A simple new approach to variable selection in regression, with application to genetic fine mapping

Bayesian Variance Change Point Detection With Credible Sets

This paper introduces a novel Bayesian approach to detect changes in the variance of a Gaussian sequence model, focusing on quantifying the uncertainty in the change point locations and providing a scalable algorithm for inference. We do that by framing the problem as a product of multiple single changes in the scale parameter. We fit the model through an iterative procedure similar to what is done for additive models. The novelty is that each iteration returns a probability distribution on time instances, which captures the uncertainty in the change point location. Leveraging a recent result in the literature, we can show that our proposal is a variational approximation of the exact model posterior distribution. We study the convergence of the algorithm and the change point localization rate. Extensive experiments in simulation studies and applications to biological data illustrate the performance of our method.

Integration of transcriptome-wide association study and gene-based association analysis identifies candidate genes for Hodgkin lymphoma

BACKGROUND

Genome-wide association studies (GWASs) have pinpointed many susceptibility loci for Hodgkin Lymphoma (HL), but their underlying biological mechanisms remain unclear.

METHODS

Utilizing GWAS data from the UK Biobank and FinnGen, along with expression quantitative trait loci (eQTL) statistics from the Genotype-Tissue Expression (GTEx) and the eQTL Catalogue, we carried out a large-scale gene-level association study using Omnibus Transcriptome Test with Expression Reference Summary data (OTTERS), and gene-based analysis with eQTL Multi-marker Analysis of Genomic Annotation (E-MAGMA).

RESULTS

We identified sixteen susceptibility genes for HL (FDR < 0.01), primarily immune-related, including HLA-DQA1, HLA-DQA2, HLA-DQB1, HLA-DRB1, HLA-DRB5, HLA-DMA, and HLA-DPB1, alongside genes involved in apoptosis, RNA processing, transcriptional regulation, and signal transduction. We identified five novel plausible genes, including HLA-DMA, HLA-DPB1, LSM2, AAR2, and NOTCH4.

CONCLUSION

These findings highlight the role of the exogenous antigen presentation pathway in HL, shedding light on potential mechanisms.

Bayesian fine-mapping and Mendelian randomization leveraging expression quantitative trait loci reveal novel candidate causal genes for body conformation traits in cattle

Cattle body size measurements constitute the conformation traits that facilitate their production, fertility, and longevity status. Prioritizing functional variants and causal genes of conformation traits is essential for understanding their genetic basis. In this study, we conducted single-trait and multitrait GWAS for 20 body conformation traits using imputed sequence data in 7,674 Chinese Holstein individuals and identified 27 QTL regions. Leveraging these QTL regions, we performed multitrait Bayesian fine-mapping to identify 30 independent credible sets of putative causal variants. Incorporating GWAS and cis-acting expression QTL data, Mendelian randomization was used to infer 153 putative causal gene-trait relationships. The previously reported genes, such as CCND2, TMTC2, and NRG3, were confirmed in our study. Of note, several novel candidate causal genes were also identified, such as C1R, RIMS1, SERPINB8, NETO2, TTYH3, TTC3, ANAPC4, and PSMD13. Our results provide new insights into the regulatory mechanisms of body conformation traits in cattle.

Methodological opportunities in genomic data analysis to advance health equity

The causes and consequences of inequities in genomic research and medicine are complex and widespread. However, it is widely acknowledged that underrepresentation of diverse populations in human genetics research risks exacerbating existing health disparities. Efforts to improve diversity are ongoing, but an often-overlooked source of inequity is the choice of analytical methods used to process, analyse and interpret genomic data. This choice can influence all areas of genomic research, from genome-wide association studies and polygenic score development to variant prioritization and functional genomics. New statistical and machine learning techniques to understand, quantify and correct for the impact of biases in genomic data are emerging within the wider genomic research and genomic medicine ecosystems. At this crucial time point, it is important to clarify where improvements in methods and practices can, or cannot, have a role in improving equity in genomics. Here, we review existing approaches to promote equity and fairness in statistical analysis for genomics, and propose future methodological developments that are likely to yield the most impact for equity.

Improved genetic discovery and fine-mapping resolution through multivariate latent factor analysis of high-dimensional traits

Genome-wide association studies (GWASs) of high-dimensional traits, such as blood cell or metabolic traits, often use univariate approaches, ignoring trait relationships. Biological mechanisms generating variation in high-dimensional traits can be captured parsimoniously through a GWAS of latent factors. Here, we introduce flashfmZero, a zero-correlation latent-factor-based multi-trait fine-mapping approach. In an application to 25 latent factors derived from 99 blood cell traits in the INTERVAL cohort, we show that latent factor GWASs enable the detection of signals generating sub-threshold associations with several blood cell traits. The 99% credible sets (CS99) from flashfmZero were equal to or smaller in size than those from univariate fine-mapping of blood cell traits in 87% of our comparisons. In all cases univariate latent factor CS99 contained those from flashfmZero. Our latent factor approaches can be applied to GWAS summary statistics and will enhance power for the discovery and fine-mapping of associations for many traits.

Variant-to-function approaches for adipose tissue: Insights into cardiometabolic disorders

Genome-wide association studies (GWASs) have identified thousands of genetic loci associated with cardiometabolic disorders. However, the functional interpretation of these loci remains a daunting challenge. This is particularly true for adipose tissue, a critical organ in systemic metabolism and the pathogenesis of various cardiometabolic diseases. We discuss how variant-to-function (V2F) approaches are used to elucidate the mechanisms by which GWAS loci increase the risk of cardiometabolic disorders by directly influencing adipose tissue. We outline GWAS traits most likely to harbor adipose-related variants and summarize tools to pinpoint the putative causal variants, genes, and cell types for the associated loci. We explain how large-scale perturbation experiments, coupled with imaging and multi-omics, can be used to screen variants' effects on cellular phenotypes and how these phenotypes can be tied to physiological mechanisms. Lastly, we discuss the challenges and opportunities that lie ahead for V2F research and propose a roadmap for future studies.

Predicting gene expression from DNA sequence using deep learning models

Transcription of genes is regulated by DNA elements such as promoters and enhancers, the activity of which are in turn controlled by many transcription factors. Owing to the highly complex combinatorial logic involved, it has been difficult to construct computational models that predict gene activity from DNA sequence. Recent advances in deep learning techniques applied to data from epigenome mapping and high-throughput reporter assays have made substantial progress towards addressing this complexity. Such models can capture the regulatory grammar with remarkable accuracy and show great promise in predicting the effects of non-coding variants, uncovering detailed molecular mechanisms of gene regulation and designing synthetic regulatory elements for biotechnology. Here, we discuss the principles of these approaches, the types of training data sets that are available and the strengths and limitations of different approaches.

A flexible machine learning Mendelian randomization estimator applied to predict the safety and efficacy of sclerostin inhibition

Mendelian randomization (MR) enables the estimation of causal effects while controlling for unmeasured confounding factors. However, traditional MR's reliance on strong parametric assumptions can introduce bias if these are violated. We describe a machine learning MR estimator named quantile instrumental variable (Quantile IV) that achieves a low estimation error in a wide range of plausible MR scenarios. Quantile IV is distinctive in its ability to estimate nonlinear and heterogeneous causal effects and offers a flexible approach for subgroup analysis. Applying quantile IV, we investigate the impact of circulating sclerostin levels on heel bone mineral density, osteoporosis, and cardiovascular outcomes. Employing various MR estimators and colocalization techniques, our analysis reveals that a genetically predicted reduction in sclerostin levels significantly increases heel bone mineral density and reduces the risk of osteoporosis while showing no discernible effect on ischemic cardiovascular diseases. As a second application, we estimated the effect of increases in low-density lipoprotein cholesterol and waist-to-hip ratio on ischemic cardiovascular diseases using this well-known association as a positive control analysis. Quantile IV contributes to the advancement of MR methodology, and the selected applications demonstrate the applicability of our estimator in various MR contexts.

Mining Genetic Variations Reveals the Differentiation of Gene Alternative Polyadenylation Involving in Rice Panicle Architecture Regulation

Panicle architecture is a critical determinant of rice yield and resilience, yet the genetic and environmental factors shaping this trait remain incompletely understood. Here, we applied an integrative genomic approach combining multi-locus association mapping, transcriptome analysis and population genomics to dissect the genetic basis of key panicle traits in rice. We identified robust genetic loci underlying the number of primary branches, panicle length and spikelets per panicle, with many showing sensitivity to temperature, underscoring the importance of gene-environment interactions for yield stability. Notably, we discovered that variation in alternative polyadenylation (APA) of specific transcripts is associated with panicle trait diversity at the population level, suggesting that regulatory mechanisms such as APA are significant contributors to phenotypic plasticity and adaptation. These findings deliver both novel candidate genes in panicle development and mechanistic insights to support the breeding of rice varieties with enhanced productivity and climate resilience.

Human genetic variation determines 24-hour rhythmic gene expression and disease risk

24-hour biological rhythms are essential to maintain physiological homeostasis. Disruption of these rhythms increases the risks of multiple diseases. Biological rhythms are known to have a genetic basis formed by core clock genes, but how individual genetic variation shapes the oscillating transcriptome and contributes to human chronophysiology and disease risk is largely unknown. Here, we mapped interactions between temporal gene expression and genotype to identify quantitative trait loci (QTLs) contributing to rhythmic gene expression. These newly identified QTLs were termed as rhythmic QTLs (rhyQTLs), which determine previously unappreciated rhythmic genes in human subpopulations with specific genotypes. Functionally, rhyQTLs and their associated rhythmic genes contribute extensively to essential chronophysiological processes, including bile acid and lipid metabolism. The identification of rhyQTLs sheds light on the genetic mechanisms of gene rhythmicity, offers mechanistic insights into variations in human disease risk, and enables precision chronotherapeutic approaches for patients.

JOB: Japan Omics Browser provides integrative visualization of multi-omics data

We present the Japan Omics Browser (JOB), which enables integrative analysis of human omics at different layers. JOB offers visualization of per-variant regulatory effects in the human blood at mRNA and protein level distinctively, quantified from statistical fine-mapping of mRNA-expression quantitative loci (eQTL) and protein QTLs (pQTLs) in 1,405 Japanese, together with fine-mapping results of 94 complex traits in UK Biobank. In addition, JOB shows per-tissue regulatory effect prediction score (EMS), trained via multi-task learning. Furthermore, validation scores from Massively Parallel Reporter Assay (MPRA) in two cell types are available for over 10,000 variants. JOB is publicly available at https://japan-omics.jp/ .

Integrating a multi-omics strategy framework to screen potential targets in cognitive impairment-related epilepsy

Epilepsy is a prevalent neurological disorder that affects over 70 million individuals worldwide and is often associated with cognitive impairments. Despite the widespread impact of epilepsy and cognitive impairments, the genetic basis and causal relationships underlying these conditions remain uncertain, prompting us to conduct a comprehensive investigation into the molecular mechanisms involved. In this study, we utilized statistical data from the third National Health and Nutrition Examination Survey (NHANES III) to evaluate correlation and large-scale pan-phenotype genome-wide association study (GWAS) data to establish genetic correlation and causality. Leveraging multi-omics datasets, we performed a comprehensive post-analysis that included variant prioritization, gene analysis, tissue and cell type enrichment, and pathway annotation. An integrated strategy-multi-trait analysis of GWAS (MTAG), transcriptome-wide association study (TWAS), summary-data-based Mendelian Randomization (SMR), and protein quantitative trait locus (pQTL)-MR-was performed to investigate the shared genetic architecture. Based on multiple orthogonal lines of evidence, we thereby identified 40 single nucleotide polymorphisms (SNPs) and 85 genes common to both conditions. Additionally, we optimized candidate genes such as GNAQ, FADS1, and PTK2 by single-cell expression analysis and molecular pathway mechanisms, thereby highlighting potential shared genetic pathways. These findings elucidate the genetic interplay and co-occurring mechanisms between epilepsy and cognitive impairments, providing crucial insights for future research and therapeutic strategies.

A 3D Genome Atlas of Genetic Variants and Their Pathological Effects in Cancer

The hierarchical organization of the eukaryotic genome is crucial for nuclear activities and cellular development. Genetic aberrations can disrupt this 3D genomic architecture, potentially driving oncogenesis. However, current research often lacks a comprehensive perspective, focusing on specific mutation types and singular 3D structural levels. Here, pathological changes from chromosomes to nucleotides are systematically cataloged, including 10 789 interchromosomal translocations (ICTs), 18 863 structural variants (SVs), and 162 769 single nucleotide polymorphisms (SNPs). The multilayered analysis reveals that fewer than 10% of ICTs disrupt territories via potent 3D interactions, and only a minimal fraction of SVs disrupt compartments or intersect topologically associated domain structures, yet these events significantly influence gene expression. Pathogenic SNPs typically show reduced interactions within the 3D genomic space. To investigate the effects of variants in the context of 3D organization, a two-phase scoring algorithm, 3DFunc, is developed to evaluate the pathogenicity of variant-gene pairs in cancer. Using 3DFunc, IGHV3-23's critical role in chronic lymphocytic leukemia is identified and it is found that three pathological SNPs (rs6605578, rs7814783, rs2738144) interact with DEFA3. Additionally, 3DGAtlas is introduced, which provides a highly accessible 3D genome atlas and a valuable resource for exploring the pathological effects of genetic mutations in cancer.

Brain and blood transcriptome-wide association studies identify five novel genes associated with Alzheimer's disease

BackgroundGenome-wide association studies (GWAS) have identified numerous genetic variants associated with Alzheimer's disease (AD), but their functional implications remain unclear. Transcriptome-wide association studies (TWAS) offer enhanced statistical power by analyzing genetic associations at the gene level rather than at the variant level, enabling assessment of how genetically-regulated gene expression influences AD risk. However, previous AD-TWAS have been limited by small expression quantitative trait loci (eQTL) reference datasets or reliance on AD-by-proxy phenotypes.ObjectiveTo perform the most powerful AD-TWAS to date using summary statistics from the largest available brain and blood cis-eQTL meta-analyses applied to the largest clinically-adjudicated AD GWAS.MethodsWe implemented the OTTERS TWAS pipeline to predict gene expression using the largest available cis-eQTL data from cortical brain tissue (MetaBrain; N = 2683) and blood (eQTLGen; N = 31,684), and then applied these models to AD-GWAS data (Cases = 21,982; Controls = 44,944).ResultsWe identified and validated five novel gene associations in cortical brain tissue (PRKAG1, C3orf62, LYSMD4, ZNF439, SLC11A2) and six genes proximal to known AD-related GWAS loci (Blood: MYBPC3; Brain: MTCH2, CYB561, MADD, PSMA5, ANXA11). Further, using causal eQTL fine-mapping, we generated sparse models that retained the strength of the AD-TWAS association for MTCH2, MADD, ZNF439, CYB561, and MYBPC3.ConclusionsOur comprehensive AD-TWAS discovered new gene associations and provided insights into the functional relevance of previously associated variants, which enables us to further understand the genetic architecture underlying AD risk.

Unravelling novel and closely linked association signals for fat-related traits in pigs using prioritised variants from whole-genome sequence data

For most production traits, the largest proportions of genetic variance remain unmapped. Dense whole-genome sequence (WGS) data enable the possibility of discovering novel associations as well as unravelling closely linked association signals with a resolution that marker arrays cannot reach. However, the identification of variants from WGS data that are causal of the variation of complex traits is hindered by the high dimensionality and linkage disequilibrium. Thus, at best, we can narrow the circle around the causal variants to prioritise a set of variants for their posterior validation. In this study, we assessed the utility of WGS data for uncovering associations of weaker effects using, as a model, fat content and composition traits in a Duroc pig population where we previously described major effects of the LEPR and SCD genes. We genotyped 971 pigs for a set of 182 variants from 154 candidate genes that were prioritised from amongst the WGS variants discovered in 205 sequenced individuals. These variants were prioritised conditional to LEPR and SCD. The association of the prioritised variants with the target traits was then tested in the confirmation set of 971 pigs. A total of 17 potentially independent quantitative trait loci (8.4% of the total number of studied genes) were significantly associated (q-value < 0.05) with at least one of the studied traits. We identified novel associations attributable to genes such as ABCC2, MOGAT2, or PLPP1 for backfat thickness, myristic acid content, and monounsaturated fatty acid content, respectively. Our results also revealed a finer granularity of weaker genetic effects in loci such as those around the DGAT2 and FADS2 genes, which may mask the effects of closely located genes like MOGAT2 and DAGLA, respectively. To refine the prioritisation of variants for validation studies, especially when targeting those of weaker effects, we recommend larger and more diverse discovery sets, more precise and complete functional gene annotation, and the integration of other omics data.

Fine-mapping in admixed populations using CARMA-X, with applications to Latin American studies

Genome-wide association studies (GWASs) in ancestrally diverse populations are rapidly expanding, opening up unique opportunities for novel gene discoveries and increased utility of genetic findings in non-European individuals. A popular technique to identify putative causal variants at GWAS loci is via statistical fine-mapping. Despite tremendous efforts, fine-mapping remains a very challenging task, even in the relatively simple scenario of studies with a single, homogeneous population. For studies with admixed individuals, such as within Latin America and the Caribbean, methods for gene discovery are still limited. Here, we propose a Bayesian model for fine-mapping in admixed populations, CARMA-X, that addresses some of the unique challenges of admixed individuals. The proposed method includes an estimation method for the linkage disequilibrium (LD) matrix that accounts for small reference panels for admixed individuals, heterogeneity across populations and cross-ancestry LD, and a Bayesian hypothesis test that leads to robust fine-mapping when relying on external reference panels of modest size for LD estimation. Using simulations, we compare performance with recently proposed fine-mapping methods for multi-ancestry studies and show that the proposed model provides higher power while controlling false discoveries, especially when using an out-of-sample LD matrix. We further illustrate our approach through applications to two Latin American genetic studies, the Estudio Familiar de Influencia Genética en Alzheimer (EFIGA) study in the Dominican Republic and the Mexican Biobank, where we show the benefit of modeling ancestry-specific effects by prioritizing putative causal variants and genes, including several findings driven by ancestry-specific effects in the African and Native American ancestries.

Rare predicted loss-of-function and damaging missense variants in CFHR5 associate with protection from age-related macular degeneration

Age-related macular degeneration (AMD) is a leading cause of blindness among older adults worldwide, but treatment options are limited. Genetics studies have implicated the CFH locus, containing CFH and five CFHR genes, CFHR1-5, in AMD. While CFH has been robustly linked with AMD risk, potential additional roles for the CFHR genes remain unclear, obscured by strong linkage disequilibrium across the locus. Investigating rare coding variants can help to identify causal genes in such regions. We used whole-exome sequencing data from 406,952 UK Biobank participants to examine AMD associations with genes at the CFH locus. For each gene, we used burden testing to examine associations of rare (minor-allele frequency [MAF] < 1%) predicted loss-of-function (pLoF) and predicted damaging missense variants with AMD. We considered "broadly defined AMD" (ICD-10 35.3; ncases = 10,700) and "strictly defined AMD" (dry or wet AMD; ncases = 346). Adjusting for CFH-region variants known to independently associate with AMD, we find that CFHR5 rare variant burden significantly associates with a decreased risk of broadly defined AMD (odds ratio [OR] = 0.75, p = 7 × 10-4), with this association primarily driven by pLoF variants. Furthermore, the association of CFHR5 rare variants with AMD protection is estimated to be stronger for individuals with the CFH rs1061170 AMD risk allele (p.Tyr402His [p.Y402H]; interaction p = 0.04). Corresponding analyses of strict AMD were underpowered. However, we observe that thinning of the photoreceptor layer outer segment strongly predicts strict AMD and find that CFHR5 rare variant burden is significantly associated with increased thickness of this retinal layer (+0.34 SD, p = 4 × 10-4, n = 45,365). These findings suggest CFHR5 inhibition as a potential therapeutic approach for AMD.

Allelic effects on KLHL17 expression underlie a pancreatic cancer genome-wide association signal at chr1p36.33

Pancreatic Ductal Adenocarcinoma (PDAC) is the third leading cause of cancer-related deaths in the U.S. Both rare and common germline variants contribute to PDAC risk. Here, we fine-map and functionally characterize a common PDAC risk signal at chr1p36.33 (tagged by rs13303010) identified through a genome wide association study (GWAS). One of the fine-mapped SNPs, rs13303160 (OR = 1.23 (95% CI 1.15-1.32), P-value = 2.74×10-9, LD r2 = 0.93 with rs13303010 in 1000 G EUR samples) demonstrated allele-preferential gene regulatory activity in vitro and binding of JunB and JunD in vitro and in vivo. Expression Quantitative Trait Locus (eQTL) analysis identified KLHL17 as a likely target gene underlying the signal. Proteomic analysis identified KLHL17 as a member of the Cullin-E3 ubiquitin ligase complex with vimentin and nestin as candidate substrates for degradation in PDAC-derived cells. In silico differential gene expression analysis of high and low KLHL17 expressing GTEx pancreas samples suggested an association between lower KLHL17 levels (risk associated) and pro-inflammatory pathways. We hypothesize that KLHL17 may mitigate cell injury and inflammation by recruiting nestin and vimentin for ubiquitination and degradation thereby influencing PDAC risk.

Proteome-Wide Genetic Study in East Asians and Europeans Identified Multiple Therapeutic Targets for Ischemic Stroke

BACKGROUND

Analyses of genomic and proteomics data in prospective biobank studies in diverse populations may discover novel or repurposing drug targets for stroke.

METHODS

We extracted individual cis-protein quantitative trait locus for 2923 proteins measured using Olink Explore panel from a genome-wide association study in prospective China Kadoorie Biobank and UK Biobank, both established ≈20 years ago. These cis-protein quantitative trait loci were used in ancestry-specific 2-sample Mendelian randomization analyses of ischemic stroke (IS) in East Asians (n=22 664 cases) and Europeans (n=62 100 cases). We further undertook colocalization analyses to examine the shared causal variants of cis-protein quantitative trait locus with stroke, along with various downstream analyses (eg, phenome-wide association study, drug development lookups) to clarify mechanisms of action and druggability.

RESULTS

In Mendelian randomization analyses, the genetically predicted plasma levels of 10 proteins were significantly associated with IS in East Asians (n=2) and Europeans (n=9), with 6 proteins (FGF5 [fibroblast growth factor 5], TMPRSS5 [transmembrane protease serine 5], FURIN, F11 [coagulation factor XI], ALDH2 [aldehyde dehydrogenase 2], and ABO) showing positive and 4 (GRK5 [G protein-coupled receptor kinase 5], KIAA0319 [dyslexia-associated protein KIAA0319], PROCR [endothelial protein C receptor], and MMP12 [macrophage metalloelastase 12]) showing inverse associations, all directionally consistent between East Asians and Europeans. Colocalization analyses provided strong evidence (posterior probabilities for the H4 hypothesis ≥0.7) of shared genetic variants with IS for 9 out of 10 proteins (except ABO). Moreover, 8 proteins were also causally associated, in the expected directions, with systolic blood pressure (positive/inverse: 4/2), low-density lipoprotein cholesterol (1 positive), body mass index (1 inverse), type 2 diabetes (2/1), or atrial fibrillation (3/1). Phenome-wide association study analyses and lookups in knock-out mouse models confirmed their importance for IS or stroke-related traits (eg, hematologic phenotypes). Of these 10 proteins, 1 was not druggable (ABO), 3 had known primary (F11) or potentially repurposed (ALDH2, MMP12) drug targets for stroke, and 6 (PROCR, GRK5, FGF5, FURIN, KIAA0319, and TMPRSS5) had no evidence of any drug targets.

CONCLUSIONS

Proteogenomic investigation in diverse ancestry populations identified the causal relevance of 10 proteins for IS, with several being potentially novel or repurposed targets that could be prioritized for further investigation.

Monoallelic TYROBP deletion is a novel risk factor for Alzheimer's disease

Biallelic loss-of-function variants in TYROBP and TREM2 cause autosomal recessive presenile dementia with bone cysts known as Nasu-Hakola disease (NHD, alternatively polycystic lipomembranous osteodysplasia with sclerosing leukoencephalopathy, PLOSL). Some other TREM2 variants contribute to the risk of Alzheimer's disease (AD) and frontotemporal dementia, while deleterious TYROBP variants are globally extremely rare and their role in neurodegenerative diseases remains unclear. The population history of Finns has favored the enrichment of deleterious founder mutations, including a 5.2 kb deletion encompassing exons 1-4 of TYROBP and causing NHD in homozygous carriers. We used here a proxy marker to identify monoallelic TYROBP deletion carriers in the Finnish biobank study FinnGen combining genome and health registry data of 520,210 Finns. We show that monoallelic TYROBP deletion associates with an increased risk and earlier onset age of AD and dementia when compared to noncarriers. In addition, we present the first reported case of a monoallelic TYROBP deletion carrier with NHD-type bone cysts. Mechanistically, monoallelic TYROBP deletion leads to decreased levels of DAP12 protein (encoded by TYROBP) in myeloid cells. Using transcriptomic and proteomic analyses of human monocyte-derived microglia-like cells, we show that upon lipopolysaccharide stimulation monoallelic TYROBP deletion leads to the upregulation of the inflammatory response and downregulation of the unfolded protein response when compared to cells with two functional copies of TYROBP. Collectively, our findings indicate TYROBP deletion as a novel risk factor for AD and suggest specific pathways for therapeutic targeting.

Multi-ancestry sequencing-based genome-wide association study of C-reactive protein in 513,273 genomes

C-reactive protein (CRP) serves as a pivotal marker of systemic inflammation, yet its genetic architecture has predominantly been explored within European populations. Our multi-ancestry sequencing-based genome-wide association study (seqGWAS) meta-analysis encompasses 447,369 Europeans, 10,389 Africans, 9685 Asians, and 9200 Hispanics in the discovery set, and 23,521 Europeans, 7160 Africans, 771 Asians, and 5178 Hispanics in the replication set. We identify 113 independent association signals (Pdiscovery ≤ 5 × 10-9 and Preplication ≤ 0.05), including 21 loci that passed the conditional analysis, among which 3 are European-specific. Cross ancestry fine-mapping pinpoints 19 of 113 independent signals within the 95% credible set. Functional annotation reveals significant enrichment in blood tissue, H3K27me3 histone marks, and exonic regions. Leveraging the Polygenic Priority Score (PoPS) and gene-based analyses, we implicate 151 genes as potential regulators of CRP levels, 55 of which have not been previously reported. Among these, 17 genes and four proteins show causal evidence or strong colocalization with CRP-related pathologies.

Evidence to shared genetic correlation of ischemic stroke and intracerebral hemorrhage and cardiovascular related traits

BACKGROUND

Previous studies have demonstrated the genetic basis of stroke and also revealed their genetic correlation with some cardiovascular related diseases or traits at the entire genome, which, however, would not give the answer which regions may mainly account for the genetic overlap. This study aims to identify specific genetic loci that contribute to the shared genetic basis between ischemic stroke subtypes and common cardiovascular traits.

METHODS

We used Local Analysis of [co]Variant Annotation (LAVA), a recent developed local genetic correlation method, to perform a system local genetic correlation analysis on GWAS summary data of two major subtypes of stroke, including any ischemic stroke (AIS) and intracerebral hemorrhage (ICH), and ten common cardiovascular related diseases or traits (CRTs). We further used colocalization analysis to explore potential shared causal genes in loci with significant local genetic correlation. In addition, we also performed Transcriptome-wide association (TWAS) analysis and fine-mapping for each phenotype to functionally annotate significant loci.

RESULTS

LAVA analysis identified a total of 3 significant local genetic correlations (Bonferroni-adjusted P <  0.05) across 3 chromosomes between AIS and systolic blood pressure (SBP), AIS and hypertension (HT), and ICH and body mass index (BMI), among which locus 7.24 explicated to harbor a shared causal variant for AIS and SBP. TWIST1 in locus 7.24 was defined to be nominally associated with SBP, but not for AIS. Fine-mapping analysis also only identified TWIST1 a credible causal gene for BMI.

CONCLUSIONS

Our study revealed the local genetic correlations between two stroke subtypes and ten common CRTs. Gene-level analyses indicated that biological explanations underlying these identified local genetic correlations may existed elsewhere beyond a common pattern of genetic-gene expression regulation.

Fast analysis of biobank-size data and meta-analysis using the BGLR R-package

Analyzing human genomic data from biobanks and large-scale genetic evaluations often requires fitting models with a sample size exceeding the number of DNA markers used (n>p). For instance, developing polygenic scores for humans and genomic prediction for genetic evaluations of agricultural species may require fitting models involving a few thousand SNPs using data with hundreds of thousands of samples. In such cases, computations based on sufficient statistics are more efficient than those based on individual genotype-phenotype data. Additionally, software that admits sufficient statistics as inputs can be used to analyze data from multiple sources jointly without the need to share individual genotype-phenotype data. Therefore, we developed functionality within the BGLR R-package that generates posterior samples for Bayesian shrinkage and variable selection models from sufficient statistics. In this article, we present an overview of the new methods incorporated in the BGLR R-package, demonstrate the use of the new software through simple examples, provide several computational benchmarks, and present a real-data example using data from the UK-Biobank, All of Us, and the Hispanic Community Health Study/Study of Latinos cohort demonstrating how a joint analysis from multiple cohorts can be implemented without sharing individual genotype-phenotype data, and how a combined analysis can improve the prediction accuracy of polygenic scores for Hispanics-a group severely under-represented in genome-wide association studies data.

A global overview of shared genetic architecture between smoking behaviors and major depressive disorder in European and East Asian ancestry

BACKGROUND

The co-occurrence of smoking behaviors and major depressive disorder (MDD) has been widely documented in populations. However, the underlying mechanism of this association remains unclear.

METHODS

Genome-wide association studies of smoking behaviors and MDD, combined with multi-omics datasets, were used to characterise genetic correlations, identify shared loci and genes, and explore underlying biological mechanisms. Mendelian randomization (MR) analyses were conducted to infer causal relationships between smoking behaviors and MDD. Druggability analyses were performed to identify potential drugs with both antidepressant and smoking cessation effects.

RESULTS

Extensive overall genetic correlations were found between smoking behaviors and MDD. Furthermore, eighteen local regions showed significant genetic correlations, which could be partly explained by gene co-expression patterns. We identified 24 shared loci and 120 genes, which were enriched in limbic system, GABAergic and dopaminergic neurons, as well as in synaptic pathways. Through integrating with tissue specific information, seven key genes (ANKK1, NEGR1, USP4, TCTA, SORCS5, SPPL3, and USP28) were pinpointed. Notably, druggability analyses supported ANKK1 as a potential drug target for the treatment of MDD and tobacco dependence. MR analyses suggested a bidirectional causal relationship between smoking initiation and MDD. Although findings in East Asian ancestry were limited, the shared locus (chr15:47613403-47,685,504) identified in European ancestry remained significant in East Asian ancestry.

CONCLUSIONS

Our findings suggest the extensive genetic overlap between smoking behaviors and MDD, support the role of limbic system and synapse involved in shared mechanisms, and implicate for prevention, intervention and treatment.

Association of Epistatic Effects of lncRNA GAS5, miR-146a, IRAK-1, and miR-155 Genetic Variants with Multiple Sclerosis Risk and Severity

The complex genetic architecture of heritability in multiple sclerosis (MS) remains undisclosed mainly. Epistasis (gene-gene interaction) substantially impacts MS; however, it is largely unexplored, especially among the non-coding RNA genes and their targets. The long non-coding RNA GAS5 exacerbates demyelination and sponges miR-146a and miR-155, impeccable contributors to MS pathogenesis. miR-146a negatively regulates the immune responses by targeting IRAK-1. We investigated the association of epistatic effects and haplotypes of five single nucleotide polymorphisms (SNPs), GAS5 rs2067079, miR-146a rs2910164 and rs57095329, IRAK-1 rs3027898, and miR-155 rs767649, with the risk of MS and its phenotypes. The expression quantitative trait locus (eQTL) associated with these variants was explored through bioinformatics analysis. The study enrolled 116 MS patients and 120 healthy controls. No strong linkage disequilibrium (D' ≥ 0.8) was detected among the studied SNPs. SNP-SNP interactions overlaid an overall magnified risk of MS and its phenotypes compared to the single-locus effects. After adjustment for multiple comparisons, the most significant interactions associated with the risk of overall MS and secondary-progressive MS were rs2067079-rs2910164, rs2910164-rs57095329, and rs3027898-rs767649. The last two former SNP-SNP interactions were highly associated with relapsing-remitting MS risk. The same pattern of interactions, as observed in association with MS risk, was female-specific. The CCAAA haplotype (alleles in the order of rs2067079, rs2910164, rs57095329, rs3027898, and rs767649) was protective against MS risk (CCAAA vs. CGAAT, adjusted OR = 0.14, 95% CI = 0.03-0.69, P = 0.009). Among MS patients, harboring the CGACT and CGAAT haplotypes was more prevalent in females and males, respectively. MS patients having EDSS ≥ 6 had a significantly higher frequency of the CCGCA haplotype than those with EDSS < 6. Functional analysis revealed rs2067079, rs57095329, and rs767649 as strong cis-eQTL regulating multiple genes, particularly in the brain and immune system. We propose that a magnified combined effect of GAS5, miR-146a, IRAK-1, and miR-155 genetic variants via epistatic interactions might impact the risk of MS and its phenotypes and could help in the risk stratification of MS patients.

Gene clusters linked to insulin resistance identified in a genome-wide study of the Taiwan Biobank population

This pioneering genome-wide association study examined surrogate markers for insulin resistance (IR) in 147,880 Taiwanese individuals using data from the Taiwan Biobank. The study focused on two IR surrogate markers: the triglyceride to high-density lipoprotein cholesterol (TG:HDL-C) ratio and the TyG index (the product of fasting plasma glucose and triglycerides). We identified genome-wide significance loci within four gene clusters: GCKR, MLXIPL, APOA5, and APOC1, uncovering 197 genes associated with IR. Transcriptome-wide association analysis revealed significant associations between these clusters and TyG, primarily in adipose tissue. Gene ontology analysis highlighted pathways related to Alzheimer's disease, glucose homeostasis, insulin resistance, and lipoprotein dynamics. The study identified sex-specific genes associated with TyG. Polygenic risk score analysis linked both IR markers to gout and hyperlipidemia. Our findings elucidate the complex relationships between IR surrogate markers, genetic predisposition, and disease phenotypes in the Taiwanese population, contributing valuable insights to the field of metabolic research.

Genome-wide association study for circulating metabolic traits in 619,372 individuals

Interpreting genetic associations with complex traits can be greatly improved by detailed understanding of the molecular consequences of these variants. However, although genome-wide association studies (GWAS) for common complex diseases routinely profile 1M+ individuals, studies of molecular phenotypes have lagged behind. We performed a GWAS meta-analysis for 249 circulating metabolic traits in the Estonian Biobank and the UK Biobank in up to 619,372 individuals, identifying 88,604 significant locus-metabolite associations and 8,774 independent lead variants, including 987 lead variants with a minor allele frequency less than 1%. We demonstrate how common and low-frequency associations converge on shared genes and pathways, bridging the gap between rare-variant burden testing and common-variant GWAS. We used Mendelian randomisation (MR) to explore putative causal links between metabolic traits, coronary artery disease and type 2 diabetes (T2D). Surprisingly, up to 85% of the tested metabolite-disease pairs had statistically significant genome-wide MR estimates, likely reflecting complex indirect effects driven by horisontal pleiotropy. To avoid these pleiotropic effects, we used cis-MR to test the phenotypic impact of inhibiting specific drug targets. We found that although plasma levels of branched-chain amino acids (BCAAs) have been associated with T2D in both observational and genome-wide MR studies, inhibiting the BCAA catabolism pathway to lower BCAA levels is unlikely to reduce T2D risk. Our publicly available results provide a valuable novel resource for GWAS interpretation and drug target prioritisation.

Whole genome sequencing analysis of body mass index identifies novel African ancestry-specific risk allele

Obesity is a major public health crisis associated with high mortality rates. Previous genome-wide association studies (GWAS) investigating body mass index (BMI) have largely relied on imputed data from European individuals. This study leveraged whole-genome sequencing (WGS) data from 88,873 participants from the Trans-Omics for Precision Medicine (TOPMed) Program, of which 51% were of non-European population groups. We discovered 18 BMI-associated signals (P < 5 × 10-9), including two secondary signals. Notably, we identified and replicated a novel low-frequency single nucleotide polymorphism (SNP) in MTMR3 that was common in individuals of African descent. Using a diverse study population, we further identified two novel secondary signals in known BMI loci and pinpointed two likely causal variants in the POC5 and DMD loci. Our work demonstrates the benefits of combining WGS and diverse cohorts in expanding current catalog of variants and genes confer risk for obesity, bringing us one step closer to personalized medicine.

Integration of functional genomics and statistical fine-mapping systematically characterizes adult-onset and childhood-onset asthma genetic associations

BACKGROUND

Genome-wide association studies (GWAS) have identified hundreds of loci underlying adult-onset asthma (AOA) and childhood-onset asthma (COA). However, the causal variants, regulatory elements, and effector genes at these loci are largely unknown.

METHODS

We performed heritability enrichment analysis to determine relevant cell types for AOA and COA, respectively. Next, we fine-mapped putative causal variants at AOA and COA loci. To improve the resolution of fine-mapping, we integrated ATAC-seq data in blood and lung cell types to annotate variants in candidate cis-regulatory elements (CREs). We then computationally prioritized candidate CREs underlying asthma risk, experimentally assessed their enhancer activity by massively parallel reporter assay (MPRA) in bronchial epithelial cells (BECs) and further validated a subset by luciferase assays. Combining chromatin interaction data and expression quantitative trait loci, we nominated genes targeted by candidate CREs and prioritized effector genes for AOA and COA.

RESULTS

Heritability enrichment analysis suggested a shared role of immune cells in the development of both AOA and COA while highlighting the distinct contribution of lung structural cells in COA. Functional fine-mapping uncovered 21 and 67 credible sets for AOA and COA, respectively, with only 16% shared between the two. Notably, one-third of the loci contained multiple credible sets. Our CRE prioritization strategy nominated 62 and 169 candidate CREs for AOA and COA, respectively. Over 60% of these candidate CREs showed open chromatin in multiple cell lineages, suggesting their potential pleiotropic effects in different cell types. Furthermore, COA candidate CREs were enriched for enhancers experimentally validated by MPRA in BECs. The prioritized effector genes included many genes involved in immune and inflammatory responses. Notably, multiple genes, including TNFSF4, a drug target undergoing clinical trials, were supported by two independent GWAS signals, indicating widespread allelic heterogeneity. Four out of six selected candidate CREs demonstrated allele-specific regulatory properties in luciferase assays in BECs.

CONCLUSIONS

We present a comprehensive characterization of causal variants, regulatory elements, and effector genes underlying AOA and COA genetics. Our results supported a distinct genetic basis between AOA and COA and highlighted regulatory complexity at many GWAS loci marked by both extensive pleiotropy and allelic heterogeneity.

An atlas of single-cell eQTLs dissects autoimmune disease genes and identifies novel drug classes for treatment

Most variants identified from genome-wide association studies (GWASs) are non-coding and regulate gene expression. However, many risk loci fail to colocalize with expression quantitative trait loci (eQTLs), potentially due to limited GWAS and eQTL analysis power or cellular heterogeneity. Population-scale single-cell RNA-sequencing (scRNA-seq) datasets are emerging, enabling mapping of eQTLs in different cell types (sc-eQTLs). Compared to eQTL data from bulk tissues (bk-eQTLs), sc-eQTL datasets are smaller. We propose a joint model of bk-eQTLs as a weighted sum of sc-eQTLs (JOBS) from constituent cell types to improve power. Applying JOBS to One1K1K and eQTLGen data, we identify 586% more eQTLs, matching the power of 4× the sample sizes of OneK1K. Integrating sc-eQTLs with GWAS data creates an atlas for 14 immune-mediated disorders, colocalizing 29.9% or 32.2% more loci than using sc-eQTL or bk-eQTL alone. Extending JOBS, we develop a drug-repurposing pipeline and identify novel drugs validated by real-world data.

CXCL12 drives natural variation in coronary artery anatomy across diverse populations

Coronary arteries have a specific branching pattern crucial for oxygenating heart muscle. Among humans, there is natural variation in coronary anatomy with respect to perfusion of the inferior/posterior left heart, which can branch from either the right arterial tree, the left, or both-a phenotype known as coronary dominance. Using angiographic data for >60,000 US veterans of diverse ancestry, we conducted a genome-wide association study of coronary dominance, revealing moderate heritability and identifying ten significant loci. The strongest association occurred near CXCL12 in both European- and African-ancestry cohorts, with downstream analyses implicating effects on CXCL12 expression. We show that CXCL12 is expressed in human fetal hearts at the time dominance is established. Reducing Cxcl12 in mice altered coronary dominance and caused septal arteries to develop away from Cxcl12 expression domains. These findings indicate that CXCL12 patterns human coronary arteries, paving the way for "medical revascularization" through targeting developmental pathways.

Machine learning reveals connections between preclinical type 2 diabetes subtypes and brain health

Previous research has established type 2 diabetes mellitus as a significant risk factor for various disorders, adversely impacting human health. While evidence increasingly links type 2 diabetes to cognitive impairment and brain disorders, understanding the causal effects of its preclinical stage on brain health is yet to be fully known. This knowledge gap hinders advancements in screening and preventing neurological and psychiatric diseases. To address this gap, we employed a robust machine learning algorithm (Subtype and Stage Inference, SuStaIn) with cross-sectional clinical data from the UK Biobank (20 277 preclinical type 2 diabetes participants and 20 277 controls) to identify underlying subtypes and stages for preclinical type 2 diabetes. Our analysis revealed one subtype distinguished by elevated circulating leptin levels and decreased leptin receptor levels, coupled with increased body mass index, diminished lipid metabolism, and heightened susceptibility to psychiatric conditions such as anxiety disorder, depression disorder, and bipolar disorder. Conversely, individuals in the second subtype manifested typical abnormalities in glucose metabolism, including rising glucose and haemoglobin A1c levels, with observed correlations with neurodegenerative disorders. A >10-year follow-up of these individuals revealed differential declines in brain health and significant clinical outcome disparities between subtypes. The first subtype exhibited faster progression and higher risk for psychiatric conditions, while the second subtype was associated with more severe progression of Alzheimer's disease and Parkinson's disease and faster progression to type 2 diabetes. Our findings highlight that monitoring and addressing the brain health needs of individuals in the preclinical stage of type 2 diabetes is imperative.

Single-cell analysis of the epigenome and 3D chromatin architecture in the human retina

Most genetic risk variants linked to ocular diseases are non-protein coding and presumably contribute to disease through dysregulation of gene expression, however, deeper understanding of their mechanisms of action has been impeded by an incomplete annotation of the transcriptional regulatory elements across different retinal cell types. To address this knowledge gap, we carried out single-cell multiomics assays to investigate gene expression, chromatin accessibility, DNA methylome and 3D chromatin architecture in human retina, macula, and retinal pigment epithelium (RPE)/choroid. We identified 420,824 unique candidate regulatory elements and characterized their chromatin states in 23 sub-classes of retinal cells. Comparative analysis of chromatin landscapes between human and mouse retina cells further revealed both evolutionarily conserved and divergent retinal gene-regulatory programs. Leveraging the rapid advancements in deep-learning techniques, we developed sequence-based predictors to interpret non-coding risk variants of retina diseases. Our study establishes retina-wide, single-cell transcriptome, epigenome, and 3D genome atlases, and provides a resource for studying the gene regulatory programs of the human retina and relevant diseases.

Cross-ancestry genome-wide association study and systems-level integrative analyses implicate new risk genes and therapeutic targets for depression

Deciphering the genetic architecture of depression is pivotal for characterizing the associated pathophysiological processes and development of new therapeutics. Here we conducted a cross-ancestry genome-wide meta-analysis on depression (416,437 cases and 1,308,758 controls) and identified 287 risk loci, of which 49 are new. Variant-level fine mapping prioritized potential causal variants and functional genomic analysis identified variants that regulate the binding of transcription factors. We validated that 80% of the identified functional variants are regulatory variants, and expression quantitative trait loci analysis uncovered the potential target genes regulated by the prioritized risk variants. Gene-level analysis, including transcriptome and proteome-wide association studies, colocalization and Mendelian randomization-based analyses, prioritized potential causal genes and drug targets. Gene prioritization analyses highlighted likely causal genes, including TMEM106B, CTNND1, AREL1 and so on. Pathway analysis indicated significant enrichment of depression risk genes in synapse-related pathways. Finally, knockdown of Tmem106b in mice resulted in depression-like behaviours, supporting the involvement of Tmem106b in depression. Our study identified new risk loci, likely causal variants and genes for depression, providing important insights into the genetic architecture of depression and potential therapeutic targets.

The identification of blood-derived response eQTLs reveals complex effects of regulatory variants on inflammatory and infectious disease risk

Hundreds of risk loci for immune mediated inflammatory and infectious diseases have been identified by genome-wide association studies (GWAS). Yet, what causal variants and genes in risk loci underpin the observed associations remains poorly understood for most. The identification of colocalized cis-expression Quantitative Trait Loci (cis-eQTLs) is a promising way to identify candidate causative genes. The catalogue of cis-eQTLs of the immune system is likely incomplete as many cis-eQTLs may be context-specific. We built a large cohort of 406 healthy individuals and expanded the immune cis-regulome through their whole blood transcriptome obtained after stimulation with specific toll-like receptor (TLR) agonists and T-cell receptor (TCR) antagonist. We report three mechanisms that may explain why an eQTL could only be revealed after immune stimulation. More than half of the cis-eQTLs detected in this study would have been overlooked without specific immune stimulations. We then mined this new catalogue of response (r)eQTLs, with public GWAS summary statistics of three diseases through a colocalization approach: inflammatory bowel diseases, rheumatoid arthritis and COVID-19 disease. We identified reQTL-specific colocalizations for risk loci for which no matching eQTL were reported before, revealing interesting new candidate causal genes.

A genome-wide meta-analysis reveals shared and population-specific variants for allergic sensitization

BACKGROUND

Allergic diseases are major causes of morbidity in both developed and developing countries and represent a global burden on health care systems. Allergic sensitization is defined as the production of IgE specific to common environmental allergens and is an important indicator in the assessment of allergic diseases.

OBJECTIVE

We sought to clarify the genetic basis of allergic sensitization.

METHODS

We performed a genome-wide association study (GWAS) of allergic sensitization in the Japanese population followed by a cross-ancestry meta-analysis with a European population including 20,492 cases and 23,342 controls for Japanese and 8,246 cases and 16,786 controls for Europeans. We also performed a polysensitization GWAS of a Japanese population including 4,923 cases and 17,009 controls.

RESULTS

Allergic sensitization GWAS identified 18 susceptibility loci for Japanese only and 23 loci for the cross-ancestry population, among which 4 loci were novel. Polysensitization GWAS identified 8 significant loci. Expression quantitative trait locus colocalization analysis revealed polysensitization GWAS significant variants affecting both the phenotype and the expression of the CD28, LPP, and LRCC32 genes. Cross-population genetic correlation analysis of allergic sensitization suggested that heterogeneity exists in allergic sensitization between Europeans and Japanese, indicating that more genetic heterogeneity may exist in allergic sensitization than allergic diseases.

CONCLUSIONS

Our investigation provides new insights into the molecular mechanism of allergic sensitization that could enhance current understanding of allergy and allergic diseases.

Genome-wide association study meta-analysis provides insights into the etiology of heart failure and its subtypes

Heart failure (HF) is a major contributor to global morbidity and mortality. While distinct clinical subtypes, defined by etiology and left ventricular ejection fraction, are well recognized, their genetic determinants remain inadequately understood. In this study, we report a genome-wide association study of HF and its subtypes in a sample of 1.9 million individuals. A total of 153,174 individuals had HF, of whom 44,012 had a nonischemic etiology (ni-HF). A subset of patients with ni-HF were stratified based on left ventricular systolic function, where data were available, identifying 5,406 individuals with reduced ejection fraction and 3,841 with preserved ejection fraction. We identify 66 genetic loci associated with HF and its subtypes, 37 of which have not previously been reported. Using functionally informed gene prioritization methods, we predict effector genes for each identified locus, and map these to etiologic disease clusters through phenome-wide association analysis, network analysis and colocalization. Through heritability enrichment analysis, we highlight the role of extracardiac tissues in disease etiology. We then examine the differential associations of upstream risk factors with HF subtypes using Mendelian randomization. These findings extend our understanding of the mechanisms underlying HF etiology and may inform future approaches to prevention and treatment.

A genome-wide association study of imaging-defined atherosclerosis

Imaging-defined atherosclerosis represents an intermediate phenotype of atherosclerotic cardiovascular disease (ASCVD). Genome-wide association studies (GWAS) on directly measured coronary plaques using coronary computed tomography angiography (CCTA) are scarce. In the so far largest population-based cohort with CCTA data, we performed a GWAS on coronary plaque burden as determined by the segment involvement score (SIS) in 24,811 European individuals. We identified 20 significant independent genetic markers for SIS, three of which were found in loci not implicated in ASCVD before. Further GWAS on coronary artery calcification showed similar results to that of SIS, whereas a GWAS on ultrasound-assessed carotid plaques identified both shared and non-shared loci with SIS. In two-sample Mendelian randomization studies using SIS-associated markers in UK Biobank and CARDIoGRAMplusC4D, one extra coronary segment with atherosclerosis corresponded to 1.8-fold increased odds of myocardial infarction. This GWAS data can aid future studies of causal pathways in ASCVD.

Integrative genomic analysis reveals shared loci for reproduction and production traits in Yorkshire pigs

BACKGROUND

Improving reproductive performance in Yorkshire pigs, a key maternal line in three-way crossbreeding systems, remains challenging due to low heritability and historical selection pressures favoring production traits. Identifying pleiotropic genetic variants that influence both reproduction and production traits is crucial for understanding their genetic interplay and enhancing molecular breeding strategies.

RESULTS

Genome-wide association studies (GWAS) using 2,764 individuals identified 264,660 significant loci associated with reproduction traits and 12,460 loci for production traits, with 73 independent signals, including genes such as SCLT1 and CAPN9. A total of 465,047 independent loci were identified, resulting in a genome-wide significance threshold of 2.15 × 10 - 6 . Genetic correlations analysis between reproduction and production traits across parities revealed varying trends, including a strengthening negative correlation between mean litter weight (MLW) and backfat thickness (BFT) with increasing parity (P1: r g =-0.0376; P2: r g =-0.1371; P3: r g =-0.1475). Given 1062 shared significant loci between MLW and BFT, local genetic correlation was calculated within the corresponding genomic regions, resulting in a weak correlation of 0.014. Transcriptome-wide association studies (TWAS) leveraging data from the PigGTEx project, which includes 9,530 RNA-sequencing samples across 34 tissues, revealed 2,143 significant genes, with 31 linked to total number of piglets born (TNB) and 133 to number of piglets born alive (NBA). These results highlight the importance of these genes in reproductive performance, with SCLT1 being notably significant in reproductive tissues. For MLW, integrating results from multiple analyses revealed CENPE as a strong candidate gene, exhibiting significant association and colocalization. Validation in an independent population (n = 300) showed that incorporating the top 0.2% of significant single nucleotide polymorphisms (SNPs) in the GFBLUP model improved predictive accuracy, increasing from 0.0168 to 0.0242 for MLW.

CONCLUSION

This study provides new insights into the pleiotropic genetic architecture underlying reproduction and production traits in Yorkshire pigs. Genetic correlations, shared loci, and candidate genes inform breeding program design. The increased accuracy of genomic selection using these significant loci highlights their practical utility in improving breeding efficiency. These findings suggest opportunities for refining selection strategies, although further research is warranted to fully realize their potential for enhancing breeding programs.

Steroid hormone levels vary with sex, aging, lifestyle, and genetics

Steroid hormone levels vary greatly among individuals, between sexes, with age, and across health and disease. What drives variance in steroid hormones and how they vary in individuals over time are not well studied. To address these questions, we measured 17 steroid hormones in a sex-balanced cohort of 949 healthy donors aged 20 to 69 years. We investigated associations between steroid levels and biological sex, age, clinical and demographic data, genetics, and plasma proteomics. Steroid hormone levels were strongly affected by sex and age, and a high number of lifestyle habits. Key observations were the broad impact of hormonal birth control in female donors and the relationship with smoking in male donors. In a 10-year follow-up study, we identified significant associations between steroid hormone levels and health status only in male donors. These observations highlight biological and lifestyle parameters affecting steroid hormones, and underlie the importance of considering sex, age, and potentially gendered behaviors in the treatment of hormone-related diseases.

Inherited resilience to clonal hematopoiesis by modifying stem cell RNA regulation

Somatic mutations that increase hematopoietic stem cell (HSC) fitness drive their expansion in clonal hematopoiesis (CH) and predispose to blood cancers. Although CH frequently occurs with aging, it rarely progresses to overt malignancy. Population variation in the growth rate and potential of mutant clones suggests the presence of genetic factors protecting against CH, but these remain largely undefined. Here, we identify a non-coding regulatory variant, rs17834140-T, that significantly protects against CH and myeloid malignancies by downregulating HSC-selective expression and function of the RNA-binding protein MSI2. By modeling variant effects and mapping MSI2 binding targets, we uncover an RNA network that maintains human HSCs and influences CH risk. Importantly, rs17834140-T is associated with slower CH expansion rates in humans, and stem cell MSI2 levels modify ASXL1-mutant HSC clonal dominance in experimental models. These findings leverage natural resilience to highlight a key role for post-transcriptional regulation in human HSCs, and offer genetic evidence supporting inhibition of MSI2 or its downstream targets as rational strategies for blood cancer prevention.

Optimizing UK biobank cloud-based research analysis platform to fine-map coronary artery disease loci in whole genome sequencing data

We conducted the first comprehensive association analysis of a coronary artery disease (CAD) cohort within the recently released UK Biobank (UKB) whole genome sequencing dataset. We employed fine mapping tool PolyFun and pinpoint rs10757274 as the most likely causal SNV within the 9p21.3 CAD risk locus. Notably, we show that machine-learning (ML) approaches, REGENIE and VariantSpark, exhibited greater sensitivity compared to traditional single-SNV logistic regression, uncovering rs28451064 a known risk locus in 21q22.11. Our findings underscore the utility of leveraging advanced computational techniques and cloud-based resources for mega-biobank analyses. Aligning with the paradigm shift of bringing compute to data, we demonstrate a 44% cost reduction and 94% speedup through compute architecture optimisation on UK Biobank's Research Analysis Platform using our RAPpoet approach. We discuss three considerations for researchers implementing novel workflows for datasets hosted on cloud-platforms, to pave the way for harnessing mega-biobank-sized data through scalable, cost-effective cloud computing solutions.

Genetic modulation of protein expression in rat brain

Genetic variations in protein expression are implicated in a broad spectrum of common diseases and complex traits but remain less explored compared to mRNA and classical phenotypes. This study systematically analyzed brain proteomes in a rat family using tandem mass tag (TMT)-based quantitative mass spectrometry. We quantified 8,119 proteins across two parental strains (SHR/Olalpcv and BN-Lx/Cub) and 29 HXB/BXH recombinant inbred (RI) strains, identifying 597 proteins with differential expression and 464 proteins linked to cis-acting quantitative trait loci (pQTLs). Proteogenomics identified 95 variant peptides, and sex-specific analyses revealed both shared and distinct cis-pQTLs. We improved the ability to pinpoint candidate genes underlying pQTLs by utilizing the rat pangenome and explored the connections between pQTLs in rats and human disorders. Collectively, this study highlights the value of large proteo-genetic datasets in elucidating protein modulation in the brain and its links to complex central nervous system (CNS) traits.

fastGxE: Powering genome-wide detection of genotype-environment interactions in biobank studies

Traditional genome-wide association studies (GWAS) have primarily focused on detecting main genotype effects, often overlooking genotype-environment interactions (GxE), which are essential for understanding context-specific genetic effects and refining disease etiology. Here, we present fastGxE, a scalable and effective genome-wide GxE method designed to identify genetic variants that interact with environmental factors to influence traits of interest. fastGxE controls for both polygenic effects and polygenic interaction effects, is robust to the number of environmental factors involved in GxE interactions, and ensures scalability for genome-wide GxE analysis in large biobank studies, achieving speed improvements of 32.98-126.49 times over existing approaches. We illustrate the benefits of fastGxE through extensive simulations and an in-depth analysis of 32 physical traits and 67 blood biomarkers from the UK Biobank. In real data applications, fastGxE identifies nine genomic loci associated with physical traits, including six novel ones, and 26 genomic loci associated with blood biomarkers, 19 of which are novel. The new discoveries highlight the dynamic interplay between genetics and the environment, uncovering potentially clinically significant pathways that could inform personalized interventions and treatment strategies.

RegionScan: a comprehensive R package for region-level genome-wide association testing with integration and visualization of multiple-variant and single-variant hypothesis testing

Summary

RegionScan is designed for scalable genome-wide association testing of both multiple-variant and single-variant region-level statistics, with visualization of the results. For detection of association under various regional architectures, it implements three classes of state-of-the-art region-level tests, including multiple-variant linear/logistic regression (with and without dimension reduction), a variance-component score test, and region-level minP tests. RegionScan also supports the analysis of multi-allelic variants and unbalanced binary phenotypes and is compatible with widely used variant call format (VCF) files for both genotyped and imputed variants. Association testing leverages linkage disequilibrium (LD) structure in pre-defined regions, for example, LD-adaptive regions obtained by genomic partitioning, and accommodates parallel processing to improve computational and memory efficiency. Detailed outputs (with allele frequencies, variant-LD bin assignment, single/joint variant effect estimates and region-level results) and utility functions are provided to assist comparison, visualization, and interpretation of results. Thus, RegionScan analysis offers valuable insights into region-level genetic architecture, which supports a wide range of potential applications.

Availability and implementation

RegionScan is freely available for download on GitHub (https://github.com/brossardMyriam/RegionScan).

Benchmarking DNA Sequence Models for Causal Regulatory Variant Prediction in Human Genetics

Machine learning holds immense promise in biology, particularly for the challenging task of identifying causal variants for Mendelian and complex traits. Two primary approaches have emerged for this task: supervised sequence-to-function models trained on functional genomics experimental data and self-supervised DNA language models that learn evolutionary constraints on sequences. However, the field currently lacks consistently curated datasets with accurate labels, especially for non-coding variants, that are necessary to comprehensively benchmark these models and advance the field. In this work, we present TraitGym, a curated dataset of regulatory genetic variants that are either known to be causal or are strong candidates across 113 Mendelian and 83 complex traits, along with carefully constructed control variants. We frame the causal variant prediction task as a binary classification problem and benchmark various models, including functional-genomics-supervised models, self-supervised models, models that combine machine learning predictions with curated annotation features, and ensembles of these. Our results provide insights into the capabilities and limitations of different approaches for predicting the functional consequences of non-coding genetic variants. We find that alignment-based models CADD and GPN-MSA compare favorably for Mendelian traits and complex disease traits, while functional-genomics-supervised models Enformer and Borzoi perform better for complex non-disease traits. Evo2 shows substantial performance gains with scale, but still lags somewhat behind alignment-based models, struggling particularly with enhancer variants. The benchmark, including a Google Colab notebook to evaluate a model in a few minutes, is available at https://huggingface.co/datasets/songlab/TraitGym.

Applied statistical methods for identifying features of heart rate that are associated with nicotine vaping

Background: Wearable devices have been increasingly adopted to collect physiological data such as heart rate that may infer momentary risk of substance use. Yet, innovative methods capable for handling these complex time series data as presented in the statistics or data science literature may not be accessible to substance use researchers.Objectives: This study introduces a series of statistical methods to analyze heart rate data and identify features that are associated with nicotine vaping.Methods: Nontechnical description of the methods coupled with the information about open-source software packages that implemented these methods was provided. The analytical procedure included 5 steps: (1) de-noising by the singular spectrum analysis (SSA); (2) sleep region identification by the Sum of Single Effects (SuSiE) model; (3) repeated heart rate pattern identification by the matrix profile; (4) dimension reduction by the linear regression; and (5) comparing repeated heart rate patterns across non-vaping and vaping regions by the linear mixed model. Secondary analysis was conducted on heart rate and ecological momentary assessment (EMA) data collected from 35 young adult e-cigarette users (66% female) for 7 days.Results: Effectiveness of the methods was demonstrated by graphical presentations showing that the extracted features characterize sleep patterns and heart rate changes before and after vaping events quite well. Secondary analysis found that heart rate was higher and changed faster before vaping.Conclusion: Statistical methods can effectively extract useful features from heart rate data that may inform momentary vaping risk and optimal timings for delivering messages in mobile-phone based interventions.

Multivariate Bayesian variable selection for multi-trait genetic fine mapping

Genome-wide association studies (GWAS) have identified thousands of single-nucleotide polymorphisms (SNPs) associated with complex traits, but determining the underlying causal variants remains challenging. Fine mapping aims to pinpoint the potentially causal variants from a large number of correlated SNPs possibly with group structure in GWAS-enriched genomic regions using variable selection approaches. In multi-trait fine mapping, we are interested in identifying the causal variants for multiple related traits. Existing multivariate variable selection methods for fine mapping select variables for all responses without considering the possible heterogeneity across different responses. Here, we develop a novel multivariate Bayesian variable selection method for multi-trait fine mapping to select causal variants from a large number of grouped SNPs that target at multiple correlated and possibly heterogeneous traits. Our new method is featured by its selection at multiple levels, incorporation of prior biological knowledge to guide selection and identification of best subset of traits the variants target at. We showed the advantage of our method over existing methods via comprehensive simulations that mimic typical fine-mapping settings and a real-world fine-mapping example in UK Biobank, where we identified critical causal variants potentially targeting at different subsets of addictive behaviours and risk factors.

Genetic, developmental, and neural changes underlying the evolution of butterfly mate preference

Many studies have linked genetic variation to behavior, but few connect to the intervening neural circuits that underlie the arc from sensation to action. Here, we used a combination of genome-wide association (GWA), developmental gene expression, and photoreceptor electrophysiology to investigate the architecture of mate choice behavior in Heliconius cydno butterflies, a clade where males identify preferred mates based on wing color patterns. We first found that the GWA variants most strongly associated with male mate choice were tightly linked to the gene controlling wing color in the K locus, consistent with previous mapping efforts. RNA-seq across developmental time points then showed that seven genes near the top GWA peaks were differentially expressed in the eyes, optic lobes, or central brain of white and yellow H. cydno males, many of which have known functions in the development and maintenance of synaptic connections. In the visual system of these butterflies, we identified a striking physiological difference between yellow and white males that could provide an evolutionarily labile circuit motif in the eye to rapidly switch behavioral preference. Using single-cell electrophysiology recordings, we found that some ultraviolet (UV)-sensitive photoreceptors receive inhibition from long-wavelength photoreceptors in the male eye. Surprisingly, the proportion of inhibited UV photoreceptors was strongly correlated with male wing color, suggesting a difference in the early stages of visual processing that could plausibly influence courtship decisions. We discuss potential links between candidate genes and this physiological signature, and suggest future avenues for experimental work. Taken together, our results support the idea that alterations to the evolutionarily labile peripheral nervous system, driven by genetic and gene expression differences, can significantly and rapidly alter essential behaviors.

Quantitative trait loci mapping of circulating metabolites in cerebrospinal fluid to uncover biological mechanisms involved in brain-related phenotypes

Genomic studies of molecular traits have provided mechanistic insights into complex disease, though these lag behind for brain-related traits due to the inaccessibility of brain tissue. We leveraged cerebrospinal fluid (CSF) to study neurobiological mechanisms in vivo, measuring 5543 CSF metabolites, the largest panel in CSF to date, in 977 individuals of European ancestry. Individuals originated from two separate cohorts including cognitively healthy subjects (n = 490) and a well-characterized memory clinic sample, the Amsterdam Dementia Cohort (ADC, n = 487). We performed metabolite quantitative trait loci (mQTL) mapping on CSF metabolomics and found 126 significant mQTLs, representing 65 unique CSF metabolites across 51 independent loci. To better understand the role of CSF mQTLs in brain-related disorders we integrated our CSF mQTL results with pre-existing summary statistics on brain traits, identifying 34 genetic associations between CSF metabolites and brain traits. Over 90% of significant mQTLs demonstrated colocalized associations with brain-specific gene expression, unveiling potential neurobiological pathways.