Ryan S. Dhindsa

AstraZeneca (United States)

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Slavé Petrovski

AstraZeneca (United Kingdom)

Quanli Wang

AstraZeneca (United States)

David B. Goldstein

Columbia University

Dimitrios Vitsios

AstraZeneca (United Kingdom)

Andrew R. Harper

AstraZeneca (United Kingdom)

Abhishek Nag

AstraZeneca (United Kingdom)

Katherine R. Smith

AstraZeneca (United Kingdom)

Fengyuan Hu

AstraZeneca (United Kingdom)

Oliver S. Burren

AstraZeneca (United Kingdom)

Keren Carss

AstraZeneca (United Kingdom)

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AstraZeneca (United Kingdom)

115

Cleveland Clinic Lerner College of Medicine

Columbia University

Harvard University

Columbia University Irving Medical Center

Broad Institute

Massachusetts General Hospital

University of California, San Francisco

Johns Hopkins University

John Wiley & Sons (United States)

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Whole-exome sequencing in undiagnosed genetic diseases: interpreting 119 trios

Genetics in Medicine (2015)

Xiaolin Zhu Slavé Petrovski Pingxing Xie Elizabeth K. Ruzzo Yifan Lu

PurposeDespite the recognized clinical value of exome-based diagnostics, methods for comprehensive genomic interpretation remain immature. Diagnoses are based on known or presumed pathogenic variants in genes already associated with a similar phenotype. Here, we extend this paradigm by evaluating novel bioinformatics approaches to aid identification of new gene–disease associations.MethodsWe analyzed 119 trios to identify both diagnostic genotypes in known genes and candidate genotypes in novel genes. We considered qualifying genotypes based on their population frequency and in silico predicted effects we also characterized the patterns of genotypes enriched among this collection of patients.ResultsWe obtained a genetic diagnosis for 29 (24%) of our patients. We showed that patients carried an excess of damaging de novo mutations in intolerant genes, particularly those shown to be essential in mice (P = 3.4 × 10−8). This enrichment is only partially explained by mutations found in known disease-causing genes.ConclusionThis work indicates that the application of appropriate bioinformatics analyses to clinical sequence data can also help implicate novel disease genes and suggest expanded phenotypes for known disease genes. These analyses further suggest that some cases resolved by whole-exome sequencing will have direct therapeutic implications.

Exome

Genotype-phenotype distinction

Candidate gene

10.1038/gim.2014.191

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Citations (319)

Author Correction: DrugnomeAI is an ensemble machine-learning framework for predicting druggability of candidate drug targets

Communications Biology (2023)

Arwa Bin Raies Ewa Tulodziecka James Stainer Lawrence Middleton Ryan S. Dhindsa

Druggability

Ensemble Learning

Drug candidate

10.1038/s42003-023-05086-5

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Citations (2)

An expanded genomic database for identifying disease-related variants

Nature (2023)

Ryan S. Dhindsa Slavé Petrovski

10.1038/d41586-023-01598-6

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Citations (0)

Issue Information

Annals of Neurology (2021)

Ryan S. Dhindsa Anthony W. Zoghbi Daniel Krizay Chirag Vasavda David Goldstein

Canada & Mexico), €1286.00(Europe), £1020.00(UK), US$1993.00 (Rest of World).Prices are exclusive of tax.Asia-Pacific GST,

Annals

10.1002/ana.26007

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Ancestry adjustment improves genome-wide estimates of regional intolerance

Zenodo (CERN European Organization for Nuclear Research) (2022)

Tristan J. Hayeck Nicholas Stong Evan H. Baugh Ryan S. Dhindsa Tychele N. Turner

Genomic regions subject to purifying selection are more likely to carry disease causing mutations than regions not under selection. Cross species conservation is often used to identify such regions but with limited resolution to detect selection on short evolutionary timescales such as that occurring in only one species. In contrast, genetic intolerance looks for depletion of variation relative to expectation within a species, allowing species specific features to be identified. When estimating the intolerance of noncoding sequence, methods strongly leverage variant frequency distributions. As the expected distributions depend on ancestry, if not properly controlled for, ancestral population source may obfuscate signals of selection. We demonstrate that properly incorporating ancestry in intolerance estimation greatly improved variant classification. We provide a genomewide intolerance map that is conditional on ancestry and likely to be particularly valuable for variant prioritization.

10.5281/zenodo.6326155

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High-impact rare genetic variants in severe schizophrenia

Proceedings of the National Academy of Sciences (2021)

Anthony W. Zoghbi Ryan S. Dhindsa Terry E. Goldberg Aydan Mehralizade Joshua E. Motelow

Extreme phenotype sequencing has led to the identification of high-impact rare genetic variants for many complex disorders but has not been applied to studies of severe schizophrenia. We sequenced 112 individuals with severe, extremely treatment-resistant schizophrenia, 218 individuals with typical schizophrenia, and 4,929 controls. We compared the burden of rare, damaging missense and loss-of-function variants between severe, extremely treatment-resistant schizophrenia, typical schizophrenia, and controls across mutation intolerant genes. Individuals with severe, extremely treatment-resistant schizophrenia had a high burden of rare loss-of-function (odds ratio, 1.91; 95% CI, 1.39 to 2.63;

10.1073/pnas.2112560118

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Citations (37)

Correction: Orion: Detecting regions of the human non-coding genome that are intolerant to variation using population genetics

PLoS ONE (2018)

Ayal B. Gussow Brett Copeland Ryan S. Dhindsa Quanli Wang Slavé Petrovski

[This corrects the article DOI: 10.1371/journal.pone.0181604.].

Population Genetics

Variation (astronomy)

10.1371/journal.pone.0191298

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Citations (1)

Genetic architecture of telomere length in 462,675 UK Biobank whole-genome sequences

medRxiv (Cold Spring Harbor Laboratory) (2023)

Oliver S. Burren Ryan S. Dhindsa Sri V. V. Deevi Sean Wen Abhishek Nag

Abstract Telomeres protect the ends of chromosomes from damage, and genetic regulation of their length is associated with human disease and ageing. We developed a joint telomere length (TL) metric, combining both qPCR and whole genome sequencing (WGS) measurements across 462,675 UK Biobank participants that increased our ability to capture TL heritability by 36% (h 2 mean= 0.058 to h 2 combined= 0.079) and improved predictions of age. Exome-wide rare variant (minor allele frequency<0.001) and gene-level collapsing association studies identified 53 variants and 22 genes significantly associated with TL that included allelic series in ACD and RTEL1 . Five of the 31 rare-variant TL associated genes (16%) were also known drivers of clonal haematopoiesis (CH), prompting somatic variant analyses. Stratifying by CH clone size, we uncovered novel gene-specific associations with TL, including lengthened telomeres in individuals with large SRSF2 -mutant clones, in contrast to the progressive telomere shortening observed with increasing clonal expansions driven by other CH genes. Our findings demonstrate the impact of rare variants on TL with larger effects in genes associated with CH, a precursor of myeloid cancers and several other non-malignant human diseases. Telomere biology is likely to be an important focus for the prevention and treatment of these conditions.

Exome

Genetic architecture

Genome-wide Association Study

10.1101/2023.09.18.23295715

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Citations (4)

Literature-based predictions of Mendelian disease therapies

The American Journal of Human Genetics (2023)

Cole A. Deisseroth Won‐Seok Lee Jiyoen Kim Hyun-Hwan Jeong Ryan S. Dhindsa

Drug target

Mendelian inheritance

10.1016/j.ajhg.2023.08.018