Deciphering climate resilience in Indian cattle breeds by selection signature analyses

doi:10.21203/rs.3.rs-3245946/v1

Download PDF

Research Article

Deciphering climate resilience in Indian cattle breeds by selection signature analyses

https://doi.org/10.21203/rs.3.rs-3245946/v1

This work is licensed under a CC BY 4.0 License

Journal Publication

published 17 Jan, 2024

Read the published version in Tropical Animal Health and Production →

You are reading this older preprint version

Read the latest preprint version →

The signature of selection is a crucial concept in evolutionary biology that refers to the pattern of genetic variation that arises in a population as a result of 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 cultivated in diverse agroecological zones exhibit genetic "footprints" within their genomes that mirror the influence of climate-induced selective pressures, subsequently impacting phenotypic variance. We examined genotypic data from eight different cattle groups (Gir, Hariana, Kankrej, Nelore, Ongole, Red Sindhi, Sahiwal, and Tharparkar) that are adapted to diverse regional climates in India. It is assumed that the genomes of animals residing in these regions have been altered through selection for various climatic adaptations. In this study, we utilized bovine 50k chip data and four genome scan techniques to assess the genetic regions of positive selection for high temperature adaptation in eight distinct cattle populations. We have also investigated the population stratification i.e. PCA, genome-wide investigation of genetic diversity, inbreeding, effective population size in our target dataset. To identify selection signature regions in this work, we used four intra-population statistics: Tajima's D, CLR, iHS, and ROH. We identified potential regions for selection that is likely to be caused by climate. We have identified many adaption genes as several potential selection signature areas. These include genes like HSPB2, HSPB3, HSP20, HSP90AB1, HSF4, HSPA1B, CLPB, GAP43, MITF, and MCHR1 that have been previously identified in the cattle population that lives in high- and low-temperature regions.

Adaptation genes

cattle

climate-mediated selection

evolution

genome-wide scans

heat stress

selection signature

Cattle are categorized into three species i.e., Bos indicus, the Indicine or "Zebu cattle," Bos taurus or "taurine" (the European) cattle (including related kinds from Asia and Africa); and the nonextant Bos primigenius, the aurochs. The aurochs are related to taurine and Zebu cattle (Ajmone-Marsan et al., 2010). Later, they were re-classified as a single species, Bos taurus, with aurochs and zebu as subspecies (Gentry et al., 2004). Modern breeds evolved from wild aurochs between 8000 and 9000 BC. Identifying the genetic foundation of cattle domestication advances our cognition of the function of artificial selection in altering the genome. Cattle are economically and socio-culturally significant all around the globe because they produce milk, meat, hide, and power for mankind. Selection and domestication have altered the genetic viewpoint of cattle breeds, resulting in a variety of commercially important features such as environmental adaption, appearance, and productivity attributes (The Bovine HapMap Consortium 2009; Randhawa et al., 2016). A population's genetic makeup evolves as a result of the process of evolution. The adaptations made by organisms to their altering environments are reflected in evolution, which can produce altered genes, unique features, and new species. It is anticipated that these selections will leave behind selection signatures, which can be recognized in the genome. The domestication of livestock and subsequent human-mediated selection for advantageous production features have characterized its demographic history. Many genomic areas and genes that have undergone selective sweeps have been effectively identified through the analysis of genomic patterns of SNP variability, frequently between diverse breeds (Gu et al. 2009; Qanbari et al. 2010; Stella et al. 2010; Amaral et al. 2011). The majority of empirical population genetic research has aimed to comprehend the evolutionary processes that have influenced the genetic diversity within a single sample obtained from a present-day population. On all inhabited continents, different breeds vary in appearance, performance, and environmental adaption. Thus, one of the primary study areas in evolutionary biology is the identification of climate-mediated selection signatures, which has the potential to reveal the genetic underpinnings of local adaptation and speciation in response to changing temperatures (MacCallum and Hill 2006; Joost et al. 2007). The signature of selection underlying climate adaptation refers to the genetic and genomic patterns that result from natural selection acting on populations in response to environmental changes related to climate. These patterns can include changes in allele frequency, genetic diversity and linkage disequilibrium, as well as the emergence of specific adaptive traits or phenotypes that enhance an organism's ability to survive and reproduce in changing environmental conditions.

By identifying the genetic factors that enable species to adapt to new environmental conditions, researchers can develop more effective strategies for conserving and managing threatened and endangered species, as well as for mitigating the impacts of climate change on ecosystems and human societies. Adaptation may take place over surprisingly short ecological timescales, with major phenotypic changes happening in natural populations within just a few generations. Living in an extreme climate, however, is quite difficult because of exposure to extremely high and low temperatures. Genome-wide scans for footprints of natural selection have been used to analyse adaptation to different climate circumstances in a range of species, including humans (Friedrich and Wiener, 2020). In a study of the genetic relatedness between Tibetans and Han in China, the team identified genetic signatures of high-altitude adaptation at seven gene loci. These findings provide important insight into our understanding of how the Tibetan genome has changed during high-altitude adaptation (Yang et al., 2017). The identification of selection signatures for different climate-related adaptations in ruminant livestock species is also of great interest for a variety of reasons. The type of selection signal that can be identified depends on the approach used to identify the genes generating adaptation (Friedrich and Wiener, 2020). Furthermore, the detection of adaptive variation has the potential to shed light on functionally significant variants (Bamshad and Wooding 2003; Nielsen et al. 2007). A trait needs to possess two qualities to be subjected to positive selection. The trait must first be advantageous or improve the likelihood that the organism will survive and reproduce. For a trait to be passed on to an organism's progeny, it must also be heritable (Schaffner and Sabeti, 2008). Nature chooses the genome for adaptation features (such as disease resistance, temperature adaptation, high-altitude adaptations, etc.) for the organism to be alive and produced in that environment. This offers helpful insights into the genetic foundation of specific and general adaptation mechanisms. The importance of adaptable breeds in demanding conditions, as well as increased reproductive and growth efficiency, must be addressed using genomic technology in livestock genetic development (van Marle-Koster and Visser, 2018). These native cattle breeds are particularly well suited to the various agro-climatic conditions of India, and some of these kinds of cattle are also doing very well in other nations across the world. Additionally, indigenous cattle breeds are essential due to their advantages in terms of disease resistance, low input management, and better survival in adverse weather conditions and climate change (Savalia et al., 2019). One of the key challenges in studying the signature of selection underlying climate adaptation is distinguishing between genetic changes that are driven by natural selection and those that result from other factors such as genetic drift or gene flow. As a result, we chose to explore the signature of selection underlying the climatic adaptation of these indigenous breeds.In the current investigation, we intended to use four complementary methods—Tajima's D, CLR, iHS, and ROH—to detect a wide variety of recent and ancient selection signatures. This study includes Tharparkar genotype data from our laboratory along with data on Gir, Hariana, Kankrej, Nelore, Ongole, Red Sindhi, and Sahiwal cattle breeds obtained from the online repositories. Moreover, we pinpointed the candidate genes indicated by selection signatures responsible for variations in adaptation potential among different Indian cattle breeds. We focus on identifying selection signatures and hypothesise that the analysis of genetic information from many species and geographical areas will make a significant contribution to our understanding of the genetic underpinnings of extreme climates.

The purpose of this work is to uncover a genome-wide signature of selection to define the genetic imprint that decades of climate-induced adaptations have left in the cattle genome of India. After identifying the crucial regions for selection, the NCBI database, PANTHER 17.0, and CattleQTL database were utilised for annotation. In the next section, we've highlighted a few significant genes and gene ontology (GO) keywords that are undergoing a selective sweep. Under the umbrella of climate-driven adaptation, we identified a group of candidate genes (GO terms) associated with the biological processes of energy metabolism, endocrine regulation, and autoimmune regulation. These findings are significant for their potential use in functional genomics, selective breeding, and the development of conservation management programmes (Luikart et al. 2003) to help livestock adapt to rapid global climate change. Our findings will contribute to demonstrating the effectiveness of selection signature approaches in identifying candidate genes and pathways involved in climate adaptation.

1.1. Description of the dataset and markers used in the study and quality control

For the current investigation, we used genotyped data on 187 animals from eight distinct cow breeds. We used 50k SNP chip data from Tharparkar (n = 72), which was generated in our laboratory. We also included data from the WIDDE online portal (Online-Interfaced Next-Generation Database for Genetic Diversity Exploration). Our analysis includes indigenous breeds like Gir (n = 24), Sahiwal (n = 17), Red Sindhi (n = 10), Kankrej (n = 10), Ongole (n = 20), Hariana (n = 10), and Nelore (n = 24). Table 1 provides a summary of the dataset we used for our investigation. In our study, only autosomal SNPs are taken into account. For quality checking, PLINK v1.90 was utilised (Purcell et al., 2007). The quality control procedures, in brief, included the exclusion of markers; SNPs with a call rate less than 0.90 and SNPs with a MAF less than 0.05 were discarded. Furthermore, we excluded SNPs on the X and Y chromosomes as well as those without physical or chromosomal positions.

Table 1

Sample description for eight different cattle breeds used in the study
Sr. No	Name	Abbreviation	Size	Origin	SNPs before QC	SNPs after QC	Purpose
1	Gir	GIR	24	Indigenous	51998	22452	Dairy
2	Hariana	HAR	10	Indigenous	52497	15606	Dual
3	Kankrej	KAN	10	Indigenous	52497	21059	Draught
4	Nelore	NEL	24	Indigenous	51998	19458	Draught
5	Ongole	ONG	20	Indigenous	52497	27474	Dual
6	Red Sindhi	RSI	10	Indigenous	52497	23118	Dairy
7	Sahiwal	SAH	17	Indigenous	52497	20154	Dairy
8	Tharparkar	THAR	72	Indigenous	47515	21943	Dairy

1.2. Population stratification

In this work, principal component analysis (PCA based on eigen-decomposition) is the primary statistical method of population stratification. Using plink and the packages in the R programme, PCA was carried out on the dataset. Using the --distance-matrix option, PLINK calculates genetic distances. It generates the previously described enormous number matrix, which is recorded in the text file dataForPCA.mdist. Using multidimensional scaling in R, it is made simpler. This will be accomplished by first loading the text file into R along with the breed names (fam variable) and person IDs (famInd variable). The next step is multidimensional scaling, and computing the eigenvectors. The cmdscale() function is used for this, accounting for the first five primary components. We next combine the PCA findings with the breed and family ID data (eigenvec_populations) and compute the variance explained by each main component (eigen_percent). PCA was displayed using the tidyverse programme (Fig. 14). The actual visualisation is the next section of code, which combines base R with tidy coding techniques. The eigenvec_populations file's first and second main components will be plotted. Eigenvectors were plotted individually along a graph's axes.

1.3. Diversity estimation

The genetic diversity, ROH, inbreeding, linkage disequilibrium, effective population size, and haplotype block structure in Tharparkar cattle from India were all thoroughly analysed across the entire genome in this study. The study included a total of 72 Tharparkar animals that had previously undergone genotyping using an Illumina BovineSNP50 array. Several indices, including observed heterozygozity (H_O), minor allele frequency (MAF), and inbreeding coefficients (F), were utilised to evaluate genetic diversity within the Tharparkar cow population. Under Hardy Weinberg Equilibrium (HWE), H_O was compared to expected heterozygozity (H_E). PLINK's —hardy flag was used to estimate the H_O and H_E. PLINK's —freqoption was used to calculate the MAFs. MAFs were classified into six categories: (0-0.05), (0.05–0.1), (0.1–0.2), (0.2–0.3), (0.3–0.4), and (0.4–0.5), and the proportion of SNPs in each was computed (Purcell et al., 2007). We calculated inbreeding coefficients using four different approaches (F_ROH, F_HOM, F_UNI, and F_GRM). For each animal, F_ROH was computed by dividing the entire genome length covered by ROH (LROH) by the total genome length covered by SNPs (L_AUTO) (McQuillan et al., 2008). The effective Population Size (Ne) was calculated using NeEstimator v2.136 and SNeP v1.1.37 based on linkage disequilibrium and recombination rate (c) between SNPs (Do et al., 2014; Barbato et al., 2015). Using pairwise LD values in SNeP software v1.1.37, we computed historical Ne for the Tharparkar population during the past generations. For SNPs, the bin width was set to 0.1 Mb, and the recombination rate (c) was adjusted in accordance with Sved and Feldman (Sved and Feldman, 1973).

1.4. Candidate Gene annotation, GO, and QTLs analysis

After quality control, the remaining SNPs were used for further investigation. To find potential functional genes under selection, we annotated the overlapping candidate genes found by different summary statistics. The CattleQTL database, PANTHER 17.0, and NCBI databases were used for annotation after determining the critical regions for selection. For gene annotation, we used the Bos_taurus_UMD_3.1.1 assembly. We've highlighted a few prominent genes, and GO terms that are experiencing selective sweep in the following section. Following are the four approaches we used in this study: Integrated haplotype score (iHS), Runs of homozygosity (ROH), Tajima's D, and Composite likelihood ratio (CLR).

1.5. Tajima’s D

It's a crucial statistic that is commonly used in population genetics. Tajima's D takes into account the difference between the mean pairwise difference and the number of segregating sites in nucleotide polymorphism data as a standard method for finding selection signatures (Tajima, 1989). The test statistic is equal to zero for neutral variation, positive when rare polymorphisms are overrepresented due to recent balancing selection for multiple alleles, and negative when high-frequency variants are overrepresented (Rajawat et al., 2022). We utilized the program VCF tools for a sliding window approach with a window size of 500 kb to estimate Tajima's D value (Danecek et al., 2011). As possible genomic regions inferring selective sweep, we chose the bottom 1% of empirical Tajima's D values.

1.6. ROH

Long sections of homozygous genotypes are known as runs of homozygosity (ROH), and they are thought to reflect portions shared exactly by descent because of processes including consanguinity, population size reduction, and natural selection (Szpiech et al., 2013). The detectRUNS R software was used to calculate the runs of homozygosity (ROH). ROHs were calculated using a sliding window-based technique with the detectRUNS R package. For ROH studies, the following threshold parameters were set: i) the maximum number of heterozygous SNPs (max-oppWindow-1); (ii) the minimum number of SNPs (minSNP-20); and (iii) the maximum number of missed calls per window (maxMissWindow-1). The ROH estimation threshold was set at 0.6, and passed SNPs were chosen as candidates for selective sweep (Almeida et al., 2019).

1.7 The integrated haplotype score (iHS)

Alleles within the same population are compared using this approach. This was calculated for each SNP using the R package’rehh’ as a representative for haplotype-based methods (Voight et al., 2006; Gautier and Vitalis, 2012). The iHS can identify incomplete or ongoing selection signatures (Sabeti et al., 2007). This approach is an expansion of EHH, which was developed by Voight et al. (2006) based on the comparison of EHH between derived and ancestor alleles within a population. We calculated |iHS| for each SNP across the genome of the breeds to find genomic areas that might have been targets of recent selection. Beagle v5.1 software was originally utilized for chromosome-wise haplotype phasing and imputation for missing genotypes in the iHS investigation (Rajawat et al., 2022). Subsequently, we calculated iHS values from a data frame including iHH values for ancestral and derived alleles using the rehh package of "R" version 3.2.2 (Gautier and Vitalis, 2012). The top 1% of regions are chosen for annotations.

1.8. Composite Likelihood Ratio (CLR)

A selective sweep alters allele frequency patterns at associated sites, excluding variation at tightly linked loci and resulting in an allele surplus at very low and very high frequencies in more remote areas (Williamson et al., 2007). The CLR test (Nielsen et al., 2005) was used as a statistical approach deployed to find a specific allele pattern following a selective sweep of frequencies along a chromosome. Then, for gene annotations, we have taken a region of 500 bp (Wang et al., 2018).

Table 2 gives an overview of the genes associated with adaptation to thermal stress that we identified during our investigation.

Table 2

List of genes found in respective breeds related to climate adaptation
BTA	START (bp)	END (bp)	METHOD	GENE	BREED
1	60008528	61681629	ROH	GAP43	NELORE
14	26003598	27730648	ROH	TOX	NELORE
15	19059568	24890676	ROH	HSPB2	HARIANA
15	19059568	24890676	ROH	CRYAB	HARIANA
15	19059568	24890676	ROH	HSP20	HARIANA
20	23333568	28244515	ROH	HSPB3	ONGOLE
23	16280653	19478135	ROH	HSP90AB1	ONGOLE
8	38976903	41052217	ROH	CDC37L1	ONGOLE
11	29774437	30298969	ROH	FBXO11	RED SINDHI
13	63733206	64875239	ROH	ACSS2	RED SINDHI
18	34750000	35250000	Tajima's D	HSF4	HARIANA
22	31250000	31750000	Tajima's D	MITF	HARIANA
15	52250000	52750000	Tajima's D	CLPB	ONGOLE
14	26250000	26750000	Tajima's D	TOX	NELORE
1	82250000	82750000	Tajima's D	MAP3K13	KANKREJ
23	26500001	27000001	Tajima's D	HSPA1B	THARPARKER
5	112271878	112771878	iHS	MCHR1	ONGOLE
5	112271878	112771878	iHS	ST13	ONGOLE
2	26278268	26778268	iHS	SSB	HARIANA
11	1695714	2195714	iHS	GPAT2	GIR
11	29840854	30340854	iHS	FBXO11	RED SINDHI
6	63000001	63500001	iHS	GRXCR1	THARPARKER
9	11000001	11500001	iHS	RIMS1	THARPARKER
25	7603870	8103870	CLR	CARHSP1	KANKREJ
26	39073333	39573333	CLR	PRLHR	KANKREJ
2	79788739	80288739	CLR	STAT1	ONGOLE
8	108513448	109013448	CLR	TLR4	SAHIWAL

2.1. PCA

After performing quality control in plink, 5726 variants were removed due to missing genotype data (--geno), 7249 variants were removed due to the Hardy-Weinberg exact test (--hwe), and 2124 variants were removed due to minor allele threshold(s) (--maf). PCA was applied to the SNP data, which had 2829 variants of 187 cattle. The total genotyping rate was 0.98. After quality control, a distance matrix is calculated. Then, distance matrix are written to .mdist, and IDs are written to .mdist.id. In order to accomplish PCA, we used the cmdscale function to extract the eigen vectors and measure the amount of variance that each eigen vector captured. On the basis of PC analysis, the dataset's eight populations were all stratified. The first principal component contributes 42.08%, and the second principal component contributes 9.65%. The amount of variance in percent that each PC accounts for is shown in brackets in the figure. Various colour schemes showed that clustering was connected to two distinct indicine lines. One cluster indicates the Tharparker cattle population, coloured in red. The second cluster includes Gir, Hariana, Kankrej, Nelore, Ongole, Red Sindhi, and Sahiwal.

Diversity Analyses

After quality control, 21,943 biallelic SNPs with genotyping rates greater than 90% and MAFs greater than 0.05 were kept. The overall mean observed (H_O) and expected heterozygosity (H_E) were 0.342 ± 0.168 and 0.335 ± 0.121, respectively. With an SD of 0.137, the minor allele frequency was on average 0.241. According to F_ROH, F_HOM, F_GRM, and F_UNI, the estimated genomic inbreeding coefficients were 0.0581, 0.0219, 0.0528, and 0.0159, respectively. Additionally, we saw that throughout the previous generations, the effective population number gradually decreased.

Because of its availability, precision, and cost-effectiveness in livestock species, SNP genotyping is one of the finest techniques to examine genetic variation. Previously, our group employed bovine 50 K SNP data to estimate inbreeding coefficient, effective population size, linkage disequilibrium, runs of homozygozygozity, and haplotype block structure in Indian zebu and crossbred cattle (Chhotaray et al., 2021b; Saravanan et al., 2020b). The admixture level and population structure of crossbred cattle populations were studied using genomic data from the Bovine 50 K SNP chip (Ahmad et al., 2020; Chhotaray et al., 2020). Saravanan and his co-workers conducted diversity analysis in Tharparkar, which had a population size of 24. A declining trend in effective population size over the past generations has been observed. (Saravanan et al., 2020).

2.2. Tajima’s D

HSF4 and CLPB are the genes that we identified using this approach that are correlated to climate adaptation in Hariana and Ongole, respectively. The gene HSF4 is found on chromosome 18 at positions 34.75–35.25 Mb. The gene CLPB is found on chromosome 15 at positions 52.25–52.75 Mb. MITF is a gene that is found in Hariana on chromosome 22 between 31.25 and 31.75 Mb. MAP3K13 (Chr 1: 82.25–82.75 Mb) and TOX (Chr 14: 26.25–26.75 Mb) have been identified in Nelore and Kankrej, respectively. Figures (Fig. 1, Fig. 2, Fig. 3, Fig. 4, and Fig. 5) demonstrate Tajima's D values with respect to various base pair locations on the corresponding chromosomal number, where we found a positive selection signature associated with adaptation.

2.2.1. Heat shock transcription factor 4 (HSF4)

Heat shock transcription factors (HSFs) are in charge of controlling the elevated heat shock gene expression in response to varied stimuli. Following the identification of the HSF family, the functional significance of many HSFs is becoming clearer day by day. HSF2, however, is resistant to conventional stressors, whereas HSF1, an HSF prototype, and HSF3 are responsible for heat-induced HSP expression. Similar to HSF1 and HSF2, HSF4 is a type of heat shock transcription factor that is expressed in a tissue-specific manner (Pirkkala et al., 2001). Under normal circumstances, HSF is dormant; nevertheless, when exposed to heat, it becomes active by causing trimerization, binding to DNA with high affinity, and opening up domains for transcriptional activity.

2.2.2. Caseinolytic peptidase B protein homolog (CLPB)

The crucial heat-shock response proteins, bacterial CLPB and its eukaryotic counterpart Hsp104, have the extraordinary ability to recover stress-damaged proteins from an aggregated state (Lee et al., 2003). They are necessary for cell survival under severe stress. Hsp104 also controls prion synthesis and breakdown. The Hsp70 or DnaK chaperone systems collaborate with Hsp104 and ClpB for the majority of their protein remodelling activities. Together, the two chaperones catalyze protein disaggregation and reactivation by a mechanism that most likely involves the forced unfolding and translocation of polypeptides from aggregates through the Hsp104 or ClpB central cavity. The polypeptides are subsequently returned to the cellular environment, where they can refold naturally or with the assistance of chaperones (Doyle and Wickner, 2009). While exploring the environmental adaptation of native Chinese cattle, Gao et al. reported the CLPB gene. One group identified genes AMPD2, PLPP3, SP4, RFX4, LEF1, SLIT2, IGFBP7, STK32B, ADRA1D, UBE2E3, CLPB, ADAMTS16 and LAMA1, which were exhibiting strong signals for environmental adaptation in Chinese cattle, with one group consisting of cold-adapted Tibetan cattle (Gao et al., 2017). Cellular reaction to heat is one of its gene ontology concepts (GO:0034605), which we observed in our analysis.

2.2.3. Melanocyte-inducing transcription factor (MITF)

MITF controls several differentiation and cell-cycle progression genes to regulate melanocyte growth, function, and survival. Selection signals in the genes MITF, FGF5, MTOR, TRHDE, and TUBB3 that have been linked to high-altitude adaptation were identified by pairwise comparison of the genetic differentiation of sheep breeds acclimated to various environments (Edea et al., 2019). Choudhary and co-workers reported that heat stress dramatically enhanced the expression of the Mc1r, Mitf, Pmel, and Tyrp1 genes, which further boosted the production of melanin (Choudhary et al., 2017). Following an exploration of gene ontology concepts, we observed in our findings that it is related to the cellular response to heat (GO:0034605).

2.2.4. TOX protein

Li and coworkers used the F_ST approach to study the selection characteristics of ruminant heat tolerance in Dehong cattle, a Chinese indigenous zebu breed. The findings suggested that genes related to heat shock (HSF1), oxidative stress response (PLCB1, PLCB4), coat colour (RAB31), feed intake (ATP8A1, SHC3), and reproduction (TP63, MAP3K13, PTPN4, PPP3CC, ADAMTSL1, SS18L1, OSBPL2, TOX, RREB1, and GRK2) may be associated with heat adaptation (Li et al., 2020).

2.3. ROH

In this approach, we have found certain genes like GAP43 (Chr 1: 60.00-61.68 Mb) and TOX (Chr 14: 26.00-27.73 Mb) in Nelore cattle. In Hariana, we have identified HSPB2 (Chr15: 19.05–24.89 Mb), CRYAB (Chr15: 19.05–24.89 Mb), and HSP20 (Chr: 19.05–24.89 Mb). In Ongole, we have identified HSPB3 (Chr20: 23.33–28.24 Mb), HSP90AB1 (Chr23: 16.28–19.47 Mb), and CDC37L1 (Chr8: 38.97–41.05 Mb) genes. We have identified genes such as FBXO11 (Chr11: 29.77–30.29 Mb) and ACSS2 (Chr13: 63.73–64.87 Mb) in Red Sindhi.

2.3.1. Growth Associated Protein 43 (GAP43)

After annotation, we learned that this gene is related to tissue regeneration (GO:0042246), wound healing (GO:0009611), cellular stress response (GO:0033554), and calmodulin binding (GO:0005516). A key protein kinase C substrate present in growing and regenerating neurons is neuromodulin, a neural-specific calmodulin-binding protein also known as GAP43, G50, F1, or pp46 (Deloulme et al., 1990).

2.3.2. Heat shock Proteins (HSPs)

In the course of our study, we identified some heat shock proteins, including HSPB2, HSPB3, HSP20, and HSP90AB1. Cattle all over the world have a variety of genetic traits that could be used to improve adaptation. These genetic biomarkers include the heat stress-related genes HSF1, HSP20, HSP70, and HSP90 (Abioja, et al., 2011). When cells and tissues are exposed to harsh temperatures that result in either acute or chronic stress, the heat shock response, which is defined by the elevated production of heat shock proteins (Hsps), is triggered. Hsps have a role in maintaining cellular homeostasis and fostering survival as molecular chaperones. Apoptosis, a signal that causes programmed cell death, is activated if the stress is too great, creating a carefully calibrated balance between survival and death (Pirkkala et al., 2001). The GO keywords like a response to heat (GO:0009408), cellular response to heat (GO:0034605), and protein folding (GO:0006457) are associated with adaptation.

2.3.3. Alpha-crystallin B chain (CRYAB)

We identified that it has related GO terms, including protein folding (GO:0006457), reaction to heat (GO:0009408), and unfolded protein binding (GO:0051082). It is a member of the small heat shock protein family. CRYAB (HSPB5) is a chaperone that is found in various organs and is a member of the SHSP family (HSPB1-HSPB10). It is a primary chaperone of the cytoskeleton (Tang et al., 2018).

2.3.4. Hsp90 co-chaperone Cdc37-like 1 (CDC37L1)

After annotation, we found that the function of this gene includes binding to heat shock protein (GO:0031072), chaperone (GO:0051087), and unfolded protein (GO:0051082). Through improved heat shock protein 90 (HSP90) and peptidylprolyl isomerase A (PPIA) binding, CDC37L1 effectively increases PPIA expression as a cochaperone (Li et al., 2022).

2.3.5. F-box protein 11 (FBXO11)

Apoptotic process (GO:0006915), regulation of apoptotic process (GO:0042981), protein ubiquitination (GO:0016567), and ubiquitin-dependent protein catabolic process (GO:0006511) are only a few of the gene ontology terms it is related to. The FBXO11 gene codes for a member of the TGF signaling and heat stress tolerance-related F-box protein family, which is involved in the ubiquitination of target proteins. Four unique SNP genotypes were reported as markers for heat stress resistance in another investigation along with a thermo-tolerance index based on rectal temperature. These SNPs, which were found in the FBXO11, PHC3, TSHR, and STAT1 genes, suggest increased thermo-tolerance via core body temperature control in response to external heat stress (Luna-Nev arez et al., 2021). According to research, the FBXO11 gene appears to suppress TGF-signalling by activating HSP70 and ubiquitination and offering thermo-protection to cells exposed to heat stress (Saadeldin et al., 2020).

2.3.6. Propionate-CoA ligase (ACSS2)

Several GO keywords are present in it, including the acyl-CoA metabolic process (GO:0006637), the amide biosynthesis process (GO:0043604), and the purine ribonucleotide biosynthetic process (GO:0009152). Candidate genes (ACSS2, ALDOC, EPAS1, EGLN1, NUCB2) and pathways that are putatively involved in hypoxia adaptation were identified in one research study using selection signature studies comparing high-altitude (> 1800 m) and low-altitude acclimated breeds (< 1500 m) (Wang et al., 2021).

2.4. iHS

The genes identified in this approach are MCHR1 (Chr5:112.27–112.77 Mb) and ST13 (Chr5:112.27–112.77 Mb) in Ongole, SSB (Chr2:26.27–26.77) in Hariana, GPAT2 (Chr11:1.69–2.19 Mb) in Gir, and FBXO11 (Chr11:29.84–30.34) in Red Sindhi. Figures (Fig. 6, Fig. 7, Fig. 8, and Fig. 9) demonstrate normalized iHSscores with respect to various base pair locations on the corresponding chromosomal number, where we found a positive selection signature associated with adaptation.

2.4.1. Melanin-Concentrating Hormone Receptor 1 (MCHR1)

It contains GO keywords for the neuropeptide signalling pathway (GO:0007218), the plasma membrane area (GO:0098590), the integral component of the plasma membrane (GO:0005887), and the projection of a neuron (GO:0043005). All mammals express the short cyclic peptide known as melanin-concentrating hormone (MCH), which is mostly found in the hypothalamus. Two receptors (MCHR1 and MCHR2), of which only MCHR1 is shared by all mammals, allow MCH to mediate its effects. Because there are so many different MCH downstream signalling pathways, MCHR1 agonists and antagonists have a lot of potential as tools for the targeted management of energy balance disorders and related metabolic complications. Translational approaches utilizing these substances also hold promise for the development of novel treatments for obesity. For energy and glucose homeostasis, MCH plays a variety of roles (Al-Massadi et al., 2021).

2.4.2. ST13 Hsp70 interacting protein (ST13)

We identified several gene ontology terms for the processes of chaperone cofactor-dependent protein refolding (GO:0051085) and Hsp70 protein binding (GO:0030544). While studying selection signatures, one group observed some genes in the Yorkshire pig breed, including ST13, which is linked to heat shock protein binding (Zhao et al., 2021).

2.4.3. Saccharomyces cerevisiae Hsp70 (SSB)

Several gene ontology terms are associated with it, including cytoplasmic stress granule (GO:0010494), translational elongation (GO:0006414), positive regulation of translation (GO:0045727), mRNA binding (GO:0003723), and cytosol (GO:0005829). The ribosome-nascent chain complex has a link with Ssb, a Saccharomyces cerevisiae Hsp70 cytosolic molecular chaperone. The two genes that make up the SSB chaperone family, SSB1 and SSB2, are nearly identical to one another. It had been investigated that the mRNA regulation of SSB under various environmental circumstances to ascertain whether this chaperone is co-regulated with ribosomal proteins (Lopez et al., 1999). As per a study, the proper regulation of SSB following a change in temperature is dependent on the Heat shock factor (HSF), which is indispensable for regulating the core components of ribosomes. When the temperature rises, a group of genes known as the heat shock genes are induced, while the expression of RP genes, SSB, and numerous other genes is decreased (Miller et al., 1982). According to earlier research, SSB is regulated properly like a fundamental ribosomal component, and HSF is necessary for its regulation (Lopez et al., 1999). To provide thermotolerance, calmodulin (CaM) activation stimulates the DNA-binding activity of HS transcription factors and causes an accumulation of HS proteins (Jia et al., 2014).

2.4.4. Glycerol-3-phosphate acyltransferase 2 (GPAT2)

Several gene ontology terms are associated with it, including the piRNA metabolic process (GO:0034587), mitochondrial membrane (GO:0031966), and mitochondrial membrane (GO:0031966). In three local sheep breeds, one group used GWAS to find genes related to heat tolerance like PLCB1, STEAP3, KSR2, UNC13C, PEBP4, and GPAT2 that are linked to heat tolerance.

2.5. CLR

In this approach, we found some significant genes like CARHSP1 (Chr25:7.60–8.10 Mb) and PRLHR (Chr26:39.07–39.57 Mb) in Kankrej, STAT1 (Chr2:79.78–80.28 Mb) in Ongole and TLR4 (Chr8:108.51–109.01 Mb) in Sahiwal. Figures (Fig. 10, Fig. 11, Fig. 12, and Fig. 13) demonstrate CLR values with respect to various base pair locations on the corresponding chromosomal number, where we found a positive selection signature associated with adaptation.

2.5.1. Calcium regulated heat stable protein 1 (CARHSP1)

Several gene ontology terms are associated with it, including mRNA 3'-UTR binding (GO:0003730), gene expression (GO:0010467), regulation of mRNA stability (GO:0043488) and mRNA catabolic process (GO:0006402). The calcium-regulated heat-stable protein 1 (CARHSP1), a 24 kDa protein commonly referred to as CRHSP-24, is another member of the human cold shock protein family. CARHSP1 is a paralog of the brain-specific cold shock protein PIPPin (Schafer et al., 2003). It was first discovered as a substrate of the calcium/calmodulin-regulated protein phosphatase calcineurin (Groblewski et al., 1998).

2.5.2. Prolactin-releasing peptide receptor (PRLHR)

It contains GO terms for neuropeptide binding (GO:0042923), neuropeptide receptor activity (GO:0008188), the G protein-coupled receptor signaling pathway (GO:0007186), the plasma membrane region (GO:0098590), and neuron projection (GO:0043005). A group identified 21 loci that were strongly linked with hibernation in a genome-wide scan with 46,996 variations. The most noticeable of them was the Prolactin-releasing hormone receptor (PRLHR), a gene that regulates food intake and energy homeostasis (Grabek et al., 2017).

2.5.3. Signal transducer and activator of transcription (STAT1)

It has associated GO terms like RNA polymerase II cis-regulatory region sequence-specific DNA binding (GO:0000978); DNA-binding transcription factor activity, RNA polymerase II-specific (GO:0000981), etc. One study on a genome-wide association analysis of a thermo-tolerance indicator in pregnant sheep exposed to an artificially hot environment reveals candidate genes for thermo-tolerance, including FBX011, PHC3, TSHR, and STAT1 (Luna-Nevárez et al., 2021).

2.5.4. Toll-like receptor 4 (TLR4)

It has to GO terms like signaling receptor activity (GO:0038023), lipopolysaccharide-binding (GO:0001530), toll-like receptor signalling pathway (GO:0002224), inflammatory response (GO:0006954), immune response (GO:0006955), defense response to Gram-negative bacterium (GO:0050829) and an integral component of the plasma membrane(GO:0005887). According to one study, the variable expression patterns of TLR 2/4 and IL 2/6 could provide thermotolerance during short- and long-term heat stress exposure, possibly depending on the time and tissue. Additionally, they concluded that TLR2/4 and IL2/6 may be crucial in triggering an immune response in Tharparkar cattle during long-term heat stress acclimation to reduce thermal insults (Bharati et al., 2017).

Common genes

We conducted venn diagram analysis to identify common climate-related genes shared by multiple methodologies (https://bioinformatics.psb.ugent.be/webtools/Venn). We observed that two genes were shared by two approaches out of 27 genes with adaption relevance (Fig. 15). We identified one gene, i.e., TOX, that was shared by ROH and Tajima's D methods. Another gene, i.e., the FBXO11 gene, is shared by the ROH and iHS methods. We have already discussed these two genes in the previous section. Considering the fact that these two genes are shared by several methods, this suggests that they have been undergoing extensive positive selection, which helps in overcoming heat stress.

Our primary goal in the current study was to use bovine 50K SNP chip data to identify selection signatures in the indicine cattle breeds (Bos indicus). Four intra-population statistics (Tajima's D, CLR, iHS, and ROH) have been performed in our study to explain signals of selection. Selection signature studies aim to identify genomic regions or genes that have been under strong selection in response to environmental changes. Climate adaptation is a complex process that involves many biological mechanisms, and the identification of the genomic basis of adaptation is still an active area of research. In addition to genetic variation, we can also look for other indicators of selection, such as changes in gene expression or protein function, that are associated with adaptation to specific environmental conditions. The identification of selection signatures in native cattle for high altitude and hot climate adaptation has helped shed light on both particular and general adaptation mechanisms. The direct or indirect involvement of numerous significant pathways and genes in animal adaptation to cold and heat has been identified in this study. It is important to note that signatures of selection can be complex and influenced by a variety of factors, including genetic drift, gene flow, and historical patterns of selection. Therefore, it is essential to use multiple lines of evidence and carefully consider the context in which selection is occurring to fully understand the genetic basis of climate adaptation.

The expression or activation of specific biological markers, such as heat shock transcription factor (HSF), heat shock proteins (HSP70, HSP90, and HSP27), slick hair gene (Dikmen et al., 2008), ATP1A1 (Liu et al., 2010), ATP1B2 (Wang et al., 2010), and AMP-activated protein kinase (AMPK) (Min et al., 2016), can be used to detect thermotolerance. Some of the genes identified in the previous study, including HSPA1B and GRXCR1 in Tharparkar cattle, the FKBP4 gene, HSPA1L and HSPA1B genes in Gir cattle, and the HSPA12A and IL6 genes in Ongole cattle, were associated with thermotolerance (Saravanan et al., 2021). These findings offer new information on the genetic processes that are probably responsible for the ability of domestic animals to adapt to their environment, along with an understanding of the molecular, biological, and cellular functions of candidate genes.

Studying the genetic signatures of adaptation to climate change helps understand the mechanisms and genetic pathways involved. This knowledge can inform conservation efforts and management practises to preserve ecosystems, especially those sensitive to climate change. Identifying the genetic basis of adaptive traits can also help develop tools like genetic engineering or assisted evolution to aid adaptation. Therefore, studying the signature of selection in climate adaptation is crucial for understanding how organisms respond to environmental change and preserving biodiversity. This study used genome-wide scanning to identify potential genes and genomic regions involved in local adaptation in cattle, with a focus on Indicine breeds. Results revealed that genes responsible for disease resistance and thermotolerance underwent extensive selection. The findings have implications for marker-assisted breeding, understanding the genetic landscape of climate-induced adaptability, and implementing breeding and conservation programmes. Future research should combine additional genomic data and environmental factors to confirm and improve these findings. However, different association analyses or recently established approaches, such as the Composite of Multiple Signals (CMS) and Approximate Bayesian Computation (ABC) test, would be needed for quantitative data on adaptation features in order to confirm the role of these genes in the adaptation mechanism.

Declaration of Competing Interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Acknowledgment

Support received from National Agricultural Science Fund (NASF), Indian Council of Agricultural Research, New Delhi, India for carrying out this work is duly acknowledged. The authors wish to thank the Director & Joint Director (Research), ICAR-Indian Veterinary Research Institute, Bareilly, India for providing necessary facilities during this study.

CRediT authorship contribution statement

Sonali Sonejita Nayak: Methodology, Writing – original draft. Manjit Panigrahi: Conceptualization, Methodology, Writing – review & editing. Divya Rajawat: Writing – review & editing. Kanika Ghildiyal: Writing – review & editing. Anurodh Sharma: Writing – review & editing. Bharat Bhushan, G.K. Gaur and Triveni Dutt: Resources, Supervision

Availability of data and materials

The WIDDE database and the Dryad repository both contain the genotypic data used for this study (http://widde.toulouse.inra.fr/widde/). The genotypic information for the 72 Tharparkar animals utilised in this work is accessible with the corresponding author, and due to some institutional liabilities, we can disclose this dataset on request.

Ethical approval

All the procedures performed in this study involving animals were following the ethical standards of the Institutional Animal Ethics Committee (IAEC) of ICAR- IVRI, Izatnagar, Bareilly, India (NO.F.1-53/2018-19/J. D(R)).

Funding Information

No funds available

Joost, S., Bonin, A., Bruford, M. W., Després, L., Conord, C., Erhardt, G., & Taberlet, P. (2007). A spatial analysis method (SAM) to detect candidate loci for selection: towards a landscape genomics approach to adaptation. Molecular ecology, 16(18), 3955-3969.
Stella A, Ajmone-Marsan P, Lazzari B, Boettcher P. 2010. Identification of selectionsignatures in cattle breeds selected for dairy production.Genetics 185: 1451-1498
Bamshad, M., & Wooding, S. P. (2003). Signatures of natural selection in the human genome. Nature Reviews Genetics, 4(2), 99-110.
Nielsen R, Hellmann I, Hubisz M, Bustamante C, Clark AG. 2007. Recent and ongoingselection in the human genome.Nat Rev Genet. 8: 857-868.
Gu JJ, Orr N, Park SD, Katz LM, Sulimova G, MacHugh DE, Hill EW. 2009. A Genome scanfor positive selection in thoroughbred horses. PLoS ONE 4: e5767.
Qanbari S, Pimentel ECG, Tetens J, Thaller G, Lichtner P, Sharifi AR, Simianer H. 2010. Agenome-wide scan for signatures of recent selection in Holstein cattle. Anim Genet. 41:377-389
Amaral AJ, Ferretti L, Megens HJ, Crooijmans RPMA, Nie HS, RamosOnsins SE, Perez-Enciso M, Schook LB, Groenen MAM. 2011. Genome-wide footprints of pig domestication and selection revealed through massive parallel sequencing of pooled DNA. PLoS One 6:e14782.
Luikart G, England PR, Tallmon D, Jordan S, Taberlet P. 2003. The power and promise of population genomics: from genotyping to genome typing. Nat Rev Genet. 4:981–994.
Tajima F. 1989. Statistical method for testing the neutral mutation hypothesis by DNA polymorphism. Genetics 123: 585–595.
Fariello MI, Boitard S, Naya H, SanCristobal M, Servin B. 2013. Detecting signatures of selection through haplotype differentiation among hierarchically structured populations. Genetics 193: 929–941
Tajima F (1989) Statistical method for testing the neutral mutation hypothesis by DNA polymorphism. Genetics 123: 585–595. pmid:2513255
Rajawat, D., Panigrahi, M., Kumar, H., Nayak, S. S., Parida, S., Bhushan, B., ... & Mishra, B. P. (2022). Identification of important genomic footprints using eight different selection signature statistics in domestic cattle breeds. Gene, 816, 146165.
Danecek, P., Auton, A., Abecasis, G., Albers, C. A., Banks, E., DePristo, M. A., ... & 1000 Genomes Project Analysis Group. (2011). The variant call format and VCFtools. Bioinformatics, 27(15), 2156-2158.
Voight BF, Kudaravalli S, Wen X, Pritchard JK: A map of recent positive selection in the human genome. PLoS Biology. 2006, 4: e72-10.1371/journal.pbio.0040072.
Sabeti PC, Varilly P, Fry B, Lohmueller J, Hostetter E, et al: Genome-wide detection and characterization of positive selection in human populations. Nature. 2007, 449: 913-918. 10.1038/nature06250.
Gautier, M., & Vitalis, R. (2012). rehh: an R package to detect footprints of selection in genome-wide SNP data from haplotype structure. Bioinformatics, 28(8), 1176-1177.
Weir B.S. & Cockerham C.C. (1984) Estimating F-statistics for the analysis of population structure. Evolution 38, 1358–70.
Randhawa I.A.S., Khatkar M.S., Thomson P.C. & Raadsma H.W. (2014) Composite selection signals can localize the trait specific genomic regions in multi-breed populations of cattle and sheep. BMC Genetics 15, 1601–8.
Lewontin RC, Krakauer J. Distribution of gene frequency as a test of the theory of the selective neutrality of polymorphisms. Genetics. 1973;74:175–95.
Kullo IJ, Ding K. Patterns of population differentiation of candidate genes for cardiovascular disease. BMC Genet. 2007;8:48.
Kijas, J. W., Lenstra, J. A., Hayes, B., Boitard, S., Porto Neto, L. R., San Cristobal, M., ...& International Sheep Genomics Consortium. (2012). Genome-wide analysis of the world’s sheep breeds reveals high levels of historic mixture and strong recent selection. PLoS biology, 10(2), e1001258.
Lopez N, Halladay J, Walter W, Craig EA. SSB, encoding a ribosome-associated chaperone, is coordinately regulated with ribosomal protein genes. J Bacteriol. 1999 May;181(10):3136-43. doi: 10.1128/JB.181.10.3136-3143.1999. PMID: 10322015; PMCID: PMC93769.
Jia, L., Chu, H., Wu, D., Feng, M., & Zhao, L. (2014). Role of calmodulin in thermotolerance. Plant signaling & behavior, 9(6), e28887.
S. Dikmen, E. Alava, E. Pontes, J.M. Fear, B.Y. Dikmen, T.A. Olson, P.J. Hansen, Differences in thermoregulatory ability between slick-haired and wild-type lactating Holstein cows in response to acute heat stress, J. Dairy Sci. 91 (2008) 3395–3402, https://doi.org/10.3168/jds.2008-1072.
Y. Liu, D. Li, H. Li, X. Zhou, G. Wang, A novel SNP of the ATP1A1 gene is associated with heat tolerance traits in dairy cows, Mol. Biol. Rep. 38 (2010) 83–88, https:// doi.org/10.1007/s11033-010-0080-8.
Z. Wang, G. Wang, J. Huang, Q. Li, C. Wang, J. Zhong, Novel SNPs in the ATP1B2 gene and their associations with milk yield, milk composition and heat-resistance traits in Chinese Holstein cows, Mol. Biol. Rep. 38 (2010) 1749–1755, https://doi. org/10.1007/s11033-010-0289-6.
L. Min, S. Zhao, H. Tian, X. Zhou, Y. Zhang, S. Li, H. Yang, N. Zheng, J. Wang, Metabolic responses and “omics” technologies for elucidating the effects of heat stress in dairy cows, Int. J. Biometeorol. 61 (2016) 1149–1158, https://doi.org/ 10.1007/s00484-016-1283-z.
Saravanan, K. A., Panigrahi, M., Kumar, H., Parida, S., Bhushan, B., Gaur, G. K., ... & Singh, R. K. (2021). Genomic scans for selection signatures revealed candidate genes for adaptation and production traits in a variety of cattle breeds. Genomics, 113(3), 955-963.
Pirkkala, L., Nykänen, P., & Sistonen, L. E. A. (2001). Roles of the heat shock transcription factors in regulation of the heat shock response and beyond. The FASEB Journal, 15(7), 1118-1131.
Lee, S., Sowa, M. E., Watanabe, Y. H., Sigler, P. B., Chiu, W., Yoshida, M., & Tsai, F. T. (2003). The structure of ClpB: a molecular chaperone that rescues proteins from an aggregated state. Cell, 115(2), 229-240.
Doyle, S. M., & Wickner, S. (2009). Hsp104 and ClpB: protein disaggregating machines. Trends in biochemical sciences, 34(1), 40-48.
Gao, Y., Gautier, M., Ding, X., Zhang, H., Wang, Y., Wang, X., ... & Zhang, Y. (2017). Species composition and environmental adaptation of indigenous Chinese cattle. Scientific reports, 7(1), 1-14.
Edea, Z.; Dadi, H.; Dessie, T.; Kim, K.S. Genomic Signatures of High-Altitude Adaptation in Ethiopian Sheep Popula-Tions. Genes Genom.2019, 41, 973–981.
Choudhary, R., Goud, T. S., Kumar, A., Sharma, A. K., Singh, S. V., Upadhyay, R. C., ... & Kumar, S. (2017). Heat stress induced adaptation in melanocytes is dependent on the level of melanin and reduction of apoptosis. Journal of Dermatological Science, 85(3), 250-252.
Li, R.; Li, H.; Chen, R.; Chong, Q.; Xiao, H.; Chen, S. Genome-Wide Scan of Selection Signatures in Dehong Humped Cattle for Heat Tolerance and Disease Resistance. Anim. Genet.2020, 51, 292–299.
Deloulme, J. C., Janet, T., Au, D., Storm, D. R., Sensenbrenner, M., & Baudier, J. (1990). Neuromodulin (GAP43): a neuronal protein kinase C substrate is also present in 0-2A glial cell lineage. Characterization of neuromodulin in secondary cultures of oligodendrocytes and comparison with the neuronal antigen. The Journal of cell biology, 111(4), 1559-1569.
Tang, S., Yin, B., Xu, J., & Bao, E. (2018). Rosemary reduces heat stress by inducing CRYAB and HSP70 expression in broiler chickens. Oxidative Medicine and Cellular Longevity, 2018.
Abioja, M. O., Logunleko, M. O., Majekodunmi, B. C., Adekunle, E. O., Shittu, O. O., Odeyemi, A. J., ... & Daramola, J. O. (2022). Roles of Candidate Genes in the Adaptation of Goats to Heat Stress: A Review. Small Ruminant Research, 106878.
Li, L., Yu, S., Chen, J., Quan, M., Gao, Y., & Li, Y. (2022). miR-15a and miR-20b sensitize hepatocellular carcinoma cells to sorafenib through repressing CDC37L1 and consequent PPIA downregulation. Cell Death Discovery, 8(1), 297.
Wang, X.; Ju, Z.; Jiang, Q.; Zhong, J.; Liu, C.; Wang, J.; Hoff, J.L.; Schnabel, R.D.; Zhao, H.; Gao, Y.; et al. Introgression, Admixture and Selection Facilitate Genetic Adaptation to High-Altitude Environments in Cattle. Genomics2021, 113, 1491–1503.
Al-Massadi, O., Dieguez, C., Schneeberger, M. et al. Multifaceted actions of melanin-concentrating hormone on mammalian energy homeostasis. Nat Rev Endocrinol17, 745–755 (2021).
Zhao, Qb., Oyelami, F.O., Qadri, Q.R. et al. Identifying the unique characteristics of the Chinese indigenous pig breeds in the Yangtze River Delta region for precise conservation. BMC Genomics22, 151 (2021).
Groblewski GE, Yoshida M, Bragado MJ, Ernst SA, Leykam J, Williams JA. Purification and characterization of a novel physiological substrate for calcineurin in mammalian cells. J Biol Chem. 1998;273(35):22738–44.
Schafer C, Steffen H, Krzykowski KJ, Goke B, Groblewski GE. CRHSP-24 phosphorylation is regulated by multiple signaling pathways in pancreatic acinar cells. Am J Physiol Gastrointest Liver Physiol. 2003;285(4):G726–34.
Lopez, N., Halladay, J., Walter, W., & Craig, E. A. (1999). SSB, encoding a ribosome-associated chaperone, is coordinately regulated with ribosomal protein genes. Journal of bacteriology, 181(10), 3136-3143.
Miller M J, Xuong N-H, Geiduschek E P. Quantitative analysis of the heat shock response of Saccharomyces cerevisiae. J Bacteriol. 1982;151:311–327.
Grabek, K. R., Cooke, T. F., Epperson, L. E., Spees, K. K., Cabral, G. F., Sutton, S. C., ... & Bustamante, C. D. (2017). Genetic architecture drives seasonal onset of hibernation in the 13-lined ground squirrel. bioRxiv, 222307.
Luna-Nevárez, G., Pendleton, A. L., Luna-Ramirez, R. I., Limesand, S. W., Reyna-Granados, J. R., & Luna-Nevárez, P. (2021). Genome-wide association study of a thermo-tolerance indicator in pregnant ewes exposed to an artificial heat-stressed environment. Journal of Thermal Biology, 101, 103095.
Bharati, J., Dangi, S. S., Mishra, S. R., Chouhan, V. S., Verma, V., Shankar, O., ... & Sarkar, M. (2017). Expression analysis of toll like receptors and interleukins in Tharparkar cattle during acclimation to heat stress exposure. Journal of Thermal Biology, 65, 48-56.
Savalia, K. B., Ahlawat, A. R., Gamit, V. V., Parikh, S. S., & Verma, A. D. (2019). Recently recognized indigenous cattle breeds of India: A review. International Journal of Current Microbiology and Applied Sciences, 8(12), 161-168.
Gentry, A., Clutton-Brock, J., & Groves, C. P. (2004). The naming of wild animal species and their domestic derivatives. Journal of Archaeological Science, 31(5), 645-651.
Grabek, K. R., Cooke, T. F., Epperson, L. E., Spees, K. K., Cabral, G. F., Sutton, S. C., ... & Bustamante, C. D. (2017). Genetic architecture drives seasonal onset of hibernation in the 13-lined ground squirrel. bioRxiv, 222307.
Li, L., Yu, S., Chen, J., Quan, M., Gao, Y., & Li, Y. (2022). miR-15a and miR-20b sensitize hepatocellular carcinoma cells to sorafenib through repressing CDC37L1 and consequent PPIA downregulation. Cell Death Discovery, 8(1), 297.
MacCallum, C., & Hill, E. (2006). Being positive about selection. PLoS biology, 4(3), e87.
Saadeldin, I. M., Swelum, A. A. A., Elsafadi, M., Mahmood, A., Osama, A., Shikshaky, H., ... & Magdeldin, S. (2020). Thermotolerance and plasticity of camel somatic cells exposed to acute and chronic heat stress. Journal of advanced research, 22, 105-118.
Neafsey, D. E., Schaffner, S. F., Volkman, S. K., Park, D., Montgomery, P., Milner, D. A., ... & Wirth, D. F. (2008). Genome-wide SNP genotyping highlights the role of natural selection in Plasmodium falciparumpopulation divergence. Genome biology, 9(12), 1-16.
Tajima, F. (1989). Statistical method for testing the neutral mutation hypothesis by DNA polymorphism. Genetics, 123(3), 585-595.
van Marle-Köster, E., & Visser, C. (2018). Genetic improvement in South African livestock: can genomics bridge the gap between the developed and developing sectors?. Frontiers in Genetics, 9, 331.
The Bovine HapMap Consortium, Gibbs, R. A., Taylor, J. F., Tassel, C. V., Barendse, W., Eversole, K. A., Gill, C. A., and Green, R. D. 2009. Genome-wide survey of SNP variation uncovers the genetic structure of cattle breeds. Science.324 (5926): 528-532.
Williamson, S. H., Hubisz, M. J., Clark, A. G., Payseur, B. A., Bustamante, C. D., & Nielsen, R. (2007). Localizing recent adaptive evolution in the human genome. PLoS genetics, 3(6), e90.
McQuillan R, Leutenegger AL, Abdel-Rahman R, et al. Runs of homozygosity in European populations. Am J Hum Genet. 2008;83(3):359–372.
Do C, Waples RS, Peel D, et al. NeEstimator v2: reimplementation of software for the estimation of contemporary effective population size (Ne) from genetic data . Mol Ecol Resour. 2014;14(1):209–214. 37.
Barbato M, Orozco-terWengel P, Tapio M, Bruford MW. SNeP: a tool to estimate trends in recent effective population size trajectories using genome-wide SNP data. Front Genet. 2015;6:109.
Sved JA, Feldman MW. Correlation and probability methods for one and two loci. Theor Popul Biol. 1973; 4(1):129–132.
Purcell S, Neale B, Todd-Brown K, et al. PLINK: a tool set for whole-genome association and population-based linkage analyses. Am J Hum Genet. 2007;81(3):559–575.
Saravanan, K. A., Panigrahi, M., Kumar, H., Parida, S., Bhushan, B., Gaur, G. K., ... & Singh, R. K. (2020). Genome-wide assessment of genetic diversity, linkage disequilibrium and haplotype block structure in Tharparkar cattle breed of India. Animal biotechnology, 33(2), 297-311.
Purcell, S., Neale, B., Todd-Brown, K., Thomas, L., Ferreira, M. A., Bender, D., and Sham, P. C. (2007). PLINK: a tool set for whole-genome association and population-based linkage analyses. American Journal of Human Genetics,81(3), 559-575.

Download PDF

Journal Publication

published 17 Jan, 2024

Read the published version in Tropical Animal Health and Production →

Reviewers agreed at journal
15 Sep, 2023
Reviewers invited by journal
15 Sep, 2023
Editor assigned by journal
15 Aug, 2023
First submitted to journal
11 Aug, 2023

You are reading this older preprint version

Read the latest preprint version →

Deciphering climate resilience in Indian cattle breeds by selection signature analyses

Status:

Journal Publication

Version 1

Abstract

Figures

Introduction

Materials and Methods

1.1. Description of the dataset and markers used in the study and quality control

1.2. Population stratification

1.3. Diversity estimation

1.4. Candidate Gene annotation, GO, and QTLs analysis

1.5. Tajima’s D

1.6. ROH

1.7 The integrated haplotype score (iHS)

1.8. Composite Likelihood Ratio (CLR)

Results and Discussion

2.1. PCA

2.2. Tajima’s D

2.2.1. Heat shock transcription factor 4 (HSF4)

2.2.2. Caseinolytic peptidase B protein homolog (CLPB)

2.2.3. Melanocyte-inducing transcription factor (MITF)

2.2.4. TOX protein

2.3. ROH

2.3.1. Growth Associated Protein 43 (GAP43)

2.3.2. Heat shock Proteins (HSPs)

2.3.3. Alpha-crystallin B chain (CRYAB)

2.3.4. Hsp90 co-chaperone Cdc37-like 1 (CDC37L1)

2.3.5. F-box protein 11 (FBXO11)

2.3.6. Propionate-CoA ligase (ACSS2)

2.4. iHS

2.4.1. Melanin-Concentrating Hormone Receptor 1 (MCHR1)

2.4.2. ST13 Hsp70 interacting protein (ST13)

2.4.3. Saccharomyces cerevisiae Hsp70 (SSB)

2.4.4. Glycerol-3-phosphate acyltransferase 2 (GPAT2)

2.5. CLR

2.5.1. Calcium regulated heat stable protein 1 (CARHSP1)

2.5.2. Prolactin-releasing peptide receptor (PRLHR)

2.5.3. Signal transducer and activator of transcription (STAT1)

2.5.4. Toll-like receptor 4 (TLR4)

Conclusion

Declarations

References

Status:

Journal Publication

Version 1