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

Population structure and selection signal analysis of indigenous sheep from the southern edge of the Taklamakan Desert

Analyzing the genetic diversity and selection characteristics of sheep (Ovis aries) holds significant value in understanding their environmental adaptability, enhancing breeding efficiency, and achieving effective conservation and rational utilization of genetic resources. In this study, we utilized Illumina Ovine SNP 50 K BeadChip data from four indigenous sheep breeds from the southern margin of the Taklamakan Desert (Duolang sheep: n = 36, Hetian sheep: n = 74, Kunlun sheep: n = 27, Qira black sheep: n = 178) and three foreign meat sheep breeds (Poll Dorset sheep: n = 105, Suffolk sheep: n = 153, Texel sheep: n = 150) to investigate the population structure, genetic diversity, and genomic signals of positive selection within the indigenous sheep. According to the Principal component analysis (PCA), the Neighbor-Joining tree (NJ tree), and Admixture, we revealed distinct clustering patterns of these seven sheep breeds based on their geographical distribution. Then used Cross Population Extended Haplotype Homozygosity (XP-EHH), Fixation Index (FST), and Integrated Haplotype Score (iHS), we identified a collective set of 32 overlapping genes under positive selection across four indigenous sheep breeds. These genes are associated with wool follicle development and wool traits, desert environmental adaptability, disease resistance, reproduction, and high-altitude adaptability. This study reveals the population structure and genomic selection characteristics in the extreme desert environments of native sheep breeds from the southern edge of the Taklimakan Desert, providing new insights into the conservation and sustainable use of indigenous sheep genetic resources in extreme environments. Additionally, these findings offer valuable genetic resources for sheep and other mammals to adapt to global climate change.

Weighted single-step genome-wide association study for direct and maternal genetic effects associated with birth and weaning weights in sheep

Body weight is an important economic trait for sheep meat production, and its genetic improvement is considered one of the main goals in the sheep breeding program. Identifying genomic regions that are associated with growth-related traits accelerates the process of animal breeding through marker-assisted selection, which leads to increased response to selection. In this study, we conducted a weighted single-step genome-wide association study (WssGWAS) to identify potential candidate genes for direct and maternal genetic effects associated with birth weight (BW) and weaning weight (WW) in Baluchi sheep. The data used in this research included 13,408 birth and 13,170 weaning records collected at Abbas-Abad Baluchi Sheep Breeding Station, Mashhad-Iran. Genotypic data of 94 lambs genotyped by Illumina 50K SNP BeadChip for 54,241 markers were used. The proportion of variance explained by genomic windows was calculated by summing the variance of SNPs within 1 megabase (Mb). The top 10 window genomic regions explaining the highest percentages of additive and maternal genetic variances were selected as candidate window genomic regions associated with body weights. Our findings showed that for BW, the top-ranked genomic regions (1 Mb windows) explained 4.30 and 4.92% of the direct additive and maternal genetic variances, respectively. The direct additive genetic variance explained by the genomic window regions varied from 0.31 on chromosome 1 to 0.59 on chromosome 8. The highest (0.84%) and lowest (0.32%) maternal genetic variances were explained by genomic windows on chromosome 10 and 17, respectively. For WW, the top 10 genomic regions explained 6.38 and 5.76% of the direct additive and maternal genetic variances, respectively. The highest and lowest contribution of direct additive genetic variances were 1.37% and 0.42%, respectively, both explained by genomic regions on chromosome 2. For maternal effects on WW, the highest (1.38%) and lowest (0.41%) genetic variances were explained by genomic windows on chromosome 2. Further investigation of these regions identified several possible candidate genes associated with body weight. Gene ontology analysis using the DAVID database identified several functional terms, such as translation repressor activity, nucleic acid binding, dehydroascorbic acid transporter activity, growth factor activity and SH2 domain binding.

Structural variant landscapes reveal convergent signatures of evolution in sheep and goats

BACKGROUND

Sheep and goats have undergone domestication and improvement to produce similar phenotypes, which have been greatly impacted by structural variants (SVs). Here, we report a high-quality chromosome-level reference genome of Asiatic mouflon, and implement a comprehensive analysis of SVs in 897 genomes of worldwide wild and domestic populations of sheep and goats to reveal genetic signatures underlying convergent evolution.

RESULTS

We characterize the SV landscapes in terms of genetic diversity, chromosomal distribution and their links with genes, QTLs and transposable elements, and examine their impacts on regulatory elements. We identify several novel SVs and annotate corresponding genes (e.g., BMPR1B, BMPR2, RALYL, COL21A1, and LRP1B) associated with important production traits such as fertility, meat and milk production, and wool/hair fineness. We detect signatures of selection involving the parallel evolution of orthologous SV-associated genes during domestication, local environmental adaptation, and improvement. In particular, we find that fecundity traits experienced convergent selection targeting the gene BMPR1B, with the DEL00067921 deletion explaining ~10.4% of the phenotypic variation observed in goats.

CONCLUSIONS

Our results provide new insights into the convergent evolution of SVs and serve as a rich resource for the future improvement of sheep, goats, and related livestock.

Genetic analyses and prediction for lodging‑related traits in a diverse Iranian hexaploid wheat collection

Lodging is one of the most important limiting environmental factors for achieving the maximum yield and quality of grains in cereals, including wheat. However, little is known about the genetic foundation underlying lodging resistance (LR) in wheat. In this study, 208 landraces and 90 cultivars were phenotyped in two cropping seasons (2018-2019 and 2019-2020) for 19 LR-related traits. A genome-wide association study (GWAS) and genomics prediction were carried out to dissect the genomic regions of LR. The number of significant marker pairs (MPs) was highest for genome B in both landraces (427,017) and cultivars (37,359). The strongest linkage disequilibrium (LD) between marker pairs was found on chromosome 4A (0.318). For stem lodging-related traits, 465, 497, and 478 marker-trait associations (MTAs) and 45 candidate genes were identified in year 1, year 2, and pooled. Gene ontology exhibited genomic region on Chr. 2B, 6B, and 7B control lodging. Most of these genes have key roles in defense response, calcium ion transmembrane transport, carbohydrate metabolic process, nitrogen compound metabolic process, and some genes harbor unknown functions that, all together may respond to lodging as a complex network. The module associated with starch and sucrose biosynthesis was highlighted. Regarding genomic prediction, the GBLUP model performed better than BRR and RRBLUP. This suggests that GBLUP would be a good tool for wheat genome selection. As a result of these findings, it has been possible to identify pivotal QTLs and genes that could be used to improve stem lodging resistance in Triticum aestivum L.

A computational framework for improving genetic variants identification from 5,061 sheep sequencing data

BACKGROUND

Pan-genomics is a recently emerging strategy that can be utilized to provide a more comprehensive characterization of genetic variation. Joint calling is routinely used to combine identified variants across multiple related samples. However, the improvement of variants identification using the mutual support information from multiple samples remains quite limited for population-scale genotyping.

RESULTS

In this study, we developed a computational framework for joint calling genetic variants from 5,061 sheep by incorporating the sequencing error and optimizing mutual support information from multiple samples' data. The variants were accurately identified from multiple samples by using four steps: (1) Probabilities of variants from two widely used algorithms, GATK and Freebayes, were calculated by Poisson model incorporating base sequencing error potential; (2) The variants with high mapping quality or consistently identified from at least two samples by GATK and Freebayes were used to construct the raw high-confidence identification (rHID) variants database; (3) The high confidence variants identified in single sample were ordered by probability value and controlled by false discovery rate (FDR) using rHID database; (4) To avoid the elimination of potentially true variants from rHID database, the variants that failed FDR were reexamined to rescued potential true variants and ensured high accurate identification variants. The results indicated that the percent of concordant SNPs and Indels from Freebayes and GATK after our new method were significantly improved 12%-32% compared with raw variants and advantageously found low frequency variants of individual sheep involved several traits including nipples number (GPC5), scrapie pathology (PAPSS2), seasonal reproduction and litter size (GRM1), coat color (RAB27A), and lentivirus susceptibility (TMEM154).

CONCLUSION

The new method used the computational strategy to reduce the number of false positives, and simultaneously improve the identification of genetic variants. This strategy did not incur any extra cost by using any additional samples or sequencing data information and advantageously identified rare variants which can be important for practical applications of animal breeding.

Genome-Wide Re-Sequencing Data Reveals the Population Structure and Selection Signatures of Tunchang Pigs in China

Tunchang pig is one population of Hainan pig in the Hainan Province of China, with the characteristics of delicious meat, strong adaptability, and high resistance to diseases. To explore the genetic diversity and population structure of Tunchang pigs and uncover their germplasm characteristics, 10 unrelated Tunchang pigs were re-sequenced using the Illumina NovaSeq 150 bp paired-end platform with an average depth of 10×. Sequencing data from 36 individuals of 7 other pig breeds (including 4 local Chinese pig breeds (5 Jinhua, 5 Meishan, 5 Rongchang, and 6 Wuzhishan), and 3 commonly used commercial pig breeds (5 Duorc, 5 Landrace, and 5 Large White)) were downloaded from the NCBI public database. After analysis of genetic diversity and population structure, it has been found that compared to commercial pigs, Tunchang pigs have higher genetic diversity and are genetically close to native Chinese breeds. Three methods, FST, θπ, and XP-EHH, were used to detect selection signals for three breeds of pigs: Tunchang, Duroc, and Landrace. A total of 2117 significantly selected regions and 201 candidate genes were screened. Gene enrichment analysis showed that candidate genes were mainly associated with good adaptability, disease resistance, and lipid metabolism traits. Finally, further screening was conducted to identify potential candidate genes related to phenotypic traits, including meat quality (SELENOV, CBR4, TNNT1, TNNT3, VPS13A, PLD3, SRFBP1, and SSPN), immune regulation (CD48, FBL, PTPRH, GNA14, LOX, SLAMF6, CALCOCO1, IRGC, and ZNF667), growth and development (SYT5, PRX, PPP1R12C, and SMG9), reproduction (LGALS13 and EPG5), vision (SLC9A8 and KCNV2), energy metabolism (ATP5G2), cell migration (EPS8L1), and olfaction (GRK3). In summary, our research results provide a genomic overview of the genetic variation, genetic diversity, and population structure of the Tunchang pig population, which will be valuable for breeding and conservation of Tunchang pigs in the future.

Co-expression of candidate genes regulating growth performance and carcass traits of Barki lambs in Egypt

Sheep are considered one of the main sources of animal protein in Egypt and the producers of sheep mutton eagers to find biological criteria for selecting fast-growing lambs that reach market weight early. Therefore, the present study aimed to find a link between the expression profile of selected candidate genes with growth performance and carcass traits of Barki lambs. Thirty-eight Barki lambs were kept and fed individually after weaning till 12 months of age and were divided into 3 groups according to growth performance (fast, intermediate, and slow-growing). Three samples were taken from different body tissues (eye muscle, liver, and fat tail) of each group, directly during slaughtering and stored at − 80 °C until RNA isolation. Real-time PCR was used to profile selected candidate genes (RPL7, CTP1, FABP4, ADIPOQ, and CAPN3) and GAPDH was used as a housekeeping gene. The results indicated that the final body weight was significantly (P ≤ 0.05) greater in the fast (49.9 kg) and intermediate (40.7 kg) compared to slow-growing animals (30.8 kg). The hot carcass weight was heavier (P ≤ 0.05) in the fast and intermediate-growing (24.57 and 19.07 kg) than slow-growing lambs (15.10 kg). The blood profiles of T3 and T4 hormones in addition to other parameters such as total protein, total lipids, and calcium level showed no clear variations among different experimental groups. At the molecular level, our data demonstrated upregulation of genes involved in protein biosynthesis (RPL7), fatty acid oxidation (CPT1), and lipolysis (FABP4) in the fast and intermediate-growing lambs in all studied tissues which facilitate protein accretion, energy expenditure, and fatty acid partitioning required for muscle building up. Moreover, the expression profile of the gene involved in muscle development (CAPN3) was increased in fast and intermediate-growing compared to slow-growing lambs in order to support muscle proper development. On the other hand, a candidate gene involved in lipogenesis (ADIPOQ) was expressed similarly in fat and liver tissues; however, its expression was increased in muscles of fast and intermediate-growing lambs compared to slow-growing animals. In conclusion, the current study indicated that the expression profile of genes involved in metabolic activities of liver, muscle, and adipose tissue is linked with the growth performance of lambs although no variations were detected in blood parameters. This provides an evidence for the importance of co-expression of these genes in body tissues to determine the final body weight and carcass characteristics of Barki sheep.

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.