Abstract:The Chantecler chicken, a unique Canadian indigenous breed, is well adapted to extremely cold environments. However, its genetic characteristics have not been well studied. Here, we analyzed the whole genomes of 10 Chantecler chickens and 121 worldwide chickens, which indicated that Chantecler chickens were derived from commercial chickens and exhibit a high level of inbreeding. Based on a genome-wide scan, we identified two vital candidate regions containing
ME3
and
ZNF536
… Show more
“…Knowledge about the patterns of LD decay can provide important insights into population history, demographic processes and breeding systems [ 68 , 69 ]. The LD estimates decreased with increasing genomic distance of SNP pairs, which was consistent with previous studies in chicken and livestock animals [ 70 , 71 ]. We found that all of the studied indigenous ecotypes followed the same pattern of decrease in LD as the genomic distance increased, however more rapid decrease in LD over increasing genomic distance was observed in White leghorn chickens (Fig.…”
Section: Discussionsupporting
confidence: 91%
“…The r 2 values obtained in this study for native ecotypes were in the ranges of 0.52 to 0.57 at marker pairs distance of 100 Kb, which agreed with those reported for Chinese and Canadian native chicken populations [ 70 , 72 ]. We further found a markedly higher level of LD across all genomic distances in White Leghorn chickens followed by Arian group (Fig.…”
Background
Understanding how evolutionary forces relating to climate have shaped the patterns of genetic variation within and between species is a fundamental pursuit in biology. Iranian indigenous chickens have evolved genetic adaptations to their local environmental conditions, such as hot and arid regions. In the present study, we provide a population genome landscape of genetic variations in 72 chickens representing nine Iranian indigenous ecotypes (Creeper, Isfahan, Lari, Marand, Mashhad, Naked neck, Sari, Shiraz and Yazd) and two commercial lines (White Leghorn and Arian). We further performed comparative population genomics to evaluate the genetic basis underlying variation in the adaptation to hot climate and immune response in indigenous chicken ecotypes. To detect genomic signatures of adaptation, we applied nucleotide diversity (θπ) and FST statistical measurements, and further analyzed the results to find genomic regions under selection for hot adaptation and immune response-related traits.
Results
By generating whole-genome data, we assessed the relationship between the genetic diversity of indigenous chicken ecotypes and their genetic distances to two different commercial lines. The results of genetic structure analysis revealed clustering of indigenous chickens in agreement with their geographic origin. Among all studied chicken groups, the highest level of linkage disequilibrium (LD) (~ 0.70) was observed in White Leghorn group at marker pairs distance of 1 Kb. The results from admixture analysis demonstrated evidence of shared ancestry between Arian individuals and indigenous chickens, especially those from the north of the country. Our search for potential genomic regions under selection in indigenous chicken ecotypes revealed several immune response and heat shock protein-related genes, such as HSP70, HSPA9, HSPH1, HSP90AB1 and PLCB4 that have been previously unknown to be involved in environmental-adaptive traits. In addition, we found some other candidate loci on different chromosomes probably related with hot adaptation and immune response-related traits.
Conclusions
The work provides crucial insights into the structural variation in the genome of Iranian indigenous chicken ecotypes, which up to now has not been genetically investigated. Several genes were identified as candidates for drought, heat tolerance, immune response and other phenotypic traits. These candidate genes may be helpful targets for understanding of the molecular basis of adaptation to hot environmental climate and as such they should be used in chicken breeding programs to select more efficient breeds for desert climate.
“…Knowledge about the patterns of LD decay can provide important insights into population history, demographic processes and breeding systems [ 68 , 69 ]. The LD estimates decreased with increasing genomic distance of SNP pairs, which was consistent with previous studies in chicken and livestock animals [ 70 , 71 ]. We found that all of the studied indigenous ecotypes followed the same pattern of decrease in LD as the genomic distance increased, however more rapid decrease in LD over increasing genomic distance was observed in White leghorn chickens (Fig.…”
Section: Discussionsupporting
confidence: 91%
“…The r 2 values obtained in this study for native ecotypes were in the ranges of 0.52 to 0.57 at marker pairs distance of 100 Kb, which agreed with those reported for Chinese and Canadian native chicken populations [ 70 , 72 ]. We further found a markedly higher level of LD across all genomic distances in White Leghorn chickens followed by Arian group (Fig.…”
Background
Understanding how evolutionary forces relating to climate have shaped the patterns of genetic variation within and between species is a fundamental pursuit in biology. Iranian indigenous chickens have evolved genetic adaptations to their local environmental conditions, such as hot and arid regions. In the present study, we provide a population genome landscape of genetic variations in 72 chickens representing nine Iranian indigenous ecotypes (Creeper, Isfahan, Lari, Marand, Mashhad, Naked neck, Sari, Shiraz and Yazd) and two commercial lines (White Leghorn and Arian). We further performed comparative population genomics to evaluate the genetic basis underlying variation in the adaptation to hot climate and immune response in indigenous chicken ecotypes. To detect genomic signatures of adaptation, we applied nucleotide diversity (θπ) and FST statistical measurements, and further analyzed the results to find genomic regions under selection for hot adaptation and immune response-related traits.
Results
By generating whole-genome data, we assessed the relationship between the genetic diversity of indigenous chicken ecotypes and their genetic distances to two different commercial lines. The results of genetic structure analysis revealed clustering of indigenous chickens in agreement with their geographic origin. Among all studied chicken groups, the highest level of linkage disequilibrium (LD) (~ 0.70) was observed in White Leghorn group at marker pairs distance of 1 Kb. The results from admixture analysis demonstrated evidence of shared ancestry between Arian individuals and indigenous chickens, especially those from the north of the country. Our search for potential genomic regions under selection in indigenous chicken ecotypes revealed several immune response and heat shock protein-related genes, such as HSP70, HSPA9, HSPH1, HSP90AB1 and PLCB4 that have been previously unknown to be involved in environmental-adaptive traits. In addition, we found some other candidate loci on different chromosomes probably related with hot adaptation and immune response-related traits.
Conclusions
The work provides crucial insights into the structural variation in the genome of Iranian indigenous chicken ecotypes, which up to now has not been genetically investigated. Several genes were identified as candidates for drought, heat tolerance, immune response and other phenotypic traits. These candidate genes may be helpful targets for understanding of the molecular basis of adaptation to hot environmental climate and as such they should be used in chicken breeding programs to select more efficient breeds for desert climate.
“…SAMM had a highest linkage disequilibrium level, followed by Chinese Merino and Australian Merino (Figure S1). These results were consistent with the observations in other domestic animals including dogs (Gou et al, 2015), pigs (Muñoz et al, 2019), chickens (Wang et al, 2021;Xu et al, 2021), and ducks (Guo et al, 2021), indicating that domestication and artificial selection induced a loss of genetic diversity and an increase of inbreeding.…”
The South African Mutton Merino (SAMM), a dual‐purpose (meat and wool) sheep breed, is characterized by its excellent performance on growth, carcass traits and meat quality compared to other fine‐wool Merino breeds. Nowadays, the SAMM breed has been widely used to cross with commercial and indigenous fine‐wool or coarse‐wool breeds to improve the growth and meat performance in many countries. To date, however, little is known about the genetic basis for its prominent characteristics. In this study, whole‐genome sequences of 10 SAMM were sequenced and the selection signatures were analyzed together with those of 39 Australian Merino and Chinese Merino (wool‐type Merino) by FST, iHS, and XP‐EHH methods. In total, 313 genes in 277 regions were identified by at least 2 methods with the signal of selection and 21 of them were identified by all three methods. We highlighted a list of interesting genes, including GHR, LCORL, SMO, NCAPG, DCC, IBSP, PPARGC1A, PACRGL, PRDM5, XYLB, AHCYL2, TEFM, AFG1L, and FAM184B, which have been shown to be involved in growth, carcass traits, and meat quality by previous studies. Herein, GHR, encoding a transmembrane receptor for growth hormone, is the most notable one. We report the first study on selection signatures analysis of SAMM at whole‐genome sequence level. These results provide new insights into the genetic mechanisms underlying the growth and carcass traits in SAMM.
“…2 b and Additional file 4: Fig. S1b), indicating potential risks of their extinction following inbreeding depression and thus calling for conservation measures [ 31 , 46 ]. Fig.…”
Section: Utility and Discussionmentioning
confidence: 99%
“…The ROH (Runs of homozygosity) of each group was calculated with PLINK parameters “–homozyg-window-snp 50 –homozyg-snp 50 –homozyg-kb 500 –homozyg-density 50 –homozyg-window-missing 5 –homozyg-window-threshold 0.05 –homozyg-window-het 3”. We then calculated genomic inbreeding coefficients ( F ROH ) by the formula: F ROH = L ROH /L total [ 30 , 31 ]. To avoid the bias due to sample size variation, we reduced the sample size of each group down to five, following random sampling 10 times, then compared with the calculation results of the original samples.…”
Background
Multi-omics data can provide a stereoscopic view to explore potential causal variations and genes, as well as underlying genetic mechanisms of complex traits. However, for many non-mammalian species, including chickens, these resources are poorly integrated and reused, greatly limiting genetic research and breeding processes of the species.
Results
Here, we constructed Galbase, an easily accessible repository that integrates public chicken multi-omics data from 928 re-sequenced genomes, 429 transcriptomes, 379 epigenomes, 15,275 QTL entries, and 7,526 associations. A total of 21.67 million SNPs, 2.71 million InDels, and 488,583 cis-regulatory elements were included. Galbase allows users to retrieve genomic variations in geographical maps, gene expression profiling in heatmaps, and epigenomic signals in peak patterns. It also provides modules for batch annotation of genes, regions, and loci based on multi-layered omics data. Additionally, a series of convenient tools, including the UCSC Genome Browser, WashU Epigenome Browser, BLAT, BLAST, and LiftOver, were also integrated to facilitate search, visualization, and analysis of sequence features.
Conclusion
Galbase grants new opportunities to research communities to undertake in-depth functional genomic studies on chicken. All features of Galbase make it a useful resource to identify genetic variations responsible for chicken complex traits. Galbase is publicly available at http://animal.nwsuaf.edu.cn/ChickenVar.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
