Expression Quantitative Trait Loci Mapping Research Articles

Response splicing QTLs in primary human chondrocytes identifies putative osteoarthritis risk genes.

Osteoarthritis affects millions worldwide, yet effective treatments remain elusive due to poorly understood molecular mechanisms. While genome-wide association studies (GWAS) have identified over 100 OA-associated loci, identifying the genes impacted at each locus remains challenging. Several studies have mapped expression quantitative trait loci (eQTL) in chondrocytes and colocalized them with OA GWAS variants to identify putative OA risk genes; however, the degree to which genetic variants influence OA risk via alternative splicing has not been explored. We investigated the role of alternative splicing in OA pathogenesis using RNA-seq data from 101 human chondrocyte samples treated with PBS (control) or fibronectin fragment (FN-f), an OA trigger. We identified 590 differentially spliced genes between conditions, with FN-f inducing splicing events similar to those in primary OA tissue. We used CRISPR/Cas9 to mimic an SNRNP70 splicing event observed in OA and FN-f-treated chondrocytes and found that it induced an OA-like expression pattern. Integration with genotyping data revealed 7,188 splicing quantitative trait loci (sQTL) affecting 3,056 genes. While many sQTLs were shared, we identified 738 and 343 condition-specific sQTLs for control and FN-f, respectively. We identified 15 RNA binding proteins whose binding sites were enriched at sQTL splice junctions and found that expression of those RNA binding proteins correlated with exon inclusion. Colocalization with OA GWAS identified 6 putative risk genes, including a novel candidate, PBRM1. Our study highlights the significant impact of alternative splicing in OA and provides potential therapeutic targets for future research.

Evaluating the causal effect of circulating proteome on the glycemic traits: Evidence from Mendelian randomization.

Exploring the mechanisms underlying abnormal glycemic traits is important for deciphering type 2 diabetes and characterizing novel drug targets. This study aimed to decipher the causal associations of circulating proteins with fasting glucose (FG), 2-h glucose after an oral glucose challenge (2hGlu), fasting insulin (FI), and glycated hemoglobin (HbA1c) using large-scale proteome-wide Mendelian randomization (MR) analyses. Genetic data on plasma proteomes were obtained from ten proteomic genome-wide association studies (GWAS). Both cis- and cis+trans-protein quantitative trait loci (pQTLs) MR analyses were conducted. Bayesian colocalization, Steiger filtering analysis, assessment of protein-altering variants, and mapping expression quantitative trait loci to protein quantitative trait loci were performed to investigate the reliability of the MR findings. Protein-protein interaction, pathway enrichment analysis, and evaluation of drug targets were performed. Thirty-three proteins were identified with causal effects on FG, FI, or HbA1c but not 2hGlu in the cis-pQTLs analysis, and 93 proteins had causal effects on glycemic traits in the cis+trans-pQTLs analysis. Most proteins were either considered druggable or drug targets. In conclusion, many novel circulating protein biomarkers were identified to be causally associated with glycemic traits. These biomarkers enhance the understanding of molecular etiology and provide insights into the screening, monitoring, and treatment of diabetes.

SNP-Based and Kmer-Based eQTL Analysis Using Transcriptome Data.

Traditional expression quantitative trait locus (eQTL) mapping associates single nucleotide polymorphisms (SNPs) with gene expression, where the SNPs are derived from large-scale whole-genome sequencing (WGS) data or transcriptome data. While WGS provides a high SNP density, it also incurs substantial sequencing costs. In contrast, RNA-seq data, which are more accessible and less expensive, can simultaneously yield gene expressions and SNPs. Thus, eQTL analysis based on RNA-seq offers significant potential applications. Two primary strategies were employed for eQTL in this study. The first involved analyzing expression levels in relation to variant sites detected between populations from RNA-seq data. The second approach utilized kmers, which are sequences of length k derived from RNA-seq reads, to represent variant sites and associated these kmer genotypes with gene expression. We discovered 87 significant association signals involving eGene on the basis of the SNP-based eQTL analysis. These genes include DYNLT1, NMNAT1, and MRLC2, which are closely related to neurological functions such as motor coordination and homeostasis, play a role in cellular energy metabolism, and function in regulating calcium-dependent signaling in muscle contraction, respectively. This study compared the results obtained from eQTL mapping using RNA-seq identified SNPs and gene expression with those derived from kmers. We found that the vast majority (23/30) of the association signals overlapping the two methods could be verified by haplotype block analysis. This comparison elucidates the strengths and limitations of each method, providing insights into their relative efficacy for eQTL identification.

Application of a deep-learning marker for morbidity and mortality prediction derived from retinal photographs: a cohort development and validation study

Biological ageing markers are useful to risk stratify morbidity and mortality more precisely than chronological age. In this study, we aimed to develop a novel deep-learning-based biological ageing marker (referred to as RetiPhenoAge hereafter) using retinal images and PhenoAge, a composite biomarker of phenotypic age. We used retinal photographs from the UK Biobank dataset to train a deep-learning algorithm to predict the composite score of PhenoAge. We used a deep convolutional neural network architecture with multiple layers to develop our deep-learning-based biological ageing marker, as RetiPhenoAge, with the aim of identifying patterns and features in the retina associated with variations of blood biomarkers related to renal, immune, liver functions, inflammation, and energy metabolism, and chronological age. We determined the performance of this biological ageing marker for the prediction of morbidity (cardiovascular disease and cancer events) and mortality (all-cause, cardiovascular disease, and cancer) in three independent cohorts (UK Biobank, the Singapore Epidemiology of Eye Diseases [SEED], and the Age-Related Eye Disease Study [AREDS] from the USA). We also compared the performance of RetiPhenoAge with two other known ageing biomarkers (hand grip strength and adjusted leukocyte telomere length) and one lifestyle factor (physical activity) for risk stratification of mortality and morbidity. We explored the underlying biology of RetiPhenoAge by assessing its associations with different systemic characteristics (eg, diabetes or hypertension) and blood metabolite levels. We also did a genome-wide association study to identify genetic variants associated with RetiPhenoAge, followed by expression quantitative trait loci mapping, a gene-based analysis, and a gene-set analysis. Cox proportional hazards models were used to estimate the hazard ratios (HRs) and corresponding 95% CIs for the associations between RetiPhenoAge and the different morbidity and mortality outcomes. Retinal photographs for 34 061UK Biobank participants were used to train the model, and data for 9429participants from the SEED cohort and for 3986participants from the AREDS cohort were included in the study. RetiPhenoAge was associated with all-cause mortality (HR 1·92 [95% CI 1·42-2·61]), cardiovascular disease mortality (1·97 [1·02-3·82]), cancer mortality (2·07 [1·29-3·33]), and cardiovascular disease events (1·70[1·17-2·47]), independent of PhenoAge and other possible confounders. Similar findings were found in the two independent cohorts (HR 1·67 [1·21-2·31] for cardiovascular disease mortality in SEED and 2·07 [1·10-3·92] in AREDS). RetiPhenoAge had stronger associations with mortality and morbidity than did hand grip strength, telomere length, and physical activity. We identified two genetic variants that were significantly associated with RetiPhenoAge (single nucleotide polymorphisms rs3791224 and rs8001273), and were linked to expression quantitative trait locis in various tissues, including the heart, kidneys, and the brain. Our new deep-learning-derived biological ageing marker is a robust predictor of mortality and morbidity outcomes and could be used as a novel non-invasive method to measure ageing. Singapore National Medical Research Council and Agency for Science, Technology and Research, Singapore.

Expression quantitative trait locus mapping of extracellular microRNAs in human plasma

MicroRNAs, crucial in regulating protein-coding gene expression, are implicated in various diseases. We performed a genome-wide association study of plasma miRNAs (ex-miRNAs) in 3,743 Framingham Heart Study (FHS) participants and identified 1,027 cis-ex-miRNA-eQTLs (cis-exQTLs) for 37 ex-miRNAs, with 55% replication in an independent study. Colocalization analyses suggested potential genetic coregulation of ex-miRNAs with whole blood mRNAs. Mendelian randomization indicated 29 ex-miRNAs potentially influencing 35 traits. Notably, the chromosome 14q23 and 14q32 miRNA clusters emerged as the top signal, contributing over 50% of the significant cis-exQTL results, and were associated with a diverse range of traits including platelet count. Correlations of 10 ex-miRNAs (such as miR-376c-3p) in 14q32 with platelet count and volume were confirmed in FHS participants. These findings shed light on the genetic basis of ex-miRNA expression and their involvement in complex traits.

Unraveling the molecular mechanisms of Ace2-mediated post-COVID-19 cognitive dysfunction through systems genetics approach

The dysregulation of Angiotensin-converting enzyme 2 (ACE2) in central nervous system is believed associates with COVID-19 induced cognitive dysfunction. However, the detailed mechanism remains largely unknown. In this study, we performed a comprehensive system genetics analysis on hippocampal ACE2 based on BXD mice panel. Expression quantitative trait loci (eQTLs) mapping showed that Ace2 was strongly trans-regulated, and the elevation of Ace2 expression level was significantly correlated with impaired cognitive functions. Further Gene co-expression analysis showed that Ace2 may be correlated with the membrane proteins in Calcium signaling pathway. Further, qRT-PCR confirmed that SARS-CoV-2 spike S1 protein upregulated ACE2 expression together with eight membrane proteins in Calcium Signaling pathway. Moreover, such elevation can be attenuated by recombinant ACE2. Collectively, our findings revealed a potential mechanism of Ace2 in cognitive dysfunction, which could be beneficial for COVID–19–induced cognitive dysfunction prevention and potential treatment.

Rare variations within the serine/arginine-rich splicing factor PtoRSZ21 modulate stomatal size to determine drought tolerance in Populus.

Rare variants contribute significantly to the 'missing heritability' of quantitative traits. The genome-wide characteristics of rare variants and their roles in environmental adaptation of woody plants remain unexplored. Utilizing genome-wide rare variant association study (RVAS), expression quantitative trait loci (eQTL) mapping, genetic transformation, and molecular experiments, we explored the impact of rare variants on stomatal morphology and drought adaptation in Populus. Through comparative analysis of five world-wide Populus species, we observed the influence of mutational bias and adaptive selection on the distribution of rare variants. RVAS identified 75 candidate genes correlated with stomatal size (SS)/stomatal density (SD), and a rare haplotype in the promoter of serine/arginine-rich splicing factor PtoRSZ21 emerged as the foremost association signal governing SS. As a positive regulator of drought tolerance, PtoRSZ21 can recruit the core splicing factor PtoU1-70K to regulate alternative splicing (AS) of PtoATG2b (autophagy-related 2). The rare haplotype PtoRSZ21hap2 weakens binding affinity to PtoMYB61, consequently affecting PtoRSZ21 expression and SS, ultimately resulting in differential distribution of Populus accessions in arid and humid climates. This study enhances the understanding of regulatory mechanisms that underlie AS induced by rare variants and might provide targets for drought-tolerant varieties breeding in Populus.

Genetic variability of aquaporin expression in maize: From eQTLs to a MITE insertion regulating PIP2;5 expression.

Plant aquaporins are involved in numerous physiological processes, such as cellular homeostasis, tissue hydraulics, transpiration, and nutrient supply, and are key players of the response to environmental cues. While varying expression patterns of aquaporin genes have been described across organs, developmental stages, and stress conditions, the underlying regulation mechanisms remain elusive. Hence, this work aimed to shed light on the expression variability of 4 plasma membrane intrinsic protein (PIP) genes in maize (Zea mays) leaves, and its genetic causes, through expression quantitative trait locus (eQTL) mapping across a 252-hybrid diversity panel. Significant genetic variability in PIP transcript abundance was observed to different extents depending on the isoforms. The genome-wide association study mapped numerous eQTLs, both local and distant, thus emphasizing the existing natural diversity of PIP gene expression across the studied panel and the potential to reveal regulatory actors and mechanisms. One eQTL associated with PIP2;5 expression variation was characterized. Genomic sequence comparison and in vivo reporter assay attributed, at least partly, the local eQTL to a transposon-containing polymorphism in the PIP2;5 promoter. This work paves the way to the molecular understanding of PIP gene regulation and its possible integration into larger networks regulating physiological and stress adaptation processes.

Genome-wide expression quantitative trait locus analysis reveals silk-preferential gene regulatory network in maize.

Silk of maize (Zea mays L.) contains diverse metabolites with complicated structures and functions, making it a great challenge to explore the mechanisms of metabolic regulation. Genome-wide identification of silk-preferential genes and investigation of their expression regulation provide an opportunity to reveal the regulatory networks of metabolism. Here, we applied the expression quantitative trait locus (eQTL) mapping on a maize natural population to explore the regulation of gene expression in unpollinated silk of maize. We obtained 3,985 silk-preferential genes that were specifically or preferentially expressed in silk using our population. Silk-preferential genes showed more obvious expression variations compared with broadly expressed genes that were ubiquitously expressed in most tissues. We found that trans-eQTL regulation played a more important role for silk-preferential genes compared to the broadly expressed genes. The relationship between 38 transcription factors and 85 target genes, including silk-preferential genes, were detected. Finally, we constructed a transcriptional regulatory network around the silk-preferential gene Bx10, which was proposed to be associated with response to abiotic stress and biotic stress. Taken together, this study deepened our understanding of transcriptome variation in maize silk and the expression regulation of silk-preferential genes, enhancing the investigation of regulatory networks on metabolic pathways.

Genome-wide QTL and eQTL mapping reveal genes associated with growth rate trait of the Pacific white shrimp (Litopenaeus vannamei)

BackgroundGrowth rate is a crucial economic trait for farmed animals, but the genetic regulation of this trait is largely unknown in non-model organisms such as shrimp.ResultsIn this study, we performed genome-wide phenotypic quantitative trait loci (QTL) and expression quantitative trait loci (eQTL) mapping analyses to identify genes affecting the growth rate of Pacific white shrimp (Litopenaeus vannamei), which is the most commercially-farmed crustacean worldwide. We used RNA-sequencing of 268 individuals in a mapping population, and subsequently validated our findings through gene silencing and shrimp growth experiments. We constructed a high-density genetic linkage map comprising 5533 markers spanning 44 linkage groups, with a total distance of 6205.75 cM and an average marker interval of 1.12 cM. Our analyses identified 11 QTLs significantly correlated with growth rate, and 117,525 eQTLs. By integrating QTL and eQTL data, we identified a gene (metalloreductase STEAP4) highly associated with shrimp growth rate. RNA interference (RNAi) analysis and growth experiments confirmed that STEAP4 was significantly correlated with growth rate in L. vannamei.ConclusionsOur results indicate that the comprehensive analysis of QTL and eQTL can effectively identify genes involved in complex animal traits. This is important for marker-assisted selection (MAS) of animals. Our work contributes to the development of shrimp breeding and available genetic resources.

Cell-type-specific and disease-associated expression quantitative trait loci in the human lung

Common genetic variants confer substantial risk for chronic lung diseases, including pulmonary fibrosis. Defining the genetic control of gene expression in a cell-type-specific and context-dependent manner is critical for understanding the mechanisms through which genetic variation influences complex traits and disease pathobiology. To this end, we performed single-cell RNA sequencing of lung tissue from 66 individuals with pulmonary fibrosis and 48 unaffected donors. Using a pseudobulk approach, we mapped expression quantitative trait loci (eQTLs) across 38 cell types, observing both shared and cell-type-specific regulatory effects. Furthermore, we identified disease interaction eQTLs and demonstrated that this class of associations is more likely to be cell-type-specific and linked to cellular dysregulation in pulmonary fibrosis. Finally, we connected lung disease risk variants to their regulatory targets in disease-relevant cell types. These results indicate that cellular context determines the impact of genetic variation on gene expression and implicates context-specific eQTLs as key regulators of lung homeostasis and disease.

Accounting for isoform expression increases power to identify genetic regulation of gene expression.

A core problem in genetics is molecular quantitative trait locus (QTL) mapping, in which genetic variants associated with changes in the molecular phenotypes are identified. One of the most-studied molecular QTL mapping problems is expression QTL (eQTL) mapping, in which the molecular phenotype is gene expression. It is common in eQTL mapping to compute gene expression by aggregating the expression levels of individual isoforms from the same gene and then performing linear regression between SNPs and this aggregated gene expression level. However, SNPs may regulate isoforms from the same gene in different directions due to alternative splicing, or only regulate the expression level of one isoform, causing this approach to lose power. Here, we examine a broader question: which genes have at least one isoform whose expression level is regulated by genetic variants? In this study, we propose and evaluate several approaches to answering this question, demonstrating that "isoform-aware" methods-those that account for the expression levels of individual isoforms-have substantially greater power to answer this question than standard "gene-level" eQTL mapping methods. We identify settings in which different approaches yield an inflated number of false discoveries or lose power. In particular, we show that calling an eGene if there is a significant association between a SNP and any isoform fails to control False Discovery Rate, even when applying standard False Discovery Rate correction. We show that similar trends are observed in real data from the GEUVADIS and GTEx studies, suggesting the possibility that similar effects are present in these consortia.

Systems genetics analysis reveals the common genetic basis for pain sensitivity and cognitive function.

There is growing evidence of a strong correlation between pain sensitivity and cognitive function under both physiological and pathological conditions. However, the detailed mechanisms remain largely unknown. In the current study, we sought to explore candidate genes and common molecular mechanisms underlying pain sensitivity and cognitive function with a transcriptome-wide association study using recombinant inbred mice from the BXD family. The pain sensitivity determined by Hargreaves' paw withdrawal test and cognition-related phenotypes were systematically analyzed in 60 strains of BXD mice and correlated with hippocampus transcriptomes, followed by quantitative trait locus (QTL) mapping and systems genetics analysis. The pain sensitivity showed significant variability across the BXD strains and co-varies with cognitive traits. Pain sensitivity correlated hippocampual genes showed a significant involvement in cognition-related pathways, including glutamatergic synapse, and PI3K-Akt signaling pathway. Moreover, QTL mapping identified a genomic region on chromosome 4, potentially regulating the variation of pain sensitivity. Integrative analysis of expression QTL mapping, correlation analysis, and Bayesian network modeling identified Ring finger protein 20 (Rnf20) as the best candidate. Further pathway analysis indicated that Rnf20 may regulate the expression of pain sensitivity and cognitive function through the PI3K-Akt signaling pathway, particularly through interactions with genes Ppp2r2b, Ppp2r5c, Col9a3, Met, Rps6, Tnc, and Kras. Our study demonstrated that pain sensitivity is associated with genetic background and Rnf20-mediated PI3K-Akt signaling may involve in the regulation of pain sensitivity and cognitive functions.

Mapping Expression Quantitative Trait Loci Targeting Candidate Genes for Pregnancy in Beef Cows.

Despite collective efforts to understand the complex regulation of reproductive traits, no causative genes and/or mutations have been reported yet. By integrating genomics and transcriptomics data, potential regulatory mechanisms may be unveiled, providing opportunities to dissect the genetic factors governing fertility. Herein, we identified regulatory variants from RNA-Seq data associated with gene expression regulation in the uterine luminal epithelial cells of beef cows. We identified 4676 cis and 7682 trans eQTLs (expression quantitative trait loci) affecting the expression of 1120 and 2503 genes, respectively (FDR < 0.05). These variants affected the expression of transcription factor coding genes (71 cis and 193 trans eQTLs) and genes previously reported as differentially expressed between pregnant and nonpregnant cows. Functional over-representation analysis highlighted pathways related to metabolism, immune response, and hormone signaling (estrogen and GnRH) affected by eQTL-regulated genes (p-value ≤ 0.01). Furthermore, eQTLs were enriched in QTL regions for 13 reproduction-related traits from the CattleQTLdb (FDR ≤ 0.05). Our study provides novel insights into the genetic basis of reproductive processes in cattle. The underlying causal mechanisms modulating the expression of uterine genes warrant further investigation.

Gene expression variation underlying tissue-specific responses to copper stress in Drosophila melanogaster.

Copper is one of a handful of biologically necessary heavy metals that is also a common environmental pollutant. Under normal conditions, copper ions are required for many key physiological processes. However, in excess, copper results in cell and tissue damage ranging in severity from temporary injury to permanent neurological damage. Because of its biological relevance, and because many conserved copper-responsive genes respond to nonessential heavy metal pollutants, copper resistance in Drosophila melanogaster is a useful model system with which to investigate the genetic control of the heavy metal stress response. Because heavy metal toxicity has the potential to differently impact specific tissues, we genetically characterized the control of the gene expression response to copper stress in a tissue-specific manner in this study. We assessed the copper stress response in head and gut tissue of 96 inbred strains from the Drosophila Synthetic Population Resource using a combination of differential expression analysis and expression quantitative trait locus mapping. Differential expression analysis revealed clear patterns of tissue-specific expression. Tissue and treatment specific responses to copper stress were also detected using expression quantitative trait locus mapping. Expression quantitative trait locus associated with MtnA, Mdr49, Mdr50, and Sod3 exhibited both genotype-by-tissue and genotype-by-treatment effects on gene expression under copper stress, illuminating tissue- and treatment-specific patterns of gene expression control. Together, our data build a nuanced description of the roles and interactions between allelic and expression variation in copper-responsive genes, provide valuable insight into the genomic architecture of susceptibility to metal toxicity, and highlight candidate genes for future functional characterization.

Exploring the Regulation and Function of Rpl3l in the Development of Early-Onset Dilated Cardiomyopathy and Congestive Heart Failure Using Systems Genetics Approach.

Cardiomyopathies, diseases affecting the myocardium, are common causes of congestive heart failure (CHF) and sudden cardiac death. Recently, biallelic variants in ribosomal protein L3-like (RPL3L) have been reported to be associated with severe neonatal dilated cardiomyopathy (DCM) and CHF. This study employs a systems genetics approach to gain understanding of the regulatory mechanisms underlying the role of RPL3L in DCM. Genetic correlation, expression quantitative trait loci (eQTL) mapping, differential expression analysis and comparative functional analysis were performed using cardiac gene expression data from the patients and murine genetic reference populations (GRPs) of BXD mice (recombinant inbred strains from a cross of C57BL/6J and DBA/2J mice). Additionally, immune infiltration analysis was performed to understand the relationship between DCM, immune cells and RPL3L expression. Systems genetics analysis identified high expression of Rpl3l mRNA, which ranged from 11.31 to 12.16 across murine GRPs of BXD mice, with an ~1.8-fold difference. Pathways such as "diabetic cardiomyopathy", "focal adhesion", "oxidative phosphorylation" and "DCM" were significantly associated with Rpl3l. eQTL mapping suggested Myl4 (Chr 11) and Sdha (Chr 13) as the upstream regulators of Rpl3l. The mRNA expression of Rpl3l, Myl4 and Sdha was significantly correlated with multiple echocardiography traits in BXD mice. Immune infiltration analysis revealed a significant association of RPL3L and SDHA with seven immune cells (CD4, CD8-naive T cell, CD8 T cell, macrophages, cytotoxic T cell, gamma delta T cell and exhausted T cell) that were also differentially infiltrated between heart samples obtained from DCM patients and normal individuals. RPL3L is highly expressed in the heart tissue of humans and mice. Expression of Rpl3l and its upstream regulators, Myl4 and Sdha, correlate with multiple cardiac function traits in murine GRPs of BXD mice, while RPL3L and SDHA correlate with immune cell infiltration in DCM patient hearts, suggesting important roles for RPL3L in DCM and CHF pathogenesis via immune inflammation, necessitating experimental validations of Myl4 and Sdha in Rpl3l regulation.

Unraveling the causal genes and transcriptomic determinants of human telomere length

Telomere length (TL) shortening is a pivotal indicator of biological aging and is associated with many human diseases. The genetic determinates of human TL have been widely investigated, however, most existing studies were conducted based on adult tissues which are heavily influenced by lifetime exposure. Based on the analyses of terminal restriction fragment (TRF) length of telomere, individual genotypes, and gene expressions on 166 healthy placental tissues, we systematically interrogate TL-modulated genes and their potential functions. We discover that the TL in the placenta is comparatively longer than in other adult tissues, but exhibiting an intra-tissue homogeneity. Trans-ancestral TL genome-wide association studies (GWASs) on 644,553 individuals identify 20 newly discovered genetic associations and provide increased polygenic determination of human TL. Next, we integrate the powerful TL GWAS with placental expression quantitative trait locus (eQTL) mapping to prioritize 23 likely causal genes, among which 4 are functionally validated, including MMUT, RRM1, KIAA1429, and YWHAZ. Finally, modeling transcriptomic signatures and TRF-based TL improve the prediction performance of human TL. This study deepens our understanding of causal genes and transcriptomic determinants of human TL, promoting the mechanistic research on fine-grained TL regulation.

Sex-biased gene regulation varies across human populations as a result of adaptive evolution.

Studies of human sexual dimorphism and gender disparities in health focus on ostensibly universal molecular sex differences, such as sex chromosomes and circulating hormone levels, while ignoring the extraordinary diversity in biology, behavior, and culture acquired by different human populations over their unique evolutionary histories. Using RNA-Seq data and whole genome sequences from 1000G and HGDP, we investigate variation in sex-biased gene expression across 11 human populations and test whether population-level variation in sex-biased expression may have resulted from adaptive evolution in regions containing sex-specific regulatory variants. We find that sex-biased gene expression in humans is highly variable, mostly population-specific, and demonstrates between population reversals. Expression quantitative trait locus mapping reveals sex-specific regulatory regions with evidence of recent positive natural selection, suggesting that variation in sex-biased expression may have evolved as an adaptive response to ancestral environments experienced by human populations. These results indicate that sex-biased gene expression is more flexible than previously thought and is not generally shared among human populations. Instead, molecular phenotypes associated with sex depend on complex interactions between population-specific molecular evolution and physiological responses to contemporary socioecologies.

Mapping the dynamic genetic regulatory architecture of HLA genes at single-cell resolution.

The human leukocyte antigen (HLA) locus plays a critical role in complex traits spanning autoimmune and infectious diseases, transplantation and cancer. While coding variation in HLA genes has been extensively documented, regulatory genetic variation modulating HLA expression levels has not been comprehensively investigated. Here we mapped expression quantitative trait loci (eQTLs) for classical HLA genes across 1,073 individuals and 1,131,414 single cells from three tissues. To mitigate technical confounding, we developed scHLApers, a pipeline to accurately quantify single-cell HLA expression using personalized reference genomes. We identified cell-type-specific cis-eQTLs for every classical HLA gene. Modeling eQTLs at single-cell resolution revealed that many eQTL effects are dynamic across cell states even within a cell type. HLA-DQ genes exhibit particularly cell-state-dependent effects within myeloid, B and T cells. For example, a T cell HLA-DQA1 eQTL ( rs3104371 ) is strongest in cytotoxic cells. Dynamic HLA regulation may underlie important interindividual variability in immune responses.

Genome-wide expression QTL mapping reveals the highly dynamic regulatory landscape of a major wheat pathogen

BackgroundIn agricultural ecosystems, outbreaks of diseases are frequent and pose a significant threat to food security. A successful pathogen undergoes a complex and well-timed sequence of regulatory changes to avoid detection by the host immune system; hence, well-tuned gene regulation is essential for survival. However, the extent to which the regulatory polymorphisms in a pathogen population provide an adaptive advantage is poorly understood.ResultsWe used Zymoseptoria tritici, one of the most important pathogens of wheat, to generate a genome-wide map of regulatory polymorphism governing gene expression. We investigated genome-wide transcription levels of 146 strains grown under nutrient starvation and performed expression quantitative trait loci (eQTL) mapping. We identified cis-eQTLs for 65.3% of all genes and the majority of all eQTL loci are within 2kb upstream and downstream of the transcription start site (TSS). We also show that polymorphism in different gene elements contributes disproportionally to gene expression variation. Investigating regulatory polymorphism in gene categories, we found an enrichment of regulatory variants for genes predicted to be important for fungal pathogenesis but with comparatively low effect size, suggesting a separate layer of gene regulation involving epigenetics. We also show that previously reported trait-associated SNPs in pathogen populations are frequently cis-regulatory variants of neighboring genes with implications for the trait architecture.ConclusionsOverall, our study provides extensive evidence that single populations segregate large-scale regulatory variation and are likely to fuel rapid adaptation to resistant hosts and environmental change.