TY - JOUR
T1 - Covariance-Based Sample Selection for Heterogeneous Data
T2 - Applications to Gene Expression and Autism Risk Gene Detection
AU - Lin, Kevin Z.
AU - Liu, Han
AU - Roeder, Kathryn
N1 - Funding Information:
Han Liu’s research is supported by the NSF BIGDATA 1840866, NSF CAREER 1841569, NSF TRIPODS 1740735, DARPA-PA-18-02-09-QED-RML-FP-003, along with an Alfred P. Sloan Fellowship and a PECASE award. Kathryn Roeder’s research is supported by NIMH grants R37MH057881 and U01MH111658-01.
PY - 2020
Y1 - 2020
N2 - Risk for autism can be influenced by genetic mutations in hundreds of genes. Based on findings showing that genes with highly correlated gene expressions are functionally interrelated, “guilt by association” methods such as DAWN have been developed to identify these autism risk genes. Previous research analyzes the BrainSpan dataset, which contains gene expression of brain tissues from varying regions and developmental periods. Since the spatiotemporal properties of brain tissue are known to affect the gene expression’s covariance, previous research has focused only on a specific subset of samples to avoid the issue of heterogeneity. This analysis leads to a potential loss of power when detecting risk genes. In this article, we develop a new method called covariance-based sample selection (COBS) to find a larger and more homogeneous subset of samples that share the same population covariance matrix for the downstream DAWN analysis. To demonstrate COBSs effectiveness, we use genetic risk scores from two sequential data freezes obtained in 2014 and 2020. We show COBS improves DAWNs ability to predict risk genes detected in the newer data freeze when using the risk scores of the older data freeze as input. Supplementary materials for this article, including a standardized description of the materials available for reproducing the work, are available as an online supplement.
AB - Risk for autism can be influenced by genetic mutations in hundreds of genes. Based on findings showing that genes with highly correlated gene expressions are functionally interrelated, “guilt by association” methods such as DAWN have been developed to identify these autism risk genes. Previous research analyzes the BrainSpan dataset, which contains gene expression of brain tissues from varying regions and developmental periods. Since the spatiotemporal properties of brain tissue are known to affect the gene expression’s covariance, previous research has focused only on a specific subset of samples to avoid the issue of heterogeneity. This analysis leads to a potential loss of power when detecting risk genes. In this article, we develop a new method called covariance-based sample selection (COBS) to find a larger and more homogeneous subset of samples that share the same population covariance matrix for the downstream DAWN analysis. To demonstrate COBSs effectiveness, we use genetic risk scores from two sequential data freezes obtained in 2014 and 2020. We show COBS improves DAWNs ability to predict risk genes detected in the newer data freeze when using the risk scores of the older data freeze as input. Supplementary materials for this article, including a standardized description of the materials available for reproducing the work, are available as an online supplement.
KW - Bootstrap covariance test
KW - Microarray
KW - Multiple testing with dependence
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U2 - 10.1080/01621459.2020.1738234
DO - 10.1080/01621459.2020.1738234
M3 - Article
AN - SCOPUS:85083570553
JO - Journal of the American Statistical Association
JF - Journal of the American Statistical Association
SN - 0162-1459
ER -