Biomedical subjects

Wenhan Lu

Publications and source records attributed to Wenhan Lu.

4 recordsLinked to original sources

PubMed2026

Rare Coding Variants Reveal Distinct Genetic Architectures Across Multidimensional Sleep Phenotypes.

Sleep and circadian traits have been widely studied using common variants, but the contribution of rare coding variation remains unclear. We analyzed rare coding variants in 397,065 whole-exome sequenced UK Biobank participants across 36 sleep phenotypes from self-report, diagnoses, sleep medication use and accelerometry, and meta-analyzed results with 171,536 whole-genome sequenced All of Us participants of diverse ancestries, with replication in the Mass General Brigham Biobank (N = 31,275). We identified 260 genes associated with sleep phenotypes, including novel associations with sleep medication use in 29 genes and 24 out of 29 have not previously been reported with any sleep phenotypes. We observed modest but significant rare variant heritability and strong genetic correlations between sleep medication use, insomnia and fatigue. Temporal gene expression trajectory analyses indicate that genes associated with self-reported sleep traits show constant high prenatal expression, whereas genes linked to sleep medication phenotypes exhibit peak expression in the late prenatal period. These findings highlight distinct biological mechanisms captured by different measurement sources of sleep phenotypes and reveal rare-variant-informed targets for therapeutic discovery.

exome sequencing↗

PubMed2026

Mechanism of age-related accumulation of mtDNA mutations in human blood.

Accumulation of mutant mitochondrial DNA (mtDNA) heteroplasmy is among the strongest signatures of ageing1. Here we investigated the underlying mechanism by calling mtDNA sequence, mtDNA abundance and mtDNA heteroplasmic variants in human blood using whole-genome sequences from approximately 750,000 individuals. We observed that mtDNA single-nucleotide variants (mtSNVs) accumulate sharply at age 60 years, occur at low levels of heteroplasmy, exhibit little evidence of positive selection and are likely to be predominantly neutral. The mutational spectrum of mtSNVs does not reflect oxidative lesions, as is commonly invoked, but is more consistent with mtDNA replication errors. To understand why mtSNVs become detectable with age, we performed a genome-wide association study for heteroplasmic mtSNV burden, identifying germline variants near TERT, TCL1A and SMC4, all of which have been linked to clonal haematopoiesis (CH)2. Rare-variant analysis also showed that high mtSNV burden is associated with mutations in numerous CH driver genes. These genetic associations persisted even after exclusion of individuals with known CH driver mutations. Our results support a model in which 'cryptic' mtDNA mutations initially arise randomly as replication errors but are undetectable in bulk. They then become apparent only through age-related expansion of cellular clones in blood. We propose that the high copy number and mutation rate of mtDNA make it a sensitive blood-based marker of somatic mosaicism due to CH. Our work mechanistically unifies three prominent signatures of ageing: common germline variants in TERT, CH and observed accrual of mtDNA mutations.

Humans↗

PubMed2026

The Biobank Rare Variant consortium powers the discovery of rare genetic associations through global collaboration.

Rare coding variants can have large effects on disease risk and provide direct routes from human genetics to disease mechanisms and therapeutic targets, but their discovery is constrained by sample size, particularly for low-prevalence diseases. Here we establish the Biobank Rare Variant Analysis (BRaVa) consortium, a global rare variant association resource that integrates sequencing and linked health-record data from ten biobanks and cohorts comprising over 1.2 million individuals across diverse ancestries. We performed gene-based meta-analyses of rare coding variation across 33 clinical endpoints and 11 quantitative traits. Aggregating evidence across biobanks and ancestries identified 514 gene-trait associations, including 31 not previously reported in prior studies or curated association resources following systematic literature review. Notably, 36.1% of gene-level associations were undetectable in any individual biobank, and 91 emerged only through cross-ancestry meta-analysis, demonstrating that federated integration enables discovery beyond the reach of single cohorts. Similar gains were observed at the variant level, where 25.0% of phenotype-locus associations were detectable only through meta-analysis. Effect size estimates were correlated across ancestries with concordant directions of effect, supporting the generalizability of rare variant associations. The identified signals implicate pathways involved in transcriptional and epigenetic regulation, metabolism, vascular and epithelial biology, and immune function, highlighting rare coding variation as an engine for biological discovery across medical record phenotypes. For example, damaging variation in ANKRD12 implicates inflammatory transcriptional dysregulation in asthma and chronic obstructive pulmonary disease, and ultra-rare predicted loss-of-function variants in NAA15 link protein acetylation processes to type 2 diabetes risk. BRaVa establishes a scalable framework and freely available community resource for rare variant meta-analysis across global biobanks. Public release of gene- and variant-level association summary statistics provides a reference map of rare coding variant associations to support disease gene discovery, biological interpretation, and therapeutic target prioritization as sequencing-linked health-record resources continue to expand.

Journal Article↗

PubMed2026

Systematic common and rare variant association testing in 392,030 whole genomes in All of Us.

Large-scale genome-wide association studies (GWAS) and rare variant association studies (RVAS) from population biobanks provide valuable resources for gene discovery in complex human traits. We present an analysis of the All of Us Research Program v8 release, which includes whole genome sequencing data and harmonized phenotypic information of 392,030 participants after quality control, enabling a unified investigation of rare and common variants across a spectrum of human traits and diseases. We build an extensive phenome- and genome-wide ("All by All") computational framework to perform GWAS and RVAS on 3,602 phenotypes and identify 49,863 approximately independent, high-quality single-variant and gene-level associations. Meta-analyses of All of Us and UK Biobank, with sample sizes as large as 786,871 participants, further enhance statistical power and find 193 pLoF gene-phenotype associations that are not significant in either cohort alone, including 22 associations not highlighted by previous studies. We also present a public interactive browser that integrates association results for common and rare variants to facilitate interpretation and rapid querying of summary statistics, along with supporting documentation, and a Featured Workspace in the All of Us Researcher Workbench. Our framework will apply to iterative data releases as All of Us grows, empowering researchers worldwide to uncover insights into the functional effects of genetic components on complex traits and diseases.

Journal Article↗