Genome-Wide Expression Quantitative Trait Loci Analysis Using Mixed Models

Genetic regulation of B cell receptor signaling pathway: Insights from expression quantitative trait locus analysis using a mixed model

The B cell receptor (BCR) signaling pathway regulates non-immune cellular response through various pathways like MAPK, NF-kB, and PI3K-Akt. This study aimed to identify expression quantitative trait loci (eQTL) and their regulatory functions on BCR signaling pathway genes. A mixed model was employed to analyze eQTL using RNA expression levels in lymphoblastoid from 376 Europeans in the GEUVADIS dataset. In total, 266 SNPs, including 115 cis-acting SNPs, were found for association with transcription of 13 genes (P < 5 × 10-8), revealing 19 independent signals for five genes through linkage disequilibrium analysis. Functional analysis, aligning them with DNase sensitive sites, transcription factor binding sites, histone modification, promoters/enhancers, CpG islands, and ChIA-PET, identified regulatory variants targeting SYK, VAV2, and PLCG2. Notably, rs2562397 was validated as a SYK promoter variant, and rs694505, rs636667, and rs4889409 were confirmed as enhancer variants for VAV2 and PLCG2. Their allelic differences in gene expression were also confirmed using ENCODE ChIP-seq and Sei neural network prediction. Persistent differential expression of these genes by alleles might impact the adaptive immune system, vascular development, and/or relevant diseases that have been previously associated with other variants of the genes. Comprehensive genetic architecture studies of the BCR signaling pathway, along with experiments demonstrating related mechanisms, will greatly contribute to understanding the underlying mechanisms of relevant disease development and implementing precision medicine.

ELF1 serves as a potential biomarker for the disease activity and renal involvement in systemic lupus erythematosus

Systemic lupus erythematosus (SLE) is an autoimmune disease that affects multiple organs, yet its underlying mechanisms remain unclear, and precise biomarkers are lacking. In this study, we employed Mendelian randomization and HEIDI tools to comprehensively analyze large-scale Genome-Wide Association Study (GWAS) and expression Quantitative Trait Loci (eQTL) data, leading to the identification of seven novel potential functional genes associated with SLE, including BLK, ELF1, STIM1, B3GALT6, APOLD1, INPP5B, and FHL3. Subsequent investigations revealed a significant downregulation of ELF1 gene expression in CD4+ T cells of SLE patients compared to healthy controls. Moreover, within various SLE subgroups, such as those with decreased serum complement C3 levels, positive urinary protein, new-onset skin rashes, and SLE Disease Activity Index (SLEDAI) scores ≥ 5, ELF1 expression displayed a consistent decreasing trend. Notably, ROC curve analysis highlighted the diagnostic potential of ELF1 expression in SLE (AUC = 0.9493), as well as its value in assessing disease activity (AUC = 0.6852) and renal involvement (AUC = 0.7363). In conclusion, this study underscores the potential of ELF1 as a SLE biomarker for diagnosis and evaluation, offering insights into the underlying mechanisms of SLE and paving the way for future therapeutic research.

A brief guide to analyzing expression quantitative trait loci

Molecular quantitative trait locus (molQTL) mapping has emerged as an important approach for elucidating the functional consequences of genetic variants and unraveling the causal mechanisms underlying diseases or complex traits. However, the variety of analysis tools and sophisticated methodologies available for molQTL studies can be overwhelming for researchers with limited computational expertise. Here, we provide a brief guideline with a curated list of methods and software tools for analyzing expression quantitative trait loci, the most widely studied type of molQTL.

Deciphering the Genetic Complexity of Classical Hodgkin Lymphoma: Insights and Effective Strategies

Understanding the genetics of susceptibility to classical Hodgkin lymphoma (cHL) is considerably limited compared to other cancers due to the rare Hodgkin and Reed-Sternberg (HRS) tumor cells, which coexist with the predominant non-malignant microenvironment. This article offers insights into genetic abnormalities in cHL, as well as nucleotide variants and their associated target genes, elucidated through recent technological advancements. Oncogenomes in HRS cells highlight the survival and proliferation of these cells through hyperactive signaling in specific pathways (e.g., NF-kB) and their interplay with microenvironmental cells (e.g., CD4+ T cells). In contrast, the susceptibility genes identified from genome-wide association studies and expression quantitative trait locus analyses only vaguely implicate their potential roles in susceptibility to more general cancers. To pave the way for the era of precision oncology, more intensive efforts are imperative, employing the following strategies: exploring genetic heterogeneity by gender and cHL subtype, investigating colocalization with various types of expression quantitative trait loci, and leveraging single-cell analysis. These approaches provide valuable perspectives for unraveling the genetic complexities of cHL.

Differential expressions of FASN, SCD, and FABP4 genes in the ribeye muscle of omega-3 oil-supplemented Tattykeel Australian White lambs

BACKGROUND

The concept of the functional nutritional value of health-beneficial omega-3 long-chain polyunsaturated fatty acids (n-3 LC-PUFA) is becoming a phenomenon among red meat consumers globally. This study examined the expressions of three lipogenic genes (fatty acid binding protein 4, FABP4, fatty acid synthase, FASN; and stearoyl-CoA desaturase, SCD) in the ribeye (Longissimus thoracis et lumborum) muscle of Tattykeel Australian White (TAW) lambs fed fortified omega-3 diets and correlations with fatty acids. To answer the research question, "are there differences in the expression of lipogenic genes between control, MSM whole grain and omega-3 supplemented lambs?", we tested the hypothesis that fortification of lamb diets with omega-3 will lead to a down-regulation of lipogenic genes. Seventy-five six-month old TAW lambs were randomly allocated to the (1) omega-3 oil-fortified grain pellets, (2) unfortified grain pellets (control) or (3) unfortified MSM whole grain pellets diet supplements to generate three treatments of 25 lambs each. The feeding trial lasted 47 days.

RESULTS

From the Kruskal-Wallis test, the results showed a striking disparity in lipogenic gene expression between the three dietary treatments in which the FABP4 gene was significantly up-regulated by 3-folds in the muscles of lambs fed MSM Milling (MSM) whole grain diet compared to the omega-3 and control diets. A negative correlation was observed between FASN gene expression and intramuscular fat (IMF), eicosapentaenoic acid (EPA), total polyunsaturated fatty acids (PUFA), omega-6 polyunsaturated fatty acids (n-6 PUFA) and monounsaturated fatty acids (MUFA). The FABP4 gene expression was positively correlated (P < 0.05) with EPA and docosahexaenoic acid (DHA).

CONCLUSION

Taken together, this study's results suggest that FABP4 and FASN genes perform an important role in the biosynthesis of fatty acids in the ribeye muscle of TAW lambs, and supplementary diet composition is an important factor influencing their expressions.

Identification and Interpretation of eQTL and eGenes for Hodgkin Lymphoma Susceptibility

Genome-wide association studies (GWAS) have revealed approximately 100 genomic signals associated with Hodgkin lymphoma (HL); however, their target genes and underlying mechanisms causing HL susceptibility remain unclear. In this study, transcriptome-wide analysis of expression quantitative trait loci (eQTL) was conducted to identify target genes associated with HL GWAS signals. A mixed model, which explains polygenic regulatory effects by the genomic covariance among individuals, was implemented to discover expression genes (eGenes) using genotype data from 462 European/African individuals. Overall, 80 eGenes were identified to be associated with 20 HL GWAS signals. Enrichment analysis identified apoptosis, immune responses, and cytoskeletal processes as functions of these eGenes. The eGene of rs27524 encodes ERAP1 that can cleave peptides attached to human leukocyte antigen in immune responses; its minor allele may help Reed-Sternberg cells to escape the immune response. The eGene of rs7745098 encodes ALDH8A1 that can oxidize the precursor of acetyl-CoA for the production of ATP; its minor allele may increase oxidization activity to evade apoptosis of pre-apoptotic germinal center B cells. Thus, these minor alleles may be genetic risk factors for HL susceptibility. Experimental studies on genetic risk factors are needed to elucidate the underlying mechanisms of HL susceptibility and improve the accuracy of precision oncology.

Integrative Analysis Between Genome-Wide Association Study and Expression Quantitative Trait Loci Reveals Bovine Muscle Gene Expression Regulatory Polymorphisms Associated With Intramuscular Fat and Backfat Thickness

Understanding the architecture of gene expression is fundamental to unravel the molecular mechanisms regulating complex traits in bovine, such as intramuscular fat content (IMF) and backfat thickness (BFT). These traits are economically important for the beef industry since they affect carcass and meat quality. Our main goal was to identify gene expression regulatory polymorphisms within genomic regions (QTL) associated with IMF and BFT in Nellore cattle. For that, we used RNA-Seq data from 193 Nellore steers to perform SNP calling analysis. Then, we combined the RNA-Seq SNP and a high-density SNP panel to obtain a new dataset for further genome-wide association analysis (GWAS), totaling 534,928 SNPs. GWAS was performed using the Bayes B model. Twenty-one relevant QTL were associated with our target traits. The expression quantitative trait loci (eQTL) analysis was performed using Matrix eQTL with the complete SNP dataset and 12,991 genes, revealing a total of 71,033 cis and 36,497 trans-eQTL (FDR &lt; 0.05). Intersecting with QTL for IMF, we found 231 eQTL regulating the expression levels of 117 genes. Within those eQTL, three predicted deleterious SNPs were identified. We also identified 109 eQTL associated with BFT and affecting the expression of 54 genes. This study revealed genomic regions and regulatory SNPs associated with fat deposition in Nellore cattle. We highlight the transcription factors FOXP4, FOXO3, ZSCAN2, and EBF4, involved in lipid metabolism-related pathways. These results helped us to improve our knowledge about the genetic architecture behind important traits in cattle.

Mixed model-based eQTL analysis reveals lncRNAs associated with regulation of genes involved in sex determination and spermatogenesis: The key to understanding human gender imbalance

BACKGROUND

An imbalance in the prenatal sex ratio in humans may be due to several factors affecting sperm physiology, including genetic features. In this study, we conducted a transcriptome-wide analysis of expression quantitative trait loci (eQTLs) to identify target genes associated with previously described QTLs associated with gender imbalance.

METHODS

A mixed model explaining polygenic effects by genomic covariance among individuals was used to identify the eQTLs using gene expression and genotype data from 462 European/African individuals.

RESULTS

Eight eGenes were associated with four QTLs (P < 4.00 × 10^-5), with strong associations found (P < 4.00 × 10^-8) between rs2485007 and eGenes ANKRD26P3 (P = 3.40 × 10^-9) and LINC00421 (P = 1.35 × 10^-9). ANKRD26P3 and LINC00421 are both lncRNAs associated with the control of testis-dominant genes PELP1, TAF15, NANOG, TEX14, TCF3, ZNF433, ZNF555, TEX37, FATE1, TCP11, and CYLC2 and Y-linked genes SRY and ZFY, as well as several genes with roles in spermatogenesis (ODF1, SPATC1, SPATA3, SPATA31E1, SPERT, SPATA16, MOSPD1, SPATA24, and SPO11) and sex determination (SOX family genes).

CONCLUSIONS

The above eGenes contribute directly or indirectly to gene regulation for sex determination and spermatogenesis, thereby serving as important functional clues for gender-biased selection.

Antagonistic regulatory effects of a single cis-acting expression quantitative trait locus between transcription and translation of the MRPL43 gene

Background

Heterogeneity of expression quantitative trait locus (eQTL) effects have been shown across gene expression processes. Knowledge on how to produce the heterogeneity is quite limited. This study aims to examine fluctuations in differential gene expression by alleles of sequence variants across expression processes.

Results

Genome-wide eQTL analyses with transcriptome-wide gene expression data revealed 20 cis-acting eQTLs associated simultaneously with mRNA expression, ribosome occupancy, and protein abundance. A 97 kb-long eQTL signal for mitochondrial ribosomal protein L43 (MRPL43) covered the gene, showing a heterogeneous effect size on gene products across expression stages. One allele of the eQTL was associated with increased mRNA expression and ribosome occupancy but decreased protein abundance. We examined the heterogeneity and found that the eQTL can be attributed to the independent functions of three nucleotide variants, with a strong linkage. NC_000010.11:g.100987606G > T, upstream of MRPL43, may regulate the binding affinity of transcription factors. NC_000010.11:g.100986746C > G, 3 bp from an MRPL43 splice donor site, may alter the splice site. NC_000010.11:g.100978794A > G, in the isoform with a long 3′-UTR, may strengthen the binding affinity of the microRNA. Individuals with the TGG haplotype at these three variants had higher levels of mRNA expression and ribosome occupancy than individuals with the GCA haplotype but lower protein levels, producing the flipped effect throughout the expression process.

Conclusions

These findings suggest that multiple functional variants in a linkage exert their regulatory functions at different points in the gene expression process, producing a complexity of single eQTLs.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12863-022-01057-7.

Multi-omics research in sarcopenia: Current progress and future prospects

Sarcopenia is a systemic disease with progressive and generalized skeletal muscle dysfunction defined by age-related low muscle mass, high content of muscle slow fibers, and low muscle function. Muscle phenotypes and sarcopenia risk are heritable; however, the genetic architecture and molecular mechanisms underlying sarcopenia remain largely unclear. In recent years, significant progress has been made in determining susceptibility loci using genome-wide association studies. In addition, recent advances in omics techniques, including genomics, epigenomics, transcriptomics, proteomics, and metabolomics, offer new opportunities to identify novel targets to help us understand the pathophysiology of sarcopenia. However, each individual technology cannot capture the entire view of the biological complexity of this disorder, while integrative multi-omics analyses may be able to reveal new insights. Here, we review the latest findings of multi-omics studies for sarcopenia and provide an in-depth summary of our current understanding of sarcopenia pathogenesis. Leveraging multi-omics data could give us a holistic understanding of sarcopenia etiology that may lead to new clinical applications. This review offers guidance and recommendations for fundamental research, innovative perspectives, and preventative and therapeutic interventions for sarcopenia.

Towards the Genetic Architecture of Complex Gene Expression Traits: Challenges and Prospects for eQTL Mapping in Humans

The discovery of expression quantitative trait loci (eQTLs) and their target genes (eGenes) has not only compensated for the limitations of genome-wide association studies for complex phenotypes but has also provided a basis for predicting gene expression. Efforts have been made to develop analytical methods in statistical genetics, a key discipline in eQTL analysis. In particular, mixed model– and deep learning–based analytical methods have been extremely beneficial in mapping eQTLs and predicting gene expression. Nevertheless, we still face many challenges associated with eQTL discovery. Here, we discuss two key aspects of these challenges: 1, the complexity of eTraits with various factors such as polygenicity and epistasis and 2, the voluminous work required for various types of eQTL profiles. The properties and prospects of statistical methods, including the mixed model method, Bayesian inference, the deep learning method, and the integration method, are presented as future directions for eQTL discovery. This review will help expedite the design and use of efficient methods for eQTL discovery and eTrait prediction.

Underestimation of Heritability across the Molecular Layers of the Gene Expression Process

We investigated the extent of the heritability underestimation for molecules from an infinitesimal model in mixed model analysis. To this end, we estimated the heritability of transcription, ribosome occupancy, and translation in lymphoblastoid cell lines from Yoruba individuals. Upon considering all genome-wide nucleotide variants, a considerable underestimation in heritability was observed for mRNA transcription (−0.52), ribosome occupancy (−0.48), and protein abundance (−0.47). We employed a mixed model with an optimal number of nucleotide variants, which maximized heritability, and identified two novel expression quantitative trait loci (eQTLs; p < 1.0 × 10−5): rs11016815 on chromosome 10 that influences the transcription of SCP2, a trans-eGene on chromosome 1—whose expression increases in response to MGMT downregulation-induced apoptosis, the cis-eGene of rs11016815—and rs1041872 on chromosome 11 that influences the ribosome occupancy of CCDC25 on chromosome 8 and whose cis-eGene encodes ZNF215, a transcription factor that potentially regulates the translation speed of CCDC25. Our results suggest that an optimal number of nucleotide variants should be used in a mixed model analysis to accurately estimate heritability and identify eQTLs. Moreover, a heterogeneous covariance structure based on gene identity and the molecular layers of the gene expression process should be constructed to better explain polygenic effects and reduce errors in identifying eQTLs.

An approach for normalization and quality control for NanoString RNA expression data

The NanoString RNA counting assay for formalin-fixed paraffin embedded samples is unique in its sensitivity, technical reproducibility and robustness for analysis of clinical and archival samples. While commercial normalization methods are provided by NanoString, they are not optimal for all settings, particularly when samples exhibit strong technical or biological variation or where housekeeping genes have variable performance across the cohort. Here, we develop and evaluate a more comprehensive normalization procedure for NanoString data with steps for quality control, selection of housekeeping targets, normalization and iterative data visualization and biological validation. The approach was evaluated using a large cohort ($N=\kern0.5em 1649$) from the Carolina Breast Cancer Study, two cohorts of moderate sample size ($N=359$ and$130$) and a small published dataset ($N=12$). The iterative process developed here eliminates technical variation (e.g. from different study phases or sites) more reliably than the three other methods, including NanoString's commercial package, without diminishing biological variation, especially in long-term longitudinal multiphase or multisite cohorts. We also find that probe sets validated for nCounter, such as the PAM50 gene signature, are impervious to batch issues. This work emphasizes that systematic quality control, normalization and visualization of NanoString nCounter data are an imperative component of study design that influences results in downstream analyses.

A comprehensive overview on the genetics of Behçet's disease

Behçet's disease (BD) is a systemic and inflammatory disease, characterized mainly by recurrent oral and genital ulcers, eye involvement, and skin lesions. Although the exact etiopathogenesis of BD remains unrevealed, a bulk of studies have implicated the genetic contributing factors as critical players in disease predisposition. In countries along the Silk Road, human leukocyte antigen (HLA)-B51 has been reported as the strongest genetically associated factor for BD. Genome-wide association studies, local genetic polymorphism studies, and meta-analysis of combined data from Turkish, Iranian, and Japanese populations have also identified new genetic associations with BD. Among these, other HLA alleles such as HLA-B*15, HLA-B*27, HLA-B*57, and HLA-A*26 have been found as independent risk factors for BD, whereas HLA-B*49 and HLA-A*03 are independent protective alleles for BD. Moreover, other genes have also reached the genome-wide significance level of association with BD susceptibility, including IL10, IL23R-IL12RB2, IL12A, CCR1-CCR3, STAT4, TNFAIP3, ERAP1, KLRC4, and FUT2. Also, several rare nonsynonymous variants in TLR4, IL23R, NOD2, and MEFV genes have been reported to be involved in BD pathogenesis. According to genetic determinants in the loci outside the MHC region that are contributed to the host defense, immunity, and inflammation pathways, it is suggested that immune responses to the pathogen as an important environmental factor and mucosal immunity contribute to BD susceptibility.

Regulatory Nucleotide Sequence Signals for Expression of the Genes Encoding Ribosomal Proteins

Ribosomal proteins (RPs) are essential components that translate genetic information from mRNA templates into proteins. Their expressional dysregulation adversely affects the survival and growth of human cells. Nevertheless, little is known about the nucleotide sequences regulating the expression of RPs. Genome-wide associations for expression level of 70 RP genes were conducted across expression stages. Eighteen expression regulatory quantitative trait loci (erQTLs) were identified for protein abundances of 21 RPs (P < 1 × 10^-5), but not for their mRNA expression and ribosome occupancy (P > 1 × 10^-5). These erQTLs for protein abundance (pQTLs) were all trans-acting. Three of the pQTLs were associated with the expression of long noncoding RNAs (lncRNAs). Target genes of these lncRNAs may produce ribosomal components or may control the metabolic cues for ribosome synthesis. mRNAs of the RP genes extensively interact with miRNAs. The protein-specific erQTLs may become engendered by intensive miRNA controls at the translational stage, which in turn can produce RPs efficient in handling instantaneous cell requirements. This study suggests that the expression levels of RPs may be greatly influenced by trans-acting regulation, presumably via interference of miRNAs and target genes of lncRNAs. Further studies are warranted to examine the molecular functions of pQTLs presented in this study and to understand the underlying regulatory mechanisms of gene expression of RPs.

A Multi-Omics Perspective of Quantitative Trait Loci in Precision Medicine

Quantitative trait loci (QTL) analysis is an important approach to investigate the effects of genetic variants identified through an increasing number of large-scale, multidimensional 'omics data sets. In this 'big data' era, the research community has identified a significant number of molecular QTLs (molQTLs) and increased our understanding of their effects. Herein, we review multiple categories of molQTLs, including those associated with transcriptome, post-transcriptional regulation, epigenetics, proteomics, metabolomics, and the microbiome. We summarize approaches to identify molQTLs and to infer their causal effects. We further discuss the integrative analysis of molQTLs through a multi-omics perspective. Our review highlights future opportunities to better understand the functional significance of genetic variants and to utilize the discovery of molQTLs in precision medicine.

Bayesian Inference for Mixed Model-Based Genome-Wide Analysis of Expression Quantitative Trait Loci by Gibbs Sampling

The importance of expression quantitative trait locus (eQTL) has been emphasized in understanding the genetic basis of cellular activities and complex phenotypes. Mixed models can be employed to effectively identify eQTLs by explaining polygenic effects. In these mixed models, the polygenic effects are considered as random variables, and their variability is explained by the polygenic variance component. The polygenic and residual variance components are first estimated, and then eQTL effects are estimated depending on the variance component estimates within the frequentist mixed model framework. The Bayesian approach to the mixed model-based genome-wide eQTL analysis can also be applied to estimate the parameters that exhibit various benefits. Bayesian inferences on unknown parameters are based on their marginal posterior distributions, and the marginalization of the joint posterior distribution is a challenging task. This problem can be solved by employing a numerical algorithm of integrals called Gibbs sampling as a Markov chain Monte Carlo. This article reviews the mixed model-based Bayesian eQTL analysis by Gibbs sampling. Theoretical and practical issues of Bayesian inference are discussed using a concise description of Bayesian modeling and the corresponding Gibbs sampling. The strengths of Bayesian inference are also discussed. Posterior probability distribution in the Bayesian inference reflects uncertainty in unknown parameters. This factor is useful in the context of eQTL analysis where a sample size is too small to apply the frequentist approach. Bayesian inference based on the posterior that reflects prior knowledge, will be increasingly preferred with the accumulation of eQTL data. Extensive use of the mixed model-based Bayesian eQTL analysis will accelerate understanding of eQTLs exhibiting various regulatory functions.