Unravelling the effect of environment on the genome of Sarda breed ewes using Runs of Homozygosity

Cesarani, Alberto; Gaspa, Giustino; Correddu, Fabio; Dimauro, Corrado; Macciotta, N.P.P.

doi:10.1111/jbg.12666

Cited by 8 publications

(8 citation statements)

References 122 publications

(118 reference statements)

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“…The mapped region (eROHi and nSL) from 51.40 to 51.94 was identified in two studies by Zhu et al (2015) ; Zhu et al (2020) . The very large signal mapped here from 56.64 to 58.09 by HRiD and containing three ( AR , OPHN1 and YIPF6 ) annotated genes was found in four other studies ( Liu et al, 2016 ; Chen et al, 2018 ; Manzari et al, 2019 ; Cesarani et al, 2022 ). This concordance was of interest to us because this region was not detected by eROHi, iHS, and nSL in this study but coincided with the most significant individual nSL outlier [−log(P) = 5.17)] at position 56.81 Mb, demonstrating the complementary potential of HRiD to identify positive selection signals.…”

Section: Discussionsupporting

confidence: 69%

“…The mapped region (iHS and nSL) from 32.20 to 32.80 Mb, without annotated genes, was also mapped by Zhu et al (2015) , Liu et al (2016) and Chen et al (2018) . Similarly, the mapped region (iHS and nSL) from 41.00 to 43.00 Mb, with NDP and EHC2 genes, was confirmed by Zhu et al (2015) ; Liu et al (2016) and Cesarani et al (2022) . Since EFHC2 is associated with fear recognition and harm avoidance, we can assume a connection of this signal with extensive husbandry (fear of guard dogs and wolves), which is characteristic of Croatian indigenous breeds.…”

Section: Discussionmentioning

confidence: 61%

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Identification of Selection Signals on the X-Chromosome in East Adriatic Sheep: A New Complementary Approach

et al. 2022

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Sheep are one of the most important livestock species in Croatia, found mainly in the Mediterranean coastal and mountainous regions along the East Adriatic coast, well adapted to the environment and mostly kept extensively. Our main objective was therefore to map the positive selection of the X-chromosome (18,983 SNPs that passed quality control), since nothing is known about the adaptation genes on this chromosome for any of the breeds from the Balkan cluster. Analyses were performed on a sample of eight native Croatian breeds (101 females and 100 males) representing the East Adriatic metapopulation and on 10 mouflons (five females and males), all sampled in Croatia. Three classical within-population approaches (extreme Runs of Homozygosity islands, integrated Haplotype Score, and number of Segregating Sites by Length) were applied along with our new approach called Haplotype Richness Drop (HRiD), which uses only the information contained in male haplotypes. We have also shown that phylogenetic analyses, such as the Median-joining network, can provide additional information when performed with the selection signals identified by HRiD. Our new approach identifies positive selection signals by searching for genomic regions that exhibit a sudden decline in haplotype richness. In total, we identified 14 positive selection signals, 11 using the classical approach and three using the HRiD approach, all together containing 34 annotated genes. The most reliable selection signal was mapped by all four approaches in the same region, overlapping between 13.17 and 13.60 Mb, and assigned to the CA5B, ZRSR2, AP1S2, and GRPR genes. High repeatability (86%) of results was observed, as 12 identified selection signals were also confirmed in other studies with sheep. HRiD offers an interesting possibility to be used complementary to other approaches or when only males are genotyped, which is often the case in genomic breeding value estimations. These results highlight the importance of the X-chromosome in the adaptive architecture of domestic ruminants, while our novel HRiD approach opens new possibilities for research.

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Section: Discussionsupporting

confidence: 69%

Section: Discussionmentioning

confidence: 61%

Identification of Selection Signals on the X-Chromosome in East Adriatic Sheep: A New Complementary Approach

et al. 2022

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“…In fact, to adapt to these backyard conditions, selective sweeps might have occurred in genomic regions that are related to immune responses and disease sensitivity [ 35 ]. Effects of the geographical area of origin on genomic regions that have been subjected to selection have also been reported in local Italian goats [ 36 ] and sheep [ 37 ]. Geographical location, coupled with smallholder farm practices, likely imposed multiple environmental stressors on the studied chicken populations that may have affected their fitness and led to their adaptation to these environments over time through changes in allele frequency of beneficial or detrimental alleles.…”

Section: Discussionmentioning

confidence: 99%

Genome-wide mapping of signatures of selection using a high-density array identified candidate genes for growth traits and local adaptation in chickens

et al. 2023

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Background Availability of single nucleotide polymorphism (SNP) genotyping arrays and progress in statistical analyses have allowed the identification of genomic regions and genes under selection in chicken. In this study, SNP data from the 600 K Affymetrix chicken array were used to detect signatures of selection in 23 local Italian chicken populations. The populations were categorized into four groups for comparative analysis based on live weight (heavy vs light) and geographical area (Northern vs Southern Italy). Putative signatures of selection were investigated by combining three extended haplotype homozygosity (EHH) statistical approaches to quantify excess of haplotype homozygosity within (iHS) and between (Rsb and XP-EHH) groups. Presence of runs of homozygosity (ROH) islands was also analysed for each group. Results After editing, 541 animals and 313,508 SNPs were available for statistical analyses. In total, 15 candidate genomic regions that are potentially under selection were detected among the four groups: eight within a group by iHS and seven by combining the results of Rsb and XP-EHH, which revealed divergent selection between the groups. The largest overlap between genomic regions identified to be under selection by the three approaches was on chicken chromosome 8. Twenty-one genomic regions were identified with the ROH approach but none of these overlapped with regions identified with the three EHH-derived statistics. Some of the identified regions under selection contained candidate genes with biological functions related to environmental stress, immune responses, and disease resistance, which indicate local adaptation of these chicken populations. Conclusions Compared to commercial lines, local populations are predominantly reared as backyard chickens, and thus, may have developed stronger resistance to environmental challenges. Our results indicate that selection can play an important role in shaping signatures of selection in local chicken populations and can be a starting point to identify gene mutations that could have a useful role with respect to climate change.

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“…We used the 99th percentile value for ROH density as the hotspot threshold, such that any SNP with a value equal to or above this threshold was classed as a hotspot SNP. This outlier/percentile approach has been used by a number of recent publications (Bertolini et al 2018 ; Cesarani et al 2022 ; Mastrangelo et al 2018 ; Purfield et al 2017 ; Zhang et al 2022 ).…”

Section: Methodsmentioning

confidence: 99%

Selection, recombination and population history effects on runs of homozygosity (ROH) in wild red deer (Cervus elaphus)

Hewett

Stoffel²,

Peters³

et al. 2023

Heredity

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The distribution of runs of homozygosity (ROH) may be shaped by a number of interacting processes such as selection, recombination and population history, but little is known about the importance of these mechanisms in shaping ROH in wild populations. We combined an empirical dataset of >3000 red deer genotyped at >35,000 genome-wide autosomal SNPs and evolutionary simulations to investigate the influence of each of these factors on ROH. We assessed ROH in a focal and comparison population to investigate the effect of population history. We investigated the role of recombination using both a physical map and a genetic linkage map to search for ROH. We found differences in ROH distribution between both populations and map types indicating that population history and local recombination rate have an effect on ROH. Finally, we ran forward genetic simulations with varying population histories, recombination rates and levels of selection, allowing us to further interpret our empirical data. These simulations showed that population history has a greater effect on ROH distribution than either recombination or selection. We further show that selection can cause genomic regions where ROH is common, only when the effective population size (Ne) is large or selection is particularly strong. In populations having undergone a population bottleneck, genetic drift can outweigh the effect of selection. Overall, we conclude that in this population, genetic drift resulting from a historical population bottleneck is most likely to have resulted in the observed ROH distribution, with selection possibly playing a minor role.

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Unravelling the effect of environment on the genome of Sarda breed ewes using Runs of Homozygosity

Cited by 8 publications

References 122 publications

Identification of Selection Signals on the X-Chromosome in East Adriatic Sheep: A New Complementary Approach

Identification of Selection Signals on the X-Chromosome in East Adriatic Sheep: A New Complementary Approach

Genome-wide mapping of signatures of selection using a high-density array identified candidate genes for growth traits and local adaptation in chickens

Selection, recombination and population history effects on runs of homozygosity (ROH) in wild red deer (Cervus elaphus)

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