{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,23]],"date-time":"2026-06-23T23:45:14Z","timestamp":1782258314001,"version":"3.54.5"},"reference-count":65,"publisher":"Wiley","issue":"1","license":[{"start":{"date-parts":[[2024,3,18]],"date-time":"2024-03-18T00:00:00Z","timestamp":1710720000000},"content-version":"vor","delay-in-days":77,"URL":"http:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0\/"}],"funder":[{"DOI":"10.13039\/100000002","name":"National Institutes of Health","doi-asserted-by":"publisher","award":["K01 AG071689"],"award-info":[{"award-number":["K01 AG071689"]}],"id":[{"id":"10.13039\/100000002","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000002","name":"National Institutes of Health","doi-asserted-by":"publisher","award":["F99 AG079792"],"award-info":[{"award-number":["F99 AG079792"]}],"id":[{"id":"10.13039\/100000002","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["alz-journals.onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["A&amp;D Transl Res &amp; Clin Interv"],"published-print":{"date-parts":[[2024,1]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec>\n                    <jats:title>INTRODUCTION<\/jats:title>\n                    <jats:p>Alzheimer's disease (AD) is a complex disease influenced by genetics and environment. More than 75 susceptibility loci have been linked to late\u2010onset AD, but most of these loci were discovered in genome\u2010wide association studies (GWAS) exclusive to non\u2010Hispanic 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)\u2010based AD genetics and the statistical implications of current representation.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>METHODS<\/jats:title>\n                    <jats:p>We 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\u2010White populations.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>RESULTS<\/jats:title>\n                    <jats:p>There is insufficient statistical power (probability &lt;80%) to detect single nucleotide polymorphisms (SNPs) with low to moderate effect sizes (odds ratio [OR]&lt;1.5) using array data from Black and Hispanic participants; studies of Asian participants are not powered to detect variants OR &lt;= 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.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>DISCUSSION<\/jats:title>\n                    <jats:p>AD genetics datasets are largely representative of US\u00a0ADRD\u00a0burden. However,\u00a0there is a wide discrepancy between proportional representation and statistically meaningful representation.\u00a0Most variation identified in GWAS of non\u2010Hispanic White individuals have low to moderate effects. Comparable risk variants in non\u2010White 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\u2010based participatory research practices.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1002\/trc2.12462","type":"journal-article","created":{"date-parts":[[2024,3,18]],"date-time":"2024-03-18T06:29:56Z","timestamp":1710743396000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":11,"title":["The power of representation: Statistical analysis of diversity in US Alzheimer's disease genetics data"],"prefix":"10.1002","volume":"10","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-7633-2253","authenticated-orcid":false,"given":"Diane","family":"Xue","sequence":"first","affiliation":[{"name":"Institute for Public Health Genetics University of Washington School of Public Health  Seattle Washington USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Elizabeth E.","family":"Blue","sequence":"additional","affiliation":[{"name":"Institute for Public Health Genetics University of Washington School of Public Health  Seattle Washington USA"},{"name":"Division of Medical Genetics Department of Medicine University of Washington  Seattle Washington USA"},{"name":"Brotman Baty Institute Seattle Washington USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Matthew P.","family":"Conomos","sequence":"additional","affiliation":[{"name":"Department of Biostatistics University of Washington School of Public Health  Seattle Washington USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Alison E.","family":"Fohner","sequence":"additional","affiliation":[{"name":"Institute for Public Health Genetics University of Washington School of Public Health  Seattle Washington USA"},{"name":"Department of Epidemiology University of Washington School of Public Health  Seattle Washington USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2024,3,18]]},"reference":[{"key":"e_1_2_8_2_1","doi-asserted-by":"publisher","DOI":"10.1002\/alz.13016"},{"key":"e_1_2_8_3_1","doi-asserted-by":"publisher","DOI":"10.1001\/jama.2022.3550"},{"key":"e_1_2_8_4_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.arcmed.2012.11.003"},{"key":"e_1_2_8_5_1","doi-asserted-by":"publisher","DOI":"10.1002\/alz.13389"},{"key":"e_1_2_8_6_1","doi-asserted-by":"publisher","DOI":"10.1002\/alz.13027"},{"key":"e_1_2_8_7_1","doi-asserted-by":"publisher","DOI":"10.1001\/archpsyc.63.2.168"},{"key":"e_1_2_8_8_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.ebiom.2023.104511"},{"key":"e_1_2_8_9_1","doi-asserted-by":"publisher","DOI":"10.1007\/s13311\u2010021\u201001152\u20100"},{"key":"e_1_2_8_10_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.jalz.2019.07.016"},{"key":"e_1_2_8_11_1","doi-asserted-by":"publisher","DOI":"10.1016\/J.BIOPSYCH.2013.06.003"},{"key":"e_1_2_8_12_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.xgen.2022.100155"},{"key":"e_1_2_8_13_1","doi-asserted-by":"publisher","DOI":"10.1146\/annurev.genom.4.070802.110226"},{"key":"e_1_2_8_14_1","doi-asserted-by":"publisher","DOI":"10.1038\/ng.139"},{"key":"e_1_2_8_15_1","doi-asserted-by":"publisher","DOI":"10.1186\/1471\u20102156\u20109\u201047"},{"key":"e_1_2_8_16_1","doi-asserted-by":"publisher","DOI":"10.1038\/s41588\u2010021\u201000921\u2010z"},{"key":"e_1_2_8_17_1","doi-asserted-by":"publisher","DOI":"10.1001\/jamaneurol.2020.3536"},{"key":"e_1_2_8_18_1","doi-asserted-by":"publisher","DOI":"10.1038\/s41588\u2010019\u20100358\u20102"},{"key":"e_1_2_8_19_1","doi-asserted-by":"publisher","DOI":"10.1038\/s41588\u2010022\u201001024\u2010z"},{"key":"e_1_2_8_20_1","unstructured":"National Academies of Sciences and Medicine E.Using population descriptors in genetics and genomics research: a new framework for an evolving field. 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