Generalization of Cortical Multivariate Genome-Wide Associations Within and Across Samples

Published on Apr 24, 2021in bioRxiv
· DOI :10.1101/2021.04.23.441215
R. J. Loughnan4
Estimated H-index: 4
,
Alexey A. Shadrin12
Estimated H-index: 12
+ 9 AuthorsAnders M. Dale2
Estimated H-index: 2
Sources
Abstract
Genome-Wide Association studies have typically been limited to single phenotypes, given that high dimensional phenotypes incur a large multiple comparisons burden: ~1 million tests across the genome times the number of phenotypes. 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 - a 10-fold increase in discovered loci compared to established approaches (min-P). One question that arises is how well these discovered loci replicate in independent data. Here we perform 10 -imes cross validation within 35,644 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 (PVS), we demonstrate a higher replication yield and comparable replication rate of discovered loci for MOSTest (# replicated loci: 428-1,037, replication rate: 95-96%) in independent data when compared with the established min-P approach (# replicated loci: 30-71, replication rate: 70-84%). An out-of-sample generalization of discovered loci was conducted with a sample of 8,336 individuals from the Adolescent Brain Cognitive Development(R) (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 compared to min-P. This finding underscores the importance of using multivariate techniques for both discovery and replication of high dimensional phenotypes in Genome-Wide Association studies.
📖 Papers frequently viewed together
9 Authors (Gleb Kichaev, ..., Alkes L. Price)
8 Citations
3 Citations
2 Citations
References20
Newest
#1Robert J. Loughnan (University of California, Berkeley)H-Index: 1
#1R. J. Loughnan (University of California, Berkeley)H-Index: 4
Last. Chun Chieh Fan (UCSD: University of California, San Diego)H-Index: 26
view all 7 authors...
Background: Findings in adults have shown more culturally sensitive 9crystallized9 measures of intelligence have greater heritability, these results were not able to be shown in children. Methods: With data from 8,518 participants, aged 9 to 11, from the Adolescent Brain Cognitive Development (ABCD) Study®, we used polygenic predictors of intelligence test performance (based on genome-wide association meta-analyses of data from 269,867 individuals) and of educational attainment (based on data fr...
6 CitationsSource
#1Joshua C. Denny (NIH: National Institutes of Health)H-Index: 1
#1Joshua C. Denny (NIH: National Institutes of Health)H-Index: 8
Last. Francis S. Collins (NIH: National Institutes of Health)H-Index: 211
view all 2 authors...
Precision medicine promises improved health by accounting for individual variability in genes, environment, and lifestyle. Precision medicine will continue to transform healthcare in the coming decade as it expands in key areas: huge cohorts, artificial intelligence (AI), routine clinical genomics, phenomics and environment, and returning value across diverse populations.
2 CitationsSource
#1Weiqi Zhao (UCLA: University of California, Los Angeles)H-Index: 5
#1Weiqi Zhao (UCLA: University of California, Los Angeles)H-Index: 1
Last. Chun Chieh Fan (UCSD: University of California, San Diego)H-Index: 26
view all 9 authors...
Despite its central role in revealing the neurobiological mechanisms of behavior, neuroimaging research faces the challenge of producing reliable biomarkers for cognitive processes and clinical outcomes. Statistically significant brain regions, identified by mass univariate statistical models commonly used in neuroimaging studies, explain minimal phenotypic variation, limiting the translational utility of neuroimaging phenotypes. This is potentially due to the observation that behavioral traits ...
6 CitationsSource
#1Dennis van der Meer (University of Oslo)H-Index: 20
#1van der Meer (University of Oslo)H-Index: 1
Last. DaleH-Index: 1
view all 17 authors...
The folding of the human cerebral cortex is a highly genetically regulated process that allows for a much larger surface area to fit into the cranial vault and optimizes functional organization. Sulcal depth is a robust, yet understudied measure of localized folding, previously associated with a range of neurodevelopmental disorders. Here, we report the first genome-wide association study of sulcal depth. Through the Multivariate Omnibus Statistical Test (MOSTest) applied to vertex-wise measures...
1 CitationsSource
#1Alexey A. Shadrin (University of Oslo)H-Index: 12
#2Tobias Kaufmann (University of Oslo)H-Index: 35
Last. Anders M. Dale (UCSD: University of California, San Diego)H-Index: 166
view all 21 authors...
Brain morphology has been shown to be highly heritable, yet only a small portion of the heritability is explained by the genetic variants discovered so far. Here we exploit the distributed nature of genetic effects across the brain and apply the Multivariate Omnibus Statistical Test (MOSTest) to genome-wide association studies (GWAS) of vertex-wise structural magnetic resonance imaging (MRI) measures from N=35,657 participants in the UK Biobank. We identified 1598 loci for cortical surface area ...
3 CitationsSource
#1Shing Wan Choi (ISMMS: Icahn School of Medicine at Mount Sinai)H-Index: 13
#2Timothy Shin Heng Mak (HKU: University of Hong Kong)H-Index: 7
Last. Paul F. O'Reilly (ISMMS: Icahn School of Medicine at Mount Sinai)H-Index: 47
view all 3 authors...
A polygenic score (PGS) or polygenic risk score (PRS) is an estimate of an individual’s genetic liability to a trait or disease, calculated according to their genotype profile and relevant genome-wide association study (GWAS) data. While present PRSs typically explain only a small fraction of trait variance, their correlation with the single largest contributor to phenotypic variation—genetic liability—has led to the routine application of PRSs across biomedical research. Among a range of applic...
62 CitationsSource
#1Dennis van der Meer (UM: Maastricht University)H-Index: 20
#2Oleksandr Frei (University of Oslo)H-Index: 19
Last. Anders M. Dale (UCSD: University of California, San Diego)H-Index: 166
view all 12 authors...
Regional brain morphology has a complex genetic architecture, consisting of many common polymorphisms with small individual effects. This has proven challenging for genome-wide association studies (GWAS). Due to the distributed nature of genetic signal across brain regions, multivariate analysis of regional measures may enhance discovery of genetic variants. Current multivariate approaches to GWAS are ill-suited for complex, large-scale data of this kind. Here, we introduce the Multivariate Omni...
9 CitationsSource
#1Cathryn M. Lewis ('KCL': King's College London)H-Index: 101
#2Evangelos Vassos ('KCL': King's College London)H-Index: 32
: Genome-wide association studies have shown unequivocally that common complex disorders have a polygenic genetic architecture and have enabled researchers to identify genetic variants associated with diseases. These variants can be combined into a polygenic risk score that captures part of an individual's susceptibility to diseases. Polygenic risk scores have been widely applied in research studies, confirming the association between the scores and disease status, but their clinical utility has...
63 CitationsSource
#1Weiqi Zhao (UCSD: University of California, San Diego)H-Index: 5
#2Clare E. Palmer (UCSD: University of California, San Diego)H-Index: 11
Last. Chun Chieh Fan (UCSD: University of California, San Diego)H-Index: 26
view all 6 authors...
The traditional brain mapping approach has greatly advanced our understanding of the localized effect of the brain on behavior. However, the statistically significant brain regions identified by the standard mass univariate models only explain minimal variance in a behavior despite increased sample sizes and statistical power, highlighting the nonsparseness of the explanatory signal in the brain. We introduced the Bayesian polyvertex score (PVS-B), a whole-brain prediction framework that aggrega...
4 CitationsSource
#1Weiqi ZhaoH-Index: 5
#2Clare E. PalmerH-Index: 11
Last. Chun Chieh FanH-Index: 26
view all 6 authors...
The traditional brain mapping approach has greatly advanced our understanding of the localized effect of the brain on behavior. However, the statistically significant brain regions identified by the standard mass univariate models only explain minimal variance in behavior despite increased sample sizes and statistical power, highlighting the nonsparseness of the explanatory signal in the brain. We introduced the Bayesian polyvertex score (PVS-B), a whole-brain prediction framework that aggregate...
2 CitationsSource
Cited By0
Newest
#1Chun Chieh Fan (UCSD: University of California, San Diego)H-Index: 26
#2R. J. Loughnan (UCSD: University of California, San Diego)H-Index: 4
Last. Anders M. Dale (UCSD: University of California, San Diego)H-Index: 166
view all 0 authors...
It is important to understand the molecular determinants for microstructures of human brain. However, past genome-wide association studies (GWAS) on microstructures of human brain have had limited results due to methodological constraints. Here, we adopt advanced imaging processing methods and multivariate GWAS on two large scale imaging genetic datasets (UK Biobank and Adolescent Brain Cognitive Development study) to identify and validate key genetic association signals. We discovered 503 uniqu...
Source