Identification of selection signatures involved in performance traits in a paternal broiler line

Runs of homozygosity and selection signature analyses reveal putative genomic regions for artificial selection in layer breeding

BACKGROUND

The breeding of layers emphasizes the continual selection of egg-related traits, such as egg production, egg quality and eggshell, which enhance their productivity and meet the demand of market. As the breeding process continued, the genomic homozygosity of layers gradually increased, resulting in the emergence of runs of homozygosity (ROH). Therefore, ROH analysis can be used in conjunction with other methods to detect selection signatures and identify candidate genes associated with various important traits in layer breeding.

RESULTS

In this study, we generated whole-genome sequencing data from 686 hens in a Rhode Island Red population that had undergone fifteen consecutive generations of intensive artificial selection. We performed a genome-wide ROH analysis and utilized multiple methods to detect signatures of selection. A total of 141,720 ROH segments were discovered in whole population, and most of them (97.35%) were less than 3 Mb in length. Twenty-three ROH islands were identified, and they overlapped with some regions bearing selection signatures, which were detected by the De-correlated composite of multiple signals methods (DCMS). Sixty genes were discovered and functional annotation analysis revealed the possible roles of them in growth, development, immunity and signaling in layers. Additionally, two-tailed analyses including DCMS and ROH for 44 phenotypes of layers were conducted to find out the genomic differences between subgroups of top and bottom 10% phenotype of individuals. Combining the results of GWAS, we observed that regions significantly associated with traits also exhibited selection signatures between the high and low subgroups. We identified a region significantly associated with egg weight near the 25 Mb region of GGA 1, which exhibited selection signatures and has higher genomic homozygosity in the low egg weight subpopulation. This suggests that the region may be play a role in the decline in egg weight.

CONCLUSIONS

In summary, through the combined analysis of ROH, selection signatures, and GWAS, we identified several genomic regions that associated with the production traits of layers, providing reference for the study of layer genome.

Genomic Inbreeding and Runs of Homozygosity Analysis of Cashmere Goat

Cashmere goats are valuable genetic resources which are famous worldwide for their high-quality fiber. Runs of homozygosity (ROHs) have been identified as an efficient tool to assess inbreeding level and identify related genes under selection. However, there is limited research on ROHs in cashmere goats. Therefore, we investigated the ROH pattern, assessed genomic inbreeding levels and examined the candidate genes associated with the cashmere trait using whole-genome resequencing data from 123 goats. Herein, the Inner Mongolia cashmere goat presented the lowest inbreeding coefficient of 0.0263. In total, we identified 57,224 ROHs. Seventy-four ROH islands containing 50 genes were detected. Certain identified genes were related to meat, fiber and milk production (FGF1, PTPRM, RERE, GRID2, RARA); fertility (BIRC6, ECE2, CDH23, PAK1); disease or cold resistance and adaptability (PDCD1LG2, SVIL, PRDM16, RFX4, SH3BP2); and body size and growth (TMEM63C, SYN3, SDC1, STRBP, SMG6). 135 consensus ROHs were identified, and we found candidate genes (FGF5, DVL3, NRAS, KIT) were associated with fiber length or color. These findings enhance our comprehension of inbreeding levels in cashmere goats and the genetic foundations of traits influenced by selective breeding. This research contributes significantly to the future breeding, reservation and use of cashmere goats and other goat breeds.

Genome-wide mining of diversity and evolutionary signatures revealed selective hotspots in Indian Sahiwal cattle

The Sahiwal cattle breed is the best indigenous dairy cattle breed, and it plays a pivotal role in the Indian dairy industry. This is due to its exceptional milk-producing potential, adaptability to local tropical conditions, and its resilience to ticks and diseases. The study aimed to identify selective sweeps and estimate intrapopulation genetic diversity parameters in Sahiwal cattle using ddRAD sequencing-based genotyping data from 82 individuals. After applying filtering criteria, 78,193 high-quality SNPs remained for further analysis. The population exhibited an average minor allele frequency of 0.221 ± 0.119. Genetic diversity metrics, including observed (0.597 ± 0.196) and expected heterozygosity (0.433 ± 0.096), nucleotide diversity (0.327 ± 0.114), the proportion of polymorphic SNPs (0.726), and allelic richness (1.323 ± 0.134), indicated ample genomic diversity within the breed. Furthermore, an effective population size of 74 was observed in the most recent generation. The overall mean linkage disequilibrium (r2) for pairwise SNPs was 0.269 ± 0.057. Moreover, a greater proportion of short Runs of Homozygosity (ROH) segments were observed suggesting that there may be low levels of recent inbreeding in this population. The genomic inbreeding coefficients, computed using different inbreeding estimates (FHOM, FUNI, FROH, and FGROM), ranged from -0.0289 to 0.0725. Subsequently, we found 146 regions undergoing selective sweeps using five distinct statistical tests: Tajima's D, CLR, |iHS|, |iHH12|, and ROH. These regions, located in non-overlapping 500 kb windows, were mapped and revealed various protein-coding genes associated with enhanced immune systems and disease resistance (IFNL3, IRF8, BLK), as well as production traits (NRXN1, PLCE1, GHR). Notably, we identified interleukin 2 (IL2) on Chr17: 35217075-35223276 as a gene linked to tick resistance and uncovered a cluster of genes (HSPA8, UBASH3B, ADAMTS18, CRTAM) associated with heat stress. These findings indicate the evolutionary impact of natural and artificial selection on the environmental adaptation of the Sahiwal cattle population.

Genome-wide runs of homozygosity signatures in diverse Indian goat breeds

The present study analyzed ROH and consensus ROH regions in 102 animals of eleven diverse Indian goat (Capra hircus) breeds using whole genome sequencing. A total of 51,705 ROH and 21,271 consensus regions were identified. The mean number of ROH per animal was highest in the meat breed, Jharkhand Black (2693) and lowest in the pashmina breed, Changthangi (60). The average length of ROH (ALROH) was maximum in Kanniadu (974.11 Kb) and minimum in Tellicherry (146.98 Kb). Long ROH is typically associated with more recent inbreeding, whereas short ROH is connected to more ancient inbreeding. The overall ROH-based genomic inbreeding (FROH) was highest for Jharkhand Black (0.602) followed by Kanniadu (0.120) and Sangamneri (0.108) among all breeds. FROH of Jharkhand Black was higher than Kanniadu  up to 5 Mb ROH length category. However, in > 20 Mb ROH length category, Kanniadu (0.98)  exhibited significantly higher FROH than Jharkhand Black (0.46). This  implies that Kanniadu had higher levels of recent inbreeding than Jharkhand Black. Despite this, due to the presence of both recent and ancient inbreeding, Jharkhand Black  demonstrated higher overall FROH compared to Kanniadu. ROH patterns revealed dual purpose (meat and dairy) and pashmina breeds as less consanguineous while recent inbreeding was apparent in meat breeds. Analysis of ROH consensus regions identified selection sweeps in key genes governing intramuscular fat deposition, meat tenderisation, lean meat production and carcass weight (CDK4, ALOX15, CASP9, PRDM16, DVL1) in meat breeds; milk fat percentage and mammary gland development (POLD1, NOTCH2, ARHGAP35) in dual purpose (meat and dairy) breeds; while cold adaptation and hair follicle development (APOBEC1, DNAJC3, F2RL1, FGF9) in pashmina breed. MAPK, RAS, BMP and Wnt signaling pathways associated with hair follicle morphogenesis in Changthangi were also identified. PCA analysis based on ROH consensus regions revealed that meat breeds are more diverse than other goat breeds/populations.

Supplementary Information

The online version contains supplementary material available at 10.1007/s13205-024-03921-y.

Dissecting the genomic regions of selection on the X chromosome in different cattle breeds

Mammalian X and Y chromosomes independently evolved from various autosomes approximately 300 million years ago (MYA). To fully understand the relationship between genomic composition and phenotypic diversity arising due to the course of evolution, we have scanned regions of selection signatures on the X chromosome in different cattle breeds. In this study, we have prepared the datasets of 184 individuals of different cattle breeds and explored the complete X chromosome by utilizing four within-population and two between-population methods. There were 23, 25, 30, 17, 17, and 12 outlier regions identified in Tajima's D, CLR, iHS, ROH, FST, and XP-EHH. Bioinformatics analysis showed that these regions harbor important candidate genes like AKAP4 for reproduction in Brown Swiss, MBTS2 for production traits in Brown Swiss and Guernsey, CXCR3 and CITED1 for health traits in Jersey and Nelore, and BMX and CD40LG for regulation of X chromosome inactivation in Nelore and Gir. We identified genes shared among multiple methods, such as TRNAC-GCA and IL1RAPL1, which appeared in Tajima's D, ROH, and iHS analyses. The gene TRNAW-CCA was found in ROH, CLR and iHS analyses. The X chromosome exhibits a distinctive interaction between demographic factors and genetic variations, and these findings may provide new insight into the X-linked selection in different cattle breeds.

Characterization of runs of Homozygosity revealed genomic inbreeding and patterns of selection in indigenous sahiwal cattle

Runs of homozygosity (ROH) are contiguous genomic regions, homozygous across all sites which arise in an individual due to the parents transmitting identical haplotypes to their offspring. The genetic improvement program of Sahiwal cattle after decades of selection needs re-assessment of breeding strategy and population phenomena. Hence, the present study was carried out to optimize input parameters in PLINK for ROH estimates, to explore ROH islands and assessment of pedigree and genome-based inbreeding in Sahiwal cattle. The sliding window approach with parameters standardized to define ROH for the specific population under study was used for the identification of runs. The optimum maximum gap, density, window-snp and window-threshold were 250 Kb, 120 Kb/SNP, 10, 0.05 respectively and ROH patterns were also characterized. ROH islands were defined as the short homozygous genomic regions shared by a large proportion of individuals in a population, containing significantly higher occurrences of ROH than the population specific threshold level. These were identified using the -homozyg-group function of the PLINK v1.9 program. Our results indicated that the Islands of ROH harbor a few candidate genes, ACAD11, RFX4, BANP, UBA5 that are associated with major economic traits. The average FPED (Pedigree based inbreeding coefficient), FROH (Genomic inbreeding coefficient), FHOM (Inbreeding estimated as the ratio of observed and expected homozygous genotypes), FGRM (Inbreeding estimated on genomic relationship method) and FGRM0.5 (Inbreeding estimated from the diagonal of a GRM with allele frequencies near to 0.5) were 0.009, 0.091, 0.035, -0.104 and -0.009, respectively. Our study revealed the optimum parameter setting in PLINK viz. maximal gaps between two SNPs, minimal density of SNPs in a segment (in kb/SNP) and scanning window size to identify ROH segments, which will enable ROH estimation more efficient and comparable across various SNP genotyping-based studies. The result further emphasized the significant role of genomics in unraveling population diversity, selection signatures and inbreeding in the ongoing Sahiwal breed improvement programs.

Deciphering climate resilience in Indian cattle breeds by selection signature analyses

The signature of selection is a crucial concept in evolutionary biology that refers to the pattern of genetic variation which arises in a population due to natural selection. In the context of climate adaptation, the signature of selection can reveal the genetic basis of adaptive traits that enable organisms to survive and thrive in changing environmental conditions. Breeds living in diverse agroecological zones exhibit genetic "footprints" within their genomes that mirror the influence of climate-induced selective pressures, subsequently impacting phenotypic variance. It is assumed that the genomes of animals residing in these regions have been altered through selection for various climatic adaptations. These regions are known as signatures of selection and can be identified using various summary statistics. We examined genotypic data from eight different cattle breeds (Gir, Hariana, Kankrej, Nelore, Ongole, Red Sindhi, Sahiwal, and Tharparkar) that are adapted to diverse regional climates. To identify selection signature regions in this investigation, we used four intra-population statistics: Tajima's D, CLR, iHS, and ROH. In this study, we utilized Bovine 50 K chip data and four genome scan techniques to assess the genetic regions of positive selection for high-temperature adaptation. We have also performed a genome-wide investigation of genetic diversity, inbreeding, and effective population size in our target dataset. We identified potential regions for selection that are likely to be caused by adverse climatic conditions. We observed many adaptation genes in several potential selection signature areas. These include genes like HSPB2, HSPB3, HSP20, HSP90AB1, HSF4, HSPA1B, CLPB, GAP43, MITF, and MCHR1 which have been reported in the cattle populations that live in varied climatic regions. The findings demonstrated that genes involved in disease resistance and thermotolerance were subjected to intense selection. The findings have implications for marker-assisted breeding, understanding the genetic landscape of climate-induced adaptation, putting breeding and conservation programs into action.

Comprehensive selection signature analyses in dairy cattle exploiting purebred and crossbred genomic data

The main objective of the current research was to locate, annotate, and highlight specific areas of the bovine genome that are undergoing intense positive selection. Here, we are analyzing selection signatures in crossbred (Bos taurus X Bos indicus), taurine (Bos taurus), and indicine (Bos indicus) cattle breeds. Indicine cattle breeds found throughout India are known for their higher heat tolerance and disease resilience. More breeds and more methods can provide a better understanding of the selection signature. So, we have worked on nine distinct cattle breeds utilizing seven different summary statistics, which is a fairly extensive approach. In this study, we carried out a thorough genome-wide investigation of selection signatures using bovine 50K SNP data. We have included the genotyped data of two taurine, two crossbreds, and five indicine cattle breeds, for a total of 320 animals. During the 1950s, these indicine (cebuine) cattle breeds were exported with the aim of enhancing the resilience of taurine breeds in Western countries. For this study, we employed seven summary statistics, including intra-population, i.e., Tajima's D, CLR, iHS, and ROH and inter-population statistics, i.e., FST, XP-EHH, and Rsb. The NCBI database, PANTHER 17.0, and CattleQTL database were used for annotation after finding the important areas under selection. Some genes, including EPHA6, CTNNA2, NPFFR2, HS6ST3, NPR3, KCNIP4, LIPK, SDCBP, CYP7A1, NSMAF, UBXN2B, UGDH, UBE2K, and DAB1, were shown to be shared by three or more different approaches. Therefore, it gives evidence of the most intense selection in these areas. These genes are mostly linked to milk production and adaptability traits. This study also reveals selection regions that contain genes which are crucial to numerous biological functions, including those associated with milk production, coat color, glucose metabolism, oxidative stress response, immunity and circadian rhythms.

Evidence for selective sweeps in the MHC gene repertoire of various cattle breeds

Major Histocompatibility Complex (MHC) genes are among the immune genes that have been extensively studied in vertebrates and are necessary for adaptive immunity. In the immunological response to infectious diseases, they play several significant roles. This research paper provides the selection signatures in the MHC region of the bovine genome as well as how certain genes related to innate immunity are undergoing a positive selective sweep. Here, we investigated signatures of historical selection on MHC genes in 15 different cattle populations and a total of 427 individuals. To identify the selection signatures, we have used three separate summary statistics. The findings show potential selection signatures in cattle from whom we isolated genes involved in the MHC. The most significant regions related to the bovine MHC are BOLA, non-classical MHC class I antigen (BOLA-NC1), Microneme protein 1 (MIC1) , Cluster of Differentiation 244 (CD244), Gap Junction Alpha-5 Protein (GJA5). It will be possible to gain new insight into immune system evolution by understanding the distinctive characteristics of MHC in cattle.

Identification of genetic associations and functional SNPs of bovine KLF6 gene on milk production traits in Chinese holstein

BACKGROUND

Our previous research identified the Kruppel like factor 6 (KLF6) gene as a prospective candidate for milk production traits in dairy cattle. The expression of KLF6 in the livers of Holstein cows during the peak of lactation was significantly higher than that during the dry and early lactation periods. Notably, it plays an essential role in activating peroxisome proliferator-activated receptor α (PPARα) signaling pathways. The primary aim of this study was to further substantiate whether the KLF6 gene has significant genetic effects on milk traits in dairy cattle.

RESULTS

Through direct sequencing of PCR products with pooled DNA, we totally identified 12 single nucleotide polymorphisms (SNPs) within the KLF6 gene. The set of SNPs encompasses 7 located in 5' flanking region, 2 located in exon 2 and 3 located in 3' untranslated region (UTR). Of these, the g.44601035G > A is a missense mutation that resulting in the replacement of arginine (CGG) with glutamine (CAG), consequently leading to alterations in the secondary structure of the KLF6 protein, as predicted by SOPMA. The remaining 7 regulatory SNPs significantly impacted the transcriptional activity of KLF6 following mutation (P < 0.005), manifesting as changes in transcription factor binding sites. Additionally, 4 SNPs located in both the UTR and exons were predicted to influence the secondary structure of KLF6 mRNA using the RNAfold web server. Furthermore, we performed the genotype-phenotype association analysis using SAS 9.2 which found all the 12 SNPs were significantly correlated to milk yield, fat yield, fat percentage, protein yield and protein percentage within both the first and second lactations (P < 0.0001 ~ 0.0441). Also, with Haploview 4.2 software, we found the 12 SNPs linked closely and formed a haplotype block, which was strongly associated with five milk traits (P < 0.0001 ~ 0.0203).

CONCLUSIONS

In summary, our study represented the KLF6 gene has significant impacts on milk yield and composition traits in dairy cattle. Among the identified SNPs, 7 were implicated in modulating milk traits by impacting transcriptional activity, 4 by altering mRNA secondary structure, and 1 by affecting the protein secondary structure of KLF6. These findings provided valuable molecular insights for genomic selection program of dairy cattle.

Genome-wide landscape of runs of homozygosity and differentiation across Egyptian goat breeds

Understanding the genomic features of livestock is essential for successful breeding programs and conservation. This information is scarce for local goat breeds in Egypt. In the current study, genomic regions with selection signatures were identified as well as runs of homozygosity (ROH), genomic inbreeding coefficients (FROH) and fixation index (FST) were detected in Egyptian Nubian, Damascus, Barki and Boer goat breeds. A total of 46,268 SNP markers and 337 animals were available for the genomic analyses. On average, 145.44, 42.02, 87.90 and 126.95 ROHs were detected per individual in the autosomal genome of the respective breeds. The mean accumulative ROH lengths ranged from 46.5 Mb in Damascus to 360 Mb in Egyptian Nubian. The short ROH segments (< 2 Mb) were most frequent in all breeds, while the longest ROH segments (> 16 Mb) were exclusively found in the Egyptian Nubian. The highest average FROH was observed in Egyptian Nubian (~ 0.12) followed by Boer (~ 0.11), while the lowest FROH was found in Damascus (~ 0.05) and Barki breed (~ 0.03). The estimated mean FST was 0.14 (Egyptian Nubian and Boer), 0.077 (Egyptian Nubian and Barki), 0.075 (Egyptian Nubian and Damascus), 0.071 (Barki and Boer), 0.064 (Damascus and Boer), and 0.015 (Damascus and Barki), for each pair of breeds. Interestingly, multiple SNPs that accounted for high FST values were observed on chromosome 6 in regions harboring ALPK1 and KCNIP4. Genomic regions overlapping both FST and ROH harbor genes related to immunity (IL4R, PHF23, GABARAP, GPS2, and CD68), reproduction (SPATA2L, TNFSF12, TMEM95, and RNF17), embryonic development (TCF25 and SOX15) and adaptation (MC1R, KDR, and KIT), suggesting potential genetic adaptations to local environmental conditions. Our results contribute to the understanding of the genetic architecture of different goat breeds and may provide valuable information for effective preservation and breeding programs of local goat breeds in Egypt.

Identification of Runs of Homozygosity Islands and Functional Variants in Wenchang Chicken

Wenchang chickens, a native breed in the Hainan province of China, are famous for their meat quality and adaptability to tropical conditions. For effective management and conservation, in the present study, we systematically investigated the characteristics of genetic variations and runs of homozygosity (ROH) along the genome using re-sequenced whole-genome sequencing data from 235 Wenchang chickens. A total of 16,511,769 single nucleotide polymorphisms (SNPs) and 53,506 ROH segments were identified in all individuals, and the ROH of Wenchang chicken were mainly composed of short segments (0-1 megabases (Mb)). On average, 5.664% of the genome was located in ROH segments across the Wenchang chicken samples. According to several parameters, the genetic diversity of the Wenchang chicken was relatively high. The average inbreeding coefficient of Wenchang chickens based on F_HOM, F_GRM, and F_ROH was 0.060 ± 0.014, 0.561 ± 0.020, and 0.0566 ± 0.01, respectively. A total of 19 ROH islands containing 393 genes were detected on 9 different autosomes. Some of these genes were putatively associated with growth performance (AMY1a), stress resistance (THEMIS2, PIK3C2B), meat traits (MBTPS1, DLK1, and EPS8L2), and fat deposition (LANCL2, PPARγ). These findings provide a better understanding of the degree of inbreeding in Wenchang chickens and the hereditary basis of the characteristics shaped under selection. These results are valuable for the future breeding, conservation, and utilization of Wenchang and other chicken breeds.

The study of selection signature and its applications on identification of candidate genes using whole genome sequencing data in chicken - a review

Chicken is a major source of protein for the increasing human population and is useful for research purposes. There are almost 1,600 distinct regional breeds of chicken across the globe, among which a large body of genetic and phenotypic variations has been accumulated due to extensive natural and artificial selection. Moreover, natural selection is a crucial force for animal domestication. Several approaches have been adopted to detect selection signatures in different breeds of chicken using whole genome sequencing (WGS) data including integrated haplotype score (iHS), cross-populated extend haplotype homozygosity test (XP-EHH), fixation index (F_ST), cross-population composite likelihood ratio (XP-CLR), nucleotide diversity (Pi), and others. In addition, gene enrichment analyses are utilized to determine KEGG pathways and gene ontology (GO) terms related to traits of interest in chicken. Herein, we review different studies that have adopted diverse approaches to detect selection signatures in different breeds of chicken. This review systematically summarizes different findings on selection signatures and related candidate genes in chickens. Future studies could combine different selection signatures approaches to strengthen the quality of the results thereby providing more affirmative inference. This would further aid in deciphering the importance of selection in chicken conservation for the increasing human population.

Selective footprints and genes relevant to cold adaptation and other phenotypic traits are unscrambled in the genomes of divergently selected chicken breeds

BACKGROUND

The genomes of worldwide poultry breeds divergently selected for performance and other phenotypic traits may also be affected by, and formed due to, past and current admixture events. Adaptation to diverse environments, including acclimation to harsh climatic conditions, has also left selection footprints in breed genomes.

RESULTS

Using the Chicken 50K_CobbCons SNP chip, we genotyped four divergently selected breeds: two aboriginal, cold tolerant Ushanka and Orloff Mille Fleur, one egg-type Russian White subjected to artificial selection for cold tolerance, and one meat-type White Cornish. Signals of selective sweeps were determined in the studied breeds using three methods: (1) assessment of runs of homozygosity islands, (2) F_ST based population differential analysis, and (3) haplotype differentiation analysis. Genomic regions of true selection signatures were identified by two or more methods or in two or more breeds. In these regions, we detected 540 prioritized candidate genes supplemented them with those that occurred in one breed using one statistic and were suggested in other studies. Amongst them, SOX5, ME3, ZNF536, WWP1, RIPK2, OSGIN2, DECR1, TPO, PPARGC1A, BDNF, MSTN, and beta-keratin genes can be especially mentioned as candidates for cold adaptation. Epigenetic factors may be involved in regulating some of these important genes (e.g., TPO and BDNF).

CONCLUSION

Based on a genome-wide scan, our findings can help dissect the genetic architecture underlying various phenotypic traits in chicken breeds. These include genes representing the sine qua non for adaptation to harsh environments. Cold tolerance in acclimated chicken breeds may be developed following one of few specific gene expression mechanisms or more than one overlapping response known in cold-exposed individuals, and this warrants further investigation.

Machine-Learning Prospects for Detecting Selection Signatures Using Population Genomics Data

Natural selection has been given a lot of attention because it relates to the adaptation of populations to their environments, both biotic and abiotic. An allele is selected when it is favored by natural selection. Consequently, the favored allele increases in frequency in the population and neighboring linked variation diminishes, causing so-called selective sweeps. A high-throughput genomic sequence allows one to disentangle the evolutionary forces at play in populations. With the development of high-throughput genome sequencing technologies, it has become easier to detect these selective sweeps/selection signatures. Various methods can be used to detect selective sweeps, from simple implementations using summary statistics to complex statistical approaches. One of the important problems of these statistical models is the potential to provide inaccurate results when their assumptions are violated. The use of machine learning (ML) in population genetics has been introduced as an alternative method of detecting selection by treating the problem of detecting selection signatures as a classification problem. Since the availability of population genomics data is increasing, researchers may incorporate ML into these statistical models to infer signatures of selection with higher predictive accuracy and better resolution. This article describes how ML can be used to aid in detecting and studying natural selection patterns using population genomic data.

Identification of important genomic footprints using eight different selection signature statistics in domestic cattle breeds

In the present study, the population genomic data of different cattle breeds were explored to decipher the genomic regions affected due to selective events and reflected in the productive, reproductive, thermo-tolerance, and health-related traits. To find out these genomic deviations due to selective sweeps, we used eight different statistical tools (Tajima's D, Fu & Li's D*, CLR, ROH, iHS, F_ST, FLK, and hapFLK) on seven indigenous and five exotic cattle breeds. We further performed composite analysis by comparing their covariance matrix. Several candidate genes were found to be related to milk production (ADARB, WDR70, and CA8), reproductive (PARN, FAM134B2, and ZBTB20), and health-related traits (SP110, CXCL2, CLXCL3, CXCL5, IRF8, and MYOM1). The outcome of this investigation provides a basis for detecting selective sweeps that explain the genetic variation of traits. They may possess functional importance for multiple cattle breeds in different subcontinents. However, further studies are required to improve the findings using high-density arrays or whole-genome sequencing with higher resolution and greater sample sizes.

Association Between Natriuretic Peptide Receptor 2 (NPR2) RS208158047 Polymorphism and Fattening Performance of Young Bulls

The objective of this study was to determine fattening performance data for Charolais, Limousin and Blonde d’Aquitaine beef cattle and associate these data with NPR2 gene 8:g.59961937 T>C (rs208158047) mutation. Experiments were conducted with 176 beef cattle (77 Charolais, 66 Limousin and 33 Blonde d’Aquitaine) at nine months of age. Experiments lasted for 9 months and animals were slaughtered at the age of 18 months. Cattle body weights were determined at four different periods: beginning of fattening (d0), 60th day of fattening (d60), 120th day of fattening (d120) and at the end of fattening (sw). In terms of rs208158047 mutation of Charolais, Limousin and Blonde d’Aquitaine breeds, TT and CT genotypes were identified, and CC genotype was not encountered. The association of average daily gain (ADG) in d0-d60, d0-d120 and d0-sw periods with the genotypes of rs208158047 mutation was found to be significant (P<0.05). Greater ADGs were observed in rs208158047-CT genotypes compared to rs208158047-TT genotypes. These results indicate that the selection of bovine NPR2 gene could be used to ensure the breeding direction for growth related traits of the beef cattle.

Investigating inbreeding in the turkey (Meleagris gallopavo) genome

The detrimental effects of increased homozygosity due to inbreeding have prompted the development of methods to reduce inbreeding. The detection of runs of homozygosity (ROH), or contiguous stretches of homozygous marker genotypes, can be used to describe and quantify the level of inbreeding in an individual. The estimation of inbreeding coefficients can be calculated based on pedigree information, ROH, or the genomic relationship matrix. The aim of this study was to detect and describe ROH in the turkey genome and compare estimates of pedigree-based inbreeding coefficients (F_PED) with genomic-based inbreeding coefficients estimated from ROH (F_ROH) and the genomic relationship matrix (F_GRM). A total of 2,616,890 pedigree records were available. Of these records, 6,371 genotyped animals from three purebred turkey (Meleagris gallopavo) lines between 2013 and 2019 were available, and these were obtained using a dense single nucleotide polymorphism array (56,452 SNPs). The overall mean length of detected ROH was 2.87 ± 0.29 Mb with a mean number of 84.87 ± 8.79 ROH per animal. Short ROH with lengths of 1 to 2 Mb long were the most abundant throughout the genome. Mean ROH coverage differed greatly between chromosomes and lines. Considering inbreeding coefficient means across all lines, genomic derived inbreeding coefficients (F_ROH = 0.27; F_GRM = 0.32) were higher than coefficients estimated from pedigree records (F_PED = 0.14). Correlations between F_ROH and F_PED, F_ROH and F_GRM, and F_PED and F_GRM ranged between 0.19 to 0.31, 0.68 to 0.73, and 0.17 to 0.30, respectively. Additionally, correlations between F_ROH from different lengths and F_PED substantially increased with ROH length from -0.06 to 0.33. Results of the current research, including the distribution of ROH throughout the genome and ROH-derived inbreeding estimates, can provide a more comprehensive description of inbreeding in the turkey genome. This knowledge can be used to evaluate genetic diversity, a requirement for genetic improvement, and develop methods to minimize inbreeding in turkey breeding programs.

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 (F_ST 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.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12711-021-00664-9.

Genome-wide analyses reveal population structure and identify candidate genes associated with tail fatness in local sheep from a semi-arid area

Under a climate change perspective, the genetic make-up of local livestock breeds showing adaptive traits should be explored and preserved as a priority. We used genotype data from the ovine 50 k Illumina BeadChip for assessing breed autozygosity based on runs of homozygosity (ROH) and fine-scale genetic structure and for detecting genomic regions under selection in 63 Tunisian sheep samples. The average genomic inbreeding coefficients based on ROH were estimated at 0.017, 0.021, and 0.024 for Barbarine (BAR, n = 26), Noire de Thibar (NDT, n = 23), and Queue fine de l'Ouest (QFO, n = 14) breeds, respectively. The genomic relationships among individuals based on identity by state (IBS) distance matrix highlighted a recent introgression of QFO into the BAR and a genetic differentiation of NDT samples, possibly explained by past introgression of European gene pools. Genome-wide scan for ROH across breeds and within the BAR sample set identified an outstanding signal on chromosome 13 (46.58-49.61 Mbp). These results were confirmed using F_ST index, differentiating fat vs. thin-tailed individuals. Candidate genes under selection pressure (CDS2, PROKR1, and BMP2) were associated to lipid storage and probably preferentially selected in fat-tailed BAR animals. Our findings suggest paying more attention to preserve the genetic integrity and adaptive alleles of local sheep breeds.

Runs of Homozygosity in Modern Chicken Revealed by Sequence Data

Runs of homozygosity (ROH) are chromosomal stretches that in a diploid genome appear in a homozygous state and display identical alleles at multiple contiguous loci. This study aimed to systematically compare the genomic distribution of the ROH islands among five populations of wild vs commercial chickens of both layer and broiler type. To this end, we analyzed whole genome sequences of 115 birds including white layer (WL, n = 25), brown layer (BL, n = 25), broiler line A (BRA, n = 20), broiler line B (BRB, n = 20) and Red Junglefowl (RJF, n = 25). The ROH segments varied in size markedly among populations, ranging from 0.3 to 21.83 Mb reflecting their past genealogy. White layers contained the largest portion of the genome in homozygous state with an average ROH length of 432.1 Mb (±18.7) per bird, despite carrying it in short segments (0.3-1 Mb). Population-wise inbreeding measures based on Wright's (F_is) and genomic (F_ROH) metrics revealed highly inbred genome of layer lines relative to the broilers and Red Junglefowl. We further revealed the ROH islands, among commercial lines overlapped with QTL related to limb development (GREM1, MEOX2), body weight (Meis2a.1, uc_338), eggshell color (GLCCI1, ICA1, UMAD1), antibody response to Newcastle virus (ROBO2), and feather pecking. Comparison of ROH landscape in sequencing resolution demonstrated that a sizable portion of genome of commercial lines segregates in homozygote state, reflecting many generations of assortative mating and intensive selection in their recent history. In contrary, wild birds carry shorter ROH segments, likely suggestive of older evolutionary events.