growth, body weight, metabolism, digestion, bile acid biosynthetic and development of muscle cells. In summary, the results of our study revealed novel candidate genomic regions and candidate genes that could be managed within a turkey breeding program and adapted in fine mapping of quantitative trait loci to enhance genetic improvement in this species.
Citation: Abdalla EAE, Makanjuola BO, Wood BJ, Baes CF (2022) Genome-wide association study reveals candidate genes relevant to body weight in female turkeys (Meleagris gallopavo). PLoS ONE 17(3): e0264838. https://doi.org/10.1371/journal.pone.0264838
Editor: Martina Zappaterra, University of Bologna, ITALY
Received: August 17, 2021; Accepted: February 17, 2022; Published: March 10, 2022
Copyright: © 2022 Abdalla et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Data Availability: The data used in this study were obtained from Hybrid Turkey. Data are available upon reasonable request from: Hybrid Turkey 650 Riverbend Drive, Suite C Kitchener, N2K3S2 Canada Email: media.hybrid@hendrix-genetics.com The authors confirm that others would be able to access these data in the same manner as the authors.
Funding: The study was conducted as part of the project entitled "Application of genomic selection in turkeys for health, welfare, efficiency and production traits". This Project was funded by the Government of Canada through Genome Canada and the Ontario Genomics Institute (OGI-133) through the Genome Canada Genomic Application Partnership Program [recipients: CB (Academic), BW (Industry)]. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Competing interests: The authors have declared that no competing interests exist.
Introduction
Turkeys are mainly raised for meat and turkey production has increased worldwide in the last few years with the global market for turkey meat increasing to approximately 6 million tonnes per year between 2016 and 2019 [1]. Producing rapidly growing turkeys has been motivating breeders and farmers in response to the global high demand for meat [2, 3]. Thus, improving growth and yield are central to the turkey breeding objectives aimed at increasing production and minimizing costs [4–6]. Studies have shown high positive genetic correlations between turkey body weight at different ages [7, 8]. However, the higher heritability of turkey body weight is between ages 17- and 24-weeks [9].
Genomic selection is a powerful tool in determining selection candidates, but the identification of causative genes underlying genetic variation provides the necessary molecular information for marker-assisted and gene-based selection [7, 8]. Moreover, genetic parameters revealed that selection may not favour all traits of interest [6, 9, 10]. In this context, genome-wide association studies (GWAS) have been widely used to better understand the genetic architecture of complex traits through the identification of quantitative trait loci (QTL) harboring candidate loci.
QTLs affecting body weight have been previously reported in chickens (e.g. [11–13]), pigs [14] and beef [15]. For turkeys, despite several QTLs detected based on QTL mapping [4], no GWAS investigations have been performed to assess body weight. QTL mapping has been useful for detecting QTLs with relatively large effects, however, it does lack power in accurately modeling QTL with small effect, especially for complex traits such as body weight [16]. The current available high-density turkey genomic data opens the door to conduct GWAS, which may boost breeding programs in this species and overcome the unfavorable genetic correlations between traits of interest such as body weight and walking ability [5]. The objective of this study was to identify genetic variants and candidate genes associated with 18-week body weight (BW) in turkeys using GWAS in a turkey population genotyped with a proprietary 65K SNP array.
Materials and methods
Ethics statement
This study was carried out in accordance with the principles of the Canadian Council on Animal Care, the Hendrix Genetics Animal Welfare Policy, and the University of Guelph Animal Care Committee. The protocol was approved by the University of Guelph Animal Care Committee (Animal Use Protocol #3782).
Study population
Phenotypic and genomic data for 24,989 female turkeys from a male line were provided by Hybrid Turkeys, Kitchener, Canada. The birds hatched between 2010 and 2019 and were reared under a standard feeding system with group housing and shared feeders and drinkers. At 18 weeks of age, bodyweight was recorded with an average of 12.91 ± 0.87 kg. Blood samples were collected from each bird to extract DNA using standard industry procedures, and animals were genotyped using a proprietary 65K single nucleotide polymorphism (SNP) array (65,000 SNP; Illumina, Inc.). Markers in non-autosomal regions were removed and missing SNPs were imputed for missing SNPs using Beagle 5.1 [17]. Quality control for the imputed data was performed using PLINK software [18], where SNPs were removed if they had a minor allele frequency lower than 0.05 or significantly deviated from Hardy Weinberg proportions (P < 1×10−8). The number of markers retained for subsequent analyses was 48,715. More details about this data are presented in Abdalla et al. [5, 19].
Statistical analyses
Prior to GWAS, principal component (PC) analysis was implemented in PLINK [18]. Using the indep-pairwise option in PLINK, SNP markers were pruned with a window size of 25 markers, a step of 5 markers, and a r2 threshold of 0.2. This procedure resulted in 3,891 independent markers which were used to derive the top two PCs. To evaluate the association between SNPs and BW, the following univariate linear mixed was fitted
where y is an n × 1 vector of phenotypic values for n individuals, W is an n × c matrix of covariates (fixed effects) that control the population structure (top 2 PCs) and hatch week-year, α is a c × 1 vector of the corresponding coefficients, x is an n × 1 vector of marker genotypes at the locus being tested, β is the effect size of the marker, u is an n × 1 vector of random polygenic effects with a covariance structure as u ~ N (0,
), where K is the genetic relationship matrix derived from SNP markers and
is the polygenic additive variance, and e is an n × 1 vector of random residuals with e ~ N (0,
), where I is an n × n identity matrix, and
is the residual variance component.
Population stratification was assessed using a quantile-quantile (Q–Q) plot in addition to an inflation factor (λ; Yang et al. [20]), which was calculated by dividing the observed median value of the Chi-squared statistic for p-values (obtained from GWAS) by the expected median value of the Chi-squared statistic (approximately 0.456 for 1 df tests). Significant SNPs were determined using a genome-wide false discovery rate (FDR) of 5% [21]. This approach was chosen as it provides a higher power while controlling false discovery rate. The negative logarithm of the P-value for each SNP was displayed in a Manhattan plot and a genome wide line for the 5% FDR was drawn to display significant SNPs. Q–Q and Manhattan plots were generated using qqPlot and Manhattan functions, respectively, available in R [22]. Heritability of BW and phenotypic variance explained by the significant SNPs associated with BW was estimated by fitting a linear model using restricted maximum likelihood implemented in GCTA software [23]. To characterize candidate regions that affect bodyweight, linkage disequilibrium (LD) analysis was performed for the chromosomal regions with multiple clustered significant SNPs using PLINK software [18].
Assignment of significant SNPs to genes
The Turkey 5.1 assembly [24] was used to assign significant SNPs to genes. In chickens, strong linkage disequilibrium (LD) has been reported to extend up to 10–150 kb [25–27]. In this study, SNPs were assigned to genes if they were located within the genomic sequence of an annotated gene or within 15 kb of the 5’ or 3’ ends of the first and last exons, respectively. This distance is expected to capture proximal regulatory regions and other functional sites that may lie outside but close to the gene such as promoter regions.
Results and discussion
Deviations from the identity line to the left of the Q-Q plot suggest strong association of BW with the SNPs as shown in Fig 1. The genomic inflation factor was 0.84 indicating the absence of population stratification, therefore no adjustment was necessary [28, 29]. Q-Q plots were also obtained after removing the significant SNPs as well as without adjustment for the population stratification. These plots are presented in the supplementary material (S1 and S2 Figs). Based on an FDR of 5%, the Manhattan plot in Fig 2 shows the 33 SNPs that were significantly associated with BW in turkeys and these are shown in Table 1. Among these significant SNPs, 8, 2, 2, 4, 5 and 12 were located on Meleagris gallopavo autosomal chromosomes (MGA) 1, 2, 3, 4, 7 and 12, respectively. The minor allele frequency for the significant SNPs ranged between 0.10 and 0.47 and their effect on BW ranged between -0.15kg ± 0.01 and 0.12kg ± 0.03. Heritability of BW was 0.52 ± 0.01 and the phenotypic variance explained by all significant SNPs considered together was equal to 6.9% with a standard error of 0.02. In chickens, the effect of significant SNPs on bodyweight was reported between 3% and 8.1% [30, 31] and our results would appear to be similar to those found in this species.
It shows the late separation between observed and expected values. The genomic inflation factor was 0.84 indicating no population stratification. The shaded area around the quantile-quantile line is the 95% confidence interval (bands).
The genomic coordinates of SNPs are displayed along the horizontal axis, the negative logarithm of the association P-value for each SNP is displayed on the vertical axis, and the red line is the significance threshold line at the genome-wise false discovery rate of 5%.
As shown in Table 2, 27 significant SNPs out of the 33 identified in this study were mapped to 37 genes based on the Turkey 5.1 assembly [24] and the 15 kb up- and downstream distances previously described. Some SNPs were mapped to two genes and this is due to the 15 kb up- and downstream distances. From these 37 genes there were 13 novel LOC-named genes, i.e. genes that have yet to be characterised. The remaining six SNPs were neither within nor near any gene (15 kb up- or downstream). The lack of ability to map these SNPs to a gene could be due to the quality of the turkey genome assembly which contains many uncharacterized regions. The failure to connect markers to genes has been previously reported in pigs and was attributed to the low quality of the assembly; and for this reason a wider window of up to 50 kb has been suggested to assign such unlocated SNPs to genes [32]. In the present study, we maintained the 15 kb threshold as we found it a more prudent approach given the smaller relative size of the turkey genome compared to that of other livestock species. Although we also reported the nearest genes to these 6 SNPs in Table 3.
The 8 SNPs significantly associated with bodyweight on MGA 1 were located between 53.8 Mb and 182.3 Mb on the turkey genome shown in Table 1 and found to be distributed into two LD blocks that is depicted in Fig 3A. SNPs M2013, which is the leading SNP, and M2015 had a strong LD constructing a 52 kb long LD block (Block 1; Fig 3A). These two SNPs are located within AKR1D1 and PARP12 genes, respectively (Table 2). A third gene, which is the novel gene LOC100548731, is near M2015 and located 3,594 bp downstream of it. The AKR1D1 gene is a key gene that plays a critical role in the synthesis of bile acid and the metabolism of steroid hormones (e.g. [33, 34]), and studies have shown that dietary supplementation of bile acids can affect the activity of intestinal and lipoprotein lipases leading to improvement of broiler chicken growth [35, 36]. PARP12 gene, on the other hand, is a polymerase family member and found to be involved in regulating fatty acid metabolism [37] as well as body weight gain and insulin resistance in rats [38].
Linkage disequilibrium plots for significant SNPs associated with 18-week bodyweight in female turkeys on Meleagris gallopavo autosomal chromosome (MGA) 1 (a), MGA 2 (b), MGA 4 (c), MGA 7 (d) and MGA 12 (e).
The second LD block (146 kb) on MGA 1 includes 5 SNPs, once again shown in Fig 3A where the leading SNP was M2985 (Table 1). Except for M2987, the SNPs in this block were both located within genes and near (up- or downstream) other genes within the 15 kb regulatory distance considered in this study. The genes are LOC100551192, CLDND1, GPR15, LOC100550884, ILDR1, CFAP44 and BOC. The latter was also the nearest gene to M2987 (34 kb upstream; Table 3). The protein encoded by BOC mediates cell-cell interactions between muscle precursor cells and promotes myogenic differentiation [39]. Such protein has been reported to be associated with bodyweight gain and obesity in mice [40]. The expression of CLDND1 gene alters the metabolism functions in the liver leading to the progression of liver diseases [41] and in a recently published study, Zhu et al. [42] indicated that the CLDND1 gene is associated with energy production and fat metabolism in laying ducks. Moreover, according to Yi et al. (2016), the deficiency of fat metabolism in the liver increases ammonia levels and subsequently growth performance [43] and body fat distribution in broilers [44].
Two significant SNPs on MGA 2 affect bodyweight (Fig 3B); both had a strong LD and were located within a haplotype block span of 23 kb which covers ADCY3, CENPO and NCOA1 genes (Table 2). Interestingly, studies have shown that mutation and loss of function in ADCY3 induces bodyweight gain in humans [45–47]. One LD block (154 kb) for three significant SNPs associated with bodyweight was detected on a candidate region on MGA 4 (Fig 3C) covering the gene SPR in addition to three novel genes (Table 2). All significant SNPs on MGA 7 showed a strong LD located with one block, which extends to 53 kb (Fig 3D). Based on the Turkey 5.1 assembly [24], five genes: PMS1, TRNAL-CAG, MSTN, C2orf88 and HIBCH, were located within this LD block (Table 2). It is noteworthy to mention that the MSTN gene encodes a secreted ligand of the transforming growth factor-beta superfamily of proteins and regulators of muscle growth in chickens [48].
Fifteen significant SNPs associated with bodyweight were found on MGA 12, which was the highest number of significant SNPs on a single chromosome in this study (Tables 2 and 3). Five out of these 15 SNPs were not located near any gene within the 15 kb distance (Table 3). The LD analysis indicated that 8 SNPs had a strong LD (r2 ≥ 0.97) located within a single 410 kb long LD block. Whereas the first 3 SNPs in this block cover two genes (GLDN and LOC100549427) within the 15 kb distance (Table 2), the last 5 SNPs were positioned near SEMA6D and LOC116217089 genes but were beyond the 15 kb distance (Table 3). The HMG20A gene plays an important role in obesity in human [49, 50] and mice [51]. The TNFAIP8L3 gene was reported to affect growth and backfat thickness in pigs [52].
Finally, the distance between the two significant SNPs on MGA 3 was more than 729 kb. The first SNP, M13167, was within the novel gene LOC104910342, and the second SNP, M13195, was upstream of SDR16C5 (693 bp) and CHCHD7 (1,232 bp) genes. Nishimura et al. [53] indicated that CHCHD7 was significantly associated with carcass weight in Japanese black cattle, and recently Edea et al. [54] reported that SDR16C5 gene is associated with weaning weight, yearling weight and bodyweight gain in Korean cattle breeds.
Conclusions
In this study, we performed a GWA study for 18-weeks bodyweight in female turkeys using a 65K SNP array. The results revealed that 33 SNPs were significantly associated with this trait based on a 5% FDR. The linkage disequilibrium analysis showed that most of these genes are grouped into blocks that extend up to 410 kb. The significant SNPs were mapped to 37 genes, of which 13 were novel. Most of the genes detected are involved in functions related to bodyweight and growth, which has been supported by gene network analyses. The functions of the significant genes included regulation of growth, metabolism, digestion, bile acid biosynthetic and development of muscle cells. These findings could contribute to a better understanding of the genetic architecture of body weight gain in turkeys. However, further examination is required to prove the novel genes discovered in this study as putative genes for body weight in female turkeys.
Supporting information
S1 Fig. A quantile–quantile plot from GWAS for 18-week body weight in turkeys using a 65K SNP Illumina panel for all SNPs (dark blue) and after excluding SNPs significantly (false discovery rate of 5%) associated with the trait (black).
The sharp deviation above an expected -log10 p-value of approximately 3 is due to a strong association of 18-week body weight in turkeys with significant SNPs. Exclusion of significantly associated SNPs may leave a residual upward deviation leading to identify more associated SNPs with the trait, which was not the case in this study.
https://doi.org/10.1371/journal.pone.0264838.s001
(TIF)
S2 Fig. A quantile–quantile plot from GWAS for 18-week body weight in turkeys using a 65K SNP Illumina panel with adjustment for population stratification using the two top principal components (light blue) and without adjustment (dark blue).
The adjustment for population stratification did not change the findings of this GWAS study. The population used in this study is a pure turkey line and confounding due to population subgroups is unlikely to be observed.
https://doi.org/10.1371/journal.pone.0264838.s002
(TIF)
Acknowledgments
The authors extend their gratitude to personnel of the Hybrid Turkeys pedigree farm (Kitchener, Canada) for collecting and providing data used in this study.
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