ORCID Profile
0000-0002-9110-5830
Current Organisations
University of Tokyo
,
University of Padua
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Publisher: Cold Spring Harbor Laboratory
Date: 21-10-2020
DOI: 10.1101/2020.10.20.347294
Abstract: The large majority of variants identified by GWAS are non-coding, motivating detailed characterization of the function of non-coding variants. Experimental methods to assess variants’ effect on gene expressions in native chromatin context via direct perturbation are low-throughput. Existing high-throughput computational predictors thus have lacked large gold standard sets of regulatory variants for training and validation. Here, we leverage a set of 14,807 putative causal eQTLs in humans obtained through statistical fine-mapping, and we use 6,121 features to directly train a predictor of whether a variant modifies nearby gene expression. We call the resulting prediction the expression modifier score (EMS). We validate EMS by comparing its ability to prioritize functional variants with other major scores. We then use EMS as a prior for statistical fine-mapping of eQTLs to identify an additional 20,913 putatively causal eQTLs, and we incorporate EMS into co-localization analysis to identify 310 additional candidate genes across UK Biobank phenotypes.
Publisher: Springer Science and Business Media LLC
Date: 27-05-2020
DOI: 10.1038/S41586-020-2308-7
Abstract: Genetic variants that inactivate protein-coding genes are a powerful source of information about the phenotypic consequences of gene disruption: genes that are crucial for the function of an organism will be depleted of such variants in natural populations, whereas non-essential genes will tolerate their accumulation. However, predicted loss-of-function variants are enriched for annotation errors, and tend to be found at extremely low frequencies, so their analysis requires careful variant annotation and very large s le sizes 1 . Here we describe the aggregation of 125,748 exomes and 15,708 genomes from human sequencing studies into the Genome Aggregation Database (gnomAD). We identify 443,769 high-confidence predicted loss-of-function variants in this cohort after filtering for artefacts caused by sequencing and annotation errors. Using an improved model of human mutation rates, we classify human protein-coding genes along a spectrum that represents tolerance to inactivation, validate this classification using data from model organisms and engineered human cells, and show that it can be used to improve the power of gene discovery for both common and rare diseases.
Publisher: Springer Science and Business Media LLC
Date: 22-01-2021
Publisher: Springer Science and Business Media LLC
Date: 03-02-2021
DOI: 10.1038/S41586-020-03174-8
Abstract: A Correction to this paper has been published: 0.1038/s41586-020-03174-8.
Publisher: Cold Spring Harbor Laboratory
Date: 10-03-2019
DOI: 10.1101/573378
Abstract: Multi-nucleotide variants (MNVs), defined as two or more nearby variants existing on the same haplotype in an in idual, are a clinically and biologically important class of genetic variation. However, existing tools for variant interpretation typically do not accurately classify MNVs, and understanding of their mutational origins remains limited. Here, we systematically survey MNVs in 125,748 whole exomes and 15,708 whole genomes from the Genome Aggregation Database (gnomAD). We identify 1,996,125 MNVs across the genome with constituent variants falling within 2 bp distance of one another, of which 31,510 exist within the same codon, including 405 predicted to result in gain of a nonsense mutation, 1,818 predicted to rescue a nonsense mutation event that would otherwise be caused by one of the constituent variants, and 16,481 additional variants predicted to alter protein sequences. We show that the distribution of MNVs is highly non-uniform across the genome, and that this non-uniformity can be largely explained by a variety of known mutational mechanisms, such as CpG deamination, replication error by polymerase zeta, or polymerase slippage at repeat junctions. We also provide an estimate of the dinucleotide mutation rate caused by polymerase zeta. Finally, we show that differential CpG methylation drives MNV differences across functional categories. Our results demonstrate the importance of incorporating haplotype-aware annotation for accurate functional interpretation of genetic variation, and refine our understanding of genome-wide mutational mechanisms of MNVs.
Publisher: Springer Science and Business Media LLC
Date: 27-05-2020
DOI: 10.1038/S41467-019-12438-5
Abstract: Multi-nucleotide variants (MNVs), defined as two or more nearby variants existing on the same haplotype in an in idual, are a clinically and biologically important class of genetic variation. However, existing tools typically do not accurately classify MNVs, and understanding of their mutational origins remains limited. Here, we systematically survey MNVs in 125,748 whole exomes and 15,708 whole genomes from the Genome Aggregation Database (gnomAD). We identify 1,792,248 MNVs across the genome with constituent variants falling within 2 bp distance of one another, including 18,756 variants with a novel combined effect on protein sequence. Finally, we estimate the relative impact of known mutational mechanisms - CpG deamination, replication error by polymerase zeta, and polymerase slippage at repeat junctions - on the generation of MNVs. Our results demonstrate the value of haplotype-aware variant annotation, and refine our understanding of genome-wide mutational mechanisms of MNVs.
Publisher: Springer Science and Business Media LLC
Date: 27-05-2020
DOI: 10.1038/S41591-020-0893-5
Abstract: Human genetic variants predicted to cause loss-of-function of protein-coding genes (pLoF variants) provide natural in vivo models of human gene inactivation and can be valuable indicators of gene function and the potential toxicity of therapeutic inhibitors targeting these genes 1,2 . Gain-of-kinase-function variants in LRRK2 are known to significantly increase the risk of Parkinson’s disease 3,4 , suggesting that inhibition of LRRK2 kinase activity is a promising therapeutic strategy. While preclinical studies in model organisms have raised some on-target toxicity concerns 5–8 , the biological consequences of LRRK2 inhibition have not been well characterized in humans. Here, we systematically analyze pLoF variants in LRRK2 observed across 141,456 in iduals sequenced in the Genome Aggregation Database (gnomAD) 9 , 49,960 exome-sequenced in iduals from the UK Biobank and over 4 million participants in the 23andMe genotyped dataset. After stringent variant curation, we identify 1,455 in iduals with high-confidence pLoF variants in LRRK2 . Experimental validation of three variants, combined with previous work 10 , confirmed reduced protein levels in 82.5% of our cohort. We show that heterozygous pLoF variants in LRRK2 reduce LRRK2 protein levels but that these are not strongly associated with any specific phenotype or disease state. Our results demonstrate the value of large-scale genomic databases and phenotyping of human loss-of-function carriers for target validation in drug discovery.
Publisher: Springer Science and Business Media LLC
Date: 07-06-2021
DOI: 10.1038/S41467-021-23134-8
Abstract: The large majority of variants identified by GWAS are non-coding, motivating detailed characterization of the function of non-coding variants. Experimental methods to assess variants’ effect on gene expressions in native chromatin context via direct perturbation are low-throughput. Existing high-throughput computational predictors thus have lacked large gold standard sets of regulatory variants for training and validation. Here, we leverage a set of 14,807 putative causal eQTLs in humans obtained through statistical fine-mapping, and we use 6121 features to directly train a predictor of whether a variant modifies nearby gene expression. We call the resulting prediction the expression modifier score (EMS). We validate EMS by comparing its ability to prioritize functional variants with other major scores. We then use EMS as a prior for statistical fine-mapping of eQTLs to identify an additional 20,913 putatively causal eQTLs, and we incorporate EMS into co-localization analysis to identify 310 additional candidate genes across UK Biobank phenotypes.
Publisher: Elsevier BV
Date: 12-2016
DOI: 10.1016/J.JVC.2016.07.004
Abstract: The size of the pulmonary veins (PVs) and pulmonary arteries (PAs) changes in response to hemodynamic alterations caused by physiological events and disease. We sought to create standardized echocardiographic methods for imaging the right ostium of the pulmonary veins (RPVs) and the right pulmonary artery (RPA) using specific landmarks and timing to quantify vessel diameters and phasic changes during the cardiac cycle. Fifty client-owned healthy dogs prospectively recruited. M-mode and 2-dimensional images were obtained from modified right parasternal long and short axis views. Right ostium of the pulmonary veins and RPA measurements were timed with electrical [peak of the QRS complex (RPV In normal dogs regardless of the echocardiographic view or time in the cardiac cycle, the RPV/RPA ratio approximated 1.0. Mechanically timed fractional changes (distensibility indices) in RPV and RPA diameters did not differ (p=0.99 36.9% and 36.8%, respectively). ECG-timed fractional changes (distensibility indices) in RPV and RPA diameter were at least 50% smaller than mechanically timed changes (p<0.05). RPV:Ao and RPA:Ao ranged between 0.3 and 0.6, with lower values obtained in diastole and larger values in systole (p<0.0001). Multiple positive and negative deflections were identified on the RPV and RPA M-mode tracings. This study provides detailed methodology and 2D and M-mode reference intervals for the RPV and RPA dimensions and the phasic changes during the cardiac cycle of the dog using echocardiography.
No related grants have been discovered for Qingbo Wang.