Construction of a circRNA- lincRNA-lncRNA-miRNA-mRNA ceRNA regulatory network identifies genes and pathways linked to goat fertility.

Background: There is growing interest in the genetic improvement of fertility traits in female goats. With high-throughput genotyping, single-cell RNA sequencing (scRNA-seq) is a powerful tool for measuring gene expression profiles. The primary objective was to investigate comparative transcriptome profiling of granulosa cells (GCs) of high- and low-fertility goats, using scRNA-seq. Methods: Thirty samples from Ji'ning Gray goats (n = 15 for high fertility and n = 15 for low fertility) were retrieved from publicly available scRNA-seq data. Functional enrichment analysis and a literature mining approach were applied to explore modules and hub genes related to fertility. Then, interactions between types of RNAs identified were predicted, and the ceRNA regulatory network was constructed by integrating these interactions with other gene regulatory networks (GRNs). Results and discussion: Comparative transcriptomics-related analyses identified 150 differentially expressed genes (DEGs) between high- and low-fertility groups, based on the fold change (≥5 and ≤-5) and false discovery rate (FDR <0.05). Among these genes, 80 were upregulated and 70 were downregulated. In addition, 81 mRNAs, 58 circRNAs, 8 lincRNAs, 19 lncRNAs, and 55 miRNAs were identified by literature mining. Furthermore, we identified 18 hub genes (SMAD1, SMAD2, SMAD3, SMAD4, TIMP1, ERBB2, BMP15, TGFB1, MAPK3, CTNNB1, BMPR2, AMHR2, TGFBR2, BMP4, ESR1, BMPR1B, AR, and TGFB2) involved in goat fertility. Identified biological networks and modules were mainly associated with ovary signature pathways. In addition, KEGG enrichment analysis identified regulating pluripotency of stem cells, cytokine-cytokine receptor interactions, ovarian steroidogenesis, oocyte meiosis, progesterone-mediated oocyte maturation, parathyroid and growth hormone synthesis, cortisol synthesis and secretion, and signaling pathways for prolactin, TGF-beta, Hippo, MAPK, PI3K-Akt, and FoxO. Functional annotation of identified DEGs implicated important biological pathways. These findings provided insights into the genetic basis of fertility in female goats and are an impetus to elucidate molecular ceRNA regulatory networks and functions of DEGs underlying ovarian follicular development.

Genome-wide association analysis and pathway enrichment provide insights into the genetic basis of photosynthetic responses to drought stress in Persian walnut.

Uncovering the genetic basis of photosynthetic trait variation under drought stress is essential for breeding climate-resilient walnut cultivars. To this end, we examined photosynthetic capacity in a diverse panel of 150 walnut families (1500 seedlings) from various agro-climatic zones in their habitats and grown in a common garden experiment. Photosynthetic traits were measured under well-watered (WW), water-stressed (WS) and recovery (WR) conditions. We performed genome-wide association studies (GWAS) using three genomic datasets: genotyping by sequencing data (∼43 K SNPs) on both mother trees (MGBS) and progeny (PGBS) and the Axiom™ Juglans regia 700 K SNP array data (∼295 K SNPs) on mother trees (MArray). We identified 578 unique genomic regions linked with at least one trait in a specific treatment, 874 predicted genes that fell within 20 kb of a significant or suggestive SNP in at least two of the three GWAS datasets (MArray, MGBS, and PGBS), and 67 genes that fell within 20 kb of a significant SNP in all three GWAS datasets. Functional annotation identified several candidate pathways and genes that play crucial roles in photosynthesis, amino acid and carbohydrate metabolism, and signal transduction. Further network analysis identified 15 hub genes under WW, WS and WR conditions including GAPB, PSAN, CRR1, NTRC, DGD1, CYP38, and PETC which are involved in the photosynthetic responses. These findings shed light on possible strategies for improving walnut productivity under drought stress.

A comprehensive study on size and definition of the core group in the proven and young algorithm for single-step GBLUP.

BACKGROUND: The algorithm for proven and young (APY) has been suggested as a solution for recursively computing a sparse representation for the inverse of a large genomic relationship matrix (G). In APY, a subset of genotyped individuals is used as the core and the remaining genotyped individuals are used as noncore. Size and definition of the core are relevant research subjects for the application of APY, especially given the ever-increasing number of genotyped individuals. METHODS: The aim of this study was to investigate several core definitions, including the most popular animals (MPA) (i.e., animals with high contributions to the genetic pool), the least popular males (LPM), the least popular females (LPF), a random set (Rnd), animals evenly distributed across genealogical paths (Ped), unrelated individuals (Unrel), or based on within-family selection (Fam), or on decomposition of the gene content matrix (QR). Each definition was evaluated for six core sizes based on prediction accuracy of single-step genomic best linear unbiased prediction (ssGBLUP) with APY. Prediction accuracy of ssGBLUP with the full inverse of G was used as the baseline. The dataset consisted of 357k pedigreed Duroc pigs with 111k pigs with genotypes and ~ 220k phenotypic records. RESULTS: When the core size was equal to the number of largest eigenvalues explaining 50% of the variation of G (n = 160), MPA and Ped core definitions delivered the highest average prediction accuracies (~ 0.41-0.53). As the core size increased to the number of eigenvalues explaining 99% of the variation in G (n = 7320), prediction accuracy was nearly identical for all core types and correlations with genomic estimated breeding values (GEBV) from ssGBLUP with the full inversion of G were greater than 0.99 for all core definitions. Cores that represent all generations, such as Rnd, Ped, Fam, and Unrel, were grouped together in the hierarchical clustering of GEBV. CONCLUSIONS: For small core sizes, the definition of the core matters; however, as the size of the core reaches an optimal value equal to the number of largest eigenvalues explaining 99% of the variation of G, the definition of the core becomes arbitrary.

Dissecting genetic trends to understand breeding practices in livestock: a maternal pig line example.

BACKGROUND: Understanding whether genomic selection has been effective in livestock and when the results of genomic selection became visible are essential questions which we have addressed in this paper. Three criteria were used to identify practices of breeding programs over time: (1) the point of divergence of estimated genetic trends based on pedigree-based best linear unbiased prediction (BLUP) versus single-step genomic BLUP (ssGBLUP), (2) the point of divergence of realized Mendelian sampling (RMS) trends based on BLUP and ssGBLUP, and (3) the partition of genetic trends into that contributed by genotyped and non-genotyped individuals and by males and females. METHODS: We used data on 282,035 animals from a commercial maternal line of pigs, of which 32,856 were genotyped for 36,612 single nucleotide polymorphisms (SNPs) after quality control. Phenotypic data included 228,427, 101,225, and 11,444 records for birth weight, average daily gain in the nursery, and feed intake, respectively. Breeding values were predicted in a multiple-trait framework using BLUP and ssGBLUP. RESULTS: The points of divergence of the genetic and RMS trends estimated by BLUP and ssGBLUP indicated that genomic selection effectively started in 2019. Partitioning the overall genetic trends into that for genotyped and non-genotyped individuals revealed that the contribution of genotyped animals to the overall genetic trend increased rapidly from ~ 74% in 2016 to 90% in 2019. The contribution of the female pathway to the genetic trend also increased since genomic selection was implemented in this pig population, which reflects the changes in the genotyping strategy in recent years. CONCLUSIONS: Our results show that an assessment of breeding program practices can be done based on the point of divergence of genetic and RMS trends between BLUP and ssGBLUP and based on the partitioning of the genetic trend into contributions from different selection pathways. However, it should be noted that genetic trends can diverge before the onset of genomic selection if superior animals are genotyped retroactively. For the pig population example, the results showed that genomic selection was effective in this population.

Genes and Pathways Affecting Sheep Productivity Traits: Genetic Parameters, Genome-Wide Association Mapping, and Pathway Enrichment Analysis.

Ewe productivity is a composite and maternal trait that is considered the most important economic trait in sheep meat production. The objective of this study was the application of alternative genome-wide association study (GWAS) approaches followed by gene set enrichment analysis (GSEA) on the ewes' genome to identify genes affecting pregnancy outcomes and lamb growth after parturition in Iranian Baluchi sheep. Three maternal composite traits at birth and weaning were considered. The traits were progeny birth weight, litter mean weight at birth, total litter weight at birth, progeny weaning weight, litter mean weight at weaning, and total litter weight at weaning. GWASs were performed on original phenotypes as well as on estimated breeding values. The significant SNPs associated with composite traits at birth were located within or near genes RDX, FDX1, ARHGAP20, ZC3H12C, THBS1, and EPG5. Identified genes and pathways have functions related to pregnancy, such as autophagy in the placenta, progesterone production by the placenta, placental formation, calcium ion transport, and maternal immune response. For composite traits at weaning, genes (NR2C1, VEZT, HSD17B4, RSU1, CUBN, VIM, PRLR, and FTH1) and pathways affecting feed intake and food conservation, development of mammary glands cytoskeleton structure, and production of milk components like fatty acids, proteins, and vitamin B-12, were identified. The results show that calcium ion transport during pregnancy and feeding lambs by milk after parturition can have the greatest impact on weight gain as compared to other effects of maternal origin.

Detecting effective starting point of genomic selection by divergent trends from best linear unbiased prediction and single-step genomic best linear unbiased prediction in pigs, beef cattle, and broilers.

Genomic selection has been adopted nationally and internationally in different livestock and plant species. However, understanding whether genomic selection has been effective or not is an essential question for both industry and academia. Once genomic evaluation started being used, estimation of breeding values with pedigree best linear unbiased prediction (BLUP) became biased because this method does not consider selection using genomic information. Hence, the effective starting point of genomic selection can be detected in two possible ways including the divergence of genetic trends and Realized Mendelian sampling (RMS) trends obtained with BLUP and single-step genomic BLUP (ssGBLUP). This study aimed to find the start date of genomic selection for a set of economically important traits in three livestock species by comparing trends obtained using BLUP and ssGBLUP. Three datasets were used for this purpose: 1) a pig dataset with 117k genotypes and 1.3M animals in pedigree, 2) an Angus cattle dataset consisted of ~842k genotypes and 11.5M animals in pedigree, and 3) a purebred broiler chicken dataset included ~154k genotypes and 1.3M birds in pedigree were used. The genetic trends for pigs diverged for the genotyped animals born in 2014 for average daily gain (ADG) and backfat (BF). In beef cattle, the trends started diverging in 2009 for weaning weight (WW) and in 2016 for postweaning gain (PWG), with little divergence for birth weight (BTW). In broiler chickens, the genetic trends estimated by ssGBLUP and BLUP diverged at breeding cycle 6 for two out of the three production traits. The RMS trends for the genotyped pigs diverged for animals born in 2014, more for ADG than for BF. In beef cattle, the RMS trends started diverging in 2009 for WW and in 2016 for PWG, with a trivial trend for BTW. In broiler chickens, the RMS trends from ssGBLUP and BLUP diverged strongly for two production traits at breeding cycle 6, with a slight divergence for another trait. Divergence of the genetic trends from ssGBLUP and BLUP indicates the onset of the genomic selection. The presence of trends for RMS indicates selective genotyping, with or without the genomic selection. The onset of genomic selection and genotyping strategies agrees with industry practices across the three species. In summary, the effective start of genomic selection can be detected by the divergence between genetic and RMS trends from BLUP and ssGBLUP.

Genome-wide association study and pathway analysis identify NTRK2 as a novel candidate gene for litter size in sheep.

Litter size is one of the most important economic traits in sheep. Identification of gene variants that are associated with the prolificacy rate is an important step in breeding program success and profitability of the farm. So, to identify genetic mechanisms underlying the variation in litter size in Iranian Baluchi sheep, a two-step genome-wide association study (GWAS) was performed. GWAS was conducted using genotype data from 91 Baluchi sheep. Estimated breeding values (EBVs) for litter size calculated for 3848 ewes and then used as the response variable. Besides, a pathway analysis using GO and KEGG databases were applied as a complementary approach. A total of three single nucleotide polymorphisms (SNPs) associated with litter size were identified, one each on OAR2, OAR10, and OAR25. The SNP on OAR2 is located within a novel putative candidate gene, Neurotrophic receptor tyrosine kinase 2. This gene product works as a receptor which is essential for follicular assembly, early follicular growth, and oocyte survival. The SNP on OAR25 is located within RAB4A which is involved in blood vessel formation and proliferation through angiogenesis. The SNP on OAR10 was not associated with any gene in the 1Mb span. Moreover, gene-set analysis using the KEGG database identified several pathways, such as Ovarian steroidogenesis, Steroid hormone biosynthesis, Calcium signaling pathway, and Chemokine signaling. Also, pathway analysis using the GO database revealed several functional terms, such as cellular carbohydrate metabolic, biological adhesion, cell adhesion, cell junction, and cell-cell adherens junction, among others. This is the first study that reports the NTRK2 gene affecting litter size in sheep and our study of this gene functions showed that this gene could be a good candidate for further analysis.

Correction to: Genomic measures of inbreeding coefficients and genome-wide scan for runs of homozygosity islands in Iranian river buffalo, Bubalus bubalis.

Following publication of the original article [1], the authors flagged that the article had published with the author 'Ali Jalil Sarghale' erroneously omitted from the author list.

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.

Deep learning versus parametric and ensemble methods for genomic prediction of complex phenotypes.

BACKGROUND: Transforming large amounts of genomic data into valuable knowledge for predicting complex traits has been an important challenge for animal and plant breeders. Prediction of complex traits has not escaped the current excitement on machine-learning, including interest in deep learning algorithms such as multilayer perceptrons (MLP) and convolutional neural networks (CNN). The aim of this study was to compare the predictive performance of two deep learning methods (MLP and CNN), two ensemble learning methods [random forests (RF) and gradient boosting (GB)], and two parametric methods [genomic best linear unbiased prediction (GBLUP) and Bayes B] using real and simulated datasets. METHODS: The real dataset consisted of 11,790 Holstein bulls with sire conception rate (SCR) records and genotyped for 58k single nucleotide polymorphisms (SNPs). To support the evaluation of deep learning methods, various simulation studies were conducted using the observed genotype data as template, assuming a heritability of 0.30 with either additive or non-additive gene effects, and two different numbers of quantitative trait nucleotides (100 and 1000). RESULTS: In the bull dataset, the best predictive correlation was obtained with GB (0.36), followed by Bayes B (0.34), GBLUP (0.33), RF (0.32), CNN (0.29) and MLP (0.26). The same trend was observed when using mean squared error of prediction. The simulation indicated that when gene action was purely additive, parametric methods outperformed other methods. When the gene action was a combination of additive, dominance and of two-locus epistasis, the best predictive ability was obtained with gradient boosting, and the superiority of deep learning over the parametric methods depended on the number of loci controlling the trait and on sample size. In fact, with a large dataset including 80k individuals, the predictive performance of deep learning methods was similar or slightly better than that of parametric methods for traits with non-additive gene action. CONCLUSIONS: For prediction of traits with non-additive gene action, gradient boosting was a robust method. Deep learning approaches were not better for genomic prediction unless non-additive variance was sizable.

Genomic measures of inbreeding coefficients and genome-wide scan for runs of homozygosity islands in Iranian river buffalo, Bubalus bubalis.

BACKGROUND: Consecutive homozygous fragments of a genome inherited by offspring from a common ancestor are known as runs of homozygosity (ROH). ROH can be used to calculate genomic inbreeding and to identify genomic regions that are potentially under historical selection pressure. The dataset of our study consisted of 254 Azeri (AZ) and 115 Khuzestani (KHZ) river buffalo genotyped for ~ 65,000 SNPs for the following two purposes: 1) to estimate and compare inbreeding calculated using ROH (FROH), excess of homozygosity (FHOM), correlation between uniting gametes (FUNI), and diagonal elements of the genomic relationship matrix (FGRM); 2) to identify frequently occurring ROH (i.e. ROH islands) for our selection signature and gene enrichment studies. RESULTS: In this study, 9102 ROH were identified, with an average number of 21.2 ± 13.1 and 33.2 ± 15.9 segments per animal in AZ and KHZ breeds, respectively. On average in AZ, 4.35% (108.8 ± 120.3 Mb), and in KHZ, 5.96% (149.1 ± 107.7 Mb) of the genome was autozygous. The estimated inbreeding values based on FHOM, FUNI and FGRM were higher in AZ than they were in KHZ, which was in contrast to the FROH estimates. We identified 11 ROH islands (four in AZ and seven in KHZ). In the KHZ breed, the genes located in ROH islands were enriched for multiple Gene Ontology (GO) terms (P ≤ 0.05). The genes located in ROH islands were associated with diverse biological functions and traits such as body size and muscle development (BMP2), immune response (CYP27B1), milk production and components (MARS, ADRA1A, and KCTD16), coat colour and pigmentation (PMEL and MYO1A), reproductive traits (INHBC, INHBE, STAT6 and PCNA), and bone development (SUOX). CONCLUSION: The calculated FROH was in line with expected higher inbreeding in KHZ than in AZ because of the smaller effective population size of KHZ. Thus, we find that FROH can be used as a robust estimate of genomic inbreeding. Further, the majority of ROH peaks were overlapped with or in close proximity to the previously reported genomic regions with signatures of selection. This tells us that it is likely that the genes in the ROH islands have been subject to artificial or natural selection.

Combining phenotype, genotype, and environment to uncover genetic components underlying water use efficiency in Persian walnut.

Walnut production is challenged by climate change and abiotic stresses. Elucidating the genomic basis of adaptation to climate is essential to breeding drought-tolerant cultivars for enhanced productivity in arid and semi-arid regions. Here, we aimed to identify loci potentially involved in water use efficiency (WUE) and adaptation to drought in Persian walnut using a diverse panel of 95 walnut families (950 seedlings) from Iran, which show contrasting levels of water availability in their native habitats. We analyzed associations between phenotypic, genotypic, and environmental variables from data sets of 609 000 high-quality single nucleotide polymorphisms (SNPs), three categories of phenotypic traits [WUE-related traits under drought, their drought stress index, and principal components (PCs)], and 21 climate variables and their combination (first three PCs). Our genotype-phenotype analysis identified 22 significant and 266 suggestive associations, some of which were for multiple traits, suggesting their correlation and a possible common genetic control. Also, genotype-environment association analysis found 115 significant and 265 suggestive SNP loci that displayed potential signals of local adaptation. Several sets of stress-responsive genes were found in the genomic regions significantly associated with the aforementioned traits. Most of the candidate genes identified are involved in abscisic acid signaling, stomatal regulation, transduction of environmental signals, antioxidant defense system, osmotic adjustment, and leaf growth and development. Upon validation, the marker-trait associations identified for drought tolerance-related traits would allow the selection and development of new walnut rootstocks or scion cultivars with superior WUE.

Whole Genome Mapping Reveals Novel Genes and Pathways Involved in Milk Production Under Heat Stress in US Holstein Cows.

Heat stress represents a major environmental factor that negatively affects the health and performance of dairy cows, causing huge economic losses to the dairy industry. Identifying and selecting animals that are thermotolerant is an attractive alternative for reducing the negative effects of heat stress on dairy cattle performance. As such, the objectives of the present study were to estimate genetic components of milk yield, fat yield, and protein yield considering heat stress and to perform whole-genome scans and a subsequent gene-set analysis for identifying candidate genes and functional gene-sets implicated in milk production under heat stress conditions. Data consisted of about 254k test-day records from 17,522 Holstein cows. Multi-trait repeatability test day models with random regressions on a function of temperature-humidity index (THI) values were used for genetic analyses. The models included herd-test-day and DIM classes as fixed effects, and general and thermotolerance additive genetic and permanent environmental as random effects. Notably, thermotolerance additive genetic variances for all milk traits increased across parities suggesting that cows become more sensitive to heat stress as they age. In addition, our study revealed negative genetic correlations between general and thermotolerance additive effects, ranging between -0.18 to -0.68 indicating that high producing cows are more susceptible to heat stress. The association analysis identified at least three different genomic regions on BTA5, BTA14, and BTA15 strongly associated with milk production under heat stress conditions. These regions harbor candidate genes, such as HSF1, MAPK8IP1, and CDKN1B that are directly involved in the cellular response to heat stress. Moreover, the gene-set analysis revealed several functional terms related to heat shock proteins, apoptosis, immune response, and oxidative stress, among others. Overall, the genes and pathways identified in this study provide a better understanding of the genetic architecture underlying dairy cow performance under heat stress conditions. Our findings point out novel opportunities for improving thermotolerance in dairy cattle through marker-assisted breeding.

Association of lipid-related genes implicated in conceptus elongation with female fertility traits in dairy cattle.

Elongation of the preimplantation conceptus is a requirement for pregnancy success in ruminants, and failures in this process are highly associated with subfertility in dairy cattle. Identifying genetic markers that are related to early conceptus development and survival and utilizing these markers in selective breeding can improve the reproductive efficiency of dairy herds. Here, we evaluated the association of 1,679 SNP markers within or close to 183 candidate genes involved in lipid metabolism of the elongating conceptus with different fertility traits in US Holstein cattle. A total of 27,371 bulls with predicted transmitting ability records for daughter pregnancy rate, cow conception rate, and heifer conception rate were used as the discovery population. The associations found in the discovery population were validated using 2 female populations (1,122 heifers and 2,138 lactating cows) each with 4 fertility traits, including success to first insemination, number of services per conception, age at first conception for heifers, or days open for cows. Marker effects were estimated using a linear mixed model with SNP genotype as a linear covariate and a random polygenic effect. After multiple testing correction, 39 SNP flagging 27 candidate genes were associated with at least one fertility trait in the discovery population. Of these 39 markers, 3 SNP were validated in the heifer population and 4 SNP were validated in the cow population. The 3 SNP validated in heifers are located within or near genes CAT, MYOF, and RBP4, and the 4 SNP validated in lactating cows are located within or close to genes CHKA, GNAI1, and HMOX2. These validated genes seem to be relevant for reducing pregnancy losses, and the SNP within these genes are excellent candidates for inclusion in genomic tests to improve reproductive performance in dairy cattle.

Deep transcriptome analysis using RNA-Seq suggests novel insights into molecular aspects of fat-tail metabolism in sheep.

Fat-tail content of sheep breeds is varied and the molecular mechanisms regulating fat-tail development have not been well characterized. Aiming at better identifying the important candidate genes and their functional pathways contributing to fat deposition in the tail, a comparative transcriptome analysis was performed between fat- (Lori-Bakhtiari) and thin-tailed (Zel) Iranian sheep breeds using RNA-seq. The experiment was conducted on six male lambs (three lambs per each breed) at seven months of age. Four different combinations of aligners and statistical methods including Hisat2 + edgeR, Hisat2 + DESeq2, STAR + edgeR and STAR + DESeq2 were used to identify the differentially expressed genes (DEGs). The DEGs were selected for functional enrichment analysis and protein-protein interaction (PPI) network construction. Module analysis was also conducted to mine the functional sub-networks from the PPI network. In total, 264 genes including 80 up- and 184 down-regulated genes were identified as DEGs. The RNA-Seq results were validated by Q-RT-PCR. Functional analysis of DEGs and the module analysis of PPI network demonstrated that in addition to pathways affecting lipid metabolism, a series of enriched functional terms related to "response to interleukin", "MAPK signaling pathways", "Wnt signaling pathway", "ECM-receptor interaction", "regulation of actin cytoskeleton", and "response to cAMP" might contribute to the deposition of fat in tails of sheep. Overall results using RNA-Seq analysis characterized important candidate genes involved in the fatty acid metabolism and regulation of fat deposition, suggesting novel insights into molecular aspects of fat-tail metabolism in sheep. Selected DEGs should be further investigated as potential markers associated with the fat-tail development in sheep breeds.

Genome-wide patterns of population structure and association mapping of nut-related traits in Persian walnut populations from Iran using the Axiom J. regia 700K SNP array.

Persian plateau (including Iran) is considered as one of the primary centers of origin of walnut. Sampling walnut trees originating from this arena and exploiting the capabilities of next-generation sequencing (NGS) can provide new insights into the degree of genetic variation across the walnut genome. The present study aimed to explore the population structure and genomic variation of an Iranian collection of Persian walnut (Juglans regia L.) and identify loci underlying the variation in nut and kernel related traits using the new Axiom J. regia 700K SNP genotyping array. We genotyped a diversity panel including 95 walnut genotypes from eight Iranian provinces with a variety of climate zones. A majority of the SNPs (323,273, 53.03%) fell into the "Poly High Resolution" class of polymorphisms, which includes the highest quality variants. Genetic structure assessment, using several approaches, divided the Iranian walnut panel into four principal clusters, reflecting their geographic partitioning. We observed high genetic variation across all of the populations (HO = 0.34 and HE = 0.38). The overall level of genetic differentiation among populations was moderate (FST = 0.07). However, the Semnan population showed high divergence from the other Iranian populations (on average FST = 0.12), most likely due to its geographical isolation. Based on parentage analysis, the level of relatedness was very low among the Iranian walnuts examined, reflecting the geographical distance between the Iranian provinces considered in our study. Finally, we performed a genome-wide association study (GWAS), identifying 55 SNPs significantly associated with nut and kernel-related traits. In conclusion, by applying the novel Axiom J. regia 700K SNP array we uncovered new unexplored genetic diversity and identified significant marker-trait associations for nut-related traits in Persian walnut that will be useful for future breeding programs in Iran and other countries.

Economic values and selection indices for production and reproduction traits of Iranian buffaloes (Bubalus bubalis).

Iranian buffalo plays a critical role in supplying a portion of the income and the necessities of the rural population. The first step to design a breeding program is difinition of breeding goal (BG), a linear combination of breeding values for various traits and their economic values (EV). The current study was aimed at determining EVs for important traits of Iranian buffaloes, namely milk yield (MY), milk fat (MF), age at the first calving (AFC), and calving interval (CI), as well as at estimating the genetic response of applying various types of selection indices. Economic and management data of 50 buffalo herds from various main regions of buffalo rearing in Iran were collected. The EVs were estimated using a simple profit function. Five selection indices were constructed by combining information on various traits. The EVs for BG traits of MY, MF, AFC, and CI were 0.18, 4.66, - 0.36, and - 1.87 US$, respectively. The highest predicted genetic gain in BG was 16.95 and came from applying the selection index that included all traits. The smallest genetic gain (4.93) was predicted for the index with only AFC included. Predicted genetic gain from an index that included production traits and AFC as a reproduction trait (16.9) was higher than that from the index with only production traits (16.15). Results showed that inclusion of reproductive traits in the selection index had a positive effect on genetic gain for breeding goal.

A review on breeding and genetic strategies in Iranian buffaloes (Bubalus bubalis).

The aim of current study was to review breeding progress and update information on genetic strategies in Iranian buffaloes. Iranian buffalo is one of the vital domestic animals throughout north, north-west, south and south-west of Iran with measurable characteristics both in milk and meat production. The species plays an important role in rural economy of the country due to its unique characteristics such as resistance to diseases and parasites, having long productive lifespan and showing higher capability of consuming low-quality forage. In Iran, total production of milk and meat devoted to buffaloes are 293,000 and 24,700 tons, respectively. Selection activities and milk yield recording are carrying out by the central government through the Animal Breeding Centre of Iran. The main breeding activities of Iranian buffaloes included the estimation of genetic parameters and genetic trends for performance traits using different models and methods, estimation of economic values and selection criteria and analysis of population structure. Incorporating different aspects of dairy buffalo management together with improved housing, nutrition, breeding and milking, is known to produce significant improvements in buffalo production. Therefore, identifying genetic potential of Iranian buffaloes, selection of superior breeds, improving nutritional management and reproduction and developing the education and increasing the skills of practical breeders can be useful in order to enhance the performance and profitability of Iranian buffaloes.

Predicting bull fertility using genomic data and biological information.

The genomic prediction of unobserved genetic values or future phenotypes for complex traits has revolutionized agriculture and human medicine. Fertility traits are undoubtedly complex traits of great economic importance to the dairy industry. Although genomic prediction for improved cow fertility has received much attention, bull fertility largely has been ignored. The first aim of this study was to investigate the feasibility of genomic prediction of sire conception rate (SCR) in US Holstein dairy cattle. Standard genomic prediction often ignores any available information about functional features of the genome, although it is believed that such information can yield more accurate and more persistent predictions. Hence, the second objective was to incorporate prior biological information into predictive models and evaluate their performance. The analyses included the use of kernel-based models fitting either all single nucleotide polymorphisms (SNP; 55K) or only markers with presumed functional roles, such as SNP linked to Gene Ontology or Medical Subject Heading terms related to male fertility, or SNP significantly associated with SCR. Both single- and multikernel models were evaluated using linear and Gaussian kernels. Predictive ability was evaluated in 5-fold cross-validation. The entire set of SNP exhibited predictive correlations around 0.35. Neither Gene Ontology nor Medical Subject Heading gene sets achieved predictive abilities higher than their counterparts using random sets of SNP. Notably, kernel models fitting significant SNP achieved the best performance with increases in accuracy up to 5% compared with the standard whole-genome approach. Models fitting Gaussian kernels outperformed their counterparts fitting linear kernels irrespective of the set of SNP. Overall, our findings suggest that genomic prediction of bull fertility is feasible in dairy cattle. This provides potential for accurate genome-guided decisions, such as early culling of bull calves with low SCR predictions. In addition, exploiting nonlinear effects through the use of Gaussian kernels together with the incorporation of relevant markers seems to be a promising alternative to the standard approach. The inclusion of gene set results into prediction models deserves further research.

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. Kernel-based 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.