Commonly used genomic arrays may lose information due to imperfect coverage of discovered variants for autism spectrum disorder.

Humans Autism Spectrum Disorder / genetics Genome-Wide Association Study Multifactorial Inheritance Genetic Predisposition to Disease Male Female Genotype Polymorphism, Single Nucleotide
Autism spectrum disorder (ASD) Information Loss Polygenic scores (PGS)

Journal

Journal of neurodevelopmental disorders
ISSN: 1866-1955
Titre abrégé: J Neurodev Disord
Pays: England
ID NLM: 101483832

Informations de publication

Date de publication:
12 Sep 2024
Historique:
received: 26 09 2023
accepted: 29 08 2024
medline: 13 9 2024
pubmed: 13 9 2024
entrez: 12 9 2024
Statut: epublish

Résumé

Common genetic variation has been shown to account for a large proportion of ASD heritability. Polygenic scores generated for autism spectrum disorder (ASD-PGS) using the most recent discovery data, however, explain less variance than expected, despite reporting significant associations with ASD and other ASD-related traits. Here, we investigate the extent to which information loss on the target study genome-wide microarray weakens the predictive power of the ASD-PGS. We studied genotype data from three cohorts of individuals with high familial liability for ASD: The Early Autism Risk Longitudinal Investigation (EARLI), Markers of Autism Risk in Babies-Learning Early Signs (MARBLES), and the Infant Brain Imaging Study (IBIS), and one population-based sample, Study to Explore Early Development Phase I (SEED I). Individuals were genotyped on different microarrays ranging from 1 to 5 million sites. Coverage of the top 88 genome-wide suggestive variants implicated in the discovery was evaluated in all four studies before quality control (QC), after QC, and after imputation. We then created a novel method to assess coverage on the resulting ASD-PGS by correlating a PGS informed by a comprehensive list of variants to a PGS informed with only the available variants. Prior to imputations, None of the four cohorts directly or indirectly covered all 88 variants among the measured genotype data. After imputation, the two cohorts genotyped on 5-million arrays reached full coverage. Analysis of our novel metric showed generally high genome-wide coverage across all four studies, but a greater number of SNPs informing the ASD-PGS did not result in improved coverage according to our metric. The studies we analyzed contained modest sample sizes. Our analyses included microarrays with more than 1-million sites, so smaller arrays such as Global Diversity and the PsychArray were not included. Our PGS metric for ASD is only generalizable to samples of European ancestries, though the coverage metric can be computed for traits that have sufficiently large-sized discovery findings in other ancestries. We show that commonly used genotyping microarrays have incomplete coverage for common ASD variants, and imputation cannot always recover lost information. Our novel metric provides an intuitive approach to reporting information loss in PGS and an alternative to reporting the total number of SNPs included in the PGS. While applied only to ASD here, this metric can easily be used with other traits.

Sections du résumé

BACKGROUND BACKGROUND
Common genetic variation has been shown to account for a large proportion of ASD heritability. Polygenic scores generated for autism spectrum disorder (ASD-PGS) using the most recent discovery data, however, explain less variance than expected, despite reporting significant associations with ASD and other ASD-related traits. Here, we investigate the extent to which information loss on the target study genome-wide microarray weakens the predictive power of the ASD-PGS.
METHODS METHODS
We studied genotype data from three cohorts of individuals with high familial liability for ASD: The Early Autism Risk Longitudinal Investigation (EARLI), Markers of Autism Risk in Babies-Learning Early Signs (MARBLES), and the Infant Brain Imaging Study (IBIS), and one population-based sample, Study to Explore Early Development Phase I (SEED I). Individuals were genotyped on different microarrays ranging from 1 to 5 million sites. Coverage of the top 88 genome-wide suggestive variants implicated in the discovery was evaluated in all four studies before quality control (QC), after QC, and after imputation. We then created a novel method to assess coverage on the resulting ASD-PGS by correlating a PGS informed by a comprehensive list of variants to a PGS informed with only the available variants.
RESULTS RESULTS
Prior to imputations, None of the four cohorts directly or indirectly covered all 88 variants among the measured genotype data. After imputation, the two cohorts genotyped on 5-million arrays reached full coverage. Analysis of our novel metric showed generally high genome-wide coverage across all four studies, but a greater number of SNPs informing the ASD-PGS did not result in improved coverage according to our metric.
LIMITATIONS CONCLUSIONS
The studies we analyzed contained modest sample sizes. Our analyses included microarrays with more than 1-million sites, so smaller arrays such as Global Diversity and the PsychArray were not included. Our PGS metric for ASD is only generalizable to samples of European ancestries, though the coverage metric can be computed for traits that have sufficiently large-sized discovery findings in other ancestries.
CONCLUSIONS CONCLUSIONS
We show that commonly used genotyping microarrays have incomplete coverage for common ASD variants, and imputation cannot always recover lost information. Our novel metric provides an intuitive approach to reporting information loss in PGS and an alternative to reporting the total number of SNPs included in the PGS. While applied only to ASD here, this metric can easily be used with other traits.

Identifiants

DOI: 10.1186/s11689-024-09571-8 PMID: 39266988
pubmed: 39266988
doi: 10.1186/s11689-024-09571-8
pii: 10.1186/s11689-024-09571-8
doi:

Types de publication

Journal Article

Langues

eng

Sous-ensembles de citation

IM

Pagination

54

Informations de copyright

© 2024. The Author(s).

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Auteurs

Michael Yao (M)

Mental Health, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA.

Jason Daniels (J)

Mental Health, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA.
Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA.

Luke Grosvenor (L)

Mental Health, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA.
Wendy Klag Center for Autism and Developmental Disabilities, JHSPH, Baltimore, MD, USA.

Valerie Morrill (V)

Mental Health, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA.
Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA.

Jason I Feinberg (JI)

Mental Health, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA.
Wendy Klag Center for Autism and Developmental Disabilities, JHSPH, Baltimore, MD, USA.

Kelly M Bakulski (KM)

Department of Epidemiology, School of Public Health, University of Michigan, Ann Arbor, MI, USA.

Joseph Piven (J)

Department of Psychiatry, University of North Carolina, North Carolina, Chapel Hill, 27599, USA.
Carolina Institute for Developmental Disabilities, Chapel Hill, NC, 27599, USA.

Heather C Hazlett (HC)

Department of Psychiatry, University of North Carolina, North Carolina, Chapel Hill, 27599, USA.
Carolina Institute for Developmental Disabilities, Chapel Hill, NC, 27599, USA.

Mark D Shen (MD)

Department of Psychiatry, University of North Carolina, North Carolina, Chapel Hill, 27599, USA.
Carolina Institute for Developmental Disabilities, Chapel Hill, NC, 27599, USA.

Craig Newschaffer (C)

7AJ Drexel Autism Institute, Drexel University, 3020 Market St, Suite 560, Philadelphia, PA, 19104, USA.
College of Health and Human Development, Penn State, University Park, PA, 16802, USA.

Kristen Lyall (K)

7AJ Drexel Autism Institute, Drexel University, 3020 Market St, Suite 560, Philadelphia, PA, 19104, USA.

Rebecca J Schmidt (RJ)

Department of Public Health Sciences, University of California, Davis, CA, 95616, USA.
UC Davis MIND (Medical Investigations of Neurodevelopmental Disorders) Institute, Sacramento, CA, 95817, USA.

Irva Hertz-Picciotto (I)

Department of Public Health Sciences, University of California, Davis, CA, 95616, USA.
UC Davis MIND (Medical Investigations of Neurodevelopmental Disorders) Institute, Sacramento, CA, 95817, USA.

Lisa A Croen (LA)

Autism Research Program, Kaiser Permanente Division of Research, 2000 Broadway, Oakland, CA, 94612, USA.

M Daniele Fallin (MD)

Mental Health, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA.
Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA.
Wendy Klag Center for Autism and Developmental Disabilities, JHSPH, Baltimore, MD, USA.
Rollins School of Public Health, Emory University, 1518 Clifton Rd, Suite 8011, Atlanta, GA, 30355, USA.

Christine Ladd-Acosta (C)

Mental Health, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA.
Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA.
Wendy Klag Center for Autism and Developmental Disabilities, JHSPH, Baltimore, MD, USA.

Heather Volk (H)

Mental Health, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA.
Wendy Klag Center for Autism and Developmental Disabilities, JHSPH, Baltimore, MD, USA.

Kelly Benke (K)

Mental Health, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA. kbenke1@jhu.edu.
Wendy Klag Center for Autism and Developmental Disabilities, JHSPH, Baltimore, MD, USA. kbenke1@jhu.edu.
ORCID: 0000-0002-5628-495X

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Classifications MeSH

questionsmedicales.fr › Eucaryotes › Animaux › Chordés › Vertébrés › Mammifères › Eutheria › Primates › Haplorhini › Catarrhini › Hominidae › Humains › Commonly used genomic arrays may lose...
questionsmedicales.fr › Troubles mentaux › Troubles du développement neurologique › Troubles généralisés du développement de l'enfant › Trouble du spectre autistique › Commonly used genomic arrays may lose...
questionsmedicales.fr › Environnement et santé publique › Santé publique › Méthodes épidémiologiques › Épidémiologie moléculaire › Étude d'association pangénomique › Commonly used genomic arrays may lose...
questionsmedicales.fr › Phénomènes génétiques › Modes de transmission héréditaire › Hérédité multifactorielle › Commonly used genomic arrays may lose...
questionsmedicales.fr › Phénomènes génétiques › Génotype › Prédisposition génétique à une maladie › Commonly used genomic arrays may lose...
questionsmedicales.fr › Phénomènes génétiques › Génotype › Commonly used genomic arrays may lose...
questionsmedicales.fr › Phénomènes génétiques › Variation génétique › Polymorphisme génétique › Polymorphisme de nucléotide simple › Commonly used genomic arrays may lose...