TY - JOUR
T1 - Genetic Control of Human Brain Transcript Expression in Alzheimer Disease
AU - Webster, Jennifer A.
AU - Gibbs, J. Raphael
AU - Clarke, Jennifer
AU - Ray, Monika
AU - Zhang, Weixiong
AU - Holmans, Peter
AU - Rohrer, Kristen
AU - Zhao, Alice
AU - Marlowe, Lauren
AU - Kaleem, Mona
AU - McCorquodale, Donald S.
AU - Cuello, Cindy
AU - Leung, Doris
AU - Bryden, Leslie
AU - Nath, Priti
AU - Zismann, Victoria L.
AU - Joshipura, Keta
AU - Huentelman, Matthew J.
AU - Hu-Lince, Diane
AU - Coon, Keith D.
AU - Craig, David W.
AU - Pearson, John V.
AU - Heward, Christopher B.
AU - Reiman, Eric M.
AU - Stephan, Dietrich
AU - Hardy, John
AU - Myers, Amanda J.
PY - 2009/4/10
Y1 - 2009/4/10
N2 - We recently surveyed the relationship between the human brain transcriptome and genome in a series of neuropathologically normal postmortem samples. We have now analyzed additional samples with a confirmed pathologic diagnosis of late-onset Alzheimer disease (LOAD; final n = 188 controls, 176 cases). Nine percent of the cortical transcripts that we analyzed had expression profiles correlated with their genotypes in the combined cohort, and approximately 5% of transcripts had SNP-transcript relationships that could distinguish LOAD samples. Two of these transcripts have been previously implicated in LOAD candidate-gene SNP-expression screens. This study shows how the relationship between common inherited genetic variants and brain transcript expression can be used in the study of human brain disorders. We suggest that studying the transcriptome as a quantitative endo-phenotype has greater power for discovering risk SNPs influencing expression than the use of discrete diagnostic categories such as presence or absence of disease.
AB - We recently surveyed the relationship between the human brain transcriptome and genome in a series of neuropathologically normal postmortem samples. We have now analyzed additional samples with a confirmed pathologic diagnosis of late-onset Alzheimer disease (LOAD; final n = 188 controls, 176 cases). Nine percent of the cortical transcripts that we analyzed had expression profiles correlated with their genotypes in the combined cohort, and approximately 5% of transcripts had SNP-transcript relationships that could distinguish LOAD samples. Two of these transcripts have been previously implicated in LOAD candidate-gene SNP-expression screens. This study shows how the relationship between common inherited genetic variants and brain transcript expression can be used in the study of human brain disorders. We suggest that studying the transcriptome as a quantitative endo-phenotype has greater power for discovering risk SNPs influencing expression than the use of discrete diagnostic categories such as presence or absence of disease.
UR - https://www.scopus.com/pages/publications/64149105182
U2 - 10.1016/j.ajhg.2009.03.011
DO - 10.1016/j.ajhg.2009.03.011
M3 - Article
C2 - 19361613
AN - SCOPUS:64149105182
SN - 0002-9297
VL - 84
SP - 445
EP - 458
JO - American journal of human genetics
JF - American journal of human genetics
IS - 4
ER -