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Biol."],"published-print":{"date-parts":[[2021,6]]},"abstract":"<jats:sec><jats:title>Background<\/jats:title><jats:p>Genome wide association studies (GWAS) have identified many genetic variants associated with increased risk of Alzheimer\u2019s disease (AD). These susceptibility loci may effect AD indirectly through a combination of physiological brain changes. Many of these neuropathologic features are detectable via magnetic resonance imaging (MRI).<\/jats:p><\/jats:sec><jats:sec><jats:title>Methods<\/jats:title><jats:p>In this study, we examine the effects of such brain imaging derived phenotypes (IDPs) with genetic etiology on AD, using and comparing the following methods: two\u2010sample Mendelian randomization (2SMR), generalized summary statistics based Mendelian randomization (GSMR), transcriptome wide association studies (TWAS) and the adaptive sum of powered score (aSPU) test. These methods do not require individual\u2010level genotypic and phenotypic data but instead can rely only on an external reference panel and GWAS summary statistics.<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>Using publicly available GWAS datasets from the International Genomics of Alzheimer\u2019s Project (IGAP) and UK Biobank\u2019s (UKBB) brain imaging initiatives, we identify 35 IDPs possibly associated with AD, many of which have well established or biologically plausible links to the characteristic cognitive impairments of this neurodegenerative disease.<\/jats:p><\/jats:sec><jats:sec><jats:title>Conclusions<\/jats:title><jats:p>Our results highlight the increased power for detecting genetic associations achieved by multiple correlated SNP\u2010based methods, <jats:italic>i.e<\/jats:italic>., aSPU, GSMR and TWAS, over MR methods based on independent SNPs (as instrumental variables).<\/jats:p><\/jats:sec><jats:sec><jats:title>Availability<\/jats:title><jats:p>Example code is available at <jats:ext-link xmlns:xlink=\"http:\/\/www.w3.org\/1999\/xlink\" xlink:href=\"https:\/\/github.com\/kathalexknuts\/ADIDP\">https:\/\/github.com\/kathalexknuts\/ADIDP<\/jats:ext-link>.<\/jats:p><\/jats:sec>","DOI":"10.1007\/s40484-020-0202-9","type":"journal-article","created":{"date-parts":[[2020,8,6]],"date-time":"2020-08-06T10:03:04Z","timestamp":1596708184000},"page":"185-200","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":8,"title":["Integrating brain imaging endophenotypes with GWAS for Alzheimer\u2019s disease"],"prefix":"10.1002","volume":"9","author":[{"given":"Katherine A.","family":"Knutson","sequence":"first","affiliation":[{"name":"Division of Biostatistics University of Minnesota Minneapolis MN 55455 USA"}]},{"given":"Wei","family":"Pan","sequence":"additional","affiliation":[{"name":"Division of Biostatistics University of Minnesota Minneapolis MN 55455 USA"}]}],"member":"311","published-online":{"date-parts":[[2021,6]]},"reference":[{"key":"e_1_2_7_2_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.jalz.2016.03.001"},{"key":"e_1_2_7_3_2","doi-asserted-by":"publisher","DOI":"10.1038\/nrneurol.2009.215"},{"key":"e_1_2_7_4_2","doi-asserted-by":"publisher","DOI":"10.1073\/pnas.0308627101"},{"key":"e_1_2_7_5_2","doi-asserted-by":"publisher","DOI":"10.1093\/brain\/awp071"},{"key":"e_1_2_7_6_2","doi-asserted-by":"publisher","DOI":"10.1038\/ng.2802"},{"key":"e_1_2_7_7_2","doi-asserted-by":"publisher","DOI":"10.1093\/aje\/kwt084"},{"key":"e_1_2_7_8_2","doi-asserted-by":"publisher","DOI":"10.1038\/s41467\u2010017\u201002317\u20102"},{"key":"e_1_2_7_9_2","doi-asserted-by":"publisher","DOI":"10.1038\/ng.3367"},{"key":"e_1_2_7_10_2","doi-asserted-by":"publisher","DOI":"10.1038\/ng.3506"},{"key":"e_1_2_7_11_2","doi-asserted-by":"publisher","DOI":"10.1534\/genetics.117.300270"},{"key":"e_1_2_7_12_2","doi-asserted-by":"publisher","DOI":"10.1093\/bioinformatics\/bty865"},{"key":"e_1_2_7_13_2","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pgen.1007889"},{"key":"e_1_2_7_14_2","doi-asserted-by":"publisher","DOI":"10.1038\/s41588\u2010019\u20100345\u20107"},{"key":"e_1_2_7_15_2","doi-asserted-by":"publisher","DOI":"10.1093\/bioinformatics\/btz880"},{"key":"e_1_2_7_16_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.neuroimage.2017.07.036"},{"key":"e_1_2_7_17_2","doi-asserted-by":"publisher","DOI":"10.1038\/s41588\u2010019\u20100516\u20106"},{"key":"e_1_2_7_18_2","doi-asserted-by":"crossref","unstructured":"Zhao B. Shan Y. Yang Y. Li T. Luo T. Zhu Z. Li Y.andZhu H.(2019b)Transcriptome\u2010wide association analysis of 211 neuroimaging traits identifies new genes for brain structures and yields insights into the gene\u2010level pleiotropy with other complex traits.bioRxiv 842872","DOI":"10.1101\/842872"},{"key":"e_1_2_7_19_2","doi-asserted-by":"publisher","DOI":"10.1038\/s41588-019-0367-1"},{"key":"e_1_2_7_20_2","doi-asserted-by":"publisher","DOI":"10.1038\/s41588\u2010019\u20100385\u2010z"},{"key":"e_1_2_7_21_2","doi-asserted-by":"publisher","DOI":"10.1002\/gepi.20402"},{"key":"e_1_2_7_22_2","doi-asserted-by":"publisher","DOI":"10.1534\/genetics.114.165035"},{"key":"e_1_2_7_23_2","doi-asserted-by":"publisher","DOI":"10.1093\/bioinformatics\/btv719"},{"key":"e_1_2_7_24_2","doi-asserted-by":"crossref","unstructured":"Yan D. HuB. Darst B. Mukherjee S. Kunkle B. Deming Y. Dumitrescu L. Wang Y. Naj A. KuzmaA. et al. 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Knutson and Wei Pan declare that they have no conflict of interests.","order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Compliance with Ethics Guidelines"}},{"value":"All procedures performed in studies were in accordance with the ethical standards of the institution.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Compliance with Ethics Guidelines"}},{"value":"This content has been made available to all.","name":"free","label":"Free to read"}]}}