Deciphering the genetic basis of behavioral traits in dogs: Observed-trait GWAS and latent-trait GWAS analysis reveal key genes and variants.

Dogs exhibit remarkable phenotypic diversity, particularly in behavioral traits, making them an excellent model for studying the genetic basis of complex behaviors. Behavioral traits such as aggression and fear are highly heritable among different dog breeds, but their genetic basis is largely unknown. We used the genome-wide association study (GWAS) to identify candidate genes associated with nine behavioral traits including; stranger-directed aggression (SDA), owner-directed aggression (ODA), dog-directed aggression (DDA), stranger-directed fear (SDF), nonsocial fear (NF), dog-directed fear (DDF), touch sensitivity (TS), separation-related behavior (SRB) and attachment attention-seeking (AAS). The observed behavioral traits were collected from 38,714 to 40,460 individuals across 108 modern dog breeds. We performed a GWAS based on a latent trait extracted using the confirmatory factor analysis (CFA) method with nine observable behavioral traits and compared the results with those from the GWAS of the observed traits. Using both observed-trait and latent-trait GWAS, we identified 41 significant SNPs that were common between both GWAS methods, of which 26 were pleiotropic, as well as 10 SNPs unique to the latent-trait GWAS, and 5 SNPs unique to the observed-trait GWAS discovered. These SNPs were associated with 21 genes in latent-trait GWAS and 22 genes in the observed-trait GWAS, with 19 genes shared by both. According to previous studies, some of the genes from this study have been reported to be related to behavioral and neurological functions in dogs. In the human population, these identified genes play a role in either the formation of the nervous system or are linked to various mental health conditions. Taken together, our findings suggest that latent-trait GWAS for behavioral traits in dogs identifies significant latent genes that are neurologically prioritized.

Genic and non-genic SNP contributions to additive and dominance genetic effects in purebred and crossbred pig traits.

The present research has estimated the additive and dominance genetic variances of genic and intergenic segments for average daily gain (ADG), backfat thickness (BFT) and pH of the semimembranosus dorsi muscle (PHS). Further, the predictive performance using additive and additive dominance models in a purebred Piétrain (PB) and a crossbred (Piétrain × Large White, CB) pig population was assessed. All genomic regions contributed equally to the additive and dominance genetic variations and lead to the same predictive ability that did not improve with the inclusion of dominance genetic effect and inbreeding in the models. Using all SNPs available, additive genotypic correlations between PB and CB performances for the three traits were high and positive (> 0.83) and dominance genotypic correlation was very inaccurate. Estimates of dominance genotypic correlations between all pairs of traits in both populations were imprecise but positive for ADG-BFT in CB and BFT-PHS in PB and CB with a high probability (> 0.98). Additive and dominance genotypic correlations between BFT and PHS were of different sign in both populations, which could indicate that genes contributing to the additive genetic progress in both traits would have an antagonistic effect when used for exploiting dominance effects in planned matings.

A genome-wide scan to identify signatures of selection in two Iranian indigenous chicken ecotypes.

BACKGROUND: Various regions of the chicken genome have been under natural and artificial selection for thousands of years. The substantial diversity that exits among chickens from different geographic regions provides an excellent opportunity to investigate the genomic regions under selection which, in turn, will increase our knowledge about the mechanisms that underlie chicken diversity and adaptation. Several statistics have been developed to detect genomic regions that are under selection. In this study, we applied approaches based on differences in allele or haplotype frequencies (FST and hapFLK, respectively) between populations, differences in long stretches of consecutive homozygous sequences (ROH), and differences in allele frequencies within populations (composite likelihood ratio (CLR)) to identify inter- and intra-populations traces of selection in two Iranian indigenous chicken ecotypes, the Lari fighting chicken and the Khazak or creeper (short-leg) chicken. RESULTS: Using whole-genome resequencing data of 32 individuals from the two chicken ecotypes, approximately 11.9 million single nucleotide polymorphisms (SNPs) were detected and used in genomic analyses after quality processing. Examination of the distribution of ROH in the two populations indicated short to long ROH, ranging from 0.3 to 5.4 Mb. We found 90 genes that were detected by at least two of the four applied methods. Gene annotation of the detected putative regions under selection revealed candidate genes associated with growth (DCN, MEOX2 and CACNB1), reproduction (ESR1 and CALCR), disease resistance (S1PR1, ALPK1 and MHC-B), behavior pattern (AGMO, GNAO1 and PSEN1), and morphological traits (IHH and NHEJ1). CONCLUSIONS: Our findings show that these two phenotypically different indigenous chicken populations have been under selection for reproduction, immune, behavioral, and morphology traits. The results illustrate that selection can play an important role in shaping signatures of differentiation across the genomic landscape of two chicken populations.

Comparative population genomic analysis uncovers novel genomic footprints and genes associated with small body size in Chinese pony.

BACKGROUND: Body size is considered as one of the most fundamental properties of an organism. Due to intensive breeding and artificial selection throughout the domestication history, horses exhibit striking variations for heights at withers and body sizes. Debao pony (DBP), a famous Chinese horse, is known for its small body size and lives in Guangxi mountains of southern China. In this study, we employed comparative population genomics to study the genetic basis underlying the small body size of DBP breed based on the whole genome sequencing data. To detect genomic signatures of positive selection, we applied three methods based on population comparison, fixation index (FST), cross population composite likelihood ratio (XP-CLR) and nucleotide diversity (θπ), and further analyzed the results to find genomic regions under selection for body size-related traits. RESULTS: A number of protein-coding genes in windows with the top 1% values of FST (367 genes), XP-CLR (681 genes), and log2 (θπ ratio) (332 genes) were identified. The most significant signal of positive selection was mapped to the NELL1 gene, probably underlies the body size and development traits, and may also have been selected for short stature in the DBP population. In addition, some other loci on different chromosomes were identified to be potentially involved in the development of body size. CONCLUSIONS: Results of our study identified some positively selected genes across the horse genome, which are possibly involved in body size traits. These novel candidate genes may be useful targets for clarifying our understanding of the molecular basis of body size and as such they should be of great interest for future research into the genetic architecture of relevant traits in horse breeding program.

Mammary tissue transcriptomic analysis for construction of integrated regulatory networks involved in lactogenesis of Ovis aries.

The mammary gland experiences vast changes between the onset of lactation and pregnancy. This remodeling involves different functions such as lactation that is controlled by innumerable regulators and various gene networks which are still not completely understood. MicroRNAs (miRNAs) are one of the important non-coding gene regulators which control an extensive range of biological processes. Thus, exploring miRNAs functions is important for solving gene regulation complexity. The main purpose in the present study is to identify the various gene regulative integrated networks involved in lactation progress in mammary gland. We analyzed ovine mammary tissue data sets which included expression profiles of mRNA (genes) and miRNAs related to six ewes in different days of lactation and nutritional treatments. We combined two different types of information: the network that is module inference by mRNAs (RNA-seq data), miRNAs and transcription factors (TFs) expression matrix and prediction of targets via computational methods. To discover the miRNAs regulatory function, 134 modules were predicted by using gene expression data and 14 TFs and 20 miRNAs were allocated to these predicted modules. By applying this integrated computation-based method, 38 miRNA-modules and 35 TF-module interactions were identified from ovine mammary tissue data during lactogenesis. A lot of these modules were involved in lipid and protein metabolism, as well as steroids and vitamin biosynthesis, which would play key roles in mammary tissue and lactation development. These results present new information about the regulatory procedures at the miRNAs and TF levels throughout lactation.

Integration of single nucleotide variants and whole-genome DNA methylation profiles for classification of rheumatoid arthritis cases from controls.

This study evaluated the use of multiomics data for classification accuracy of rheumatoid arthritis (RA). Three approaches were used and compared in terms of prediction accuracy: (1) whole-genome prediction (WGP) using SNP marker information only, (2) whole-methylome prediction (WMP) using methylation profiles only, and (3) whole-genome/methylome prediction (WGMP) with combining both omics layers. The number of SNP and of methylation sites varied in each scenario, with either 1, 10, or 50% of these preselected based on four approaches: randomly, evenly spaced, lowest p value (genome-wide association or epigenome-wide association study), and estimated effect size using a Bayesian ridge regression (BRR) model. To remove effects of high levels of pairwise linkage disequilibrium (LD), SNPs were also preselected with an LD-pruning method. Five Bayesian regression models were studied for classification, including BRR, Bayes-A, Bayes-B, Bayes-C, and the Bayesian LASSO. Adjusting methylation profiles for cellular heterogeneity within whole blood samples had a detrimental effect on the classification ability of the models. Overall, WGMP using Bayes-B model has the best performance. In particular, selecting SNPs based on LD-pruning with 1% of the methylation sites selected based on BRR included in the model, and fitting the most significant SNP as a fixed effect was the best method for predicting disease risk with a classification accuracy of 0.975. Our results showed that multiomics data can be used to effectively predict the risk of RA and identify cases in early stages to prevent or alter disease progression via appropriate interventions.

Genetic structure of Iranian indigenous sheep breeds: insights for conservation.

Genetic structure and genetic diversity levels of indigenous Iranian sheep breeds are not clear, despite the interest this region has in itself as an important center for domestication of livestock. Early population genetic studies have reported high levels of diversity among Iranian sheep breeds until recently, when high admixture levels and genetic homogeneity have been detected. The rapid reduction of diversity observed in Iranian breeds might be due to an increasing trend of intensive crossbreeding practices or even total replacement of native breeds by highly specialized and productive ones. From a conservative perspective, this situation is highly concerning; thus, it might be wise to consider a conservation program in Iran to preserve the original genetic diversity in native sheep breeds. In this study, a total of 1065 animals with the purest morphological features representing 24 Iranian indigenous sheep breeds were sampled, corresponding to ancestral breed diversity. These samples were genotyped for 17 microsatellite loci in order to (1) determine the native ancestral diversity of Iranian breeds, (2) define the degree of genetic relationship among studied breeds, and (3) assess conservation priorities among defined groups. Our results showed no recent loss of diversity, but high genetic diversity levels for indigenous sheep breeds in Iran. Indeed, the analysis of conservation priorities pointed out the importance of 8 breeds for maintaining Iranian sheep breeds' maximum genetic diversity. Thus, under a genetic perspective, these 8 breeds should be the ones included into conservation programs for restocking endangered areas.

Performance of pedigree and various forms of marker-derived relationship coefficients in genomic prediction and their correlations.

In recent years, with development and validation of different genotyping panels, several methods have been proposed to build efficient similarity matrices among individuals to be used for genomic selection. Consequently, the estimated genetic parameters from such information may deviate from their counterpart using traditional family information. In this study, we used a pedigree-based numerator relationship matrix (A) and three types of marker-based relationship matrices ( G ) including two identical by descent, that is G K and G M and one identical by state, G V as well as four Gaussian kernel ( GK ) similarity kernels with different smoothing parameters to predict yet to be observed phenotypes. Also, we used different kinship matrices that are a linear combination of marker-derived IBD or IBS matrices with A, constructed as K = λ G + 1 - λ A , where the weight ( λ ) assigned to each source of information varied over a grid of values. A Bayesian multiple-trait Gaussian model was fitted to estimate the genetic parameters and compare the prediction accuracy in terms of predictive correlation, mean square error and unbiasedness. Results show that the estimated genetic parameters (heritability and correlations) are affected by the source of the information used to create kinship or the weight placed on the sources of genomic and pedigree information. The superiority of GK-based model depends on the smoothing parameters (θ) so that with an optimum θ value, the GK-based model statistically yielded better performance (higher predictive correlation, lowest MSE and unbiased estimates) and more stable correlations and heritability than the model with IBD, IBS or A kinship matrices or any of the linear combinations.

Whole-genome sequence analysis reveals candidate genomic footprints and genes associated with reproductive traits in Thoroughbred horse.

The primary objective of most horse breeding operations was to maximize reproductive efficiency and minimize the cost of producing live foals. Here, we compared individual horses from the Thoroughbred population (n = 17), known as a horse breed with poor reproductive performance, with other six horse populations (n = 28), to detect genomic signatures of positive selection underlying of reproductive traits. A number of protein-coding genes with significant (p-value <.01) higher FST values (616 genes) and a lower value for nucleotide diversity (π) (310 genes) were identified. The results of our study revealed some candidate genes such as IGFBP2, IGFBP5, GDF9, BRINP3 and GRID1 are possibly associated with functions influencing reproductive traits. These genes may have been under selection due to their essential roles in reproduction performance in horses. The candidate selected genes identified in this work should be of great interest for future research into genetic architecture of traits relevant to horse breeding programmes.

Comparative population genomics unveils candidate genes for athletic performance in Hanoverians.

Equine athletes have a genetic heritage that has been evolved for millions of years, which provides an opportunity to study the genetics of locomotion pattern and performance in mammals. The Hanoverian, a breed originating in Germany, is arguably among the most athletic of horse breeds, as well as possessing a balanced character and beautiful appearance. Here, we compared the whole genomes of Hanoverian with three other horse breeds (Akhal-Teke, Franches-Montagnes, and Standardbred), using the fixation index (Fst) and cross-population composite likelihood ratio (XP-CLR) methods for testing the multi-locus allele frequency differentiation between populations. We identified 299 and 485 positively selected genes using the Fst and XP-CLR methods, respectively. Further functional analyses showed that the ACTA1 gene is potentially involved in athletic performance in the Hanoverian breed, consistent with its role observed in human population. In addition, three other loci on chromosomes 1 and 20 were identified to be potentially involved in equine physical performance. The selected candidate genes identified in this study may be useful in current breeding efforts to develop improved breeds in regard to athletic performance.

Including Phenotypic Causal Networks in Genome-Wide Association Studies Using Mixed Effects Structural Equation Models.

Network based statistical models accounting for putative causal relationships among multiple phenotypes can be used to infer single-nucleotide polymorphism (SNP) effect which transmitting through a given causal path in genome-wide association studies (GWAS). In GWAS with multiple phenotypes, reconstructing underlying causal structures among traits and SNPs using a single statistical framework is essential for understanding the entirety of genotype-phenotype maps. A structural equation model (SEM) can be used for such purposes. We applied SEM to GWAS (SEM-GWAS) in chickens, taking into account putative causal relationships among breast meat (BM), body weight (BW), hen-house production (HHP), and SNPs. We assessed the performance of SEM-GWAS by comparing the model results with those obtained from traditional multi-trait association analyses (MTM-GWAS). Three different putative causal path diagrams were inferred from highest posterior density (HPD) intervals of 0.75, 0.85, and 0.95 using the inductive causation algorithm. A positive path coefficient was estimated for BM → BW, and negative values were obtained for BM → HHP and BW → HHP in all implemented scenarios. Further, the application of SEM-GWAS enabled the decomposition of SNP effects into direct, indirect, and total effects, identifying whether a SNP effect is acting directly or indirectly on a given trait. In contrast, MTM-GWAS only captured overall genetic effects on traits, which is equivalent to combining the direct and indirect SNP effects from SEM-GWAS. Although MTM-GWAS and SEM-GWAS use the similar probabilistic models, we provide evidence that SEM-GWAS captures complex relationships in terms of causal meaning and mediation and delivers a more comprehensive understanding of SNP effects compared to MTM-GWAS. Our results showed that SEM-GWAS provides important insight regarding the mechanism by which identified SNPs control traits by partitioning them into direct, indirect, and total SNP effects.