High-coverage whole-genome sequencing of the expanded 1000 Genomes Project cohort including 602 trios

Bidirectional Mendelian randomization and colocalization analysis of gut microbiota on lipid profile

The gut microbiota plays a crucial role in human health, but its impact on lipid metabolism remains unclear. Understanding the causal relationship between gut bacteria and lipid profiles is essential for developing strategies to prevent and treat dyslipidemia and cardiovascular diseases. This study aimed to assess this relationship using two-sample Mendelian randomization (MR). Data for both exposure and outcomes were obtained from the IEU-GWAS database, with lipid profile data sourced from a publication. Genome-wide significant single nucleotide polymorphisms (SNPs), which were independent of outcome factors but correlated with exposure variables, were identified as instrumental variables. Several MR methods, including weighted analysis, maximum likelihood, inverse variance weighting (IVW), MR-Egger, and weighted median, were applied. Colocalization analysis further validated the findings. The analysis revealed microbial groups with causal relationships to ApoA1, ApoB, high-density lipoprotein cholesterol, low-density lipoprotein cholesterol, total cholesterol, and triglycerides. Reverse MR and colocalization analysis provided additional confirmation of these results. This study offers new evidence of the causal link between gut microbiota and lipid profiles, providing insights for improving lipid profiles and reducing cardiovascular disease risk.

An integrated approach for genetic risk profiling of typhoid, tuberculosis, and cholera in local population of tehsil Haripur

Despite notable progress in public health throughout the 21st century, infectious diseases like tuberculosis, typhoid, and cholera remain serious threats to global health, particularly in high-risk regions. Understanding the genetic factors that influence susceptibility and resistance to these diseases is essential for developing more effective strategies for their prevention and treatment. This study investigates the genetic variations associated with these infectious diseases with a focus on regions where these diseases are most prevalent. The aim of this study is to identify genetic variants that may influence susceptibility to tuberculosis, typhoid, and cholera. A thorough analysis of genetic variants associated with susceptibility and resistance to tuberculosis, typhoid, and cholera was conducted. Using publicly available genetic data from various ethnic groups. The allele frequency of the prioritized variants was calculated to assess their distribution within the different populations, including Middle Eastern, Ashkenazi Jewish, European (Non-Finnish), Latino/Admixed American, South Asian, East Asian, European (Finnish), North Asian, Southeast Asian, African American, and Swedish populations. The variants of the IL1β gene exhibiting the highest allele frequencies in the South Asian population were identified and subsequently examined within the local population. Specifically, the variants rs1143627 and rs1143629, which demonstrate the highest allele frequencies and are associated with typhoid, tuberculosis, and cholera, were subjected to detailed analysis. To determine their distribution and potential impact on disease susceptibility. In the local population, statistical analysis of the available sample revealed allele frequencies of 0.1128 % for IL1β (rs1143627) and 0.18 % for IL1β (rs1143629). Furthermore, these findings revealed that certain genetic profiles may play a role in the population's overall response to infectious diseases such as tuberculosis, typhoid, and cholera. This research has the potential to guide future public health strategies for more effective management and prevention of these diseases.

A quantitative comparison of the deleteriousness of missense and nonsense mutations using the structurally resolved human protein interactome

The complex genotype-to-phenotype relationships in Mendelian diseases can be elucidated by mutation-induced disturbances to the networks of molecular interactions (interactomes) in human cells. Missense and nonsense mutations cause distinct perturbations within the human protein interactome, leading to functional and phenotypic effects with varying degrees of severity. Here, we structurally resolve the human protein interactome at atomic-level resolutions and perform structural and thermodynamic calculations to assess the biophysical implications of these mutations. We focus on a specific type of missense mutation, known as "quasi-null" mutations, which destabilize proteins and cause similar functional consequences (node removal) to nonsense mutations. We propose a "fold difference" quantification of deleteriousness, which measures the ratio between the fractions of node-removal mutations in datasets of Mendelian disease-causing and non-pathogenic mutations. We estimate the fold differences of node-removal mutations to range from 3 (for quasi-null mutations with folding ΔΔG ≥2 kcal/mol) to 20 (for nonsense mutations). We observe a strong positive correlation between biophysical destabilization and phenotypic deleteriousness, demonstrating that the deleteriousness of quasi-null mutations spans a continuous spectrum, with nonsense mutations at the extreme (highly deleterious) end. Our findings substantiate the disparity in phenotypic severity between missense and nonsense mutations and suggest that mutation-induced protein destabilization is indicative of the phenotypic outcomes of missense mutations. Our analyses of node-removal mutations allow for the potential identification of proteins whose removal or destabilization lead to harmful phenotypes, enabling the development of targeted therapeutic approaches, and enhancing comprehension of the intricate mechanisms governing genotype-to-phenotype relationships in clinically relevant diseases.

New Resources to Identify Characterized DNA Reference Materials for Pharmacogenetic (PGx) and Human Leukocyte Antigen (HLA) Testing: The Genetic Testing Reference Material (GeT-RM) Program PGx Search Tool and GeT-RM Consolidated PGx and HLA Table

Regulations, accreditation standards, and professional guidance require laboratories to use reference materials for assay development, validation, quality control, and proficiency testing of clinical genetic tests. There are, however, few publicly available reference materials for most genetic tests. To address this issue, the CDC's Genetic Testing Reference Material Program (GeT-RM), the Coriell Institute for Medical Research, and the genetic testing community have conducted 19 studies, including nine for pharmacogenetic (PGx) and human leukocyte antigen (HLA) testing, to generate characterized, renewable, and publicly available DNA samples for use as reference materials. Because new PGx alleles are frequently identified, and allele designations change over time, many samples were reanalyzed for the same gene(s) in subsequent GeT-RM studies. These studies used more comprehensive and sensitive methods and panels that examined additional single-nucleotide variants and/or star alleles to expand and update the consensus genotypes. Up-to-date information is available in two newly established resources: the GeT-RM Consolidated PGx and HLA Table and the GeT-RM PGx Search Tool. These resources contain all available PGx and HLA genotypes for 363 publicly available samples characterized during nine GeT-RM PGx or HLA studies for 34 genes/loci in a consolidated and searchable format.

The Eating Disorders Genetics Initiative 2 (EDGI2): study protocol

BACKGROUND

The Eating Disorders Genetics Initiative 2 (EDGI2) is designed to explore the role of genes and environment in anorexia nervosa, bulimia nervosa, binge-eating disorder, and avoidant/restrictive food intake disorder (ARFID) with a focus on broad population representation and severe and/or longstanding illness.

METHODS

A total of 20,000 new participants (18,700 cases and 1,300 controls) will be ascertained from the United States (US), Mexico (MX), Australia (AU), Aotearoa New Zealand (NZ), Sweden (SE), and Denmark (DK). Comprehensive phenotyping and genotyping will be performed for participants in US, MX, AU, NZ, and SE using the EDGI2 questionnaire battery and participant saliva samples. In DK, case identification and genotyping will be through the National Patient Register and bloodspots archived near birth. Case-control and case-case genome-wide association studies will be conducted within EDGI2 and enhanced via meta-analysis with external data from the Eating Disorders Working Group of the Psychiatric Genomics Consortium (PGC-ED). Additional analyses will explore genetic correlations between eating disorders (EDs) and other psychiatric and metabolic traits, calculate polygenic risk scores (PRS), and leverage functional biology to evaluate clinical outcomes. Moreover, analyzing PRS for patient stratification and linking identified risk loci to clinically relevant phenotypes highlight the potential of EDGI2 for clinical translation.

DISCUSSION

EDGI2 is a global expansion of the EDGI study to increase sample size, increase participant representation across multiple ancestral backgrounds, and to include ARFID. ED genetics research has historically lagged behind other psychiatric disorders, and EDGI2 is designed to rapidly advance the study of the genetics of the major EDs. Exploring EDs at both the diagnostic level and the symptom level will provide an unprecedented look at the genetic architecture underlying EDs.

TRIAL REGISTRATION

EDGI2 is a registered clinical trial: clinicaltrials.gov NCT06594913. https://clinicaltrials.gov/study/NCT06594913 (posted September 19, 2024).

scAI-SNP: a method for inferring ancestry from single-cell data

Background

Collaborative efforts, such as the Human Cell Atlas, are rapidly accumulating large amounts of single-cell data. To ensure that single-cell atlases are representative of human genetic diversity, we need to determine the ancestry of the donors from whom single-cell data are generated. Self-reporting of race and ethnicity, although important, can be biased and is not always available for the datasets already collected.

Methods

Here, we introduce scAI-SNP, a tool to infer ancestry directly from single-cell genomics data. To train scAI-SNP, we identified 4.5 million ancestry-informative single-nucleotide polymorphisms (SNPs) in the 1000 Genomes Project dataset across 3201 individuals from 26 population groups. For a query single-cell dataset, scAI-SNP uses these ancestry-informative SNPs to compute the contribution of each of the 26 population groups to the ancestry of the donor from whom the cells were obtained.

Results

Using diverse single-cell datasets with matched whole-genome sequencing data, we show that scAI-SNP is robust to the sparsity of single-cell data, can accurately and consistently infer ancestry from samples derived from diverse types of tissues and cancer cells, and can be applied to different modalities of single-cell profiling assays, such as single-cell RNA-seq and single-cell ATAC-seq.

Discussion

Finally, we argue that ensuring that single-cell atlases represent diverse ancestry, ideally alongside race and ethnicity, is ultimately important for improved and equitable health outcomes by accounting for human diversity.

Supplementary Information

The online version contains supplementary material available at 10.1186/s44330-025-00029-4.

The evolution and convergence of mutation spectra across mammals

Despite the key role genetic mutations play in shaping phenotypic differences between species, little is currently known about the evolution of germline mutation spectra across mammals. Domesticated species are likely particularly interesting case studies because of their high mutation rates and complex evolutionary histories, which can span multiple founding events and genetic bottlenecks. Here we have developed a new reusable workflow, nSPECTRa, that can undertake the key steps in characterising mutation spectra, from determining ancestral alleles to characterising multiple forms of variation. We apply nSPECTRa to seven species, including several that have undergone domestication, and highlight how nSPECTRa can provide important insights into mutation rate evolution. While mutation spectra most often show marked differences between species and even breeds, certain mutation types have risen to a high frequency in subpopulations of different species, indicative of convergent evolution in mutation rates. This includes the previously characterized TCC- > TTC change enriched among European humans, which is also enriched among East Asian cattle. We show Indicine cattle are particularly interesting examples of how different mutation spectra segregate within a population and subsequently spread across the globe. Together, this work has important implications for understanding the control and evolution of mammalian mutation rates.

Tracing human genetic histories and natural selection with precise local ancestry inference

Local ancestry inference is crucial for unraveling demographic histories, discovering selection signals, and including admixed individuals in genomic studies for improved equity and portability. To date, the precision and resolution of local ancestry inference were limited by technical and dataset issues. To address them, we present Orchestra, a model we train on over 10,000 single-origin individuals from 35 worldwide populations that demonstrates superior accuracy in benchmarking analyzes. We employ Orchestra to shed light on the demographic history of Latin Americans, finding trace ancestries supported by historical records. We then deploy it to offer insight on the debated Ashkenazi Jewish origins, highlighting their South European heritage. Finally, Orchestra enables us to map selection signatures, identifying trace Scandinavian ancestry in British samples and unveiling an immune-rich region linked to respiratory infections passed down from the Viking conquests. Our work significantly advances the field of local ancestry inference, highlighting its use in admixed populations.

Elucidating the Genetic Underpinnings of Human Musculoskeletal System Aging Through Genomic Structural Equation Modeling

The genetic architecture underlying traits related to Human Musculoskeletal System Aging (MSA) remains largely unexplored. In this study, we conducted a large-scale multivariate genome-wide association study (GWAS) of MSA utilizing Genomic Structural Equation Modeling (Genomic SEM). We estimated causal single nucleotide polymorphisms (SNPs) associated with independent variation and identified 14 genome-wide significant loci (mean.PP > 0.95). We employed multiple transcriptome-wide association methods to analyze tissue, cellular levels, and genomic elements, identifying loci with high relevance to MSA susceptibility, along with associated element information. Our research represents the first comprehensive delineation of the genetic architecture of Musculoskeletal System Aging through a GWAS of unmeasured phenotype.

Genome diversity and signatures of natural selection in mainland Southeast Asia

Mainland Southeast Asia (MSEA) has rich ethnic and cultural diversity with a population of nearly 300 million1,2. However, people from MSEA are underrepresented in the current human genomic databases. Here we present the SEA3K genome dataset (phase I), generated by deep short-read whole-genome sequencing of 3,023 individuals from 30 MSEA populations, and long-read whole-genome sequencing of 37 representative individuals. We identified 79.59 million small variants and 96,384 structural variants, among which 22.83 million small variants and 24,622 structural variants are unique to this dataset. We observed a high genetic heterogeneity across MSEA populations, reflected by the varied combinations of genetic components. We identified 44 genomic regions with strong signatures of Darwinian positive selection, covering 89 genes involved in varied physiological systems such as physical traits and immune response. Furthermore, we observed varied patterns of archaic Denisovan introgression in MSEA populations, supporting the proposal of at least two distinct instances of Denisovan admixture into modern humans in Asia3. We also detected genomic regions that suggest adaptive archaic introgressions in MSEA populations. The large number of novel genomic variants in MSEA populations highlight the necessity of studying regional populations that can help answer key questions related to prehistory, genetic adaptation and complex diseases.

Genetic Contributions to Alzheimer's Disease and Frontotemporal Dementia in Admixed Latin American Populations

Latin America's diverse genetic landscape provides a unique opportunity to study Alzheimer's disease (AD) and frontotemporal dementia (FTD). The Multi-Partner Consortium to Expand Dementia Research in Latin America (ReDLat) recruited 2,162 participants with AD, FTD, or as healthy controls from six countries: Argentina, Brazil, Chile, Colombia, Mexico, and Peru. Participants underwent genomic sequencing and population structure analyses were conducted using Principal Component Analysis and ADMIXTURE. The study revealed a predominant mix of American, African, and European ancestries, with an additional East Asian component in Brazil. Variant curation identified 17 pathogenic variants, pathogenic C9orf72 expansion, and 44 variants of uncertain significance. Seventy families showed autosomal dominant inheritance, with 48 affected by AD and 22 by FTD. This represents the first large-scale genetic study of AD and FTD in Latin America, highlighting the need to consider diverse ancestries, social determinants of health, and cultural factors when assessing genetic risk for neurodegenerative diseases.

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

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

Characterization of NAT, GST, and CYP2E1 Genetic Variation in Sub-Saharan African Populations: Implications for Treatment of Tuberculosis and Other Diseases

Tuberculosis (TB) is a major health burden in Africa. Although TB is treatable, anti-TB drugs are associated with adverse drug reactions (ADRs), which are partly attributed to pharmacogenetic variation. The distribution of star alleles (haplotypes) influencing anti-TB drug metabolism is unknown in many African populations. This presents challenges in implementing genotype-guided therapy in Africa to decrease the occurrence of ADRs and enhance the efficacy of anti-TB drugs. In this study, we used StellarPGx to call variants and star alleles in NAT1, NAT2, GSTM1, GSTT1, GSTP1, and CYP2E1, from 1079 high-depth African whole genomes. We present the distribution of common, rare, and potential novel star alleles across various Sub-Saharan African (SSA) populations, in comparison with other global populations. NAT1*10 (53.6%), GSTT1*0 (65%), GSTM1*0 (48%), and NAT2*5 (17.5%) were among the predominant functionally relevant star alleles. Additionally, we predicted varying phenotype distributions for NAT1 and NAT2 (acetylation) and the glutathione-S-transferase (GST) enzymes (detoxification activity) between SSA and other global populations. Forty-seven potentially novel haplotypes were identified computationally across the genes. This study provides insight into the distribution of key variants and star alleles potentially relevant to anti-TB drug metabolism and other drugs prescribed across various African populations. The high number of potentially novel star alleles exemplifies the need for pharmacogenomics studies in the African context. Overall, our study provides a foundation for functional pharmacogenetic studies and potential implementation of pharmacogenetic testing in Africa to reduce the risk of ADRs related to treatment of TB and other diseases.

Precision proteogenomics reveals pan-cancer impact of germline variants

We investigate the impact of germline variants on cancer patients' proteomes, encompassing 1,064 individuals across 10 cancer types. We introduced an approach, "precision peptidomics," mapping 337,469 coding germline variants onto peptides from patients' mass spectrometry data, revealing their potential impact on post-translational modifications, protein stability, allele-specific expression, and protein structure by leveraging the relevant protein databases. We identified rare pathogenic and common germline variants in cancer genes potentially affecting proteomic features, including variants altering protein abundance and structure and variants in kinases (ERBB2 and MAP2K2) impacting phosphorylation. Precision peptidome analysis predicted destabilizing events in signal-regulatory protein alpha (SIRPA) and glial fibrillary acid protein (GFAP), relevant to immunomodulation and glioblastoma diagnostics, respectively. Genome-wide association studies identified quantitative trait loci for gene expression and protein levels, spanning millions of SNPs and thousands of proteins. Polygenic risk scores correlated with distal effects from risk variants. Our findings emphasize the contribution of germline genetics to cancer heterogeneity and high-throughput precision peptidomics.

JoGo-LILR caller: Unveiling and navigating the complex diversity of LILRB3-LILRA6 copy number haplotype structures with whole-genome sequencing

Leukocyte immunoglobulin-like receptors (LILRs), encoded on human chromosome 19q13.4, comprise a set of 11 immunoglobulin superfamily receptors known for their genetic heterogeneity. Notably, LILRB3 and LILRA6 within this cluster exhibit pronounced sequence homology in immunoglobulin-like domains involved in ligand binding and variable copy number (CN) states. However, understanding their precise role remains challenging. To address this difficulty, we developed an algorithm and tool named JoGo-LILR Caller, which jointly calls CNs of LILRB3 and LILRA6 from a population-scale whole-genome short-read sequencing dataset. This tool was applied to 2,504 international HapMap samples and yielded a global CN profile. The 100 % concordance rate corroborated this profile with the CN data obtained from 40 samples of pangenome reference assemblies provided by the Human Pangenome Reference Consortium (HPRC). The frequencies of LILRB3-LILRA6 CN haplotype structures were also estimated for five continental groups with a global CN profile. The established allele frequency profile allowed our tool to estimate LILRB3-LILRA6 CN haplotype combinations. JoGo-LILR-trio enhanced the prediction reliability for haplotype pairs within trio datasets, with trio analysis on 40 child samples demonstrating a 100 % concordance between the predicted pair of haploid CN types and the diploid reference assemblies. Its utility will extend to facilitating software advancements for imputing LILRB3-LILRA6 CN types from SNP array genotyping data, enabling subsequent association analyses that link these CN types to diverse phenotypic traits and diseases, e.g., inflammatory bowel diseases and Takayasu arteritis.

Low-cost generation of clinical-grade, layperson-friendly pharmacogenetic passports using oligonucleotide arrays

Pharmacogenomic (PGx) information is essential for precision medicine, enabling drug prescriptions to be personalized according to an individual's genetic background. Almost all individuals will carry a genetic marker that affects their drug response, so the ideal drug prescription for these individuals will differ from the population-level guidelines. Currently, PGx information is often not available at first prescription, reducing its effectiveness. In the Netherlands, pharmacogenetic information is most often obtained using dedicated single-gene assays, making it expensive and time consuming to generate complete multi-gene PGx profiles. We therefore hypothesized that we could also use genome-wide oligonucleotide genotyping arrays to generate comprehensive PGx information (PGx passports), thereby decreasing the cost and time required for PGx testing and lowering the barrier to generating PGx information prior to first prescription. Taking advantage of existing genetic data generated in two biobanks, we developed and validated Asterix, a low-cost, clinical-grade PGx passport pipeline for 12 PGx genes. In these biobanks, we performed and clinically validated genetic variant calling and statistical phasing and imputation. In addition, we developed and validated a CYP2D6 copy-number-variant-calling tool, forgoing the need to use separate PCR-based copy-number detection. Ultimately, we returned 1,227 PGx passports to biobank participants via a layperson-friendly app, improving knowledge of PGx among citizens. Our study demonstrates the feasibility of a low-cost, clinical-grade PGx passport pipeline that could be readily implemented in clinical settings to enhance personalized healthcare, ensuring that patients receive the most effective and safe drug therapy based on their unique genetic makeup.

Leveraging long-read sequencing technologies for pharmacogenomic testing: applications, analytical strategies, challenges, and future perspectives

Long-read sequencing (LRS) was introduced as the third generation of next-generation sequencing technologies with a high accuracy rate in genomic variant identification for some of its platforms. Due to the structural complexity of many pharmacogenes, the presence of rare variants, and the limitations of genotyping and short-read sequencing approaches in detecting pharmacovariants, LRS methods are likely to become increasingly utilized in the near future. In this review, we aim to provide a comprehensive discussion of current and future applications of long-read genotyping methods by introducing the opportunities and advantages as well as the challenges and disadvantages of state-of-the-art LRS platforms for the implementation of pharmacogenomic tests in clinical and research settings. New approaches to data processing, as well as the challenges and pitfalls of performing such tests in daily practice, will be explored in detail. We provide references to resources for those who are interested or intend to employ LRS in pharmacogenomics screening, both in clinical and research settings.

Picuris Pueblo oral history and genomics reveal continuity in US Southwest

Indigenous groups often encounter significant challenges when asserting ancestral claims and cultural affiliations based on oral histories, particularly in the USA where such narratives have historically been undervalued. Although ancient DNA offers a tool to complement traditional knowledge and address gaps in oral history, longstanding disregard for Indigenous sovereignty and beliefs has understandably led many Indigenous communities to distrust DNA studies1-4. Earlier research often focused on repatriation claims5-7, whereas more recent work has increasingly moved towards enhancing Tribal histories8,9. Here we present a collaborative study initiated by a federally recognized Native American tribe, the sovereign nation of Picuris Pueblo in the Northern Rio Grande region of New Mexico, USA, to address gaps in traditional knowledge and further their understanding of their population history and ancestry. We generated genomes from 16 ancient Picuris individuals and 13 present-day members of Picuris Pueblo, providing genomic data spanning the last millennium. We show genetic continuity between ancient and present-day Picuris, and more broadly with Ancestral Puebloans from Pueblo Bonito in Chaco Canyon10, 275 km to the west. This suggests a firm spatiotemporal link among these Puebloan populations of the North American Southwest. Furthermore, we see no evidence of population decline before European arrival11-13, and no Athabascan ancestry in individuals predating 1500 CE, challenging earlier migration hypotheses14-16. This work prioritizes Indigenous control of genetic data and brings together oral tradition, archaeology, ethnography and genetics.

Identifying therapeutic target genes for diabetic retinopathy using systematic druggable genome-wide Mendelian randomization

INTRODUCTION

The treatment and prevention of diabetic retinopathy (DR) remain significant challenges. Mendelian randomization (MR) has been widely used to explore novel therapeutic targets. In this study, we conducted a systematic druggable genome-wide MR analysis to explore potential therapeutic targets for DR.

METHODS

We obtained data on druggable genes and screened for genes within blood expression quantitative trait loci (eQTL), which were then subjected to MR analysis and colocalization analysis with DR genome-wide association studies data to identify genes strongly associated with DR. Additionally, Gene Ontology (GO) analysis, Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis, protein-protein interaction (PPI) network construction, drug candidate prediction, and molecular docking were performed to provide valuable insights for the development of more effective and targeted therapeutic drugs.

RESULTS

MR analysis of blood eQTLs revealed 30 significant DR-associated druggable genes, with PRKAB1 (OR = 0.935, 95% CI: 0.892 to 0.980) and CNR1 (OR = 0.814, 95% CI: 0.696 to 0.951) being protective genes, whereas CACNA1E (OR = 1.282, 95% CI: 1.050 to 1.565), NME1 (OR = 1.198, 95% CI: 1.028 to 1.397), and CHRNA2 (OR = 1.192, 95% CI: 1.025 to 1.386) were associated with increased risk. KEGG analysis highlighted significant pathways, including adrenergic signaling in cardiomyocytes (hsa04261), the oxytocin signaling pathway (hsa04921), and arrhythmogenic right ventricular cardiomyopathy (hsa05412). PPI network analysis identified two key modules: one comprising BIN1, CDH2, ACTN1, EPAS1, CEBPA, and CTSD nodes, and the other consisting of CACNG6, CACNA1E, CACNA2D3, and RASGRP3 nodes. Drug candidate prediction suggested ethanol and isoflupredone as potential therapeutic interventions, and molecular docking revealed C5's strong protein binding affinity.

CONCLUSIONS

This study utilized MR and colocalization analysis to identify potential drug targets for DR. The findings provide promising leads for the treatments of DR, potentially reducing drug development costs.

Identification of Risk Loci for Radiotherapy-Induced Tinnitus and Hearing Loss Through Integrated Genomic Analysis

Radiotherapy-induced hearing impairment significantly affects patients' quality of life, yet its genetic basis remains poorly understood. This study seeks to identify genetic variants associated with radiotherapy-induced tinnitus and hearing loss and explore their functional implications. A genome-wide association study (GWAS) was conducted to identify single-nucleotide polymorphisms (SNPs) associated with radiotherapy-induced tinnitus and hearing loss. Protein-protein interaction networks and functional enrichment analyses were performed to explore underlying biological pathways. A phenome-wide association study (PheWAS) analysis across five databases examined associations between identified SNPs and various phenotypes. The GWAS identified 97 SNPs significantly associated with radiotherapy-induced tinnitus and 76 SNPs with hearing loss. Tinnitus-associated variants were enriched in pathways involving Wnt signaling and telomerase RNA regulation, while hearing-loss-associated variants were linked to calcium-dependent cell adhesion and neurotransmitter receptor regulation. The PheWAS analysis revealed significant associations between these hearing-impairment-related SNPs and metabolic phenotypes, particularly BMI and metabolic disorders. A chromosomal distribution analysis showed concentrated significant SNPs on chromosomes 1, 2, 5, and 10. This study identified distinct genetic architectures underlying radiotherapy-induced tinnitus and hearing loss, revealing different molecular pathways involved in their pathogenesis. The unexpected association with metabolic phenotypes suggests potential interactions between metabolic status and susceptibility to radiotherapy-induced hearing complications. These findings provide insights for developing genetic screening tools and targeted interventions to prevent or mitigate radiotherapy-related hearing damage.

Genome-wide association study of plasma amino acids and Mendelian randomization for cardiometabolic traits

Plasma amino acids (AAs) have emerged as promising biomarkers for metabolic disorders, yet their causality remains unclear. We aimed to investigate the genetic determinants of AA levels in a cohort of 10,333 individuals and their causal effects on cardiometabolic traits using Mendelian randomization (MR). Plasma levels of 20 AAs were quantified using capillary electrophoresis mass spectrometry. Genome-wide association studies were conducted using BOLT-LMM and heritability estimation via LDSC analysis. Causal effects of AAs on 11 cardiometabolic traits were examined using two-sample MR analyses. We identified 85 locus-metabolite associations across 43 genes for 18 AAs, including 44 novel loci linked to metabolic genes. Heritability for AAs was estimated at 16%. MR analysis demonstrated cystine to positively associate with systolic blood pressure (SBP) (β = 0.056, SE = 0.010), while serine indicated protective effects on SBP (β = - 0.040, SE = 0.011), diastolic BP (β = - 0.044, SE = 0.010), and coronary artery disease (odds ratio 0.888, SE = 0.028). We identified potentially novel genetic loci associated with AA levels and demonstrated robust causal associations between several AAs and cardiometabolic traits. These findings reinforce the importance of AAs as potential biomarkers and therapeutic targets in cardiometabolic health.

Inferring past demography and genetic adaptation in Spain using the GCAT cohort

Located in the southwestern corner of Europe, the Iberian Peninsula is separated from the rest of the continent by the Pyrenees Mountains and from Africa by the Strait of Gibraltar. This geographical position may have conditioned distinct selective pressures compared to the rest of Europe and influenced differential patterns of gene flow. In this work, we analyse 704 whole-genome sequences from the GCAT reference panel to quantify gene flow into Spain from various historical sources and identify the top signatures of positive (adaptive) selection. While we found no clear evidence of a 16th-century admixture event putatively related to the French diaspora during the Wars of Religion, we detected signals of North African admixture matching the Muslim period and the subsequent Christian Reconquista. Notably, besides finding that well-known candidate genes previously described in Eurasians also seem to be adaptive in Spain, we discovered novel top candidates for positive selection putatively associated with immunity and diet (UBL7, SMYD1, VAC14 and FDFT1). Finally, local ancestry deviation analysis revealed that the MHCIII genomic region underwent post-admixture selection following the post-Neolithic admixture with Steppe ancestry.

Genome-wide association study of common side effects following COVID-19 booster vaccination in a cohort of corporate employees in Japan

Individual differences have been observed in side effects after vaccination for COVID-19, and host genetic factors have been suggested as a contributing factor. Here, we conducted a genome-wide association study (GWAS) involving 2,554 Japanese corporate employees who received a third booster dose of BNT162b2/Pfizer or mRNA-1273/Moderna vaccine. Although no genome-wide significant association was found for the presence of adverse symptoms, the GWAS for severity revealed six associated loci. The most significant association was observed between the severity of swelling of lymph nodes and chromosome 2q12 locus, including the IL1RL1, IL18R1, and IL18RAP genes (lead variant: rs76152249; P = 1.46 × 10-9). Pathway analysis suggested associations between immune pathways related to the MHC locus, including HLA genes, and the occurrence and severity of fever, and the NF-κB binding pathway and those of itching at the injection site. In addition, a meta-analysis of previous GWAS studies for the primary first or second dose of COVID-19 vaccine revealed 818 variants from 72 loci that demonstrated genome-wide significant associations with any of 12 symptoms, and pathway analysis identified immune pathways related to the MHC locus, suggesting shared genetic risks among primary and booster vaccinations. These results may help control side effects following COVID-19 vaccination.

Genotyping sequence-resolved copy number variation using pangenomes reveals paralog-specific global diversity and expression divergence of duplicated genes

Copy number variant (CNV) genes are important in evolution and disease, yet sequence variation in CNV genes remains a blind spot in large-scale studies. We present ctyper, a method that leverages pangenomes to produce allele-specific copy numbers with locally phased variants from next-generation sequencing (NGS) reads. Benchmarking on 3,351 CNV genes, including HLA, SMN, and CYP2D6, and 212 challenging medically relevant (CMR) genes that are poorly mapped by NGS, ctyper captures 96.5% of phased variants with ≥99.1% correctness of copy number on CNV genes and 94.8% of phased variants on CMR genes. Applying alignment-free algorithms, ctyper requires 1.5 hours per genome on a single CPU. The results improve prediction of gene expression compared to known expression quantitative trait loci (eQTL) variants. Allele-specific expression quantified divergent expression on 7.94% of paralogs and tissue-specific biases on 4.68% of paralogs. We found reduced expression of SMN-2 due to SMN1 conversion, potentially affecting spinal muscular atrophy, and increased expression of translocated duplications of AMY2B. Overall, ctyper enables biobank-scale genotyping of CNV and CMR genes.

Transcriptome-wide analyses delineate the genetic architecture of expression variation in atopic dermatitis

Genome-wide association studies (GWASs) for atopic dermatitis (AD) have uncovered 81 risk loci in European participants; however, translating these findings into functional and therapeutic insights remains challenging. We conducted a transcriptome-wide association study (TWAS) in AD leveraging cis-eQTL data from sun exposed (n = 517), non-sun exposed skin (n = 602) and whole blood (n = 670) tissues and the latest GWAS of AD in Europeans (n = 864982). We implemented the OTTERS pipeline that combines polygenic risk score (PRS) techniques accommodating diverse assumptions in the architecture of gene regulation. We also used differential expression meta-analysis and co-expression networks (n = 186) to characterize the transcriptomic landscape of AD. We identified 176 gene-tissue associations covering 126 unique genes (53 previously unreported). Most TWAS risk genes were identified by adaptive PRS frameworks, with non-significant differences compared with clumping and thresholding approaches. TWAS risk genes were enriched in allergic reactions (e.g., AQP7, AFF4), skin barrier integrity (e.g., ACER3), and inflammatory pathways (e.g., TAPBPL). By integrating co-expression networks of lesional AD skin, we identified 16 hub genes previously identified as TWAS risk genes (six previously unreported) that orchestrate inflammatory responses (e.g., HSPA4) and keratinization (e.g., LCE3E, LCE3D), serving as potential drug targets through drug-gene interactions. Consistent associations between all analyses were reported for FOSL1 and RORC. Collectively, our findings provide additional risk genes for AD with potential implications in therapeutic approaches.

Evaluation of imputation performance of multiple reference panels in a Pakistani population

Genotype imputation is crucial for genome-wide association studies (GWASs), but reference panels and existing benchmarking studies prioritize European individuals. Consequently, it is unclear which publicly available reference panel should be used for Pakistani individuals, and whether ancestry composition or sample size of the panel matters more for imputation accuracy. Our study compared different reference panels to impute genotype data in 1,814 Pakistani individuals, finding the best performance balancing accuracy and coverage with meta-imputation with TOPMed and the expanded 1000 Genomes (ex1KG) reference. Imputation accuracy of ex1KG outperformed TOPMed for common variants despite its 30-fold smaller sample size, supporting efforts to create future panels with diverse populations.

Common variation in meiosis genes shapes human recombination phenotypes and aneuploidy risk

The leading cause of human pregnancy loss is aneuploidy, often tracing to errors in chromosome segregation during female meiosis. While abnormal crossover recombination is known to confer risk for aneuploidy, limited data have hindered understanding of the potential shared genetic basis of these key molecular phenotypes. To address this gap, we performed retrospective analysis of preimplantation genetic testing data from 139,416 in vitro fertilized embryos from 22,850 sets of biological parents. By tracing transmission of haplotypes, we identified 3,656,198 crossovers, as well as 92,485 aneuploid chromosomes. Counts of crossovers were lower in aneuploid versus euploid embryos, consistent with their role in chromosome pairing and segregation. Our analyses further revealed that a common haplotype spanning the meiotic cohesin SMC1B is significantly associated with both crossover count and maternal meiotic aneuploidy, with evidence supporting a non-coding cis-regulatory mechanism. Transcriptome- and phenome-wide association tests also implicated variation in the synaptonemal complex component C14orf39 and crossover-regulating ubiquitin ligases CCNB1IP1 and RNF212 in meiotic aneuploidy risk. More broadly, recombination and aneuploidy possess a partially shared genetic basis that also overlaps with reproductive aging traits. Our findings highlight the dual role of recombination in generating genetic diversity, while ensuring meiotic fidelity.

Gut microbiota, blood metabolites, & pan-cancer: a bidirectional Mendelian randomization & mediation analysis

We propose using Mendelian randomization analysis on GWAS data and MetaboAnalyst to model gut microbiota, metabolic pathways, blood metabolites, and cancer risk. We examined 473 gut microbiota, 205 pathways, 1400 metabolites, and 8 cancers. Results were validated through bidirectional two-sample Mendelian Randomization (MR), heterogeneity tests, and pathway enrichment, leading to a mediation pathway model. We identified 129 gut microbiota, 57 pathways, and 463 metabolites linked to cancer, and 34 significant plasma pathways. 15 microbiota, 8 pathways, and 58 metabolites implicated in multiple cancers. Eight plasma metabolic pathways are involved in the development of multiple types of cancer. Through Multivariate Mendelian Randomization (MVMR) and mediation analysis, we found 9 mediation pathways, offering novel targets and research directions for cancer pathogenesis and treatment.

SNP-array profiling data from breast cancer patients and healthy women's blood DNA samples

Breast cancer is commonly acknowledged as the primary type of cancer on a global scale, exerting a substantial influence on death rates, particularly in developing countries. The aforementioned discovery provides evidence in favor of the concept that genetic factors may contribute to the onset of breast cancer. This paper presents the unprocessed idat data containing single nucleotide polymorphisms (SNPs) acquired from breast cancer patients and a control group comprising of healthy women. The DNA was obtained from stored blood samples that were collected from a total of 48 female patients diagnosed with breast cancer at Cipto Mangunkusumo National Hospital Jakarta and Dharmais National Cancer Center Hospital Jakarta. Additionally, 24 healthy women were included as control subjects. Subsequently, the DNA samples were subjected to hybridization onto Infinium Asian Screening Array (ASA)'s beadchips. The chip was then subjected to fluorescence intensity measurements using an iScan machine manufactured by Illumina. The data output is produced in the form of a .idat file for each sample. Subsequently, further quality control measures and population stratification analysis were conducted using PLINK (v1.9). After the conclusion of the quality control procedure, 72 individuals and a dataset consisting of 424,285 genetic variants were selected for further analysis. The idat raw data files have been added to the Gene Expression Omnibus (GEO) with accession number: GSE245794 (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE245794).

Forensic investigative genetic genealogy: expanding pedigree tracing and genetic inquiry in the genomic era

Genetic genealogy provides crucial insights into the complex biological relationships within contemporary and ancient human populations by analyzing shared alleles and chromosomal segments that are identical by descent to understand kinship, migration patterns, and population dynamics. Within forensic science, forensic investigative genetic genealogy (FIGG) has gained prominence by leveraging next-generation sequencing technologies and population-specific genomic resources, opening useful investigative avenues. In this review, we synthesize current knowledge, underscore recent advancements, and discuss the growing role of FIGG in forensic genomics. FIGG has been pivotal in revitalizing dormant inquiries and offering genetic leads in numerous cold cases. Its effectiveness relies on the extensive single-nucleotide polymorphism profiles contributed by individuals from diverse populations to specialized genomic databases. Advances in computational genomics and the growth of human genomic databases have spurred a profound shift in the application of genetic genealogy across forensics, anthropology, and ancient DNA studies. As the field progresses, FIGG is evolving from a nascent practice into a more sophisticated and specialized discipline, shaping the future of forensic investigations.

DPImpute: A Genotype Imputation Framework for Ultra-Low Coverage Whole-Genome Sequencing and its Application in Genomic Selection

Whole-genome sequencing is pivotal for elucidating the complex relationships between genotype and phenotype. However, its widespread application is hindered by the high sequencing depth and large sample sizes required, especially for genomic selection (GS) reliant on precise phenotype prediction from high-density genotype data. To address this, DPImpute (Dual-Phase Impute) is developed, an two-step imputation pipeline enabling accurate whole-genome SNP genotyping under ultra-low coverage whole-genome sequencing (ulcWGS) depths, small testing sample sizes, and limited reference populations. DPImpute achieved 98.06% SNP imputation accuracy with minimal testing samples (≤10), reference samples (≤100), and an ultra-low sequencing depth of 0.3X, surpassing the accuracy of existing imputation methods. Moreover, this high accuracy is maintained across multi-ancestry human populations. Remarkably, DPImpute demonstrated accurate SNP imputation from low-coverage sequencing data from single blood cells and single blastocyst cells, highlighting its potential in embryo GS. To enhance the accessibility of DPImpute, a user-friendly web server (https://agdb.ecenr.com/DPImpute/home) is developed and a Docker container for seamless implementation. In summary, DPImpute can significantly expedite breeding programs through precise and cost-effective genotyping and serve as a valuable tool for diverse population genotyping, encompassing both human and animal studies.

Pilot work of the 10K Chinese People Genomic Diversity Project along the Silk Road suggests a complex east-west admixture landscape and biological adaptations

Genomic sources from China are underrepresented in the population-specific reference database. We performed whole-genome sequencing or genome-wide genotyping on 1,207 individuals from four linguistically diverse groups (1,081 Sinitic, 56 Mongolic, 40 Turkic, and 30 Tibeto-Burman people) living in North China included in the 10K Chinese People Genomic Diversity Project (10K_CPGDP) to characterize the genetic architecture and adaptative history of ethnic groups in the Silk Road Region of China. We observed a population split between Northwest Chinese minorities (NWCMs) and Han Chinese since the Upper Paleolithic and later Neolithic genetic differentiation within NWCMs. The observed population substructures among ethnically/linguistically diverse NWCMs suggested that differentiated admixture events contributed to the differences in their genomic and phenotypic diversity. We estimated that the Dongxiang, Tibetan, and Yugur people inherited more than 10% of the Western Eurasian ancestry, which is much greater than that of the Salar and Tu people (<7%), while Han neighbors showed less West Eurasian ancestry (∼1%-3%). Male-biased admixture introduced Western Eurasian ancestry in the Dongxiang, Tibetan, and Yugur populations. We found that the eastern-western admixture in NWCMs occurred ∼800-1,100 years ago, coinciding with intensive economic and cultural exchanges during the Tang and Song dynasties. Additionally, we identified the signatures of natural selection associated with cardiovascular system diseases or lipid metabolism and developmental/neurogenetic disorders. Moreover, the EPAS1 gene showed relatively high population branch statistic values in NWCMs. The well-fitted demographical models presented the vast landscape of complex admixture processes of the Silk Road people, and the newly reported functionally important variations suggested the importance of including ethnolinguistically diverse populations in Chinese genetic studies for uncovering the genetic basis of complex traits/diseases.

A structured coalescent model reveals deep ancestral structure shared by all modern humans

Understanding the history of admixture events and population size changes leading to modern humans is central to human evolutionary genetics. Here we introduce a coalescence-based hidden Markov model, cobraa, that explicitly represents an ancestral population split and rejoin, and demonstrate its application on simulated and real data across multiple species. Using cobraa, we present evidence for an extended period of structure in the history of all modern humans, in which two ancestral populations that diverged ~1.5 million years ago came together in an admixture event ~300 thousand years ago, in a ratio of ~80:20%. Immediately after their divergence, we detect a strong bottleneck in the major ancestral population. We inferred regions of the present-day genome derived from each ancestral population, finding that material from the minority correlates strongly with distance to coding sequence, suggesting it was deleterious against the majority background. Moreover, we found a strong correlation between regions of majority ancestry and human-Neanderthal or human-Denisovan divergence, suggesting the majority population was also ancestral to those archaic humans.

Tracing the Evolution of Human Immunity Through Ancient DNA

Infections have imposed strong selection pressures throughout human evolution, making the study of natural selection's effects on immunity genes highly complementary to disease-focused research. This review discusses how ancient DNA studies, which have revolutionized evolutionary genetics, increase our understanding of the evolution of human immunity. These studies have shown that interbreeding between modern humans and Neanderthals or Denisovans has influenced present-day immune responses, particularly to viruses. Additionally, ancient genomics enables the tracking of how human immunity has evolved across cultural transitions, highlighting strong selection since the Bronze Age in Europe (<4,500 years) and potential genetic adaptations to epidemics raging during the Middle Ages and the European colonization of the Americas. Furthermore, ancient genomic studies suggest that the genetic risk for noninfectious immune disorders has gradually increased over millennia because alleles associated with increased risk for autoimmunity and inflammation once conferred resistance to infections. The challenge now is to extend these findings to diverse, non-European populations and to provide a more global understanding of the evolution of human immunity.

Inter-platform evaluation of the MPSplex large-scale tri-allelic SNP panel for forensic identification

MPSplex is a large-scale forensic massively parallel sequencing (MPS) panel with 1,270 tri-allelic SNPs, 44 microhaplotypes (MH) and 55 ancestry-informative bi-allelic SNPs (aiSNPs) designed for missing persons identification. We have evaluated MPSplex with the most widely used MPS platforms in the forensic field: the Illumina MiSeq, the Thermo Fisher Scientific Ion S5 and the Qiagen GeneReader. The tri-allelic SNPs of MPSplex were previously identified from the most polymorphic loci with three common alleles in 1000 Genomes Phase III data and combined with the 44 MH and 55 aiSNPs, then implemented into a QIAseq Targeted DNA Custom Panel (Qiagen), a marker panel which uses Unique Molecular Indices or UMIs. The UMI random-sequence DNA molecules are incorporated onto DNA fragments before the Target Enrichment PCR, allowing the identification of reads that originated from the same template and consequently they can be used to correct the errors that may arise within the PCR or the sequencing process. In this study, we present the results of an inter-platform evaluation of the MPSplex panel, characterizing its performance in different forensic scenarios, which assessed aspects that include sensitivity, genotyping accuracy and mixture analysis. MPSplex aims to provide a tool designed for kinship analysis that can be applied beyond the resolution of first- or second-degree relationships, avoiding the need for much bigger forensic panels designed for genealogy purposes, which usually require significantly more sequencing resources. This study provides evaluation of MPSplex using the MPS systems in routine use for forensic genotyping of large-scale panels of SNPs.

Cell-free DNA from clinical testing as a resource of population genetic analysis

As a noninvasive biomarker, cell-free DNA (cfDNA) has achieved remarkable success in clinical applications. Notably, cfDNA is essentially DNA, and conducting whole-genome sequencing (WGS) can yield a wealth of genetic information. These invaluable data should not be confined to one-time use; instead, they should be leveraged for more comprehensive population genetic analysis, including genetic variation spectrum, population structure and genetic selection, and genome-wide association studies (GWASs), among others. Such research findings can, in turn, facilitate clinical practice, enabling more advanced and accurate disease predictions. This review explores the advantages, challenges, and current research areas of cfDNA in population genetics. We hope that this review can serve as a new chapter in the repurposing of cfDNA sequence data generated from clinical testing in population genetics.

Whole-Genome Sequencing Reveals Individual and Cohort Level Insights into Chromosome 9p Syndromes

Previous genomic efforts on chromosome 9p deletion and duplication syndromes have utilized low resolution strategies (i.e., karyotypes, chromosome microarrays). We present the first large-scale whole-genome sequencing (WGS) study of 100 individuals from families with 9p-related syndromes including 85 unrelated probands through the 9P-ARCH (Advanced Research in Chromosomal Health: Genomic, Phenotypic, and Functional Aspects of 9p-Related syndromes) research network. We analyzed the genomic architecture of these syndromes, highlighting fundamental features and their commonalities and differences across individuals. This work includes a machine-learning model that predicts 9p deletion syndrome from gene copy number estimates using WGS data. Two Late Replicating Regions (LRR1 [a previously un-named human fragile site], LRR2) were identified that contain most structural variant breakpoints in 9p deletion syndrome pointing to replication-based issues in structural variant formation. Furthermore, we show the utility of using WGS information to obtain a comprehensive understanding of 9p-related variation in an individual with complex structural variation where chromothripsis is the likely mechanism. Genes on 9p were prioritized based on statistical assessment of human genomic variation. Furthermore, through application of spatial transcriptomics to embryonic mouse tissue we examined 9p-gene expression in craniofacial and brain development. Through these strategies, we identified 24 important genes for the majority (83%) of individuals with 9p deletion syndrome including AK3, BRD10, CD274, CDC37L1, DMRT1, DMRT2, DMRT3, DOCK8, GLIS3, JAK2, KANK1, KDM4C, PLPP6, PTPRD, PUM3, RANBP6, RCL1, RFX3, RIC1, SLC1A1, SMARCA2, UHRF2, VLDLR, and ZNG1A. Two genes (AK3, ZNG1A) are involved in mitochondrial function and testing of the mitochondrial genome revealed excess copy number in individuals with 9p deletion syndrome. This study presents the most comprehensive genomic analysis of 9p-related syndromes to date, with plans for further expansion through our 9P-ARCH research network.

A complex phylogeny of lineage plasticity in metastatic castration resistant prostate cancer

Aggressive variant and androgen receptor (AR)-independent castration resistant prostate cancers (CRPC) represent the most significant diagnostic and therapeutic challenges in prostate cancer. This study examined a case of simultaneous progression of both adenocarcinoma and squamous tumors from the same common origin. Using whole-genome and transcriptome sequencing from 17 samples collected over >6 years, we established the clonal relationship of all samples, defined shared complex structural variants, and demonstrated both divergent and convergent evolution at AR. Squamous CRPC-associated circulating tumor DNA was identified at clinical progression prior to biopsy detection of any squamous differentiation. Dynamic changes in the detection rate of histology-specific clones in circulation reflected histology-specific sensitivity to treatment. This dataset serves as an illustration of non-neuroendocrine transdifferentiation and highlights the importance of serial sampling at progression in CRPC for the detection of emergent non-adenocarcinoma histologies with implications for the treatment of lineage plasticity and transdifferentiation in metastatic CRPC.

Mapping a complex evolutionary history

Tracking the geographic origins of genetic ancestors reveals past human migrations.

Monocyte CCL2 signaling possibly contributes to increased asthma susceptibility in type 2 diabetes

In recent years, the respiratory system has been increasingly recognized as a key target organ in diabetes. Although observational studies have established significant clinical associations between type 2 diabetes (T2D), antidiabetic medication use, and asthma, the causal relationships and underlying molecular mechanisms remain unclear. This study employed a bidirectional two-sample Mendelian randomization (MR) approach combined with bioinformatics analysis to explore the causal relationships between T2D and asthma subtypes and complications, with a focus on immune-regulatory mechanisms. The MR analysis utilized inverse-variance weighted (IVW) and meta-analysis methods to evaluate overall effects, with sensitivity analyses confirming the robustness of the findings. Bioinformatics analysis focused on differential gene expression and pathway enrichment to identify potential molecular networks. The MR analysis showed that T2D has a significant positive causal effect on asthma (P < 0.05), with severe autoimmune T2D showing strong associations with specific asthma subtypes (eosinophilic and mixed asthma) and complications (e.g., acute respiratory infections and pneumonia) (P < 0.05). Bioinformatics analysis identified the monocyte-CCL2 signaling axis as a key mechanism linking T2D and asthma, where hyperglycemia-induced monocyte activation may promote asthma development. These findings reveal shared inflammatory pathways and deepen our understanding of the molecular mechanisms linking these two chronic diseases.

Intraplaque haemorrhage quantification and molecular characterisation using attention based multiple instance learning

Intraplaque haemorrhage (IPH) represents a critical feature of plaque vulnerability as it is robustly associated with adverse cardiovascular events, including stroke and myocardial infarction. How IPH drives plaque instability is unknown. However, its identification and quantification in atherosclerotic plaques is currently performed manually, with high interobserver variability, limiting its accurate assessment in large cohorts. Leveraging the Athero-Express biobank, an ongoing study comprising a comprehensive dataset of histological, transcriptional, and clinical information from 2,595 carotid endarterectomy patients, we developed an attention-based additive multiple instance learning (MIL) framework to automate the detection and quantification of IPH across whole-slide images of nine distinct histological stains. We demonstrate that routinely available Haematoxylin and Eosin (H&E) staining outperformed all other plaque relevant Immunohistochemistry (IHC) stains tested (AUROC = 0.86), underscoring its utility in quantifying IPH. When combining stains through ensemble models, we see that H&E + CD68 (a macrophage marker) as well as H&E + Verhoeff-Van Gieson elastic fibers staining (EVG) leads to a substantial improvement (AUROC = 0.92). Using our model, we could derive IPH area from the MIL-derived patch-level attention scores, enabling not only classification but precise localisation and quantification of IPH area in each plaque, facilitating downstream analyses of its association and cellular composition with clinical outcomes. By doing so, we demonstrate that IPH presence and area are the most significant predictors of both preoperative symptom presentation and major adverse cardiovascular events (MACE), outperforming manual scoring methods. Automating IPH detection also allowed us to characterise IPH on a molecular level at scale. Pairing IPH measurements with single-cell transcriptomic analyses revealed key molecular pathways involved in IPH, including TNF-α signalling, extracellular matrix remodelling and the presence of foam cells. This study represents the largest effort in the cardiovascular field to integrate digital pathology, machine learning, and molecular data to predict and characterize IPH which leads to better understanding how it drives symptoms and MACE. Our model provides a scalable, interpretable, and reproducible method for plaque phenotyping, enabling the derivation of plaque phenotypes for predictive modelling of MACE outcomes.

Performance evaluation of structural variation detection using DNBSEQ whole-genome sequencing

BACKGROUND

DNBSEQ platforms have been widely used for variation detection, including single-nucleotide variants (SNVs) and short insertions and deletions (INDELs), which is comparable to Illumina. However, the performance and even characteristics of structural variations (SVs) detection using DNBSEQ platforms are still unclear.

RESULTS

In this study, we assessed the detection of SVs using 40 tools on eight DNBSEQ whole-genome sequencing (WGS) datasets and two Illumina WGS datasets of NA12878. Our findings confirmed that the performance of SVs detection using the same tool on DNBSEQ and Illumina datasets was highly consistent, with correlations greater than 0.80 on metrics of number, size, precision and sensitivity, respectively. Furthermore, we constructed a "DNBSEQ" SV set (4,785 SVs) from the DNBSEQ datasets and an "Illumina" SV set (6,797 SVs) from the Illumina datasets. We found that these two SV sets were highly consistent of SV sites and genomic characteristics, including repetitive regions, GC distribution, difficult-to-sequence regions, and gene features, indicating the robustness of our comparative analysis and highlights the value of both platforms in understanding the genomic context of SVs.

CONCLUSIONS

Our study systematically analyzed and characterized germline SVs detected on WGS datasets sequenced from DNBSEQ platforms, providing a benchmark resource for further studies of SVs using DNBSEQ platforms.

Genetic analysis reveals the shared genetic architecture between breast cancer and atrial fibrillation

Background

Epidemiological studies have observed an association between atrial fibrillation (AF) and breast cancer (BC). However, the underlying mechanisms linking these two conditions remain unclear. This study aims to systematically explore the genetic association between AF and BC.

Methods

We utilized the largest available genome-wide association study (GWAS) datasets for European individuals, including summary data for AF (N = 1,030,836) and BC (N = 247,173). Multiple approaches were employed to systematically investigate the genetic relationship between AF and BC from the perspectives of pleiotropy and causality.

Results

Global genetic analysis using LDSC and HDL revealed a genetic correlation between AF and BC (rg = 0.0435, P = 0.039). Mixer predicted genetic overlap between non-MHC regions of the two conditions (n = 125, rg = 0.05). Local genetic analyses using LAVA and GWAS-PW identified 22 regions with potential genetic sharing. Cross-trait meta-analysis by CPASSOC identified one novel pleiotropic SNP and 14 pleiotropic SNPs, which were subsequently annotated. Eight of these SNPs passed Bayesian colocalization tests, including one novel pleiotropic SNP. Further fine-mapping analysis identified a set of causal SNPs for each significant SNP. TWAS analyses using JTI and FOCUS models jointly identified 10 pleiotropic genes. Phenome-wide association study (PheWAS) of novel pleiotropic SNPs identified two eQTLs (PELO, ITGA1). Gene-based PheWAS results showed strong associations with BMI, height, and educational attainment. PCGA methods combining GTEx V8 tissue data and single-cell RNA data identified 16 co-enriched tissue types (including cardiovascular, reproductive, and digestive systems) and 5 cell types (including macrophages and smooth muscle cells). Finally, univariable and multivariable bidirectional Mendelian randomization analyses excluded a causal relationship between AF and BC.

Conclusion

This study systematically investigated the shared genetic overlap between AF and BC. Several pleiotropic SNPs and genes were identified, and co-enriched tissue and cell types were revealed. The findings highlight common mechanisms from a genetic perspective rather than a causal relationship. This study provides new insights into the AF-BC association and suggests potential experimental targets and directions for future research. Additionally, the results underscore the importance of monitoring the potential risk of one disease in patients diagnosed with the other.

A sequence context-based approach for classifying tumor structural variants without paired normal samples

Although several recent studies have characterized structural variants (SVs) in germline and cancer genomes independently, the genomic contexts of these SVs have not been comprehensively compared. We examined similarities and differences between 2 million germline and 115 thousand tumor SVs from a cohort of 963 patients from The Cancer Genome Atlas. We found significant differences in features related to their genomic sequences and localization that suggest differences between SV-generating processes and selective pressures. For example, our results show that features linked to transposon-mediated processes are associated with germline SVs, while somatic SVs more frequently show features characteristic of chromoanagenesis. These genomic differences enabled us to develop a classifier-the Germline and Tumor Structural Variant or "the great GaTSV" -that accurately distinguishes between germline and cancer SVs in tumor samples that lack a matched normal sample.

Telomere Length as Both Cause and Consequence in Type 1 Diabetes: Evidence from Bidirectional Mendelian Randomization

Background/Objectives: Diabetes is the most prevalent metabolic disease globally, characterized by dysregulated glucose control and accompanied by multiple refractory complications. As a critical marker of cellular homeostasis, telomere length (TL) may be associated with the progression of diabetes. However, the causal relationship between diabetes and TL remains unclear, particularly whether cellular homeostasis imbalance acts as a consequence of diabetic complications or a precipitating factor in disease development. Methods: We performed a bidirectional Mendelian randomization (MR) analysis using genome-wide association study (GWAS) data. Following the three core assumptions of MR analysis, we conducted quality control on all instrumental variables to ensure methodological rigor. The inverse variance weighted (IVW) method served as the primary analytical method, supplemented by additional MR methods to evaluate the significance of the results. Furthermore, we performed sensitivity analyses to ensure the reliability and robustness of the findings. Results: Forward analysis revealed that shortened TL significantly increases the risk of broadly defined Type 1 diabetes (T1D) and unspecified types of diabetes (p < 0.05). Additionally, we identified a positive causal relationship between TL and several diabetes-related complications, including co-morbidities, diabetic nephropathy, and diabetic ketoacidosis (p < 0.05). Interestingly, the reverse analysis demonstrated a positive causal effect of T1D and its complications on TL (p < 0.05); however, this effect disappeared after adjusting for insulin use (p > 0.05). Conclusions: Bidirectional MR analyses revealed a complex relationship between TL and T1D, where shortened telomeres increase T1D risk while T1D itself may trigger compensatory mechanisms affecting telomere maintenance, with insulin playing a crucial regulatory role in this relationship. These findings suggest telomere biology may be fundamentally involved in T1D pathogenesis and could inform future therapeutic approaches.

Rare and common genetic variants underlying the risk of Hirschsprung's disease

Hirschsprung's disease (HSCR) is a congenital enteric neuropathic disorder characterized by high heritability (>80%) and polygenic inheritance (>20 genes). The previous genome-wide association studies (GWAS) identified several common variants associated with HSCR and demonstrated increased predictive performance for HSCR risk in Europeans using a genetic risk score, there remains a notable gap in knowledge regarding Chinese populations. We conducted whole exome sequencing in a HSCR case cohort in Chinese. By using the common controls (505 controls from 1KG EAS and 10 588 controls from ChinaMAP), we conducted GWAS for the common variants in the exome and gene-based association for rare variants. We further validated the associated variants and genes in replicated samples and in vitro and vivo experiments. We identified one novel gene PLK5 by GWAS and suggested 45 novel putative genes based the gene-based test. By using genetic variant at RET and PLK5, we constructed a genetic risk score that could identify the individuals with very high genetic risk for HSCR. Compared with patients with zero or one risk allele from the three variants, the risk for HSCR was 36.61 times higher with six alleles. In addition, we delineated a HSCR risk gene landscape that encompasses 57 genes, which explains 88.5% and 54.5% of HSCR in Chinese and European, respectively. In summary, this study improved the understanding of genetic architecture of HSCR and provided a risk prediction approach for HSCR in the Chinese.

Neanderthal adaptive introgression shaped LCT enhancer region diversity without linking to lactase persistence in East Asian populations

Positive selection at the 2q21.3 enhancer region for lactase gene (LCT) expression in Europeans and Africans has long been attributed to selection for lactase persistence (LP), the capacity of adults to digest lactose in milk, presumably because of the benefits associated with milk consumption. While considered a classic example of gene-culture coevolution, recently doubts have been raised about the link between selection at 2q21.3 and LP. Analysis of additional populations could shed further light; here, we demonstrate that a haplotype spanning ~467 kb at the 2q21.3 locus has risen to high frequency in East Asians (~25%) but is absent from Africans and Europeans. This haplotype likely derived from Neanderthals and has been under positive selection in East Asians. The East Asian-specific haplotype is associated with alterations in LCT expression and promoter methylation in certain cell types, similar to what is observed with LP-associated haplotypes in Europeans. Moreover, its frequency is comparable to that of LP in East Asians, suggesting a potential association with LP in East Asians. However, it is highly unlikely that selection in East Asians was related to milk-drinking habits. We find that this haplotype impacts the expression of UBXN4, DARS1, and DARS1-AS1 in immune cells and is associated with neutrophil and white blood cell counts. Hence, the selection might be linked to certain aspects of immune function. This implies that selection on 2q21.3 has thus either occurred for different reasons in different populations or the selection observed in other populations is also not due to LP.

Evaluating the effect of marker panel sizes on estimation of bio-geographical co-ancestry proportions

A large number of ancestry-informative marker panels have been developed for forensic bio-geographical ancestry (BGA) analysis during the past decade, which offer valuable investigative tools for cold cases. The developed assays for capillary electrophoresis (CE) and massively parallel sequencing (MPS) focus on the differentiation of major populations, with MPS allowing larger numbers of markers that can be multiplexed at the same time and therefore improved differentiation of more closely related Eurasian populations. One limitation of BGA inference tools is the handling of co-ancestry in individuals with admixted backgrounds, which leads to two situations being indistinguishable: (i) the individual belongs to an admixed population, or (ii) the individual has recent ancestors from different populations. Accurate and precise co-ancestry estimates can help, as first or second-degree admixture would show a ∼ 50-50 % or ∼ 75-25 % ratio of co-ancestry proportions. Here we compared the co-ancestry proportion estimations obtained for the set of 2504 individuals from the 1000 Genomes Project with dedicated BGA and human identification (ID) assays of different sizes compared to those obtained with the > 500,000 SNP Affymetrix Human Origins panel as the point of reference for each individual. The results of the correlation analysis performed with > 500 admixed individuals indicate that panel size plays a major role in the accuracy of the co-ancestry estimates. Therefore, the large-scale forensic MPS ID panels we evaluated constitute a valuable alternative to small- and medium-scale BGA panels, especially when admixture is expected.

Deep indel mutagenesis reveals the impact of amino acid insertions and deletions on protein stability and function

Amino acid insertions and deletions (indels) are an abundant class of genetic variants. However, compared to substitutions, the effects of indels on protein stability are not well understood. To better understand indels here we analyse new and existing large-scale deep indel mutagenesis (DIM) of structurally diverse proteins. The effects of indels on protein stability vary extensively among and within proteins and are not well predicted by existing computational methods. To address this shortcoming we present INDELi, a series of models that combine experimental or predicted substitution effects and secondary structure information to provide good prediction of the effects of indels on both protein stability and pathogenicity. Moreover, quantifying the effects of indels on protein-protein interactions suggests that insertions can be an important class of gain-of-function variants. Our results provide an overview of the impact of indels on proteins and a method to predict their effects genome-wide.

Long-read transcriptomics of a diverse human cohort reveals widespread ancestry bias in gene annotation

Accurate gene annotations are fundamental for interpreting genetic variation, cellular function, and disease mechanisms. However, current human gene annotations are largely derived from transcriptomic data of individuals with European ancestry, introducing potential biases that remain uncharacterized. Here, we generate over 800 million full-length reads with long-read RNA-seq in 43 lymphoblastoid cell line samples from eight genetically-diverse human populations and build a cross-ancestry gene annotation. We show that transcripts from non-European samples are underrepresented in reference gene annotations, leading to systematic biases in allele-specific transcript usage analyses. Furthermore, we show that personal genome assemblies enhance transcript discovery compared to the generic GRCh38 reference assembly, even though genomic regions unique to each individual are heavily depleted of genes. These findings underscore the urgent need for a more inclusive gene annotation framework that accurately represents global transcriptome diversity.