Whole-genome sequencing analysis identifies rare, large-effect noncoding variants and regulatory regions associated with circulating protein levels.

The contribution of rare noncoding genetic variation to common phenotypes is largely unknown, as a result of a historical lack of population-scale whole-genome sequencing data and the difficulty of categorizing noncoding variants into functionally similar groups. To begin addressing these challenges, we performed a cis association analysis using whole-genome sequencing data, consisting of 1.1 billion variants, 123 million noncoding aggregate-based tests and 2,907 circulating protein levels in ~50,000 UK Biobank participants.

Examining the role of common variants in rare neurodevelopmental conditions.

Although rare neurodevelopmental conditions have a large Mendelian component, common genetic variants also contribute to risk. However, little is known about how this polygenic risk is distributed among patients with these conditions and their parents nor its interplay with rare variants. It is also unclear whether polygenic background affects risk directly through alleles transmitted from parents to children, or whether indirect genetic effects mediated through the family environment also play a role.

Phenotypic spectrum of dual diagnoses in developmental disorders.

As more patients receive genome-wide sequencing, the number of individuals diagnosed with multiple monogenic conditions is increasing. We sought to investigate the relative phenotypic contribution of dual diagnoses using both manual curation and computational approaches. First, we computed 1,003,236 semantic similarity scores for all possible pairs of 1,417 genes in the Developmental Disorder Gene2Phenotype (DDG2P) database using Human Phenotype Ontology terms.

Federated analysis of autosomal recessive coding variants in 29,745 developmental disorder patients from diverse populations.

Autosomal recessive coding variants are well-known causes of rare disorders. We quantified the contribution of these variants to developmental disorders in a large, ancestrally diverse cohort comprising 29,745 trios, of whom 20.4% had genetically inferred non-European ancestries.

Investigating the role of common cis-regulatory variants in modifying penetrance of putatively damaging, inherited variants in severe neurodevelopmental disorders.

Recent work has revealed an important role for rare, incompletely penetrant inherited coding variants in neurodevelopmental disorders (NDDs). Additionally, we have previously shown that common variants contribute to risk for rare NDDs. Here, we investigate whether common variants exert their effects by modifying gene expression, using multi-cis-expression quantitative trait loci (cis-eQTL) prediction models.

Genomic Diagnosis of Rare Pediatric Disease in the United Kingdom and Ireland.

Background: Pediatric disorders include a range of highly penetrant, genetically heterogeneous conditions amenable to genomewide diagnostic approaches. Finding a molecular diagnosis is challenging but can have profound lifelong benefits.

Methods: We conducted a large-scale sequencing study involving more than 13,500 families with probands with severe, probably monogenic, difficult-to-diagnose developmental disorders from 24 regional genetics services in the United Kingdom and Ireland.

Rare genetic variants underlie outlying levels of DNA methylation and gene-expression.

Testing the effect of rare variants on phenotypic variation is difficult due to the need for extremely large cohorts to identify associated variants given expected effect sizes. An alternative approach is to investigate the effect of rare genetic variants on DNA methylation (DNAm) as effect sizes are expected to be larger for molecular traits compared with complex traits. Here, we investigate DNAm in healthy ageing populations-the Lothian Birth Cohorts of 1921 and 1936-and identify both transient and stable outlying DNAm levels across the genome.

Examining the Impact of Imputation Errors on Fine-Mapping Using DNA Methylation QTL as a Model Trait.

Genetic variants disrupting DNA methylation at CpG dinucleotides (CpG-SNP) provide a set of known causal variants to serve as models to test fine-mapping methodology. We use 1716 CpG-SNPs to test three fine-mapping approaches (Bayesian imputation-based association mapping, Bayesian sparse linear mixed model, and the J-test), assessing the impact of imputation errors and the choice of reference panel by using both whole-genome sequence (WGS), and genotype array data on the same individuals ( = 1166). The choice of imputation reference panel had a strong effect on imputation accuracy, with the 1000 Genomes Project Phase 3 (1000G) reference panel ( = 2504 from 26 populations) giving a mean nonreference discordance rate between imputed and sequenced genotypes of 3.