<HashMap><database>biostudies-literature</database><scores><citationCount>0</citationCount><reanalysisCount>0</reanalysisCount><viewCount>46</viewCount><searchCount>0</searchCount></scores><additional><submitter>Cruchaga C</submitter><funding>NIA NIH HHS</funding><pagination>23-29</pagination><full_dataset_link>https://www.ebi.ac.uk/biostudies/studies/S-EPMC3979575</full_dataset_link><repository>biostudies-literature</repository><omics_type>Unknown</omics_type><volume>2(1)</volume><pubmed_abstract>The use of cerebrospinal fluid levels of Aβ42 and pTau181 as endophenotypes for genetic studies of Alzheimer's disease (AD) has led to successful identification of both rare and common AD risk variants. In addition, this approach has provided meaningful hypotheses for the biological mechanisms by which known AD risk variants modulate the disease process. In this article we discuss these successes and outline challenges to effective and continued applications of this approach. We contrast the statistical power of this approach with traditional case-control designs and discuss solutions to address challenges in quality control and data analysis for these phenotypes. Finally, we discuss the potential for the use of this approach with larger samples as well as the incorporation of next generation sequencing and for future work with other endophenotypes for AD.</pubmed_abstract><journal>Current genetic medicine reports</journal><pubmed_title>Genetic discoveries in AD using CSF amyloid and tau.</pubmed_title><pmcid>PMC3979575</pmcid><funding_grant_id>RF1 AG044546</funding_grant_id><funding_grant_id>R01 AG044546</funding_grant_id><funding_grant_id>R01 AG042611</funding_grant_id><pubmed_authors>Cruchaga C</pubmed_authors><pubmed_authors>Ebbert MT</pubmed_authors><pubmed_authors>Kauwe JS</pubmed_authors><view_count>46</view_count></additional><is_claimable>false</is_claimable><name>Genetic discoveries in AD using CSF amyloid and tau.</name><description>The use of cerebrospinal fluid levels of Aβ42 and pTau181 as endophenotypes for genetic studies of Alzheimer's disease (AD) has led to successful identification of both rare and common AD risk variants. In addition, this approach has provided meaningful hypotheses for the biological mechanisms by which known AD risk variants modulate the disease process. In this article we discuss these successes and outline challenges to effective and continued applications of this approach. We contrast the statistical power of this approach with traditional case-control designs and discuss solutions to address challenges in quality control and data analysis for these phenotypes. Finally, we discuss the potential for the use of this approach with larger samples as well as the incorporation of next generation sequencing and for future work with other endophenotypes for AD.</description><dates><release>2014-01-01T00:00:00Z</release><publication>2014 Mar</publication><modification>2024-12-03T21:05:28.463Z</modification><creation>2019-03-27T01:24:37Z</creation></dates><accession>S-EPMC3979575</accession><cross_references><pubmed>24729949</pubmed><doi>10.1007/s40142-014-0031-0</doi></cross_references></HashMap>