Vignesh Kartik Chundru

Most heritability studies have focused on common genetic variation and have largely excluded non-European or highly admixed populations. Here, we estimated the whole-genome heritability (h ² _WGS ) of seven complex traits using 73,600,953 autosomal and chromosome X variants for 198,931 unrelated individuals from three genetic similarity groups. Adjusting for population stratification, we showed that rare variants (minor allele frequency2 _WGS . We used simulations to show how functionally-stratified heritability can be inflated due to linkage with missing or lower-quality variants. Finally, after accounting for confounding effects, we found substantial heritability enrichment from variants in non-coding RNA exons (maximum fold enrichment, FE=8.7, P =3.56e-7), UTRs (23.0, P =3.46e-30), distal regulatory regions (33.8, P =3.26e-42), and protein-coding exons (58.0, P =7.78e-15). Our study provides a framework for investigating the genetic architecture of complex human phenotypes using diverse whole-genome sequencing data and highlights the contribution of rare and non-coding genetic variants to the heritability of complex traits.

Missense variant pathogenicity is usually interpreted according to the predicted effect on the protein via polypeptide alterations. But missense variants can also cause disease by disrupting RNA splicing, here defined as missense spliceogenic variants (MSVs). Although splice prediction is increasingly incorporated into variant classification pipelines, it is not a standardised process, so the scale and clinical relevance of MSVs remains poorly defined. We integrated whole-genome sequencing and circulating proteomic data from ∼50,000 UK Biobank participants to characterise MSVs using ∼8.3 million missense variants. We found that 2.7% of missense variants are predicted to alter splicing (SpliceAIΔ ≥ 0.2), comparable with synonymous variants (2.8%). Notably, 57% had benign/neutral AlphaMissense and REVEL scores (<0.34 and <0.5 respectively), rising to 85.2% in intrinsically disordered regions, indicating that many MSVs would not be prioritised by protein-centric predictors. MSVs were associated with reduced circulating levels of the cognate protein, approaching the effects of predicted loss-of-function variants. Analysis of raw SpliceAI scores revealed that events where novel cryptic sites outscored canonical sites resulted in the largest protein depletion, particularly when a premature frameshift was introduced. Validation of 14 predicted MSVs of uncertain significance in a clinical cohort provided strong pathogenic/likely pathogenic evidence, and screening unsolved cases from the 100,000 Genomes Project identified two novel candidate diagnostic MSVs. These findings establish the scale and functional consequences of MSVs, and demonstrate that RNA-centric interpretation can reveal clinically-relevant loss-of-function mechanisms missed by protein-centric missense variant interpretation.

A major challenge in genomics is deciphering the functional consequences of non-coding genetic variation. Here we present AlphaGenome Atlas, a comprehensive resource that enables the joint interpretation and prioritization of variant effects across the entire human genome. Using AlphaGenome, we predicted the regulatory effects across thousands of molecular phenotypes for every possible human single nucleotide variant and many observed indels. These predictions were then used to derive a unified and interpretable AlphaGenome Variant Impact (AVI) score and to map cis-regulatory motifs across the genome. AVI achieved state-of-the-art performance across diverse benchmarks with improved prioritization of deleterious non-coding variants. Application of the combined Atlas resource helped solve an epileptic encephalopathy rare disease case, increased the statistical power to detect rare non-coding variants driving population-level phenotypes, and enhanced the mechanistic interpretation of these variants. Thus, AlphaGenome Atlas improves the prioritization and molecular interpretation of non-coding variants with genetic and clinical significance.

Structural variants (SVs) can disrupt gene function and contribute to pathogenesis of rare disorders. Here, we created a genome-wide knockout dataset across 125,730 individuals with genome sequencing data in the UK’s National Genomic Research Library by leveraging the distinct read-depth signal associated with homozygous deletions. We curated 535,699 rare high-confidence homozygous deletion SVs, of which 48,735 were rare. These deletions collectively covered 213Mb or 6.92% of the human genome (4.58% of autosomal sequence), revealing substantial tolerance to complete sequence loss. From a subset of 58,022 individuals with rare disease, we identified 295 individuals with likely diagnostic homozygous deletions impacting protein-coding regions of known disease genes. A further 32 individuals had candidate non-coding SVs in or near to known disease genes, 19/32 (59.37%) of which disrupted 5’-UTR/promoter regions, revealing promoter deletion as an underappreciated cause of rare disorders. Finally, we identify 43 genes with no known rare-disease association but with exonic homozygous deletions in two or more individuals with consistent phenotypes. We describe in detail PDC (phosducin) in Leber Congenital Amaurosis, GCG (glucagon) for a syndromic neurodevelopmental disorder with gastrointestinal involvement, and ENTPD3 for intellectual disability with autism, as candidate novel disease-associated genes. Overall, we create a genome-wide map of homozygous deletions and demonstrate the power of this dataset for rare disease diagnosis and novel disease-gene discovery.

Aims/hypothesis: GCK -MODY (glucokinase MODY) causes lifelong, mild hyperglycaemia with high penetrance. Variation in glycaemic phenotype among carriers remains unexplained. We hypothesised that polygenic background contributes to this variability and that this influence differs from that of HNF1A -MODY. Methods: To test whether polygenic background contributes to the GCK -MODY clinical phenotype, we analysed polygenic risk scores (PGS) for nine diabetes-related traits in 897 clinically referred individuals with GCK -MODY. We compared these to 7645 non-diabetic control participants, 4773 participants with type 2 diabetes and 601 participants with HNF1A -MODY and assessed associations between PGS and glycaemic measures. Additionally, we evaluated 158 clinically unselected GCK variant carriers from the UK Biobank to examine polygenic effects independent of clinical referral. Results: We observed independent polygenic enrichment for HbA _1c (including both glycaemic and non-glycaemic components), fasting glucose and type 2 diabetes in clinically referred GCK -MODY individuals compared with non-diabetes controls (0.16–0.33 SD higher, all p 1c and fasting glucose PGSs were higher than in both type 2 diabetes and HNF1A -MODY groups, whereas type 2 diabetes PGS was lower. GCK -MODY and HNF1A -MODY showed distinct patterns of polygenic enrichment, with only the type 2 diabetes PGS contributing independently in HNF1A -MODY (0.33 SD, p 27 ). By contrast, no polygenic enrichment was seen in GCK pathogenic variant carriers from a clinically unselected population-based cohort. In both settings, HbA _1c PGS were associated with measured HbA _1c levels in GCK carriers (clinically referred: β=0.97, clinically unselected: β=0.91, both p GCK -MODY cases in the top HbA _1c quintile had a 3-to-6-fold risk of exceeding the diabetes diagnostic HbA _1c threshold (≥48 mmol/mol) in clinically selected and clinically unselected cohort respectively. Conclusions/interpretation: Our findings suggest that polygenic background and GCK variants interact to modify the glycaemic expression of GCK -MODY, influencing clinical diagnosis despite high penetrance. The pattern of polygenic contribution differs from that of HNF1A -MODY, highlighting the aetiology specific interaction. Our study highlights the importance of integrating both monogenic and polygenic factors to better understand phenotypic variability in monogenic diseases.

GWAS have generally focused on common variants from genotyping arrays or rare protein-coding variants from exome sequencing. Here, we use whole-genome sequencing data to evaluate the contribution to and architecture of rare non-coding variants for three commonly studied anthropometric traits: height, BMI and waist-hip ratio adjusted for BMI. Analysing 447,461 individuals in the UK Biobank for discovery and 225,515 individuals in All of Us for replication, we identify 90 rare and low-frequency single variant associations, including two independent rare variants upstream of IGF2BP2 that substantially reduce waist-hip ratio adjusted for BMI, but have distinct effects on other adiposity traits. We further identify 135 coding variant aggregates. For example, UBR3 protein-truncating variants are associated with a 2.7 kg/m2 increase in BMI. We additionally identify 51 non-coding variant aggregate associations, including one in the 5’UTR of FGF18 associated with up to 6 cm effects on height. We show that 97% of rare variant associations occur near GWAS-identified loci, demonstrating convergence of rare and common variant associations. Finally, we show that ultra rare variants explain a small fraction of heritability compared to common variants for these traits, that heritability is largely shared across ancestries, and that it concentrates around common variant loci.

Whole genome sequencing in diverse population-scale biobanks offers new insights into the genetic architecture of complex traits from rare and non-coding variants. However, rare single variant and aggregate associations are often confounded by linkage disequilibrium and haplotype structure, resulting in large numbers of false-positive associations. Previous methods that rely on reference panels or linkage disequilibrium-matrices to determine conditional independence in meta-analyses do not scale to very rare variants, which may be observed in only one biobank and can exhibit long-range haplotypes. Here, we implement a federated approach to perform iterative conditional meta-analysis on individual-level genotype and phenotype data across biobanks while adhering to data sharing policies. We applied our methodology to a meta-analysis of LDL-C in 614,375 individuals from UK Biobank and All of Us, encompassing six genetic ancestry groups. After conditioning, only 4.3% of significantly associated rare single variants and 6.9% of aggregates remained statistically independent. The proportion of significant aggregates that remained independent after conditioning was higher for coding-based tests than non-coding. We further validate that our approach effectively suppresses false-positive associations using simulations centred on the LDLR locus. We identify allelic series of variants associated with reduced LDL-C, including loss-of-function variants in DNAJC13 and variants in the 3-prime untranslated region of LDLR . Our results highlight that federated conditioning can distinguish independent rare variant signals from linkage and haplotype structure artifacts in multi-ancestry meta-analyses across separate biobanks.

Rare genetic disorders collectively impact over 300 million people worldwide, yet around 95% have no specific treatments. For the many rare disorders caused by haploinsufficiency, effective therapies need to upregulate protein expression. However, therapeutic upregulation is often not straightforward. Increasing protein translation of the wildtype allele through inhibiting repressive upstream open reading frames (uORFs) has been proposed as a therapeutic approach for a few specific genes. The widespread success of steric-block antisense oligonucleotides (ASOs) for this purpose is, however, debated. Here, we explore an alternative approach, using splice-switching to exclude uORF-containing exons from the mRNA. Through a genome-wide computational screening approach, we identified 79 uORF-containing 5'UTR exons in haploinsufficient monogenic disease genes as candidate exon skipping targets. We demonstrate that removing the target exon significantly increased protein translation (between 1.4-5.5 fold) in a luciferase reporter assay for four of six prioritised target 5'UTR exons in neurodevelopmental disorder genes (CTCF, GRIN2B, KRIT1, and TSC1). Overall, this work supports the widespread application of 5'UTR exon skipping to boost translation of clinically relevant haploinsufficient genes.

Population-scale whole-genome sequencing data will contain many individual-level genotype errors, even after allele-level quality control (QC). We establish the need for genotype-level QC using UK Biobank (N=490,726) and All of Us v8 (N=414,830), where we remove up to 100 million (∼9%) additional low-quality variants. We demonstrate reduced false positive rate in downstream genetic association studies, highlight the power of parent-offspring trios for QC, and illustrate the need for sex-specific X-chromosome filtering. We provide a QCed All of Us v8 dataset in plink -pgen format, and an efficient pipeline for QC and conversion from VCF to plink- pgen for UK Biobank.

Young-onset monogenic disorders often show variable penetrance, yet the underlying causes remain poorly understood. Uncovering these influences could reveal new biological mechanisms and enhance risk prediction for monogenic diseases. Here we show that polygenic background substantially shapes the clinical presentation of maturity-onset diabetes of the young (MODY), a common monogenic form of diabetes that typically presents in adolescence or early adulthood. We find strong enrichment of type 2 diabetes (T2D) polygenic risk, but not type 1 diabetes risk, in genetically confirmed MODY cases (n = 1,462). This T2D polygenic burden, primarily through beta-cell dysfunction pathways, is strongly associated with earlier age of diagnosis and increased diabetes severity. Common genetic variants collectively account for 24% (P < 0.0001) of the phenotypic variability. Using a large population cohort (n = 424,553), we demonstrate that T2D polygenic burden substantially modifies diabetes onset in individuals with pathogenic variants, with diabetes risk ranging from 11% to 81%. Finally, we show that individuals with MODY-like phenotypes (n = 300) without a causal variant have elevated polygenic burden for T2D and related traits, representing potential polygenic phenocopies. These findings reveal substantial influence of common genetic variation in shaping the clinical presentation of early-onset monogenic disorders. Incorporating these may improve risk estimates for individuals carrying pathogenic variants.