Is there a way to reduce the inequity in variant interpretation on the basis of ancestry?

Appelbaum, Paul S.; Burke, Wylie; Parens, Erik; Zeevi, David A.; Arbour, Laura; Garrison, Nanibaa’ A.; Bonham, Vence L.; Chung, Wendy K.

doi:10.1016/j.ajhg.2022.04.012

Cited by 23 publications

(26 citation statements)

References 29 publications

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“…The individuals included within the ALS Knowledge Portal and Project MinE ALS sequencing consortium cohorts were largely of European ancestry (30,31), but it is well established that rates of VUS are much higher in underrepresented populations (68,69). As larger, more diverse cohorts of ALS patients become available, it will be important to replicate our analyses using those datasets to ensure that our results remain applicable across all populations.…”

Section: Discussionmentioning

confidence: 99%

Characterizing proteomic and transcriptomic features of missense variants in amyotrophic lateral sclerosis genes

Dilliott

Rouleau

Iqbal

et al. 2022

Preprint

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Background: Within recent years, there has been a growing number of genes associated with amyotrophic lateral sclerosis (ALS), resulting in an increasing number of novel variants, particularly missense variants, many of which are of unknown clinical significance. Here, we leverage the sequencing efforts of the ALS Knowledge Portal (3,864 individuals with ALS and 7,839 controls) and Project MinE ALS Sequencing Consortium (4,366 individuals with ALS and 1,832 controls) to perform proteomic and transcriptomic characterization of missense variants in 24 ALS-associated genes. Results: Using predicted human protein structures from AlphaFold, we determined that missense variants carried by individuals with ALS were significantly enriched in β-sheets and α-helices, as well as in core, buried, or moderately buried regions. At the same time, we identified that hydrophobic amino acid residues, compositionally biased protein regions and protein-protein interaction regions are predominantly enriched in missense variants carried by individuals with ALS. Assessment of expression level based on transcriptomics also revealed enrichment of variants of high and medium expression across all tissues and within the brain. We further explored enriched features of interest using burden analyses to determine whether individual genes were driving the enrichment signal. A case study is presented forSOD1to demonstrate proof of concept of how enriched features may aid in defining variant pathogenicity. Conclusions: Our results present proteomic and transcriptomic features that are important indicators of missense variant pathogenicity in ALS and are distinct from features associated with neurodevelopmental disorders.

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Section: Discussionmentioning

confidence: 99%

Characterizing proteomic and transcriptomic features of missense variants in amyotrophic lateral sclerosis genes

Dilliott

Rouleau

Iqbal

et al. 2022

Preprint

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“…For example, it is well documented that individuals with some genetic ancestries receive more VUSs than others because of a paucity of population frequency data in public databases such as gnomAD (Appelbaum et al, 2022;Florentine et al, 2022)…”

Section: Considerations For Use Of Ai In Clinical Laboratory Genomicsmentioning

confidence: 99%

“…A third challenge facing AI approaches to variant classification is that the use of genomic datasets with underrepresentation of individuals of non‐Northern European ancestry could perpetuate inadequacies in the delivery of definitive variant classification for such groups. For example, it is well documented that individuals with some genetic ancestries receive more VUSs than others because of a paucity of population frequency data in public databases such as gnomAD (Appelbaum et al, 2022; Florentine et al, 2022). Because some genetic ancestry groups are better represented in public databases, they are also better represented in the data used for training and validating AI models for variant interpretation.…”

Section: Considerations For Use Of Ai In Clinical Laboratory Genomicsmentioning

confidence: 99%

Applications of artificial intelligence in clinical laboratory genomics

Aradhya

Facio

Metz

et al. 2023

American J of Med Genetics Pt C

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The transition from analog to digital technologies in clinical laboratory genomics is ushering in an era of “big data” in ways that will exceed human capacity to rapidly and reproducibly analyze those data using conventional approaches. Accurately evaluating complex molecular data to facilitate timely diagnosis and management of genomic disorders will require supportive artificial intelligence methods. These are already being introduced into clinical laboratory genomics to identify variants in DNA sequencing data, predict the effects of DNA variants on protein structure and function to inform clinical interpretation of pathogenicity, link phenotype ontologies to genetic variants identified through exome or genome sequencing to help clinicians reach diagnostic answers faster, correlate genomic data with tumor staging and treatment approaches, utilize natural language processing to identify critical published medical literature during analysis of genomic data, and use interactive chatbots to identify individuals who qualify for genetic testing or to provide pre‐test and post‐test education. With careful and ethical development and validation of artificial intelligence for clinical laboratory genomics, these advances are expected to significantly enhance the abilities of geneticists to translate complex data into clearly synthesized information for clinicians to use in managing the care of their patients at scale.

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“…Indeed, most genomic research has been performed on populations of European ancestry and therefore benefits them more (Landry et al, 2018). Several projects around the world are working toward reducing inequities and improving health care for individuals affected with rare genetic diseases from diverse populations (Appelbaum et al, 2022; Caron et al, 2020).…”

Section: Introductionmentioning

confidence: 99%

The variant catalogue pipeline: A workflow to generate a background variant library from Whole Genome Sequences

Correard

Abdallah

Hewitson

et al. 2022

Preprint

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Today, several projects are working toward reducing inequities and improving health care for individuals affected with rare genetic diseases from diverse populations. One route to reduce inequities is to generate variant catalogues for diverse populations. To that end, we developed CAFE (Cohort Allele Frequency Estimation) pipeline, an open-source pipeline implemented in the NextFlow framework. CAFE pipeline includes detection of single nucleotide variants, small insertions and deletions, mitochondrial variants, structural variants, mobile element insertions, and short tandem repeats. Sample and variant quality control, allele frequency calculation (for whole and sex-stratified cohorts) and annotation steps are also included, delivering vcf files with annotated variants and their frequency in the cohort. Successful application of CAFE pipeline to 100 publicly available human genomes is described. We hope that, by making this pipeline available, more under-represented populations benefit from enhanced capacity to generate high-quality variant catalogues.

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Is there a way to reduce the inequity in variant interpretation on the basis of ancestry?

Cited by 23 publications

References 29 publications

Characterizing proteomic and transcriptomic features of missense variants in amyotrophic lateral sclerosis genes

Characterizing proteomic and transcriptomic features of missense variants in amyotrophic lateral sclerosis genes

Applications of artificial intelligence in clinical laboratory genomics

The variant catalogue pipeline: A workflow to generate a background variant library from Whole Genome Sequences

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