Complex Variation in Measures of General Intelligence and Cognitive Change

Rowe, Suzanne; Rowlatt, Amy; Davies, Gail; Harris, Sarah E.; Porteous, David J.; Liewald, David C.; McNeill, Geraldine; Starr, John M.; Deary, Ian J.; Tenesa, Albert

doi:10.1371/journal.pone.0081189

Cited by 7 publications

(12 citation statements)

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“…We assumed a range of significance thresholds (a = 10 24 to 10 212 ) to detect significant genomic regions of 10 SNPs each. As expected, the number of QTL detected increased as a increased (Table S3), but the proportion of significant regions actually including QTL was correspondingly smaller, as expected, indicating that higher type I errors increased with increasing a (see also Rowe et al 2013). The contribution to the total additive variance of the QTL detected was not dramatically increased by increasing a, i.e., 46% with a = 10 24 and 31% with a = 10 28 (Table S3).…”

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The Nature of Genetic Variation for Complex Traits Revealed by GWAS and Regional Heritability Mapping Analyses

2015

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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.KEYWORDS quantitative trait variation; complex traits; missing heritability; fitness; additive genetic variance S TUDY of the nature of variation for quantitative traits, also known as complex traits, is one of the most active areas of research in genetics. This is particularly the case in human genetics because many common genetic diseases, including cancers, obesity, heart disease, and stroke, are complex traits controlled by many loci and influenced by multiple environmental factors. Large numbers of genetic loci influencing complex traits have been discovered using genome-wide association studies (GWAS) by associating putatively neutral SNPs with variation for the trait. For example, analysis of 160 complex disease phenotypes and quantitative traits has revealed associations with more than 2000 SNPs in humans (Visscher et al. 2012). A key general finding from GWAS is that the significantly associated SNPs account for only a small proportion of the trait's genetic variation, the so-called missing heritability problem (Manolio et al. 2009). For example, based on the resemblance between relatives, narrow-sense heritability for human height has been estimated to be as high as h 2 = 0.7-0.8 (Visscher 2008;Zaitlen et al. 2013), but the 679 variants identified in the latest GWAS meta-analysis of 79 studies analyzing more than 250,000 individuals accounted for only 16% of that her...

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The Nature of Genetic Variation for Complex Traits Revealed by GWAS and Regional Heritability Mapping Analyses

2015

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“…Moreover, 24% of the cognitive change between childhood (11 years) and older adult cognitive performance at ages 65 and older (residual based) is accounted for by common SNPs as measured using GWA SNP data on a subset of 1940 persons from the CAGES consortium (Deary, et al, 2012). Reanalysis of three of the CAGES consortium samples (N=1804) applied GCTA using a genome-scan approach to estimate the contribution of about 500k autosomal SNPs, reporting “population-sense heritabilities (h 2 ps )” of .36 for crystalized ability, .19 for fluid abilities, and .26 for cognitive change (Rowe et al, 2013). The range of SNP-based heritability estimates across the two studies for fluid ability is attributed to broader age composition of the larger CAGES sample set (Davies, et al, 2011)versus the older ages represented in the reduced sample set for which the genome scan was applied (Rowe, et al, 2013).…”

Section: Overstating the Case For Heritable Influences? Missing Heritmentioning

confidence: 99%

“…Reanalysis of three of the CAGES consortium samples (N=1804) applied GCTA using a genome-scan approach to estimate the contribution of about 500k autosomal SNPs, reporting “population-sense heritabilities (h 2 ps )” of .36 for crystalized ability, .19 for fluid abilities, and .26 for cognitive change (Rowe et al, 2013). The range of SNP-based heritability estimates across the two studies for fluid ability is attributed to broader age composition of the larger CAGES sample set (Davies, et al, 2011)versus the older ages represented in the reduced sample set for which the genome scan was applied (Rowe, et al, 2013). Nonetheless, these SNP-based heritability estimates for fluid and crystalized ability measures are lower than our meta-analytic heritability estimates, i.e., 56-62% at the peak for verbal, spatial and speed traits (see Figures 1, 2, and 4).…”

Section: Overstating the Case For Heritable Influences? Missing Heritmentioning

confidence: 99%

“…Models that aggregate effects across all possible contributing variants at particular loci, i.e., “local” heritability or loci-based heritability, as well as in aggregate across regions or the entire genome collectively, may provide avenues for discerning particular gene or regional contributions to an outcome (Gusev, et al, 2013; Rowe, et al, 2013) and inform the missing heritability debate. Via simulation and application to nine disease traits, Gusev and colleagues (Gusev, et al, 2013) considered the contribution of the most significant GWAS SNP at a locus (h 2 GWAS ), the contribution of all significant SNPs at a locus based on conditional linear modeling (h 2 GWAS,Joint ), the contribution of all SNPs at a locus constructed using the standard variance component approach (h 2 g ; i.e., using GCTA), and the contribution of SNPs at a locus using a variance component adjusted for linkage disequilibrium (LD) (h 2 g,LD ).…”

Section: Overstating the Case For Heritable Influences? Missing Heritmentioning

confidence: 99%

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A Meta-analysis of Heritability of Cognitive Aging: Minding the “Missing Heritability” Gap

Reynolds

Finkel

2015

Neuropsychol Rev

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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, suggest that verbal ability demonstrated declining heritability after 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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“…In addition, Nagamine et al [6] proposed a regional genomic relationship or regional heritability mapping that typically uses 10 to 100 sequential SNPs to detect additive regional genomic variances and their effects. This method has been applied to animal and human data to find quantitative trait loci (QTLs) complementing the GWAS approach [7–14]. Conventional methods in animal breeding that use only pedigree information assume that many undetectable polygenes control complex traits, and the properties of effective regions that harbor loci with detectable effects have not been thoroughly investigated.…”

Section: Introductionmentioning

confidence: 99%

Antagonistic genetic correlations for milking traits within the genome of dairy cattle

et al. 2017

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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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Complex Variation in Measures of General Intelligence and Cognitive Change

Cited by 7 publications

References 43 publications

The Nature of Genetic Variation for Complex Traits Revealed by GWAS and Regional Heritability Mapping Analyses

The Nature of Genetic Variation for Complex Traits Revealed by GWAS and Regional Heritability Mapping Analyses

A Meta-analysis of Heritability of Cognitive Aging: Minding the “Missing Heritability” Gap

Antagonistic genetic correlations for milking traits within the genome of dairy cattle

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