Heritability and genomics of gene expression in peripheral blood

Transcriptomic and Metabolomic Analyses in Monozygotic and Dizygotic Twins

Monozygotic (MZ) and dizygotic (DZ) twins are studied to understand genetic and environmental influences on complex traits, however the mechanisms behind twinning are not completely understood. (Epi)genomic studies identified SNPs associated with DZ twinning and DNA methylation sites with MZ twinning. To find molecular biomarkers of twinning, we compared transcriptomics and metabolomics data from MZ and DZ twins. We analyzed 42,663 RNA transcripts in 1453 MZ twins and 1294 DZ twins from the Netherlands Twin Register (NTR), followed by sex-stratified analyses. The top 5% transcripts with lowest p-values were analyzed for replication in 217 MZ and 158 DZ twins from the older Finnish Twin cohort (FTC). In the NTR, one transcript (PURG) was significantly differentially expressed between MZ and DZ twins; but this did not replicate in FTC. Pathway analyses highlighted the WNT-pathway, previously associated with MZ twinning, and the TGF-B and SMAD pathway, previously associated with DZ twinning. Meta-analysis of 169 serum metabolites in 2797 MZ and 2040 DZ twins from the NTR, FTC and FinnTwin12, showed no metabolomic differences. Overall, we did not find replicable transcript-level expression differences in blood between MZ and DZ twins, but highlighted the TGF-B/SMAD pathway as a potential transcriptional biomarker for DZ twinning.

Deciphering the coordinated roles of the host genome, duodenal mucosal genes, and microbiota in regulating complex traits in chickens

BACKGROUND

The complex interactions between host genetics and the gut microbiome are well documented. However, the specific impacts of gene expression patterns and microbial composition on each other remain to be further explored.

RESULTS

Here, we investigated this complex interplay in a sizable population of 705 hens, employing integrative analyses to examine the relationships among the host genome, mucosal gene expression, and gut microbiota. Specific microbial taxa, such as the cecal family Christensenellaceae, which showed a heritability of 0.365, were strongly correlated with host genomic variants. We proposed a novel concept of regulatability ( r b 2 ), which was derived from h2, to quantify the cumulative effects of gene expression on the given phenotypes. The duodenal mucosal transcriptome emerged as a potent influencer of duodenal microbial taxa, with much higher r b 2 values (0.17 ± 0.01, mean ± SE) than h2 values (0.02 ± 0.00). A comparative analysis of chickens and humans revealed similar average microbiability values of genes (0.18 vs. 0.20) and significant differences in average r b 2 values of microbes (0.17 vs. 0.04). Besides, cis ( h cis 2 ) and trans heritability ( h trans 2 ) were estimated to assess the effects of genetic variations inside and outside the cis window of the gene on its expression. Higher h trans 2 values than h cis 2 values and a greater prevalence of trans-regulated genes than cis-regulated genes underscored the significant role of loci outside the cis window in shaping gene expression levels. Furthermore, our exploration of the regulatory effects of duodenal mucosal genes and the microbiota on 18 complex traits enhanced our understanding of the regulatory mechanisms, in which the CHST14 gene and its regulatory relationships with Lactobacillus salivarius jointly facilitated the deposition of abdominal fat by modulating the concentration of bile salt hydrolase, and further triglycerides, total cholesterol, and free fatty acids absorption and metabolism.

CONCLUSIONS

Our findings highlighted a novel concept of r b 2 to quantify the phenotypic variance attributed to gene expression and emphasize the superior role of intestinal mucosal gene expressions over host genomic variations in elucidating host‒microbe interactions for complex traits. This understanding could assist in devising strategies to modulate host-microbe interactions, ultimately improving economic traits in chickens.

Classification of schizophrenia, bipolar disorder and major depressive disorder with comorbid traits and deep learning algorithms

Many psychiatric disorders share genetic liabilities, but whether these shared liabilities can be utilized to classify and differentiate psychiatric disorders remains unclear. In this study, we use polygenic risk scores (PRSs) of 42 traits comorbid with schizophrenia (SCZ), bipolar disorder (BIP), and major depressive disorder (MDD) to evaluate their utilities. We found that combining target specific PRS with PRSs of comorbid traits can improve the classification of the target disorders. Importantly, without inclusion of PRSs from targeted disorders, we can still classify SCZ (accuracy 0.710 ± 0.008, AUC 0.789 ± 0.011), BIP (accuracy 0.782 ± 0.006, AUC 0.852 ± 0.004), and MDD (accuracy 0.753 ± 0.019, AUC 0.822 ± 0.010). Furthermore, PRSs from comorbid traits alone can effectively differentiate unaffected controls and patients with SCZ, BIP, and MDD (accuracy 0.861 ± 0.003, AUC 0.961 ± 0.041). Our results demonstrate that shared liabilities can be used effectively to improve the classification and differentiation of these disorders. The finding that PRSs from comorbid traits alone can classify and differentiate SCZ, BIP and MDD reasonably well implies that a majority of the risk variants composing target PRSs are shared with comorbid traits. Overall, our results suggest that a data-driven approach may be feasible to classify and differentiate these disorders.

A twin analysis to estimate genetic and environmental factors contributing to variation in weighted gene co-expression network module eigengenes

Multivariate network-based analytic methods such as weighted gene co-expression network analysis are frequently applied to human and animal gene-expression data to estimate the first principal component of a module, or module eigengene (ME). MEs are interpreted as multivariate summaries of correlated gene-expression patterns and network connectivity across genes within a module. As such, they have the potential to elucidate the mechanisms by which molecular genomic variation contributes to individual differences in complex traits. Although increasingly used to test for associations between modules and complex traits, the genetic and environmental etiology of MEs has not been empirically established. It is unclear if, and to what degree, individual differences in blood-derived MEs reflect random variation versus familial aggregation arising from heritable or shared environmental influences. We used biometrical genetic analyses to estimate the contribution of genetic and environmental influences on MEs derived from blood lymphocytes collected on a sample of N = 661 older male twins from the Vietnam Era Twin Study of Aging (VETSA) whose mean age at assessment was 67.7 years (SD = 2.6 years, range = 62-74 years). Of the 26 detected MEs, 14 (56%) had statistically significant additive genetic variation with an average heritability of 44% (SD = 0.08, range = 35%-64%). Despite the relatively small sample size, this demonstration of significant family aggregation including estimates of heritability in 14 of the 26 MEs suggests that blood-based MEs are reliable and merit further exploration in terms of their associations with complex traits and diseases.

Allele frequency impacts the cross-ancestry portability of gene expression prediction in lymphoblastoid cell lines

Population-level genetic studies are overwhelmingly biased toward European ancestries. Transferring genetic predictions from European ancestries to other ancestries results in a substantial loss of accuracy. Yet, it remains unclear how much various genetic factors, such as causal effect differences, linkage disequilibrium (LD) differences, or allele frequency differences, contribute to the loss of prediction accuracy across ancestries. In this study, we used gene expression levels in lymphoblastoid cell lines to understand how much each genetic factor contributes to lowered portability of gene expression prediction from European to African ancestries. We found that cis-genetic effects on gene expression are highly similar between European and African individuals. However, we found that allele frequency differences of causal variants have a striking impact on prediction portability. For example, portability is reduced by more than 32% when the causal cis-variant is common (minor allele frequency, MAF >5%) in European samples (training population) but is rarer (MAF <5%) in African samples (prediction population). While large allele frequency differences can decrease portability through increasing LD differences, we also determined that causal allele frequency can significantly impact portability when the impact from LD is substantially controlled. This observation suggests that improving statistical fine-mapping alone does not overcome the loss of portability resulting from differences in causal allele frequency. We conclude that causal cis-eQTL effects are highly similar in European and African individuals, and allele frequency differences have a large impact on the accuracy of gene expression prediction.

Genome-wide studies define new genetic mechanisms of IgA vasculitis

IgA vasculitis (IgAV) is a pediatric disease with skin and systemic manifestations. Here, we conducted genome, transcriptome, and proteome-wide association studies in 2,170 IgAV cases and 5,928 controls, generated IgAV-specific maps of gene expression and splicing from blood of 255 pediatric cases, and reconstructed myeloid-specific regulatory networks to define disease master regulators modulated by the newly identified disease driver genes. We observed significant association at the HLA-DRB1 (OR=1.55, P=1.1×10-25) and fine-mapped specific amino-acid risk substitutions in DRβ1. We discovered two novel non-HLA loci: FCAR (OR=1.51, P=1.0×10-20) encoding a myeloid IgA receptor FcαR, and INPP5D (OR=1.34, P=2.2×10-9) encoding a known inhibitor of FcαR signaling. The FCAR risk locus co-localized with a cis-eQTL increasing FCAR expression; the risk alleles disrupted a PRDM1 binding motif within a myeloid enhancer of FCAR. Another risk locus was associated with a higher genetically predicted levels of plasma IL6R. The IL6R risk haplotype carried a missense variant contributing to accelerated cleavage of IL6R into a soluble form. Using systems biology approaches, we prioritized IgAV master regulators co-modulated by FCAR, INPP5D and IL6R in myeloid cells. We additionally identified 21 shared loci in a cross-phenotype analysis of IgAV with IgA nephropathy, including novel loci PAID4, WLS, and ANKRD55.

GLP1R Gene Expression and Kidney Disease Progression

Importance

Glucagon-like peptide 1 receptor agonists (GLP-1RAs) may have nephroprotective properties beyond those related to weight loss and glycemic control.

Objective

To investigate the association of genetically proxied GLP-1RAs with kidney disease progression.

Design, Setting, and Participants

This genetic association study assembled a national retrospective cohort of veterans aged 18 years or older from the US Department of Veterans Affairs Million Veteran Program between January 10, 2011, and December 31, 2021. Data were analyzed from November 2023 to February 2024.

Exposures

Genetic risk score for systemic GLP1R gene expression that was calculated for each study participant based on genetic variants associated with GLP1R mRNA levels across all tissue samples within the Genotype-Tissue Expression project.

Main Outcomes and Measures

The primary composite outcome was incident end-stage kidney disease or a 40% decline in estimated glomerular filtration rate. Cox proportional hazards regression survival analysis assessed the association between genetically proxied GLP-1RAs and kidney disease progression.

Results

Among 353 153 individuals (92.5% men), median age was 66 years (IQR, 58.0-72.0 years) and median follow-up was 5.1 years (IQR, 3.1-7.2 years). Overall, 25.7% had diabetes, and 45.0% had obesity. A total of 4.6% experienced kidney disease progression. Overall, higher genetic GLP1R gene expression was associated with a lower risk of kidney disease progression in the unadjusted model (hazard ratio [HR], 0.96; 95% CI, 0.92-0.99; P = .02) and in the fully adjusted model accounting for baseline patient characteristics, body mass index, and the presence or absence of diabetes (HR, 0.96; 95% CI, 0.92-1.00; P = .04). The results were similar in sensitivity analyses stratified by diabetes or obesity status.

Conclusions and Relevance

In this genetic association study, higher GLP1R gene expression was associated with a small reduction in risk of kidney disease progression. These findings support pleiotropic nephroprotective mechanisms of GLP-1RAs independent of their effects on body weight and glycemic control.

Novel insights into the pleiotropic health effects of growth differentiation factor 11 gained from genome-wide association studies in population biobanks

BACKGROUND

Growth differentiation factor 11 (GDF11) is a member of the transforming growth factor-β (TGF-β) superfamily that has gained considerable attention over the last decade for its observed ability to reverse age-related deterioration of multiple tissues, including the heart. Yet as many researchers have struggled to confirm the cardioprotective and anti-aging effects of GDF11, the topic has grown increasingly controversial, and the field has reached an impasse. We postulated that a clearer understanding of GDF11 could be gained by investigating its health effects at the population level.

METHODS AND RESULTS

We employed a comprehensive strategy to interrogate results from genome-wide association studies in population Biobanks. Interestingly, phenome-wide association studies (PheWAS) of GDF11 tissue-specific cis-eQTLs revealed associations with asthma, immune function, lung function, and thyroid phenotypes. Furthermore, PheWAS of GDF11 genetic variants confirmed these results, revealing similar associations with asthma, immune function, lung function, and thyroid health. To complement these findings, we mined results from transcriptome-wide association studies, which uncovered associations between predicted tissue-specific GDF11 expression and the same health effects identified from PheWAS analyses.

CONCLUSIONS

In this study, we report novel relationships between GDF11 and disease, namely asthma and hypothyroidism, in contrast to its formerly assumed role as a rejuvenating factor in basic aging and cardiovascular health. We propose that these associations are mediated through the involvement of GDF11 in inflammatory signaling pathways. Taken together, these findings provide new insights into the health effects of GDF11 at the population level and warrant future studies investigating the role of GDF11 in these specific health conditions.

Heritability of Gene Expression Measured from Peripheral Blood in Older Adults

The contributions of genetic variation and the environment to gene expression may change across the lifespan. However, few studies have investigated the heritability of blood gene expression in older adults. The current study therefore aimed to investigate this question in a community sample of older adults. A total of 246 adults (71 MZ and 52 DZ twins, 69.91% females; mean age-75.79 ± 5.44) were studied. Peripheral blood gene expression was assessed using Illumina microarrays. A heritability analysis was performed using structural equation modelling. There were 5269 probes (19.9%) from 4603 unique genes (23.9%) (total 26,537 probes from 19,256 genes) that were significantly heritable (mean h2 = 0.40). A pathway analysis of the top 10% of significant genes showed enrichment for the immune response and ageing-associated genes. In a comparison with two other gene expression twin heritability studies using adults from across the lifespan, there were 38 out of 9479 overlapping genes that were significantly heritable. In conclusion, our study found ~24% of the available genes for analysis were heritable in older adults, with only a small number common across studies that used samples from across adulthood, indicating the importance of examining gene expression in older age groups.

Control of false discoveries in grouped hypothesis testing for eQTL data

BACKGROUND

Expression quantitative trait locus (eQTL) analysis aims to detect the genetic variants that influence the expression of one or more genes. Gene-level eQTL testing forms a natural grouped-hypothesis testing strategy with clear biological importance. Methods to control family-wise error rate or false discovery rate for group testing have been proposed earlier, but may not be powerful or easily apply to eQTL data, for which certain structured alternatives may be defensible and may enable the researcher to avoid overly conservative approaches.

RESULTS

In an empirical Bayesian setting, we propose a new method to control the false discovery rate (FDR) for grouped hypotheses. Here, each gene forms a group, with SNPs annotated to the gene corresponding to individual hypotheses. The heterogeneity of effect sizes in different groups is considered by the introduction of a random effects component. Our method, entitled Random Effects model and testing procedure for Group-level FDR control (REG-FDR), assumes a model for alternative hypotheses for the eQTL data and controls the FDR by adaptive thresholding. As a convenient alternate approach, we also propose Z-REG-FDR, an approximate version of REG-FDR, that uses only Z-statistics of association between genotype and expression for each gene-SNP pair. The performance of Z-REG-FDR is evaluated using both simulated and real data. Simulations demonstrate that Z-REG-FDR performs similarly to REG-FDR, but with much improved computational speed.

CONCLUSION

Our results demonstrate that the Z-REG-FDR method performs favorably compared to other methods in terms of statistical power and control of FDR. It can be of great practical use for grouped hypothesis testing for eQTL analysis or similar problems in statistical genomics due to its fast computation and ability to be fit using only summary data.

Genetic imputation of kidney transcriptome, proteome and multi-omics illuminates new blood pressure and hypertension targets

Genetic mechanisms of blood pressure (BP) regulation remain poorly defined. Using kidney-specific epigenomic annotations and 3D genome information we generated and validated gene expression prediction models for the purpose of transcriptome-wide association studies in 700 human kidneys. We identified 889 kidney genes associated with BP of which 399 were prioritised as contributors to BP regulation. Imputation of kidney proteome and microRNAome uncovered 97 renal proteins and 11 miRNAs associated with BP. Integration with plasma proteomics and metabolomics illuminated circulating levels of myo-inositol, 4-guanidinobutanoate and angiotensinogen as downstream effectors of several kidney BP genes (SLC5A11, AGMAT, AGT, respectively). We showed that genetically determined reduction in renal expression may mimic the effects of rare loss-of-function variants on kidney mRNA/protein and lead to an increase in BP (e.g., ENPEP). We demonstrated a strong correlation (r = 0.81) in expression of protein-coding genes between cells harvested from urine and the kidney highlighting a diagnostic potential of urinary cell transcriptomics. We uncovered adenylyl cyclase activators as a repurposing opportunity for hypertension and illustrated examples of BP-elevating effects of anticancer drugs (e.g. tubulin polymerisation inhibitors). Collectively, our studies provide new biological insights into genetic regulation of BP with potential to drive clinical translation in hypertension.

Integrative cross-omics and cross-context analysis elucidates molecular links underlying genetic effects on complex traits

Genetic effects on functionally related 'omic' traits often co-occur in relevant cellular contexts, such as tissues. Motivated by the multi-tissue methylation quantitative trait loci (mQTLs) and expression QTLs (eQTLs) analysis, we propose X-ING (Cross-INtegrative Genomics) for cross-omics and cross-context integrative analysis. X-ING takes as input multiple matrices of association statistics, each obtained from different omics data types across multiple cellular contexts. It models the latent binary association status of each statistic, captures the major association patterns among omics data types and contexts, and outputs the posterior mean and probability for each input statistic. X-ING enables the integration of effects from different omics data with varying effect distributions. In the multi-tissue cis-association analysis, X-ING shows improved detection and replication of mQTLs by integrating eQTL maps. In the trans-association analysis, X-ING reveals an enrichment of trans-associations in many disease/trait-relevant tissues.

Classification of Schizophrenia, Bipolar Disorder and Major Depressive Disorder with Comorbid Traits and Deep Learning Algorithms

Recent GWASs have demonstrated that comorbid disorders share genetic liabilities. But whether and how these shared liabilities can be used for the classification and differentiation of comorbid disorders remains unclear. In this study, we use polygenic risk scores (PRSs) estimated from 42 comorbid traits and the deep neural networks (DNN) architecture to classify and differentiate schizophrenia (SCZ), bipolar disorder (BIP) and major depressive disorder (MDD). Multiple PRSs were obtained for individuals from the schizophrenia (SCZ) (cases = 6,317, controls = 7,240), bipolar disorder (BIP) (cases = 2,634, controls 4,425) and major depressive disorder (MDD) (cases = 1,704, controls = 3,357) datasets, and classification models were constructed with and without the inclusion of PRSs of the target (SCZ, BIP or MDD). Models with the inclusion of target PRSs performed well as expected. Surprisingly, we found that SCZ could be classified with only the PRSs from 35 comorbid traits (not including the target SCZ and directly related traits) (accuracy 0.760 ± 0.007, AUC 0.843 ± 0.005). Similar results were obtained for BIP (33 traits, accuracy 0.768 ± 0.007, AUC 0.848 ± 0.009), and MDD (36 traits, accuracy 0.794 ± 0.010, AUC 0.869 ± 0.004). Furthermore, these PRSs from comorbid traits alone could effectively differentiate unaffected controls, SCZ, BIP, and MDD patients (average categorical accuracy 0.861 ± 0.003, average AUC 0.961 ± 0.041). These results suggest that the shared liabilities from comorbid traits alone may be sufficient to classify SCZ, BIP and MDD. More importantly, these results imply that a data-driven and objective diagnosis and differentiation of SCZ, BIP and MDD may be feasible.

Proteins in scalp hair of preschool children

Background

(1)Early childhood experiences have long-lasting effects on subsequent mental and physical health, education, and employment. Measurement of these effects relies on insensitive behavioral signs, subjective assessments by adult observers, neuroimaging or neurophysiological studies, or retrospective epidemiologic outcomes. Despite intensive search, the underlying mechanisms for these long-term changes in development and health status remain unknown.

Methods

(2)We analyzed scalp hair from healthy children and their mothers using an unbiased proteomics platform using tandem mass spectrometry, ultra-performance liquid chromatography, and collision induced dissociation to reveal commonly observed hair proteins with spectral count of 3 or higher.

Results

(3)We observed 1368 non-structural hair proteins in children, 1438 non-structural hair proteins in mothers, with 1288 proteins showing individual variability. Mothers showed higher numbers of peptide spectral matches and hair proteins compared to children, with important age-related differences between mothers and children. Age-related differences were also observed in children, with differential protein expression patterns between younger (2 years and below) and older children (3-5 years). We observed greater similarity in hair protein patterns between mothers and their biological children as compared to mothers and unrelated children. The top 5% proteins driving population variability represent biological pathways associated with brain development, immune signaling, and stress response regulation.

Conclusion

(4)Non-structural proteins observed in scalp hair include promising biomarkers to investigate the long-term developmental changes and health status associated with early childhood experiences.

Large-scale integrative analysis of juvenile idiopathic arthritis for new insight into its pathogenesis

BACKGROUND

Juvenile idiopathic arthritis (JIA) is one of the most prevalent rheumatic disorders in children and is classified as an autoimmune disease (AID). While a robust genetic contribution to JIA etiology has been established, the exact pathogenesis remains unclear.

METHODS

To prioritize biologically interpretable susceptibility genes and proteins for JIA, we conducted transcriptome-wide and proteome-wide association studies (TWAS/PWAS). Then, to understand the genetic architecture of JIA, we systematically analyzed single-nucleotide polymorphism (SNP)-based heritability, a signature of natural selection, and polygenicity. Next, we conducted HLA typing using multi-ethnicity RNA sequencing data. Additionally, we examined the T cell receptor (TCR) repertoire at a single-cell level to explore the potential links between immunity and JIA risk.

RESULTS

We have identified 19 TWAS genes and two PWAS proteins associated with JIA risks. Furthermore, we observe that the heritability and cell type enrichment analysis of JIA are enriched in T lymphocytes and HLA regions and that JIA shows higher polygenicity compared to other AIDs. In multi-ancestry HLA typing, B*45:01 is more prevalent in African JIA patients than in European JIA patients, whereas DQA1*01:01, DQA1*03:01, and DRB1*04:01 exhibit a higher frequency in European JIA patients. Using single-cell immune repertoire analysis, we identify clonally expanded T cell subpopulations in JIA patients, including CXCL13+BHLHE40+ TH cells which are significantly associated with JIA risks.

CONCLUSION

Our findings shed new light on the pathogenesis of JIA and provide a strong foundation for future mechanistic studies aimed at uncovering the molecular drivers of JIA.

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.

A phenome-wide association study of tandem repeat variation in 168,554 individuals from the UK Biobank

Most genetic association studies focus on binary variants. To identify the effects of multi-allelic variation of tandem repeats (TRs) on human traits, we performed direct TR genotyping and phenome-wide association studies in 168,554 individuals from the UK Biobank, identifying 47 TRs showing causal associations with 73 traits. We replicated 23 of 31 (74%) of these causal associations in the All of Us cohort. While this set included several known repeat expansion disorders, novel associations we found were attributable to common polymorphic variation in TR length rather than rare expansions and include e.g. a coding polyhistidine motif in HRCT1 influencing risk of hypertension and a poly(CGC) in the 5'UTR of GNB2 influencing heart rate. Causal TRs were strongly enriched for associations with local gene expression and DNA methylation. Our study highlights the contribution of multi-allelic TRs to the "missing heritability" of the human genome.

Non-additive genetic components contribute significantly to population-wide gene expression variation

Gene expression variation, an essential step between genotype and phenotype, is collectively controlled by local (cis) and distant (trans) regulatory changes. Nevertheless, how these regulatory elements differentially influence gene expression variation remains unclear. Here, we bridge this gap by analyzing the transcriptomes of a large diallel panel consisting of 323 unique hybrids originating from genetically divergent Saccharomyces cerevisiae isolates. Our analysis across 5,087 transcript abundance traits showed that non-additive components account for 36% of the gene expression variance on average. By comparing allele-specific read counts in parent-hybrid trios, we found that trans-regulatory changes underlie the majority of gene expression variation in the population. Remarkably, most cis-regulatory variations are also exaggerated or attenuated by additional trans effects. Overall, we showed that the transcriptome is globally buffered at the genetic level mainly due to trans-regulatory variation in the population.

Distributed eQTL analysis with auxiliary information

Expression quantitative trait locus (eQTL) analysis is a useful tool to identify genetic loci that are associated with gene expression levels. Large collaborative efforts such as the Genotype-Tissue Expression (GTEx) project provide valuable resources for eQTL analysis in different tissues. Most existing methods, however, either focus on one tissue at a time, or analyze multiple tissues to identify eQTLs jointly present in multiple tissues. There is a lack of powerful methods to identify eQTLs in a target tissue while effectively borrowing strength from auxiliary tissues. In this paper, we propose a novel statistical framework to improve the eQTL detection efficacy in the tissue of interest with auxiliary information from other tissues. This framework can enhance the power of the hypothesis test for eQTL effects by incorporating shared and specific effects from multiple tissues into the test statistics. We also devise data-driven and distributed computing approaches for efficient implementation of eQTL detection when the number of tissues is large. Numerical studies in simulation demonstrate the efficacy of the proposed method, and the real data analysis of the GTEx example provides novel insights into eQTL findings in different tissues.

Longitudinal multi-omics study reveals common etiology underlying association between plasma proteome and BMI trajectories in adolescent and young adult twins

BACKGROUND

The influence of genetics and environment on the association of the plasma proteome with body mass index (BMI) and changes in BMI remains underexplored, and the links to other omics in these associations remain to be investigated. We characterized protein-BMI trajectory associations in adolescents and adults and how these connect to other omics layers.

METHODS

Our study included two cohorts of longitudinally followed twins: FinnTwin12 (N = 651) and the Netherlands Twin Register (NTR) (N = 665). Follow-up comprised 4 BMI measurements over approximately 6 (NTR: 23-27 years old) to 10 years (FinnTwin12: 12-22 years old), with omics data collected at the last BMI measurement. BMI changes were calculated in latent growth curve models. Mixed-effects models were used to quantify the associations between the abundance of 439 plasma proteins with BMI at blood sampling and changes in BMI. In FinnTwin12, the sources of genetic and environmental variation underlying the protein abundances were quantified by twin models, as were the associations of proteins with BMI and BMI changes. In NTR, we investigated the association of gene expression of genes encoding proteins identified in FinnTwin12 with BMI and changes in BMI. We linked identified proteins and their coding genes to plasma metabolites and polygenic risk scores (PRS) applying mixed-effects models and correlation networks.

RESULTS

We identified 66 and 14 proteins associated with BMI at blood sampling and changes in BMI, respectively. The average heritability of these proteins was 35%. Of the 66 BMI-protein associations, 43 and 12 showed genetic and environmental correlations, respectively, including 8 proteins showing both. Similarly, we observed 7 and 3 genetic and environmental correlations between changes in BMI and protein abundance, respectively. S100A8 gene expression was associated with BMI at blood sampling, and the PRG4 and CFI genes were associated with BMI changes. Proteins showed strong connections with metabolites and PRSs, but we observed no multi-omics connections among gene expression and other omics layers.

CONCLUSIONS

Associations between the proteome and BMI trajectories are characterized by shared genetic, environmental, and metabolic etiologies. We observed few gene-protein pairs associated with BMI or changes in BMI at the proteome and transcriptome levels.

Genetic analysis of the blood transcriptome of young healthy pigs to improve disease resilience

BACKGROUND

Disease resilience is the ability of an animal to maintain productive performance under disease conditions and is an important selection target. In pig breeding programs, disease resilience must be evaluated on selection candidates without exposing them to disease. To identify potential genetic indicators for disease resilience that can be measured on selection candidates, we focused on the blood transcriptome of 1594 young healthy pigs with subsequent records on disease resilience. Transcriptome data were obtained by 3'mRNA sequencing and genotype data were from a 650 K genotyping array.

RESULTS

Heritabilities of the expression of 16,545 genes were estimated, of which 5665 genes showed significant estimates of heritability (p < 0.05), ranging from 0.05 to 0.90, with or without accounting for white blood cell composition. Genes with heritable expression levels were spread across chromosomes, but were enriched in the swine leukocyte antigen region (average estimate > 0.2). The correlation of heritability estimates with the corresponding estimates obtained for genes expressed in human blood was weak but a sizable number of genes with heritable expression levels overlapped. Genes with heritable expression levels were significantly enriched for biological processes such as cell activation, immune system process, stress response, and leukocyte activation, and were involved in various disease annotations such as RNA virus infection, including SARS-Cov2, as well as liver disease, and inflammation. To estimate genetic correlations with disease resilience, 3205 genotyped pigs, including the 1594 pigs with transcriptome data, were evaluated for disease resilience following their exposure to a natural polymicrobial disease challenge. Significant genetic correlations (p < 0.05) were observed with all resilience phenotypes, although few exceeded expected false discovery rates. Enrichment analysis of genes ranked by estimates of genetic correlations with resilience phenotypes revealed significance for biological processes such as regulation of cytokines, including interleukins and interferons, and chaperone mediated protein folding.

CONCLUSIONS

These results suggest that expression levels in the blood of young healthy pigs for genes in biological pathways related to immunity and endoplasmic reticulum stress have potential to be used as genetic indicator traits to select for disease resilience.

Metabolomic epidemiology offers insights into disease aetiology

Metabolomic epidemiology is the high-throughput study of the relationship between metabolites and health-related traits. This emerging and rapidly growing field has improved our understanding of disease aetiology and contributed to advances in precision medicine. As the field continues to develop, metabolomic epidemiology could lead to the discovery of diagnostic biomarkers predictive of disease risk, aiding in earlier disease detection and better prognosis. In this Review, we discuss key advances facilitated by the field of metabolomic epidemiology for a range of conditions, including cardiometabolic diseases, cancer, Alzheimer's disease and COVID-19, with a focus on potential clinical utility. Core principles in metabolomic epidemiology, including study design, causal inference methods and multi-omic integration, are briefly discussed. Future directions required for clinical translation of metabolomic epidemiology findings are summarized, emphasizing public health implications. Further work is needed to establish which metabolites reproducibly improve clinical risk prediction in diverse populations and are causally related to disease progression.

Phenotypic variance partitioning by transcriptomic gene expression levels and environmental variables for anthropometric traits using GTEx data

Phenotypic variation in human is the results of genetic variation and environmental influences. Understanding the contribution of genetic and environmental components to phenotypic variation is of great interest. The variance explained by genome-wide single nucleotide polymorphisms (SNPs) typically represents a small proportion of the phenotypic variance for complex traits, which may be because the genome is only a part of the whole biological process to shape the phenotypes. In this study, we propose to partition the phenotypic variance of three anthropometric traits, using gene expression levels and environmental variables from GTEx data. We use the gene expression of four tissues that are deemed relevant for the anthropometric traits (two adipose tissues, skeletal muscle tissue and blood tissue). Additionally, we estimate the transcriptome-environment correlation that partly underlies the phenotypes of the anthropometric traits. We found that genetic factors play a significant role in determining body mass index (BMI), with the proportion of phenotypic variance explained by gene expression levels of visceral adipose tissue being 0.68 (SE = 0.06). However, we also observed that environmental factors such as age, sex, ancestry, smoking status, and drinking alcohol status have a small but significant impact (0.005, SE = 0.001). Interestingly, we found a significant negative correlation between the transcriptomic and environmental effects on BMI (transcriptome-environment correlation = -0.54, SE = 0.14), suggesting an antagonistic relationship. This implies that individuals with lower genetic profiles may be more susceptible to the effects of environmental factors on BMI, while those with higher genetic profiles may be less susceptible. We also show that the estimated transcriptomic variance varies across tissues, e.g., the gene expression levels of whole blood tissue and environmental variables explain a lower proportion of BMI phenotypic variance (0.16, SE = 0.05 and 0.04, SE = 0.004 respectively). We observed a significant positive correlation between transcriptomic and environmental effects (1.21, SE = 0.23) for this tissue. In conclusion, phenotypic variance partitioning can be done using gene expression and environmental data even with a small sample size (n = 838 from GTEx data), which can provide insights into how the transcriptomic and environmental effects contribute to the phenotypes of the anthropometric traits.

Non-additive genetic components contribute significantly to population-wide gene expression variation

Gene expression variation, an essential step between genomic variation and phenotypic landscape, is collectively controlled by local (cis) and distant (trans) regulatory changes. Nevertheless, how these regulatory elements differentially influence the heritability of expression traits remains unclear. Here, we bridge this gap by analyzing the transcriptomes of a large diallel panel consisting of 323 unique hybrids originated from genetically divergent yeast isolates. We estimated the broad- and narrow-sense heritability across 5,087 transcript abundance traits and showed that non-additive components account for 36% of the phenotypic variance on average. By comparing allelic expression ratios in the hybrid and the corresponding parental pair, we identified regulatory changes in 25% of all cases, with a majority acting in trans. We further showed that trans-regulation could underlie coordinated expression variation across highly connected genes, resulting in significantly higher non-additive variance and most likely in some of the missing heritability of gene expression traits.

Longitudinal multi-omics study reveals common etiology underlying association between plasma proteome and BMI trajectories in adolescent and young adult twins

Background

The influence of genetics and environment on the association of the plasma proteome with body mass index (BMI) and changes in BMI remain underexplored, and the links to other omics in these associations remain to be investigated. We characterized protein-BMI trajectory associations in adolescents and adults and how these connect to other omics layers.

Methods

Our study included two cohorts of longitudinally followed twins: FinnTwin12 (N=651) and the Netherlands Twin Register (NTR) (N=665). Follow-up comprised four BMI measurements over approximately 6 (NTR: 23-27 years old) to 10 years (FinnTwin12: 12-22 years old), with omics data collected at the last BMI measurement. BMI changes were calculated using latent growth curve models. Mixed-effects models were used to quantify the associations between the abundance of 439 plasma proteins with BMI at blood sampling and changes in BMI. The sources of genetic and environmental variation underlying the protein abundances were quantified using twin models, as were the associations of proteins with BMI and BMI changes. In NTR, we investigated the association of gene expression of genes encoding proteins identified in FinnTwin12 with BMI and changes in BMI. We linked identified proteins and their coding genes to plasma metabolites and polygenic risk scores (PRS) using mixed-effect models and correlation networks.

Results

We identified 66 and 14 proteins associated with BMI at blood sampling and changes in BMI, respectively. The average heritability of these proteins was 35%. Of the 66 BMI-protein associations, 43 and 12 showed genetic and environmental correlations, respectively, including 8 proteins showing both. Similarly, we observed 6 and 4 genetic and environmental correlations between changes in BMI and protein abundance, respectively. S100A8 gene expression was associated with BMI at blood sampling, and the PRG4 and CFI genes were associated with BMI changes. Proteins showed strong connections with many metabolites and PRSs, but we observed no multi-omics connections among gene expression and other omics layers.

Conclusions

Associations between the proteome and BMI trajectories are characterized by shared genetic, environmental, and metabolic etiologies. We observed few gene-protein pairs associated with BMI or changes in BMI at the proteome and transcriptome levels.

Pathogen-specific innate immune response patterns are distinctly affected by genetic diversity

Innate immune responses vary by pathogen and host genetics. We analyze quantitative trait loci (eQTLs) and transcriptomes of monocytes from 215 individuals stimulated by fungal, Gram-negative or Gram-positive bacterial pathogens. We identify conserved monocyte responses to bacterial pathogens and a distinct antifungal response. These include 745 response eQTLs (reQTLs) and corresponding genes with pathogen-specific effects, which we find first in samples of male donors and subsequently confirm for selected reQTLs in females. reQTLs affect predominantly upregulated genes that regulate immune response via e.g., NOD-like, C-type lectin, Toll-like and complement receptor-signaling pathways. Hence, reQTLs provide a functional explanation for individual differences in innate response patterns. Our identified reQTLs are also associated with cancer, autoimmunity, inflammatory and infectious diseases as shown by external genome-wide association studies. Thus, reQTLs help to explain interindividual variation in immune response to infection and provide candidate genes for variants associated with a range of diseases.

Evidence for a maintenance cost for birds maintaining highly flexible basal, but not summit, metabolic rates

Reversible phenotypic flexibility allows organisms to better match phenotypes to prevailing environmental conditions and may produce fitness benefits. Costs and constraints of phenotypic flexibility may limit the capacity for flexible responses but are not well understood nor documented. Costs could include expenses associated with maintaining the flexible system or with generating the flexible response. One potential cost of maintaining a flexible system is an energetic cost reflected in the basal metabolic rate (BMR), with elevated BMR in individuals with more flexible metabolic responses. We accessed data from thermal acclimation studies of birds where BMR and/or M_sum (maximum cold-induced metabolic rate) were measured before and after acclimation, as a measure of metabolic flexibility, to test the hypothesis that flexibility in BMR (ΔBMR), M_sum (ΔM_sum), or metabolic scope (M_sum - BMR; ΔScope) is positively correlated with BMR. When temperature treatments lasted at least three weeks, three of six species showed significant positive correlations between ΔBMR and BMR, one species showed a significant negative correlation, and two species showed no significant correlation. ΔM_sum and BMR were not significantly correlated for any species and ΔScope and BMR were significantly positively correlated for only one species. These data suggest that support costs exist for maintaining high BMR flexibility for some bird species, but high flexibility in M_sum or metabolic scope does not generally incur elevated maintenance costs.

A computational method for cell type-specific expression quantitative trait loci mapping using bulk RNA-seq data

Mapping cell type-specific gene expression quantitative trait loci (ct-eQTLs) is a powerful way to investigate the genetic basis of complex traits. A popular method for ct-eQTL mapping is to assess the interaction between the genotype of a genetic locus and the abundance of a specific cell type using a linear model. However, this approach requires transforming RNA-seq count data, which distorts the relation between gene expression and cell type proportions and results in reduced power and/or inflated type I error. To address this issue, we have developed a statistical method called CSeQTL that allows for ct-eQTL mapping using bulk RNA-seq count data while taking advantage of allele-specific expression. We validated the results of CSeQTL through simulations and real data analysis, comparing CSeQTL results to those obtained from purified bulk RNA-seq data or single cell RNA-seq data. Using our ct-eQTL findings, we were able to identify cell types relevant to 21 categories of human traits.

Maximizing the value of twin studies in health and behaviour

In the classical twin design, researchers compare trait resemblance in cohorts of identical and non-identical twins to understand how genetic and environmental factors correlate with resemblance in behaviour and other phenotypes. The twin design is also a valuable tool for studying causality, intergenerational transmission, and gene-environment correlation and interaction. Here we review recent developments in twin studies, recent results from twin studies of new phenotypes and recent insights into twinning. We ask whether the results of existing twin studies are representative of the general population and of global diversity, and we conclude that stronger efforts to increase representativeness are needed. We provide an updated overview of twin concordance and discordance for major diseases and mental disorders, which conveys a crucial message: genetic influences are not as deterministic as many believe. This has important implications for public understanding of genetic risk prediction tools, as the accuracy of genetic predictions can never exceed identical twin concordance rates.

Assisted clustering of gene expression data using regulatory data from partially overlapping sets of individuals

BACKGROUND

As omics measurements profiled on different molecular layers are interconnected, integrative approaches that incorporate the regulatory effect from multi-level omics data are needed. When the multi-level omics data are from the same individuals, gene expression (GE) clusters can be identified using information from regulators like genetic variants and DNA methylation. When the multi-level omics data are from different individuals, the choice of integration approaches is limited.

METHODS

We developed an approach to improve GE clustering from microarray data by integrating regulatory data from different but partially overlapping sets of individuals. We achieve this through (1) decomposing gene expression into the regulated component and the other component that is not regulated by measured factors, (2) optimizing the clustering goodness-of-fit objective function. We do not require the availability of different omics measurements on all individuals. A certain amount of individual overlap between GE data and the regulatory data is adequate for modeling the regulation, thus improving GE clustering.

RESULTS

A simulation study shows that the performance of the proposed approach depends on the strength of the GE-regulator relationship, degree of missingness, data dimensionality, sample size, and the number of clusters. Across the various simulation settings, the proposed method shows competitive performance in terms of accuracy compared to the alternative K-means clustering method, especially when the clustering structure is due mostly to the regulated component, rather than the unregulated component. We further validate the approach with an application to 8,902 Framingham Heart Study participants with data on up to 17,873 genes and regulation information of DNA methylation and genotype from different but partially overlapping sets of participants. We identify clustering structures of genes associated with pulmonary function while incorporating the predicted regulation effect from the measured regulators. We further investigate the over-representation of these GE clusters in pathways of other diseases that may be related to lung function and respiratory health.

CONCLUSION

We propose a novel approach for clustering GE with the assistance of regulatory data that allowed for different but partially overlapping sets of individuals to be included in different omics data.

Pre-infection antiviral innate immunity contributes to sex differences in SARS-CoV-2 infection

Male sex is a major risk factor for SARS-CoV-2 infection severity. To understand the basis for this sex difference, we studied SARS-CoV-2 infection in a young adult cohort of United States Marine recruits. Among 2,641 male and 244 female unvaccinated and seronegative recruits studied longitudinally, SARS-CoV-2 infections occurred in 1,033 males and 137 females. We identified sex differences in symptoms, viral load, blood transcriptome, RNA splicing, and proteomic signatures. Females had higher pre-infection expression of antiviral interferon-stimulated gene (ISG) programs. Causal mediation analysis implicated ISG differences in number of symptoms, levels of ISGs, and differential splicing of CD45 lymphocyte phosphatase during infection. Our results indicate that the antiviral innate immunity set point causally contributes to sex differences in response to SARS-CoV-2 infection. A record of this paper's transparent peer review process is included in the supplemental information.

Stroke genetics informs drug discovery and risk prediction across ancestries

Previous genome-wide association studies (GWASs) of stroke - the second leading cause of death worldwide - were conducted predominantly in populations of European ancestry1,2. Here, in cross-ancestry GWAS meta-analyses of 110,182 patients who have had a stroke (five ancestries, 33% non-European) and 1,503,898 control individuals, we identify association signals for stroke and its subtypes at 89 (61 new) independent loci: 60 in primary inverse-variance-weighted analyses and 29 in secondary meta-regression and multitrait analyses. On the basis of internal cross-ancestry validation and an independent follow-up in 89,084 additional cases of stroke (30% non-European) and 1,013,843 control individuals, 87% of the primary stroke risk loci and 60% of the secondary stroke risk loci were replicated (P < 0.05). Effect sizes were highly correlated across ancestries. Cross-ancestry fine-mapping, in silico mutagenesis analysis3, and transcriptome-wide and proteome-wide association analyses revealed putative causal genes (such as SH3PXD2A and FURIN) and variants (such as at GRK5 and NOS3). Using a three-pronged approach4, we provide genetic evidence for putative drug effects, highlighting F11, KLKB1, PROC, GP1BA, LAMC2 and VCAM1 as possible targets, with drugs already under investigation for stroke for F11 and PROC. A polygenic score integrating cross-ancestry and ancestry-specific stroke GWASs with vascular-risk factor GWASs (integrative polygenic scores) strongly predicted ischaemic stroke in populations of European, East Asian and African ancestry5. Stroke genetic risk scores were predictive of ischaemic stroke independent of clinical risk factors in 52,600 clinical-trial participants with cardiometabolic disease. Our results provide insights to inform biology, reveal potential drug targets and derive genetic risk prediction tools across ancestries.

Cross-Ancestry Investigation of Venous Thromboembolism Genomic Predictors

BACKGROUND

Venous thromboembolism (VTE) is a life-threatening vascular event with environmental and genetic determinants. Recent VTE genome-wide association studies (GWAS) meta-analyses involved nearly 30 000 VTE cases and identified up to 40 genetic loci associated with VTE risk, including loci not previously suspected to play a role in hemostasis. The aim of our research was to expand discovery of new genetic loci associated with VTE by using cross-ancestry genomic resources.

METHODS

We present new cross-ancestry meta-analyzed GWAS results involving up to 81 669 VTE cases from 30 studies, with replication of novel loci in independent populations and loci characterization through in silico genomic interrogations.

RESULTS

In our genetic discovery effort that included 55 330 participants with VTE (47 822 European, 6320 African, and 1188 Hispanic ancestry), we identified 48 novel associations, of which 34 were replicated after correction for multiple testing. In our combined discovery-replication analysis (81 669 VTE participants) and ancestry-stratified meta-analyses (European, African, and Hispanic), we identified another 44 novel associations, which are new candidate VTE-associated loci requiring replication. In total, across all GWAS meta-analyses, we identified 135 independent genomic loci significantly associated with VTE risk. A genetic risk score of the significantly associated loci in Europeans identified a 6-fold increase in risk for those in the top 1% of scores compared with those with average scores. We also identified 31 novel transcript associations in transcriptome-wide association studies and 8 novel candidate genes with protein quantitative-trait locus Mendelian randomization analyses. In silico interrogations of hemostasis and hematology traits and a large phenome-wide association analysis of the 135 GWAS loci provided insights to biological pathways contributing to VTE, with some loci contributing to VTE through well-characterized coagulation pathways and others providing new data on the role of hematology traits, particularly platelet function. Many of the replicated loci are outside of known or currently hypothesized pathways to thrombosis.

CONCLUSIONS

Our cross-ancestry GWAS meta-analyses identified new loci associated with VTE. These findings highlight new pathways to thrombosis and provide novel molecules that may be useful in the development of improved antithrombosis treatments.

Impact of long-term exposure to PM2.5 on peripheral blood gene expression pathways involved in cell signaling and immune response

BACKGROUND

Exposure to ambient air pollution, even at low levels, is a major environmental health risk. The peripheral blood transcriptome provides a potential avenue for the elucidation of ambient air pollution related biological perturbations. We assessed the association between long-term estimates for seven priority air pollutants and perturbations in peripheral blood transcriptomics data collected in the Dutch National Twin Register (NTR) and Netherlands Study of Depression and Anxiety (NESDA) cohorts.

METHODS

In both the discovery (n = 2438) and replication (n = 1567) cohort, outdoor concentration of 7 air pollutants (NO₂, NO_x, particulate matter (PM_2.5, PM_2.5abs, PM₁₀, PM_coarse)_, and ultrafine particles) was predicted with land use regression models. Gene expression was assessed by Affymetrix U219 arrays. Multi-variable univariate mixed-effect models were applied to test for an association between the air pollutants and the transcriptome. Functional analysis was conducted in DAVID.

RESULTS

In the discovery cohort, we observed for 335 genes (374 probes with FDR < 5 %) a perturbation in peripheral blood gene expression that was associated with long-term average levels of PM_2.5. For 69 genes pooled effect estimates from the NTR and NESDA cohorts were significant. Identified genes play a role in biological pathways related to cell signaling and immune response. Sixty-two out of 69 genes had a similar direction of effect in an analysis in which we regressed the probes on differential PM_2.5 exposure within monozygotic twin pairs, indicating that the observed differences in gene expression were likely driven by differences in air pollution, rather than by confounding by genetic factors.

CONCLUSION

Our results indicate that PM_2.5 can elicit a response in cell signaling and the immune system, both hallmarks of environmental diseases. The differential effect that we observed between air pollutants may aid in the understanding of differential health effects that have been observed with these exposures.

Diverse environmental perturbations reveal the evolution and context-dependency of genetic effects on gene expression levels

There is increasing appreciation that, in addition to being shaped by an individual's genotype and environment, most complex traits are also determined by poorly understood interactions between these two factors. So-called "genotype × environment" (G×E) interactions remain difficult to map at the organismal level but can be uncovered using molecular phenotypes. To do so at large scale, we used TM3'seq to profile transcriptomes across 12 cellular environments in 544 immortalized B cell lines from the 1000 Genomes Project. We mapped the genetic basis of gene expression levels across environments and revealed a context-dependent genetic architecture: The average heritability of gene expression levels increased in treatment relative to control conditions, and on average, each treatment revealed new expression quantitative trait loci (eQTLs) at 11% of genes. Across our experiments, 22% of all identified eQTLs were context-dependent, and this group was enriched for trait- and disease-associated loci. Further, evolutionary analyses suggested that positive selection has shaped G×E loci involved in responding to immune challenges and hormones but not to man-made chemicals. We hypothesize that this reflects a reduced opportunity for selection to act on responses to molecules recently introduced into human environments. Together, our work highlights the importance of considering an exposure's evolutionary history when studying and interpreting G×E interactions, and provides new insight into the evolutionary mechanisms that maintain G×E loci in human populations.

A transcriptome-wide association study of uterine fibroids to identify potential genetic markers and toxic chemicals

Uterine fibroid is one of the most prevalent benign tumors in women, with high socioeconomic costs. Although genome-wide association studies (GWAS) have identified several loci associated with uterine fibroid risks, they could not successfully interpret the biological effects of genomic variants at the gene expression levels. To prioritize uterine fibroid susceptibility genes that are biologically interpretable, we conducted a transcriptome-wide association study (TWAS) by integrating GWAS data of uterine fibroid and expression quantitative loci data. We identified nine significant TWAS genes including two novel genes, RP11-282O18.3 and KBTBD7, which may be causal genes for uterine fibroid. We conducted functional enrichment network analyses using the TWAS results to investigate the biological pathways in which the overall TWAS genes were involved. The results demonstrated the immune system process to be a key pathway in uterine fibroid pathogenesis. Finally, we carried out chemical–gene interaction analyses using the TWAS results and the comparative toxicogenomics database to determine the potential risk chemicals for uterine fibroid. We identified five toxic chemicals that were significantly associated with uterine fibroid TWAS genes, suggesting that they may be implicated in the pathogenesis of uterine fibroid. In this study, we performed an integrative analysis covering the broad application of bioinformatics approaches. Our study may provide a deeper understanding of uterine fibroid etiologies and informative notifications about potential risk chemicals for uterine fibroid.

Transcriptome-wide association study of HIV-1 acquisition identifies HERC1 as a susceptibility gene

The host genetic factors conferring protection against HIV type 1 (HIV-1) acquisition remain elusive, and in particular the contributions of common genetic variants. Here, we performed the largest genome-wide association meta-analysis of HIV-1 acquisition, which included 7,303 HIV-1-positive individuals and 587,343 population controls. We identified 25 independent genetic loci with suggestive association, of which one was genome-wide significant within the major histocompatibility complex (MHC) locus. After exclusion of the MHC signal, linkage disequilibrium score regression analyses revealed a SNP heritability of 21% and genetic correlations with behavioral factors. A transcriptome-wide association study identified 15 susceptibility genes, including HERC1, UEVLD, and HIST1H4K. Convergent evidence from conditional analyses and fine-mapping identified HERC1 downregulation in immune cells as a robust mechanism associated with HIV-1 acquisition. Functional studies on HERC1 and other identified candidates, as well as larger genetic studies, have the potential to further our understanding of the host mechanisms associated with protection against HIV-1.

A resource for integrated genomic analysis of the human liver

In this study, we generated whole-transcriptome RNA-Seq from n = 192 genotyped liver samples and used these data with existing data from the GTEx Project (RNA-Seq) and previous liver eQTL (microarray) studies to create an enhanced transcriptomic sequence resource in the human liver. Analyses of genotype-expression associations show pronounced enrichment of associations with genes of drug response. The associations are primarily consistent across the two RNA-Seq datasets, with some modest variation, indicating the importance of obtaining multiple datasets to produce a robust resource. We further used an empirical Bayesian model to compare eQTL patterns in liver and an additional 20 GTEx tissues, finding that MHC genes, and especially class II genes, are enriched for liver-specific eQTL patterns. To illustrate the utility of the resource to augment GWAS analysis with small sample sizes, we developed a novel meta-analysis technique to combine several liver eQTL data sources. We also illustrate its application using a transcriptome-enhanced re-analysis of a study of neutropenia in pancreatic cancer patients. The associations of genotype with liver expression, including splice variation and its genetic associations, are made available in a searchable genome browser.

eQTL Set-Based Association Analysis Identifies Novel Susceptibility Loci for Barrett Esophagus and Esophageal Adenocarcinoma

BACKGROUND

Over 20 susceptibility single-nucleotide polymorphisms (SNP) have been identified for esophageal adenocarcinoma (EAC) and its precursor, Barrett esophagus (BE), explaining a small portion of heritability.

METHODS

Using genetic data from 4,323 BE and 4,116 EAC patients aggregated by international consortia including the Barrett's and Esophageal Adenocarcinoma Consortium (BEACON), we conducted a comprehensive transcriptome-wide association study (TWAS) for BE/EAC, leveraging Genotype Tissue Expression (GTEx) gene-expression data from six tissue types of plausible relevance to EAC etiology: mucosa and muscularis from the esophagus, gastroesophageal (GE) junction, stomach, whole blood, and visceral adipose. Two analytical approaches were taken: standard TWAS using the predicted gene expression from local expression quantitative trait loci (eQTL), and set-based SKAT association using selected eQTLs that predict the gene expression.

RESULTS

Although the standard approach did not identify significant signals, the eQTL set-based approach identified eight novel associations, three of which were validated in independent external data (eQTL SNP sets for EXOC3, ZNF641, and HSP90AA1).

CONCLUSIONS

This study identified novel genetic susceptibility loci for EAC and BE using an eQTL set-based genetic association approach.

IMPACT

This study expanded the pool of genetic susceptibility loci for EAC and BE, suggesting the potential of the eQTL set-based genetic association approach as an alternative method for TWAS analysis.

The whole blood transcriptional regulation landscape in 465 COVID-19 infected samples from Japan COVID-19 Task Force

Coronavirus disease 2019 (COVID-19) is a recently-emerged infectious disease that has caused millions of deaths, where comprehensive understanding of disease mechanisms is still unestablished. In particular, studies of gene expression dynamics and regulation landscape in COVID-19 infected individuals are limited. Here, we report on a thorough analysis of whole blood RNA-seq data from 465 genotyped samples from the Japan COVID-19 Task Force, including 359 severe and 106 non-severe COVID-19 cases. We discover 1169 putative causal expression quantitative trait loci (eQTLs) including 34 possible colocalizations with biobank fine-mapping results of hematopoietic traits in a Japanese population, 1549 putative causal splice QTLs (sQTLs; e.g. two independent sQTLs at TOR1AIP1), as well as biologically interpretable trans-eQTL examples (e.g., REST and STING1), all fine-mapped at single variant resolution. We perform differential gene expression analysis to elucidate 198 genes with increased expression in severe COVID-19 cases and enriched for innate immune-related functions. Finally, we evaluate the limited but non-zero effect of COVID-19 phenotype on eQTL discovery, and highlight the presence of COVID-19 severity-interaction eQTLs (ieQTLs; e.g., CLEC4C and MYBL2). Our study provides a comprehensive catalog of whole blood regulatory variants in Japanese, as well as a reference for transcriptional landscapes in response to COVID-19 infection.

Limited evidence for blood eQTLs in human sexual dimorphism

BACKGROUND

The genetic underpinning of sexual dimorphism is very poorly understood. The prevalence of many diseases differs between men and women, which could be in part caused by sex-specific genetic effects. Nevertheless, only a few published genome-wide association studies (GWAS) were performed separately in each sex. The reported enrichment of expression quantitative trait loci (eQTLs) among GWAS-associated SNPs suggests a potential role of sex-specific eQTLs in the sex-specific genetic mechanism underlying complex traits.

METHODS

To explore this scenario, we combined sex-specific whole blood RNA-seq eQTL data from 3447 European individuals included in BIOS Consortium and GWAS data from UK Biobank. Next, to test the presence of sex-biased causal effect of gene expression on complex traits, we performed sex-specific transcriptome-wide Mendelian randomization (TWMR) analyses on the two most sexually dimorphic traits, waist-to-hip ratio (WHR) and testosterone levels. Finally, we performed power analysis to calculate the GWAS sample size needed to observe sex-specific trait associations driven by sex-biased eQTLs.

RESULTS

Among 9 million SNP-gene pairs showing sex-combined associations, we found 18 genes with significant sex-biased cis-eQTLs (FDR 5%). Our phenome-wide association study of the 18 top sex-biased eQTLs on >700 traits unraveled that these eQTLs do not systematically translate into detectable sex-biased trait-associations. In addition, we observed that sex-specific causal effects of gene expression on complex traits are not driven by sex-specific eQTLs. Power analyses using real eQTL- and causal-effect sizes showed that millions of samples would be necessary to observe sex-biased trait associations that are fully driven by sex-biased cis-eQTLs. Compensatory effects may further hamper their detection.

CONCLUSIONS

Our results suggest that sex-specific eQTLs in whole blood do not translate to detectable sex-specific trait associations of complex diseases, and vice versa that the observed sex-specific trait associations cannot be explained by sex-specific eQTLs.

Influence of gonadal steroids on cortical surface area in infancy

Sex differences in the human brain emerge as early as mid-gestation and have been linked to sex hormones, particularly testosterone. Here, we analyzed the influence of markers of early sex hormone exposure (polygenic risk score (PRS) for testosterone, salivary testosterone, number of CAG repeats, digit ratios, and PRS for estradiol) on the growth pattern of cortical surface area in a longitudinal cohort of 722 infants. We found PRS for testosterone and right-hand digit ratio to be significantly associated with surface area, but only in females. PRS for testosterone at the most stringent P value threshold was positively associated with surface area development over time. Higher right-hand digit ratio, which is indicative of low prenatal testosterone levels, was negatively related to surface area in females. The current work suggests that variation in testosterone levels during both the prenatal and postnatal period may contribute to cortical surface area development in female infants.

Integrative transcriptome-wide analysis of atopic dermatitis for drug repositioning

Atopic dermatitis (AD) is one of the most common inflammatory skin diseases, which significantly impact the quality of life. Transcriptome-wide association study (TWAS) was conducted to estimate both transcriptomic and genomic features of AD and detected significant associations between 31 expression quantitative loci and 25 genes. Our results replicated well-known genetic markers for AD, as well as 4 novel associated genes. Next, transcriptome meta-analysis was conducted with 5 studies retrieved from public databases and identified 5 additional novel susceptibility genes for AD. Applying the connectivity map to the results from TWAS and meta-analysis, robustly enriched perturbations were identified and their chemical or functional properties were analyzed. Here, we report the first research on integrative approaches for an AD, combining TWAS and transcriptome meta-analysis. Together, our findings could provide a comprehensive understanding of the pathophysiologic mechanisms of AD and suggest potential drug candidates as alternative treatment options.

Integrative genomic and transcriptomic analyses on publicly available data-sets together with in silico drug repositioning identifies alternative therapeutic options to treat atopic dermatitis.

TwinEQTL: Ultra Fast and Powerful Association Analysis for eQTL and GWAS in Twin Studies

We develop a computationally efficient alternative, TwinEQTL, to a linear mixed-effects model (LMM) for twin genome-wide association study (GWAS) data. Instead of analyzing all twin samples together with LMM, TwinEQTL first splits twin samples into two independent groups on which multiple linear regression analysis can be validly performed separately, followed by an appropriate meta-analysis-like approach to combine the two non-independent test results. Through mathematical derivations, we prove the validity of TwinEQTL algorithm and show that the correlation between two dependent test statistics at each single-nucleotide polymorphism (SNP) are independent of its minor allele frequency (MAF). Thus the correlation is constant across all SNPs. Through simulations, we show empirically that TwinEQTL has well controlled type I error with negligible power loss compared to the gold-standard linear mixed effects models. To accommodate eQTL analysis with twin subjects, we further implement TwinEQTL into a R package with much improved computational efficiency. Our approaches provide a significant leap in terms of computing speed for GWAS and eQTL analysis with twin samples.

Transcriptome-based identification of novel endotypes in adult atopic dermatitis

BACKGROUND

Atopic dermatitis (AD) is a frequent and heterogeneous inflammatory skin disease, for which personalized medicine remains a challenge. High-throughput approaches have improved understanding of the complex pathophysiology of AD. However, a purely data-driven AD classification is still lacking.

METHODS

To address this question, we applied an original unsupervised approach on the largest available transcriptome dataset of AD lesional (n = 82) and healthy (n = 213) skin biopsies.

RESULTS

Taking into account pathological and physiological state, a variance-based filtering revealed 222 AD-specific hyper-variable genes that efficiently classified the AD samples into 4 clusters that turned out to be clinically and biologically distinct. Comparison of gene expressions between clusters identified 3 sets of upregulated genes used to derive metagenes (MGs): MG-I (19 genes) was associated with IL-1 family signaling (including IL-36A and 36G) and skin remodeling, MG-II (23 genes) with negative immune regulation (including IL-34 and 37) and skin architecture, and MG-III (17 genes) with B lymphocyte immunity. Sample clusters differed in terms of disease severity (p = .02) and S. aureus (SA) colonization (p = .02). Cluster 1 contained the most severe AD, highest SA colonization, and overexpressed MG-I. Cluster 2 was characterized by less severe AD, low SA colonization, and high MG-II expression. Cluster 3 included mild AD, mild SA colonization, and mild expression of all MGs. Cluster 4 had the same clinical features as cluster 3 but had hyper-expression of MG-III. Last, we successfully validated our method and results in an independent cohort.

CONCLUSION

Our study revealed unrecognized AD endotypes with specific underlying biological pathways, highlighting novel pathophysiological mechanisms. These data could provide new insights into personalized treatment strategies.

Immune disease variants modulate gene expression in regulatory CD4+ T cells

Identifying cellular functions dysregulated by disease-associated variants could implicate novel pathways for drug targeting or modulation in cell therapies. However, follow-up studies can be challenging if disease-relevant cell types are difficult to sample. Variants associated with immune diseases point toward the role of CD4+ regulatory T cells (Treg cells). We mapped genetic regulation (quantitative trait loci [QTL]) of gene expression and chromatin activity in Treg cells, and we identified 133 colocalizing loci with immune disease variants. Colocalizations of immune disease genome-wide association study (GWAS) variants with expression QTLs (eQTLs) controlling the expression of CD28 and STAT5A, involved in Treg cell activation and interleukin-2 (IL-2) signaling, support the contribution of Treg cells to the pathobiology of immune diseases. Finally, we identified seven known drug targets suitable for drug repurposing and suggested 63 targets with drug tractability evidence among the GWAS signals that colocalized with Treg cell QTLs. Our study is the first in-depth characterization of immune disease variant effects on Treg cell gene expression modulation and dysregulation of Treg cell function.

Gene-based association tests using GWAS summary statistics and incorporating eQTL

Although genome-wide association studies (GWAS) have been successfully applied to a variety of complex diseases and identified many genetic variants underlying complex diseases via single marker tests, there is still a considerable heritability of complex diseases that could not be explained by GWAS. One alternative approach to overcome the missing heritability caused by genetic heterogeneity is gene-based analysis, which considers the aggregate effects of multiple genetic variants in a single test. Another alternative approach is transcriptome-wide association study (TWAS). TWAS aggregates genomic information into functionally relevant units that map to genes and their expression. TWAS is not only powerful, but can also increase the interpretability in biological mechanisms of identified trait associated genes. In this study, we propose a powerful and computationally efficient gene-based association test, called Overall. Using extended Simes procedure, Overall aggregates information from three types of traditional gene-based association tests and also incorporates expression quantitative trait locus (eQTL) information into a gene-based association test using GWAS summary statistics. We show that after a small number of replications to estimate the correlation among the integrated gene-based tests, the p values of Overall can be calculated analytically. Simulation studies show that Overall can control type I error rates very well and has higher power than the tests that we compared with. We also apply Overall to two schizophrenia GWAS summary datasets and two lipids GWAS summary datasets. The results show that this newly developed method can identify more significant genes than other methods we compared with.

A novel cis-regulatory variant modulating TIE1 expression associated with attention deficit hyperactivity disorder in Han Chinese children

BACKGROUND

The genetic factors of attention deficit hyperactivity disorder (ADHD) are far from fully elucidated. This study aims to get additional insight into the genetic structure of ADHD.

METHODS

First, a transcriptome-wide association study and summary data-based Mendelian randomization analysis were performed to identify ADHD susceptibility genes. Then, genetic variants influencing the expression of the identified susceptibility genes were tested for association with ADHD risk in a sample of Han Chinese children (543 cases and 560 controls). Dual-luciferase reporter gene assays and electrophoretic mobility shift assays were performed to verify the transcriptional regulatory functions of the identified ADHD-associated variants. Additionally, real-time quantitative polymerase chain reaction was applied to quantify the expression levels of target genes in blood samples.

RESULTS

Both TIE1 and MED8 were identified as ADHD susceptibility genes. Furthermore, we first found the G allele of rs3768046 was significantly associated with an increased risk of ADHD (recessive model: GG vs AA+AG, OR= 1.659, 95% CI= (1.262, 2.181); additive model: GG vs GA vs AA, OR= 1.493, 95% CI= (1.179, 1.890)). Additionally, in vitro functional experiments revealed that rs3768046 might alter TIE1 expression by affecting the binding sites of transcription factors. Moreover, the expression level of TIE1 in the blood samples of patients was significantly higher than that of controls.

LIMITATIONS

Given the moderate statistical power of this study, it is necessary to verify our findings in other larger samples.

CONCLUSIONS

Together, this study presents the first systematic evidence of TIE1 with potential implications for the genetic basis of ADHD.

Multiple epistatic DNA variants in a single gene affect gene expression in trans

DNA variants that alter gene expression in trans are important sources of phenotypic variation. Nevertheless, the identity of trans-acting variants remains poorly understood. Single causal variants in several genes have been reported to affect the expression of numerous distant genes in trans. Whether these simple molecular architectures are representative of trans-acting variation is unknown. Here, we studied the large RAS signaling regulator gene IRA2, which contains variants with extensive trans-acting effects on gene expression in the yeast Saccharomyces cerevisiae. We used systematic CRISPR-based genome engineering and a sensitive phenotyping strategy to dissect causal variants to the nucleotide level. In contrast to the simple molecular architectures known so far, IRA2 contained at least seven causal nonsynonymous variants. The effects of these variants were modulated by nonadditive, epistatic interactions. Two variants at the 5'-end affected gene expression and growth only when combined with a third variant that also had no effect in isolation. Our findings indicate that the molecular basis of trans-acting genetic variation may be considerably more complex than previously appreciated.

Electronic health record-based genome-wide meta-analysis provides insights on the genetic architecture of non-alcoholic fatty liver disease

Non-alcoholic fatty liver disease (NAFLD) is a complex disease linked to several chronic diseases. We aimed at identifying genetic variants associated with NAFLD and evaluating their functional consequences. We performed a genome-wide meta-analysis of 4 cohorts of electronic health record-documented NAFLD in participants of European ancestry (8,434 cases and 770,180 controls). We identify 5 potential susceptibility loci for NAFLD (located at or near GCKR, TR1B1, MAU2/TM6SF2, APOE, and PNPLA3). We also report a potentially causal effect of lower LPL expression in adipose tissue on NAFLD susceptibility and an effect of the FTO genotype on NAFLD. Positive genetic correlations between NAFLD and cardiometabolic diseases and risk factors such as body fat accumulation/distribution, lipoprotein-lipid levels, insulin resistance, and coronary artery disease and negative genetic correlations with parental lifespan, socio-economic status, and acetoacetate levels are observed. This large GWAS meta-analysis reveals insights into the genetic architecture of NAFLD.