Generalization of cortical MOSTest genome-wide associations within and across samples
Genome-Wide Association studies have typically been limited to univariate analysis in which a single outcome measure is tested against millions of variants. Recent work demonstrates that a Multivariate Omnibus Statistic Test (MOSTest) is well powered to discover genomic effects distributed across mu...
| Main Authors: | , , , , , , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Elsevier
2022-11-01
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| Series: | NeuroImage |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S1053811922007479 |
| _version_ | 1811192608718848000 |
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| author | Robert J. Loughnan Alexey A. Shadrin Oleksandr Frei Dennis van der Meer Weiqi Zhao Clare E. Palmer Wesley K. Thompson Carolina Makowski Terry L. Jernigan Ole A. Andreassen Chun Chieh Fan Anders M. Dale |
| author_facet | Robert J. Loughnan Alexey A. Shadrin Oleksandr Frei Dennis van der Meer Weiqi Zhao Clare E. Palmer Wesley K. Thompson Carolina Makowski Terry L. Jernigan Ole A. Andreassen Chun Chieh Fan Anders M. Dale |
| author_sort | Robert J. Loughnan |
| collection | DOAJ |
| description | Genome-Wide Association studies have typically been limited to univariate analysis in which a single outcome measure is tested against millions of variants. Recent work demonstrates that a Multivariate Omnibus Statistic Test (MOSTest) is well powered to discover genomic effects distributed across multiple phenotypes. Applied to cortical brain MRI morphology measures, MOSTest has resulted in a drastic improvement in power to discover loci when compared to established approaches (min-P). One question that arises is how well these discovered loci replicate in independent data. Here we perform 10 times cross validation within 34,973 individuals from UK Biobank for imaging measures of cortical area, thickness and sulcal depth (>1,000 dimensionality for each). By deploying a replication method that aggregates discovered effects distributed across multiple phenotypes, termed PolyVertex Score (MOSTest-PVS), we demonstrate a higher replication yield and comparable replication rate of discovered loci for MOSTest (# replicated loci: 242–496, replication rate: 96–97%) in independent data when compared with the established min-P approach (# replicated loci: 26–55, replication rate: 91–93%). An out-of-sample replication of discovered loci was conducted with a sample of 4,069 individuals from the Adolescent Brain Cognitive Development® (ABCD) study, who are on average 50 years younger than UK Biobank individuals. We observe a higher replication yield and comparable replication rate of MOSTest-PVS compared to min-P. This finding underscores the importance of using well-powered multivariate techniques for both discovery and replication of high dimensional phenotypes in Genome-Wide Association studies. |
| first_indexed | 2024-04-11T23:55:18Z |
| format | Article |
| id | doaj.art-8dacec5e225a4a2cacafea5ac3c0f6d5 |
| institution | Directory Open Access Journal |
| issn | 1095-9572 |
| language | English |
| last_indexed | 2024-04-11T23:55:18Z |
| publishDate | 2022-11-01 |
| publisher | Elsevier |
| record_format | Article |
| series | NeuroImage |
| spelling | doaj.art-8dacec5e225a4a2cacafea5ac3c0f6d52022-12-22T03:56:23ZengElsevierNeuroImage1095-95722022-11-01263119632Generalization of cortical MOSTest genome-wide associations within and across samplesRobert J. Loughnan0Alexey A. Shadrin1Oleksandr Frei2Dennis van der Meer3Weiqi Zhao4Clare E. Palmer5Wesley K. Thompson6Carolina Makowski7Terry L. Jernigan8Ole A. Andreassen9Chun Chieh Fan10Anders M. Dale11Department of Cognitive Science, University of California, 9500 Gilman Drive, La Jolla, San Diego, CA 92093, USA; Population Neuroscience and Genetics, University of California, 9500 Gilman Drive, La Jolla, San Diego, CA 92161, USA; Center for Multimodal Imaging and Genetics, San Diego School of Medicine, University of California, 9444 Medical Center Dr, La Jolla, CA 92037, USA; Corresponding authors at: Center for Human Development, University of California, 9500 Gilman Drive, La Jolla, San Diego, CA 92161, USADepartment of Informatics, Centre for Bioinformatics, University of Oslo, Oslo, NorwayDivision of Mental Health and Addiction, NORMENT Centre, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Oslo, Norway; Department of Informatics, Centre for Bioinformatics, University of Oslo, Oslo, NorwayDivision of Mental Health and Addiction, NORMENT Centre, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Oslo, Norway; Faculty of Health, Medicine and Life Sciences, School of Mental Health and Neuroscience, Maastricht University, the NetherlandsDepartment of Cognitive Science, University of California, 9500 Gilman Drive, La Jolla, San Diego, CA 92093, USACenter for Human Development, University of California, 9500 Gilman Drive, La Jolla, San Diego, CA 92161, USAPopulation Neuroscience and Genetics, University of California, 9500 Gilman Drive, La Jolla, San Diego, CA 92161, USA; Herbert Wertheim School of Public Health, University of California, La Jolla, San Diego, CA, USA; Center for Population Neuroscience and Genetics, Laureate Institute for Brain Research, Tulsa, OK, USACenter for Multimodal Imaging and Genetics, San Diego School of Medicine, University of California, 9444 Medical Center Dr, La Jolla, CA 92037, USA; Department of Radiology, San Diego School of Medicine, University of California, 9500 Gilman Drive, La Jolla, CA 92037, USADepartment of Cognitive Science, University of California, 9500 Gilman Drive, La Jolla, San Diego, CA 92093, USA; Center for Human Development, University of California, 9500 Gilman Drive, La Jolla, San Diego, CA 92161, USA; Department of Radiology, San Diego School of Medicine, University of California, 9500 Gilman Drive, La Jolla, CA 92037, USA; Department of Psychiatry, San Diego School of Medicine, University of California, 9500 Gilman Drive, La Jolla, CA 92037, USADepartment of Informatics, Centre for Bioinformatics, University of Oslo, Oslo, NorwayPopulation Neuroscience and Genetics, University of California, 9500 Gilman Drive, La Jolla, San Diego, CA 92161, USA; Center for Population Neuroscience and Genetics, Laureate Institute for Brain Research, Tulsa, OK, USA; Department of Radiology, San Diego School of Medicine, University of California, 9500 Gilman Drive, La Jolla, CA 92037, USA; Department of Radiology, Schoold of Medicine, University of California, San Diego, 9500 Gilman Drive, La Jolla, CA, 92037, USADepartment of Cognitive Science, University of California, 9500 Gilman Drive, La Jolla, San Diego, CA 92093, USA; Center for Multimodal Imaging and Genetics, San Diego School of Medicine, University of California, 9444 Medical Center Dr, La Jolla, CA 92037, USA; Department of Radiology, San Diego School of Medicine, University of California, 9500 Gilman Drive, La Jolla, CA 92037, USA; Department of Neuroscience, San Diego School of Medicine, University of California, 9500 Gilman Drive, La Jolla, CA 92037, USA; Corresponding authors at: Center for Human Development, University of California, 9500 Gilman Drive, La Jolla, San Diego, CA 92161, USAGenome-Wide Association studies have typically been limited to univariate analysis in which a single outcome measure is tested against millions of variants. Recent work demonstrates that a Multivariate Omnibus Statistic Test (MOSTest) is well powered to discover genomic effects distributed across multiple phenotypes. Applied to cortical brain MRI morphology measures, MOSTest has resulted in a drastic improvement in power to discover loci when compared to established approaches (min-P). One question that arises is how well these discovered loci replicate in independent data. Here we perform 10 times cross validation within 34,973 individuals from UK Biobank for imaging measures of cortical area, thickness and sulcal depth (>1,000 dimensionality for each). By deploying a replication method that aggregates discovered effects distributed across multiple phenotypes, termed PolyVertex Score (MOSTest-PVS), we demonstrate a higher replication yield and comparable replication rate of discovered loci for MOSTest (# replicated loci: 242–496, replication rate: 96–97%) in independent data when compared with the established min-P approach (# replicated loci: 26–55, replication rate: 91–93%). An out-of-sample replication of discovered loci was conducted with a sample of 4,069 individuals from the Adolescent Brain Cognitive Development® (ABCD) study, who are on average 50 years younger than UK Biobank individuals. We observe a higher replication yield and comparable replication rate of MOSTest-PVS compared to min-P. This finding underscores the importance of using well-powered multivariate techniques for both discovery and replication of high dimensional phenotypes in Genome-Wide Association studies.http://www.sciencedirect.com/science/article/pii/S1053811922007479 |
| spellingShingle | Robert J. Loughnan Alexey A. Shadrin Oleksandr Frei Dennis van der Meer Weiqi Zhao Clare E. Palmer Wesley K. Thompson Carolina Makowski Terry L. Jernigan Ole A. Andreassen Chun Chieh Fan Anders M. Dale Generalization of cortical MOSTest genome-wide associations within and across samples NeuroImage |
| title | Generalization of cortical MOSTest genome-wide associations within and across samples |
| title_full | Generalization of cortical MOSTest genome-wide associations within and across samples |
| title_fullStr | Generalization of cortical MOSTest genome-wide associations within and across samples |
| title_full_unstemmed | Generalization of cortical MOSTest genome-wide associations within and across samples |
| title_short | Generalization of cortical MOSTest genome-wide associations within and across samples |
| title_sort | generalization of cortical mostest genome wide associations within and across samples |
| url | http://www.sciencedirect.com/science/article/pii/S1053811922007479 |
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