Transcriptome-wide association study of the plasma proteome reveals cis and trans regulatory mechanisms underlying complex traits

Regulation of transcription and translation are mechanisms through which genetic variants affect complex traits. Expression quantitative trait locus (eQTL) studies have been more successful at identifying cis-eQTL (within 1 Mb of the transcription start site) than trans-eQTL. Here, we tested the cis component of gene expression for association with observed plasma protein levels to identify cis- and trans-acting genes that regulate protein levels. We used transcriptome prediction models from 49 Genotype-Tissue Expression (GTEx) Project tissues to predict the cis component of gene expression and tested the predicted expression of every gene in every tissue for association with the observed abundance of 3,622 plasma proteins measured in 3,301 individuals from the INTERVAL study. We tested significant results for replication in 971 individuals from the Trans-omics for Precision Medicine (TOPMed) Multi-Ethnic Study of Atherosclerosis (MESA). We found 1,168 and 1,210 cis- and trans-acting associations that replicated in TOPMed (FDR < 0.05) with a median expected true positive rate (π1) across tissues of 0.806 and 0.390, respectively. The target proteins of trans-acting genes were enriched for transcription factor binding sites and autoimmune diseases in the GWAS catalog. Furthermore, we found a higher correlation between predicted expression and protein levels of the same underlying gene (R = 0.17) than observed expression (R = 0.10, p = 7.50 × 10-11). This indicates the cis-acting genetically regulated (heritable) component of gene expression is more consistent across tissues than total observed expression (genetics + environment) and is useful in uncovering the function of SNPs associated with complex traits.

Quantitative trait loci on chromosomes 9 and 19 modulate AII amacrine cell number in the mouse retina

Sequence variants modulating gene function or expression affect various heritable traits, including the number of neurons within a population. The present study employed a forward-genetic approach to identify candidate causal genes and their sequence variants controlling the number of one type of retinal neuron, the AII amacrine cell. Data from twenty-six recombinant inbred (RI) strains of mice derived from the parental C57BL/6J (B6/J) and A/J laboratory strains were used to identify genomic loci regulating cell number. Large variation in cell number is present across the RI strains, from a low of ∼57,000 cells to a high of ∼87,000 cells. Quantitative trait locus (QTL) analysis revealed three prospective controlling genomic loci, on Chromosomes (Chrs) 9, 11, and 19, each contributing additive effects that together approach the range of variation observed. Composite interval mapping validated two of these loci, and chromosome substitution strains, in which the A/J genome for Chr 9 or 19 was introgressed on a B6/J genetic background, showed increased numbers of AII amacrine cells as predicted by those two QTL effects. Analysis of the respective genomic loci identified candidate controlling genes defined by their retinal expression, their established biological functions, and by the presence of sequence variants expected to modulate gene function or expression. Two candidate genes, Dtx4 on Chr 19, being a regulator of Notch signaling, and Dixdc1 on Chr 9, a modulator of the WNT-β-catenin signaling pathway, were explored in further detail. Postnatal overexpression of Dtx4 was found to reduce the frequency of amacrine cells, while Dixdc1 knockout retinas contained an excess of AII amacrine cells. Sequence variants in each gene were identified, being the likely sources of variation in gene expression, ultimately contributing to the final number of AII amacrine cells.

Cis-eQTL Analysis and Functional Validation of Candidate Genes for Carcass Yield Traits in Beef Cattle

Carcass yield traits are of considerable economic importance for farm animals, which act as a major contributor to the world’s food supply. Genome-wide association studies (GWASs) have identified many genetic variants associated with carcass yield traits in beef cattle. However, their functions are not effectively illustrated. In this study, we performed an integrative analysis of gene-based GWAS with expression quantitative trait locus (eQTL) analysis to detect candidate genes for carcass yield traits and validate their effects on bovine skeletal muscle satellite cells (BSCs). The gene-based GWAS and cis-eQTL analysis revealed 1780 GWAS and 1538 cis-expression genes. Among them, we identified 153 shared genes that may play important roles in carcass yield traits. Notably, the identified cis-eQTLs of PON3 and PRIM2 were significantly (p < 0.001) enriched in previous GWAS loci for carcass traits. Furthermore, overexpression of PON3 and PRIM2 promoted the BSCs’ proliferation, increased the expression of MYOD and downregulated the expression of MYOG, which indicated that these genes may inhibit myogenic differentiation. In contrast, PON3 and PRIM2 were significantly downregulated during the differentiation of BSCs. These findings suggested that PON3 and PRIM2 may promote the proliferation of BSCs and inhibit them in the pre-differentiation stage. Our results further contribute to the understanding of the molecular mechanisms of carcass yield traits in beef cattle.

The Upregulation of Cathepsin G Is Associated with Resistance to Bovine Paratuberculosis

An in silico genomic-transcriptomic combined approach allowed the identification of a polymorphism (cis-eQTL-rs41976219) in the Bos taurus genome associated with the CTSG mRNA expression in bovine blood samples, which suggests that individual genetic variation might modulate the CTSG transcriptional response. In the current study, a sandwich ELISA is used to measure the CTSG protein levels in supernatants of monocyte-derived macrophages (MDMs) isolated from cows with the AA (n = 5) and AC (n = 11) genotypes for the rs41976219 and infected ex vivo with MAP. Cows with the AC genotype have significantly higher CTSG protein levels (1.85 ng/mL) in the supernatants of enriched CD14+-MDMs after 2 h of infection when compared with infected CD14+-MDMs from cows with the AA genotype (1.68 ng/mL). Statistically significant differences in the intracellular MAP load at 7 d p.i. are observed between animals with the AA (2.16 log CFUs) and AC (1.44 log CFUs) genotypes. Finally, the association between the rs41976219 allelic variants and resistance to PTB is tested in a larger cattle population (n = 943) classified according to the presence (n = 442) or absence (n = 501) of PTB-associated lesions. The presence of the two minor alleles in the rs41976219 (CC) is more frequent among healthy cows than in cows with PTB-associated lesions in gut tissues (2.2% vs. 1.4%, OR = 0.61). In agreement with this, the CTSG levels in plasma samples of cows without lesions in gut tissues and with the CC (n = 8) genotype are significantly higher than in the plasmas of cows with the AA + AC (n = 36) genotypes.

Brain region-specific effects of nearly fixed sapiens-derived alleles

The availability of high-coverage genomes of our extinct relatives, the Neanderthals and Denisovans, and the emergence of large, tissue-specific databases of modern human genetic variation, offer the possibility of probing the effects of modern-derived alleles in specific tissues, such as the brain, and its specific regions. While previous research has explored the effects of introgressed variants in gene expression, the effects of Homo sapiens-specific gene expression variability are still understudied. Here we identify derived, Homo sapiens-specific high-frequency (≥90%) alleles that are associated with differential gene expression across 15 brain structures derived from the GTEx database. We show that regulation by these derived variants targets regions under positive selection more often than expected by chance, and that high-frequency derived alleles lie in functional categories related to transcriptional regulation. Our results highlight the role of these variants in gene regulation in specific regions like the cerebellum and pituitary.

ACE2 and COVID-19 Susceptibility and Severity

Sick, male, and older populations are more vulnerable to COVID-19. However, it remains unclear whether a common mechanism exists across different demographic characteristics. SARS-CoV-2 infection is initiated by the specific binding of the viral spike protein to angiotensin-converting enzyme 2 (ACE2). This study analyzed the demographics of pulmonary ACE2 expression, Mendelian randomization (MR) of ACE2 and COVID-19, and comparative tropism of SARS-CoV-2. The key features of SARS-CoV-2 tropism, including pulmonary ACE2 expression and ACE2-expressing cell types, showed distinct subphenotypes associated with the demographics of vulnerable COVID-19 populations, suggesting a hypothesis centered on “ACE2” to explain their interplay. Next, by integrating multiple COVID-19 cohorts of genome-wide association studies (GWASs) and cis-expression quantitative trait loci (cis-eQTLs) of ACE2, MR analysis demonstrated that ACE2 played a causal role in COVID-19 susceptibility and severity, suggesting ACE2 as a promising target for early COVID-19 treatment. Next, by analyzing the expression of host cell receptors using single-cell RNA sequencing (scRNA-seq) data of human lung tissues, comparative tropism analysis showed that SARS-CoV-2 and other respiratory viruses, but not non-respiratory viruses, had remarkably overlapping and enriched cellular tropism in alveolar type 2 (AT2) cells. This finding indicates the possibility of coinfection with SARS-CoV-2 and other respiratory viruses, perhaps implying sociovirology at the cellular level. Moreover, the binding of viral entry proteins to the compatible host cell receptors is under strong natural selection pressure. Therefore, comparative tropism might reveal the footprint of natural selection that shapes the virus population, which provides a novel perspective for understanding zoonotic spillover events.

TIGAR-V2: Efficient TWAS tool with nonparametric Bayesian eQTL weights of 49 tissue types from GTEx V8

Standard transcriptome-wide association study (TWAS) methods first train gene expression prediction models using reference transcriptomic data and then test the association between the predicted genetically regulated gene expression and phenotype of interest. Most existing TWAS tools require cumbersome preparation of genotype input files and extra coding to enable parallel computation. To improve the efficiency of TWAS tools, we developed Transcriptome-Integrated Genetic Association Resource V2 (TIGAR-V2), which directly reads Variant Call Format (VCF) files, enables parallel computation, and reduces up to 90% of computation cost (mainly due to loading genotype data) compared to the original version. TIGAR-V2 can train gene expression imputation models using either nonparametric Bayesian Dirichlet process regression (DPR) or Elastic-Net (as used by PrediXcan), perform TWASs using either individual-level or summary-level genome-wide association study (GWAS) data, and implement both burden and variance-component statistics for gene-based association tests. We trained gene expression prediction models by DPR for 49 tissues using Genotype-Tissue Expression (GTEx) V8 by TIGAR-V2 and illustrated the usefulness of these Bayesian cis-expression quantitative trait locus (eQTL) weights through TWASs of breast and ovarian cancer utilizing public GWAS summary statistics. We identified 88 and 37 risk genes, respectively, for breast and ovarian cancer, most of which are either known or near previously identified GWAS (∼95%) or TWAS (∼40%) risk genes and three novel independent TWAS risk genes with known functions in carcinogenesis. These findings suggest that TWASs can provide biological insight into the transcriptional regulation of complex diseases. The TIGAR-V2 tool, trained Bayesian cis-eQTL weights, and linkage disequilibrium (LD) information from GTEx V8 are publicly available, providing a useful resource for mapping risk genes of complex diseases.

LORSEN: Fast and Efficient eQTL Mapping With Low Rank Penalized Regression

Characterization of genetic variations that are associated with gene expression levels is essential to understand cellular mechanisms that underline human complex traits. Expression quantitative trait loci (eQTL) mapping attempts to identify genetic variants, such as single nucleotide polymorphisms (SNPs), that affect the expression of one or more genes. With the availability of a large volume of gene expression data, it is necessary and important to develop fast and efficient statistical and computational methods to perform eQTL mapping for such large scale data. In this paper, we proposed a new method, the low rank penalized regression method (LORSEN), for eQTL mapping. We evaluated and compared the performance of LORSEN with two existing methods for eQTL mapping using extensive simulations as well as real data from the HapMap3 project. Simulation studies showed that our method outperformed two commonly used methods for eQTL mapping, LORS and FastLORS, in many scenarios in terms of area under the curve (AUC). We illustrated the usefulness of our method by applying it to SNP variants data and gene expression levels on four chromosomes from the HapMap3 Project.

Bayesian Genome-wide TWAS Method to Leverage both cis- and trans-eQTL Information through Summary Statistics

Transcriptome-wide association studies (TWASs) have been widely used to integrate gene expression and genetic data for studying complex traits. Due to the computational burden, existing TWAS methods do not assess distant trans-expression quantitative trait loci (eQTL) that are known to explain important expression variation for most genes. We propose a Bayesian genome-wide TWAS (BGW-TWAS) method that leverages both cis- and trans-eQTL information for a TWAS. Our BGW-TWAS method is based on Bayesian variable selection regression, which not only accounts for cis- and trans-eQTL of the target gene but also enables efficient computation by using summary statistics from standard eQTL analyses. Our simulation studies illustrated that BGW-TWASs achieved higher power compared to existing TWAS methods that do not assess trans-eQTL information. We further applied BWG-TWAS to individual-level GWAS data (N = ∼3.3K), which identified significant associations between the genetically regulated gene expression (GReX) of ZC3H12B and Alzheimer dementia (AD) (p value = 5.42 × 10-13), neurofibrillary tangle density (p value = 1.89 × 10-6), and global measure of AD pathology (p value = 9.59 × 10-7). These associations for ZC3H12B were completely driven by trans-eQTL. Additionally, the GReX of KCTD12 was found to be significantly associated with β-amyloid (p value = 3.44 × 10-8) which was driven by both cis- and trans-eQTL. Four of the top driven trans-eQTL of ZC3H12B are located within APOC1, a known major risk gene of AD and blood lipids. Additionally, by applying BGW-TWAS with summary-level GWAS data of AD (N = ∼54K), we identified 13 significant genes including known GWAS risk genes HLA-DRB1 and APOC1, as well as ZC3H12B.

eQTLMAPT: Fast and Accurate eQTL Mediation Analysis With Efficient Permutation Testing Approaches

Expression quantitative trait locus (eQTL) analyses are critical in understanding the complex functional regulatory natures of genetic variation and have been widely used in the interpretation of disease-associated variants identified by genome-wide association studies (GWAS). Emerging evidence has shown that trans-eQTL effects on remote gene expression could be mediated by local transcripts, which is known as the mediation effects. To discover the genome-wide eQTL mediation effects combing genomic and transcriptomic profiles, it is necessary to develop novel computational methods to rapidly scan large number of candidate associations while controlling for multiple testing appropriately. Here, we present eQTLMAPT, an R package aiming to perform eQTL mediation analysis with implementation of efficient permutation procedures in multiple testing correction. eQTLMAPT is advantageous in threefold. First, it accelerates mediation analysis by effectively pruning the permutation process through adaptive permutation scheme. Second, it can efficiently and accurately estimate the significance level of mediation effects by modeling the null distribution with generalized Pareto distribution (GPD) trained from a few permutation statistics. Third, eQTLMAPT provides flexible interfaces for users to combine various permutation schemes with different confounding adjustment methods. Experiments on real eQTL dataset demonstrate that eQTLMAPT provides higher resolution of estimated significance of mediation effects and is an order of magnitude faster than compared methods with similar accuracy.

Identifying Putative Susceptibility Genes and Evaluating Their Associations with Somatic Mutations in Human Cancers

Genome-wide association studies (GWASs) have identified hundreds of genetic risk variants for human cancers. However, target genes for the majority of risk loci remain largely unexplored. It is also unclear whether GWAS risk-loci-associated genes contribute to mutational signatures and tumor mutational burden (TMB) in cancer tissues. We systematically conducted cis-expression quantitative trait loci (cis-eQTL) analyses for 294 GWAS-identified variants for six major types of cancer-colorectal, lung, ovary, prostate, pancreas, and melanoma-by using transcriptome data from the Genotype-Tissue Expression (GTEx) Project, the Cancer Genome Atlas (TCGA), and other public data sources. By using integrative analysis strategies, we identified 270 candidate target genes, including 99 with previously unreported associations, for six cancer types. By analyzing functional genomic data, our results indicate that 180 genes (66.7% of 270) had evidence of cis-regulation by putative functional variants via proximal promoter or distal enhancer-promoter interactions. Together with our previously reported associations for breast cancer risk, our results show that 24 genes are shared by at least two cancer types, including four genes for both breast and ovarian cancer. By integrating mutation data from TCGA, we found that expression levels of 33 and 66 putative susceptibility genes were associated with specific mutational signatures and TMB of cancer-driver genes, respectively, at a Bonferroni-corrected p < 0.05. Together, these findings provide further insight into our understanding of how genetic risk variants might contribute to carcinogenesis through the regulation of susceptibility genes that are related to the biogenesis of somatic mutations.

A computational approach to identify blood cell-expressed Parkinson's disease biomarkers that are coordinately expressed in brain tissue

Identification of genes whose regulation of expression is functionally similar in both brain tissue and blood cells could in principle enable monitoring of significant neurological traits and disorders by analysis of blood samples. We thus employed transcriptional analysis of pathologically affected tissues, using agnostic approaches to identify overlapping gene functions and integrating this transcriptomic information with expression quantitative trait loci (eQTL) data. Here, we estimate the correlation of gene expression in the top-associated cis-eQTLs of brain tissue and blood cells in Parkinson's Disease (PD). We introduced quantitative frameworks to reveal the complex relationship of various biasing genetic factors in PD, a neurodegenerative disease. We examined gene expression microarray and RNA-Seq datasets from human brain and blood tissues from PD-affected and control individuals. Differentially expressed genes (DEG) were identified for both brain and blood cells to determine common DEG overlaps. Based on neighborhood-based benchmarking and multilayer network topology approaches we then developed genetic associations of factors with PD. Overlapping DEG sets underwent gene enrichment using pathway analysis and gene ontology methods, which identified candidate common genes and pathways. We identified 12 significantly dysregulated genes shared by brain and blood cells, which were validated using dbGaP (gene SNP-disease linkage) database for gold-standard benchmarking of their significance in disease processes. Ontological and pathway analyses identified significant gene ontology and molecular pathways that indicate PD progression. In sum, we found possible novel links between pathological processes in brain tissue and blood cells by examining cell pathway commonalities, corroborating these associations using well validated datasets. This demonstrates that for brain-related pathologies combining gene expression analysis and blood cell cis-eQTL is a potentially powerful analytical approach. Thus, our methodologies facilitate data-driven approaches that can advance knowledge of disease mechanisms and may, with clinical validation, enable prediction of neurological dysfunction using blood cell transcript profiling.

The Effect of Genetic Variation on the Placental Transcriptome in Humans

The knowledge of genetic variants shaping human placental transcriptome is limited and they are not cataloged in the Genotype-Tissue Expression project. So far, only one whole genome analysis of placental expression quantitative trait loci (eQTLs) has been published by Peng et al. (2017) with no external independent validation. We report the second study on the landscape of placental eQTLs. The study aimed to generate a high-confidence list of placental cis-eQTLs and to investigate their potential functional implications. Analysis of cis-eQTLs (±100 kbp from the gene) utilized 40 placental RNA sequencing and respective whole genome genotyping datasets. The identified 199 placental cis-eSNPs represented 88 independent eQTL signals (FDR < 5%). The most significant placental eQTLs (FDR < 10-5) modulated the expression of ribosomal protein RPL9, transcription factor ZSCAN9 and aminopeptidase ERAP2. The analysis confirmed 50 eSNP-eGenes pairs reported by Peng et al. (2017) and thus, can be claimed as robust placental eQTL signals. The study identified also 13 novel placental eGenes. Among these, ZSCAN9 is modulated by several eSNPs (experimentally validated: rs1150707) that have been also shown to affect the methylation level of genes variably escaping X-chromosomal inactivation. The identified 63 placental eGenes exhibited mostly mixed or ubiquitous expression. Functional enrichment analysis highlighted 35 Gene Ontology categories with the top ranking pathways "ruffle membrane" (FDR = 1.81 × 10-2) contributing to the formation of motile cell surface and "ATPase activity, coupled" (FDR = 2.88 × 10-2), critical for the membrane transport. Placental eGenes were also significantly enriched in pathways implicated in development, signaling and immune function. However, this study was not able to confirm a significant overrepresentation of genome-wide association studies top hits among the placental eSNP and eGenes, reported by Peng et al. (2017). The identified eSNPs were further analyzed in association with newborn and pregnancy traits. In the discovery step, a suggestive association was detected between the eQTL of ALPG (rs11678251) and reduced placental, newborn's and infant's weight. Meta-analysis across REPROMETA, HAPPY PREGNANCY, ALSPAC cohorts (n = 6830) did not replicate these findings. In summary, the study emphasizes the role of genetic variation in driving the transcriptome profile of the human placenta and the importance to explore further its functional implications.

Xkr8 Modulates Bipolar Cell Number in the Mouse Retina

The present study interrogated a quantitative trait locus (QTL) on Chr 4 associated with the population sizes of two types of bipolar cell in the mouse retina. This locus was identified by quantifying the number of rod bipolar cells and Type 2 cone bipolar cells across a panel of recombinant inbred (RI) strains of mice derived from two inbred laboratory strains, C57BL/6J (B6/J) and A/J, and mapping a proportion of that variation in cell number, for each cell type, to this shared locus. There, we identified the candidate gene X Kell blood group precursor related family member 8 homolog (Xkr8). While Xkr8 has no documented role in the retina, we localize robust expression in the mature retina via in situ hybridization, confirm its developmental presence via immunolabeling, and show that it is differentially regulated during the postnatal period between the B6/J and A/J strains using qPCR. Microarray analysis, derived from whole eye mRNA from the entire RI strain set, demonstrates significant negative correlation of Xkr8 expression with the number of each of these two types of bipolar cells, and the variation in Xkr8 expression across the strains maps a cis-eQTL, implicating a regulatory variant discriminating the parental genomes. Xkr8 plasmid electroporation during development yielded a reduction in the number of bipolar cells in the retina, while sequence analysis of Xkr8 in the two parental strain genomes identified a structural variant in the 3^′ UTR that may disrupt mRNA stability, and two SNPs in the promoter that create transcription factor binding sites. We propose that Xkr8, via its participation in mediating cell death, plays a role in the specification of bipolar cell number in the retina.

A Comprehensive cis-eQTL Analysis Revealed Target Genes in Breast Cancer Susceptibility Loci Identified in Genome-wide Association Studies

Genome-wide association studies (GWASs) have identified more than 150 common genetic loci for breast cancer risk. However, the target genes and underlying mechanisms remain largely unknown. We conducted a cis-expression quantitative trait loci (cis-eQTL) analysis using normal or tumor breast transcriptome data from the Molecular Taxonomy of Breast Cancer International Consortium (METABRIC), The Cancer Genome Atlas (TCGA), and the Genotype-Tissue Expression (GTEx) project. We identified a total of 101 genes for 51 lead variants after combing the results of a meta-analysis of METABRIC and TCGA, and the results from GTEx at a Benjamini-Hochberg (BH)-adjusted p < 0.05. Using luciferase reporter assays in both estrogen-receptor positive (ER⁺) and negative (ER^-) cell lines, we showed that alternative alleles of potential functional single-nucleotide polymorphisms (SNPs), rs11552449 (DCLRE1B), rs7257932 (SSBP4), rs3747479 (MRPS30), rs2236007 (PAX9), and rs73134739 (ATG10), could significantly change promoter activities of their target genes compared to reference alleles. Furthermore, we performed in vitro assays in breast cancer cell lines, and our results indicated that DCLRE1B, MRPS30, and ATG10 played a vital role in breast tumorigenesis via certain disruption of cell behaviors. Our findings revealed potential target genes for associations of genetic susceptibility risk loci and provided underlying mechanisms for a better understanding of the pathogenesis of breast cancer.

A cis-eQTL of HLA-DRB1 and a frameshift mutation of MICA contribute to the pattern of association of HLA alleles with cervical cancer

The association of classic human leukocyte antigen (HLA) alleles with risk of cervical cancer has been extensively studied, and a protective effect has consistently been found for DRB1*1301, DQA1*0103, and/or DQB1*0603 (these three alleles are in perfect linkage disequilibrium [LD] and often occur on the same haplotype in Europeans), while reports have differed widely with respect to the effect of HLA-B*07, DRB1*1501, and/or DQB1*0602 (the last two alleles are also in perfect LD in Europeans). It is not clear whether the reported HLA alleles are responsible for the differences in cervical cancer susceptibility, or if functional variants at other locations within the major histocompatibility complex (MHC) region may explain the effect. In order to assess the relative contribution of both classic HLA alleles and single-nucleotide polymorphisms (SNPs) within the MHC region to cervical cancer susceptibility, we have imputed classic HLA alleles in 1034 cervical cancer patients and 3948 controls in a Swedish population for an integrated analysis. We found that the protective haplotype DRB1*1301-DQA1*0103-DQB1*0603 has a direct effect on cervical cancer and always occurs together with the C allele of a HLA-DRB1 cis-eQTL (rs9272143), which increases the expression of HLA-DRB1. The haplotype rs9272143C-DRB1*1301-DQA1*0103-DQB1*0603 conferred the strongest protection against cervical cancer (odds ratio [OR] = 0.41, 95% confidence interval [CI] = 0.32–0.52, P = 6.2 × 10⁻¹³). On the other hand, the associations with HLA-B*0702 and DRB1*1501-DQB1*0602 are attributable to the joint effects of both the HLA-DRB1 cis-eQTL (rs9272143) and a frameshift mutation (G inserion of rs67841474, also known as A5.1) of the MHC class I polypeptide-related sequence A gene (MICA). Variation in LD between the classic HLA loci, rs9272143 and rs67841474 between populations may explain the different associations of HLA-B*07 and DRB1*1501-DQB1*0602 with cervical cancer between studies. The mechanism suggested may also explain similar inconsistent results for other HLA-associated diseases.

A variant upstream of HLA-DRB1 and multiple variants in MICA influence susceptibility to cervical cancer in a Swedish population

In a genome-wide association study, we have previously identified and performed the initial replication of three novel susceptibility loci for cervical cancer: rs9272143 upstream of HLA-DRB1, rs2516448 adjacent to MHC class I polypeptide-related sequence A gene (MICA), and rs3117027 at HLA-DPB2. The risk allele T of rs2516448 is in perfect linkage disequilibrium with a frameshift mutation (A5.1) in MICA exon 5, which results in a truncated protein. To validate these associations in an independent study and extend our prior work to MICA exon 5, we genotyped the single-nucleotide polymorphisms at rs9272143, rs2516448, rs3117027 and the MICA exon 5 microsatellite in a nested case–control study of 961 cervical cancer patients (827 carcinoma in situ and 134 invasive carcinoma) and 1725 controls from northern Sweden. The C allele of rs9272143 conferred protection against cervical cancer (odds ratio [OR] = 0.73, 95% confidence interval [CI] = 0.65–0.82; P = 1.6 × 10⁻⁷), which is associated with higher expression level of HLA-DRB1, whereas the T allele of rs2516448 increased the susceptibility to cervical cancer (OR = 1.33, 95% CI = 1.19–1.49; P = 5.8 × 10⁻⁷), with the same association shown with MICA-A5.1. The direction and the magnitude of these associations were consistent with our previous findings. We also identified protective effects of the MICA-A4 (OR = 0.80, 95% CI = 0.68–0.94; P = 6.7 × 10⁻³) and MICA-A5 (OR = 0.60, 95% CI = 0.50–0.72; P = 3.0 × 10⁻⁸) alleles. The associations with these variants are unlikely to be driven by the nearby human leukocyte antigen (HLA) alleles. No association was observed between rs3117027 and risk of cervical cancer. Our results support the role of HLA-DRB1 and MICA in the pathogenesis of cervical cancer.