Differential contribution of genomic regions to marked genetic variation and prediction of quantitative traits in broiler chickens

Background: Genome-wide association studies in humans have found enrichment of trait-associated single nucleotide polymorphisms (SNPs) in coding regions of the genome and depletion of these in intergenic regions. However, a recent release of the ENCyclopedia of DNA elements showed that ~80 % of the human genome has a biochemical function. Similar studies on the chicken genome are lacking, thus assessing the relative contribution of its genic and non-genic regions to variation is relevant for biological studies and genetic improvement of chicken populations. Methods: A dataset including 1351 birds that were genotyped with the 600K Affymetrix platform was used. We partitioned SNPs according to genome annotation data into six classes to characterize the relative contribution of genic and non-genic regions to genetic variation as well as their predictive power using all available quality-filtered SNPs. Target traits were body weight, ultrasound measurement of breast muscle and hen house egg production in broiler chickens. Six genomic regions were considered: intergenic regions, introns, missense, synonymous, 5′ and 3′ untranslated regions, and regions that are located 5 kb upstream and downstream of coding genes. Genomic relationship matrices were constructed for each genomic region and fitted in the models, separately or simultaneously. Kernelbased ridge regression was used to estimate variance components and assess predictive ability. Contribution of each class of genomic regions to dominance variance was also considered. Results: Variance component estimates indicated that all genomic regions contributed to marked additive genetic variation and that the class of synonymous regions tended to have the greatest contribution. The marked dominance genetic variation explained by each class of genomic regions was similar and negligible (~0.05). In terms of prediction mean-square error, the whole-genome approach showed the best predictive ability. Conclusions: All genic and non-genic regions contributed to phenotypic variation for the three traits studied. Overall, the contribution of additive genetic variance to the total genetic variance was much greater than that of dominance variance. Our results show that all genomic regions are important for the prediction of the targeted traits, and the whole-genome approach was reaffirmed as the best tool for genome-enabled prediction of quantitative traits

Synthesis and characterization of Sn‑doped TiO2 flm for antibacterial applications

Simple sol–gel method has been exploited to deposit Sn-doped TiO2 thin flms on glass substrates. The resultant coatings were characterized by X-ray difraction (XRD), UV–visible techniques (UV–Vis), Fourier transform infrared spectroscopy (FTIR), and photoluminescence analysis (PL). The XRD pattern reveals an increase in crystallite size of the prepared samples with the increasing doping concentration. A decrease in doping concentrating resulted in the decrease in bandgap values. The diferent chemical bonds on these flms were identifed from their FTIR spectra. The photoluminescence analysis shows an increase in the emission peak intensity with increasing dopant concentration, and this can be attributed to the efect created due to surface states. The prepared samples were tested as antibacterial agent toward both Gram-positive and Gram-negative bacteria like S.aureus (Staphylococcus aureus) and E.coli (Escherichia coli), respectively. The size of the inhibition zones indicates that the sample shows maximum inhibitory property toward E.coli when compared to S.aureus

Estimates of genomic heritability and genome-wide association study for fatty acids profile in Santa Inês sheep

Background: Despite the health concerns and nutritional importance of fatty acids, there is a relative paucity of studies in the literature that report genetic or genomic parameters, especially in the case of sheep populations. To investigate the genetic architecture of fatty acid composition of sheep, we conducted genome-wide association studies (GWAS) and estimated genomic heritabilities for fatty acid profile in Longissimus dorsi muscle of 216 male sheep. Results: Genomic heritability estimates for fatty acid content ranged from 0.25 to 0.46, indicating that substantial genetic variation exists for the evaluated traits. Therefore, it is possible to alter fatty acid profiles through selection. Twenty-seven genomic regions of 10 adjacent SNPs associated with fatty acids composition were identified on chromosomes 1, 2, 3, 5, 8, 12, 14, 15, 16, 17, and 18, each explaining ≥0.30% of the additive genetic variance. Twenty-three genes supporting the understanding of genetic mechanisms of fat composition in sheep were identified in these regions, such as DGAT2, TRHDE, TPH2, ME1, C6, C7, UBE3D, PARP14, and MRPS30. Conclusions: Estimates of genomic heritabilities and elucidating important genomic regions can contribute to a better understanding of the genetic control of fatty acid deposition and improve the selection strategies to enhance meat quality and health attributes

A multiple-phenotype imputation method for genetic studies

Genetic association studies have yielded a wealth of biologic discoveries. However, these have mostly analyzed one trait and one SNP at a time, thus failing to capture the underlying complexity of these datasets. Joint genotypephenotype analyses of complex, high-dimensional datasets represent an important way to move beyond simple GWAS with great potential. The move to high-dimensional phenotypes will raise many new statistical problems. In this paper we address the central issue of missing phenotypes in studies with any level of relatedness between samples. We propose a multiple phenotype mixed model and use a computationally efficient variational Bayesian algorithm to fit the model. On a variety of simulated and real datasets from a range of organisms and trait types, we show that our method outperforms existing state-of-the-art methods from the statistics and machine learning literature and can boost signals of associatio

A predictive assessment of genetic correlations between traits in chickens using markers

International audienceAbstractBackgroundGenomic selection has been successfully implemented in plant and animal breeding programs to shorten generation intervals and accelerate genetic progress per unit of time. In practice, genomic selection can be used to improve several correlated traits simultaneously via multiple-trait prediction, which exploits correlations between traits. However, few studies have explored multiple-trait genomic selection. Our aim was to infer genetic correlations between three traits measured in broiler chickens by exploring kinship matrices based on a linear combination of measures of pedigree and marker-based relatedness. A predictive assessment was used to gauge genetic correlations.MethodsA multivariate genomic best linear unbiased prediction model was designed to combine information from pedigree and genome-wide markers in order to assess genetic correlations between three complex traits in chickens, i.e. body weight at 35 days of age (BW), ultrasound area of breast meat (BM) and hen-house egg production (HHP). A dataset with 1351 birds that were genotyped with the 600 K Affymetrix platform was used. A kinship kernel (K) was constructed as K = λG + (1 − λ)A, where A is the numerator relationship matrix, measuring pedigree-based relatedness, and G is a genomic relationship matrix. The weight (λ) assigned to each source of information varied over the grid λ = (0, 0.2, 0.4, 0.6, 0.8, 1). Maximum likelihood estimates of heritability and genetic correlations were obtained at each λ, and the “optimum” λ was determined using cross-validation.ResultsEstimates of genetic correlations were affected by the weight placed on the source of information used to build K. For example, the genetic correlation between BW–HHP and BM–HHP changed markedly when λ varied from 0 (only A used for measuring relatedness) to 1 (only genomic information used). As λ increased, predictive correlations (correlation between observed phenotypes and predicted breeding values) increased and mean-squared predictive error decreased. However, the improvement in predictive ability was not monotonic, with an optimum found at some 0 < λ < 1, i.e., when both sources of information were used together.ConclusionsOur findings indicate that multiple-trait prediction may benefit from combining pedigree and marker information. Also, it appeared that expected correlated responses to selection computed from standard theory may differ from realized responses. The predictive assessment provided a metric for performance evaluation as well as a means for expressing uncertainty of outcomes of multiple-trait selection

The impact of QTL allele frequency distribution on the accuracy of genomic prediction

The accuracy of genomic prediction of quantitative traits based on single nucleotide polymorphism (SNP) markers depends among other factors on the allele frequency distribution of quantitative trait loci (QTL). Therefore, the aim of this study was to investigate different QTL allele frequency distributions and their effect on the accuracy of genomic estimated breeding values (GEBVs) using best linear unbiased genomic prediction (GBLUP) in simulated data. A population of 1000 individuals composed of 500 males and 500 females as well as a genome of 1000 cM consisting of 10 chromosomes and with a mutation rate of 2.5  ×  10−5 per locus was simulated. QTL frequencies were derived from five distributions of allele frequency including constant, uniform, U-shaped, L-shaped and minor allele frequency (MAF) less than 0.01 (lowMAF). QTL effects were generated from a standard normal distribution. The number of QTL was assumed to be 500, and the simulation was done in 10 replications. The genomic prediction accuracy in the first-validation generation in constant, and the uniform allele frequency distribution was 0.59 and 0.57, respectively. Results showed that the highest accuracy of GEBVs was obtained with constant and uniform distributions followed by L-shaped, U-shaped and lowMAF QTL allele frequency distribution. The regression of true breeding values on predicted breeding values in the first-validation generation was 0.94, 0.92, 0.88, 0.85 and 0.75 for constant, uniform, L-shaped, U-shaped and lowMAF distributions, respectively. Depite different values of regression coefficients, in all scenarios GEBVs are biased downward. Overall, results showed that when QTL had a lower MAF relative to SNP markers, a low linkage disequilibrium (LD) was observed, which had a negative effect on the accuracy of GEBVs. Hence, the effect of the QTL allele frequency distribution on prediction accuracy can be alleviated through using a genomic relationship weighted by MAF or an LD-adjusted relationship matrix

Genetic analysis of heat tolerance for production and health traits in US Holstein cows.

ABSTRACT..Heat stress reduces milk production, depresses fertility and increases the incidence of health disorders in dairy cows. Our first goal was to estimate genetic components of milk yield (MY) and somatic cell score (SCS) across lactations considering heat stress. Our second goal was to reveal genes responsible for thermotolerance. Data included 254k MY and 356k SCR test-day records of 20k Holstein cows. Multi-trait repeatability test-day models with random regressions on THI values were used to estimate variance components. The models included herd-test-date and DIM classes as fixed effects, and generic and heat tolerance additive and permanent environmental as random effects. Genetic variances for MY under-heat stress increased 3.9 and 6.5% between consecutive parities, suggesting that cows become more sensitive as they age. Heritability estimates for MY at THI 78 were between 0.17 to 0.32. Genetic correlations between general merit and heat tolerance ranged from -0.30 and -0.55, indicating production and thermotolerance are antagonistic. For SCS, heritability estimates for SCS at THI 78 were between 0.10 and 0.16. For this trait, genetic correlations between general merit and thermotolerance were always positive, ranged from +0.10 to +0.43. Whole-genome scans were performed using ssGBLUP. For MY, as expected, the region on BTA14 that harbors DGAT1 was associated with general merit in all three parities. One region on BTA15 was associated with thermotolerance across lactations; this region harbors PEX16, MAPK8IP1, and CREB3L1, genes implicated in thermogenesis and cellular response to heat stress. For SCS, regions on BTA6 and BTA29 were implicated in general udder health in all parities. These regions harbor genes, such as CXCL13, SCARB2, and FAT3, that are involved in immune response. Notably, genes DLX1 and DLX2 which downregulate cytokine signalling pathway were associated with SCS thermotolerance in all lactations. Overall, this study contributes to better understanding of the genetics underlying heat stress and point out novel opportunities for improving thermotolerance in dairy cattle. Keywords: heat stress, variance components, repeatability test-day model, ssGBLUP

Comparison of Poisson, probit and linear models for genetic analysis of number of inseminations to conception and success at first insemination in Iranian Holstein cows

The goals of this study were to estimate genetic parameters and to assess alternative models for genetic evaluation of number of inseminations to conception (INS) and success at first insemination (SF) in Iranian Holstein cows. Two models were considered for each trait: linear and probit models for SF, and linear and Poisson models for INS. Data consisted of 72,124 records of parities 1 to 6 from 27,113 cows having lactation between 1981 and 2007 and distributed over 15 large Holstein herds. Genetic parameters and goodness of fit statistics were estimated using the whole data set and predictive ability of models was assessed via a 4-fold cross-validation based on mean squared error of prediction (MSEP) and correlation between observed and fitted values. Estimates of heritability ranged from 0.039 to 0.062 for SF and 0.040 to 0.165 for INS. The performance of linear and probit models was very similar for SF. Predictions of random effects from these models were highly correlated, and both models exhibited similar predictive ability. For INS, the linear model performed better than the Poisson model according to goodness of fit statistics, but these two models showed the same predictive ability. Overall, nonlinear models did not outperform linear models for genetic evaluations of SF and INS in Iranian Holstein cows