Genome‐wide association study for egg production and quality in layer chickens

Discovery of genes with large effects on economically important traits has for many years been of interest to breeders. The development of SNP panels which cover the whole genome with high density and, more importantly, that can be genotyped on large numbers of individuals at relatively low cost, has opened new opportunities for genome‐wide association studies (GWAS). The objective of this study was to find genomic regions associated with egg production and quality traits in layers using analysis methods developed for the purpose of whole genome prediction. Genotypes on over 4500 birds and phenotypes on over 13 000 hens from eight generations of a brown egg layer line were used. Birds were genotyped with a custom 42K Illumina SNP chip. Recorded traits included two egg production and 11 egg quality traits (puncture score, albumen height, yolk weight and shell colour) at early and late stages of production, as well as body weight and age at first egg. Egg weight was previously analysed by Wolc et al. ( 2012 ). The Bayesian whole genome prediction model – BayesB (Meuwissen et al. 2001 ) was used to locate 1 Mb regions that were most strongly associated with each trait. The posterior probability of a 1 Mb window contributing to genetic variation was used as the criterion for suggesting the presence of a quantitative trait locus (QTL) in that window. Depending upon the trait, from 1 to 7 significant (posterior probability >0.9) 1 Mb regions were found. The largest QTL, a region explaining 32% of genetic variance, was found on chr4 at 78 Mb for body weight but had pleiotropic effects on other traits. For the other traits, the largest effects were much smaller, explaining <7% of genetic variance, with regions on chromosomes 2, 12 and 17 explaining above 5% of genetic variance for albumen height, shell colour and egg production, respectively. In total, 45 of 1043 1 Mb windows were estimated to have a non‐zero effect with posterior probability > 0.9 for one or more traits.     

Tag-SNP selection using Bayesian genomewide association study for growth traits in Hereford and Braford cattle

The aim of this study was to perform a Bayesian genomewide association study (GWAS) to identify genomic regions associated with growth traits in Hereford and Braford cattle, and to select Tag-SNPs to represent these regions in low-density panels useful for genomic predictions. In addition, we propose candidate genes through functional enrichment analysis associated with growth traits using Medical Subject Headings (MeSH). Phenotypic data from 126,290 animals and genotypes for 131 sires and 3,545 animals were used. The Tag-SNPs were selected with BayesB (π = 0.995) method to compose low-density panels. The number of Tag-single nucleotide polymorphism (SNP) ranged between 79 and 103 SNP for the growth traits at weaning and between 78 and 100 SNP for the yearling growth traits. The average proportion of variance explained by Tag-SNP with BayesA was 0.29, 0.23, 0.32 and 0.19 for birthweight (BW), weaning weight (WW205), yearling weight (YW550) and postweaning gain (PWG345), respectively. For Tag-SNP with BayesA method accuracy values ranged from 0.13 to 0.30 for k-means and from 0.30 to 0.65 for random clustering of animals to compose reference and validation groups. Although genomic prediction accuracies were higher with the full marker panel, predictions with low-density panels retained on average 76% of the accuracy obtained with BayesB with full markers for growth traits. The MeSH analysis was able to translate genomic information providing biological meanings of more specific gene products related to the growth traits. The proposed Tag-SNP panels may be useful for future fine mapping studies and for lower-cost commercial genomic prediction applications.     

A genome‐wide association study for canine cryptorchidism in Siberian Huskies

Cryptorchidism is a condition whereby one or both testes fail to descend into the scrotal sac. Here, we performed a genome‐wide association study (GWAS) with both a case–control analysis using the GEMMA software accounting for population structure and a BayesB approach in the GenSel software applied to every 1 Mb window of SNPs or haplotypes. The haplotypes were constructed from a genealogical tree using the population of 204 Siberian Huskies. The BayesB analyses identified six putative genomic candidate regions on CFA6, 9, 24, 27 and X. These regions explained a high percentage of genetic variance when compared with other genomic regions. The positional candidate genes Q9TSI5_CANFA (matrix metalloproteinase 9 precursor) on CFA24, ADAMTS20 (ADAM metallopeptidase with thrombospondin type 1 motif, 20) on CFA27 and MID1IP1 (MID1 interacting protein 1) on CFAX are known to be functionally related to extracellular matrix remodelling, which might be important for gubernaculum elongation and thus interrupting normal testicular descent. Further mutation screening in these candidate regions on CFA6, 9, 24, 27 and X is needed. Next generation sequencing will help to uncover rare variants associated with cryptorchidism in this dog population.     

Mixture models detect large effect QTL better than GBLUP and result in more accurate and persistent predictions

Background: Accurate evaluation of SNP effects is important for genome wide association studies and for genomic prediction. The genetic architecture of quantitative traits differs widely, with some traits exhibiting few if any quantitative trait loci(QTL) with large effects, while other traits have one or several easily detectable QTL with large effects.Methods: Body weight in broilers and egg weight in layers are two examples of traits that have QTL of large effect.A commonly used method for genome wide association studies is to fit a mixture model such as Bayes B that assumes some known proportion of SNP effects are zero. In contrast, the most commonly used method for genomic prediction is known as GBLUP, which involves fitting an animal model to phenotypic data with the variance-covariance or genomic relationship matrix among the animals being determined by genome wide SNP genotypes. Genotypes at each SNP are typically weighted equally in determining the genomic relationship matrix for GBLUP. We used the equivalent marker effects model formulation of GBLUP for this study. We compare these two classes of models using egg weight data collected over 8 generations from 2,324 animals genotyped with a42 K SNP panel.Results: Using data from the first 7 generations, both Bayes B and GBLUP found the largest QTL in a similar well-recognized QTL region, but this QTL was estimated to account for 24 % of genetic variation with Bayes B and less than 1 % with GBLUP. When predicting phenotypes in generation 8 Bayes B accounted for 36 % of the phenotypic variation and GBLUP for 25 %. When using only data from any one generation, the same QTL was identified with Bayes B in all but one generation but never with GBLUP. Predictions of phenotypes in generations 2 to 7 based on only 295 animals from generation 1 accounted for 10 % phenotypic variation with Bayes B but only6 % with GBLUP. Predicting phenotype using only the marker effects in the 1 Mb region that accounted for the largest effect on egg weight from generation 1 data alone accounted for almost 8 % variation using Bayes B but had no predictive power with GBLUP.Conclusions: In conclusion, In the presence of large effect QTL, Bayes B did a better job of QTL detection and its genomic predictions were more accurate and persistent than those from GBLUP.     

Theoretical efficiency of multiple-trait quantitative trait loci-assisted selection

The effectiveness of five selection methods for genetic improvement of net merit comprising trait 1 of low heritability (h² = 0.1) and trait 2 of high heritability (h² = 0.4) was examined: (i) two‐trait quantitative trait loci (QTL)‐assisted selection; (ii) partial QTL‐assisted selection based on trait 1; (iii) partial QTL‐assisted selection based on trait 2; (iv) QTL‐only selection; and (v) conventional selection index without QTL information. These selection methods were compared under 72 scenarios with different combinations of the relative economic weights, the genetic correlations between traits, the ratio of QTL variance to total genetic variance of the trait, and the ratio of genetic variances between traits. The results suggest that the detection of QTL for multiple‐trait QTL‐assisted selection is more important when the index traits are negatively correlated than when they are positively correlated. In contrast to literature reports that single‐trait marker‐assisted selection (MAS) is the most efficient for low heritability traits, this study found that the identified QTL of the low heritability trait contributed negligibly to total response in net merit. This is because multiple‐trait QTL‐assisted selection is designed to maximize total net merit rather than the genetic response of the individual index trait as in the case of single‐trait MAS. Therefore, it is not economical to identify the QTL of the low heritability traits for the improvement of total net merit. The efficient, cost‐effective selection strategy is to identify the QTL of the moderate or high heritability traits of the QTL‐assisted selection index to facilitate total economic returns. Detection of the QTL of the low h² traits for the QTL‐assisted index selection is justified when the low h² traits have high negative genetic correlation with the other index traits and/or when both economic weights and genetic variances of the low h² traits are larger as compared to the other index traits of higher h². This study deals with theoretical efficiency of QTL‐assisted selection, but the same principle applies to SNP‐based genomic selection when the proportion of the genetic variance ‘explained by the identified QTLs’ in this study is replaced by ‘explained by SNPs’.     

Signatures of selection in Charolais beef cattle identified by genome‐wide analysis

Charolais cattle are one of the most important breeds for meat production worldwide; in México, its selection is mainly made by live weight traits. One strategy for mapping important genomic regions that might influence productive traits is the identification of signatures of selection. This type of genomic features contains loci with extended linkage disequilibrium (LD) and homozygosity patterns that are commonly associated with sites of quantitative trait locus (QTL). Therefore, the objective of this study was to identify the signatures of selection in Charolais cattle genotyped with the GeneSeek Genomic Profiler Bovine HD panel consisting of 77 K single nucleotide polymorphisms (SNPs). A total 61,311 SNPs and 819 samples were used for the analysis. Identification of signatures of selection was carried out using the integrated haplotype score (iHS) methodology implemented in the rehh R package. The top ten SNPs with the highest piHS values were located on BTA 4, 5, 6 and 14. By identifying markers in LD with top ten SNPs, the candidate regions defined were mapped to 52.8–59.3 Mb on BTA 4; 67.5–69.3 on BTA 5; 39.5–41.0 Mb on BTA 6; and 26.4–29.6 Mb on BTA 14. The comparison of these candidate regions with the bovine QTLdb effectively confirmed the association (p < 0.05) with QTL related to growth traits and other important productive traits. The genomic regions identified in this study indicated selection for growth traits on the Charolais population via the conservation of haplotypes on various chromosomes. These genomic regions and their associated genes could serve as the basis for haplotype association studies and for the identification of causal genes related to growth traits.     

A note on transgenerational epigenetics affecting egg quality traits in meat-type quail

ABSTRACT

1. The aim of the following experiment was to estimate transgenerational epigenetic variance for egg quality traits using genealogical and phenotypic information in meat-type quail. Measured traits included egg length (EL) and width (EWD), albumen weight (AW), shell weight (SW), yolk weight (YW) and egg weight (EW).

2. A total of 391 birds were evaluated for egg quality by collecting a sample of one egg per bird, during three consecutive days, starting on the 14th d of production. Analyses were performed using mixed models including the random epigenetic effect. Variance components were estimated by the restricted maximum likelihood method. A grid-search for values for the auto-recursive parameter (λ) was used in the variance components estimation. This parameter is directly related to the reset (v) and epigenetic transmissibility (1 ? v) coefficients.

3. The epigenetic effect was not significant for any of the egg quality traits evaluated. Direct heritability estimates for egg quality traits ranged in magnitude from 0.06 to 0.33, whereby the higher estimates were found for AW and SW. Epigenetic heritability estimates were low and close to zero (ranging from 0.00 to 0.07) for all evaluated traits.

4. The current breeding strategies accounting for additive genetic effect seem to be suitable for egg quality traits in meat-type quail.     

Dissection of additive genetic variability for quantitative traits in chickens using SNP markers

The aim of this study was to separate marked additive genetic variability for three quantitative traits in chickens into components associated with classes of minor allele frequency (MAF), individual chromosomes and marker density using the genomewide complex trait analysis (GCTA) approach. Data were from 1351 chickens measured for body weight (BW), ultrasound of breast muscle (BM) and hen house egg production (HHP), each bird with 354 364 SNP genotypes. Estimates of variance components show that SNPs on commercially available genotyping chips marked a large amount of genetic variability for all three traits. The estimated proportion of total variation tagged by all autosomal SNPs was 0.30 (SE 0.04) for BW, 0.33 (SE 0.04) for BM, and 0.19 (SE 0.05) for HHP. We found that a substantial proportion of this variation was explained by low frequency variants (MAF <0.20) for BW and BM, and variants with MAF 0.10–0.30 for HHP. The marked genetic variance explained by each chromosome was linearly related to its length (R² = 0.60) for BW and BM. However, for HHP, there was no linear relationship between estimates of variance and length of the chromosome (R² = 0.01). Our results suggest that the contribution of SNPs to marked additive genetic variability is dependent on the allele frequency spectrum. For the sample of birds analysed, it was found that increasing marker density beyond 100K SNPs did not capture additional additive genetic variance.     

Identification of genetic markers for fat deposition and meat tenderness on bovine chromosome 5: development of a low-density single nucleotide polymorphism map

As genetic markers, SNP are well suited for the development of genetic tests for production traits in livestock. They are stable through many generations and can provide direct assessment of individual animal's genetic merit if they are in linkage disequilibrium and phase with functional genetic variation. Bovine chromosome 5 has been shown to harbor genetic variation affecting production traits in multiple cattle populations; thus, this chromosome was targeted for SNP-based marker development and subsequent association analysis with carcass and growth phenotypes. Discovery of SNP was performed in a panel of 16 sires representing two sires from each of seven beef breeds and two Holstein sires by PCR amplification and sequencing using primers designed from genomic sequence obtained by low-coverage sequencing of bacterial artificial chromosome (BAC) clones. From 550 SNP, 296 (54%) were tentatively identified as having a minor allele frequency >10%. Forty-five SNP derived from 15 BAC were chosen based on minor allele frequency and were genotyped in 564 steers and their sires. Production and carcass data were collected on the steers as a part of the Germplasm Evaluation (GPE), Cycle VII Project at the U.S. Meat Animal Research Center (Clay Center, NE), which involves of the evaluation of sires from seven of the most popular U.S. breeds. Haplotypes based on seven SNP derived from a BAC containing the bovine genes HEM1 and PDE1B were associated with traits related to carcass fat. Steers homozygous for the major haplotype had 0.15 +/- 0.04 cm less subcutaneous fat, 0.57 +/- 0.18 kg less rib fat, 0.18 +/- 0.07 lower yield grade, 1.11 +/- 0.35% less predicted fat yield, and 0.79 +/- 0.3% greater predicted retail product yield than heterozygotes. The frequency of the major haplotype was 0.70 in the steers, and it ranged from 0.44 (Limousin) to 0.98 (Simmental and Gelbvieh) in a panel consisting of an average of 20 purebred sires from each of the seven breeds. A second set of haplotypes based on four SNP derived from a BAC containing the genes NOL1 and CHD4 was associated with Warner-Bratzler shear force. Steers homozygous for the major haplotype had 0.27 +/- 0.11 kg greater shear force than those heterozygous for the major haplotype and one of two minor haplotypes. The frequency of the major haplotype was 0.59 in the steers and ranged from 0.27 (Hereford) to approximately 0.95 (Angus and Red Angus) in the panel of purebred sires. These results demonstrate the feasibility of targeting QTL regions for SNP-based marker development and that a low level of coverage can identify markers associated with phenotypic traits.     

Polymorphisms in GnRHI and GnRHII genes and their association with egg production and egg quality traits in chicken

1. Two candidate genes, namely, Gonadotropin releasing hormone I (GnRHI) and Gonadotropin releasing hormone II (GnRHII) play pivotal roles in ovulation and egg production in chicken. The objective of this study was to explore polymorphism in these genes and to estimate the effects of polymorphism of these two genes on egg production and egg quality traits in White Leghorn laying hens.

2. Single strand conformation polymorphism followed by sequencing was performed to detect polymorphism in these genes.

3. The coding regions of the GnRHI and GnRHII genes were found to be polymorphic. In the GnRH1 gene, 12 haplotypes were determined, of which the h1 haplotype was predominant and the h5, h9 and h11 haplotypes were the least frequent ones. In the GnRHII gene, eight haplotypes were found, of which the h1 haplotype was the most frequent and the h6 was the least frequent haplotype in the White Leghorn population.

4. The haplogroups of GnRHI had a significant effect on body weight and egg production up to 64 weeks of age, yolk content, Haugh units and egg shell parameters. The h1h2 haplogroup of the GnRHI gene showed the highest egg production, with 211.0 ± 24.3 eggs up to 64 weeks of age, while the highest yolk content and Haugh unit was found in h3h10 haplogrouped birds. The haplogroups of GnRHII had a significant effect on age at sexual maturity (ASM) where the shortest ASM was found in the h1h4 birds (147.3 ± 5.9 d) and the longest ASM was observed in the h1h3 birds (160.6 ± 23.4 d).

5. It was concluded that GnRHI and GnRHII genes are polymorphic and have a significant effect on body weight, egg production and egg quality traits in White Leghorn laying hens.     

欣华鸡DRD2基因多态性及其与蛋用性状的关联分析

试验旨在研究多巴胺受体D2(dopamine receptor D2,DRD2)基因多态性及其与欣华鸡蛋用性状的相关性,寻找可用于欣华鸡蛋用性状选育的分子遗传标记。应用Primer Premier 5.0软件设计4对引物,利用PCR-RFLP技术对欣华E系鸡群的473个个体进行基因型鉴定,使用SPSS 19.0软件将欣华鸡的蛋用性状与DRD2基因多态性进行关联分析。群体多态性分析结果表明,存在4个SNPs位点:A-16105G(SNP1)、G-12510T(SNP2)、G+3360A(SNP3)和T+5042C(SNP4),其中SNP1和SNP2位点符合哈代-温伯格平衡(P>0.05),且杂合度较低,但SNP3和SNP4位点显著偏离哈代-温伯格平衡(P<0.05)。关联分析表明,DRD2基因4个SNPs位点与欣华E系鸡群体蛋用性状存在关联,其中与产蛋性状关联结果:SNP1与开产日龄显著关联(P<0.05),SNP2与33周龄产蛋数、300日龄产蛋总数极显著关联(P<0.01),SNP3与开产体重(P<0.05)、300日龄产蛋总数(P<0.01)、平均连产(P<0.01)和最长连产(P<0.05)关联,SNP4与开产日龄极显著关联(P<0.01);与蛋品质关联结果:SNP1与蛋形指数(P<0.01)、蛋黄颜色(P<0.01)和哈氏单位(P<0.05)关联,SNP2与蛋黄重显著关联(P<0.05),SNP3与蛋壳强度极显著关联(P<0.01),SNP4与蛋黄重、蛋清重和哈氏单位显著关联(P<0.05)。单倍型分析发现,DRD2基因4个SNPs位点的不同单倍型组合与欣华鸡的最长连产长度呈极显著相关(P<0.01),与哈氏单位呈显著相关(P<0.05)。组织表达谱分析发现,DRD2基因主要在欣华E系鸡垂体中表达,在58周龄鸡胸肌中有较高表达,在36周龄鸡脑中有少量表达。结果表明,DRD2基因可作为候选基因辅助用于欣华E系鸡群体蛋用性状的遗传改良。     

Accurate genomic predictions for BCWD resistance in rainbow trout are achieved using low‐density SNP panels: Evidence that long‐range LD is a major contributing factor

Previously accurate genomic predictions for Bacterial cold water disease (BCWD) resistance in rainbow trout were obtained using a medium‐density single nucleotide polymorphism (SNP) array. Here, the impact of lower‐density SNP panels on the accuracy of genomic predictions was investigated in a commercial rainbow trout breeding population. Using progeny performance data, the accuracy of genomic breeding values (GEBV) using 35K, 10K, 3K, 1K, 500, 300 and 200 SNP panels as well as a panel with 70 quantitative trait loci (QTL)‐flanking SNP was compared. The GEBVs were estimated using the Bayesian method BayesB, single‐step GBLUP (ssGBLUP) and weighted ssGBLUP (wssGBLUP). The accuracy of GEBVs remained high despite the sharp reductions in SNP density, and even with 500 SNP accuracy was higher than the pedigree‐based prediction (0.50–0.56 versus 0.36). Furthermore, the prediction accuracy with the 70 QTL‐flanking SNP (0.65–0.72) was similar to the panel with 35K SNP (0.65–0.71). Genomewide linkage disequilibrium (LD) analysis revealed strong LD (r² ≥ 0.25) spanning on average over 1 Mb across the rainbow trout genome. This long‐range LD likely contributed to the accurate genomic predictions with the low‐density SNP panels. Population structure analysis supported the hypothesis that long‐range LD in this population may be caused by admixture. Results suggest that lower‐cost, low‐density SNP panels can be used for implementing genomic selection for BCWD resistance in rainbow trout breeding programs.     

A genomewide association study in divergently selected lines in rabbits reveals novel genomic regions associated with litter size traits

Uterine capacity (UC), defined as the total number of kits from unilaterally ovariectomized does at birth, has a high genetic correlation with litter size. The aim of our research was to identify genomic regions associated with litter size traits through a genomewide association study using rabbits from a divergent selection experiment for UC. A high-density SNP array (200K) was used to genotype 181 does from a control population, high and low UC lines. Traits included total number born (TNB), number born alive (NBA), number born dead, ovulation rate (OR), implanted embryos (IE) and embryo, foetal and prenatal survivals at second parity. We implemented the Bayes B method and the associations were tested by Bayes factors and the percentage of genomic variance (GV) explained by windows. Different genomic regions associated with TNB, NBA, IE and OR were found. These regions explained 7.36%, 1.27%, 15.87% and 3.95% of GV, respectively. Two consecutive windows on chromosome 17 were associated with TNB, NBA and IE. This genomic region accounted for 6.32% of GV of TNB. In this region, we found the BMP4, PTDGR, PTGER2, STYX and CDKN3 candidate genes which presented functional annotations linked to some reproductive processes. Our findings suggest that a genomic region on chromosome 17 has an important effect on litter size traits. However, further analyses are needed to validate this region in other maternal rabbit lines.     

Study on association of single nucleotide polymorphism of CAPN1 gene with muscle fibre and carcass traits in quality chicken populations

This study was aimed at investigating the effect of the calpain 1 (CAPN1) gene on carcass and meat quality traits in eight meat-type chicken populations, including five pure lines (developed from Chinese local breeds) and three cross-breeds. Primer pairs for the Coding Sequence (CDS) region in CAPN1 were designed from the chicken genomic sequence database. Polymorphisms were detected by polymerase chain reaction (PCR)-Single Strand Conformation Polymorphism (SSCP) and DNA sequencing. Three single nucleotide polymorphisms (SNP; C2546T, G3535A and C7198A) were detected among individuals in each population. The associations of their haplotypes (H1 = CGA, H2 = CGC, H3 = CAA, H4 = CAC, H5 = TGA and H7 = TAA) with chicken breast muscle fibre and carcass traits were analysed. Results showed that the haplotypes were associated with live weight (LW), carcass weight (CW), breast muscle weight (BMW) and leg muscle weight (LMW) (p < 0.05), and were also related to eviscerated percentage (%EP) and breast muscle fibre density (p < 0.01). H1H3 haplotype was dominant for LW, CW and BMW; H1H5 haplotype was dominant for EP; H3H4 haplotype was dominant for LMW and H1H1 haplotype was dominant for BFD. It was concluded that the CAPN1 gene may be a major gene affecting meat quality traits of chicken or it is linked with the major gene. H1H3, H1H5 and H3H4 were the most advantageous haplotypes for carcass traits whereas H1H1 was the positive haplotype for breast muscle fibre trait.     

Detection of single nucleotide polymorphisms associated with ultrasonic backfat depth in a segregating Meishan x White Composite population

Multiple genomic scans have identified QTL for backfat deposition across the porcine genome. The objective of this study was to detect SNP and genomic regions associated with ultrasonic backfat. A total of 74 SNP across 5 chromosomes (SSC 1, 3, 7, 8, and 10) were selected based on their proximity to backfat QTL or to QTL for other traits of interest in the experimental population. Gilts were also genotyped for a SNP thought to influence backfat in the thyroxine-binding globulin gene (TBG) on SSC X. Genotypic data were collected on 298 gilts, divided between the F8 and F10 generations of the US Meat Animal Research Center Meishan resource population (composition, one-quarter Meishan). Backfat depths were recorded by ultrasound from 3 locations along the back at approximately 210 and 235 d of age in the F8 and F10 generations, respectively. Ultrasound measures were averaged for association analyses. Regressors for additive, dominant, and parent-of-origin effects of each SNP were calculated using genotypic probabilities computed by allelic peeling algorithms in GenoProb. The association model included the fixed effects of scan date and TBG genotype, the covariates of weight and SNP regressors, and random additive polygenic effects to account for genetic similarities between animals not explained by known genotypes. Variance components for polygenic effects and error were estimated using MTDFREML. Initially, each SNP was fitted (once with and once without parent-of-origin effects) separately due to potential multi-collinearity between regressions of closely linked markers. To form a final model, all significant SNP across chromosomes were included in a common model and were individually removed in successive iterations based on their significance. Across all analyses, TBG was significant, with an additive effect of approximately 1.2 to 1.6 mm of backfat. Three SNP on SSC3 remained in the final model even though few studies have identified QTL for backfat on this chromosome. Two of these SNP exhibited irregular parent-of-origin effects and may not have been detected in other genome scans. One significant SNP on SSC7 remained in the final, backward-selected model; the estimated effect of this marker was similar in magnitude and direction to previously identified QTL. This SNP can potentially be used to introgress the leaner Meishan allele into commercial swine populations.     

The importance of haplotype length and heritability using genomic selection in dairy cattle

Reliabilities for genomic estimated breeding values (GEBV) were investigated by simulation for a typical dairy cattle breeding setting. Scenarios were simulated with different heritabilites ( h ²) and for different haplotype sizes, and seven generations with only genotypes were generated to investigate reliability of GEBV over time. A genome with 5000 single nucleotide polymorphisms (SNP) at distances of 0.1 cM and 50 quantitative trait loci (QTL) was simulated, and a Bayesian variable selection model was implemented to predict GEBV. Highest reliabilities were obtained for 10 SNP haplotypes. At optimal haplotype size, reliabilities in generation 1 without phenotypes ranged from 0.80 for h ² = 0.02 to 0.93 for h ² = 0.30, and in the seventh generation without phenotypes ranged from 0.69 for h ² = 0.02 to 0.86 for h ² = 0.30. Reliabilities of GEBV were found sufficiently high to implement dairy selection schemes without progeny testing in which case a data time-lag of two to three generations may be present. Reliabilities were also relatively high for low heritable traits, implying that genomic selection could be especially beneficial to improve the selection on, e.g. health and fertility.     

Polymorphisms of the PNPLA3 gene and their associations with chicken growth and carcass traits

1. An F₂ resource population of Gushi chickens crossed with Anka broilers was used to investigate the genetic effects of the chicken PNPLA3 gene on growth and adipose accumulation.

2. Associations between three SNPs (g.40006G?>?T, g.42344T?>?C and g.42404A?>?T) and broiler traits were determined using linkage disequilibrium, haplotype construction and association analysis.

3. The g.40006G?>?T mutation was associated with body weights at 4, 6, 8, 10 and 12 weeks of age, carcass weight, evisceration weight and semi-evisceration weight (P? 4. Haplotypes of the g.42344T?>?C and g.42404A?>?T mutations were associated with body weight at 12 weeks, carcass weight, evisceration weight, and semi-evisceration weight (P? 5. The results suggest that the PNPLA3 gene may be in linkage with the causative mutation or a QTL controlling growth traits in chickens. In contrast to human studies, the polymorphisms were not associated with fat related traits.     

Fine mapping of a calving QTL on Bos taurus autosome 18 in Holstein cattle

Decreased calving performance not only directly impacts the economic efficiency of dairy cattle farming but also influences public concern for animal welfare. Previous studies have revealed a QTL on Bos taurus autosome (BTA) 18 that has a large effect on calving traits in Holstein cattle. In this study, fine mapping of this QTL was performed using imputed high‐density SNP chip (HD) genotypes followed by imputed next‐generation sequencing (NGS) variants. BTA18 was scanned for seven direct calving traits in 6113 bulls with imputed HD genotypes. SNP rs136283363 (BTA18: 57 548 213) was consistently the most significantly associated SNP across all seven traits [e.g. p‐value = 2.04 × 10^?59 for birth index (BI)]. To finely map the QTL region and to explore pleiotropic effects, we studied NGS variants within the targeted region (BTA18: 57 321 450–57 625 355) for associations with direct calving traits and with three conformation traits. Significant variants were prioritized, and their biological relevance to the traits was interpreted. Considering their functional relationships with direct calving traits, SIGLEC12, CD33 and CEACAM18 were proposed as candidate genes. In addition, pleiotropic effects of this QTL region on direct calving traits and conformation traits were observed. However, the extent of linkage disequilibrium combined with the lack of complete annotation and potential errors in the Bos taurus genome assembly hampered our efforts to pinpoint the causal mutation.     

Fine-mapping of quantitative trait loci for body weight and bone traits and positional cloning of the RB1 gene in chicken

Previously, a quantitative trait locus (QTL) that affects body weight (BW) at 4-12 weeks of age and carcass weight at 12 weeks of age had been mapped on chicken chromosome 1. After including more markers and individuals, the confidence interval was narrowed down to approximately 5.5 Mbps and located this QTL near a microsatellite marker (ADL328). This QTL is the same as the QTL for 12 bone traits, including metatarsus length and metatarsus circumference at 4, 6, 8, 10 and 12 weeks of age and keel length and metatarsus claw weight at 12 weeks of age, that was identified using the same population. In the current study, 1010 individuals from the Northeast Agricultural University F(2) resource population were used and 14 single-nucleotide polymorphism (SNPs) around ADL328 were developed to construct haplotypes, and an association analysis was performed to fine-map the QTL. The haplotypes were constructed on the basis of a sliding 'window', with three SNP markers included in each 'window'. The association analysis results indicated that the haplotypes in 'windows' 6-12 were significantly associated with BW and bone traits and suggested that the QTL for BW and bone traits was located between SNP8 and SNP14 or was in linkage disequilibrium with this region. The interval from SNP8 to SNP14 was approximately 400 kbps. This region contained five RefSeq genes (RB1, P2RY5, FNDC3A, MLNR and CAB39L) on the University of California Santa Cruz website. The RB1 gene was selected as a candidate gene and five SNPs were identified in the gene. The association results indicated that the RB1 gene was a major gene for BW and bone traits. The SNPs g.39692 G>A and g.77260 A>G in RB1 gene might be two quantitative trait nucleotides for BW and bone traits.