{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,12]],"date-time":"2026-10-12T14:04:33Z","timestamp":1791813873211,"version":"4.3.4"},"reference-count":41,"publisher":"Wiley","issue":"8","license":[{"start":{"date-parts":[[2026,10,12]],"date-time":"2026-10-12T00:00:00Z","timestamp":1791763200000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"},{"start":{"date-parts":[[2026,10,12]],"date-time":"2026-10-12T00:00:00Z","timestamp":1791763200000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/doi.wiley.com\/10.1002\/tdm_license_1.1"}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Genetic Epidemiology"],"published-print":{"date-parts":[[2026,12]]},"abstract":"<jats:title>ABSTRACT<\/jats:title>\n                  <jats:p>\n                    Imputation can cost\u2010effectively generate genotypes for millions of variants. This study evaluated the power of performing rare\u2010variant aggregate association tests using imputed genotypes. UK Biobank exome\u2010sequence data and variants obtained via imputation using the HRC (r1.1) (\n                    <jats:italic>N<\/jats:italic>\n                    \u2009=\u200932,488 samples) or TOPMed (r3) (\n                    <jats:italic>N<\/jats:italic>\n                    \u2009=\u2009133,597 samples) reference panels were analyzed. Simulations were used to compare the power of performing rare\u2010variant aggregate association analysis using either sequence or imputed data. The number of genes available for analysis, that is, those with \u2265\u20092 rare variants, was approximately the same for exome\u2010sequence and TOPMed imputed data, but HRC imputed data had ~10% fewer genes. For imputed data, using a less stringent R\n                    <jats:sup>2<\/jats:sup>\n                    threshold (i.e., &gt;\u20090.3 vs. &gt;\u20090.8) led to greater power to detect aggregate associations due to additional rare variants included in the analysis. Exome\u2010sequence data provided the highest power to detect rare\u2010variant aggregate associations. TOPMed imputed variants usually had &lt;\u200920% reduction in power in comparison to exome\u2010sequence data, whereas HRC imputed variants provided substantially less power. Rare\u2010variant aggregate association analyses using exome\u2010sequence data, and imputed variants for\n                    <jats:italic>PCSK9<\/jats:italic>\n                    and low\u2010density lipoprotein and\n                    <jats:italic>APOC3<\/jats:italic>\n                    and triglyceride levels were also performed. For these analyses, even when only ultra\u2010rare variants (minor allele frequency\u2009&lt;\u20090.001) were analyzed, significant aggregate associations could be detected for all data types. Although there is a decrease in power for rare\u2010variant aggregate association tests when analyzing imputed variants compared to sequence data due to missing variants and uncertainty in genotypes, analysis of imputed data is a viable approach to detect rare\u2010variant aggregate associations when sequence data are unavailable.\n                  <\/jats:p>","DOI":"10.1002\/gepi.70061","type":"journal-article","created":{"date-parts":[[2026,10,12]],"date-time":"2026-10-12T13:43:55Z","timestamp":1791812635000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Imputed Rare\u2010Variant Aggregate Analysis Is a Powerful Approach to Detect Genetic Associations"],"prefix":"10.1002","volume":"50","author":[{"ORCID":"https:\/\/orcid.org\/0009-0005-4283-9386","authenticated-orcid":false,"given":"Tianyi","family":"Liu","sequence":"first","affiliation":[{"name":"Center for Statistical Genetics, Gertrude H. Sergievsky Center, Department of Neurology Columbia University Medical Center New York New York USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1735-8044","authenticated-orcid":false,"given":"Paul L.","family":"Auer","sequence":"additional","affiliation":[{"name":"Division of Biostatistics, Data Science Institute, and Cancer Center Medical College of Wisconsin Milwaukee Wisconsin USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Gao T.","family":"Wang","sequence":"additional","affiliation":[{"name":"Center for Statistical Genetics, Gertrude H. Sergievsky Center, Department of Neurology Columbia University Medical Center New York New York USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1671-7471","authenticated-orcid":false,"given":"Elnaz","family":"Naderi","sequence":"additional","affiliation":[{"name":"Center for Statistical Genetics, Gertrude H. Sergievsky Center, Department of Neurology Columbia University Medical Center New York New York USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7679-8704","authenticated-orcid":false,"given":"Andrew T.","family":"DeWan","sequence":"additional","affiliation":[{"name":"Department of Chronic Disease Epidemiology and Center for Perinatal Pediatric and Environmental Epidemiology, Yale School of Public Health New Haven Connecticut USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1231-8174","authenticated-orcid":false,"given":"Suzanne M.","family":"Leal","sequence":"additional","affiliation":[{"name":"Center for Statistical Genetics, Gertrude H. Sergievsky Center, Department of Neurology Columbia University Medical Center New York New York USA"},{"name":"Taub Institute for Alzheimer's Disease and the Aging Brain Columbia University Medical Center New York New York USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2026,10,12]]},"reference":[{"key":"e_1_2_10_2_1","doi-asserted-by":"publisher","DOI":"10.1038\/nature11632"},{"key":"e_1_2_10_3_1","doi-asserted-by":"publisher","DOI":"10.1093\/bioinformatics\/btx299"},{"key":"e_1_2_10_4_1","doi-asserted-by":"publisher","DOI":"10.1002\/gepi.21736"},{"key":"e_1_2_10_5_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.ajhg.2025.07.002"},{"key":"e_1_2_10_6_1","doi-asserted-by":"publisher","DOI":"10.1038\/s41586\u2010018\u20100579\u2010z"},{"key":"e_1_2_10_7_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.ajhg.2024.03.011"},{"key":"e_1_2_10_8_1","doi-asserted-by":"publisher","DOI":"10.1038\/s41467\u2010022\u201030526\u2010x"},{"key":"e_1_2_10_9_1","doi-asserted-by":"publisher","DOI":"10.1038\/s41591\u2010024\u201003438\u20100"},{"key":"e_1_2_10_10_1","doi-asserted-by":"publisher","DOI":"10.1038\/s41431\u2010023\u201001302\u20102"},{"key":"e_1_2_10_11_1","doi-asserted-by":"publisher","DOI":"10.1093\/gigascience\/giab008"},{"key":"e_1_2_10_12_1","doi-asserted-by":"publisher","DOI":"10.1038\/nmeth.1785"},{"key":"e_1_2_10_13_1","doi-asserted-by":"publisher","DOI":"10.1093\/bioinformatics\/btu704"},{"key":"e_1_2_10_14_1","doi-asserted-by":"publisher","DOI":"10.1038\/s41588\u2010024\u201001930\u20104"},{"key":"e_1_2_10_15_1","doi-asserted-by":"publisher","DOI":"10.1038\/nature13997"},{"key":"e_1_2_10_16_1","doi-asserted-by":"publisher","DOI":"10.1038\/s41586\u2010020\u20102308\u20107"},{"key":"e_1_2_10_17_1","doi-asserted-by":"publisher","DOI":"10.1038\/s41586\u2010022\u201005473\u20108"},{"key":"e_1_2_10_18_1","doi-asserted-by":"publisher","DOI":"10.1093\/biostatistics\/kxs014"},{"key":"e_1_2_10_19_1","doi-asserted-by":"publisher","DOI":"10.1093\/ije\/dyt268"},{"key":"e_1_2_10_20_1","doi-asserted-by":"publisher","DOI":"10.1093\/bioinformatics\/btp324"},{"key":"e_1_2_10_21_1","doi-asserted-by":"crossref","unstructured":"Li J. 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