Michael N. Weedon

The paper estimates whole-genome heritability of complex traits across diverse populations, finding rare and non-coding variants significantly contribute to heritability.

AlphaGenome Atlas predicts regulatory effects of non-coding variants across the human genome, improving their prioritization and interpretation.

This study estimates the prevalence and penetrance of heritable retinoblastoma in adult cohorts using genomic data and health records, finding lower penetrance than clinical cohorts.

The paper introduces a masked-MLP neural network to improve genetic risk score calculation from noisy and missing data, showing better robustness than standard methods.

The paper identifies 260 genes associated with sleep phenotypes using rare coding variants from large-scale sequencing data, revealing distinct genetic architectures and biological mechanisms across different sleep measurements.

The paper combines genetic risk scores with PSA test results and age to improve prostate cancer prediction, showing better performance than using either method alone.

This study shows that polygenic risk scores for diabetes traits are associated with higher HbA1c and increased diabetes diagnosis risk in GCK-MODY carriers, highlighting the interaction between monogenic and polygenic factors.

This study evaluates control selection strategies in prostate cancer GWAS using UK Biobank data, finding that matched controls balance power and cost but may miss novel associations.

This study uses whole-genome sequencing in 672,976 individuals to show rare and common variants converge in genetic associations for anthropometric traits, with ultra-rare variants explaining less than 10% of heritability.

The paper uses a federated approach to conditionally analyze genetic variants across biobanks, identifying LDL-C lowering effects of DNAJC13 haploinsufficiency and LDLR regulation.

The paper develops a machine learning-based T1D risk model tailored to Middle Eastern populations, showing improved performance over existing models in this region.

This study identifies genetic associations for multiple sleep disorders and medications in over 1.6 million individuals, revealing shared and unique genetic factors across sleep traits.

The paper reduces error rates in population-scale whole-genome sequencing by applying genotype-level quality control, demonstrating its effectiveness using UK Biobank and All of Us datasets.

This study examines genotype-treatment interactions in endocrine therapy for breast cancer using UK Biobank data and finds no significant associations between genomic variants and adverse drug effects.

The paper introduces vcf2agds, a toolkit converting WGS data to aGDS format, enabling efficient analysis of UK Biobank data and identifying 480 genome-wide significant associations for total cholesterol.

The paper shows that common genetic variants influence disease risk and severity in monogenic diabetes, modifying clinical outcomes in individuals with pathogenic variants.

This study performs a genome-wide association study of T1D stratified by HLA DR3 and DR4 status, revealing genetic and biological heterogeneity in T1D progression.

The paper identifies non-canonical FBN1 splice variants in undiagnosed Marfan syndrome families using genome sequencing and RNA testing, finding 20 candidate variants with significant enrichment in FTAAD cases.

The paper assesses the genetic evidence for NEUROD1, PDX1, APPL1, and WFS1 in MODY, finding that only NEUROD1 and PDX1 variants are enriched, while APPL1 and WFS1 are not.

This paper demonstrates that deep learning can generate polygenic risk scores with high accuracy, even with missing data, and shows that a single model can predict multiple scores.

This study performs genome-wide association analysis on composite sleep health scores, identifying 28 novel loci and confirming 341 previously reported loci, with insights into genetic mechanisms and pathways.

This study estimates that ~6% of insulin-treated diabetic individuals with ambiguous features in British Pakistanis and Bangladeshis have T1D, highlighting the need for autoantibody and C-peptide testing.

The paper evaluates the performance of a type 1 diabetes genetic risk score in an Indian cohort, finding lower discriminative ability compared to Europeans due to HLA diversity.

This paper develops a polygenic risk score model to predict colorectal cancer risk in symptomatic patients using UK Biobank data, achieving high predictive accuracy.

This paper simulates genetic risk scores using summary statistics to enable comparison across datasets, showing similar performance to real data in type 1 diabetes.

The paper investigates how rare variants in monogenic developmental disorder genes interact with common variants to affect cognitive and socio-economic traits, finding that higher educational attainment polygenic scores can partially counterbalance adverse effects.

The paper identifies chromosome 20p11.2 deletions as a cause of congenital hyperinsulinism by disrupting FOXA2 expression in pancreatic beta-cells.

The paper uses whole-genome sequencing data from UK Biobank participants to identify rare non-coding variants and regions associated with circulating protein levels.