Gabriel E Hoffman1,2,3, Panos Roussos1,2,3,4,5. 1. Pamela Sklar Division of Psychiatric Genomics, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA. 2. Icahn Institute for Data Science and Genomic Technology, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA. 3. Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA. 4. Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA. 5. Mental Illness Research, Education, and Clinical Center (VISN 2 South), James J. Peters VA Medical Center, Bronx, NY 10468, USA.
Abstract
SUMMARY: Large-scale transcriptome studies with multiple samples per individual are widely used to study disease biology. Yet, current methods for differential expression are inadequate for cross-individual testing for these repeated measures designs. Most problematic, we observe across multiple datasets that current methods can give reproducible false-positive findings that are driven by genetic regulation of gene expression, yet are unrelated to the trait of interest. Here, we introduce a statistical software package, dream, that increases power, controls the false positive rate, enables multiple types of hypothesis tests, and integrates with standard workflows. In 12 analyses in 6 independent datasets, dream yields biological insight not found with existing software while addressing the issue of reproducible false-positive findings. AVAILABILITY AND IMPLEMENTATION: Dream is available within the variancePartition Bioconductor package at http://bioconductor.org/packages/variancePartition. CONTACT: gabriel.hoffman@mssm.edu. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
SUMMARY: Large-scale transcriptome studies with multiple samples per individual are widely used to study disease biology. Yet, current methods for differential expression are inadequate for cross-individual testing for these repeated measures designs. Most problematic, we observe across multiple datasets that current methods can give reproducible false-positive findings that are driven by genetic regulation of gene expression, yet are unrelated to the trait of interest. Here, we introduce a statistical software package, dream, that increases power, controls the false positive rate, enables multiple types of hypothesis tests, and integrates with standard workflows. In 12 analyses in 6 independent datasets, dream yields biological insight not found with existing software while addressing the issue of reproducible false-positive findings. AVAILABILITY AND IMPLEMENTATION:Dream is available within the variancePartition Bioconductor package at http://bioconductor.org/packages/variancePartition. CONTACT: gabriel.hoffman@mssm.edu. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
Authors: Elisa Navarro; Evan Udine; Katia de Paiva Lopes; Madison Parks; Giulietta Riboldi; Brian M Schilder; Jack Humphrey; Gijsje J L Snijders; Ricardo A Vialle; Maojuan Zhuang; Tamjeed Sikder; Charalambos Argyrou; Amanda Allan; Michael J Chao; Kurt Farrell; Brooklyn Henderson; Sarah Simon; Deborah Raymond; Sonya Elango; Roberto A Ortega; Vicki Shanker; Matthew Swan; Carolyn W Zhu; Ritesh Ramdhani; Ruth H Walker; Winona Tse; Mary Sano; Ana C Pereira; Tim Ahfeldt; Alison M Goate; Susan Bressman; John F Crary; Lotje de Witte; Steven Frucht; Rachel Saunders-Pullman; Towfique Raj Journal: Nat Aging Date: 2021-09-14
Authors: Julia Tcw; Lu Qian; Nina H Pipalia; Michael J Chao; Shuang A Liang; Yang Shi; Bharat R Jain; Sarah E Bertelsen; Manav Kapoor; Edoardo Marcora; Elizabeth Sikora; Elizabeth J Andrews; Alessandra C Martini; Celeste M Karch; Elizabeth Head; David M Holtzman; Bin Zhang; Minghui Wang; Frederick R Maxfield; Wayne W Poon; Alison M Goate Journal: Cell Date: 2022-06-23 Impact factor: 66.850
Authors: Georgios Voloudakis; Gabriel Hoffman; Sanan Venkatesh; Kyung Min Lee; Kristina Dobrindt; James M Vicari; Wen Zhang; Noam D Beckmann; Shan Jiang; Daisy Hoagland; Jiantao Bian; Lina Gao; André Corvelo; Kelly Cho; Jennifer S Lee; Sudha K Iyengar; Shiuh-Wen Luoh; Schahram Akbarian; Robert Striker; Themistocles L Assimes; Eric E Schadt; Miriam Merad; Benjamin R tenOever; Alexander W Charney; Kristen J Brennand; Julie A Lynch; John F Fullard; Panos Roussos Journal: medRxiv Date: 2021-06-02
Authors: Gabriel E Hoffman; Yixuan Ma; Kelsey S Montgomery; Jaroslav Bendl; Manoj Kumar Jaiswal; Alex Kozlenkov; Mette A Peters; Stella Dracheva; John F Fullard; Andrew Chess; Bernie Devlin; Solveig K Sieberts; Panos Roussos Journal: Biol Psychiatry Date: 2021-03-25 Impact factor: 13.382
Authors: John F Fullard; Hao-Chih Lee; Georgios Voloudakis; Shengbao Suo; Behnam Javidfar; Zhiping Shao; Cyril Peter; Wen Zhang; Shan Jiang; André Corvelo; Heather Wargnier; Emma Woodoff-Leith; Dushyant P Purohit; Sadhna Ahuja; Nadejda M Tsankova; Nathalie Jette; Gabriel E Hoffman; Schahram Akbarian; Mary Fowkes; John F Crary; Guo-Cheng Yuan; Panos Roussos Journal: Genome Med Date: 2021-07-19 Impact factor: 15.266
Authors: Kathryn R Bowles; M Catarina Silva; Kristen Whitney; Taylor Bertucci; Joshua E Berlind; Jesse D Lai; Jacob C Garza; Nathan C Boles; Sidhartha Mahali; Kevin H Strang; Jacob A Marsh; Cynthia Chen; Derian A Pugh; Yiyuan Liu; Ronald E Gordon; Susan K Goderie; Rebecca Chowdhury; Steven Lotz; Keith Lane; John F Crary; Stephen J Haggarty; Celeste M Karch; Justin K Ichida; Alison M Goate; Sally Temple Journal: Cell Date: 2021-07-26 Impact factor: 66.850