Data summary<\/p>\n
We collected 386 blood samples with matched genotype and gene expression data from the PigGTEx project [9<\/a>]. The details about these blood samples were provided in Table S1<\/a>. We predicted the missing sex information using MetaPred (https:\/\/github.com\/FarmGTEx\/metadata-prediction-v1<\/a>) based on gene expression data. For the prediction of sex information, we utilized the gene expression data (transcripts per million, TPM) derived from PigGTEx project and utilized the 20-fold\u2009\u00d7\u20095-repeat times to obtain the prediction probability of sex information for each sample. Then, we retained the samples with the prediction probability of sex information higher than 95% for downstream analysis. For the genotype data, the low-density single nucleotide polymorphisms (SNPs) were called from the RNA-seq data using GATK (v4.0.8.1) [22<\/a>]. After that, the low-density genotypes were imputed to high-density genotypes using Pig Genomics Reference Panel by Beagle (v5.1) [23<\/a>]. After quality control, 2,833,585 common bi-allele SNPs (minor allele frequency (MAF)\u2009>\u20090.05) across samples were maintained for further analysis. The details about the processing of genotype data could be found in the PigGTEx project [9<\/a>]. After quality control, 384 samples with known sex information (including 156 males and 228 females) were kept for the downstream analysis.<\/p>\n Differentially expressed genes analysis<\/p>\n Low expression gene (mean read counts\u200924]. Next, we yielded the surrogate variables (confounding factors) of normalized gene expression data except the sex using smartSVA R package (v 0.1.3) [25<\/a>]. The procedure about the adjustment of confounding factors was as follow. First, we utilized the num.sv() function grouped by sex information to calculate the number of surrogate variables (including 2 surrogate variables). Next, we utilized the smartsva.cpp(n.sv\u2009=\u20092) function to compute the surrogate variables. After that, we adjusted the surrogate variables for normalized gene expression data using removeBatchEffect() function from limma R package (v3.60.6) [26<\/a>]. We leveraged the adjusted gene expression data to perform differentially expressed genes analysis using Wilcox test method. Finally, we set the false discovery rate (FDR)\u20092(fold change)|>\u20091.5 as the threshold to obtain the sex-biased genes.<\/p>\n Sex-combined and sex-stratified cis-h2<\/p>\n To explore the similarity and difference of genetic variance of gene expression between male and female, we estimated the sex-combined and sex-stratified cis-h2 of gene expression in blood using OmiGA (v1.0.4.250515_beta2) [27<\/a>]. For the processing of gene expression data, we first maintained the genes located on autosome. Next, we removed the genes with TPM\u200928] using edgeR R package, and then performed inversed normal transformation of the TMM. To estimate sex-combined cis-h2, we utilized the entire dataset to estimate cis-h2. To estimate sex-stratified cis-h2, we separated the genotypes and gene expression data into male and female-stratified dataset to estimate male-stratified and female-stratified cis-h2, respectively. The statistical model was as follows:<\/p>\n $${\\varvec{y}}={\\varvec{X}}{\\varvec{b}}+{{\\varvec{g}}}_{{\\varvec{a}}{\\varvec{c}}}+{\\varvec{e}}$$<\/p>\n where \\({\\varvec{y}}\\) is a vector of normalized expression for each gene, \\({\\varvec{b}}\\) is covariates including the phenotype and genotype principal components (PCs) estimated by OmiGA, \\({{\\varvec{g}}}_{{\\varvec{a}}{\\varvec{c}}}\\sim {\\varvec{N}}\\left(0,{{\\varvec{G}}}_{{\\varvec{a}}{\\varvec{c}}}{{\\varvec{\\upsigma}}}_{{\\varvec{a}}{\\varvec{c}}}^{2}\\right)\\) is the cis-additive genetic effect, \\({{\\varvec{\\upsigma}}}_{{\\varvec{a}}{\\varvec{c}}}^{2}\\) is the cis-additive genetic variance, \\({{\\varvec{G}}}_{{\\varvec{a}}{\\varvec{c}}}\\) is additive cis-genomic relationship matrix (cis-GRM) constructed by genotypes located in cis-region that around 1\u00a0Mb of the transcript start site (TSS) of gene. \\({\\varvec{e}}\\sim {\\varvec{N}}\\left(0,{\\varvec{I}}{{\\varvec{\\sigma}}}_{{\\varvec{e}}}^{2}\\right)\\) is the residual effect, \\({{\\varvec{\\sigma}}}_{{\\varvec{e}}}^{2}\\) is the residual variance. \\({\\varvec{X}}\\) is the designed matrix for \\({\\varvec{b}}\\).<\/p>\n For the phenotype and genotype PCs, we considered the PC with parameter \u201c\u2013dprop-pc-covar 0.001\u201d in OmiGA. We used the parameter \u201c\u2013h2-model Ac\u201d for estimating the sex-combined and sex-stratified cis-h2.<\/p>\n Sex-combined and sex-stratified cis-eQTL mapping<\/p>\n To investigate the genetic effect of gene expression in blood, we performed cis-eQTL mapping using OmiGA. For the combined cis-eQTL mapping, we utilized the whole dataset to perform cis-eQTL mapping. for the sex-stratified cis-eQTL mapping, we separated the genotypes and gene expression data into male and female-stratified dataset to conduct male-stratified and female-stratified cis-eQTL mapping, respectively. The statistical model was as follows:<\/p>\n $${\\varvec{y}}={\\varvec{X}}{\\varvec{b}}+{{\\varvec{s}}}_{{\\varvec{g}}}{{\\varvec{\\beta}}}_{{\\varvec{g}}}+{{\\varvec{g}}}_{{\\varvec{a}}}+{\\varvec{e}}$$<\/p>\n where \\({{\\varvec{\\beta}}}_{{\\varvec{g}}}\\) is the genetic effect of SNPs. \\({{\\varvec{s}}}_{{\\varvec{g}}}\\) is the vector of genotypes coding as 0, 1 and 2. \\({{\\varvec{g}}}_{{\\varvec{a}}}\\sim {\\varvec{N}}\\left(0,{{\\varvec{G}}}_{{\\varvec{a}}}{{\\varvec{\\upsigma}}}_{{\\varvec{a}}}^{2}\\right)\\) is the additive genetic effect. \\({{\\varvec{\\upsigma}}}_{{\\varvec{a}}}^{2}\\) is the additive genetic variance. \\({{\\varvec{G}}}_{{\\varvec{a}}}\\) is the additive GRM constructed by genotypes. The other variables are the same as statistical model of cis-h2.<\/p>\n Sex-interaction cis-eQTL mapping<\/p>\n We performed the sex-interaction cis-eQTL mapping using OmiGA. The statistical models included linear model (LM) and linear mixed model (LMM). The LMM for the sex-interaction cis-eQTL mapping was as follows:<\/p>\n $$\\begin{aligned}{\\varvec{y}}=&{\\varvec{X}}{\\varvec{b}}+{{\\varvec{s}}}_{{\\varvec{g}}}{{\\varvec{\\beta}}}_{{\\varvec{g}}}+{{\\varvec{s}}}_{{\\varvec{g}}}\\times {\\varvec{s}}{\\varvec{e}}{\\varvec{x}}\\boldsymbol{*}{{\\varvec{\\beta}}}_{{\\varvec{g}}\\times {\\varvec{s}}{\\varvec{e}}{\\varvec{x}}}\\\\&+{\\varvec{s}}{\\varvec{e}}{\\varvec{x}}\\boldsymbol{*}{{\\varvec{\\beta}}}_{{\\varvec{s}}{\\varvec{e}}{\\varvec{x}}}+{{\\varvec{g}}}_{{\\varvec{a}}}+{\\varvec{e}}\\end{aligned}$$<\/p>\n where \\({\\varvec{s}}{\\varvec{e}}{\\varvec{x}}\\) is the vector of sex. \\({{\\varvec{\\beta}}}_{{\\varvec{g}}\\times {\\varvec{s}}{\\varvec{e}}{\\varvec{x}}}\\) is the effect of sex-genotype interaction. \\({{\\varvec{\\beta}}}_{{\\varvec{s}}{\\varvec{e}}{\\varvec{x}}}\\) is the effect of sex. The other variables are the same as statistical model of cis-eQTL mapping.<\/p>\n To investigate the differences and similarities between LM and LMM, we also utilized the linear model to perform sex-interaction cis-eQTL mapping. The LM was as follow:<\/p>\n $$\\begin{aligned}{\\varvec{y}}=&{\\varvec{X}}{\\varvec{b}}+{{\\varvec{s}}}_{{\\varvec{g}}}{{\\varvec{\\beta}}}_{{\\varvec{g}}}+{{\\varvec{s}}}_{{\\varvec{g}}}\\times {\\varvec{s}}{\\varvec{e}}{\\varvec{x}}\\boldsymbol{*}{{\\varvec{\\beta}}}_{{\\varvec{g}}\\times {\\varvec{s}}{\\varvec{e}}{\\varvec{x}}}\\\\&+{\\varvec{s}}{\\varvec{e}}{\\varvec{x}}\\boldsymbol{*}{{\\varvec{\\beta}}}_{{\\varvec{s}}{\\varvec{e}}{\\varvec{x}}}+{\\varvec{e}}\\end{aligned}$$<\/p>\n For the linear model, we removed the component of additive genetic effect \\({{\\varvec{g}}}_{{\\varvec{a}}}\\) in the statistical model to perform the sex-interaction cis-eQTL mapping.<\/p>\n We performed aggregated Cauchy association test (ACAT) [29<\/a>] method to calculate the q-value (false discovery rate, FDR), and set the FDR\u2009cis-eQTL mapping, we classified genes into four categories, including male-specific genes (significant only in males), female-specific genes (significant only in females), shared genes (significant in both sexes,”Both”), and non-significant genes (not significant in either sex,”Neither”).<\/p>\n Phenome-wide association study (PheWAS) in pig and human<\/p>\n To investigate the genetic connection between sb-eQTL\/eGenes and complex traits, we first performed PheWAS analysis using PigBiobank [30<\/a>]. We utilized the top sb-eQTL of sb-eGenes to obtain the association of GWAS meta-analysis across 25 complex traits in pigs (Table S2). The detailed information of GWAS meta-analysis could be found in the previous study [1<\/a>]. In addition, we also performed the PheWAS of sb-eGenes based on ortholog genes derived from BioMart in human complex traits using GWAS atlas [31<\/a>] (https:\/\/atlas.ctglab.nl\/PheWAS<\/a>). We considered the Bonferroni correction (P\u2009=\u20090.05\/number of traits) as the significant threshold.<\/p>\n","protected":false},"excerpt":{"rendered":"Data summary We collected 386 blood samples with matched genotype and gene expression data from the PigGTEx project…\n","protected":false},"author":2,"featured_media":111196,"comment_status":"","ping_status":"","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[74],"tags":[2569,90,18,69112,7341,910,19,17,3544,9693,6720,69113,6719,3549,69111,82],"class_list":{"0":"post-111195","1":"post","2":"type-post","3":"status-publish","4":"format-standard","5":"has-post-thumbnail","7":"category-technology","8":"tag-animal-genetics-and-genomics","9":"tag-blood","10":"tag-eire","11":"tag-expression-quantitative-trait-locus","12":"tag-gene-expression","13":"tag-general","14":"tag-ie","15":"tag-ireland","16":"tag-life-sciences","17":"tag-microarrays","18":"tag-microbial-genetics-and-genomics","19":"tag-pig","20":"tag-plant-genetics-and-genomics","21":"tag-proteomics","22":"tag-sex-biased-gene","23":"tag-technology"},"share_on_mastodon":{"url":"","error":""},"_links":{"self":[{"href":"https:\/\/www.europesays.com\/ie\/wp-json\/wp\/v2\/posts\/111195","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.europesays.com\/ie\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/www.europesays.com\/ie\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/www.europesays.com\/ie\/wp-json\/wp\/v2\/users\/2"}],"replies":[{"embeddable":true,"href":"https:\/\/www.europesays.com\/ie\/wp-json\/wp\/v2\/comments?post=111195"}],"version-history":[{"count":0,"href":"https:\/\/www.europesays.com\/ie\/wp-json\/wp\/v2\/posts\/111195\/revisions"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/www.europesays.com\/ie\/wp-json\/wp\/v2\/media\/111196"}],"wp:attachment":[{"href":"https:\/\/www.europesays.com\/ie\/wp-json\/wp\/v2\/media?parent=111195"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.europesays.com\/ie\/wp-json\/wp\/v2\/categories?post=111195"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.europesays.com\/ie\/wp-json\/wp\/v2\/tags?post=111195"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}