Literature DB >> 35399757

Integrating brain imaging endophenotypes with GWAS for Alzheimer's disease.

Katherine A Knutson1, Wei Pan1.   

Abstract

Background: Genome wide association studies (GWAS) have identified many genetic variants associated with increased risk of Alzheimer's 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).
Methods: 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-sample 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-level genotypic and phenotypic data but instead can rely only on an external reference panel and GWAS summary statistics.
Results: Using publicly available GWAS datasets from the International Genomics of Alzheimer's Project (IGAP) and UK Biobank's (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. Conclusions: Our results highlight the increased power for detecting genetic associations achieved by multiple correlated SNP-based methods, i.e., aSPU, GSMR and TWAS, over MR methods based on independent SNPs (as instrumental variables).

Entities:  

Keywords:  MRI; Mendelian randomization; SPU tests; Sum test; TWAS; aSPU test

Year:  2021        PMID: 35399757      PMCID: PMC8993183          DOI: 10.1007/s40484-020-0202-9

Source DB:  PubMed          Journal:  Quant Biol        ISSN: 2095-4689


  31 in total

1.  Accelerating Structural Degeneration in Temporal Regions and Their Effects on Cognition in Aging of MCI Patients.

Authors:  Xin Li; Jianan Xia; Chao Ma; Kewei Chen; Kai Xu; Junying Zhang; Yaojing Chen; He Li; Dongfeng Wei; Zhanjun Zhang
Journal:  Cereb Cortex       Date:  2020-01-10       Impact factor: 5.357

Review 2.  Opportunities and challenges for transcriptome-wide association studies.

Authors:  Michael Wainberg; Nasa Sinnott-Armstrong; Nicholas Mancuso; Alvaro N Barbeira; David A Knowles; David Golan; Raili Ermel; Arno Ruusalepp; Thomas Quertermous; Ke Hao; Johan L M Björkegren; Hae Kyung Im; Bogdan Pasaniuc; Manuel A Rivas; Anshul Kundaje
Journal:  Nat Genet       Date:  2019-03-29       Impact factor: 38.330

3.  A Powerful Framework for Integrating eQTL and GWAS Summary Data.

Authors:  Zhiyuan Xu; Chong Wu; Peng Wei; Wei Pan
Journal:  Genetics       Date:  2017-09-11       Impact factor: 4.562

4.  Testing the white matter retrogenesis hypothesis of cognitive aging.

Authors:  Adam M Brickman; Irene B Meier; Mayuresh S Korgaonkar; Frank A Provenzano; Stuart M Grieve; Karen L Siedlecki; Ben T Wasserman; Leanne M Williams; Molly E Zimmerman
Journal:  Neurobiol Aging       Date:  2011-07-23       Impact factor: 4.673

5.  Asymptotic tests of association with multiple SNPs in linkage disequilibrium.

Authors:  Wei Pan
Journal:  Genet Epidemiol       Date:  2009-09       Impact factor: 2.135

6.  Impaired White Matter Connections of the Limbic System Networks Associated with Impaired Emotional Memory in Alzheimer's Disease.

Authors:  Xiaoshu Li; Haibao Wang; Yanghua Tian; Shanshan Zhou; Xiaohu Li; Kai Wang; Yongqiang Yu
Journal:  Front Aging Neurosci       Date:  2016-10-27       Impact factor: 5.750

7.  A statistical framework for cross-tissue transcriptome-wide association analysis.

Authors:  Yiming Hu; Mo Li; Qiongshi Lu; Haoyi Weng; Jiawei Wang; Seyedeh M Zekavat; Zhaolong Yu; Boyang Li; Jianlei Gu; Sydney Muchnik; Yu Shi; Brian W Kunkle; Shubhabrata Mukherjee; Pradeep Natarajan; Adam Naj; Amanda Kuzma; Yi Zhao; Paul K Crane; Hui Lu; Hongyu Zhao
Journal:  Nat Genet       Date:  2019-02-25       Impact factor: 38.330

8.  Causal associations between risk factors and common diseases inferred from GWAS summary data.

Authors:  Zhihong Zhu; Zhili Zheng; Futao Zhang; Yang Wu; Maciej Trzaskowski; Robert Maier; Matthew R Robinson; John J McGrath; Peter M Visscher; Naomi R Wray; Jian Yang
Journal:  Nat Commun       Date:  2018-01-15       Impact factor: 14.919

9.  Integrating predicted transcriptome from multiple tissues improves association detection.

Authors:  Alvaro N Barbeira; Milton Pividori; Jiamao Zheng; Heather E Wheeler; Dan L Nicolae; Hae Kyung Im
Journal:  PLoS Genet       Date:  2019-01-22       Impact factor: 5.917

10.  Genome-wide association analysis of 19,629 individuals identifies variants influencing regional brain volumes and refines their genetic co-architecture with cognitive and mental health traits.

Authors:  Bingxin Zhao; Tianyou Luo; Tengfei Li; Yun Li; Jingwen Zhang; Yue Shan; Xifeng Wang; Liuqing Yang; Fan Zhou; Ziliang Zhu; Hongtu Zhu
Journal:  Nat Genet       Date:  2019-11-01       Impact factor: 38.330

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