Genome-wide scan for selection signatures in Mexican Sardo Negro Zebu cattle

Abstract

The Sardo Negro cattle (SN) is the only zebu cattle breed developed in Mexico. Since its development, the selection could have led to an increase in the homozygosity level in some regions of the genome and made differentiation with other cattle populations. We aimed to identify and characterize selection signatures in SN using medium-density SNP data using four approaches: 1) Runs of homozygosity (ROH) 2) Nucleotide Diversity 3) Tajima’s D and 4) the Wright’s fixation index (F_ST). A sample of 555 SN animals genotyped for 65k SNPs was used to obtain ROH segments considered regions under selection. The F_ST values were estimated by comparing the sample of genotyped SN animals with samples of genotyped animals from the Gir, Brahman, and Ongole breeds. Only one region mapped to 35.78–42.51 Mb on BTA6 was considered a selection signature by the ROH method. This selection signature overlapped with the lowest diversity, negative values of Tajima’s D and a diversification region between SN and the other Zebu breeds by F_ST. We found several candidate genes (LCORL, NCAPG, and SLIT2) related to growth and other economically important productive traits in this common region. Using the F_ST method, different regions, such as regions on BTA8 (8:93.4–93.9 Mb), BTA11 (11:99.2–99.7), and BTA14 (14: 26.1–26.8) related to growth and milk traits also were defined as candidate selection signatures. The selective signals identified in this study reflected the direction of the selection pressure that primarily involves the increase of live weight traits in the Sardo Negro cattle breeding program.

Introduction

The Sardo Negro Zebu breed (SN) has been developed as a dual-purpose breed, i.e., the final target is to breed animals able to produce weaned calves with high growth potential and able to produce a good amount of milk. Because the SN animals exhibit good growth potential and milk production combined with superior heat tolerance, this cattle breed is mainly used in production systems in tropical regions of Mexico. It already has ~50,000 animals registered in its pedigree [1]. This breed was developed in the 1950s using Gir and Indubrasil bulls, obtaining black and white fur animals, and since then, the genealogy of this breed has started to be recorded [2]. Until now, SN has been breeding independently of other Zebu breeds and with specific breeding goals; therefore, it is considered a new Mexican Zebu breed.

During the years of the SN establishment as a new breed, selecting specific phenotypes for inter se matings could shape the pattern of genetic variation so that some allele frequencies tend to change in a particular direction, and even allelic fixation could occur. Saravanan [3] mentioned that selection strategies reduce variation in specific genomic regions that control the breed characteristics such as morphology, body conformation, production, etc. Selection signatures are considered regions of the genome where genetic variability has diminished throughout generations due to selection practices. When directional selection leads to the fixation at some loci, other alleles in linkage disequilibrium also increase their frequencies, a phenomenon known as hitchhiking [4]. When this phenomenon favors retaining the favorable alleles, these genome regions are considered selective sweeps [5]. Selective sweeps are a process by which a new advantageous mutation eliminates or reduces variation in linked neutral sites as it increases in frequency.

The methods for detecting selection signatures based on SNP genotyping technologies can be broadly classified into two groups: intra-population and inter-population statistics [3]. Based on the hitchhiking theory, the selective sweeps should have stretches of homozygous loci that exhibit higher homozygosity than the average of the genome; therefore, runs of homozygosity (ROH) can be used to identify selection signatures [6]. Wright’s Fixation index (F_ST) [7] is based on differences in allele frequencies between populations; highly differentiated allele frequencies at any given locus between two populations indicate positive selection. We aimed to identify and characterize selection signatures in the SN cattle population using medium-density SNP data using two approaches: ROH and F_ST.

Materials and methods

Ethics statement

This study involved the analysis of pre-collected hair samples from animals and did not include any direct interaction with live animals. According to the Canadian Council on Animal Care (CCAC) guidelines, specifically the conditions outlined in the “Guide to the Care and Use of Experimental Animals”, research using non-invasive methods and pre-existing samples do not require formal ethical review. As this research did not involve new procedures or the handling of live animals, and adhered to the ethical standards in place at the time of sample collection, it qualifies for exemption criteria.

Animals and sampling

We use hair follicle samples from the SN, which were pre-collected by the Asociación Mexicana de Criadores de Cebú (AMCC). The sampled SN animals included 40 males and 515 females born between 2013 and 2019 in six farms in Mexico’s tropical areas. The study samples were randomly selected, including only animals with no parent-offspring relationship. Only animals were considered if they were purebred based on pedigree and farmer interviews. The hair samples were submitted for genotyping with a 65k SNP chip (Axiom Bovine Genotyping v3 Array, aligned to the Bos taurus genome assembly ARS-UCD1.2).

Genotyping data

In this study, we used five sets of SNPs, i.e., genotypes from 1) 555 SN bovines; 2) 97 Mexican Brahman Zebu bovines (MxBr); 3) 20 Gir Zebu bovines; 4) 20 American Brahman Zebu bovines (Brah); and 5) 20 Ongole Zebu bovines (Ong).

Quality controls on animals and markers of the first two sets were performed using the PLINK software version 1.90 [8] according to the following parameters: minimum SNP call rate of 90%, minor allele frequencies > 0.01, only autosomal SNPs were considered, and only individuals with high genotyping (at least 90% complete call rate) were included; 35,321 SNPs and the 555 animals passed filters and quality control for SN and 35,294 SNPs and 97 animals for MxBr. The last three data sets were taken from a public repository [9]. These animals were genotyped with the Illumina Bovine SNP50 BeadChip array developed using UMD3.1 assembly. The authors already had done quality control for these datasets with the same parameters considered in this study. Only SN animal genotypes were considered for the selection signatures identification with ROH. For the identification of selection signatures using the F_ST methods, the SN genotypes were merged with the other genotypes of each breed at a time. We identified the common genetic markers mapping SNPs to a common assembly using SNPchiMp database [10]. The representation an strand orientations were checked using the—flip and—flip-scan functions of PLINK. Finally, the combined datasets included SN-MxBr = 33,924 SNPs, SN-Gir = 18,410 SNPs, SN-Brah = 18,464 SNPs, and SN-Ong = 18,460 SNPs.

Selection signatures intra-population

We calculated the ROH using the ’detectRUNS’ R package [11], which implements a sliding-window-based method similar to that implemented in the PLINK software. The ROHs were calculated separately for every animal with the following parameters: (1) the minimum length that constituted the ROH was 1 Mb, (2) a minimum number of 20 consecutive homozygous SNPs was required to call a segment an ROH, and a maximum of 1 heterozygous SNPs was allowed in the homozygous SNP windows, (3) the maximum gap between consecutive SNPs was set to 1000 kb, and (4) the maximum number of missing allowed genotypes was set to 1. The minimum density considered and the maximum gap between consecutive SNPs were 1 SNP every 1000 kb and 1 Mb, respectively. The ROHs were classified into four categories according to length: 1 to < 2 Mb, 2 to < 4 Mb, 4 to < 8 Mb, and > 8 Mb.

The idea of detecting selection signatures by ROH relies on searching for chunks of the genome without heterozygosity in the diploid state on a genome-wide scale to identify signals of past selection [12]. In our study, the percentage of SNP existing within an ROH was estimated by counting the number of times each SNP appeared in an ROH and dividing that number by the number of animals [13]. Shared ROHs within a livestock population identify chromosome regions in which a reduced haplotype variability produces ROH islands. ROH islands were identified as a percentile threshold of the frequency of an SNP in ROH calculated within a breed (called percentile-based threshold), the threshold used in this work was the top 0.1% of SNPs with the highest occurrences [14,15].

Both the nucleotide diversity and Tajima’s D were estimated based on a sliding window approach with windows of 50 kb and a step of 50 kb using VCFtools [16].

Selection signatures inter-population

The F_ST was estimated using pairwise breeds, i.e., considering Sardo Negro, and the other breeds as control. The F_ST was calculated using the—Fst function of the PLINK software, which calculates the F_ST for each autosomal diploid variant using the method introduced by Weir and Cockerham [17]. Excessively high F_ST values at any given locus indicate a greater divergent selection between the populations than expected under drift. In contrast, excessively low F_ST values indicate less divergence than expected under drift and subtle balancing selection between the tested populations. The regions with the top 0.1% F_ST values and the SNP with higher F_ST values in more than three pairs of cattle breeds were considered to represent the signatures of selection. For each F_ST peak, a window of 500 Kb (250 Kb upstream and 250 Kb downstream) was evaluated for gene annotation.

Bioinformatic analyses

The identified selection signatures were compared for genes annotated to the Bos taurus genome assembly ASR-UCD2.0 using Genome Data Viewer (NCBI; https://www.ncbi.nlm.nih.gov/datasets/genome/GCA_002263795.4). Gene ontology (GO) significantly overrepresented terms were identified using PANTHER 18.0 (http://www.pantherdb.org/) [18].

Results

In total, 2,674 ROH were identified among the 555 SN samples. Descriptive statistics of each length category are given in Table 1. Our results show that 21% of the ROH were shorter than 4 Mb. Most of the ROH for SN cattle population comprised segments longer than 4 Mb, i.e., segments 4–8 Mb and >8 Mb represented 83%. In general, animals with a larger ROH number tend to have a greater total length of ROH segments regardless of the length of single ROH regions (Fig 1).

[Figure omitted. See PDF.]

[Figure omitted. See PDF.]

A Manhattan plot shows the incidence of each SNP in the ROH across individuals (Fig 2A). The genomic regions most associated with ROH in all individuals were defined by the top 0.1% of SNPs observed in the ROH. The adjacent SNPs over this threshold were merged into genomic regions corresponding to a ROH island (Table 2).

[Figure omitted. See PDF.]

a) Manhattan plot of the SNP frequency within runs of homozygosity b) Manhattan plot of nucleotide diversity. c) Manhattan plot of Tajima´s D values.

[Figure omitted. See PDF.]

We applied the nucleotide diversity analysis and Tajima’s D method to detect genomic regions related to selection in the Sardo Negro breed (Fig 2B and 2C). Two methods showed outlier signals (lowest 1% with nucleotide diversity analysis; values under zero with Tajima’s D) in overlapping regions and were therefore considered candidate selective regions. Our results showed that in the three intra-population methods, the selection signature on BTA6 is confirmed.

The identification of only one ROH island on BTA 6 (37.39–39.57 Mb) was the result of this approach. The annotation analyses revealed 25 candidate genes overlapping with that genomic region under positive selection detected using the F_ST metric. Several QTLs have been reported in that genomic region. Most of the traits associated with those QTLs are related to growth.

Wright’s F_ST was calculated pairwise for each SNP in each group of animals to examine differentiation among Sardo Negro and the other Zebu breeds to determine signals of diversifying selection. The regions above 0.1% of the highest F_ST values were considered signals of positive selection (S1 Table). We identified 80 genomic regions (50 unique regions) under divergent selection. The genome-wide distribution of selection signatures by F_ST was plotted for the four breed pairs (S1–S4 Figs) and jointly (Fig 3). Nine SNP above the threshold coincided in the three comparisons with Sardo Negro (S2 Table).

[Figure omitted. See PDF.]

Sardo Negro (SN) were compared with Gir, Brahman [American (Brah-US) and Mexican (Brah-Mx)] samples, and Ongole zebu breeds.

Only one candidate region overlapped between all four methods, ROH, Nucleotide diversity, Tajima’s D, and F_ST (34.8.47–43.2 MB). That region involves the CCSER1, MMRN1, SNCA, FAM13A, HERC3, NAP1L5, PIGY, PYURF, HERC5, PPM1K, ABCG2, PKD2, SPP1, IBSP, MEPE, TRNAA-CGC, LAP3, MED28, FAM184B, NCAPG, DCAF16, LCORL, SLIT2, PACRGL, and KCNIP4 genes and QTLs related to pelvic area, body weight, marbling score, milk alpha-S1-casein percentage, and milk C18 index [19].

Discussion

Several researchers have explored the selection signatures of cattle, pigs, sheep, and horses [20]. In the present study, we aimed to identify signatures of selection in the Sardo Negro Zebu breed using bovine 50K SNP chip data. Many studies have used the bovine 50K SNP chip to detect selection signatures in various cattle breeds [21,22]. However, compared to Bos taurus cattle breeds, the number of studies in Bos indicus cattle has been limited [3].

Four complementary methodologies were applied to uncover selection signatures in the studied population, which should improve detection accuracy and eliminate unknown bias [20,23]. In cattle, using a low-density SNP microarray tends to overestimate the number of ROH ≤ 4 Mb. In contrast, using a high-density chip could lead to an underestimation of the number of long ROH (i.e., ≥8 Mb). However, De Roos et al. [24] suggested that 50k SNPs yield reasonable parameters when studying one breed. Using the ROH approach, we detected a selection sweep based on identifying genomic regions with reduced variation relative to the genome average, as Broman and Weber [25] suggested for the first time.

We found only one selective sweep on BTA6 by ROH. Maiorano et al. [26] also showed the presence of a selection signature on BTA6 for the Gir dairy population. These authors mentioned that LCORL and NCAPG genes on BTA6 were found to be associated with growth, meat, and carcass traits. This similarity can be explained because the founder breeds of the Sardo Negro breed are the Gir and Indubrasil [1]. In other words, the selective sweep detected on the BTA6 could be due to selection history in their founder breeds. The length of the ROH is considered a helpful time indicator of its associated inbreeding event. Long ROHs are related to recent inbreeding events in a breed’s history, whereas short ROHs indicate a more ancient event [27].

The positive selection signals around this region are further confirmed by significantly lower values of Tajima’s D. The negative value shows the fixation of rare alleles. This is indicated by a smaller value (more negative) of Tajima’s D, suggesting an abundance of rare alleles and positive selection [28]. Positive values show the balancing selection, which reflects an abundance of intermediate allele frequencies. The zero D value is the indication of neutral variation.

Many studies relate that region on BTA6 to beef cattle’s feed intake, gain, meat, and carcass traits [29,30]. Also, other genes found on this selective sweep, like LAP3, MED28, SLIT2, and IBSP, have been previously reported to be associated with carcass traits, body measurements, growth traits, and bone weight [31–33]. Niu et al. [34] reported that the region on BTA6, found like a selective sweep in our study, provides valuable information for understanding the genetic basis of body stature in beef cattle.

Dominguez Viveros et al. [35] characterized the growth of five zebu breeds including Sardo Negro. Sardo Negro had the highest values in the parameter related to mature weight, (871 kg in males, and 615 kg in females). The Gir and Indubrasil breeds, considered as the ancestor breeds of Sardo Negro animals, had lower values in mature weight (Indbrasil:744kg and 506 kg; Gir:695 kg and 527 kg) than Sardo Negro [36].

Comparing the ROHs of the Sardo Negro Zebu breed with the ROHs observed in the samples of other Zebu breeds like Gir, Brahman, and Ongole (S5–S8 Figs). None of the reference populations exhibit ROHs similar to those found in the Sardo Negro population. Additionally, the ROHs in BTA6, identified as selection signatures, were not observed in any other reference population.

Other signals of diversifying selection were found comparing the Sardo Negro Zebu breed and other Zebu breeds like Gir, Brahman, and Ongole. We only compared Sardo Negro with Zebu breeds because of the known genomic divergence of zebu and taurine subspecies [37]. Nine SNPs above the threshold of the top 0.1% F_ST value were commonly detected among the comparison between Sardo Negro and Gir, Brahman, and Ongole Zebu breeds. The top SNPs above the F_ST threshold between Sardo Negro and Mexican Brahman only coincide by region, not by SNP, with the other F_ST comparisons, because of the difference in the used SNP chip. The candidate divergent regions detected (± 250kb of each F_ST peak) in the present study contain several QTLs for live weight traits. A region mapped on BTA8 (8:93.4–93.9 Mb) had been related to selenoproteins that play crucial roles in the regulation of oxidative stress and calcium homeostasis in striated muscle regarding the maintenance of functions and disease progression [37,38].

The window on BTA11 (11:99.2–99.7 Mb), which includes the OLFML2A, WDR38, RPL35, ARPC5L, and GOLGA1 genes, which have been associated with the fat percentage in milk. The diversifying selection region on BTA14 harbors a QTL previously reported to be involved in milk and fat protein percentage. Soares et al. [39] identified a relation between the CHD7 and RAB2A genes with fertility traits in Brahman cattle. CHD7 has been reported to be associated with age at first corpus luteum and scrotal circumference.

The selection signatures found in this study may be affected by several factors, including sample size, the SNP density, and ascertainment bias in the genomic data [23]. However, using two approaches strengthens the identification of the selection signatures, highlighting the 35.78–42.51 Mb region on BTA6.

Conclusions

Using two complementary statistical approaches (ROH and F_ST) facilitated the broader spectrum of detection of selection signatures. The selective signals identified in this study reflected the direction of the selection pressure that primarily involves the increase of live weight traits in the Sardo Negro cattle population. The candidate region 35.78–42.51 Mb on BTA6 might be related to body weight identified in Sardo Negro in Mexico.

Supporting information

S1 Fig. Genome-wide distribution of selection signatures detected by F_ST between Sardo Negro and Gir.

The horizontal line represents the threshold level of 0.1%.

https://doi.org/10.1371/journal.pone.0312453.s001

(TIF)

S2 Fig. Genome-wide distribution of selection signatures detected by F_ST between Sardo Negro and Brahman (American sample).

The horizontal line represents the threshold level of 0.1%.

https://doi.org/10.1371/journal.pone.0312453.s002

(TIF)

S3 Fig. Genome-wide distribution of selection signatures detected by F_ST between Sardo Negro and Ongole.

The horizontal line represents the threshold level of 0.1%.

https://doi.org/10.1371/journal.pone.0312453.s003

(TIF)

S4 Fig. Genome-wide distribution of selection signatures detected by F_ST between Sardo Negro and Brahman (Mexican sample).

The horizontal line represents the threshold level of 0.1%.

https://doi.org/10.1371/journal.pone.0312453.s004

(TIF)

S5 Fig. Manhattan plot of the SNP frequency within runs of homozygosity by autosome in a Mexican Brahman zebu cattle population sample.

The horizontal dashed line represents the threshold level of 0.1%.

https://doi.org/10.1371/journal.pone.0312453.s005

(TIF)

S6 Fig. Manhattan plot of the SNP frequency within runs of homozygosity by autosome in an American Brahman zebu cattle population sample.

The horizontal dashed line represents the threshold level of 0.1%.

https://doi.org/10.1371/journal.pone.0312453.s006

(TIF)

S7 Fig. Manhattan plot of the SNP frequency within runs of homozygosity by autosome in a Gir zebu cattle population sample.

The horizontal dashed line represents the threshold level of 0.1%.

https://doi.org/10.1371/journal.pone.0312453.s007

(TIF)

S8 Fig. Manhattan plot of the SNP frequency within runs of homozygosity by autosome in an Ongole zebu cattle population sample.

The horizontal dashed line represents the threshold level of 0.1%.

https://doi.org/10.1371/journal.pone.0312453.s008

(TIF)

S1 Table. SNPs above 0.1% of the highest F_ST values comparing Sardo Negro with Gir, Brahman, and Ongole.

https://doi.org/10.1371/journal.pone.0312453.s009

(XLSX)

S2 Table. Genomic regions under divergent selection identified by FST approach and associated candidate genes, QTLs and GO terms.

https://doi.org/10.1371/journal.pone.0312453.s010

(XLSX)

Acknowledgments

We thank the Asociación Mexicana de Criadores de Cebu (AMCC) for providing information for the present study. We also are grateful to DGAPA-UNAM PAPIIT-IA202323.

References

1. 1. Domínguez-Viveros J, Reyes-Cerón A, Saiz-Pineda JF, Villegas-Gutiérrez C, Aguilar-Palma GN, Rodríguez-Almeida FA. Structure and genetic variability of the Mexican Sardo Negro breed. Ciência Rural. 2021; 52. https://doi.org/10.1590/0103-8478cr20210116.

* View Article

* Google Scholar

2. 2. AMCC. Reglamento técnico. Asociación Mexicana de Criadores de Cebú. Tampico, Tamaulipas, México; 2019.

3. 3. Saravanan KA, Panigrahi M, Kumar H, Bhushan B, Dutt T, Mishra BP. Selection signatures in livestock genome: A review of concepts, approaches, and applications. Livest Sci. 2020; 241, 104257. https://doi.org/10.1016/j.livsci.2020.104257.

* View Article

* Google Scholar

4. 4. Walsh B, Lynch M. Evolution and selection of quantitative traits. United Kingdom: Oxford; 2018.

5. 5. Urbinati I, Stafuzza NB, Oliveira MT, Chud TCS, Higa RH, Regitano LCDA et al. Selection signatures in Canchim beef cattle. J Anim Sci Biotechnol. 2016; 7, 1–9. https://doi.org/10.1186/s40104-016-0089-5.

* View Article

* Google Scholar

6. 6. Almeida OAC, Moreira GCM, Rezende FM, Boschiero C, de Oliveira Peixoto J, Ibelli AMG et al. Identification of selection signatures involved in performance traits in a paternal broiler line. BMC Genomics. 2019; 20(1), 1–20. https://doi.org/10.1186/s12864-019-5811-1.

* View Article

* Google Scholar

7. 7. Wright S. The genetical structure of populations. Ann Eugen. 1949; 15(1), 323–354. https://doi.org/10.1111/j.1469-1809.1949.tb02451.x.

* View Article

* Google Scholar

8. 8. Purcell S, Neale B, Todd-Brown K, Thomas L, Ferreira MA, Bender D et al. PLINK: a tool set for whole-genome association and population-based linkage analyses. Am J Hum Genet. 2007; 81(3), 559–575. pmid:17701901

* View Article

* PubMed/NCBI

* Google Scholar

9. 9. Decker JE, McKay SD, Rolf MM, Kim J, Molina Alcalá A, Sonstegard TS et al. Worldwide patterns of ancestry, divergence, and admixture in domesticated cattle. PLoS Genet. 2014; 10(3), e1004254. pmid:24675901

* View Article

* PubMed/NCBI

* Google Scholar

10. 10. Nicolazzi EL, Picciolini M, Strozzi F, Schnabel RD, Lawley C, Pirani A et al. SNPchiMp: a database to disentangle the SNPchip jungle in bovine livestock. BMC genomics. 2014; 15, 1–6.

* View Article

* Google Scholar

11. 11. Biscarini F, Cozzi P, Gaspa G, Marras G. DetectRUNS: Detect Runs of Homozygosity and Runs of Heterozygosity in Diploid Genomes, 0.9.5. 2018[cited 8 December 2023]. Available from: https://github.com/bioinformatics-ptp/detectRUNS/tree/master/detectRUNS.

* View Article

* Google Scholar

12. 12. Qanbari S, Simianer H. Mapping signatures of positive selection in the genome of livestock. Livest Sci. 2014; 166, 133–143. https://doi.org/10.1016/j.livsci.2014.05.003.

* View Article

* Google Scholar

13. 13. Zhang Q, Calus MP, Bosse M, Sahana G, Lund MS, Guldbrandtsen B. Human-mediated introgression of haplotypes in a modern dairy cattle breed. Genetics. 2014; 209(4), 1305–1317. https://doi.org/10.1534/genetics.118.301143.

* View Article

* Google Scholar

14. 14. Purfield DC, McParland S, Wall E, Berry DP. The distribution of runs of homozygosity and selection signatures in six commercial meat sheep breeds. PLoS ONE. 2017; 12(5): e0176780. pmid:28463982

* View Article

* PubMed/NCBI

* Google Scholar

15. 15. Schiavo G, Bovo S, Bertolini F, Dall’Olio S, Costa LN, Tinarelli et al. Runs of homozygosity islands in Italian cosmopolitan and autochthonous pig breeds identify selection signatures in the porcine genome. Livest Sci. 2020; 240, 104219. https://doi.org/10.1016/j.livsci.2020.104219.

* View Article

* Google Scholar

16. 16. Danecek P, Auton A, Abecasis G, Albers CA, Banks E, DePristo MA, Handsaker RE, Lunter G, Marth GT, Sherry ST, et al. The variant call format and VCFtools. Bioinformatics (Oxford, England). 2011;27(15):2156–8. pmid:21653522

* View Article

* PubMed/NCBI

* Google Scholar

17. 17. Weir BS, Cockerham CC. Estimating F-statistics for the analysis of population structure. Evolution. 1984; 1358–1370. pmid:28563791

* View Article

* PubMed/NCBI

* Google Scholar

18. 18. Mi H, Muruganujan A, Ebert D, Huang X, Thomas PD. PANTHER version 14: more genomes, a new PANTHER GO-slim, and improvements in enrichment analysis tools. Nucleic Acids Res. 2019; 47(D1), D419–D426. pmid:30407594

* View Article

* PubMed/NCBI

* Google Scholar

19. 19. Hu ZL, Park CA, Reecy JM. Bringing the Animal QTLdb and CorrDB into the future: Meeting new challenges and providing updated services. Nucleic Acids Res. 2022; 50(D1), D956–D961. pmid:34850103

* View Article

* PubMed/NCBI

* Google Scholar

20. 20. Liu D, Chen Z, Zhao W, Guo L, Sun H, Zhu K et al. Genome-wide selection signatures detection in Shanghai Holstein cattle population identified genes related to adaption, health and reproduction traits. BMC genomics. 2021; 22(1), 1–19. https://doi.org/10.1186/s12864-021-08042-x.

* View Article

* Google Scholar

21. 21. Hosokawa D, Ishii A, Yamaji K, Sasazaki S, Oyama K, Mannen H. Identification of divergently selected regions between Japanese Black and Holstein cattle using bovine 50k SNP array. Anim Sci Jour. 2011; 83(1), 7–13. pmid:22250733

* View Article

* PubMed/NCBI

* Google Scholar

22. 22. Makina SO, Muchadeyi FC, van Marle-Köster E, Taylor JF, Makgahlela ML, Maiwashe A. Genome-wide scan for selection signatures in six cattle breeds in South Africa. Genet Sel Evol. 2015; 47(1), 1–14. https://doi.org/10.1186/s12711-015-0173-x.

* View Article

* Google Scholar

23. 23. Saravanan KA, Panigrahi M, Kumar H, Parida S, Bhushan B, Gaur GK et al. Genomic scans for selection signatures revealed candidate genes for adaptation and production traits in a variety of cattle breeds. Genomics. 2021; 113(3), 955–963. pmid:33610795

* View Article

* PubMed/NCBI

* Google Scholar

24. 24. De Roos APW, Hayes BJ, Spelman RJ, Goddard ME. Linkage disequilibrium and persistence of phase in Holstein–Friesian, Jersey and Angus cattle. Genetics. 2008; 179(3), 1503–1512. pmid:18622038

* View Article

* PubMed/NCBI

* Google Scholar

25. 25. Broman KW, Weber JL. Long homozygous chromosomal segments in reference families from the centre d’Etude du polymorphisme humain. Am J Hum Genet. 1999; 65(6), 1493–1500. pmid:10577902

* View Article

* PubMed/NCBI

* Google Scholar

26. 26. Maiorano AM, Lourenco DL, Tsuruta S, Ospina AMT, Stafuzza NB, Masuda et al. Assessing genetic architecture and signatures of selection of dual purpose Gir cattle populations using genomic information. PLoS One. 2018; 13(8), e0200694. pmid:30071036

* View Article

* PubMed/NCBI

* Google Scholar

27. 27. Kirin M, McQuillan R, Franklin CS, Campbell H, McKeigue PM, Wilson JF. Genomic runs of homozygosity record population history and consanguinity. PloS one. 2010; 5(11), e13996. pmid:21085596

* View Article

* PubMed/NCBI

* Google Scholar

28. 28. Korneliussen T. S., Moltke I., Albrechtsen A., & Nielsen R. (2013). Calculation of Tajima’s D and other neutrality test statistics from low depth next-generation sequencing data. BMC bioinformatics, 14, 1–14.

* View Article

* Google Scholar

29. 29. Setoguchi K, Furuta M, Hirano T, Nagao T, Watanabe T, Sugimoto Y, Takasuga A. Cross-breed comparisons identified a critical 591-kb region for bovine carcass weight QTL (CW-2) on chromosome 6 and the Ile-442-Met substitution in NCAPG as a positional candidate. BMC Genetics. 2009; 10(1), 1–12. pmid:19653884

* View Article

* PubMed/NCBI

* Google Scholar

30. 30. Lindholm-Perry AK, Sexten AK, Kuehn LA, Smith TP, King DA, Shackelford SD et al. Association, effects, and validation of polymorphisms within the NCAPG-LCORL locus located on BTA6 with feed intake, gain, meat, and carcass traits in beef cattle. BMC Genetics. 2011; 12(1), 1–13. pmid:22168586

* View Article

* PubMed/NCBI

* Google Scholar

31. 31. Gutiérrez-Gil B, Wiener P, Williams JL, Haley CS. Investigation of the Genetic Architecture of a Bone Carcass Weight QTL on BTA6. Anim. Genet. 2012; 43, 654–661. pmid:22497262

* View Article

* PubMed/NCBI

* Google Scholar

32. 32. Santiago GG, Siqueira F, Cardoso FF, Regitano LCA, Ventura R, Sollero BP et al. Genome-wide Association Study for Production and Meat Quality Traits in Canchim Beef Cattle. J. Anim. Sci. 2017; 95, 3381–3390. pmid:28805909

* View Article

* PubMed/NCBI

* Google Scholar

33. 33. Weng Z, Su H, Saatchi M, Lee J, Thomas MG, Dunkelberger JR et al. Genome-wide Association Study of Growth and Body Composition Traits in Brangus Beef Cattle. Livest Sci. 2016; 183, 4–11. https://doi.org/10.1016/j.livsci.2015.11.011.

* View Article

* Google Scholar

34. 34. Niu Q, Zhang T, Xu L, Wang T, Wang Z, Zhu B et al. Identification of candidate variants associated with bone weight using whole genome sequence in beef cattle. Front Genet. 2021; 12, 750746. pmid:34912371

* View Article

* PubMed/NCBI

* Google Scholar

35. 35. Domínguez-Viveros J, Rodríguez-Almeida FA, Aguilar-Palma GN, Castillo-Rangel F, Saiz-Pineda JF, Villegas-Gutiérrez C. Fitting of non-linear models to characterize the growth of five zebu cattle breeds. Livest Sci. 2020; 104303. https://doi.org/10.1016/j.livsci.2020.104303.

* View Article

* Google Scholar

36. 36. Porto-Neto LR, Sonstegard TS, Liu GE, Bickhart DM, Da Silva MV, Machado MA, et al. Genomic divergence of zebu and taurine cattle identified through high-density SNP genotyping. BMC Genomics. 2013; 14, 1–12. https://doi.org/10.1186/1471-2164-14-876.

* View Article

* Google Scholar

37. 37. Seeher S, Atassi T, Mahdi Y, Carlson BA, Braun D, Wirth EK, et al. Secisbp2 is essential for embryonic development and enhances selenoprotein expression. Antioxid Redox Signal. 2014; 21(6), 835–849. pmid:24274065

* View Article

* PubMed/NCBI

* Google Scholar

38. 38. de Souza Fonseca PA, Caldwell T, Mandell I, Wood K, Cánovas A. Genome-wide association study for meat tenderness in beef cattle identifies patterns of the genetic contribution in different post-mortem stages. Meat Sci. 2022; 186, 108733. pmid:35007800

* View Article

* PubMed/NCBI

* Google Scholar

39. 39. Soares ACC, Guimarães SEF, Kelly MJ, Fortes MRS, de Silva FF, Verardo LL et al. Multiple-trait genomewide mapping and gene network analysis for scrotal circumference growth curves in Brahman cattle. J Anim Sci. 2017; 95(8), 3331–3345. pmid:28805926

* View Article

* PubMed/NCBI

* Google Scholar

Citation: Garduño López VI, Martínez-Rocha R, Núñez Domínguez R, Ramírez Valverde R, Domínguez Viveros J, Reyes Ceron A, et al. (2024) Genome-wide scan for selection signatures in Mexican Sardo Negro Zebu cattle. PLoS ONE 19(11): e0312453. https://doi.org/10.1371/journal.pone.0312453

About the Authors:

Victor Isaias Garduño López

Roles: Writing – original draft, Writing – review & editing

Affiliation: Facultad de Estudios Superiores Cuautitlán, Universidad Nacional Autónoma de México, Cuautitlán Izcalli, Estado de México, México

Ricardo Martínez-Rocha

Roles: Formal analysis, Writing – original draft

E-mail: [email protected]

Affiliation: Facultad de Estudios Superiores Cuautitlán, Universidad Nacional Autónoma de México, Cuautitlán Izcalli, Estado de México, México

ORICD: https://orcid.org/0000-0002-7316-4589

Rafael Núñez Domínguez

Roles: Methodology, Supervision, Writing – review & editing

Affiliation: Posgrado en Producción Animal, Universidad Autónoma Chapingo, Texcoco, Estado de México, México

Rodolfo Ramírez Valverde

Roles: Investigation, Writing – original draft, Writing – review & editing

Affiliation: Posgrado en Producción Animal, Universidad Autónoma Chapingo, Texcoco, Estado de México, México

Joel Domínguez Viveros

Roles: Conceptualization, Data curation, Investigation, Writing – original draft

Affiliation: Facultad de Zootecnia y Ecología, Universidad Autónoma de Chihuahua, Chihuahua, México

Antonio Reyes Ceron

Roles: Data curation

Affiliation: Asociación Mexicana de Criadores de Cebú, Tampico, Tamaulipas, México

Jorge Hidalgo

Roles: Formal analysis, Methodology, Writing – review & editing

Affiliation: Department of Animal and Dairy Science, University of Georgia, Athens, Georgia, United States of America

ORICD: https://orcid.org/0000-0002-0783-381X

[/RAW_REF_TEXT]

[/RAW_REF_TEXT]

References

1. Domínguez-Viveros J, Reyes-Cerón A, Saiz-Pineda JF, Villegas-Gutiérrez C, Aguilar-Palma GN, Rodríguez-Almeida FA. Structure and genetic variability of the Mexican Sardo Negro breed. Ciência Rural. 2021; 52. https://doi.org/10.1590/0103-8478cr20210116.

2. AMCC. Reglamento técnico. Asociación Mexicana de Criadores de Cebú. Tampico, Tamaulipas, México; 2019.

3. Saravanan KA, Panigrahi M, Kumar H, Bhushan B, Dutt T, Mishra BP. Selection signatures in livestock genome: A review of concepts, approaches, and applications. Livest Sci. 2020; 241, 104257. https://doi.org/10.1016/j.livsci.2020.104257.

4. Walsh B, Lynch M. Evolution and selection of quantitative traits. United Kingdom: Oxford; 2018.

5. Urbinati I, Stafuzza NB, Oliveira MT, Chud TCS, Higa RH, Regitano LCDA et al. Selection signatures in Canchim beef cattle. J Anim Sci Biotechnol. 2016; 7, 1–9. https://doi.org/10.1186/s40104-016-0089-5.

6. Almeida OAC, Moreira GCM, Rezende FM, Boschiero C, de Oliveira Peixoto J, Ibelli AMG et al. Identification of selection signatures involved in performance traits in a paternal broiler line. BMC Genomics. 2019; 20(1), 1–20. https://doi.org/10.1186/s12864-019-5811-1.

7. Wright S. The genetical structure of populations. Ann Eugen. 1949; 15(1), 323–354. https://doi.org/10.1111/j.1469-1809.1949.tb02451.x.

8. Purcell S, Neale B, Todd-Brown K, Thomas L, Ferreira MA, Bender D et al. PLINK: a tool set for whole-genome association and population-based linkage analyses. Am J Hum Genet. 2007; 81(3), 559–575. pmid:17701901

9. Decker JE, McKay SD, Rolf MM, Kim J, Molina Alcalá A, Sonstegard TS et al. Worldwide patterns of ancestry, divergence, and admixture in domesticated cattle. PLoS Genet. 2014; 10(3), e1004254. pmid:24675901

10. Nicolazzi EL, Picciolini M, Strozzi F, Schnabel RD, Lawley C, Pirani A et al. SNPchiMp: a database to disentangle the SNPchip jungle in bovine livestock. BMC genomics. 2014; 15, 1–6.

11. Biscarini F, Cozzi P, Gaspa G, Marras G. DetectRUNS: Detect Runs of Homozygosity and Runs of Heterozygosity in Diploid Genomes, 0.9.5. 2018[cited 8 December 2023]. Available from: https://github.com/bioinformatics-ptp/detectRUNS/tree/master/detectRUNS.

12. Qanbari S, Simianer H. Mapping signatures of positive selection in the genome of livestock. Livest Sci. 2014; 166, 133–143. https://doi.org/10.1016/j.livsci.2014.05.003.

13. Zhang Q, Calus MP, Bosse M, Sahana G, Lund MS, Guldbrandtsen B. Human-mediated introgression of haplotypes in a modern dairy cattle breed. Genetics. 2014; 209(4), 1305–1317. https://doi.org/10.1534/genetics.118.301143.

14. Purfield DC, McParland S, Wall E, Berry DP. The distribution of runs of homozygosity and selection signatures in six commercial meat sheep breeds. PLoS ONE. 2017; 12(5): e0176780. pmid:28463982

15. Schiavo G, Bovo S, Bertolini F, Dall’Olio S, Costa LN, Tinarelli et al. Runs of homozygosity islands in Italian cosmopolitan and autochthonous pig breeds identify selection signatures in the porcine genome. Livest Sci. 2020; 240, 104219. https://doi.org/10.1016/j.livsci.2020.104219.

16. Danecek P, Auton A, Abecasis G, Albers CA, Banks E, DePristo MA, Handsaker RE, Lunter G, Marth GT, Sherry ST, et al. The variant call format and VCFtools. Bioinformatics (Oxford, England). 2011;27(15):2156–8. pmid:21653522

17. Weir BS, Cockerham CC. Estimating F-statistics for the analysis of population structure. Evolution. 1984; 1358–1370. pmid:28563791

18. Mi H, Muruganujan A, Ebert D, Huang X, Thomas PD. PANTHER version 14: more genomes, a new PANTHER GO-slim, and improvements in enrichment analysis tools. Nucleic Acids Res. 2019; 47(D1), D419–D426. pmid:30407594

19. Hu ZL, Park CA, Reecy JM. Bringing the Animal QTLdb and CorrDB into the future: Meeting new challenges and providing updated services. Nucleic Acids Res. 2022; 50(D1), D956–D961. pmid:34850103

20. Liu D, Chen Z, Zhao W, Guo L, Sun H, Zhu K et al. Genome-wide selection signatures detection in Shanghai Holstein cattle population identified genes related to adaption, health and reproduction traits. BMC genomics. 2021; 22(1), 1–19. https://doi.org/10.1186/s12864-021-08042-x.

21. Hosokawa D, Ishii A, Yamaji K, Sasazaki S, Oyama K, Mannen H. Identification of divergently selected regions between Japanese Black and Holstein cattle using bovine 50k SNP array. Anim Sci Jour. 2011; 83(1), 7–13. pmid:22250733

22. Makina SO, Muchadeyi FC, van Marle-Köster E, Taylor JF, Makgahlela ML, Maiwashe A. Genome-wide scan for selection signatures in six cattle breeds in South Africa. Genet Sel Evol. 2015; 47(1), 1–14. https://doi.org/10.1186/s12711-015-0173-x.

23. Saravanan KA, Panigrahi M, Kumar H, Parida S, Bhushan B, Gaur GK et al. Genomic scans for selection signatures revealed candidate genes for adaptation and production traits in a variety of cattle breeds. Genomics. 2021; 113(3), 955–963. pmid:33610795

24. De Roos APW, Hayes BJ, Spelman RJ, Goddard ME. Linkage disequilibrium and persistence of phase in Holstein–Friesian, Jersey and Angus cattle. Genetics. 2008; 179(3), 1503–1512. pmid:18622038

25. Broman KW, Weber JL. Long homozygous chromosomal segments in reference families from the centre d’Etude du polymorphisme humain. Am J Hum Genet. 1999; 65(6), 1493–1500. pmid:10577902

26. Maiorano AM, Lourenco DL, Tsuruta S, Ospina AMT, Stafuzza NB, Masuda et al. Assessing genetic architecture and signatures of selection of dual purpose Gir cattle populations using genomic information. PLoS One. 2018; 13(8), e0200694. pmid:30071036

27. Kirin M, McQuillan R, Franklin CS, Campbell H, McKeigue PM, Wilson JF. Genomic runs of homozygosity record population history and consanguinity. PloS one. 2010; 5(11), e13996. pmid:21085596

28. Korneliussen T. S., Moltke I., Albrechtsen A., & Nielsen R. (2013). Calculation of Tajima’s D and other neutrality test statistics from low depth next-generation sequencing data. BMC bioinformatics, 14, 1–14.

29. Setoguchi K, Furuta M, Hirano T, Nagao T, Watanabe T, Sugimoto Y, Takasuga A. Cross-breed comparisons identified a critical 591-kb region for bovine carcass weight QTL (CW-2) on chromosome 6 and the Ile-442-Met substitution in NCAPG as a positional candidate. BMC Genetics. 2009; 10(1), 1–12. pmid:19653884

30. Lindholm-Perry AK, Sexten AK, Kuehn LA, Smith TP, King DA, Shackelford SD et al. Association, effects, and validation of polymorphisms within the NCAPG-LCORL locus located on BTA6 with feed intake, gain, meat, and carcass traits in beef cattle. BMC Genetics. 2011; 12(1), 1–13. pmid:22168586

31. Gutiérrez-Gil B, Wiener P, Williams JL, Haley CS. Investigation of the Genetic Architecture of a Bone Carcass Weight QTL on BTA6. Anim. Genet. 2012; 43, 654–661. pmid:22497262

32. Santiago GG, Siqueira F, Cardoso FF, Regitano LCA, Ventura R, Sollero BP et al. Genome-wide Association Study for Production and Meat Quality Traits in Canchim Beef Cattle. J. Anim. Sci. 2017; 95, 3381–3390. pmid:28805909

33. Weng Z, Su H, Saatchi M, Lee J, Thomas MG, Dunkelberger JR et al. Genome-wide Association Study of Growth and Body Composition Traits in Brangus Beef Cattle. Livest Sci. 2016; 183, 4–11. https://doi.org/10.1016/j.livsci.2015.11.011.

34. Niu Q, Zhang T, Xu L, Wang T, Wang Z, Zhu B et al. Identification of candidate variants associated with bone weight using whole genome sequence in beef cattle. Front Genet. 2021; 12, 750746. pmid:34912371

35. Domínguez-Viveros J, Rodríguez-Almeida FA, Aguilar-Palma GN, Castillo-Rangel F, Saiz-Pineda JF, Villegas-Gutiérrez C. Fitting of non-linear models to characterize the growth of five zebu cattle breeds. Livest Sci. 2020; 104303. https://doi.org/10.1016/j.livsci.2020.104303.

36. Porto-Neto LR, Sonstegard TS, Liu GE, Bickhart DM, Da Silva MV, Machado MA, et al. Genomic divergence of zebu and taurine cattle identified through high-density SNP genotyping. BMC Genomics. 2013; 14, 1–12. https://doi.org/10.1186/1471-2164-14-876.

37. Seeher S, Atassi T, Mahdi Y, Carlson BA, Braun D, Wirth EK, et al. Secisbp2 is essential for embryonic development and enhances selenoprotein expression. Antioxid Redox Signal. 2014; 21(6), 835–849. pmid:24274065

38. de Souza Fonseca PA, Caldwell T, Mandell I, Wood K, Cánovas A. Genome-wide association study for meat tenderness in beef cattle identifies patterns of the genetic contribution in different post-mortem stages. Meat Sci. 2022; 186, 108733. pmid:35007800

39. Soares ACC, Guimarães SEF, Kelly MJ, Fortes MRS, de Silva FF, Verardo LL et al. Multiple-trait genomewide mapping and gene network analysis for scrotal circumference growth curves in Brahman cattle. J Anim Sci. 2017; 95(8), 3331–3345. pmid:28805926