Evidence map›Paper›PMID 38500778›Full record

ArticleAlzheimer's & dementia (New York, N. Y.)

The power of representation: Statistical analysis of diversity in US Alzheimer's disease genetics data.

Diane Xue, Elizabeth E Blue, Matthew P Conomos, Alison E Fohner

Open access · goldAbstract read
In one paragraph

Article in Alzheimer's & dementia (New York, N. Y.). The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
12citing papers in PubMed, 1 pooled it
5.0field-weighted citation impact, top 5% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

12 citing papers in PubMed, 1 synthesis or guideline pooled it, 9 citations in OpenAlex.

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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

4 authors at 2 institutions in 1 country.

Diane XueInstitute for Public Health Genetics University of Washington School of Public Health Seattle Washington USA.ORCID https://orcid.org/0000-0001-7633-2253
Elizabeth E BlueInstitute for Public Health Genetics University of Washington School of Public Health Seattle Washington USA.
Matthew P ConomosDepartment of Biostatistics University of Washington School of Public Health Seattle Washington USA.
Alison E FohnerInstitute for Public Health Genetics University of Washington School of Public Health Seattle Washington USA.
Washington State Department of Health · USUniversity of Washington · US

Funding

Integrated multi-omics approach to identify early protein biomarkers in Alzheimer's disease and cognitive declineK01AG071689 · NIA · UNIVERSITY OF WASHINGTON · PI FOHNER, ALISON ELIZABETH · 2022 to 2025
$488k
Integrating Polygenic Risk and Environmental Exposures to Uncover Biological Mechanisms Underlying Dementia in a Diverse CohortF99AG079792 · NIA · UNIVERSITY OF WASHINGTON · PI XUE, DIANE · 2022 to 2024
$92k
NIA NIH HHS F99 AG079792NIA NIH HHS K01 AG071689
6 · The paper itself

Abstract

introductionAlzheimer's disease (AD) is a complex disease influenced by genetics and environment. More than 75 susceptibility loci have been linked to late-onset AD, but most of these loci were discovered in genome-wide association studies (GWAS) exclusive to non-Hispanic White individuals. There are wide disparities in AD risk across racially stratified groups, and while these disparities are not due to genetic differences, underrepresentation in genetic research can further exacerbate and contribute to their persistence. We investigated the racial/ethnic representation of participants in United States (US)-based AD genetics and the statistical implications of current representation.

methodsWe compared racial/ethnic data of participants from array and sequencing studies in US AD genetics databases, including National Institute on Aging Genetics of Alzheimer's Disease Data Storage Site (NIAGADS) and NIAGADS Data Sharing Service (dssNIAGADS), to AD and related dementia (ADRD) prevalence and mortality. We then simulated the statistical power of these datasets to identify risk variants from non-White populations.

resultsThere is insufficient statistical power (probability <80%) to detect single nucleotide polymorphisms (SNPs) with low to moderate effect sizes (odds ratio [OR]<1.5) using array data from Black and Hispanic participants; studies of Asian participants are not powered to detect variants OR <= 2. Using available and projected sequencing data from Black and Hispanic participants, risk variants with OR = 1.2 are detectable at high allele frequencies. Sample sizes remain insufficiently powered to detect these variants in Asian populations. DISCUSSION: AD genetics datasets are largely representative of US ADRD burden. However, there is a wide discrepancy between proportional representation and statistically meaningful representation. Most variation identified in GWAS of non-Hispanic White individuals have low to moderate effects. Comparable risk variants in non-White populations are not detectable given current sample sizes, which could lead to disparities in future studies and drug development. We urge AD genetics researchers and institutions to continue investing in recruiting diverse participants and use community-based participatory research practices.

Indexed as

Alzheimer's diseaseancestrycommunity‐based participatory researchdiversityethnicitygeneticsracerepresentationstatistical power

Identifiers

PMID38500778
PMCPMC10945594
OpenAlexW4392931332

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
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Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.