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

Genetic analysis of comorbidities between osteoarthritis, sarcopenia, and osteoporosis

BACKGROUND

Osteoarthritis (OA), sarcopenia (SCP), and osteoporosis (OP) pose a substantial global morbidity and mortality burden, and previous studies have observed potential associations among them. This study aims to comprehensively characterize the common genetic structure, biological basis, and underlying causal relationship among OA, SCP, and OP.

METHODS

We used pooled statistics from the largest European genome-wide association study to investigate the genetic overlap and underlying causal relationships among OA, SCP, and OP. LD Score Regression (LDSC) was first used for estimating global and local genetic associations, cross-trait meta-analysis was then conducted to identify shared loci, and mendelian randomization (MR) analysis was performed to test causal association.

RESULTS

In global and local genetic correlation analysis, we found strong positive correlations among OA, SCP, and OP. Cross-trait meta-analysis revealed 9 novel pleiotropic loci for HandOA_SCP trait-pairs, 1 for ThumbOA_SCP (females), and 6 for KneeOA_SCP (males)0.10 novel pleiotropic loci were also identified for HipOA_TBMD, while none for WLM_FinOP. Bidirectional MR analyses indicated significant causal associations between HandOA and SCP(Forward: OR: 1.41, 95 % CI: 1.25-1.60, p < 0.01,Reverse: OR: 1.77, 95 % CI: 1.34-2.35, p < 0.01). Reverse analyses suggested that ThumbOA.female (OR: 1.92, 95 % CI:1.18-3.13, p < 0.01) and KneeOA.male (OR: 1.58, 95 % CI: 1.13-2.12, p < 0.01) were positively correlated with SCP, while TBMD was positively correlated with HipOA (OR: 1.23, 95 % CI: 1.16-1.31, p < 0.01).

CONCLUSIONS

Our work demonstrates a shared genetic basis, pleiotropic loci, and putative causal relationships among OA, SCP, and OP, highlighting the intrinsic links behind these three complex skeletal diseases.

Polyconnectomic Scoring of Functional Connectivity Patterns Across Eight Neuropsychiatric and Three Neurodegenerative Disorders

BACKGROUND

Neuropsychiatric and neurodegenerative disorders involve diverse changes in brain functional connectivity. As an alternative to approaches that search for specific mosaic patterns of affected connections and networks, we used polyconnectomic scoring to quantify disorder-related whole-brain connectivity signatures into interpretable, personalized scores.

METHODS

The polyconnectomic score (PCS) measures the extent to which an individual's functional connectivity mirrors the whole-brain circuitry characteristics of a trait. We computed PCSs for 8 neuropsychiatric conditions (attention-deficit/hyperactivity disorder, anxiety-related disorders, autism spectrum disorder, obsessive-compulsive disorder, bipolar disorder, major depressive disorder, schizoaffective disorder, and schizophrenia) and 3 neurodegenerative conditions (Alzheimer's disease, frontotemporal dementia, and Parkinson's disease) across 22 datasets with resting-state functional magnetic resonance imaging data from 10,667 individuals (5325 patients, 5342 control participants). We also examined PCSs in 26,673 individuals from the population-based UK Biobank cohort.

RESULTS

PCSs were consistently higher in out-of-sample patients across 6 of the 8 neuropsychiatric and across all 3 investigated neurodegenerative disorders ([minimum, maximum]: area under the receiver operating characteristic curve = [0.55, 0.73], false discovery rate-corrected p [pFDR] = [1.8 × 10-16, 4.5 × 10-2]). Individuals with elevated PCS levels for neuropsychiatric conditions exhibited higher neuroticism (pFDR < 9.7 × 10-5), lower cognitive performance (pFDR < 5.3 × 10-5), and lower general well-being (pFDR < 9.7 × 10-4).

CONCLUSIONS

Our findings reveal generalizable whole-brain connectivity alterations in brain disorders. Polyconnectomic scoring effectively aggregates disorder-related signatures across the entire brain into an interpretable, participant-specific metric. A toolbox is provided for PCS computation.

Genetic analysis of psychosis Biotypes: shared Ancestry-adjusted polygenic risk and unique genomic associations

The Bipolar-Schizophrenia Network for Intermediate Phenotypes (B-SNIP) created psychosis Biotypes based on neurobiological measurements in a multi-ancestry sample. These Biotypes cut across DSM diagnoses of schizophrenia, schizoaffective disorder, and bipolar disorder with psychosis. Two recently developed post hoc ancestry adjustment methods of Polygenic Risk Scores (PRSs) generate Ancestry-Adjusted PRSs (AAPRSs), which allow for PRS analysis of multi-ancestry samples. Applied to schizophrenia PRS, we found the Khera AAPRS method to show superior portability and comparable prediction accuracy as compared with the Ge method. The three Biotypes of psychosis disorders had similar AAPRSs across ancestries. In genomic analysis of Biotypes, 12 genes, and isoforms showed significant genomic associations with specific Biotypes in a Transcriptome-Wide Association Study (TWAS) of genetically regulated expression (GReX) in the adult brain and fetal brain. TWAS inflation was addressed by the inclusion of genotype principal components in the association analyses. Seven of these 12 genes/isoforms satisfied Mendelian Randomization (MR) criteria for putative causality, including four genes TMEM140, ARTN, C1orf115, CYREN, and three transcripts ENSG00000272941, ENSG00000257176, ENSG00000287733. These genes are enriched in the biological pathways of Rearranged during Transfection (RET) signaling, Neural Cell Adhesion Molecule 1 (NCAM1) interactions, and NCAM signaling for neurite out-growth. The specific associations with Biotypes suggest that pharmacological clinical trials and biological investigations might benefit from analyzing Biotypes separately.

PTPN2 and Leukopenia in Individuals With Normal TPMT and NUDT15 Metabolizer Status Taking Azathioprine

Leukopenia is a common dose-dependent side effect of azathioprine, often leading to drug discontinuation. Variants in TPMT and NUDT15 are associated with azathioprine-induced leukopenia but only explain 25% of cases. Thus, we aimed to identify novel genetic risk factors among TPMT and NUDT15 normal metabolizers through a genome-wide association study (GWAS). Using BioVU, Vanderbilt's electronic health record linked to genetic data, we assembled a discovery cohort of new users of azathioprine. The analysis was conducted in 1184 new users of azathioprine who had no history of prior thiopurine use or an organ transplant. A replication cohort of 521 patients was derived from All of Us, an NIH-funded project that links healthcare data and genetics. The GWAS was adjusted for sex, age, indication (inflammatory bowel disease, systemic lupus erythematosus, other autoimmune condition, or unknown), concurrent use of xanthine oxidase inhibitors (allopurinol or febuxostat) or immunosuppressants, prior TPMT or NUDT15 testing, and 10 principal components of ancestry. In BioVU, 65% of patients were female with a median age of 44 [IQR: 30, 57] and 125 patients developed leukopenia. In All of Us, 69% were female with a median age of 51 [36, 61], and 44 patients developed leukopenia. An intronic variant in PTPN2, rs11664064, reached genome-wide significance in BioVU (OR = 3.61; p = 1.96E-8) and replicated in All of Us (OR = 2.42, p = 0.039). Our finding suggests an association between rs11664064 in PTPN2 and azathioprine-induced leukopenia. PTPN2 plays a role in immune cell development and differentiation, providing a plausible mechanism for this association.

Genetic associations of sex hormones with cerebral aneurysm formation and subarachnoid hemorrhage: A two-sample Mendelian randomization analysis

BACKGROUND AND AIM

Women are more likely than men to develop aneurysmal subarachnoid hemorrhage, and this difference is more pronounced in women after menopause, suggesting a possible correlation between sex hormone levels and cerebral aneurysm formation and rupture.

METHODS AND RESULTS

We selected genetic variants closely related to estrogen (estradiol), bioavailable testosterone (Bio T), and sex hormone-binding globulin (SHBG) as instrumental variables from the pooled data of the IEU Open GWAS project and cerebral aneurysm (CA) and subarachnoid hemorrhage (SAH) data from two independent datasets from the same study. Two-sample Mendelian randomization was subsequently performed to determine whether the relevant sex hormones and SHBG are causally associated with the formation and rupture of CA. We identified 14 causal associations of related sex hormones and their binding proteins with cerebral aneurysm formation and rupture. Inverse-variance weighting revealed that genetically predicted increased BioT levels reduced the risk of SAH development and genetically predicted increased levels of SHBG in females influenced reduced the risk of cerebral aneurysm formation. After excluding sex differences, weighted mode revealed opposite results, but there was no difference in the IVW, MR-Egger regression, weighted median, or simple mode analyses. No significant effects of the concentrations of other relevant sex hormones or SHBG on the risk of cerebral aneurysm formation or rupture were found.

CONCLUSIONS

Our study may explain the mechanisms underlying the increased incidence of cerebral aneurysms in menopausal women and provide new directions for intervention in cerebral aneurysm formation and rupture, but further studies are needed.

A meta-analysis of genome-wide association studies revealed significant QTL and candidate genes for loin muscle area in three breeding pigs

Loin muscle area (LMA) is an important production trait in pigs and is highly correlated with lean meat percentage. However, the genetic architecture of LMA has not yet been fully elucidated. This study conducted genome-wide association studies (GWAS) and meta-analyses of LMA in Duroc (n = 337), Landrace (n = 662), and Yorkshire pigs (n = 3,176) using imputed whole-genome sequencing to identify new QTLs and candidate genes associated with LMA traits. A total of 108, 34, and 232 significant variants were identified in the Duroc, Landrace, and Yorkshire populations, respectively. The meta-analysis revealed 143 genome-wide significant SNPs and 276 suggestive SNPs, among which 213 were not identified in single population GWAS. Notably, 229 and 413 SNPs were located on SSC16 in the Yorkshire population and meta-analysis, respectively. Based on the 2-LOD drop-off interval, the SSC16 QTL in the Yorkshire population was further narrowed to a 679.835 kb interval (from 32.818 Mb to 33.498 Mb). The most significant variant within this QTL, 16_33228254 (P = 4.45 × 10-9), explained 1.11% phenotypic variance, representing a potential novel locus for LMA. Further bioinformatics analysis determined seven promising candidate genes (NDUFS4, ARL15, FST, ADAM12, DAB2, PLPP1, and SGMS2) with biological processes such as myoblast fusion and positive regulation of transforming growth factor beta receptor signaling pathway. Among them, ARL15 was previously reported in LMA studies, while the other six genes represent novel candidate genes. These findings reveal potential functional genes and pathways associated with LMA, providing valuable insights for future genetic improvement in pigs.

PIGEON: a statistical framework for estimating gene-environment interaction for polygenic traits

Understanding gene-environment interaction (GxE) is crucial for deciphering the genetic architecture of human complex traits. However, current statistical methods for GxE inference face challenges in both scalability and interpretability. Here we introduce PIGEON-a unified statistical framework for quantifying polygenic GxE using a variance component analytical approach. Based on this framework, we outline the main objectives in GxE studies and introduce an estimation procedure that requires only summary statistics data as input. We demonstrate the effectiveness of PIGEON through theoretical and empirical analyses, including a quasi-experimental gene-by-education study of health outcomes and gene-by-sex interaction for 530 traits using UK Biobank. We also identify genetic interactors that explain the treatment effect heterogeneity in a clinical trial on smoking cessation. PIGEON suggests a path towards polygenic, summary statistics-based inference in future GxE studies.

Reveal genomic insights into cotton domestication and improvement using gene level functional haplotype-based GWAS

Genome-wide association studies (GWAS) are widely used to detect associations between genetic variants and phenotypes. However, few studies have thoroughly analyzed genes, the fundamental and most crucial functional units. Here, we develop an innovative strategy to translate genomic variants into gene-level functional haplotypes (FHs), effectively reducing the interference from complex genome structure and linkage disequilibrium (LD) present in the conventional genetic mapping framework. Using refined mixed linear models, gene-level FH is regressed with 20 cotton agronomic traits across 245 sets of phenotypic values in 3,724 accessions, directly identifying 532 quantitative trait genes (QTGs) with significant breeding potential. The biological function of a superior fiber quality QTG encoding ferulic acid 5-hydroxylase 1 is experimentally validated. Thereafter, we systematically analyze the genetic basis of cotton domestication and improvement at the gene level. This report provides genomic insight into the genetic dissection and efficient mapping of functional genes in plants.

Causal association of hypertension in family members with preeclampsia-eclampsia in pregnant women: A two-sample Mendelian randomization study

OBJECTIVES

The genetic risk factors for hypertension are also high-risk factors for preeclampsia-eclampsia. This study examined the association of hypertension in family members with preeclampsia-eclampsia in pregnant women through two-sample Mendelian randomization (MR).

STUDY DESIGN

Mendelian randomization.

MAIN OUTCOME MEASURES

The data for hypertension in siblings, mother, and father were from the UK Biobank, including 364,661, 426,391, and 402,899 individuals, respectively. The data for preeclampsia-eclampsia were FinnGEN R9 (7217 cases and 194,266 controls). Inverse-variance weighted was used as the main analysis method. Weighted median, MR-Egger, simple mode, and weighted mode were complementary MR methods. Heterogeneity was detected using Cochran's Q-test, horizontal pleiotropy using MR-Egger regression, and driving single-nucleotide polymorphisms (SNPs) using the leave-one-out method.

RESULTS

Mendelian randomization analysis showed that hypertension in family members was positively correlated with preeclampsia-eclampsia risk. The risk of preeclampsia-eclampsia in pregnant women who have siblings with hypertension was the highest (OR = 179.41, 95 % CI: 23.10-1393.65, P = 6.98E-07), followed by hypertension in the mothers (OR = 26.83, 95 % CI: 5.42-132.87, P = 5.56E-05) and the fathers (OR = 18.97, 95 % CI: 1.28-281.29, P = 0.032). The MR-Egger regression test indicated no horizontal pleiotropy (P > 0.05). Cochran's Q-test showed that the effects of the included SNPs exhibited heterogeneity (P < 0.05). The leave-one-out analysis did not reveal SNPs driving the results by themselves.

CONCLUSION

The risk of preeclampsia-eclampsia in pregnant women who have siblings with hypertension was the highest, followed by pregnant women with a mother or father with hypertension. Having siblings with hypertension should be considered as a high-risk factor for the early prediction of preeclampsia-eclampsia.

Genome-scale metabolic modelling identifies reactions mediated by SNP-SNP interactions associated with yeast sporulation

Genome-scale metabolic models (GEMs) are powerful tools used to understand the functional effects of genetic variants. However, the impact of single nucleotide polymorphisms (SNPs) in transcription factors and their interactions on metabolic fluxes remains largely unexplored. Using gene expression data from a yeast allele replacement panel grown during sporulation, we constructed co-expression networks and SNP-specific GEMs. Analysis of co-expression networks revealed that during sporulation, SNP-SNP interactions impact the connectivity of metabolic regulators involved in glycolysis, steroid and histidine biosynthesis, and amino acid metabolism. Further, genome-scale differential flux analysis identified reactions within six major metabolic pathways associated with sporulation efficiency variation. Notably, autophagy was predicted to act as a pentose pathway-dependent compensatory mechanism supplying critical precursors like nucleotides and amino acids, enhancing sporulation. Our study highlights how transcription factor polymorphisms interact to shape metabolic pathways in yeast, offering insights into genetic variants associated with metabolic traits in genome-wide association studies.

Shared genetic architecture between leukocyte telomere length and Alzheimer's disease

BACKGROUND

Epidemiological and clinical studies have reported an association between leukocyte telomere length (LTL) and Alzheimer's disease (AD). However, genetic association between the two phenotypes remains largely unknown. We aimed to elucidate the potential shared genetic architecture between LTL and AD.

METHODS

Summary statistics from genome-wide association studies were obtained from large-scale biobank in European-ancestry populations for LTL (N = 472,174) and AD (71,880 cases, 383,378 controls). We examined the global and local genetic correlation between LTL and AD using linkage-disequilibrium score regression and ρ-HESS. We applied the bivariate causal mixture model (MiXeR) to calculate the number of shared genetic causal variants, and the conditional/conjunctional false discovery rate (condFDR/conjFDR) framework to identify specific shared loci between LTL and AD. Bidirectional two-sample Mendelian randomization (MR) were used to explore the causal associations between LTL and AD.

RESULTS

We detected a significant genetic correlation between LTL and AD (rg = -0.168). Partitioning the whole genome into 1703 almost independent regions, we observed a significant local genetic correlation for LTL and AD at 19q13.32. MiXeR estimated a total of 360 variants affecting LTL, of which 16 was estimated to influence AD. The condFDR revealed an essential genetic enrichment in LTL conditional on associations with AD, and vice versa. We next identified 8 shared genomic loci between LTL and AD using conjFDR method, of which 4 are novel loci for both the phenotypes. Moreover, 3 shared loci were identified as eQTLs (rs3098168, rs4780338 and rs2680702). All shared loci mapped a subset of 48 credible genes, including USP8, DEXI and APOE. Gene-set analysis identified 18 putative gene sets enriched with the genes mapped to the shared loci. MR analysis suggested that genetically determined AD was causally associated with LTL.

CONCLUSION

Our study identified specific shared loci between LTL and AD, providing new insights for polygenic overlap and molecular mechanisms, and highlighting new opportunities for future experimental validation.

Genome-wide association analysis in peanut accessions uncovers the genetic basis regulating oil and fatty acid variation

BACKGROUND

The cultivated peanut, Arachis hypogaea L., is a critical oil and food crop worldwide. Improving seed oil quality in peanut has long been an aim of breeders. However, our knowledge of the genetic basis of selecting for seed nutritional traits is limited. Based on AhFAD2A and AhFAD2B, scientists have now developed higher oleic acid (80-84%) in peanut. Decoding the genetic makeup behind natural variation in kernel oil and fatty acid concentrations is crucial for molecular breeding-based nutrient quantity and quality manipulation.

RESULTS

Herein, we recognized 87 quantitative trait loci (QTLs) in 45 genomic regions for the concentrations of oil, oleic acid, and linoleic acid, as well as the oleic acid to linoleic acid (O/L) ratio via a genome-wide association study (GWAS) involving 499 peanut accessions. Eight QTLs explained more than 15% of the phenotypic variation in peanut accessions. Among the 45 potential genes significantly related to the four traits, only three genes displayed annotation to the fatty acid pathway. Furthermore, on the basis of pleiotropism or linkage data belonging to the identified singular QTLs, we generated a trait-locus axis to better elucidate the genetic background behind the observed oil and fatty acid concentration association. Expression analysis indicated that arahy.AV6GAN and arahy.NNA8KD have higher expressions in the seeds.

CONCLUSION

This natural population consisting of 499 peanut accessions combined with high-density SNPs will provide a better choice for identifying peanut QTLs/genes in the future. Together, our results provide strong evidence for the genetic mechanism behind oil biosynthesis in peanut, facilitating future advances in multiple fatty acid component generation via pyramiding of desirable QTLs.

Genome-wide association studies on malaria in Sub-Saharan Africa: A scoping review

BACKGROUND

Malaria remains one of the leading causes of death in Sub-Saharan Africa (SSA). The scoping review mapped evidence in research on existing studies on malaria genome-wide association studies (GWAS) in SSA.

METHODS

A scoping review was conducted to map existing studies in genome-wide association on malaria in SSA, with a review period between 1st January 2000 and 31st December 2024. The searches were made with the last search done in January 2025. The extracted data were analyzed using R software and SRplot. Relevant studies were identified through electronic searching of Google Scholar, Pubmed, Scopus, and Web of Science databases. Two independent reviewers followed the inclusion-exclusion criteria to extract relevant studies. Data from the studies were collected and synthesized using Excel and Zotero software.

RESULTS

We identified 89 studies for inclusion. Most of these studies (n = 42, [Formula: see text]) used a case-control study design, while the rest used cross-sectional, cohort, longitudinal, family-based, and experimental study designs. These studies were conducted between 2000 and 2024, with a noticeable increase in publications from 2012. Most studies were carried out in Kenya (n = 23), Gambia (n = 18), Cameroon (n = 15), and Tanzania (n = 9), primarily exploring genetic variants associated with malaria susceptibility, resistance, and severity.

CONCLUSION

Many case-control studies in Kenya and Gambia reported genetic variants in malaria susceptibility, resistance, and severity. GWAS on malaria is scarce in SSA, and even fewer studies are model-based. Consequently, there is a pressing need for more genome-wide research on malaria in SSA.

Mathematical bounds on r2 and the effect size in case-control genome-wide association studies

Case-control genome-wide association studies (GWAS) are often used to find associations between genetic variants and diseases. When case-control GWAS are conducted, researchers must make decisions regarding how many cases and how many controls to include in the study. Connections between variants and diseases are made using association statistics, including χ2. Previous work in population genetics has shown that LD statistics, including r2, are bounded by the allele frequencies in the population being studied. Since varying the case fraction changes sample allele frequencies, we use the known bounds on r2 to explore how the fraction of cases included in a study can affect statistical power to detect associations. We analyze a simple mathematical model and use simulations to study a quantity proportional to the χ2 noncentrality parameter, which is closely related to r2, under various conditions. Varying the case fraction changes the χ2 noncentrality parameter, and by extension the statistical power, with effects depending on the dominance, penetrance, and frequency of the risk allele. Our framework explains previously observed results, such as asymmetries in power to detect risk vs. protective alleles, and the fact that a balanced sample of cases and controls does not always give the best power to detect associations, particularly for highly penetrant minor risk alleles that are either dominant or recessive. We show by simulation that our results can be used as a rough guide to statistical power for association tests other than χ2 tests of independence.

Genome-wide enhancer-gene regulatory maps of liver reveal novel regulatory mechanisms underlying NAFLD pathogenesis

INTRODUCTION

Non-alcoholic fatty liver disease (NAFLD) represents the most widespread liver disease globally, ranging from non-alcoholic fatty liver (NAFL) and steatohepatitis (NASH) to fibrosis/cirrhosis, with potential progression to hepatocellular carcinoma (HCC). Genome-wide association studies (GWASs) have identified several single nucleotide polymorphisms (SNPs) associated with NAFLD. However, numerous GWAS signals associated with NAFLD locate in non-coding regions, posing a challenge for interpreting their functional annotation.

RESULTS

In this study, we utilized the Activity-by-Contact (ABC) model to construct the enhancer-gene maps of liver by integrating epigenomic data from 15 liver tissues and cell lines. We constructed the most comprehensive genome-wide regulatory maps of the liver, identifying 543,486 enhancer-gene connections, including 267,857 enhancers and 16,872 target genes. Enrichment analyses revealed that the ABC SNPs are significantly enriched in active chromatin regions and active chromatin state. By combining the ABC regulatory maps and NAFLD GWAS data, we systematically identified ABC SNPs associated with NAFLD risk. Through the functional annotations, such as pathway enrichment and drug-gene interaction analyses, we identified 6 genes (GGT1, ACTG1, SPP1, EPHA2, PROZ and SHMT1) as candidate NAFLD genes, with SHMT1 previously reported. Among the SNPs connected to the candidate genes, the ABC SNP rs2017869 (odds ratio [OR] for the C allele = 1.10, 95% CI = 1.04-1.16, P = 5.97 × 10- 4) had the highest ABC score. According to the ABC maps, rs2017869 links to GGT1, and several drugs targeting this gene, such as liothyronine, showed potential benefits to patients with NAFLD. Furthermore, we identified that another novel gene, EPHA2, may play a crucial role in NAFLD by regulating the GGT levels.

CONCLUSIONS

Our study provides the most comprehensive ABC regulatory maps of the liver to date. This resource offers a valuable reference for identifying regulatory variants and prioritizing susceptibility genes of liver diseases, such as NAFLD.

EasyOmics: A graphical interface for population-scale omics data association, integration, and visualization

The rapid growth of population-scale whole-genome resequencing, RNA sequencing, bisulfite sequencing, and metabolomic and proteomic profiling has led quantitative genetics into the era of big omics data. Association analyses of omics data, such as genome-, transcriptome-, proteome-, and methylome-wide association studies, along with integrative analyses of multiple omics datasets, require various bioinformatics tools, which rely on advanced programming skills and command-line interfaces and thus pose challenges for wet-lab biologists. Here, we present EasyOmics, a stand-alone R Shiny application with a user-friendly interface that enables wet-lab biologists to perform population-scale omics data association, integration, and visualization. The toolkit incorporates multiple functions designed to meet the increasing demand for population-scale omics data analyses, including data quality control, heritability estimation, genome-wide association analysis, conditional association analysis, omics quantitative trait locus mapping, omics-wide association analysis, omics data integration, and visualization. A wide range of publication-quality graphs can be prepared in EasyOmics by pointing and clicking. EasyOmics is a platform-independent software that can be run under all operating systems, with a docker container for quick installation. It is freely available to non-commercial users at Docker Hub https://hub.docker.com/r/yuhan2000/easyomics.

Leveraging transcriptome-wide association studies identifies the relationship between upper respiratory flora and cell type-specific gene expression in severe respiratory disease

ObjectivesThe upper respiratory tract flora may influence host immunity and modulate susceptibility to viral respiratory infections. This study aimed to investigate the associations between upper respiratory tract flora and immune cells in severe ILI, identify specific microbial taxa and immune response pathways contributing to disease severity, and elucidate how flora influences ILI progression by modulating immune cell functions.MethodsHeritability of GWAS summary data was estimated using LDSC (v1.0.1). Gene-level genetic associations were analyzed with MAGMA. scRNA-seq data were integrated with genetic association data using scDRS. FUSION was used to construct cell type-specific expression quantitative trait locus models based on genotypes and scRNA-seq data from the onek1k project, which were combined with flora abundance-related GWAS data for a transcriptome-wide association study.ResultsFrom the LDSC analysis, data from 1195 severe ILI-associated GWASs with upper respiratory flora(h2 > 0.1) were included in subsequent analysis. TWAS identified 19 significant association pairs (Padj < 0.05), and 1226 differentially expressed genes between mild and severe ILI patients (Padj < 0.05 and | log2FC|>0.25). Functional enrichment analyses using GO, KEGG, and Reactome databases revealed that immune cells,such as CD4 + T effector memory cells, cDCs, NK cells, were enriched in multiple biological processes or pathways.ConclusionsThis study identified associations between severe ILI-related upper respiratory tract flora and cell type-specific gene expression, potentially explaining how differential flora influences ILI progression. CD16 + monocytes exhibited the most differentially expressed genes, followed by proliferating cells and cDCs, highlighting the significant role of immune cell-enriched pathways in ILI progression.

Genetic parameters and haplotype-based genome-wide association study of indicator traits for gastrointestinal parasite resistance in Santa Ines sheep

Genetic parameters are of great importance in animal breeding since they determine the strategies necessary to increase the genetic progress in economic traits, for example indicator traits for resistance to endoparasites in sheep. Furthermore, genetic markers have been used to identify genomic regions associated with economically important traits, which can help increase the genetic response through genomic selection. Therefore, this study aimed to estimate genetic parameters and perform a haplotype-based genome wide association study (GWAS) to identify genomic regions associated with indicator traits for endoparasite resistance in Santa Ines sheep. Haplotype GWAS was performed using linkage disequilibrium blocks (haploblocks) defined by the Haploview software. Records from 1725 animals for Famacha© (FAM, N = 5560), packed cell volume (PCV, N = 5135), total plasma protein (TPP, N = 4356), and fecal egg count (FEC, N = 4248) were used for analysis. The pedigree file contained information from 4821 animals; of these, 638 animals were genotyped using the Ovine SNP50 Genotyping BeadChip. Heritability estimates were moderate for FAM (0.26), PCV (0.26), TPP (0.16), and log-transformed FEC (0.19). Genomic regions that explained more than 0.3 % of the total additive genetic variance of the traits were defined as significant. These regions overlapped with quantitative traits loci associated with eosinophil number, fecal egg count, hematocrit, and immunoglobulin levels. In the top regions for the traits evaluated we found genes individually involved in inflammatory response, immunity, macrophage function in host-pathogen interactions, and other biological functions. All indicator traits for resistance to gastrointestinal parasites evaluated in this study exhibited sufficient genetic variability to respond to selection and can be used to improve the health and consequently the production of Santa Ines sheep.

Expanding scope of genetic studies in the era of biobanks

Biobanks have become pivotal in genetic research, particularly through genome-wide association studies (GWAS), driving transformative insights into the genetic basis of complex diseases and traits through the integration of genetic data with phenotypic, environmental, family history, and behavioral information. This review explores the distinct design and utility of different biobanks, highlighting their unique contributions to genetic research. We further discuss the utility and methodological advances in combining data from disease-specific study or consortia with that of biobanks, especially focusing on summary statistics based meta-analysis. Subsequently we review the spectrum of additional advantages offered by biobanks in genetic studies in representing population differences, calibration of polygenic scores, assessment of pleiotropy and improving post-GWAS in silico analyses. Advances in sequencing technologies, particularly whole-exome and whole-genome sequencing, have further enabled the discovery of rare variants at biobank scale. Among recent developments, the integration of large-scale multi-omics data especially proteomics and metabolomics, within biobanks provides deeper insights into disease mechanisms and regulatory pathways. Despite challenges like ascertainment strategies and phenotypic misclassification, biobanks continue to evolve, driving methodological innovation and enabling precision medicine. We highlight the contributions of biobanks to genetic research, their growing integration with multi-omics, and finally discuss their future potential for advancing healthcare and therapeutic development.

IFI27, a potential candidate molecular marker for primary Sjogren's syndrome

OBJECTIVE

The etiology of primary Sjogren's syndrome (pSS) is complex and not completely clear. This study was to identify key genes in pSS based on Gene Expression Omnibus (GEO).

METHODS

We downloaded the GSE40568, GSE80805, GSE127952, and GSE164885 mRNA expression profiles from GEO. Differentially expressed genes (DEGs) analyses were carried out by using the online analysis tool GEO2R and R. Gene Ontology (GO) annotation and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses were performed to elucidate the biological processes, molecular function, cellular component, and KEGG signaling pathways for the DEGs in salivary glands (SGs) and peripheral blood mononuclear cells (PBMCs). Genes co-expressed were found in PBMCs and SGs of pSS patients. RT-qPCR was performed for validation. Finally, clinical correlation analysis and receiver operator characteristic (ROC) curve analysis were performed.

RESULTS

A total of thirty-nine up-regulated and one down-regulated genes were identified in pSS SGs. GO and KEGG pathway revealed that these DEGs were related to response to virus, and type I interferon signaling pathway. It was verified that fourteen genes were up-regulated in the SGs of pSS by RT-qPCR. Twenty up-regulated genes were identified in pSS patients PBMCs. Two genes were up-regulated in SGs and PBMCs of pSS patients, including IFI27 and IFI44L. The mRNA level of IFI27 was positively correlated with the disease activity of pSS patients. Furthermore, ROC analyses proved IFI27 may have diagnostic value for pSS.

CONCLUSION

IFI27 might serve as a potential biomarker for the early diagnosis and therapy of pSS. Key Points • IFI27 expression was significantly increased in PBMCs and SGs of pSS patients. • IFI27 was positively correlated with disease activity in pSS patients. • IFI27 might have a good diagnostic value for pSS.

A regulatory variant rs9379874 in T1D risk region 6p22.2 affects BTN3A1 expression regulating T cell function

OBJECTIVE

Genome-wide association studies (GWAS) have identified that 6p22.2 region is associated with type 1 diabetes (T1D) risk in the Chinese Han population. This study aims to reveal associations between this risk region and T1D subgroups and related clinical features, and further identify causal variant(s) and target gene(s) in this region.

METHODS

2608 T1D and 4814 healthy controls were recruited from East, Central, and South China. Baseline data and genotyping for rs4320356 were collected. The most likely causal variant and gene were identified by bioinformatics analysis, dual-luciferase reporter assays, expression quantitative trait loci (eQTL), and functional annotation of the non-coding region within the 6p22.2 region.

RESULTS

The leading variant rs4320356 in the 6p22.2 region was associated with T1D risk in the Chinese and Europeans. However, this variant was not significantly associated with islet function or autoimmunity. In silico analysis suggested rs9379874 was the most potential causal variant for T1D risk among thymus, spleen, and T cells, overlapping with the enhancer-related histone mark in multiple T cell subsets. Dual luciferase reporter assay and eQTL showed that the T allele of rs9379874 increased BTN3A1 expression by binding to FOXA1. Public single-cell RNA sequencing analysis indicated that BTN3A1 was related to T-cell activation, ATP metabolism, and cytokine metabolism pathways, which might contribute to T1D development.

CONCLUSION

This study indicates that a functional variant rs9379874 regulates BTN3A1 expression, expanding the genomic landscape of T1D risk and offering a potential target for developing novel therapies.

Planting Genomes in the Wild: Arabidopsis from Genetics History to the Ecology and Evolutionary Genomics Era

The genetics model system Arabidopsis thaliana (L.) Heynh. lives across a vast geographic range with contrasting climates, in response to which it has evolved diverse life histories and phenotypic adaptations. In the last decade, the cataloging of worldwide populations, DNA sequencing of whole genomes, and conducting of outdoor field experiments have transformed it into a powerful evolutionary ecology system to understand the genomic basis of adaptation. Here, we summarize new insights on Arabidopsis following the coordinated efforts of the 1001 Genomes Project, the latest reconstruction of biogeographic and demographic history, and the systematic genomic mapping of trait natural variation through 15 years of genome-wide association studies. We then put this in the context of local adaptation across climates by summarizing insights from 73 Arabidopsis outdoor common garden experiments conducted to date. We conclude by highlighting how molecular and genomic knowledge of adaptation can help us to understand species' (mal)adaptation under ongoing climate change.

Integrating large-scale meta-GWAS and PigGTEx resources to decipher the genetic basis of 232 complex traits in pigs

Understanding the molecular and cellular mechanisms underlying complex traits in pigs is crucial for enhancing genetic gain via artificial selection and utilizing pigs as models for human disease and biology. Here, we conducted comprehensive genome-wide association studies (GWAS) followed by a cross-breed meta-analysis for 232 complex traits and a within-breed meta-analysis for 12 traits, using 28.3 million imputed sequence variants in 70 328 animals across 14 pig breeds. We identified 6878 quantitative trait loci (QTL) for 139 complex traits. Leveraging the Pig Genotype-Tissue Expression resource, we systematically investigated the biological context and regulatory mechanisms behind these trait-QTLs, ultimately prioritizing 14 829 variant-gene-tissue-trait regulatory circuits. For instance, rs344053754 regulates UGT2B31 expression in the liver and intestines, potentially by modulating enhancer activity, ultimately influencing litter weight at weaning in pigs. Furthermore, we observed conservation of certain genetic and regulatory mechanisms underlying complex traits between humans and pigs. Overall, our cross-breed meta-GWAS in pigs provides invaluable resources and novel insights into the genetic regulatory and evolutionary mechanisms of complex traits in mammals.

Genomic Prediction and Genome-Wide Association Studies for Productivity, Conformation and Heat Tolerance Traits in Tropical Smallholder Dairy Cows

Genomic selection (GS) and genome-wide association studies (GWAS) have not been investigated in Vietnamese dairy cattle, even for basic milk production traits, largely due to the scarcity of individual phenotype recording in smallholder dairy farms (SDFs). This study aimed to estimate heritability (h 2) and test the applicability of GS and GWAS for milk production, body conformation and novel heat tolerance traits using single test day phenotypic data. Thirty-two SDFs located in either the north (a lowland vs. a highland) or the south (a lowland vs. a highland) of Vietnam were each visited for an afternoon and the next morning to collect phenotype data of all lactating cows (n = 345). Tail hair from each cow was sampled for subsequent genotyping with a 50K SNP chip at that same visit. Milk production traits (single-test day) were milk yield (MILK, kg/cow/day), energy corrected milk yield adjusted for body weight (ECMbw, kg/100 kg BW/day), fat (mFA, %), protein (mPR, %) and dry matter (mDM, %). Conformation traits were body weight (BW, kg) and body condition score (BCS, 1 = thin to 5 = obese). Heat tolerance traits were panting score (PS, 0 = normal to 4.5 = extremely heat-stressed) and infrared temperatures (IRTs, °C) at 11 areas on the external body surface of the cow (inner vulval lip, outer vulval surface, inner tail base surface, ocular area, muzzle, armpit area, paralumbar fossa area, fore udder, rear udder, forehoof and hind hoof), assessed by an Infrared Camera. Univariate linear mixed models and a 10-fold cross-validation approach were applied for GS. Univariate single SNP mixed linear models were applied for the GWAS. Estimated h 2 (using the genotype information to build relationships among animals) were moderate (0.20-0.37) for ECMbw, mFA, mPR, mRE, BW, BCS and IRT at rear udder; low (0.08-0.19) for PS and other IRTs; and very low (≤ 0.07) for MILK, ECM and mDM. Accuracy of genomic estimated breeding values (GEBVs) was low (≤ 0.12) for MILK, ECM, mDM and IRT at hind hoof; and moderate to high (0.32-0.46) for all other traits. The most significant regions on chromosomes (BTA) associated with milk production traits were 0.47-1.18 Mb on BTA14. Moderate to high h 2 and moderate accuracies of GEBVs for mFA, mPR, ECMbw, BCS, BW, PS and IRTs at rear udder and outer vulval surface suggested that GS using single test day phenotypic data could be applied for these traits. However, a greater sample size is required to decrease the bias of GEBVs by GS and increase the power of detecting significant quantitative trait loci (QTLs) by GWAS.

From rare to more common: The emerging role of omics in improving understanding and treatment of severe inflammatory and hyperinflammatory conditions

Inflammation is a pathogenic driver of many diseases, including atherosclerosis and rheumatoid arthritis. Hyperinflammation can be seen as any inflammatory response that is deleterious to the host, regardless of cause. In medicine, hyperinflammation is defined as severe, deleterious, and fluctuating systemic or local inflammation with presence of a cytokine storm. It has been associated with rare autoinflammatory disorders. However, advances in omics technologies, including genomics, proteomics, and metabolomics, have revealed it to be more common, occurring in sepsis and severe coronavirus disease 2019. With a focus on proteomics, this review highlights the key role of omics in this shift. Through an exploration of research, we present how omics technologies have contributed to improved diagnostics, prognostics, and targeted therapeutics in the field of hyperinflammation. We also discuss the integration of advanced technologies, multiomics approaches, and artificial intelligence in analyzing complex datasets to develop targeted therapies, and we address their potential for revolutionizing the clinical aspects of hyperinflammation. We emphasize personalized medicine approaches for effective treatments and outline challenges, including the need for standardized methodologies, robust bioinformatics tools, and ethical considerations regarding data privacy. This review aims to provide a comprehensive overview of the molecular mechanisms underpinning hyperinflammation and underscores the potential of omics technologies in enabling successful clinical management.

Machine learning: Python tools for studying biomolecules and drug design

The increasing adoption of computational methods and artificial intelligence in scientific research has led to a growing interest in versatile tools like Python. In the fields of medical chemistry, biochemistry, and bioinformatics, Python has emerged as a key language for tackling complex challenges. It is used to solve various tasks, such as drug discovery, high-throughput and virtual screening, protein and genome analysis, and predicting drug efficacy. This review presents a list of tools for these tasks, including scripts, libraries, and ready-made programs, and serves as a starting point for scientists wishing to apply automation or optimization to routine tasks in medical chemistry and bioinformatics.

Biological age acceleration and interaction with genetic predisposition in the risk of type 2 diabetes and coronary artery disease

Biological age (BA), compared to chronological age, offers a more accurate reflection of aging status. In this prospective UK Biobank study, BA acceleration was measured using the Klemera-Doubal method BA (KDM-BA) and Phenotypic age (PhenoAge). Cox models estimated associations of BA acceleration with incident T2D (n = 271,885) and CAD (n = 270,054). Both additive and multiplicative interactions between BA acceleration and polygenic risk score (PRS) were examined. Predictive performance was assessed by adding BA, PRS, and their interactions to traditional risk models. BA acceleration was positively associated with incident T2D (HRQ4 vs Q1 for KDM-BA: 2.38 [95% CI, 2.22-2.56]; HRQ4 vs Q1 for PhenoAge: 1.85 [95% CI, 1.72-1.99]) and CAD (HRQ4 vs Q1 for KDM-BA: 1.67 [95% CI, 1.58-1.76]; HRQ4 vs Q1 for PhenoAge: 1.33 [95% CI, 1.27-1.39]). Significant multiplicative interactions were observed between BA acceleration and PRS (all P for multiplicative interaction ≤ 0.002). Individuals with highest BA acceleration and PRS had strongest risk elevation for T2D (HR for KDM-BA, 6.89 [95% CI, 6.03-7.87]; HR for PhenoAge, 6.28 [95% CI, 5.28-7.46]) and CAD (HR for KDM-BA, 2.80 [95% CI, 2.59-3.02]; HR for PhenoAge, 2.25 [95% CI, 2.07-2.45]). Additive interactions were observed for T2D with 18-28% of risk attributable to BA-genetic interaction. Adding BA measures and PRS to traditional risk models significantly improved prediction for both diseases (Δ C-statistic 0.024-0.034). In conclusion, BA acceleration was positively associated with incident T2D and CAD, especially in individuals with high genetic predisposition, and improved T2D and CAD prediction beyond traditional risk factors.

Widespread recessive effects on common diseases in a cohort of 44,000 British Pakistanis and Bangladeshis with high autozygosity

Genetic association studies have focused on testing additive models in cohorts with European ancestry. Little is known about recessive effects on common diseases, specifically for non-European ancestry. Genes & Health is a cohort of British Pakistani and Bangladeshi individuals with elevated rates of consanguinity and endogamy, making it suitable to study recessive effects. We imputed variants into a genotyped dataset (n = 44,190) by using two reference panels: a set of 4,982 whole-exome sequences from within the cohort and the Trans-Omics for Precision Medicine (TOPMed-r2) panel. We performed association testing with 898 diseases from electronic health records. 185 independent loci reached genome-wide significance (p < 5 × 10-8) under the recessive model, with p values lower than under the additive model, and >40% of these were novel. 140 loci demonstrated nominally significant (p < 0.05) dominance deviation p values, confirming a recessive association pattern. Sixteen loci in three clusters were significant at a Bonferroni threshold, accounting for multiple phenotypes tested (p < 5.4 × 10-12). In FinnGen, we replicated 44% of the expected number of Bonferroni-significant loci we were powered to replicate, at least one from each cluster, including an intronic variant in patatin-like phospholipase domain-containing protein 3 (PNPLA3; rs66812091) and non-alcoholic fatty liver disease, a previously reported additive association. We present evidence suggesting that the association is recessive instead (odds ratio [OR] = 1.3, recessive p = 2 × 10-12, additive p = 2 × 10-11, dominance deviation p = 3 × 10-2, and FinnGen recessive OR = 1.3 and p = 6 × 10-12). We identified a novel protective recessive association between a missense variant in SGLT4 (rs61746559), a sodium-glucose transporter with a possible role in the renin-angiotensin-aldosterone system, and hypertension (OR = 0.2, p = 3 × 10-8, dominance deviation p = 7 × 10-6). These results motivate interrogating recessive effects on common diseases more widely.

A Breast Cancer Polygenic Risk Score Validation in 15,490 Brazilians Using Exome Sequencing

Background/Objectives: Brazil has a highly admixed population. Polygenic risk scores (PRSs) have mostly been developed from European population studies, and their application to other populations is challenging. To assess the use of PRS for breast cancer (BC) risk in Brazil, we evaluated four PRSs in the Brazilian population. Methods: We analyzed a Brazilian cohort composed of 6206 women with a history of breast cancer and 8878 unphenotyped adults as controls. Genomic variants were imputed from exomes, and scores were calculated for all samples. Results: After individuals with known pathogenic or likely pathogenic variants in BRCA1, BRCA2, PALB2, PTEN, or TP53 genes, and first-degree relatives of the probands were excluded, 5598 cases and 8767 controls remained. Four PRS models were compared, and PRS3820 achieved the best performance, with an odds ratio (OR) of 1.43 per standard deviation increase (p value < 0.001) and an OR of 1.88 (p value < 0.001) for the top decile. PRS3820 also performed well for different ancestry groups: East Asian majority (OR 1.59, p value 0.004), Non-European majority (OR 1.45, p value < 0.001), and European majority (OR 1.43, p value < 0.001). Conclusions: Among the different PRSs, PRS313 and PRS3820 could be validated in our Brazilian cohort, with the latter exhibiting the best performance. While further clinical studies are necessary to guide clinical practice, this work represents an important step toward improving BC precision medicine in Brazil.

A bidirectional mendelian-randomization analyses of genetically predicted circulating levels of systemic inflammatory regulators with risk of sepsis

Whether there is a causal relationship between circulating levels of systemic inflammatory regulators and sepsis remains unclear. To determine whether genetically predicted circulating levels of cytokines are associated with risk of sepsis, a bidirectional two-sample Mendelian randomization (MR) analysis based on the a STROBE-compliant cross-sectional observational study was conducted utilizing gene-wide association study (GWAS) data. Selected with rigor, single-nucleotide polymorphisms served as instrumental variables for subsequent MR analysis. The preferred method for the MR analysis was the inverse-variance weighted approach. However, for comprehensive sensitivity analyses, 6 additional MR methods were employed. Cochrane's Q test was performed to examine heterogeneity. A leave-one-out method ensured the stability of MR results. Our findings suggest an inverse association between the levels of beta-nerve growth factor (BNGF) and the risk of sepsis development (OR = 0.769, 95% CI = 0.599-0.987, P = .039). In contrast, higher levels of TNF-related apoptosis-inducing ligand and vascular endothelial growth factor A (VEGF-A) are positively correlated with sepsis risk (OR = 1.094, 95% CI = 1.012-1.183, P = .025; OR = 1.182, 95% CI = 1.016-1.375, P = .031, respectively). Reverse MR Analysis indicated that sepsis risk is linked with lower circulating levels of adenosine deaminase and Interleukin-17A (β = -0.043, 95% CI = -0.085 to -0.002, P = .042; β = -0.061, 95% CI = -0.108 to -0.013, P = .012, respectively), and also with higher circulating levels of BNGF, delta/notchlike epidermal growth factor-related receptor, fibroblast growth factor 23, leukemia inhibitory factor, monocyte chemoattractant protein-1, and osteoprotegerin (β = 0.056, 95% CI = 0.015-0.096, P = .007; β = 0.137, 95% CI = 0.035-0.240, P = .009; β = 0.118, 95% CI = 0.020-0.216, P = .018; β = 0.136, 95% CI = 0.020-0.252, P = .022; β = 0.143, 95% CI = 0.043-0.242, P = .005; β = 0.116, 95% CI = 0.010-0.222, P = .031, respectively). Sum up, our study provides evidence supporting a bidirectional causal relationship between sepsis and genetically predicted circulating levels of systemic inflammatory regulators.

Identification of Gene Expression and Splicing QTLs in Porcine Muscle Associated with Meat Quality Traits

Understanding the genetic regulation of gene expression and splicing in muscle tissues is critical for elucidating the molecular mechanisms of meat quality traits. In this study, we integrated large-scale whole-genome sequencing and strand-specific RNA-seq data from 582 F2 hybrid pigs (White Duroc × Erhualian) to characterize the expression and splicing quantitative trait loci (eQTLs/sQTL) in longissimus dorsi muscle. We identified 11,058 cis-eQTL-associated genes (eGenes) and 5139 cis-sQTL-associated genes (sGenes), of which 29% of eGenes and 80% of sGenes were previously unreported in the PigGTEx database. Functional analyses revealed distinct genomic features: eQTLs were enriched near transcription start sites (TSSs) and associated with active TSS-proximal transcribed regions and enhancers, whereas sQTLs clustered at splice junctions, underscoring their distinct roles in gene expression and splicing regulation. Colocalization analysis of e/sQTLs with GWAS signals prioritized PHKG1 as a key candidate gene (PPH4 > 0.9) for glycogen metabolism. Notably, we confirmed that an sQTL-driven alternative splicing event in exon 10 of PHKG1 was significantly correlated with phenotypic variation (R = -0.39, p = 9.5 × 10-21). Collectively, this study provides novel insights into the genetic regulation of gene expression and alternative splicing in porcine muscle tissue, advancing our understanding of the molecular mechanisms underlying economically important meat quality traits.

Large language models deconstruct the clinical intuition behind diagnosing autism

Efforts to use genome-wide assays or brain scans to diagnose autism have seen diminishing returns. Yet the clinical intuition of healthcare professionals, based on longstanding first-hand experience, remains the gold standard for diagnosis of autism. We leveraged deep learning to deconstruct and interrogate the logic of expert clinician intuition from clinical reports to inform our understanding of autism. After pre-training on hundreds of millions of general sentences, we finessed large language models (LLMs) on >4,000 free-form health records from healthcare professionals to distinguish confirmed versus suspected autism cases. By introducing an explainability strategy, our extended language model architecture could pin down the most salient single sentences in what drives clinical thinking toward correct diagnoses. Our framework flagged the most autism-critical DSM-5 criteria to be stereotyped repetitive behaviors, special interests, and perception-based behaviors, which challenges today's focus on deficits in social interplay, suggesting necessary revision of long-trusted diagnostic criteria in gold-standard instruments.

Limitations of genomics to predict and treat autism: a disorder born in the womb

Brain development involves the sequential expression of vulnerable biological processes including cell proliferation, programmed cell death, neuronal migration, synapse and functional unit formation. All these processes involve gene and activity-dependent events that can be distorted by many extrinsic and intrinsic environmental factors, including stress, microbiota, inflammatory signals, hormonal signals and epigenetic factors, hence leading to disorders born in the womb that are manifested later in autism spectrum disorders (ASDs) and other neurodevelopmental disorders. Predicting and treating such disorders call for a conceptual framework that includes all aspects of developmental biology. Here, taking the high incidence of ASDs as an example, we first discuss the intrinsic limitations of the genetic approach, notably the widely used twin studies and SNPs. We then review the long list of in utero events that can deviate developmental sequences, leading to persistent aberrant activity generated by immature misplaced and misconnected neuronal ensembles that are the direct cause of ASD. In a clinical perspective, we suggest analysing non-genetic maternity data to enable an early prediction of babies who will develop ASD years later, thereby facilitating early psycho-educative techniques. Subsequently, agents capable of selectively silencing malformed immature networks offer promising therapeutic perspectives. In summary, understanding developmental processes is critical to predicting, understanding and treating ASD, as well as most other disorders that arise in the womb.

A novel genome-wide association study method for detecting quantitative trait loci interacting with complex population structures in plant genetics

In plant genetics, most modern association analyses are performed on panels that bring together individuals from several populations, including admixed individuals whose genomes comprise chromosomal regions from different populations. These panels can identify quantitative trait loci (QTLs) with population-specific effects and epistatic interactions between QTLs and polygenic backgrounds. However, analyzing a diverse panel constitutes a challenge for statistical analysis. The statistical model must account for possible interactions between a QTL and the panel structure while strictly controlling the detection error rate. Although models to detect population-specific QTLs have already been developed, they rely on prior information about the population structure. In practice, this prior information may be missing as many genome-wide association study (GWAS) panels exhibit complex population structures. The present study introduces 2 new models for detecting QTLs interacting with complex population structures. Both incorporate an interaction term between single nucleotide polymorphism/haplotype block and genetic background into conventional GWAS models. The proposed models were compared with state-of-the-art models through simulation studies that considered QTLs with different levels of interaction with their genetic backgrounds. Results showed that models matching simulation settings were most effective for detecting corresponding QTLs while the proposed models outperformed classical models in detecting QTLs interacting with polygenes. Additionally, when applied to a soybean dataset, one of our models identified putative associated QTLs that conventional models failed to detect. The new models, implemented in the RAINBOWR package available on CRAN, are expected to help uncover complex trait genetic architectures.

Genetic influences for distinct impulsivity domains are differentially associated with early substance use initiation: Results from the ABCD Study

Background

Impulsivity is among the strongest correlates of substance involvement and its distinct domains (e.g., sensation seeking, urgency) are differentially correlated, phenotypically and genetically, with unique substance involvement stages. Understanding whether polygenic influences for distinct impulsivity domains are differentially predictive of early substance use initiation, a major risk factor for later problematic use, will improve our understanding of the role of impulsivity in addiction etiology.

Methods

Data collected from participants (n=4,808) of genetically-inferred European ancestry enrolled in the Adolescent Brain Cognitive Development StudySM were used to estimate associations between polygenic scores (PGS) for UPPS-P impulsivity domains (i.e., sensation seeking, lack of premeditation/perseverance, and negative/positive urgency) and substance (i.e., any, alcohol, nicotine, cannabis) use initiation before age 15. Mediation models examined whether child impulsivity (ages 9-11) mediated links between PGSs and substance use initiation.

Results

Sensation seeking PGS was significantly associated with any substance and alcohol use initiation (ORs>1.10, psFDR<0.006). Lack of perseverance and urgency (negative/positive) PGSs were nominally associated with alcohol and nicotine use initiation, respectively (ORs>1.06, ps<0.05, psFDR>0.05). No significant associations were observed for lack of premeditation PGS or cannabis use initiation. Measured impulsivity domains accounted for 5-9% of associations between UPPS-P PGSs and substance use initiation.

Conclusions

Genetic influences for distinct impulsivity domains have differential associations with early substance use initiation with sensation seeking showing the most robust associations. Evaluating genetic influences for distinct impulsivity domains can yield valuable etiologic insight into the earliest stages of substance involvement that may be missed when adopting broad impulsivity definitions.

Correcting for volunteer bias in GWAS increases SNP effect sizes and heritability estimates

Selection bias in genome-wide association studies (GWASs) due to volunteer-based sampling (volunteer bias) is poorly understood. The UK Biobank (UKB), one of the largest and most widely used cohorts, is highly selected. Using inverse probability (IP) weights we estimate inverse probability weighted GWAS (WGWAS) to correct GWAS summary statistics in the UKB for volunteer bias. Our IP weights were estimated using UK Census data - the largest source of population-representative data - made representative of the UKB's sampling population. These weights have a substantial SNP-based heritability of 4.8% (s.e. 0.8%), suggesting they capture volunteer bias in GWAS. Across ten phenotypes, WGWAS yields larger SNP effect sizes, larger heritability estimates, and altered gene-set tissue expression, despite decreasing the effective sample size by 62% on average, compared to GWAS. The impact of volunteer bias on GWAS results varies by phenotype. Traits related to disease, health behaviors, and socioeconomic status are most affected. We recommend that GWAS consortia provide population weights for their data sets, or use population-representative samples.

A genotype-phenotype transformer to assess and explain polygenic risk

Genome-wide association studies have linked millions of genetic variants to biomedical phenotypes, but their utility has been limited by lack of mechanistic understanding and widespread epistatic interactions. Recently, Transformer models have emerged as a powerful machine learning architecture with potential to address these and other challenges. Accordingly, here we introduce the Genotype-to-Phenotype Transformer (G2PT), a framework for modeling hierarchical information flow among variants, genes, multigenic systems, and phenotypes. As proof-of-concept, we use G2PT to model the genetics of TG/HDL (triglycerides to high-density lipoprotein cholesterol), an indicator of metabolic health. G2PT predicts this trait via attention to 1,395 variants underlying at least 20 systems, including immune response and cholesterol transport, with accuracy exceeding state-of-the-art. It implicates 40 epistatic interactions, including epistasis between APOA4 and CETP in phospholipid transfer, a target pathway for cholesterol modification. This work positions hierarchical graph transformers as a next-generation approach to polygenic risk.

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 (GWASs) 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 809 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 78% of cases. We conclude that fpQTLs can reveal causal GWAS variants, define the role of TF binding-site disruption in complex traits, and provide functional insights into non-coding variants, ultimately informing novel treatments for common diseases.

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.

Incorporating multiple functional annotations to improve polygenic risk prediction accuracy

We present OmniPRS, a scalable biobank-scale framework that improves genetic risk prediction for complex traits by integrating genome-wide association study (GWAS) summary statistics and functional annotations. It employs a mixed model incorporating tissue-specific genetic variance components from annotations to re-estimate single-nucleotide polymorphism (SNP) effects and constructs tissue-specific polygenic risk scores (PRSs) and aggregates them into the final OmniPRS. Our experiments, encompassing 135 simulation scenarios and 11 representative traits, demonstrate that OmniPRS is flexible and robust, delivering efficient and accurate predictions comparable to ten leading PRS methods. For quantitative (binary) traits, OmniPRS achieved an average improvement of 52.31% (19.83%) versus the clumping and thresholding (C+T) method, 3.92% (1.31%) versus the annotation-integrated PRSs (LDpred-funct), and 8.44% (2.27%) versus the Bayesian-based PRSs (PRScs). Notably, it achieved 35× faster computation than the PRScs. This rapid, precise framework enables efficient polygenic risk scoring with multi-annotation integration for large-scale genomic studies.

Genome-Wide Association Studies of Body Weight and Average Daily Gain in Chinese Dongliao Black Pigs

In the domain of swine production, body weight (BW) and average daily gain (ADG) are recognized as the primary performance indicators. Nevertheless, the genetic architecture of ADG and BW in Dongliao black (DLB) pigs remains to be fully elucidated. In this study, we performed a genome-wide association analysis of BW, ADG, and body mass index (BMI) in 358 DLB pigs of different days of age. The genome-wide association study (GWAS) showed the following: (1) The most significant single nucleotide polymorphism (SNP) detected for BW was on Sus scrofa chromosome (SSC) 11:100,808 (p-value = 1.16 × 10-6) that was also the most significant SNP for ADG. (2) The most significant SNP associated with BMI was SSC17:51,463,521 (p-value = 5.16 × 10-8). (3) SNPs SSC10:6,523,844 and SSC17:23,852,682 were identified in both BW and ADG. A meta-analysis was conducted on BW at different days and demonstrated SSC5:39,028,335 (p-value = 8.37 × 10-6) which was not identified in the results of each single trait. The regions of two SNPs (SSC11:100,808, SSC4:10,703,277) exhibited considerable influence on both BW and ADG and the related regions were selected for linkage disequilibrium (LD) analyses that exhibited a notable linkage. In addition, several genes were identified that are associated with obesity and play roles in lipid metabolism, including MACROD2, PHLPP2, CYP2E1, and STT3B.

No Associations Between Genetically Predicted Chronotype, Insomnia, Daytime Sleepiness, or Physical Activity and Acne Vulgaris: A Two-Sample Mendelian Randomization Study

Purpose

The exact factors leading to the development of acne vulgaris are poorly understood. Besides diet, lifestyle habits like sleep and physical activity have received attention. This study explored the causal associations between genetically predicted sleep traits and exercise and acne vulgaris.

Patients and Methods

The genome-wide association study (GWAS) data for sleep, physical activity, and acne vulgaris were retrieved from the FinnGen Project (1092/211,139 patients/controls) to carry out a two-sample Mendelian randomization (MR) analysis. Validation was performed using a dataset from the Estonian Biobank (34,422/364,991 patients/controls). The inverse variance weighted (IVW) method was the primary analytical method, with robustness tested using the weighted median, weighted mode, and MR-Egger analyses. Heterogeneity was tested using Cochran's Q-test, horizontal pleiotropy using MR-Egger regression, outliers using MR-PRESSO, and driving SNPs using the leave-one-out method.

Results

The results revealed that genetically predicted chronotype (OR=1.021, 95% CI: 0.786-1.326, P=0.875), insomnia (OR=1.475, 95% CI: 0.676-3.216, P=0.329), daytime sleepiness (OR=0.466, 95% CI: 0.046-4.708, P=0.518), or physical activity (OR=0.990, 95% CI: 0.925-1.059, P=0.767) were not causally associated with acne vulgaris. Cochran's Q-test detected no heterogeneity (all P>0.05). No horizontal pleiotropy was detected (all P>0.05), indicating that the selected IVs met the third MR assumption. MR-PRESSO revealed no outliers. No single SNP drove the results according to the leave-one-out analysis. These results were validated through the use of additional datasets.

Conclusion

Our study found no causal associations between sleep traits and physical activity and acne vulgaris. However, further research is needed to explore other potential factors and validate these results in more diverse populations.

Genetics of Suicide

Over the past two decades, suicide has consistently ranked among the leading causes of death in the United States. While suicide deaths are closely associated with uicidal ideation and attempts, these are not good predictors of future suicide deaths. Establishing who is at risk of suicide remains a challenge that is mostly hampered by the lack of understanding of its pathophysiology. Nonetheless, evidence continues to accumulate suggesting that suicide is driven by a complex and dynamic interaction between environmental factors and genetics. The identification of genes that place people at risk of suicide remains elusive, but data are rapidly evolving. In this narrative review, we describe how Tryptophan hydroxylase (TPH) genes, particularly TPH1 and TPH2, have been associated with suicide in various publications. There is also replicated evidence linking the brain-derived neurotrophic factor gene to suicide, with its most consistent results originating from epigenetic studies. Not surprisingly, many genes involved in the hypothalamic-pituitary-adrenal axis have been connected with suicide, but these data require replication. Finally, among the inflammatory genes studied in suicide, only specific polymorphisms in TNF-alpha and IL-6 may increase susceptibility to suicidal behavior. In conclusion, significant work remains to be performed as inconsistencies undermine the reliability of genetic results in suicide. Potential avenues for future research are proposed.

Opportunities and challenges of local ancestry in genetic association analyses

Recently, admixed populations make up an increasing percentage of the US and global populations, and the admixture is not uniform over space or time or across genomes. Therefore, it becomes indispensable to evaluate local ancestry in addition to global ancestry to improve genetic epidemiological studies. Recent advances in representing human genome diversity, coupled with large-scale whole-genome sequencing initiatives and improved tools for local ancestry inference, have enabled studies to demonstrate that incorporating local ancestry information enhances both genetic association analyses and polygenic risk predictions. Along with the opportunities that local ancestry provides, there exist challenges preventing its full usage in genetic analyses. In this review, we first summarize methods for local ancestry inference and illustrate how local ancestry can be utilized in various analyses, including admixture mapping, association testing, and polygenic risk score construction. In addition, we discuss current challenges in research involving local ancestry, both in terms of the inference itself and its role in genetic association studies. We further pinpoint some future study directions and methodology development opportunities to help more effectively incorporate local ancestry in genetic analyses. It is worth the effort to pursue those future directions and address these analytical challenges because the appropriate use of local ancestry estimates could help mitigate inequality in genomic medicine and improve our understanding of health and disease outcomes.

An evolving understanding of multiple causal variants underlying genetic association signals

Understanding how genetic variation contributes to phenotypic variation is a fundamental question in genetics. Genome-wide association studies (GWASs) have discovered numerous genetic associations with various human phenotypes, most of which contain co-inherited variants in strong linkage disequilibrium (LD) with indistinguishable statistical significance. The experimental and analytical difficulty in identifying the "causal variant" among the co-inherited variants has traditionally led mechanistic studies to focus on relatively simple loci, where a single functional variant is presumed to explain most of the association signal and affect a target gene. The notion that a single causal variant is responsible for an association signal, while other variants in LD are merely correlated, has often been assumed in functional studies. However, emerging evidence powered by high-throughput experimental tools and context-specific functional databases argues that even a single independent signal may involve multiple functional variants in strong LD, each contributing to the observed genetic association. In this perspective, we articulate this evolving understanding of causal variants through examples from both traditional locus-by-locus approaches and more recent high-throughput functional studies. We then discuss the implications and prospects of this notion in understanding the genetic architecture of complex traits and interpreting the variant-level causality in GWAS follow-up studies.

Potentially diagnostic and prognostic roles of piRNAs/PIWIs in pancreatic cancer: A review

Pancreatic ductal adenocarcinoma (PDAC) is a highly aggressive malignancy with limited early diagnostic methods and therapeutic options, contributing to its poor prognosis. Recent advances in high-throughput sequencing have highlighted the critical roles of noncoding RNAs (ncRNAs), particularly PIWI-interacting RNAs (piRNAs), in cancer biology. In this review, we systematically summarize the emerging roles of piRNAs and their associated PIWI proteins in PDAC pathogenesis, progression, and prognosis. We provide a comprehensive analysis of the molecular mechanisms by which piRNAs/PIWIs regulate gene expression and cellular signaling pathways in PDAC. Furthermore, we discuss their potential as novel biomarkers for early diagnosis and therapeutic targets. Importantly, this review identifies key piRNAs/PIWIs involved in PDAC and proposes innovative strategies for improving diagnosis and treatment outcomes. Our work not only consolidates current knowledge but also offers new perspectives for future research and clinical applications in PDAC management.

Unleashing the Power of Multiomics: Unraveling the Molecular Landscape of Peripheral Neuropathy

Peripheral neuropathies (PNs) affect over 20 million individuals in the United States, manifesting as a wide range of sensory, motor, and autonomic nerve symptoms. While various conditions such as diabetes, metabolic disorders, trauma, autoimmune disease, and chemotherapy-induced neurotoxicity have been linked to PN, approximately one-third of PN cases remain idiopathic, underscoring a critical gap in our understanding of these disorders. Over the years, considerable efforts have focused on unraveling the complex molecular pathways underlying PN to advance diagnosis and treatment. Traditional methods such as linkage analysis, fluorescence in situ hybridization, polymerase chain reaction, and Sanger sequencing identified initial genetic variants associated with PN. However, the establishment and application of next-generation sequencing (NGS) and, more recently, long-read/single-cell sequencing have revolutionized the field, accelerating the discovery of novel disease-causing variants and challenging previous assumptions about pathogenicity. This review traces the evolution of genomic technologies in PN research, emphasizing the pivotal role of NGS in uncovering genetic complexities. We provide a comprehensive analysis of established genomic approaches such as genome-wide association studies, targeted gene panel sequencing, and whole-exome/genome sequencing, alongside emerging multiomic technologies including RNA sequencing and proteomics. Integrating these approaches promises holistic insights into PN pathophysiology, potentially revealing new biomarkers and therapeutic targets. Furthermore, we discuss the clinical implications of genomic and multiomic integration, highlighting their potential to enhance diagnostic accuracy, prognostic assessment, and personalized treatment strategies for PN. Challenges and questions in standardizing these technologies for clinical use are raised, underscoring the need for robust guidelines to maximize their clinical utility.

A rare KLHDC4 variant Glu510Lys is associated with genetic susceptibility and promotes tumor metastasis in nasopharyngeal carcinoma

Various genetic association studies have identified numerous single nucleotide polymorphisms (SNPs) associated with nasopharyngeal carcinoma (NPC) risk. However, these studies have predominantly focused on common variants, leaving the contribution of rare variants to the "missing heritability" largely unexplored. Here, we integrate genotyping data from 3925 NPC cases and 15,048 healthy controls to identify a rare SNP, rs141121474, resulting in a Glu510Lys mutation in KLHDC4 gene linked to increased NPC risk. Subsequent analyses reveal that KLHDC4 is highly expressed in NPC and correlates with poorer prognosis. Functional characterizations demonstrate that KLHDC4 acts as an oncogene in NPC cells, enhancing their migratory and metastatic capabilities, with these effects being further augmented by the Glu510Lys mutation. Mechanistically, the Glu510Lys mutant exhibits increased interaction with Vimentin compared to the wild-type KLHDC4 (KLHDC4-WT), leading to elevated Vimentin protein stability and modulation of the epithelial-mesenchymal transition process, thereby promoting tumor metastasis. Moreover, Vimentin knockdown significantly mitigates the oncogenic effects induced by overexpression of both KLHDC4-WT and the Glu510Lys variant. Collectively, our findings highlight the critical role of the rare KLHDC4 variant rs141121474 in NPC progression and propose its potential as a diagnostic and therapeutic target for NPC patients.

A Next-Generation Combinatorial Genomic Strategy Scans Genomic Loci Governing Heat Stress Tolerance in Chickpea

In the wake of rising earth temperature, chickpea crop production is haunted by the productivity crisis. Chickpea, a cool season legume manifests tolerance in several agro-physiological level, which is complex quantitative in nature, and regulated by multiple genes and genetic networks. Understanding the molecular genetic basis of this tolerance and identifying key regulators can leverage chickpea breeding against heat stress. This study employed a genomics-assisted breeding strategy utilizing multi-locus GWAS to identify 10 key genomic regions linked to traits contributing to heat stress tolerance in chickpea. These loci subsequently delineated few key candidates and hub regulatory genes, such as RAD23b, CIPK25, AAE19, CK1 and WRKY40, through integrated genomics, transcriptomics and interactive analyses. The differential transcript accumulation of these identified candidates in contrasting chickpea accessions suggests their potential role in heat stress tolerance. Differential ROS accumulation along with their scavengers' transcript abundance aligning with the expression of identified candidates in the contrasting chickpea accessions persuade their regulatory significance. Additionally, their functional significance is ascertained by heterologous expression and subsequent heat stress screening. The high confidence genomic loci and the superior genes and natural alleles delineated here has great potential for swift genomic interventions to enhance heat resilience and yield stability in chickpea.

An enhanced framework for local genetic correlation analysis

Genetic correlation is a key parameter in the joint genetic model of complex traits, but it is usually estimated on a global genomic scale. Understanding local genetic correlations provides more detailed insight into the shared genetic architecture of complex traits. However, a state-of-the-art tool for local genetic correlation analysis, LAVA, is prone to false inference. Here we extend the high-definition likelihood (HDL) method to a local version, HDL-L, which performs genetic correlation analysis in small, approximately independent linkage disequilibrium blocks. HDL-L allows a more granular estimation of genetic variances and covariances. Simulations show that HDL-L offers more consistent heritability estimates and more efficient genetic correlation estimates compared with LAVA. HDL-L demonstrated robust performance across a wide range of simulations conducted under varying parameter settings. In the analysis of 30 phenotypes from the UK Biobank, HDL-L identified 109 significant local genetic correlations and showed a notable computational advantage. HDL-L proves to be a powerful tool for uncovering the detailed genetic landscape that underlies complex human traits, offering both accuracy and computational efficiency.