Uncovering the Genetic Architectures of Quantitative Traits

Lee, James J.; Vattikuti, Shashaank; Chow, Carson C.

doi:10.1016/j.csbj.2015.10.002

Cited by 15 publications

(9 citation statements)

References 63 publications

(78 reference statements)

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“…The methodology of GWAS is enabling this revolution because certain special properties of genomic datasuch as the natural randomization of genotypes within the offspring of the same parentsenable a high degree of trust in the causal inferences that can be drawn from it (Lee 2012;Lee & Chow 2013;Lee et al 2016). Unfortunately, genetic methods along these lines may be somewhat difficult to apply to nonhuman species because of the large sample sizes required for adequate statistical power (Chabris et al 2015).…”

Section: A C K N O W L E D G M E N T Smentioning

confidence: 99%

The evolution of general intelligence

Burkart¹,

Schubiger²,

Schaik³

2016

Behav Brain Sci

171

222

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The presence of general intelligence poses a major evolutionary puzzle, which has led to increased interest in its presence in nonhuman animals. The aim of this review is to critically evaluate this question and to explore the implications for current theories about the evolution of cognition. We first review domain-general and domain-specific accounts of human cognition in order to situate attempts to identify general intelligence in nonhuman animals. Recent studies are consistent with the presence of general intelligence in mammals (rodents and primates). However, the interpretation of a psychometric g factor as general intelligence needs to be validated, in particular in primates, and we propose a range of such tests. We then evaluate the implications of general intelligence in nonhuman animals for current theories about its evolution and find support for the cultural intelligence approach, which stresses the critical importance of social inputs during the ontogenetic construction of survival-relevant skills. The presence of general intelligence in nonhumans implies that modular abilities can arise in two ways, primarily through automatic development with fixed content and secondarily through learning and automatization with more variable content. The currently best-supported model, for humans and nonhuman vertebrates alike, thus construes the mind as a mix of skills based on primary and secondary modules. The relative importance of these two components is expected to vary widely among species, and we formulate tests to quantify their strength.

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Section: A C K N O W L E D G M E N T Smentioning

confidence: 99%

The evolution of general intelligence

Burkart¹,

Schubiger²,

Schaik³

2016

Behav Brain Sci

171

222

View full text Add to dashboard Cite

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“…The GWAS field has been fortunate in that it offers a variety of methods to check whether confounding has produced spurious correlations between genetic and phenotypic variation. These methods have led to a strong consensus that confounding has a minimal impact on GWAS results (Goldstein, 2011; Lee, 2012; Lee, Vattikuti, & Chow, 2016; Visscher, Brown, McCarthy, & Yang, 2012).…”

Section: Introductionmentioning

confidence: 99%

The accuracy of LD Score regression as an estimator of confounding and genetic correlations in genome‐wide association studies

Lee

McGue

Iacono

et al. 2018

Genetic Epidemiology

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To infer that a single-nucleotide polymorphism (SNP) either affects a phenotype or is linkage disequilibrium with a causal site, we must have some assurance that any SNP-phenotype correlation is not the result of confounding with environmental variables that also affect the trait. In this study, we study the properties of linkage disequilibrium (LD) Score regression, a recently developed method for using summary statistics from genome-wide association studies to ensure that confounding does not inflate the number of false positives. We do not treat the effects of genetic variation as a random variable and thus are able to obtain results about the unbiasedness of this method. We demonstrate that LD Score regression can produce estimates of confounding at null SNPs that are unbiased or conservative under fairly general conditions. This robustness holds in the case of the parent genotype affecting the offspring phenotype through some environmental mechanism, despite the resulting correlation over SNPs between LD Scores and the degree of confounding. Additionally, we demonstrate that LD Score regression can produce reasonably robust estimates of the genetic correlation, even when its estimates of the genetic covariance and the two univariate heritabilities are substantially biased.

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“…Importantly, the multivariate approach may also be useful in the context of genetic association studies. The effects of individual genetic variants on complex human traits are usually small [24]. Genome wide association studies for hematological parameters have now implicated several loci in the regulation of hematological indices, but the power is currently insufficient to detect all loci involved [25][26][27][28].…”

Section: Discussionmentioning

confidence: 99%