Genomic insight into X chromosome dynamics in high altitude adaptation of trans-Himalayan yaks

doi:10.21203/rs.3.rs-5033046/v1

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Genomic insight into X chromosome dynamics in high altitude adaptation of trans-Himalayan yaks

https://doi.org/10.21203/rs.3.rs-5033046/v1

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This study delves into the genomic foundations of high-altitude adaptation in Indian yaks, with a specific emphasis on the X chromosome and its role in traits related to production, reproduction, and immunity. Utilizing whole-genome resequencing, we identified 319,015 high-quality X chromosomal SNPs from 30 unrelated Indian yaks and 8 Jinchuan yaks. These SNPs were analyzed through various statistical methodologies, including composite likelihood ratio (CLR) statistics, Tajima’s D, iHS, F_ST, and XP-EHH. Our findings highlight several genes associated with high-altitude adaptation, such as AIFM1, APOOL, ATRX, CHST7, DACH2, DGAT2L6, DIAPH2, and EIF2S3B, identified through iHS, Tajima’s D, and CLR approaches. Additionally, genes including GPR119, HS6ST2, MAGED1, MOSPD1, PQBP1, SLC25A14, SLC35A2, TIMM17B, and WDR44 exhibited common selection signatures across F_ST and XP-EHH methods. Unique genes and loci specific to each yak population were uncovered on the X chromosome, which are critical for adaptability, immunity, reproduction, and production traits. Notably, our study identified selection regions containing the RLIM gene in Himachali yaks, which is crucial for Dosage Compensation on the X chromosome. This research offers new insights into X-linked selection across different yak populations, enhancing our understanding of the genomic mechanisms underlying high-altitude adaptation.

High altitude adaptation

X chromosome

Whole-genome resequencing

Selection signatures

In mammals, sex determination is governed by the sex chromosomes, with females possessing two X chromosomes and males having one X and one Y chromosome, where the presence of a Y chromosome determines a male phenotype (Goodfellow and Lovell-Badge, 1993). The X chromosome, evolutionarily conserved, differs significantly from the Y chromosome in size, structure, and gene content across species (Hughes and Page, 2015). Unlike autosomes, which carry a mix of genes expressed variably in different tissues, sex chromosomes are enriched with sex-biased genes crucial for sexual development and reproduction, particularly in spermatogenesis and male fertility. The X chromosome notably harbors three times more genes expressed in female reproductive organs and fifteen times more genes specific to male germ cells, impacting testicular functions (Saifl and Chandra, 1999). Additionally, the X chromosome plays a pivotal role in brain development and cognitive functions, with a six-fold increase in intelligence-related genes compared to autosomes (Graves, 2006), earning it the moniker of the "sexy and smart" chromosome (Graves et al., 2002).

Despite the comprehensive assembly of the yak genome in recent years (Qiu et al., 2012), the genetic basis of high-altitude adaptation in yak breeds remains predominantly explored through autosomal selective scans. The X chromosome, with its unique characteristics such as reduced effective population size and increased linkage disequilibrium, presents a promising avenue for understanding the genetic underpinnings of high-altitude adaptation (Villegas-Mirón et al., 2021; Shihabi et al., 2022). While several studies have identified candidate genes related to adaptation, growth, and immune response in yaks (Wang et al., 2021; Lan et al., 2021; Bao et al., 2022; Guo et al., 2024; Peng et al., 2024; Mahar et al., 2024), a comprehensive investigation of the X chromosome's role in high-altitude adaptation is lacking.

The X-chromosome has high density of genes and therefore, will be a good target for detecting signatures of selection (Zhu et al., 2015). Our study therefore aims to fill this gap by conducting a systematic analysis of the X chromosome in Indian yaks from the Trans-Himalayan region. We seek to elucidate the X chromosome's contribution to high-altitude adaptation and its impact on production, reproduction, and immunity traits. Therefore, the present investigation, identifies unique signatures of selection on the often-overlooked X chromosome in three population of Indian yak (Arunachali yak, Himachali yak, Ladakhi yak) along with Chinese yak (Jinchuan yak).

2.1 Sample collection and whole genome resequencing

Three distinct populations of Indian yaks (Arunachali yak, Himachali yak and Ladakhi yak) situated in different geographical regions and at varying elevations, were examined in this analysis. A total of 30 genetically unrelated samples were collected, with 10 sample each from 3 populations. The genomic DNA was obtained from the repository maintained by NBAGR. These samples were subsequently resequenced using the Illumina NovaSeq 6000 platform, employing 150 base pair paired-end sequencing. The library preparation was carried out using the NEBNext UltraII DNA library preparation kit designed for Illumina sequencing. This approach resulted in a sequencing depth of approximately 10×, generating a robust dataset for subsequent analyses. To facilitate comparative evaluation, the study also included an additional 8 samples of Chinese yak. The raw fastQ files for these supplementary samples were retrieved from the European Nucleotide Archive, and the accession IDs are provided in Table S1 for reference (Wang et al., 2020)

2.2 Sequence Quality Assessment, Alignment, and Variant Calling

The raw sequencing data underwent an initial quality assessment using FastQC (v0.12.0) (Andrews, 2010), which evaluated various sequence quality metrics. Trimmomatic (v0.39) was then employed to perform quality trimming and adapter removal, generating clean reads suitable for further analysis (Bolger et al., 2014). These clean reads were subsequently aligned to the yak reference genome (BosGru3.1) (GenBank GCA_005887515.3) using the Burrows-Wheeler Aligner (BWA-MEM) (v 0.7.17-r1188), accurately mapping them to their genomic positions (Li and Durbin, 2009). To refine the data, the BAM files were sorted, and duplicate reads originating from the library preparation process were identified and marked using the "MarkDuplicates" option in Picard (v1.114). Variant calling was then performed using the 'HaplotypeCaller' tool within the GATK pipeline (v4.3.0.0), generating individual genomic Variant Call Format (gVCF) files for each sample. These gVCF files were merged using the "CombineGVCFs" option, followed by joint genotyping using "GenotypeGVCFs" to consolidate variant calls across all samples. A stringent variant filtering process was implemented using GATK's "VariantFiltration" tool, applying multiple criteria to filter the variants. The statistics of the variant call format files were assessed using 'bcftools stats' to generate comprehensive statistics on the identified variants (Danecek et al., 2021). Finally, only the variants present on the X chromosome were retained, while those on autosomes, Y chromosomes, and mitochondria were removed. Separate analyses of X chromosome and autosome variants should be conducted to avoid potential biases arising from differences in effective population size (Bahbahani et al., 2015; Sukhija et al., 2024; Mahar et al., 2024 Preprint)

2.3 Genotype quality control

PLINK v1.9 software (Purcell et al., 2007) was employed for quality control of single-nucleotide polymorphisms on the X chromosomes of diverse yak populations. SNPs were retained based on the following criteria: a genotyping rate exceeding 90%, a minor allele frequency greater than 0.05, and adherence to Hardy-Weinberg Equilibrium with a p-value above 0.001. Additionally, individuals with a missing genotype rate per individual above 0.10 were excluded to eliminate samples with excessive missing data. Notably, the dataset was not pruned for linkage disequilibrium to avoid the potential removal of loci under selection. This decision was made to mitigate biases in the analysis arising from population admixture with varying gene frequencies (Falconer, 1996; Kanaka et al., 2023)

2.4 Identification of Selection Signatures on X chromosome of Yak populations

Various statistical methodologies have been developed for detecting selection signatures in livestock species (Saravanan et al., 2020)

2.4.1 Extended Haplotype Homozygosity (EHH): EHH calculates the probability that two randomly chosen chromosomes carry homozygous SNPs for a given interval, indicating extended haplotypes. This measure is used in two haplotype-based statistics, iHS and XP-EHH, which assess the EHH at a tested SNP relative to the ancestral and derived allele state. iHS, derived from EHH, is designed to identify incomplete hard sweeps (Voight et al., 2006), characteristic of recent or ongoing selection where the selected allele's frequency increases, reducing linked genetic variation. XP-EHH, developed by Sabeti et al. (2007), detects haplotype-based differentiation, with positive scores indicating selection in the test population and negative scores suggesting selection in the reference population. The genetic distance between adjacent SNPs is assumed to be 1 cM for XP-EHH normalization (Sabeti et al., 2007).

2.4.2 Site Frequency Spectrum (SFS) (Tajima’s D and Composite Likelihood Ratio): SFS-based tests, such as Tajima’s D, assess the distribution of allele frequencies within a population (Ronen et al., 2013). Tajima’s D was computed using VCFtools with a non-overlapping window-based strategy of 10 kb to detect departures from neutral evolution (Danecek et al., 2011). CLR analysis, performed using SweeD v3.3.2 software, examines asymmetry in the site-frequency spectrum of alleles across multiple loci along each chromosome to detect selective sweeps (Nielsen et al., 2005; Pavlidis et al., 2013). It compares the likelihood of a selective sweep at a specific position to a null model, identifying regions under selection (López et al., 2021)

2.4.3 FST Analysis: FST, a measure of population differentiation, was calculated across different yak populations using VCFtools. Genome regions with high-FST values, based on the distribution of weighted FST, were identified. A region was considered a high-FST outlier if its FST value fell within the upper 1% of the empirical genome-wide distribution of FST (Weir and Cockerham, 1984).

2.5 Annotation of genes for the regions under selection

The 'GALLO' package in R software was utilized to identify genes within regions under selection across all yak populations. Subsequently, gene ontology and pathway analyses were conducted using the DAVID (Database for Annotation, Visualization, and Integrated Discovery) tool (https://david.ncifcrf.gov). The genes identified by GALLO were imported into DAVID for analysis to determine significant genes and to identify important pathways. Common genes identified across different selection signature detection methods were further analyzed. A Venn diagram was created using the interactive Venn tool (http://www.interactivenn.net/) to visualize the overlap of genes identified by Tajima’s D, iHS, CLR, XP-EHH, and FST methods among the four yak populations. Additionally, an X Chromosome map of selective sweeps was constructed using MG2C (http://mg2c.iask.in mg2c_v2) (Chao et al., 2021) to highlight genes present among the four yak populations identified through various methods.

3.1 SNP calling

A total of 319,015 X chromosome variants from 36 individuals across four yak populations successfully passed the quality filter criteria, which included metrics for missing genotypes (MG), Hardy-Weinberg equilibrium (HWE), minor allele frequency (MAF) and missing individual (MIND). LD pruning was purposefully omitted since some of the statistical analysis rely on LD signals (iHS). Furthermore, implementing LD pruning on sex-chromosomes is not advisable since sex-chromosomes comprises of long blocks of haplotype due to their lack of recombination. Consequently, all subsequent analyses were conducted on the unpruned set of SNPs.

3.2 Empirical distribution of test statistics of various methods

The empirical distributions of various test statistics based on the X chromosome are demonstrated in Figures 1, 2, and 3. The analysis of the distributions for iHS was found to be consistent among 3 Indian yak populations while in Jinchuan yak, the distribution was flatter on top distinguishing it from Indian yak population. Distribution of F_ST, shows skewness towards lower F_ST value while comparing among Indian yaks, while distribution was skewed toward higher F_ST on comparison with Jinchuan yak showing distinction among Indian and Chinese yak. XP-EHH test statistics similarly revealed demarcation on comparing Indian yaks with Chinese yaks revealing haplotype-based differentiation. This comparability underscores the robustness and reliability of the detected selection signatures. The detailed visual representations in the figures highlight the distinct patterns and commonalities in the genetic makeup and evolutionary pressures experienced by these yak populations.

3.3 Identification of selective sweeps on X chromosome

A range of techniques was employed to investigate selection signatures on the X chromosomes. For the within-population analysis, three distinct methods were utilized: Tajima’s D, Composite Likelihood Ratio (CLR) and integrated Haplotype Score (iHS) as these methods represent various categories, including site frequency spectrum (SFS) and haplotype-based approaches. For the between-population analysis, Cross-Population Extended Haplotype Homozygosity (XP-EHH) and Fixation Index (F_ST) were used to assess the degree of population differentiation.

3.3.1 Tajima’s D Analysis

In our study, we focused on identifying candidate regions under positive selection using Tajima’s D. We isolated the bottom 1% of Tajima’s D values, which revealed 461 candidate regions in each yak population. Tajima’s D values below -2 were observed for all yak populations, indicating positive selection with the fixation of rare alleles. A total of 460 candidate genes were found to overlap with the selected regions. Specifically, 123 genes were identified in Arunachali yaks, 108 in Himachali yaks, 121 in Ladakhi yaks, and 108 in Jinchuan yaks. The distribution of Tajima’s D values along the X chromosome is illustrated in Figure 4. To elucidate the roles and functions of the identified genes, a comprehensive functional annotation process was conducted.

3.3.2 Composite Likelihood ratio (CLR) Analysis

Using the SweeD tool, we calculated likelihood values and annotated candidate regions with a boundary of 50 kb upstream and downstream from each region. Retaining the top 1% likelihood values, we identified 12,103, 10,118, 12,003, and 3,837 candidate regions in Arunachali yak, Himachali yak, Ladakhi yak, and Jinchuan yak, respectively. Figure 5 depicts the distribution of Composite Likelihood Ratio (CLR) values along the X chromosome of different yak populations, providing a visual representation of the selection signatures detected. Our analysis identified a total of 2,119 candidate genes overlapping with these selected regions. Specifically, 503 genes were found in Arunachali yak, 644 in Himachali yak, 376 in Ladakhi yak, and 596 in Jinchuan yak, respectively.

3.3.3 Integrated Haplotype Score (iHS) Analysis

The Selscan tool, designed for haplotype-based selection signature scanning, was employed to analyze signatures of selection using the Integrated Haplotype Score (iHS) along the X chromosome. For each SNP, the iHS score was calculated, normalized, and a threshold of |iHS| > 2 was applied. The top 1% of normalized scores greater than 2 (|iHS| > 2) were considered to enhance the accuracy of selection signature detection. Our analysis revealed 57, 167, 165, and 196 regions under selective sweep, harboring 366 candidate genes within these regions and their flanking regions. Specifically, the number of genes found in Arunachali yak, Himachali yak, Ladakhi yak, and Jinchuan yak were 53, 117, 89, and 107, respectively. The empirical distributions of iHS values across the various yak populations, as depicted in Figure 1. Moreover, we identified a list of genes that were common to the intra-population statistics (Tajima’s D, CLR, and iHS). Figure 6 presents a composite diagram illustrating the intersection of detected genes among different yak populations.

3.3.4 Fixation Index (F_ST) Analysis

The fixation index (F_ST), first proposed by Wright in 1951, is a measure that uses allele frequency fluctuations to infer genetic differentiation between populations. It quantifies the degree of genetic differentiation between populations due to factors such as selection pressure. Higher FST values indicate greater genetic differentiation and potential selection signals. In our study, we compared allele frequency differences between various yak populations (Arunachali/Himachali, Arunachali/Ladakhi, Himachali/Ladakhi, Jinchuan/Arunachali, Jinchuan/Himachali, and Jinchuan/Ladakhi) based on the X chromosome. By selecting the top 1% of empirical F_STvalues, we identified 68 regions in each pair of populations. We annotated 312 genes located in and around these regions with a flanking threshold of 50 kb. Population-wise analysis revealed the presence of 47, 18, 53, 51, 70, and 73 genes in the Arunachali/Himachali, Arunachali/Ladakhi, Arunachali/Himachali, Jinchuan/Arunachali, Jinchuan/Himachali, and Jinchuan/Ladakhi pairs, respectively.

3.3.5 Cross population extended haplotype homozygosity (XP-EHH) Analysis

XP-EHH is a method based on measuring the difference in haplotype frequency and length between two populations, effectively detecting recent and ongoing positive selection. In our study, the Selscan tool was utilized to measure haplotype-based differentiation between pairs of populations (Arunachali/Himachali, Himachali/Ladakhi, Arunachali/Ladakhi, Jinchuan/Arunachali, Jinchuan/Himachali, and Jinchuan/Ladakhi) based on the X chromosome. Regions with the top 1% of |XP-EHH| values greater than 2 were defined as undergoing selection between pairs of populations. Specifically, 175, 173, 172, 150, 150, and 153 candidate regions were detected between Arunachali/Himachali, Himachali/Ladakhi, Arunachali/Ladakhi, Jinchuan/Arunachali, Jinchuan/Himachali, and Jinchuan/Ladakhi pairs, respectively. A total of 64 genes were identified, with distributions of 4, 7, 13, 14, 13, and 13 in the Arunachali/Himachali, Himachali/Ladakhi, Arunachali/Ladakhi, Jinchuan/Arunachali, Jinchuan/Himachali, and Jinchuan/Ladakhi pairs, respectively. Moreover, a list of genes common to inter-population statistics, i.e., FST and XP-EHH, is depicted in Figure 7. An X chromosome map was constructed displaying genes and their positions across different yak populations identified using Tajima’s D, CLR, iHS, FST, and XP-EHH (Supplementary file F1, F2, F3, F4).

3.4 Functional analyses of the extracted candidate genes

For structural annotation of potential selected regions, we accessed the ENA database (https://www.ncbi.nlm.nih.gov) and utilized gene location data from the BosGru3.0 yak genome assembly. Our gene enrichment analysis covered various categories, including cellular components, molecular functions, biological processes, and KEGG pathways. Additionally, we performed a detailed functional annotation, examining biological processes, molecular functions, and cellular components using the DAVID v2022q2 gene ontology tool (https://david.ncifcrf.gov). This comprehensive approach allowed us to gain a deeper understanding of the functional roles and pathways associated with the selected genes (Table 1) (Supplementary Table S2, S3, S4, and S5).

Here we also found some of the unique X chromosome genes and loci that are identified by various statistics and population-specific, i.e., MSN, ENSBGRG00000011790, PABPC5, CCNQ in Arunachali yak, RPL36A, UBA1, ENSBGRG00000009234, ENSBGRG00000007912, ENSBGRG00000007899, ENSBGRG00000008814, SLC9A in Himachali yak, ENSBGRG00000024060, MTM1, ENSBGRG00000024700, KCND1, SUV39H1, GPR173 in Ladakhi yak, and HSD17B10, ENSBGRG00000022974, ENSBGRG00000020907, ENSBGRG00000013035, AFF2, SH2D1A, MIR222, LPAR4 in Jinchuan yak (Table 2) (Supplementary Table S2, S3, S4, and S5).

Table 1: Genes and Novel Loci Associated with Selection Signatures Identified by Tajima’s D, iHS, CLR, FST, and XP-EHH Analyses, with Gene Ontology (GO) Terms (Supplementary Data)

Methods

Genes associated with selection signature

Tajima’s D, iHS and CLR

AIFM1, APOOL, ATRX, CHST7, DACH2, DGAT2L6, DIAPH2, EIF2S3B,

ENSBGRG00000008170, ENSBGRG00000008355, ENSBGRG00000008903, ENSBGRG00000008920, ENSBGRG00000010041,

ENSBGRG00000010189, ENSBGRG00000010672, ENSBGRG00000010695, ENSBGRG00000012630, ENSBGRG00000013098,

ENSBGRG00000013114, ENSBGRG00000013130, ENSBGRG00000013264, ENSBGRG00000016546, ENSBGRG00000016563,

ENSBGRG00000016583, ENSBGRG00000019326, ENSBGRG00000019489,

ENSBGRG00000019527, ENSBGRG00000019584, ENSBGRG00000020695, ENSBGRG00000021228, ENSBGRG00000021324,

ENSBGRG00000021353, ENSBGRG00000021428, ENSBGRG00000023145, ENSBGRG00000023158, ENSBGRG00000023191,

ENSBGRG00000023217, ENSBGRG00000023464, ENSBGRG00000024623, ENSBGRG00000024765, ENSBGRG00000025313,

ENSBGRG00000025461, ENSBGRG00000025945, ENSBGRG00000026272, ENSBGRG00000026304,FAM133A,

FGF13,FMR1,FRMD7,G6PD,GPC3,GRIA3,HDX,HS6ST2,HTR2C,HUWE1,IKBKG,

IL1RAPL2,INTS6L,KIF4A,KLHL15,MAGT1,MBTPS2,Metazoa_SRP,MID2,MIR18A,MIR19B2,MIR20B,

MIR363,MIR92A2,NBDY,NKAP,NR0B1,PGK1,PGRMC1,PHEX,RAB33A,RPS6KA6,SLC9A7,SPIN4,TENM1,TRPC5,U6,ZC4H2,ZNF280C

F_ST and XP-EHH

ARHGEF6, COL4A5, ENSBGRG00000008907, ENSBGRG00000009057, ENSBGRG00000020148, ENSBGRG00000020495, ENSBGRG00000021828, ENSBGRG00000024024, ENSBGRG00000025650, GPR119, HS6ST2, MAGED1, MOSPD1, PQBP1, SLC25A14, SLC35A2, TIMM17B, WDR44

Table 2: Genes and novel loci unique to Arunachali, Himachali, Ladakhi, and Jinchuan Yak identified by various statistical methods

Yak Populations	Genes identified
Arunachali yak	MSN, ENSBGRG00000011790, PABPC5, CCNQ, ENSBGRG00000010049, ENSBGRG00000024354 ENSBGRG00000020934, ENSBGRG00000024406 ENSBGRG00000018988, LDOC1
Himachali yak	ENSBGRG00000007503, ENSBGRG00000013880, ENSBGRG00000018466, ENSBGRG00000020038, ENSBGRG00000026084, ENSBGRG00000018983, RPL36A, UBA1, ENSBGRG00000009234, ENSBGRG00000007912, ENSBGRG00000007899, ENSBGRG00000008814, SLC9A6, ENSBGRG00000013446, TAF7L, CDX4, ENSBGRG00000024296, ENSBGRG00000009915, ENSBGRG00000026379, ENSBGRG00000020074, ENSBGRG00000019979, ENSBGRG00000018671, CLDN2, ENSBGRG00000008968, TKTL1, ENSBGRG00000018162, CITED1, PABIR2, GLA, ENSBGRG00000008003, ENSBGRG00000026384, MED14, ENSBGRG00000020673, USP9X, BTK, ENSBGRG00000018982, ENSBGRG00000024248, ENSBGRG00000024024, ENSBGRG00000025438, RIPPLY1, SOX3, MAGEE2, ENSBGRG00000020495, TMEM35A, CETN2, ENSBGRG00000019681, CENPI, ENSBGRG00000026345, ENSBGRG00000018143, HAUS7, RLIM, HNRNPH2, ENSBGRG00000024259, DRP2, ENSBGRG00000008290, ENSBGRG00000018656, ENSBGRG00000023465, SNORA35B, ENSBGRG00000007883, ENSBGRG00000008800, TEX11, BGN, FUNDC1, NSDHL, ENSBGRG00000024121
Ladakhi yak	ENSBGRG00000024060, MTM1, ENSBGRG00000024700, KCND1, SUV39H1, GPR173, ENSBGRG00000024081, TSPYL2 ENSBGRG00000024069, SNORA35
Jinchuan yak	ENSBGRG00000017892, HSD17B10, ENSBGRG00000022974, ENSBGRG00000020907, ENSBGRG00000013035, AFF2, SH2D1A, MIR222, LPAR4, ENSBGRG00000012747, ENSBGRG00000008066, ENSBGRG00000012409, ENSBGRG00000007551, ENSBGRG00000009197, ENSBGRG00000007923, RIBC1, ENSBGRG00000007060, NKRF, ENSBGRG00000026097, ENSBGRG00000023969, PBDC1, SRPK3, ENSBGRG00000013049, ENSBGRG00000018938, TMEM164, ENSBGRG00000017398, ABCD1, ENSBGRG00000017469, PRDX4, PRAF2, OTUD6A, ENSBGRG00000015557, ENSBGRG00000020119, GATA1 IDH3G CCDC120, ENSBGRG00000012421, ERAS, ENSBGRG00000011954, TFE3, ENSBGRG00000009995, ENSBGRG00000017943, MIR221, CCDC22, ENSBGRG00000019586, PLP1, ENSBGRG00000025435, CACNA1F, ENSBGRG00000008034, ENSBGRG00000008253, ZIC3, ENSBGRG00000026351, ENSBGRG00000019967, ENSBGRG00000020770, ENSBGRG00000025261, SAT1, ENSBGRG00000022376, HDAC6, ENSBGRG00000009057

Locally adapted breeds and populations are invaluable reservoirs of genetic polymorphisms and selection signatures, crucial for their adaptation to climate change (Groeneveld et al., 2010). These populations exhibit unique adaptive traits, making them prime candidates for conservation efforts aimed at preserving genetic diversity and resilience (Weldenegodguad et al., 2019; Goli et al., 2024). While previous studies have focused on identifying genetic variants and selection signatures in Indian yak populations, most have concentrated on autosomal regions (Sivalingam et al., 2020; Kour et al., 2022, Kumar et al., 2024, Mahar et al., 2024). Our research, is first of its kind attempt to unravel selection signatures on X-chromosome utilizing whole genome sequence data on the least explored bovine species with 10X coverage. This improvement substantially enhances our ability to identify high-quality SNPs and a greater number of positive selection signatures. By leveraging these advanced datasets, our study provides deeper insights into the genetic adaptations that enable yaks to thrive in high-altitude environments. Our findings not only contribute to the field of evolutionary biology but also have practical implications for the conservation and breeding of yak populations, ensuring their continued resilience in the face of changing environmental conditions. Our focus on uncovering selection hotspots on the X chromosome of Indian yaks, particularly in Arunachali, Himachali, and Ladakhi populations, adds a new dimension to the study of high-altitude adaptation in these animals. Previous research in animal domestication and breeding has identified selective scans on the X chromosome, shedding light on regions or genes likely involved in the domestication processes of other species such as pigs, horses, and sheep (Ma et al., 2014; Liu et al., 2018; Zhu et al., 2020; Shihabi et al., 2022). However, our study breaks new ground by conducting the first comprehensive X-chromosome-wide scans to investigate the genetic basis of high-altitude adaptation in Indian yaks.

4.1 SNP calling

The SNP calling process in our study involved stringent quality control measures, resulting in a total of 319,015 X-chromosome SNPs from 36 animals across four yak populations. These SNPs were filtered based on parameters such as Hardy-Weinberg Equilibrium (HWE), Minor Allele Frequency (MAF), and Missing Genotype (MG) criteria. Notably, two samples from the Jinchuan yak population were excluded due to missing individual parameters.

There is ongoing debate regarding the exclusion of variants under selection by the HWE test, potentially affecting the detection of significant genetic signals (Abramovs et al., 2020). However, in our study, we employed this test as part of our filtering strategy to enhance the accuracy of SNP identification. LD pruning of the X chromosome was also omitted because of X chromosome's low recombination rate and the formation of large LD blocks. This phenomenon is consistent with studies in other species, such as pigs (Tong et al., 2020). This decision retained several potentially informative variants for our analysis.

4.2 Detection of selection signatures on X chromosome of Indian yaks

Detecting selection signatures on the X chromosome of Indian yaks required a comprehensive and integrated approach, given the complex genetic landscape of adaptation in these populations. In line with recommendations from previous studies (Utsunomiya et al., 2015), we employed a combination of five different methods: Tajima’s D, iHS, CLR, XP-EHH, and FST. This approach, similar to studies on the X chromosome of pigs (Ma et al., 2014), allowed us to capture a broad spectrum of selection signatures within and between yak populations. By integrating these complementary methods, our research aimed to provide a robust and thorough analysis of selection signatures, thereby enhancing the reliability and validity of our findings. This multi-faceted approach will uncover unique signatures of selection that contribute to the unique characteristics of different Indian yak populations. Through systematic analysis, we identified regions under selection and subsequently annotated these regions to elucidate the genes responsible for key adaptive traits such as adaptation to high-altitude environments, reproduction, and immunity.

4.3 Overlapping signatures with adaptation related genes on X chromosome of yak

The identification of overlapping selection signatures with adaptation-related genes on the X chromosome of Indian yaks sheds light on the genetic basis of their resilience and adaptation to high-altitude environments. Among these genes, KCND1, uniquely found in Ladakhi yaks, is known to regulate heart rate and smooth muscle contraction, potentially contributing to their adaptation to the extreme altitudes. Additionally, the presence of GPR173, which regulates G protein-coupled receptors (GPCRs) associated with various sensory and immune system functions, underscores the importance of sensory perception and immune response in high-altitude survival (Groeneveld et al., 2010; Ma et al., 2014). At high altitudes, the importance of vision, taste, smell, behavior, and mood becomes paramount for survival and adaptation. Vision is essential for navigating the rugged and often treacherous terrain, allowing animals to find food and avoid predators. Taste and smell play crucial roles in identifying edible plants and detecting environmental hazards (Kour et al., 2022). In Ladakhi yaks, genes such as TSPYL2 and SUV39H1 are integral for maintaining genomic stability and DNA repair mechanisms, crucial for coping with genotoxic stress induced by environmental factors prevalent at high altitudes, such as increased UV radiation and hypoxia (Peters et al., 2001; Tao et al., 2011; Cardano et al., 2023). Similarly, the presence of the FUNDC1 gene in Himachali yaks suggests their adaptation to hypoxic conditions (Wang et al., 2021), while the identification of ZIC3, MIR221, and MIR222 genes in Jinchuan yaks indicates their involvement in cardioprotection mechanisms and heart health under hypoxic stress (Paulussen et al., 2016; Li et al., 2018; Zhou et al., 2019). Furthermore, several genes shared among yak populations, including DGKK, ABCB7, AMOT, and LHFPL1, are known to be associated with oxidative stress response and high-altitude adaptation in various species, including humans, Tibetan mastiffs, and Tibetan chickens (Shihabi et al., 2022; Witt and Huertta, 2019; Wu et al., 2016; Liu et al., 2020). The presence of these genes highlights the resilience of yak populations to harsh cold climates, enabling them to thrive on the highest plateau on Earth.

4.4 Overlapping signatures with production related genes on X chromosome of yak

The identification of overlapping selection signatures with production-related genes on the X chromosome of Indian yaks provides insights into the genetic basis of their milk production traits. Among these genes, MBTPS2 and DGAT associated with quantitative trait loci (QTLs) related to milk fatty acid profiles and milk fat synthesis respectively earlier reported in Goats, Maiwa yak and Brown Swiss cattle (Grisart et al., 2002; Mohamed et al., 2023; Rajawat et al., 2024; Xia et al., 2023), was found in Arunachali, Himachali, and Jinchuan yaks. This gene's presence suggests its potential role in regulating milk fat content, which is higher in yaks compared to cattle and rich in long-chain unsaturated fatty acids (PUFA) (Guo et al., 2014). Similarly, the presence of the APLN gene, associated with milk production in Nellore cattle, was observed in all three Indian yak populations, indicating its potential involvement in regulating milk production traits (Rajawat et al., 2024). Furthermore, several functional candidate genes were identified across all four yak populations, including GPC3, MBNL3, and HS6ST genes. Additionally, Ladakhi and Jinchuan yaks exhibited the presence of the DMD gene, associated with QTLs for milk production and composition traits, suggesting its role in regulating milk-related phenotypes in these populations (Sanchez et al., 2023). Other important genes associated with udder health, such as TMEM164, ACSL4, ENOX2, HTR2C, and AMOT, were also identified, highlighting their potential contribution to milk production traits in Indian yaks.

4.5 Overlapping signatures with reproduction related genes on X chromosome of yak

The identification of overlapping selection signatures with reproduction-related genes on the X chromosome of Indian yaks provides insights into the genetic basis of their reproductive traits. In Arunachali, Ladakhi, and Jinchuan yaks, several genes associated with reproduction were identified, including AR (Androgen receptor), SOX3 (SRY related HMG box gene 3), FATE (fetal and adult expressed), and AKAP4 (A-kinase anchor protein 82). The AR gene, which is well-studied, is known to play a role in reproductive ability, with mutations leading to a range of phenotypes affecting fertility (Stouffs et al., 2009). AKAP4 is crucial for sperm motility as it provides structural components to the sperm flagellum (Blommaert et al., 2019), while SOX3, a homolog of the SRY gene, is essential for normal reproductive function, as evidenced by reduced fertility in mice models with hemizygous SOX3 mutations (Weiss et al., 2003; Stouffs et al., 2009). Additionally, Himachali yaks harbored the TAF7L (TATA box binding protein (TBP)-associated factor 7 like) and TEX11 (testis-expressed gene 11) genes on their X chromosome. TAF7L gene knockout in mice resulted in reduced sperm production and abnormal sperm morphology and motility (Cheng et al., 2007), while TEX11 controls spermatocyte meiotic division, crucial for male fertility (Boroujeni et al., 2018). Furthermore, genes such as AKAP4, COL4A6, PGRMC1, RGN, and HTR2C were shared among all yak populations, suggesting their essential role in reproduction. Additionally, the presence of CCNB3 (cyclin B3) gene, crucial for mice spermatogenesis, and DACH2 (Dachshund Family Transcription Factor 2) gene, associated with organogenesis, myogenesis control, and early ovarian failure, highlights the complexity and importance of these genetic factors in yak reproduction (Karasu and Keeney, 2019; Shihabi et al., 2022).

4.6 Overlapping signatures with immunity related genes on X chromosome of yak

The X chromosome of Indian yaks exhibits a variety of immunity-related genes, highlighting the importance of these genes in their adaptation to environmental challenges. Arunachali yak possesses unique genes involved in immune responses, including MSN (moesin), CCNQ (cyclin Q), and LDOC1 (LDOC1 regulator of NFKB signaling). The MSN gene codes for the moesin protein, crucial for immune function, and variations in this gene can lead to X-linked recessive primary immunodeficiency (Kovács et al., 2022). LDOC1 regulates NF-κB signaling, a key pathway in innate and adaptive immunity, while mutations in CCNQ can cause syndactyly and telecanthus (Liu et al., 2017; Che et al., 2023). In Himachali yak, the BTK gene (Bruton tyrosine kinase), essential for B-cell maturation and development, was identified. This gene is critical for adaptive immunity (Chear et al., 2023). Ladakhi yak harbors the MTM1 (Myotubularin) gene, involved in muscle cell development and maintenance. Mutations in MTM1 can lead to X-linked myotubular myopathy, impacting muscle function (Laporte et al., 1997). Common immunity-related genes across all yak populations include TRPC5 (transient receptor potential cation channel subfamily C member 5), which fights against cardiovascular disease, CYBB (cytochrome b-245 beta chain), part of the NADPH oxidase enzyme complex crucial for the immune system, and GATA1 (GATA binding factor 1), governing the development of hematopoietic progenitor cells and enhancing immune response (Ma et al., 2014; Hu et al., 2009; Bianchi et al., 2012; Hwang et al., 2022). Additionally, WAS (WASP actin nucleation promoting factor) gene, responsible for signaling between blood cell surface and the actin cytoskeleton in white blood cells, plays a vital role in immune response (Jaillon et al., 2019). Furthermore, candidate genes like ATP6AP2, CD40LG, CITED1, CXCR3, FMR1, and SH2D1A, previously reported in cattle breeds, were found in this study, highlighting their role in immune response and disease resistance (Rajawat et al., 2024). Genes such as MAGT1, ATRX, and FGF16, identified in sheep populations, are also involved in immunity (Blommaert et al., 2019; Shihabi et al., 2022).

4.7 Overlapping signatures with dosage compensation related genes on X chromosome of yak

X chromosome inactivation (XCI) is a vital process for dosage compensation in mammals, ensuring equal expression of X-linked genes between males and females (Lyon, 1961). In our study, we identified the RLIM (also known as Rnf12) gene in Himachali yak, which is involved in the random inactivation of the X chromosome. This gene has also been found in Gir and Nellore cattle breeds under selective sweep, indicating its importance in dosage compensation (Rajawat et al., 2024). RLIM/Rnf12, initially identified as a transcriptional cofactor associated with LIM domain transcription factors, is now recognized as a key regulator of XCI and is conserved across species, mapping to the X chromosome (Ostendorff et al., 2000).

Our findings suggest that the X chromosome, similar to autosomes, plays a significant role in the adaptation of Indian yaks (Arunachali, Himachali, and Ladakhi) to the hypoxic conditions at high altitudes. Additionally, the X chromosome is crucial for traits related to immunity, reproduction, and production, as it contains several crucial genes associated with these functions. This underscores the importance of considering the X chromosome in future studies of adaptive evolution.

Ethics approval:

All applicable international, national, and/or institutional guidelines for the care and use of animals were followed.

Consent for publication:

The manuscript has consent of all the authors for its publication in current format.

Conflict of interest:

The authors declare that there is no conflict of interest.

Funding:

This study was not supported by any external grants.

Acknowledgment:

The authors thank the Director, ICAR-National Bureau of Animal Genetic Resources, Karnal, India for providing necessary facilities and support to carry out the work.

Data Availability:

Data has been submitted to the SRA database NCBI.

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Genomic insight into X chromosome dynamics in high altitude adaptation of trans-Himalayan yaks

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Abstract

Figures

1. Introduction

2. Material and Methods

2.1 Sample collection and whole genome resequencing

2.2 Sequence Quality Assessment, Alignment, and Variant Calling

2.3 Genotype quality control

2.4 Identification of Selection Signatures on X chromosome of Yak populations

2.5 Annotation of genes for the regions under selection

3. Results

4. Discussion

4.1 SNP calling

4.2 Detection of selection signatures on X chromosome of Indian yaks

4.3 Overlapping signatures with adaptation related genes on X chromosome of yak

4.4 Overlapping signatures with production related genes on X chromosome of yak

4.5 Overlapping signatures with reproduction related genes on X chromosome of yak

4.6 Overlapping signatures with immunity related genes on X chromosome of yak

4.7 Overlapping signatures with dosage compensation related genes on X chromosome of yak

5 Conclusion

Declarations

Funding:

Acknowledgment:

Data Availability:

References

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