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Record W3128316783 · doi:10.3389/fgene.2020.629373

Recent Consanguinity and Outbred Autozygosity Are Associated With Increased Risk of Late-Onset Alzheimer’s Disease

2021· article· en· W3128316783 on OpenAlexfundaboutno aff
Valerio Napolioni, Marzia A. Scelsi, Raiyan R. Khan, André Altmann, Michael D. Greicius

Bibliographic record

VenueFrontiers in Genetics · 2021
Typearticle
Languageen
FieldMedicine
TopicHemoglobinopathies and Related Disorders
Canadian institutionsnot available
FundersNational Institute on AgingEngineering and Physical Sciences Research CouncilNational Institute of Biomedical Imaging and BioengineeringCanadian Institutes of Health ResearchNational Institutes of HealthIXICOServierEisaiNorthern California Institute for Research and EducationIllinois Department of Public HealthRush UniversityGenentechNational Alzheimer's Coordinating CenterPfizerBiogenBioClinicaF. Hoffmann-La RocheUniversity of Southern CaliforniaEuropean Federation of Pharmaceutical Industries and AssociationsTranslational Genomics Research InstituteNovartis Pharmaceuticals CorporationU.S. Department of DefenseEli Lilly and CompanyBristol-Myers SquibbH. Lundbeck A/SMcKnight FoundationAlzheimer's AssociationFoundation for the National Institutes of HealthEuropean CommissionAlzheimer's Disease Neuroimaging InitiativeMedical Research CouncilMeso Scale Diagnostics
KeywordsConsanguinityInbreedingRuns of HomozygosityDemographyPopulationEthnic groupGenome-wide association studyMedicineBiologyGeneticsPediatricsGenotypeSingle-nucleotide polymorphismPolitical science

Abstract

fetched live from OpenAlex

Prior work in late-onset Alzheimer’s disease (LOAD) has resulted in discrepant findings as to whether recent consanguinity and outbred autozygosity are associated with LOAD risk. In the current study, we tested the association between consanguinity and outbred autozygosity with LOAD in the largest such analysis to date, in which 20 LOAD GWAS datasets were retrieved through public databases. Our analyses were restricted to eight distinct ethnic groups: African–Caribbean, Ashkenazi–Jewish European, European–Caribbean, French–Canadian, Finnish European, North-Western European, South-Eastern European, and Yoruba African for a total of 21,492 unrelated subjects (11,196 LOAD and 10,296 controls). Recent consanguinity determination was performed using FSuite v1.0.3, according to subjects’ ancestral background. The level of autozygosity in the outbred population was assessed by calculating inbreeding estimates based on the proportion (F ROH ) and the number (N ROH ) of runs of homozygosity (ROHs). We analyzed all eight ethnic groups using a fixed-effect meta-analysis, which showed a significant association of recent consanguinity with LOAD ( N = 21,481; OR = 1.262, P = 3.6 × 10 –4 ), independently of APOE ∗ 4 ( N = 21,468, OR = 1.237, P = 0.002), and years of education ( N = 9,257; OR = 1.274, P = 0.020). Autozygosity in the outbred population was also associated with an increased risk of LOAD, both for F ROH ( N = 20,237; OR = 1.204, P = 0.030) and N ROH metrics ( N = 20,237; OR = 1.019, P = 0.006), independently of APOE ∗ 4 [( F ROH , N = 20,225; OR = 1.222, P = 0.029) ( N ROH , N = 20,225; OR = 1.019, P = 0.007)]. By leveraging the Alzheimer’s Disease Sequencing Project (ADSP) whole-exome sequencing (WES) data, we determined that LOAD subjects do not show an enrichment of rare, risk-enhancing minor homozygote variants compared to the control population. A two-stage recessive GWAS using ADSP data from 201 consanguineous subjects in the discovery phase followed by validation in 10,469 subjects led to the identification of RPH3AL p.A303V (rs117190076) as a rare minor homozygote variant increasing the risk of LOAD [discovery: Genotype Relative Risk (GRR) = 46, P = 2.16 × 10 –6 ; validation: GRR = 1.9, P = 8.0 × 10 –4 ]. These results confirm that recent consanguinity and autozygosity in the outbred population increase risk for LOAD. Subsequent work, with increased samples sizes of consanguineous subjects, should accelerate the discovery of non-additive genetic effects in LOAD.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.017
Threshold uncertainty score0.572

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.012
GPT teacher head0.229
Teacher spread0.217 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations8
Published2021
Admission routes2
Has abstractyes

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