Evaluating brain structure traits as endophenotypes using polygenicity and discoverability

Matoba, Nana; Love, Michael I.; Stein, Jason L.

doi:10.1101/2020.07.17.208843

Cited by 4 publications

(5 citation statements)

References 50 publications

(49 reference statements)

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Section: Discussionmentioning

confidence: 99%

“…Brain morphological measures have been hypothesized to be less polygenic than schizophrenia, forming the basis for the endophenotype approach 49 in schizophrenia. Our findings further support this hypothesis by revealing subcortical volumes and ICV have a lower polygenicity and a higher discoverability than schizophrenia, consistent with previous reports 49 . The positive AIC values indicate sufficient power for univariate analysis, suggesting the difference in polygenicity and discoverability is not mainly driven by different GWAS sample sizes.…”

Section: Discussionmentioning

confidence: 99%

“…A possible explanation is that schizophrenia is a complex and heterogeneous disorder with a broad range of symptoms and functional deficits 50 , so its high polygenicity is likely the result of a sum of SNPs associated with those clinical and course features. In contrast, brain volumetric phenotypes are less complex and have been proposed as "closer to the underlying biology," which may lead to a smaller polygenicity 49 .…”

Section: Discussionmentioning

confidence: 99%

“…Moreover, polygenicity has been hypothesized to be related to etiological heterogeneity, i.e., how many etiological mechanisms are underlying the development of the phenotype 49,50 . A further investigation on the underlying etiological mechanisms is therefore suggested.…”

Section: Discussionmentioning

confidence: 99%

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Shared genetic architecture between schizophrenia and subcortical brain volumes implicates early neurodevelopmental processes and brain development in childhood

et al. 2022

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Patients with schizophrenia have consistently shown brain volumetric abnormalities, implicating both etiological and pathological processes. However, the genetic relationship between schizophrenia and brain volumetric abnormalities remains poorly understood. Here, we applied novel statistical genetic approaches (MiXeR and conjunctional false discovery rate analysis) to investigate genetic overlap with mixed effect directions using independent genome-wide association studies of schizophrenia (n=130,644) and brain volumetric phenotypes, including subcortical brain and intracranial volumes (n=33,735). We found brain volumetric phenotypes share substantial genetic variants (74%~96%) with schizophrenia, and observed 107 distinct shared loci with sign consistency in independent samples. Genes mapped by shared loci revealed (1) significant enrichment in neurodevelopmental biological processes, (2) three co-expression clusters with peak expression at the prenatal stage, and(3) genetically imputed thalamic expression of CRHR1 and ARL17A was associated with the thalamic volume as early as in childhood. Together, our findings provide evidence of shared genetic architecture between schizophrenia and brain volumetric phenotypes and suggest altered early neurodevelopmental processes and brain development in childhood may be involved in schizophrenia development.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

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Shared genetic architecture between schizophrenia and subcortical brain volumes implicates early neurodevelopmental processes and brain development in childhood

et al. 2022

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“…To a large extent the heritability of many human brain features can be captured by common genetic variation as measured by genome-wide association studies 3,4 (GWAS). Similar to other multifactorial traits such as behavioral and psychiatric traits 5 , the polygenic nature of brain traits complicates translation of GWAS into an understanding of (causal) biological processes 6 . Analytical algorithms that can help translate high-dimensional MRI-GWAS output into mechanistic interpretations and quantify interindividual biological heterogeneity, could have a tremendous impact both in research, and potentially for clinical applications in personalized medicine.…”

Section: Introduction Backgroundmentioning

confidence: 99%

Principal and Independent Genomic Components of Brain Structure and Function

Soheili-Nezhad

Beckmann

Sprooten

2022

Preprint

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IntroductionHuman brain structure and function, as measured using magnetic resonance imaging (MRI), is heritable. The genetic associations are highly polygenic and pleiotropic, complicating translation of genetic association studies to concrete biological processes. We recently proposed genomic independent component analysis (ICA) to reduce a large array of genome-wide association study (GWAS) statistics into a number of more interpretable multivariate components. Here, we extend this work with genomic principal component analysis (PCA). We also estimate the inter-trait stability and test-retest reliability of genomic PCA and ICA. As a benchmark, we provide a head-to-head comparison with the reproducibility of original (i.e., raw univariate) GWAS variant effect sizes from which the components were derived.MethodsWe considered summary statistics of two rounds of brain MRI GWAS in UK biobank encompassing 22,138 and 11,086 subjects and thousands of imaging-derived phenotypes (IDPs) retrieved from Oxford BIG-40 server. We decomposed the clumped brain-wide genome-wide matrix (n=157,893 variants and m=2,240 brain IDPs) by PCA and ICA to obtain up to 50 principal and independent genome-wide components. We tested the reproducibility of these genomic components using tail overlap coefficient and Pearson’s correlation. Decompositions were repeated 20 times on randomly permuted split-half matrices to arrive at an empirical distribution of the above reproducibility metrics. As a benchmark, we also provide comparisons with the reproducibility of the original univariate GWAS effect sizes from which the components were derived.ResultsThe first ten principal genomic components were highly stable across trait-subsets within samples (r=0.82±0.15; mean ± standard deviation). PCA cross-sample reproducibility dropped to r=0.16±0.07 (r-max=0.30) was still a substantial improvement over univariate GWAS reproducibility r=0.09±0.05. ICA components showed higher within-sample reproducibility at lower dimensions (ICA dimension-5: r=0.81±0.16 vs. ICA dimension-50: r=0.41±0.13). The same trend was observed for their cross-sample reproducibility, achieving an r-max of 0.27 at the lowest ICA dimension of five (average r=0.20±0.3) vs. r-max of 0.11 at dimension 50 (average r=0.05±0.02).ConclusionOverall, the cross-sample reproducibility of PCA and ICA components are moderate, but considerably better than the reproducibility of the raw univariate GWAS effect sizes. Genomic PCA identified highly reproducible and pleiotropic genomic effects across many brain traits in just 1-3 factors. Genomic ICA captured mutually independent genome-wide effects that may be more sensitive to distinct biological pathways. As GWAS samples continue to increase and more (endo)phenotypes become available in biobanks, multi-trait genome-wide associations of the brain decomposed by ICA and PCA will likely become more reproducible and interpretable.

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Improving reporting standards for polygenic scores in risk prediction studies

Wand

Tamburro

Iacocca

et al. 2020

Preprint

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Over the past decade, genome-wide association studies have identified genetic variation associated with a wide range of human diseases and traits. These findings are now commonly aggregated into polygenic risk scores, which can bridge the gap between the initial discovery efforts and clinical applications for disease risk estimation. However, there is remarkable heterogeneity in the reporting of these risk scores due to a lack of accepted standards for the development, reporting, and application of PRS. This lack of rigorous standards hinders the translation of PRS into clinical care. The ClinGen Complex Disease Working Group, in a collaboration with the Polygenic Score (PGS) Catalog, have developed a novel PRS Reporting Statement (PRS-RS), updating previous standards to the current state of the field. Drawing upon experts in epidemiology, statistics, disease-specific applications, implementation, and policy, this 33-item reporting framework defines the minimal information needed to interpret and evaluate a PRS, especially with respect to any downstream clinical applications. Items span detailed descriptions of the study population (recruitment method, key demographics, inclusion/exclusion criteria, and phenotype definition), statistical methods for both PRS development and validation, and considerations for potential limitations of the published risk score and downstream clinical utility. Additionally, emphasis has been placed on data availability and transparency to facilitate reproducibility and benchmarking against other PRS, such as deposition in the publicly available PGS Catalog. By providing these criteria in a structured format that borrows from existing standards and ontologies, the use of this framework in publishing PRS will facilitate PRS translation into clinical care and progress towards defining best practices.

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Evaluating brain structure traits as endophenotypes using polygenicity and discoverability

Cited by 4 publications

References 50 publications

Shared genetic architecture between schizophrenia and subcortical brain volumes implicates early neurodevelopmental processes and brain development in childhood

Shared genetic architecture between schizophrenia and subcortical brain volumes implicates early neurodevelopmental processes and brain development in childhood

Principal and Independent Genomic Components of Brain Structure and Function

Improving reporting standards for polygenic scores in risk prediction studies

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