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Blokland GAM, Mesholam-Gately RI, Toulopoulou T, del Re EC, Lam M, DeLisi LE, Donohoe G, Walters JTR, Seidman LJ, Petryshen TL. Heritability of Neuropsychological Measures in Schizophrenia and Nonpsychiatric Populations: A Systematic Review and Meta-analysis. Schizophr Bull 2017; 43:788-800. [PMID: 27872257 PMCID: PMC5472145 DOI: 10.1093/schbul/sbw146] [Citation(s) in RCA: 55] [Impact Index Per Article: 7.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/18/2022]
Abstract
Schizophrenia is characterized by neuropsychological deficits across many cognitive domains. Cognitive phenotypes with high heritability and genetic overlap with schizophrenia liability can help elucidate the mechanisms leading from genes to psychopathology. We performed a meta-analysis of 170 published twin and family heritability studies of >800 000 nonpsychiatric and schizophrenia subjects to accurately estimate heritability across many neuropsychological tests and cognitive domains. The proportion of total variance of each phenotype due to additive genetic effects (A), shared environment (C), and unshared environment and error (E), was calculated by averaging A, C, and E estimates across studies and weighting by sample size. Heritability ranged across phenotypes, likely due to differences in genetic and environmental effects, with the highest heritability for General Cognitive Ability (32%-67%), Verbal Ability (43%-72%), Visuospatial Ability (20%-80%), and Attention/Processing Speed (28%-74%), while the lowest heritability was observed for Executive Function (20%-40%). These results confirm that many cognitive phenotypes are under strong genetic influences. Heritability estimates were comparable in nonpsychiatric and schizophrenia samples, suggesting that environmental factors and illness-related moderators (eg, medication) do not substantially decrease heritability in schizophrenia samples, and that genetic studies in schizophrenia samples are informative for elucidating the genetic basis of cognitive deficits. Substantial genetic overlap between cognitive phenotypes and schizophrenia liability (average rg = -.58) in twin studies supports partially shared genetic etiology. It will be important to conduct comparative studies in well-powered samples to determine whether the same or different genes and genetic variants influence cognition in schizophrenia patients and the general population.
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Affiliation(s)
- Gabriëlla A. M. Blokland
- Psychiatric and Neurodevelopmental Genetics Unit, Department of Psychiatry and Center for Human Genetic Research, Massachusetts General Hospital, Boston, MA;,Department of Psychiatry, Harvard Medical School, Boston, MA;,Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA
| | - Raquelle I. Mesholam-Gately
- Department of Psychiatry, Harvard Medical School, Boston, MA;,Commonwealth Research Center, Harvard Medical School, Boston, MA;,Massachusetts Mental Health Center Public Psychiatry Division of the Beth Israel Deaconess Medical Center, Boston, MA
| | - Timothea Toulopoulou
- Psychology Department, Bilkent University, Ankara, Turkey;,Department of Psychology, University of Hong Kong, Pokfulam, Hong Kong;,Department of Psychosis Studies, Institute of Psychiatry, King’s College London, London, UK
| | - Elisabetta C. del Re
- Department of Psychiatry, Harvard Medical School, Boston, MA;,Clinical Neuroscience Division, Laboratory of Neuroscience, Department of Psychiatry, Veterans Affairs Boston Healthcare System, Brockton, MA
| | - Max Lam
- Institute of Mental Health, Woodbridge Hospital, Singapore
| | - Lynn E. DeLisi
- Department of Psychiatry, Harvard Medical School, Boston, MA;,Clinical Neuroscience Division, Laboratory of Neuroscience, Department of Psychiatry, Veterans Affairs Boston Healthcare System, Brockton, MA
| | - Gary Donohoe
- School of Psychology, National University of Ireland, Galway, Ireland;,Neuropsychiatric Genetics Group, Department of Psychiatry and Trinity College Institute of Neuroscience, Trinity College, Dublin, Ireland
| | - James T. R. Walters
- Institute of Psychological Medicine and Clinical Neurosciences, Cardiff University School of Medicine, Cardiff, UK
| | | | - Larry J. Seidman
- Department of Psychiatry, Harvard Medical School, Boston, MA;,Commonwealth Research Center, Harvard Medical School, Boston, MA;,Massachusetts Mental Health Center Public Psychiatry Division of the Beth Israel Deaconess Medical Center, Boston, MA
| | - Tracey L. Petryshen
- Psychiatric and Neurodevelopmental Genetics Unit, Department of Psychiatry and Center for Human Genetic Research, Massachusetts General Hospital, Boston, MA;,Department of Psychiatry, Harvard Medical School, Boston, MA;,Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA
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Gervais O, Pong-Wong R, Navarro P, Haley CS, Nagamine Y. Antagonistic genetic correlations for milking traits within the genome of dairy cattle. PLoS One 2017; 12:e0175105. [PMID: 28380033 PMCID: PMC5381921 DOI: 10.1371/journal.pone.0175105] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/06/2016] [Accepted: 03/21/2017] [Indexed: 01/18/2023] Open
Abstract
Genome-wide association studies can be applied to identify useful SNPs associated with complex traits. Furthermore, regional genomic mapping can be used to estimate regional variance and clarify the genomic relationships within and outside regions but has not previously been applied to milk traits in cattle. We applied both single SNP analysis and regional genomic mapping to investigate SNPs or regions associated with milk yield traits in dairy cattle. The de-regressed breeding values of three traits, total yield (kg) of milk (MLK), fat (FAT), and protein (PRT) in 305 days, from 2,590 Holstein sires in Japan were analyzed. All sires were genotyped with 40,646 single-nucleotide polymorphism (SNP) markers. A genome-wide significant region (P < 0.01) common to all three traits was identified by regional genomic mapping on chromosome (BTA) 14. In contrast, single SNP analysis identified significant SNPs only for MLK and FAT (P < 0.01), but not PRT in the same region. Regional genomic mapping revealed an additional significant region (P < 0.01) for FAT on BTA5 that was not identified by single SNP analysis. The additive whole-genomic effects estimated in the regional genomic mapping analysis for the three traits were positively correlated with one another (0.830–0.924). However, the regional genomic effects obtained by using a window size of 20 SNPs for FAT on BTA14 were negatively correlated (P < 0.01) with the regional genomic effect for MLK (–0.940) and PRT (–0.878). The BTA14 regional effect for FAT also showed significant negative correlations (P < 0.01) with the whole genomic effects for MLK (–0.153), FAT (–0.172), and PRT (–0.181). These negative genomic correlations between loci are consistent with the negative linkage disequilibrium expected for traits under directional selection. Such antagonistic correlations may hamper the fixation of the FAT increasing alleles on BTA14. In summary, regional genomic mapping found more regions associated with milk production traits than did single SNP analysis. In addition, the existence of non-zero covariances between regional and whole genomic effects may influence the detection of regional effects, and antagonistic correlations could hamper the fixation of major genes under intensive selection.
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Affiliation(s)
- Olivier Gervais
- Kyoto University, Graduate School of Informatics, Kyoto Japan
| | - Ricardo Pong-Wong
- The Roslin Institute and R(D)SVS, University of Edinburgh, Midlothian, United Kingdom
| | - Pau Navarro
- MRC Human Genetics Unit, MRC Institute of Genetics and Molecular Medicine, University of Edinburgh, Western General Hospital, Edinburgh, United Kingdom
| | - Chris S. Haley
- The Roslin Institute and R(D)SVS, University of Edinburgh, Midlothian, United Kingdom
- MRC Human Genetics Unit, MRC Institute of Genetics and Molecular Medicine, University of Edinburgh, Western General Hospital, Edinburgh, United Kingdom
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Imputation of DNA Methylation Levels in the Brain Implicates a Risk Factor for Parkinson's Disease. Genetics 2016; 204:771-781. [PMID: 27466229 PMCID: PMC5068861 DOI: 10.1534/genetics.115.185967] [Citation(s) in RCA: 14] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/18/2015] [Accepted: 07/12/2016] [Indexed: 01/04/2023] Open
Abstract
Understanding how genetic variation affects intermediate phenotypes, like DNA methylation or gene expression, and how these in turn vary with complex human disease provides valuable insight into disease etiology. However, intermediate phenotypes are typically tissue and developmental stage specific, making relevant phenotypes difficult to assay. Assembling large case–control cohorts, necessary to achieve sufficient statistical power to assess associations between complex traits and relevant intermediate phenotypes, has therefore remained challenging. Imputation of such intermediate phenotypes represents a practical alternative in this context. We used a mixed linear model to impute DNA methylation (DNAm) levels of four brain tissues at up to 1826 methylome-wide sites in 6259 patients with Parkinson’s disease and 9452 controls from across five genome-wide association studies (GWAS). Six sites, in two regions, were found to associate with Parkinson’s disease for at least one tissue. While a majority of identified sites were within an established risk region for Parkinson’s disease, suggesting a role of DNAm in mediating previously observed genetic effects at this locus, we also identify an association with four CpG sites in chromosome 16p11.2. Direct measures of DNAm in the substantia nigra of 39 cases and 13 control samples were used to independently replicate these four associations. Only the association at cg10917602 replicated with a concordant direction of effect (P = 0.02). cg10917602 is 87 kb away from the closest reported GWAS hit. The employed imputation methodology implies that variation of DNAm levels at cg10917602 is predictive for Parkinson’s disease risk, suggesting a possible causal role for methylation at this locus. More generally this study demonstrates the feasibility of identifying predictive epigenetic markers of disease risk from readily available data sets.
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The Nature of Genetic Variation for Complex Traits Revealed by GWAS and Regional Heritability Mapping Analyses. Genetics 2015; 201:1601-13. [PMID: 26482794 DOI: 10.1534/genetics.115.177220] [Citation(s) in RCA: 42] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/10/2015] [Accepted: 10/09/2015] [Indexed: 02/08/2023] Open
Abstract
We use computer simulations to investigate the amount of genetic variation for complex traits that can be revealed by single-SNP genome-wide association studies (GWAS) or regional heritability mapping (RHM) analyses based on full genome sequence data or SNP chips. We model a large population subject to mutation, recombination, selection, and drift, assuming a pleiotropic model of mutations sampled from a bivariate distribution of effects of mutations on a quantitative trait and fitness. The pleiotropic model investigated, in contrast to previous models, implies that common mutations of large effect are responsible for most of the genetic variation for quantitative traits, except when the trait is fitness itself. We show that GWAS applied to the full sequence increases the number of QTL detected by as much as 50% compared to the number found with SNP chips but only modestly increases the amount of additive genetic variance explained. Even with full sequence data, the total amount of additive variance explained is generally below 50%. Using RHM on the full sequence data, a slightly larger number of QTL are detected than by GWAS if the same probability threshold is assumed, but these QTL explain a slightly smaller amount of genetic variance. Our results also suggest that most of the missing heritability is due to the inability to detect variants of moderate effect (∼0.03-0.3 phenotypic SDs) segregating at substantial frequencies. Very rare variants, which are more difficult to detect by GWAS, are expected to contribute little genetic variation, so their eventual detection is less relevant for resolving the missing heritability problem.
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Reynolds CA, Finkel D. A meta-analysis of heritability of cognitive aging: minding the "missing heritability" gap. Neuropsychol Rev 2015; 25:97-112. [PMID: 25732892 DOI: 10.1007/s11065-015-9280-2] [Citation(s) in RCA: 34] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/26/2014] [Accepted: 01/26/2015] [Indexed: 12/19/2022]
Abstract
The etiologies underlying variation in adult cognitive performance and cognitive aging have enjoyed much attention in the literature, but much of that attention has focused on broad factors, principally general cognitive ability. The current review provides meta-analyses of age trends in heritability of specific cognitive abilities and considers the profile of genetic and environmental factors contributing to cognitive aging to address the 'missing heritability' issue. Our findings, based upon evaluating 27 reports in the literature, indicate that verbal ability demonstrated declining heritability, after about age 60, as did spatial ability and perceptual speed more modestly. Trends for general memory, working memory, and spatial ability generally indicated stability, or small increases in heritability in mid-life. Equivocal results were found for executive function. A second meta-analysis then considered the gap between twin-based versus SNP-based heritability derived from population-based GWAS studies. Specifically, we considered twin correlation ratios to agnostically re-evaluate biometrical models across age and by cognitive domain. Results modestly suggest that nonadditive genetic variance may become increasingly important with age, especially for verbal ability. If so, this would support arguments that lower SNP-based heritability estimates result in part from uncaptured non-additive influences (e.g., dominance, gene-gene interactions), and possibly gene-environment (GE) correlations. Moreover, consistent with longitudinal twin studies of aging, as rearing environment becomes a distal factor, increasing genetic variance may result in part from nonadditive genetic influences or possible GE correlations. Sensitivity to life course dynamics is crucial to understanding etiological contributions to adult cognitive performance and cognitive aging.
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Affiliation(s)
- Chandra A Reynolds
- Department of Psychology, University of California Riverside, Riverside, CA, 92521, USA,
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