10 Years of GWAS Discovery: Biology, Function, and Translation

Integrative genomic analysis of RNA-modification-single nucleotide polymorphisms associated with kidney function

Introduction

Increasing evidence suggests that RNA modification plays a significant role in the kidney and may be an ideal target for the treatment of kidney diseases. However, the specific mechanisms underlying RNA modifications in the pathogenesis of kidney disease remain unclear. Genome-wide association studies (GWAS) have identified numerous genetic loci involved in kidney function and RNA modifications. The identification and exploration of RNA modification-related single-nucleotide polymorphisms (RNAm-SNPs) associated with kidney function can help us to comprehensively understand the underlying mechanism of kidney disease and identify potential therapeutic targets.

Methods

First, we examined the association of RNAm-SNPs with eGFR. Second, we performed expression quantitative trait locus (eQTL) and protein quantitative trait locus (pQTL) analyses to explore the functions of the identified RNAm-SNPs. Finally, we evaluated the causality between RNAm-SNP-associated gene expression and circulating proteins and kidney function using a Mendelian randomization (MR) analysis.

Results

A total of 252 RNA m-SNPs related to m6A, m1A, A-to-I, m5C, m7G, and m5U were identified. All these factors were significantly associated with the eGFR. A total of 119(47.22 %) RNAm-SNPs showed cis-eQTL effects in blood cells, whereas 72 (28.57 %) RNAm-SNPs showed cis-pQTL effects in plasma. 47 (18.65 %) RNAm-SNPs exhibited cis-eQTL and cis-pQTL effects. In addition, we demonstrated a causal association between RNAm-SNP-associated gene expression, circulating protein levels, and eGFR decline. Some of the identified genes and proteins have been reported to be associated with kidney diseases, such as CDK10 and SDCCAG8.

Conclusions

This study reveals an association between RNAm-SNPs and kidney function. These SNPs regulate gene expression and protein levels through RNA modifications, eventually leading to kidney dysfunction. Our study provides novel insights that connect the genetic risk of kidney disease to RNA modification and suggests potential therapeutic targets for the prevention and treatment of kidney disease.

Accounting for genetic effect heterogeneity in fine-mapping and improving power to detect gene-environment interactions with SharePro

Classical gene-by-environment interaction (GxE) analysis can be used to characterize genetic effect heterogeneity but has a high multiple testing burden in the context of genome-wide association studies (GWAS). We adapt a colocalization method, SharePro, to account for effect heterogeneity in fine-mapping and identify candidates for GxE analysis with reduced multiple testing burden. SharePro demonstrates improved power for both fine-mapping and GxE analysis compared to existing methods as well as well-controlled false type I error in simulations. Using smoking status stratified GWAS summary statistics, we identify genetic effects on lung function modulated by smoking status that are not identified by existing methods. Additionally, using sex stratified GWAS summary statistics, we characterize sex differentiated genetic effects on fat distribution. In summary, we have developed an analytical framework to account for effect heterogeneity in fine-mapping and subsequently improve power for GxE analysis. The SharePro software for GxE analysis is openly available at https://github.com/zhwm/SharePro_gxe .

Quantitative omnigenic model discovers interpretable genome-wide associations

As their statistical power grows, genome-wide association studies (GWAS) have identified an increasing number of loci underlying quantitative traits of interest. These loci are scattered throughout the genome and are individually responsible only for small fractions of the total heritable trait variance. The recently proposed omnigenic model provides a conceptual framework to explain these observations by postulating that numerous distant loci contribute to each complex trait via effect propagation through intracellular regulatory networks. We formalize this conceptual framework by proposing the "quantitative omnigenic model" (QOM), a statistical model that combines prior knowledge of the regulatory network topology with genomic data. By applying our model to gene expression traits in yeast, we demonstrate that QOM achieves similar gene expression prediction performance to traditional GWAS with hundreds of times less parameters, while simultaneously extracting candidate causal and quantitative chains of effect propagation through the regulatory network for every individual gene. We estimate the fraction of heritable trait variance in cis- and in trans-, break the latter down by effect propagation order, assess the trans- variance not attributable to transcriptional regulation, and show that QOM correctly accounts for the low-dimensional structure of gene expression covariance. We furthermore demonstrate the relevance of QOM for systems biology, by employing it as a statistical test for the quality of regulatory network reconstructions, and linking it to the propagation of nontranscriptional (including environmental) effects.

Ancestry and genome-wide association study of domestic pigs that survive African swine fever in Uganda

African swine fever (ASF) is endemic to Uganda and causes annual outbreaks. Some pigs survive these outbreaks and remain asymptomatic but are African swine fever virus (ASFV) positive. The potential heritability and genetic disparities in disease susceptibility among Ugandan pigs are not fully understood. In a 12-year study, whole blood and tissue samples were collected from 212 pigs across 19 districts in Uganda. Polymerase chain reaction (PCR) assays were used to determine ASFV infection status and genotyping was completed using a commercial porcine array. The point prevalence of ASF was calculated for each district, and breed composition origins were quantified for the sampled pigs by implementing established ancestry analyses. Genome-wide associated studies (GWAS) were conducted using all available domestic swine samples (full study population; n = 206) as well as a reduced dataset (farm-level study population; n = 129). This study revealed a greater number of ASFV-positive pigs in border districts than in non-border districts, a high level of admixture among domestic pigs sampled from Ugandan smallholder farms, and 48 loci that were associated with ASFV infection status. The discovery of 48 significant SNPs and 28 putative candidate genes may imply the possibility of heritability for resistance to ASFV. However, additional investigations in ASFV-endemic regions are required to fully elucidate the heritability of ASFV susceptibility among surviving pigs in Uganda.

Cysteine cathepsins and autoimmune diseases: A bidirectional Mendelian randomization

Cysteine cathepsins are proteolytic enzymes crucial in various physiological and pathological processes, primarily operating within lysosomes. Their functions include protein degradation, immune system regulation, and involvement in various diseases. While some cysteine cathepsins play important roles in the immune system, their connection to autoimmune diseases remains unclear. This study proposes using Mendelian randomization to explore the causal relationship between cysteine cathepsins and autoimmune diseases. Single nucleotide polymorphisms (SNPs) for cysteine cathepsins were obtained from a publicly available genome-wide association study (GWAS) dataset, while outcome SNP data were sourced from 10 separate GWAS datasets. Mendelian randomization (MR) analysis employed the Wald ratio (WR) and inverse variance weighted (IVW) approach as primary methods, supplemented by the weighted median and MR-Egger methods. Heterogeneity was assessed using Cochran Q test, and sensitivity analysis was conducted using the MR-PRESSO method. The association strength between exposure and outcome was evaluated using odds ratios (OR) with 95% confidence intervals (CI). The study identified a potential positive correlation between elevated cathepsin B and psoriasis (Wald ratio OR = 1.449, 95% CI: 1.053-1.993, P = .0227). Elevated cathepsin F was potentially linked to ulcerative colitis (WR OR = 1.073, 95% CI: 1.021-1.127, P = .0056), ankylosing spondylitis (WR OR = 1.258, 95% CI: 1.082-1.463, P = .0029), and primary biliary cholangitis(PBC) (WR OR = 1.958, 95% CI: 1.326-2.889, P = .0007). Conversely, cathepsin H appeared protective against celiac disease (WR OR = 0.881, 95% CI: 0.838-0.926, P = 6.5e-7), though elevated levels may increase the risk of type 1 diabetes (IVW OR = 1.121, 95% CI: 1.053-1.194, P = .0003) and PBC (WR OR = 1.792, 95% CI: 1.062-3.024, P = .0288). Cathepsin Z was also associated with an increased risk of type 1 diabetes (IVW OR = 1.090, 95% CI: 1.006-1.181, P = .0349). The MR analysis suggests potential risks of cathepsin B with psoriasis, cathepsin F with ulcerative colitis, ankylosing spondylitis, and PBC, and cathepsin Z with type 1 diabetes. Conversely, cathepsin H may protect against celiac disease but could increase the risk of type 1 diabetes and PBC.

Stimulation of insulin secretion induced by low 4-cresol dose involves the RPS6KA3 signalling pathway

4-cresol (4-methylphenol, p-cresol) is a xenobiotic substance negatively correlated with type 2 diabetes and associated with health improvement in preclinical models of diabetes. We aimed at refining our understanding of the physiological role of this metabolite and identifying potential signalling mechanisms. Functional studies revealed that 4-cresol does not deteriorate insulin sensitivity in human primary adipocytes and exhibits an additive effect to that of insulin on insulin sensitivity in mouse C2C12 myoblasts. Experiments in mouse isolated islets showed that 4-cresol potentiates glucose induced insulin secretion. We demonstrated the absence of off target effects of 4-cresol on a panel of 44 pharmacological compounds. Screening large panels of 241 G protein-coupled receptors (GPCRs) and 468 kinases identified binding of 4-cresol only to TNK1, EIF2AK4 (GCN2) and RPS6KA3 (RSK2), a kinase strongly expressed in human and rat pancreatic islets. Islet expression of RPS6KA3 is reduced in spontaneously diabetic rats chronically treated with 4-cresol and Rps6ka3 deficient mice exhibit reduction in both body weight and fasting glycemia, modest improvement in glycemic control and enhanced insulin release in vivo. Similar to low doses of 4-cresol, incubation of isolated rat islets with low concentrations of the RPS6KA3 inhibitor BIX 02565 stimulates both glucose induced insulin secretion and β-cell proliferation. These results provide further information on the role of low 4-cresol doses in the regulation of insulin secretion.

Genome wide association studies are enriched for interacting genes

Background

With recent advances in single cell technology, high-throughput methods provide unique insight into disease mechanisms and more importantly, cell type origin. Here, we used multi-omics data to understand how genetic variants from genome-wide association studies influence development of disease. We show in principle how to use genetic algorithms with normal, matching pairs of single-nucleus RNA- and ATAC-seq, genome annotations, and protein-protein interaction data to describe the genes and cell types collectively and their contribution to increased risk.

Results

We used genetic algorithms to measure fitness of gene-cell set proposals against a series of objective functions that capture data and annotations. The highest information objective function captured protein-protein interactions. We observed significantly greater fitness scores and subgraph sizes in foreground vs.matching sets of control variants. Furthermore, our model reliably identified known targets and ligand-receptor pairs, consistent with prior studies.

Conclusions

Our findings suggested that application of genetic algorithms to association studies can generate a coherent cellular model of risk from a set of susceptibility variants. Further, we showed, using breast cancer as an example, that such variants have a greater number of physical interactions than expected due to chance.

A network-based systems genetics framework identifies pathobiology and drug repurposing in Parkinson's disease

Parkinson's disease (PD) is the second most prevalent neurodegenerative disorder. However, current treatments are directed at symptoms and lack ability to slow or prevent disease progression. Large-scale genome-wide association studies (GWAS) have identified numerous genomic loci associated with PD, which may guide the development of disease-modifying treatments. We presented a systems genetics approach to identify potential risk genes and repurposable drugs for PD. First, we leveraged non-coding GWAS loci effects on multiple human brain-specific quantitative trait loci (xQTLs) under the protein-protein interactome (PPI) network. We then prioritized a set of PD likely risk genes (pdRGs) by integrating five types of molecular xQTLs: expression (eQTLs), protein (pQTLs), splicing (sQTLs), methylation (meQTLs), and histone acetylation (haQTLs). We also integrated network proximity-based drug repurposing and patient electronic health record (EHR) data observations to propose potential drug candidates for PD treatments. We identified 175 pdRGs from QTL-regulated GWAS findings, such as SNCA, CTSB, LRRK2, DGKQ, CD38 and CD44. Multi-omics data validation revealed that the identified pdRGs are likely to be druggable targets, differentially expressed in multiple cell types and impact both the parkin ubiquitin-proteasome and alpha-synuclein (a-syn) pathways. Based on the network proximity-based drug repurposing followed by EHR data validation, we identified usage of simvastatin as being significantly associated with reduced incidence of PD (fall outcome: hazard ratio (HR) = 0.91, 95% confidence interval (CI): 0.87-0.94; for dementia outcome: HR = 0.88, 95% CI: 0.86-0.89), after adjusting for 267 covariates. Our network-based systems genetics framework identifies potential risk genes and repurposable drugs for PD and other neurodegenerative diseases if broadly applied.

Unveiling the shared genetic architecture between testosterone and polycystic ovary syndrome

Testosterone (T) is a critical predictor of polycystic ovary syndrome (PCOS) but the genetic overlap between T and PCOS has not been established. Here by leveraging genetic datasets from large-scale genome-wide association studies, we assessed the genetic correlation and polygenic overlap between PCOS and three T-related traits using linkage disequilibrium score regression and the bivariate causal mixture model methods. The conjunctional false discovery rate (conjFDR) method was employed to identify shared causal variants. Functional annotation of variants was conducted using FUMA. Total T and bioavailable T exhibited positive correlations with PCOS, while sex hormone-binding globulin (SHBG) showed a negative correlation. All three traits demonstrated extensive genetic overlap with PCOS, with a minimum of 68% of T-related variants influencing PCOS. The conjFDR revealed 4 to 6 causal variants within joint genomic loci shared between PCOS and T-related traits. Functional annotations suggested that these variants might impact PCOS by modulating nearby genes, such as FSHB. Our findings support the hypothesis that PCOS is significantly influenced by androgen abnormalities. Additionally, this study identified several causal variants potentially involved in shared biological mechanisms between PCOS and T regulation.

A scalable adaptive quadratic kernel method for interpretable epistasis analysis in complex traits

Our knowledge of the contribution of genetic interactions (epistasis) to variation in human complex traits remains limited, partly due to the lack of efficient, powerful, and interpretable algorithms to detect interactions. Recently proposed approaches for set-based association tests show promise in improving the power to detect epistasis by examining the aggregated effects of multiple variants. Nevertheless, these methods either do not scale to large Biobank data sets or lack interpretability. We propose QuadKAST, a scalable algorithm focused on testing pairwise interaction effects (quadratic effects) within small to medium-sized sets of genetic variants (window size ≤100) on a trait and provide quantified interpretation of these effects. Comprehensive simulations show that QuadKAST is well-calibrated. Additionally, QuadKAST is highly sensitive in detecting loci with epistatic signals and accurate in its estimation of quadratic effects. We applied QuadKAST to 52 quantitative phenotypes measured in ≈300,000 unrelated white British individuals in the UK Biobank to test for quadratic effects within each of 9515 protein-coding genes. We detect 32 trait-gene pairs across 17 traits and 29 genes that demonstrate statistically significant signals of quadratic effects (accounting for the number of genes and traits tested). Across these trait-gene pairs, the proportion of trait variance explained by quadratic effects is comparable to additive effects, with five pairs having a ratio >1. Our method enables the detailed investigation of epistasis on a large scale, offering new insights into its role and importance.

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

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

Utilizing non-invasive prenatal test sequencing data for human genetic investigation

Non-invasive prenatal testing (NIPT) employs ultra-low-pass sequencing of maternal plasma cell-free DNA to detect fetal trisomy. Its global adoption has established NIPT as a large human genetic resource for exploring genetic variations and their associations with phenotypes. Here, we present methods for analyzing large-scale, low-depth NIPT data, including customized algorithms and software for genetic variant detection, genotype imputation, family relatedness, population structure inference, and genome-wide association analysis of maternal genomes. Our results demonstrate accurate allele frequency estimation and high genotype imputation accuracy (R2>0.84) for NIPT sequencing depths from 0.1× to 0.3×. We also achieve effective classification of duplicates and first-degree relatives, along with robust principal-component analysis. Additionally, we obtain an R2>0.81 for estimating genetic effect sizes across genotyping and sequencing platforms with adequate sample sizes. These methods offer a robust theoretical and practical foundation for utilizing NIPT data in medical genetic research.

Genome-wide association study of maternal plasma metabolites during pregnancy

Metabolites are key indicators of health and therapeutic targets, but their genetic underpinnings during pregnancy-a critical period for human reproduction-are largely unexplored. Using genetic data from non-invasive prenatal testing, we performed a genome-wide association study on 84 metabolites, including 37 amino acids, 24 elements, 13 hormones, and 10 vitamins, involving 34,394 pregnant Chinese women, with sample sizes ranging from 6,394 to 13,392 for specific metabolites. We identified 53 metabolite-gene associations, 23 of which are novel. Significant differences in genetic effects between pregnant and non-pregnant women were observed for 16.7%-100% of these associations, indicating gene-environment interactions. Additionally, 50.94% of genetic associations exhibited pleiotropy among metabolites and between six metabolites and eight pregnancy phenotypes. Mendelian randomization revealed potential causal relationships between seven maternal metabolites and 15 human traits and diseases. These findings provide new insights into the genetic basis of maternal plasma metabolites during pregnancy.

Connecting dementia risk loci to the CSF proteome identifies pathophysiological leads for dementia

Genome-wide association studies have successfully identified many genetic risk loci for dementia, but exact biological mechanisms through which genetic risk factors contribute to dementia remains unclear. Integrating CSF proteomic data with dementia risk loci could reveal intermediate molecular pathways connecting genetic variance to the development of dementia. We tested to what extent effects of known dementia risk loci can be observed in CSF levels of 665 proteins [proximity extension-based (PEA) immunoassays] in a deeply-phenotyped mixed memory clinic cohort [n = 502, mean age (standard deviation, SD) = 64.1 (8.7) years, 181 female (35.4%)], including patients with Alzheimer's disease (AD, n = 213), dementia with Lewy bodies (DLB, n = 50) and frontotemporal dementia (FTD, n = 93), and controls (n = 146). Validation was assessed in independent cohorts (n = 99 PEA platform, n = 198, mass reaction monitoring-targeted mass spectroscopy and multiplex assay). We performed additional analyses stratified according to diagnostic status (AD, DLB, FTD and controls separately), to explore whether associations between CSF proteins and genetic variants were specific to disease or not. We identified four AD risk loci as protein quantitative trait loci (pQTL): CR1-CR2 (rs3818361, P = 1.65 × 10-8), ZCWPW1-PILRB (rs1476679, P = 2.73 × 10-32), CTSH-CTSH (rs3784539, P = 2.88 × 10-24) and HESX1-RETN (rs186108507, P = 8.39 × 10-8), of which the first three pQTLs showed direct replication in the independent cohorts. We identified one AD-specific association between a rare genetic variant of TREM2 and CSF IL6 levels (rs75932628, P = 3.90 × 10-7). DLB risk locus GBA showed positive trans effects on seven inter-related CSF levels in DLB patients only. No pQTLs were identified for FTD loci, either for the total sample as for analyses performed within FTD only. Protein QTL variants were involved in the immune system, highlighting the importance of this system in the pathophysiology of dementia. We further identified pQTLs in stratified analyses for AD and DLB, hinting at disease-specific pQTLs in dementia. Dissecting the contribution of risk loci to neurobiological processes aids in understanding disease mechanisms underlying dementia.

Genome-wide assessment of shared genetic landscape of idiopathic pulmonary fibrosis and its comorbidities

Idiopathic pulmonary fibrosis (IPF) is a progressive interstitial lung disease accompanied by both local and systemic comorbidities. Genetic factors play a role in the development of IPF and certain associated comorbidities. Nevertheless, it is uncertain whether there are shared genetic factors underlying IPF and these comorbidities. To bridge this knowledge gap, we conducted a systematic investigation into the shared genetic architecture between IPF and ten prevalent heritable comorbidities (i.e., body mass index [BMI], coronary artery disease [CAD], chronic obstructive pulmonary disease [COPD], gastroesophageal reflux disease, lung cancer, major depressive disorder [MDD], obstructive sleep apnoea, pulmonary hypertension [PH], stroke, and type 2 diabetes), by utilizing large-scale summary data from their respective genome-wide association studies and multi-omics studies. We revealed significant (false discovery rate [FDR] < 0.05) and moderate genetic correlations between IPF and seven comorbidities, excluding lung cancer, MDD and PH. Evidence suggested a partially putative causal effect of IPF on CAD. Notably, we observed FDR-significant genetic enrichments in lung for the cross-trait between IPF and CAD and in liver for the cross-trait between IPF and COPD. Additionally, we identified 65 FDR-significant genes over-represented in 20 biological pathways related to the etiology of IPF, BMI, and COPD, including inflammation-related mucin gene clusters. Several of these genes were associated with clinically relevant drugs for the treatment of IPF, CAD, and/or COPD. Our results underscore the pervasive shared genetic basis between IPF and its common comorbidities and hold future implications for early diagnosis of IPF-related comorbidities, drug repurposing, and the development of novel therapies for IPF.

Role of the X Chromosome in Alzheimer Disease Genetics

Importance

The X chromosome has remained enigmatic in Alzheimer disease (AD), yet it makes up 5% of the genome and carries a high proportion of genes expressed in the brain, making it particularly appealing as a potential source of unexplored genetic variation in AD.

Objectives

To perform the first large-scale X chromosome-wide association study (XWAS) of AD.

Design, Setting, and Participants

This was a meta-analysis of genetic association studies in case-control, family-based, population-based, and longitudinal AD-related cohorts from the US Alzheimer's Disease Genetics Consortium, the Alzheimer's Disease Sequencing Project, the UK Biobank, the Finnish health registry, and the US Million Veterans Program. Risk of AD was evaluated through case-control logistic regression analyses. Data were analyzed between January 2023 and March 2024. Genetic data available from high-density single-nucleotide variant microarrays and whole-genome sequencing and summary statistics for multitissue expression and protein quantitative trait loci available from published studies were included, enabling follow-up genetic colocalization analyses. A total of 1 629 863 eligible participants were selected from referred and volunteer samples, 477 596 of whom were excluded for analysis exclusion criteria. The number of participants who declined to participate in original studies was not available.

Main Outcome and Measures

Risk of AD, reported as odds ratios (ORs) with 95% CIs. Associations were considered at X chromosome-wide (P < 1 × 10-5) and genome-wide (P < 5 × 10-8) significance. Primary analyses are nonstratified, while secondary analyses evaluate sex-stratified effects.

Results

Analyses included 1 152 284 participants of non-Hispanic White, European ancestry (664 403 [57.7%] female and 487 881 [42.3%] male), including 138 558 individuals with AD. Six independent genetic loci passed X chromosome-wide significance, with 4 showing support for links between the genetic signal for AD and expression of nearby genes in brain and nonbrain tissues. One of these 4 loci passed conservative genome-wide significance, with its lead variant centered on an intron of SLC9A7 (OR, 1.03; 95% CI, 1.02-1.04) and colocalization analyses prioritizing both the SLC9A7 and nearby CHST7 genes. Of these 6 loci, 4 displayed evidence for escape from X chromosome inactivation with regard to AD risk.

Conclusion and Relevance

This large-scale XWAS of AD identified the novel SLC9A7 locus. SLC9A7 regulates pH homeostasis in Golgi secretory compartments and is anticipated to have downstream effects on amyloid β accumulation. Overall, this study advances our knowledge of AD genetics and may provide novel biological drug targets. The results further provide initial insights into elucidating the role of the X chromosome in sex-based differences in AD.

Small-group originating model: Optimized individual-level GWAS simulation featured by SLiM and using open-access data

The development of analytical methods for Genome-wide Association Studies (GWAS) has outpaced the evolution of simulation techniques and pipelines. This disparity underscores the importance of innovative simulation methods that can keep pace with the rapidly increasing scale of GWAS. The median sample size of GWAS over the past ten years has exceeded 50,000 individuals, a trend that emphasizes the need for simulation tools capable of generating data on a similar or larger scale. This paper introduces a novel method, the small-group originating (SGO) model, utilizing the SLiM software for simulating individual-level GWAS data. Our standardized protocol facilitates the generation of tens of thousands of pseudo-individuals with millions of variants from small (30-90) open-access datasets. SGO stands out, especially when compared to the widely-used resampling method in HapGen, showcasing superior simulation efficiency for large sample sizes (> 13,000) of unrelated individuals. This capability is particularly relevant given the current trajectory towards larger GWAS, necessitating tools that can simulate datasets reflective of this growth. Additionally, SGO provides customization options and can model dynamic life cycles and mating across generations, positioning it as a highly promising alternative for GWAS simulations. In a case study, sensitivity analyses of chromosome-level principal component analysis and kinship coefficient estimation were conducted. The results highlighted the poor robustness of chromosome-level quality control (QC) indexes and the uneven distribution of population structure across chromosomes and ancestries, advocating for the caution against relying solely on chromosome-level QC statistics. With its flexible and efficient approach to generating pseudo GWAS data, our standardized SGO protocol emerges as a crucial asset for method development, power analysis, and benchmarking in GWAS research. It is especially vital in the context of accommodating the demands for large-scale simulations, aligning with the current and future scale of GWAS.

Causal association of circulating inflammatory proteins on neurodegenerative diseases: Insights from a mendelian randomization study

Neuroinflammation is increasingly recognized as a pivotal factor in the development and progression of neurodegenerative disorders. While correlations between inflammatory cytokines and these diseases are documented, the definitive causal dynamics remain to be elucidated. We explored the causal association between 91 circulating inflammatory cytokines and Alzheimer's disease (AD), amyotrophic lateral sclerosis (ALS), multiple sclerosis (MS) and Parkinson's disease (PD) through Mendelian randomization analysis. Leveraging genetic variants from the most comprehensive genome-wide association studies (GWAS) available for these cytokines, AD, ALS, MS and PD, we sought to uncover the causality. Our study validated a causal influence of genetically determined cytokine levels on the susceptibility to AD, with notable cytokines including C-X-C motif chemokine 1 (OR = 0.9993, p = 0.0424), Interleukin-18 (OR = 0.9994, p = 0.0186), Leukaemia inhibitory factor receptor (OR = 0.9993, p = 0.0122) and Monocyte chemoattractant protein-1 (OR = 0.9992, p = 0.0026) in risk attenuation. Additionally, a positive causal relationship was identified between two cytokines-C-C motif chemokine 19 (OR = 1.0005, p = 0.0478) and Fms-related tyrosine kinase 3 ligand (OR = 1.0005, p = 0.0210)-and AD incidence. Conversely, transforming growth factor-alpha (OR = 0.8630, p = 0.0298), CD40L receptor (OR = 0.7737, p = 1.1265E-09) and Interleukin-12 subunit beta (OR = 0.8987, p = 0.0333) showed inverse associations with ALS, MS and PD, respectively. The consistency observed in various MR analyses, alongside sensitivity analysis, underscored the absence of horizontal pleiotropy, thus supporting our causal findings. This study reveals, for the first time, a genetically anchored causal nexus between levels of circulating inflammatory cytokines and the risk of neurodegenerative diseases.

A comprehensive framework for trans-ancestry pathway analysis using GWAS summary data from diverse populations

As more multi-ancestry GWAS summary data become available, we have developed a comprehensive trans-ancestry pathway analysis framework that effectively utilizes this diverse genetic information. Within this framework, we evaluated various strategies for integrating genetic data at different levels-SNP, gene, and pathway-from multiple ancestry groups. Through extensive simulation studies, we have identified robust strategies that demonstrate superior performance across diverse scenarios. Applying these methods, we analyzed 6,970 pathways for their association with schizophrenia, incorporating data from African, East Asian, and European populations. Our analysis identified over 200 pathways significantly associated with schizophrenia, even after excluding genes near genome-wide significant loci. This approach substantially enhances detection efficiency compared to traditional single-ancestry pathway analysis and the conventional approach that amalgamates single-ancestry pathway analysis results across different ancestry groups. Our framework provides a flexible and effective tool for leveraging the expanding pool of multi-ancestry GWAS summary data, thereby improving our ability to identify biologically relevant pathways that contribute to disease susceptibility.

Polygenic Risk Score, Cardiorespiratory Fitness, and Cardiometabolic Risk Factors: WASEDA'S Health Study

PURPOSE

This study estimated an individual's genetic liability to cardiometabolic risk factors by polygenic risk score (PRS) construction and examined whether high cardiorespiratory fitness (CRF) modifies the association between PRS and cardiometabolic risk factors.

METHODS

This cross-sectional study enrolled 1296 Japanese adults aged ≥40 yr. The PRS for each cardiometabolic trait (blood lipids, glucose, hypertension, and obesity) was calculated using the LDpred2 and clumping and thresholding methods. Participants were divided into low-, intermediate-, and high-PRS groups according to PRS tertiles for each trait. CRF was quantified as peak oxygen uptake (V̇O 2peak ) per kilogram body weight. Participants were divided into low-, intermediate-, and high-CRF groups according to the tertile V̇O 2peak value.

RESULTS

Linear regression analysis revealed a significant interaction between PRS for triglyceride (PRS TG ) and CRF groups on serum TG levels regardless of the PRS calculation method, and the association between PRS TG and TG levels was attenuated in the high-CRF group. Logistic regression analysis revealed a significant sub-additive interaction between LDpred2 PRS TG and CRF on the prevalence of high TG, indicating that high CRF attenuated the genetic predisposition to high TG. Furthermore, a significant sub-additive interaction between PRS for body mass index and CRF on obesity was detected regardless of the PRS calculation method. These significant interaction effects on high TG and obesity were diminished in the sensitivity analysis using V̇O 2peak per kilogram fat-free mass as the CRF index. Effects of PRSs for other cardiometabolic traits were not significantly attenuated in the high-CRF group regardless of PRS calculation methods.

CONCLUSIONS

The findings of the present study suggest that individuals with high CRF overcome the genetic predisposition to high TG levels and obesity.

Whole exome sequencing analyses identified novel genes for Alzheimer's disease and related dementia

INTRODUCTION

The heritability of Alzheimer's disease (AD) is estimated to be 58%-79%. However, known genes can only partially explain the heritability.

METHODS

Here, we conducted gene-based exome-wide association study (ExWAS) of rare variants and single-variant ExWAS of common variants, utilizing data of 54,569 clinically diagnosed/proxy AD and related dementia (ADRD) and 295,421 controls from the UK Biobank.

RESULTS

Gene-based ExWAS identified 11 genes predicting a higher ADRD risk, including five novel ones, namely FRMD8, DDX1, DNMT3L, MORC1, and TGM2, along with six previously reported ones, SORL1, GRN, PSEN1, ABCA7, GBA, and ADAM10. Single-variant ExWAS identified two ADRD-associated novel genes, SLCO1C1 and NDNF. The identified genes were predominantly enriched in amyloid-β process pathways, microglia, and brain regions like hippocampus. The druggability evidence suggests that DDX1, DNMT3L, TGM2, SLCO1C1, and NDNF could be effective drug targets.

DISCUSSION

Our study contributes to the current body of evidence on the genetic etiology of ADRD.

HIGHLIGHTS

Gene-based analyses of rare variants identified five novel genes for Alzheimer's disease and related dementia (ADRD), including FRMD8, DDX1, DNMT3L, MORC1, and TGM2. Single-variant analyses of common variants identified two novel genes for ADRD, including SLCO1C1 and NDNF. The identified genes were predominantly enriched in amyloid-β process pathways, microglia, and brain regions like hippocampus. DDX1, DNMT3L, TGM2, SLCO1C1, and NDNF could be effective drug targets.

Providing biological context for GWAS results using eQTL regulatory and co-expression networks in Populus

Our study utilized genome-wide association studies (GWAS) to link nucleotide variants to traits in Populus trichocarpa, a species with rapid linkage disequilibrium decay. The aim was to overcome the challenge of interpreting statistical associations at individual loci without sufficient biological context, which often leads to reliance solely on gene annotations from unrelated model organisms. We employed an integrative approach that included GWAS targeting multiple traits using three individual techniques for lignocellulose phenotyping, expression quantitative trait loci (eQTL) analysis to construct transcriptional regulatory networks around each candidate locus and co-expression analysis to provide biological context for these networks, using lignocellulose biosynthesis in Populus trichocarpa as a case study. The research identified three candidate genes potentially involved in lignocellulose formation, including one previously recognized gene (Potri.005G116800/VND1, a critical regulator of secondary cell wall formation) and two genes (Potri.012G130000/AtSAP9 and Potri.004G202900/BIC1) with newly identified putative roles in lignocellulose biosynthesis. Our integrative approach offers a framework for providing biological context to loci associated with trait variation, facilitating the discovery of new genes and regulatory networks.

Trait association and prediction through integrative k-mer analysis

Genome-wide association study (GWAS) with single nucleotide polymorphisms (SNPs) has been widely used to explore genetic controls of phenotypic traits. Alternatively, GWAS can use counts of substrings of length k from longer sequencing reads, k-mers, as genotyping data. Using maize cob and kernel color traits, we demonstrated that k-mer GWAS can effectively identify associated k-mers. Co-expression analysis of kernel color k-mers and genes directly found k-mers from known causal genes. Analyzing complex traits of kernel oil and leaf angle resulted in k-mers from both known and candidate genes. A gene encoding a MADS transcription factor was functionally validated by showing that ectopic expression of the gene led to less upright leaves. Evolution analysis revealed most k-mers positively correlated with kernel oil were strongly selected against in maize populations, while most k-mers for upright leaf angle were positively selected. In addition, genomic prediction of kernel oil, leaf angle, and flowering time using k-mer data resulted in a similarly high prediction accuracy to the standard SNP-based method. Collectively, we showed k-mer GWAS is a powerful approach for identifying trait-associated genetic elements. Further, our results demonstrated the bridging role of k-mers for data integration and functional gene discovery.

Identifying genetic variants that influence the abundance of cell states in single-cell data

Disease risk alleles influence the composition of cells present in the body, but modeling genetic effects on the cell states revealed by single-cell profiling is difficult because variant-associated states may reflect diverse combinations of the profiled cell features that are challenging to predefine. We introduce Genotype-Neighborhood Associations (GeNA), a statistical tool to identify cell-state abundance quantitative trait loci (csaQTLs) in high-dimensional single-cell datasets. Instead of testing associations to predefined cell states, GeNA flexibly identifies the cell states whose abundance is most associated with genetic variants. In a genome-wide survey of single-cell RNA sequencing peripheral blood profiling from 969 individuals, GeNA identifies five independent loci associated with shifts in the relative abundance of immune cell states. For example, rs3003-T (P = 1.96 × 10-11) associates with increased abundance of natural killer cells expressing tumor necrosis factor response programs. This csaQTL colocalizes with increased risk for psoriasis, an autoimmune disease that responds to anti-tumor necrosis factor treatments. Flexibly characterizing csaQTLs for granular cell states may help illuminate how genetic background alters cellular composition to confer disease risk.

Impact of Epigenetics, Diet, and Nutrition-Related Pathologies on Wound Healing

Chronic wounds pose a significant challenge to healthcare. Stemming from impaired wound healing, the consequences can be severe, ranging from amputation to mortality. This comprehensive review explores the multifaceted impact of chronic wounds in medicine and the roles that diet and nutritional pathologies play in the wound-healing process. It has been well established that an adequate diet is crucial to proper wound healing. Nutrients such as vitamin D, zinc, and amino acids play significant roles in cellular regeneration, immune functioning, and collagen synthesis and processing. Additionally, this review discusses how patients with chronic conditions like diabetes, obesity, and nutritional deficiencies result in the formation of chronic wounds. By integrating current research findings, this review highlights the significant impact of the genetic make-up of an individual on the risk of developing chronic wounds and the necessity for adequate personalized dietary interventions. Addressing the nutritional needs of individuals, especially those with chronic conditions, is essential for improving wound outcomes and overall patient care. With new developments in the field of genomics, there are unprecedented opportunities to develop targeted interventions that can precisely address the unique metabolic needs of individuals suffering from chronic wounds, thereby enhancing treatment effectiveness and patient outcomes.

A cell type-aware framework for nominating non-coding variants in Mendelian regulatory disorders

Unsolved Mendelian cases often lack obvious pathogenic coding variants, suggesting potential non-coding etiologies. Here, we present a single cell multi-omic framework integrating embryonic mouse chromatin accessibility, histone modification, and gene expression assays to discover cranial motor neuron (cMN) cis-regulatory elements and subsequently nominate candidate non-coding variants in the congenital cranial dysinnervation disorders (CCDDs), a set of Mendelian disorders altering cMN development. We generate single cell epigenomic profiles for ~86,000 cMNs and related cell types, identifying ~250,000 accessible regulatory elements with cognate gene predictions for ~145,000 putative enhancers. We evaluate enhancer activity for 59 elements using an in vivo transgenic assay and validate 44 (75%), demonstrating that single cell accessibility can be a strong predictor of enhancer activity. Applying our cMN atlas to 899 whole genome sequences from 270 genetically unsolved CCDD pedigrees, we achieve significant reduction in our variant search space and nominate candidate variants predicted to regulate known CCDD disease genes MAFB, PHOX2A, CHN1, and EBF3 - as well as candidates in recurrently mutated enhancers through peak- and gene-centric allelic aggregation. This work delivers non-coding variant discoveries of relevance to CCDDs and a generalizable framework for nominating non-coding variants of potentially high functional impact in other Mendelian disorders.

Characterization of non-coding variants associated with transcription factor binding through ATAC-seq-defined footprint QTLs in liver

Non-coding variants discovered by genome-wide association studies (GWAS) are enriched in regulatory elements harboring transcription factor (TF) binding motifs, strongly suggesting a connection between disease association and the disruption of cis-regulatory sequences. Occupancy of a TF inside a region of open chromatin can be detected in ATAC-seq where bound TFs block the transposase Tn5, leaving a pattern of relatively depleted Tn5 insertions known as a "footprint". Here, we sought to identify variants associated with TF-binding, or "footprint quantitative trait loci" (fpQTLs) in ATAC-seq data generated from 170 human liver samples. We used computational tools to scan the ATAC-seq reads to quantify TF binding likelihood as "footprint scores" at variants derived from whole genome sequencing generated in the same samples. We tested for association between genotype and footprint score and observed 693 fpQTLs associated with footprint-inferred TF binding (FDR < 5%). Given that Tn5 insertion sites are measured with base-pair resolution, we show that fpQTLs can aid GWAS and QTL fine-mapping by precisely pinpointing TF activity within broad trait-associated loci where the underlying causal variant is unknown. Liver fpQTLs were strongly enriched across ChIP-seq peaks, liver expression QTLs (eQTLs), and liver-related GWAS loci, and their inferred effect on TF binding was concordant with their effect on underlying sequence motifs in 80% of cases. We conclude that fpQTLs can reveal causal GWAS variants, define the role of TF binding site disruption in disease and provide functional insights into non-coding variants, ultimately informing novel treatments for common diseases.

Semi-supervised learning with pseudo-labeling compares favorably with large language models for regulatory sequence prediction

Predicting molecular processes using deep learning is a promising approach to provide biological insights for non-coding single nucleotide polymorphisms identified in genome-wide association studies. However, most deep learning methods rely on supervised learning, which requires DNA sequences associated with functional data, and whose amount is severely limited by the finite size of the human genome. Conversely, the amount of mammalian DNA sequences is growing exponentially due to ongoing large-scale sequencing projects, but in most cases without functional data. To alleviate the limitations of supervised learning, we propose a novel semi-supervised learning (SSL) based on pseudo-labeling, which allows to exploit unlabeled DNA sequences from numerous genomes during model pre-training. We further improved it incorporating principles from the Noisy Student algorithm to predict the confidence in pseudo-labeled data used for pre-training, which showed improvements for transcription factor with very few binding (very small training data). The approach is very flexible and can be used to train any neural architecture including state-of-the-art models, and shows in most cases strong predictive performance improvements compared to standard supervised learning. Moreover, small models trained by SSL showed similar or better performance than large language model DNABERT2.

Understanding the Genetic Landscape of Gestational Diabetes: Insights into the Causes and Consequences of Elevated Glucose Levels in Pregnancy

Background/Objectives: During pregnancy, physiological changes in maternal circulating glucose levels and its metabolism are essential to meet maternal and fetal energy demands. Major changes in glucose metabolism occur throughout pregnancy and consist of higher insulin resistance and a compensatory increase in insulin secretion to maintain glucose homeostasis. For some women, this change is insufficient to maintain normoglycemia, leading to gestational diabetes mellitus (GDM), a condition characterized by maternal glucose intolerance and hyperglycaemia first diagnosed during the second or third trimester of pregnancy. GDM is diagnosed in approximately 14.0% of pregnancies globally, and it is often associated with short- and long-term adverse health outcomes in both mothers and offspring. Although recent studies have highlighted the role of genetic determinants in the development of GDM, research in this area is still lacking, hindering the development of prevention and treatment strategies. Methods: In this paper, we review recent advances in the understanding of genetic determinants of GDM and glycaemic traits during pregnancy. Results/Conclusions: Our review highlights the need for further collaborative efforts as well as larger and more diverse genotyped pregnancy cohorts to deepen our understanding of the genetic aetiology of GDM, address research gaps, and further improve diagnostic and treatment strategies.

Genetic history and biological adaptive landscape of the Tujia people inferred from shared haplotypes and alleles

BACKGROUND

High-quality genomic datasets from under-representative populations are essential for population genetic analysis and medical relevance. Although the Tujia are the most populous ethnic minority in southwestern China, previous genetic studies have been fragmented and only partially reveal their genetic diversity landscape. The understanding of their fine-scale genetic structure and potentially differentiated biological adaptive features remains nascent.

OBJECTIVES

This study aims to explore the demographic history and genetic architecture related to the natural selection of the Tujia people, focusing on a meta-Tujia population from the central regions of the Yangtze River Basin.

RESULTS

Population genetic analyses conducted on the meta-Tujia people indicate that they occupy an intermediate position in the East Asian North-South genetic cline. A close genetic affinity was identified between the Tujia people and neighboring Sinitic-speaking populations. Admixture models suggest that the Tujia can be modeled as a mixture of northern and southern ancestries. Estimates of f3/f4 statistics confirmed the presence of ancestral links to ancient Yellow River Basin millet farmers and the BaBanQinCen-related groups. Furthermore, population-specific natural selection signatures were explored, revealing highly differentiated functional variants between the Tujia and southern indigenous populations, including genes associated with hair morphology (e.g., EDAR) and skin pigmentation (e.g., SLC24A5). Additionally, both shared and unique selection signatures were identified among ethnically diverse but geographically adjacent populations, highlighting their extensive admixture and the biological adaptations introduced by this admixture.

CONCLUSIONS

The study unveils significant population movements and genetic admixture among the Tujia and other ethno-linguistically diverse East Asian groups, elucidating the differentiated adaptation processes across geographically diverse populations from the current genetic landscape.

An efficient, not-only-linear correlation coefficient based on clustering

Identifying meaningful patterns in data is crucial for understanding complex biological processes, particularly in transcriptomics, where genes with correlated expression often share functions or contribute to disease mechanisms. Traditional correlation coefficients, which primarily capture linear relationships, may overlook important nonlinear patterns. We introduce the clustermatch correlation coefficient (CCC), a not-only-linear coefficient that utilizes clustering to efficiently detect both linear and nonlinear associations. CCC outperforms standard methods by revealing biologically meaningful patterns that linear-only coefficients miss and is faster than state-of-the-art coefficients such as the maximal information coefficient. When applied to human gene expression data from genotype-tissue expression (GTEx), CCC identified robust linear relationships and nonlinear patterns, such as sex-specific differences, that are undetectable by standard methods. Highly ranked gene pairs were enriched for interactions in integrated networks built from protein-protein interactions, transcription factor regulation, and chemical and genetic perturbations, suggesting that CCC can detect functional relationships missed by linear-only approaches. CCC is a highly efficient, next-generation, not-only-linear correlation coefficient for genome-scale data. A record of this paper's transparent peer review process is included in the supplemental information.

EmbedGEM: a framework to evaluate the utility of embeddings for genetic discovery

Summary

Machine learning-derived embeddings are a compressed representation of high content data modalities. Embeddings can capture detailed information about disease states and have been qualitatively shown to be useful in genetic discovery. Despite their promise, embeddings have a major limitation: it is unclear if genetic variants associated with embeddings are relevant to the disease or trait of interest. In this work, we describe EmbedGEM (Embedding Genetic Evaluation Methods), a framework to systematically evaluate the utility of embeddings in genetic discovery. EmbedGEM focuses on comparing embeddings along two axes: heritability and disease relevance. As measures of heritability, we consider the number of genome-wide significant associations and the meanχ 2 statistic at significant loci. For disease relevance, we compute polygenic risk scores for each embedding principal component, then evaluate their association with high-confidence disease or trait labels in a held-out evaluation patient set. While our development of EmbedGEM is motivated by embeddings, the approach is generally applicable to multivariate traits and can readily be extended to accommodate additional metrics along the evaluation axes. We demonstrate EmbedGEM's utility by evaluating embeddings and multivariate traits in two separate datasets: (i) a synthetic dataset simulated to demonstrate the ability of the framework to correctly rank traits based on their heritability and disease relevance and (ii) a real data from the UK Biobank, including metabolic and liver-related traits. Importantly, we show that greater disease relevance does not automatically follow from greater heritability.

Availability and implementation

https://github.com/insitro/EmbedGEM.

A sex-stratified analysis of the genetic architecture of human brain anatomy

Large biobanks have dramatically advanced our understanding of genetic influences on human brain anatomy. However, most studies have combined rather than compared male and female participants. Here we screen for sex differences in the common genetic architecture of over 1000 neuroanatomical phenotypes in the UK Biobank and establish a general concordance between male and female participants in heritability estimates, genetic correlations, and variant-level effects. Notable exceptions include higher mean heritability in the female group for regional volume and surface area phenotypes; between-sex genetic correlations that are significantly below 1 in the insula and parietal cortex; and a common variant with stronger effect in male participants mapping to RBFOX1 - a gene linked to multiple neuropsychiatric disorders more common in men. This work suggests that common variant influences on human brain anatomy are largely consistent between males and females, with a few exceptions that will guide future research in growing datasets.

Natural selection acting on complex traits hampers the predictive accuracy of polygenic scores in ancient samples

The prediction of phenotypes from ancient humans has gained interest due to its potential to investigate the evolution of complex traits. These predictions are commonly performed using polygenic scores computed with DNA information from ancient humans along with genome-wide association studies (GWAS) data from present-day humans. However, numerous evolutionary processes could impact the prediction of phenotypes from ancient humans based on polygenic scores. In this work we investigate how natural selection impacts phenotypic predictions on ancient individuals using polygenic scores. We use simulations of an additive trait to analyze how natural selection impacts phenotypic predictions with polygenic scores. We simulate a trait evolving under neutrality, stabilizing selection and directional selection. We find that stabilizing and directional selection have contrasting effects on ancient phenotypic predictions. Stabilizing selection accelerates the loss of large-effect alleles contributing to trait variation. Conversely, directional selection accelerates the loss of small and large-effect alleles that drive individuals farther away from the optimal phenotypic value. These effects result in specific shared genetic variation patterns between ancient and modern populations which hamper the accuracy of polygenic scores to predict phenotypes. Furthermore, we conducted simulations that include realistic strengths of stabilizing selection and heritability estimates to show how natural selection could impact the predictive accuracy of ancient polygenic scores for two widely studied traits: height and body mass index. We emphasize the importance of considering how natural selection can decrease the reliability of ancient polygenic scores to perform phenotypic predictions on an ancient population.

The effect of family structure on the still-missing heritability and genomic prediction accuracy of type 2 diabetes

This study aims to assess the effect of familial structures on the still-missing heritability estimate and prediction accuracy of Type 2 Diabetes (T2D) using pedigree estimated risk values (ERV) and genomic ERV. We used 11,818 individuals (T2D cases: 2,210) with genotype (649,932 SNPs) and pedigree information from the ongoing periodic cohort study of the Iranian population project. We considered three different familial structure scenarios, including (i) all families, (ii) all families with ≥ 1 generation, and (iii) families with ≥ 1 generation in which both case and control individuals are presented. Comprehensive simulation strategies were implemented to quantify the difference between estimates of [Formula: see text] and [Formula: see text]. A proportion of still-missing heritability in T2D could be explained by overestimation of pedigree-based heritability due to the presence of families with individuals having only one of the two disease statuses. Our research findings underscore the significance of including families with only case/control individuals in cohort studies. The presence of such family structures (as observed in scenarios i and ii) contributes to a more accurate estimation of disease heritability, addressing the underestimation that was previously overlooked in prior research. However, when predicting disease risk, the absence of these families (as seen in scenario iii) can yield the highest prediction accuracy and the strongest correlation with Polygenic Risk Scores. Our findings represent the first evidence of the important contribution of familial structure for heritability estimations and genomic prediction studies in T2D.

Causal associations between gut microbiota, circulating inflammatory proteins, and epilepsy: a multivariable Mendelian randomization study

Background

Previous studies have suggested that gut microbiota (GM) may be involved in the pathogenesis of epilepsy through the microbiota-gut-brain axis (MGBA). However, the causal relationship between GM and different epilepsy subtypes and whether circulating inflammatory proteins act as mediators to participate in epileptogenesis through the MGBA remain unclear. Therefore, it is necessary to identify specific GM associated with epilepsy and its subtypes and explore their underlying inflammatory mechanisms for risk prediction, personalized treatment, and prognostic monitoring of epilepsy.

Methods

We hypothesized the existence of a pathway GM-inflammatory proteins-epilepsy. We found genetic variants strongly associated with GM, circulating inflammatory proteins, epilepsy and its subtypes, including generalized and partial seizures, from large-scale genome-wide association studies (GWAS) summary data and used Multivariate Mendelian Randomization to explore the causal relationship between the three and whether circulating inflammatory proteins play a mediating role in the pathway from GM to epilepsy, with inverse variance weighted (IVW) method as the primary statistical method, supplemented by four methods: MR-Egger, weighted median estimator (WME), Weighted mode and Simple mode.

Results

16 positive and three negative causal associations were found between the genetic liability of GM and epilepsy and its subtypes. There were nine positive and nine negative causal associations between inflammatory proteins and epilepsy and its subtypes. Furthermore, we found that C-X-C motif chemokine 11 (CXCL11) levels mediated the causal association between Genus Family XIII AD3011 group and epilepsy.

Conclusion

Our study highlights the possible causal role of specific GM and specific inflammatory proteins in the development of epilepsy and suggests that circulating inflammatory proteins may mediate epileptogenesis through the MGBA.

Single-nucleus multi-omics identifies shared and distinct pathways in Pick's and Alzheimer's disease

The study of neurodegenerative diseases, particularly tauopathies like Pick's disease (PiD) and Alzheimer's disease (AD), offers insights into the underlying regulatory mechanisms. By investigating epigenomic variations in these conditions, we identified critical regulatory changes driving disease progression, revealing potential therapeutic targets. Our comparative analyses uncovered disease-enriched non-coding regions and genome-wide transcription factor (TF) binding differences, linking them to target genes. Notably, we identified a distal human-gained enhancer (HGE) associated with E3 ubiquitin ligase (UBE3A), highlighting disease-specific regulatory alterations. Additionally, fine-mapping of AD risk genes uncovered loci enriched in microglial enhancers and accessible in other cell types. Shared and distinct TF binding patterns were observed in neurons and glial cells across PiD and AD. We validated our findings using CRISPR to excise a predicted enhancer region in UBE3A and developed an interactive database (http://swaruplab.bio.uci.edu/scROAD) to visualize predicted single-cell TF occupancy and regulatory networks.

A blended genome and exome sequencing method captures genetic variation in an unbiased, high-quality, and cost-effective manner

We deployed the Blended Genome Exome (BGE), a DNA library blending approach that generates low pass whole genome (1-4× mean depth) and deep whole exome (30-40× mean depth) data in a single sequencing run. This technology is cost-effective, empowers most genomic discoveries possible with deep whole genome sequencing, and provides an unbiased method to capture the diversity of common SNP variation across the globe. To evaluate this new technology at scale, we applied BGE to sequence >53,000 samples from the Populations Underrepresented in Mental Illness Associations Studies (PUMAS) Project, which included participants across African, African American, and Latin American populations. We evaluated the accuracy of BGE imputed genotypes against raw genotype calls from the Illumina Global Screening Array. All PUMAS cohorts hadR 2 concordance ≥95% among SNPs with MAF≥1%, and never fell below ≥90%R 2 for SNPs with MAF<1%. Furthermore, concordance rates among local ancestries within two recently admixed cohorts were consistent among SNPs with MAF≥1%, with only minor deviations in SNPs with MAF<1%. We also benchmarked the discovery capacity of BGE to access protein-coding copy number variants (CNVs) against deep whole genome data, finding that deletions and duplications spanning at least 3 exons had a positive predicted value of ~90%. Our results demonstrate BGE scalability and efficacy in capturing SNPs, indels, and CNVs in the human genome at 28% of the cost of deep whole-genome sequencing. BGE is poised to enhance access to genomic testing and empower genomic discoveries, particularly in underrepresented populations.

VUS next in rare diseases? Deciphering genetic determinants of biomolecular condensation

The diagnostic odysseys for rare disease patients are getting shorter as next-generation sequencing becomes more widespread. However, the complex genetic diversity and factors influencing expressivity continue to challenge accurate diagnosis, leaving more than 50% of genetic variants categorized as variants of uncertain significance.Genomic expression intricately hinges on localized interactions among its products. Conventional variant prioritization, biased towards known disease genes and the structure-function paradigm, overlooks the potential impact of variants shaping the composition, location, size, and properties of biomolecular condensates, genuine membraneless organelles swiftly sensing and responding to environmental changes, and modulating expressivity.To address this complexity, we propose to focus on the nexus of genetic variants within biomolecular condensates determinants. Scrutinizing variant effects in these membraneless organelles could refine prioritization, enhance diagnostics, and unveil the molecular underpinnings of rare diseases. Integrating comprehensive genome sequencing, transcriptomics, and computational models can unravel variant pathogenicity and disease mechanisms, enabling precision medicine. This paper presents the rationale driving our proposal and describes a protocol to implement this approach. By fusing state-of-the-art knowledge and methodologies into the clinical practice, we aim to redefine rare diseases diagnosis, leveraging the power of scientific advancement for more informed medical decisions.

The goldmine of GWAS summary statistics: a systematic review of methods and tools

Genome-wide association studies (GWAS) have revolutionized our understanding of the genetic architecture of complex traits and diseases. GWAS summary statistics have become essential tools for various genetic analyses, including meta-analysis, fine-mapping, and risk prediction. However, the increasing number of GWAS summary statistics and the diversity of software tools available for their analysis can make it challenging for researchers to select the most appropriate tools for their specific needs. This systematic review aims to provide a comprehensive overview of the currently available software tools and databases for GWAS summary statistics analysis. We conducted a comprehensive literature search to identify relevant software tools and databases. We categorized the tools and databases by their functionality, including data management, quality control, single-trait analysis, and multiple-trait analysis. We also compared the tools and databases based on their features, limitations, and user-friendliness. Our review identified a total of 305 functioning software tools and databases dedicated to GWAS summary statistics, each with unique strengths and limitations. We provide descriptions of the key features of each tool and database, including their input/output formats, data types, and computational requirements. We also discuss the overall usability and applicability of each tool for different research scenarios. This comprehensive review will serve as a valuable resource for researchers who are interested in using GWAS summary statistics to investigate the genetic basis of complex traits and diseases. By providing a detailed overview of the available tools and databases, we aim to facilitate informed tool selection and maximize the effectiveness of GWAS summary statistics analysis.

Impacts of pleiotropy and migration on repeated genetic adaptation

Observations of genetically repeated evolution (repeatability) in complex organisms are incongruent with the Fisher-Orr model, which implies that repeated use of the same gene should be rare when mutations are pleiotropic (i.e. affect multiple traits). When spatially divergent selection occurs in the presence of migration, mutations of large effect are more strongly favored, and hence, repeatability is more likely, but it is unclear whether this observation is limited by pleiotropy. Here, we explore this question using individual-based simulations of a two-patch model incorporating multiple quantitative traits governed by mutations with pleiotropic effects. We explore the relationship between fitness trade-offs and repeatability by varying the alignment between mutation effect and spatial variation in trait optima. While repeatability decreases with increasing trait dimensionality, trade-offs in mutation effects on traits do not strongly limit the contribution of a locus of large effect to repeated adaptation, particularly under increased migration. These results suggest that repeatability will be more pronounced for local rather than global adaptation. Whereas pleiotropy limits repeatability in a single-population model, when there is local adaptation with gene flow, repeatability can occur if some loci are able to produce alleles of large effect, even when there are pleiotropic trade-offs.

The Genetic Architecture of Recombination Rates is Polygenic and Differs Between the Sexes in Wild House Sparrows (Passer domesticus)

Meiotic recombination through chromosomal crossing-over is a fundamental feature of sex and an important driver of genomic diversity. It ensures proper disjunction, allows increased selection responses, and prevents mutation accumulation; however, it is also mutagenic and can break up favorable haplotypes. This cost-benefit dynamic is likely to vary depending on mechanistic and evolutionary contexts, and indeed, recombination rates show huge variation in nature. Identifying the genetic architecture of this variation is key to understanding its causes and consequences. Here, we investigate individual recombination rate variation in wild house sparrows (Passer domesticus). We integrate genomic and pedigree data to identify autosomal crossover counts (ACCs) and intrachromosomal allelic shuffling (r¯intra) in 13,056 gametes transmitted from 2,653 individuals to their offspring. Females had 1.37 times higher ACC, and 1.55 times higher r¯intra than males. ACC and r¯intra were heritable in females and males (ACC h2 = 0.23 and 0.11; r¯intra  h2 = 0.12 and 0.14), but cross-sex additive genetic correlations were low (rA = 0.29 and 0.32 for ACC and r¯intra). Conditional bivariate analyses showed that all measures remained heritable after accounting for genetic values in the opposite sex, indicating that sex-specific ACC and r¯intra can evolve somewhat independently. Genome-wide models showed that ACC and r¯intra are polygenic and driven by many small-effect loci, many of which are likely to act in trans as global recombination modifiers. Our findings show that recombination rates of females and males can have different evolutionary potential in wild birds, providing a compelling mechanism for the evolution of sexual dimorphism in recombination.

Body fluid multiomics in 3PM-guided ischemic stroke management: health risk assessment, targeted protection against health-to-disease transition, and cost-effective personalized approach are envisaged

Because of its rapid progression and frequently poor prognosis, stroke is the third major cause of death in Europe and the first one in China. Many independent studies demonstrated sufficient space for prevention interventions in the primary care of ischemic stroke defined as the most cost-effective protection of vulnerable subpopulations against health-to-disease transition. Although several studies identified molecular patterns specific for IS in body fluids, none of these approaches has yet been incorporated into IS treatment guidelines. The advantages and disadvantages of individual body fluids are thoroughly analyzed throughout the paper. For example, multiomics based on a minimally invasive approach utilizing blood and its components is recommended for real-time monitoring, due to the particularly high level of dynamics of the blood as a body system. On the other hand, tear fluid as a more stable system is recommended for a non-invasive and patient-friendly holistic approach appropriate for health risk assessment and innovative screening programs in cost-effective IS management. This article details aspects essential to promote the practical implementation of highlighted achievements in 3PM-guided IS management.

Supplementary Information

The online version contains supplementary material available at 10.1007/s13167-024-00376-2.

Association between vitamin D metabolism gene polymorphisms and schizophrenia

Schizophrenia (SZ) is a multifactorial and neurodegenerative disorder that results from the interaction between genetic and environmental factors. Notably, hundreds of single nucleotide polymorphisms (SNPs) are associated with the susceptibility to SZ. Vitamin D (VD) plays an essential role in regulating several genes important for maintaining brain function and health. To the best of the authors' knowledge, no studies have yet been conducted on the association between the VD pathway and patients with SZ. Therefore, the present study aimed to assess the potential association between eight SNPs in genes related to the VD pathway, including CYP2R1, CYP27B1, CYP24A1 and VDR among patients with SZ. A case-control study was conducted, involving a total of 400 blood samples drawn from 200 patients and 200 healthy controls. Genomic DNA was extracted and variants were genotyped using the tetra-amplification refractory mutation system-polymerase chain reaction method. The present study revealed statistically significant differences between patients with SZ and controls regarding the genotypes and allele distributions of three SNPs [CYP2R1 (rs10741657), CYP27B1 (rs10877012) and CYP24A1 (rs6013897) (P<0.0001)]. The AA genotype of rs10741657 was identified to be associated with SZ (P<0.0001) and the frequency of the A allele was higher in patients with SZ (P<0.0001) compared with the control group. Similarly, the TT genotype of rs10877012 was revealed to be associated with SZ (P<0.0001) and the T allele was more frequent in patients with SZ (P<0.0001) than in the control group. Moreover, the AA genotype of rs6013897 was revealed to be associated with SZ (P<0.0001), although no significant difference was detected between the two groups regarding the A allele (P=0.055). VDR (rs2228570, rs1544410, rs731236 and rs7975232) and CYP27B1 (rs4646536) gene polymorphisms did not exhibit a significant association with SZ. While the studied SNPs revealed promising discriminatory capacity between patients with SZ and controls, the rs10741657 SNP exhibited the most optimal area under the curve value at 0.615. A logistic model was applied considering only the significant SNPs and VD levels, which revealed that rs6013897 (T/A) and VD may have protective effects (0.267, P<0.001; 0.888, P<0.001, respectively). Moreover, a low serum VD level was highly prevalent in patients with SZ compared with the controls. Based on this finding, an association between serum 25(OH)D and SZ could be demonstrated. The present study revealed that CYP2R1 (rs10741657), CYP27B1 (rs10877012) and CYP24A1 (rs6013897) gene SNPs may be associated with SZ susceptibility.

Genetic risk score based on obesity-related genes and progression in weight loss after bariatric surgery: a 60-month follow-up study

BACKGROUND

Obesity is a polygenic multifactorial disease. Recent genome-wide association studies have identified several common loci associated with obesity-related phenotypes. Bariatric surgery (BS) is the most effective long-term treatment for patients with severe obesity. The huge variability in BS outcomes between patients suggests a moderating effect of several factors, including the genetic architecture of the patients.

OBJECTIVE

To examine the role of a genetic risk score (GRS) based on 7 polymorphisms in 5 obesity-candidate genes (FTO, MC4R, SIRT1, LEP, and LEPR) on weight loss after BS.

SETTING

University hospital in Spain.

METHODS

We evaluated a cohort of 104 patients with severe obesity submitted to BS (Roux-en-Y gastric bypass or sleeve gastrectomy) followed up for >60 months (lost to follow-up, 19.23%). A GRS was calculated for each patient, considering the number of carried risk alleles for the analyzed genes. During the postoperative period, the percentage of excess weight loss total weight loss and changes in body mass index were evaluated. Generalized estimating equation models were used for the prospective analysis of the variation of these variables in relation to the GRS.

RESULTS

The longitudinal model showed a significant effect of the GRS on the percentage of excess weight loss (P = 1.5 × 10-5), percentage of total weight loss (P = 3.1 × 10-8), and change in body mass index (P = 7.8 × 10-16) over time. Individuals with a low GRS seemed to experience better outcomes at 24 and 60 months after surgery than those with a higher GRS.

CONCLUSION

The use of the GRS in considering the polygenic nature of obesity seems to be a useful tool to better understand the outcome of patients with obesity after BS.

Genome-wide association study for high-temperature tolerance in the Japanese flounder

This study addresses the critical issue of high-temperature stress in Japanese flounder (Paralichthys olivaceus), a factor threatening both their survival and the growth of the aquaculture industry. The research aims to identify genetic markers associated with high-temperature tolerance, unravel the genetic regulatory mechanisms, and lay the foundation for breeding Japanese flounder with increased resistance to high temperatures. In this study, using a genome-wide association study was performed to identify single nucleotide polymorphisms (SNPs) and genes associated with high-temperature tolerance for Japanese flounder using 280 individuals with 342 311 high-quality SNPs. The traits of high-temperature tolerance were defined as the survival time and survival status of Japanese flounder at high water temperature (31℃) for 15 days cultivate. A genome-wide association study identified six loci on six chromosomes significantly correlated with survival time under high-temperature stress. Six candidate genes were successfully annotated. Additionally, 34 loci associated with survival status were identified and mapped to 15 chromosomes, with 22 candidate genes annotated. Functional analysis highlighted the potential importance of genes like traf4 and ppm1l in regulating apoptosis, impacting high-temperature tolerance in Japanese flounder. These findings provide a valuable theoretical framework for integrating molecular markers into Japanese flounder breeding programmes, serving as a molecular tool to enhance genetic traits linked to high-temperature tolerance in cultured Japanese flounder.

Multi-Task Learning and Sparse Discriminant Canonical Correlation Analysis for Identification of Diagnosis-Specific Genotype-Phenotype Association

The primary objective of imaging genetics research is to investigate the complex genotype-phenotype association for the disease under study. For example, to understand the impact of genetic variations over the brain functions and structure, the genotypic data such as single nucleotide polymorphism (SNP) is integrated with the phenotypic data such as imaging quantitative traits. The sparse models, based on canonical correlation analysis (CCA), are popular in this area to find the complex bi-multivariate genotype-phenotype association, as the number of features in genotypic and/or phenotypic data is significantly higher as compared to the number of samples. However, the sparse CCA based methods are, in general, unsupervised in nature, and fail to identify the diagnose-specific features those play an important role for the diagnosis and prognosis of the disease under study. In this regard, a new supervised model is proposed to study the complex genotype-phenotype association, by judiciously integrating the merits of CCA, linear discriminant analysis (LDA) and multi-task learning. The proposed model can identify the diagnose-specific as well as the diagnose-consistent features with significantly lower computational complexity. The performance of the proposed method, along with a comparison with the state-of-the-art methods, is evaluated on several synthetic data sets and one real imaging genetics data collected from Alzheimer's Disease Neuroimaging Initiative (ADNI) cohort. In the current study, the SNP as genetic data and resting state functional MRI ( fMRI) as imaging data are integrated to find the complex genotype-phenotype association. An important finding is that the proposed method has better correlation value, improved noise resistance and stability, and also has better feature selection ability. All the results illustrate the power and capability of the proposed method to find the diagnostic group-specific imaging genetic association, which may help to understand the neurodegenerative disorder in a more comprehensive way.

DiGAS: Differential gene allele spectrum as a descriptor in genetic studies

Diagnosing individuals with complex genetic diseases is a challenging task. Computational methodologies exploit information at the genotype level by taking into account single nucleotide polymorphisms (SNPs) leveraging the results of genome-wide association studies analysis to assign a statistical significance to each SNP. Recent methodologies extend such an approach by aggregating SNP significance at the genetic level to identify genes that are related to the condition under study. However, such methodologies still suffer from the initial SNP analysis limitations. Here, we present DiGAS, a tool for diagnosing genetic conditions by computing significance, by means of SNP information, directly at the complex level of genetic regions. Such an approach is based on a generalized notion of allele spectrum, which evaluates the complete genetic alterations of the SNP set belonging to a genetic region at the population level. The statistical significance of a region is then evaluated through a differential allele spectrum analysis between the conditions of individuals belonging to the population. Tests, performed on well-established datasets regarding Alzheimer's disease, show that DiGAS outperforms the state of the art in distinguishing between sick and healthy subjects.

Identification of genetic variants associated with clinical features of sickle cell disease

Sickle cell disease (SCD) is an inherited blood disorder marked by homozygosity of hemoglobin S, which is a defective hemoglobin caused by a missense mutation in the β-globin gene. However, clinical phenotypes of SCD vary among patients. To investigate genetic variants associated with various clinical phenotypes of SCD, we genotyped DNA samples from 520 SCD subjects and used a genome-wide association study (GWAS) approach to identify genetic variants associated with phenotypic features of SCD. For HbF levels, the previously reported 2p16.1 locus (BCL11A) reached genome significance (rs1427407, P = 8.58 × 10-10) in our GWAS as expected. In addition, we found a new genome-wide significance locus at 15q14 (rs8182015, P = 2.07 × 10-8) near gene EMC7. GWAS of acute chest syndrome (ACS) detected a locus (rs79915189, P = 3.70 × 10-8) near gene IDH2 at 15q26.1. The SNP, rs79915189, is also an expression quantitative trait locus (eQTL) of IDH2 in multiple tissues. For vasoocclusive episode (VOE), GWAS detected multiple significant signals at 2p25.1 (rs62118798, P = 4.27 × 10-8), 15q26.1 (rs62020555, P = 2.04 × 10-9) and 15q26.3 (rs117797325, P = 4.63 × 10-8). Our findings provide novel insights into the genetic mechanisms of SCD suggesting that common genetic variants play an important role in the presentation of the clinical phenotypes of patients with SCD.

Leveraging Functional Genomics for Understanding Beef Quality Complexities and Breeding Beef Cattle for Improved Meat Quality

Consumer perception of beef is heavily influenced by overall meat quality, a critical factor in the cattle industry. Genomics has the potential to improve important beef quality traits and identify genetic markers and causal variants associated with these traits through genomic selection (GS) and genome-wide association studies (GWAS) approaches. Transcriptomics, proteomics, and metabolomics provide insights into underlying genetic mechanisms by identifying differentially expressed genes, proteins, and metabolic pathways linked to quality traits, complementing GWAS data. Leveraging these functional genomics techniques can optimize beef cattle breeding for enhanced quality traits to meet high-quality beef demand. This paper provides a comprehensive overview of the current state of applications of omics technologies in uncovering functional variants underlying beef quality complexities. By highlighting the latest findings from GWAS, GS, transcriptomics, proteomics, and metabolomics studies, this work seeks to serve as a valuable resource for fostering a deeper understanding of the complex relationships between genetics, gene expression, protein dynamics, and metabolic pathways in shaping beef quality.