|
1
|
Petrill SA, Klamer BG, Buyske S, Willcutt EG, Gruen JR, Francis DJ, Flax JF, Brzustowicz LM, Bartlett CW. The Rosetta Phenotype Harmonization Method Facilitates Finding a Relationship Quantitative Trait Locus for a Complex Cognitive Trait. Genes (Basel) 2023; 14:1748. [PMID: 37761888 PMCID: PMC10531321 DOI: 10.3390/genes14091748] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/21/2023] [Revised: 08/28/2023] [Accepted: 08/28/2023] [Indexed: 09/29/2023] Open
Abstract
Genetics researchers increasingly combine data across many sources to increase power and to conduct analyses that cross multiple individual studies. However, there is often a lack of alignment on outcome measures when the same constructs are examined across studies. This inhibits comparison across individual studies and may impact the findings from meta-analysis. Using a well-characterized genotypic (brain-derived neurotrophic factor: BDNF) and phenotypic constructs (working memory and reading comprehension), we employ an approach called Rosetta, which allows for the simultaneous examination of primary studies that employ related but incompletely overlapping data. We examined four studies of BDNF, working memory, and reading comprehension with a combined sample size of 1711 participants. Although the correlation between working memory and reading comprehension over all participants was high, as expected (ρ = 0.45), the correlation between working memory and reading comprehension was attenuated in the BDNF Met/Met genotype group (ρ = 0.18, n.s.) but not in the Val/Val (ρ = 0.44) or Val/Met (ρ = 0.41) groups. These findings indicate that Met/Met carriers may be a unique and robustly defined subgroup in terms of memory and reading comprehension. This study demonstrates the utility of the Rosetta method when examining complex phenotypes across multiple studies, including psychiatric genetic studies, as shown here, and also for the mega-analysis of cohorts generally.
Collapse
Affiliation(s)
- Stephen A. Petrill
- Department of Psychology, College of Arts and Sciences, The Ohio State University, Columbus, OH 43210, USA;
| | - Brett G. Klamer
- Center for Biostatistics, The Ohio State University, Columbus, OH 43210, USA;
| | - Steven Buyske
- Department of Statistics, Rutgers, The State University of New Jersey, Piscataway, NJ 08854, USA;
| | - Erik G. Willcutt
- Department of Psychology, University of Colorado Boulder, Boulder, CO 80309, USA;
| | - Jeffrey R. Gruen
- Departments of Pediatrics and of Genetics, Yale Medical School, New Haven, CT 06511, USA;
| | - David J. Francis
- Texas Institute for Measurement, Evaluation, and Statistics, University of Houston, Houston, TX 77004, USA;
| | - Judy F. Flax
- Department of Genetics, Rutgers, The State University of New Jersey, Piscataway, NJ 08854, USA; (J.F.F.); (L.M.B.)
| | - Linda M. Brzustowicz
- Department of Genetics, Rutgers, The State University of New Jersey, Piscataway, NJ 08854, USA; (J.F.F.); (L.M.B.)
| | - Christopher W. Bartlett
- The Steve & Cindy Rasmussen Institute for Genomic Medicine in the Abigail Wexner Research Institute at Nationwide Children’s Hospital, Columbus, OH 43205, USA
- Department of Pediatrics, College of Medicine, The Ohio State University, Columbus, OH 43205, USA
| |
Collapse
|
|
2
|
Guo Y, Ning X, Mathé E, Wang K, Li L, Zhang C, Zhao Z. Innovating Computational Biology and Intelligent Medicine: ICIBM 2019 Special Issue. Genes (Basel) 2020; 11:E437. [PMID: 32316483 DOI: 10.3390/genes11040437] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/14/2020] [Accepted: 04/15/2020] [Indexed: 12/03/2022] Open
Abstract
The International Association for Intelligent Biology and Medicine (IAIBM) is a nonprofit organization that promotes intelligent biology and medical science. It hosts an annual International Conference on Intelligent Biology and Medicine (ICIBM), which was established in 2012. The ICIBM 2019 was held from 9 to 11 June 2019 in Columbus, Ohio, USA. Out of the 105 original research manuscripts submitted to the conference, 18 were selected for publication in a Special Issue in Genes. The topics of the selected manuscripts cover a wide range of current topics in biomedical research including cancer informatics, transcriptomic, computational algorithms, visualization and tools, deep learning, and microbiome research. In this editorial, we briefly introduce each of the manuscripts and discuss their contribution to the advance of science and technology.
Collapse
|