Multifactorial structure of cognitive assessment tests in the UK Biobank: A combined exploratory factor and structural equation modeling analyses

Ciobanu, Liliana G; Stankov, Lazar; Ahmed, Muktar Beshir; Heathcote, Andrew; Clark, Stanley J.; Aidman, Eugene

doi:10.3389/fpsyg.2023.1054707

Cited by 6 publications

(3 citation statements)

References 43 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…We applied Genomic SEM to GWAS summary statistics for 12 cognitive traits in the UKB and found that a three-factor model best explained the genetic correlations between the cognitive traits. This is consistent with findings from a previous study that applied structural equation modeling to phenotypic data from the UKB cognitive assessments and found that a three-factor solution fit the data best 32 . The three cognitive factors identified in the current study may be characterized using the framework provided by the Cattell–Horn–Carrol (CHC) theory of human cognitive abilities, which proposes a three-stratum model of human intelligence 24 , 33 .…”

Section: Discussionsupporting

confidence: 91%

Genomic insights into the shared and distinct genetic architecture of cognitive function and schizophrenia

Wootton,

Shadrin,

Bjella

et al. 2024

Sci Rep

View full text Add to dashboard Cite

Cognitive impairment is a major determinant of functional outcomes in schizophrenia, however, understanding of the biological mechanisms underpinning cognitive dysfunction in the disorder remains incomplete. Here, we apply Genomic Structural Equation Modelling to identify latent cognitive factors capturing genetic liabilities to 12 cognitive traits measured in the UK Biobank. We identified three broad factors that underly the genetic correlations between the cognitive tests. We explore the overlap between latent cognitive factors, schizophrenia, and schizophrenia symptom dimensions using a complementary set of statistical approaches, applied to data from the latest schizophrenia genome-wide association study (Ncase = 53,386, Ncontrol = 77,258) and the Thematically Organised Psychosis study (Ncase = 306, Ncontrol = 1060). Global genetic correlations showed a significant moderate negative genetic correlation between each cognitive factor and schizophrenia. Local genetic correlations implicated unique genomic regions underlying the overlap between schizophrenia and each cognitive factor. We found substantial polygenic overlap between each cognitive factor and schizophrenia and biological annotation of the shared loci implicated gene-sets related to neurodevelopment and neuronal function. Lastly, we show that the common genetic determinants of the latent cognitive factors are not predictive of schizophrenia symptoms in the Norwegian Thematically Organized Psychosis cohort. Overall, these findings inform our understanding of cognitive function in schizophrenia by demonstrating important differences in the shared genetic architecture of schizophrenia and cognitive abilities.

show abstract

Section: Discussionsupporting

confidence: 91%

Genomic insights into the shared and distinct genetic architecture of cognitive function and schizophrenia

Wootton,

Shadrin,

Bjella

et al. 2024

Sci Rep

View full text Add to dashboard Cite

show abstract

“…This is consistent with findings from a previous study that applied structural equation modeling to phenotypic data from the UKB cognitive assessments and found that a three-factor solution fit the data best. 45 The three cognitive factors identified in the current study may be characterized using the framework provided by the Cattell-Horn-Carrol (CHC) theory of human cognitive abilities, which proposes a three-stratum model of human intelligence. 44 46 Consistent with the relatively high genetic correlation between factor 1 and factor 2, the first two latent cognitive factors appear to capture the same broad cognitive ability, fluid reasoning ( Gf ).…”

Section: Discussionmentioning

confidence: 99%

Genomic Insights into the Shared and Distinct Genetic Architecture of Cognitive Function and Schizophrenia

Wootton,

Shadrin,

Bjella

et al. 2023

Preprint

View full text Add to dashboard Cite

Cognitive impairment is a major determinant of functional outcomes in schizophrenia, and efforts to understand the biological basis of cognitive dysfunction in the disorder are ongoing. Previous studies have suggested genetic overlap between global cognitive ability and schizophrenia, but further work is needed to delineate the shared genetic architecture. Here, we apply genomic structural equation modelling to identify latent cognitive factors capturing genetic liabilities to 12 cognitive traits measured in the UK Biobank (UKB). We explore the overlap between latent cognitive factors, schizophrenia, and schizophrenia symptom dimensions using a complementary set of statistical approaches, applied to data from the latest schizophrenia genome-wide association study (Ncase = 53,386, Ncontrol = 77,258) and the Thematically Organised Psychosis study (Ncase = 306, Ncontrol = 1060). We identified three broad factors (visuo-spatial, verbal analytic and decision/reaction time) that underly the genetic correlations between the UKB cognitive tests. Global genetic correlations showed a significant but moderate negative genetic correlation between each cognitive factor and schizophrenia. Local genetic correlations implicated unique genomic regions underlying the overlap between schizophrenia and each cognitive factor. We found evidence of substantial polygenic overlap between each cognitive factor and schizophrenia but show that most loci shared between the latent cognitive factors and schizophrenia have unique patterns of association with the cognitive factors. Biological annotation of the shared loci implicated gene-sets related to neurodevelopment and neuronal function. Lastly, we find that the common genetic determinants of the latent cognitive factors are not predictive of schizophrenia symptom dimensions. Overall, these findings inform our understanding of cognitive function in schizophrenia by demonstrating important differences in the shared genetic architecture of schizophrenia and cognitive abilities.

show abstract

“…Cognitive tests used by the UK Biobank are not standardized and at the time of administration had not been tested for psychometric quality. Since then, psychometric quality of the UK Biobank cognitive tests has been assessed in several studies (Lyall et al, 2016;Fawns-Ritchie et al, 2020;Ciobanu et al, 2023).Psychometric quality in terms of internal consistency, short-term test-retest reliability and concurrent validity of the UK Biobank cognitive tests have been found to be reasonable to good in general (Lyall et al, 2016;Fawns-Ritchie, 2020), although the long-term test-retest reliability of some of the tests may not be ideal (Lyall et al, 2016).…”

Section: Cognitionmentioning

confidence: 99%

Human social complexity: Evolutionary and methodological considerations

Roozen

View full text Add to dashboard Cite

Throughout the evolution of primate and early hominid species, more complex social environments have been associated with increases in the size of the neocortex. However, despite the continuing increase in the social complexity of the human environment, evidence indicates that in recent millennia the human brain has not only stopped growing, but may have started to decrease in size. Several hypotheses have been suggested to explain this apparent decrease, including the collective intelligence hypothesis and the self-domestication hypothesis. Methodological advances now allow for the detection of signals of selection in human genetic data, meaning selection pressure affecting human social complexity and structural characteristics of the human brain can be evaluated. Pleiotropy, a mechanism underlying several hypotheses explaining the evolution of brain size, can similarly be examined using genetic data. Such analyses may aid in our understanding of how the modern environment may affect the human brain in the long term. 14Chapter 1 social complexity or brain size, it is possible to assess the effects of recent selection on these traits.A notable example of a study which used a large genetic database to study human evolution using (among others) MR and SDS analyses is the study by Song et al (2021), in which the authors examined selection pressure for 870 human traits across four different time scales (since human speciation, pan-Neolithic period, 2,000-3,000 years and modern), using summary statistics from genome-wide association study (GWAS) studies carried out in previous studies. An issue with this study is that the GWAS results used for the analyses were found in previous studies, which did not appear to anticipate that these results may be used for studies about selection pressure in the future. As a result, most likely all (or most) of these studies removed SNPs which deviated significantly from Hardy-Weinberg equilibrium (HWE), as this is considered a necessary test of genotyping quality. However, deviation from the Hardy-Weinberg equilibrium may also occur in case of selection. Hence, the removal of SNPs that deviate from HWE may preclude the detection of signatures of selection. We have included a supplementary analysis at the end of this chapter to demonstrate the effect of recent selection on HWE.Another overview of methods of measuring selection using human genome-wide data is provided in the review by Guo et al (2018). They mention that selection could affect allele frequencies at focal loci as well as at linked loci and it may affect linkage disequilibrium at loci under selection. In loci under selection, minor allele frequencies and effect sizes could become correlated, as alleles of larger effects are expected to be more affected by selection. The authors also mention several methods to leverage between-population genotypic differences, for example by examining the relation between trait-related loci and the principal components resulting from the principal components analysis (PCA) which are usuall...

show abstract

Multifactorial structure of cognitive assessment tests in the UK Biobank: A combined exploratory factor and structural equation modeling analyses

Cited by 6 publications

References 43 publications

Genomic insights into the shared and distinct genetic architecture of cognitive function and schizophrenia

Genomic insights into the shared and distinct genetic architecture of cognitive function and schizophrenia

Genomic Insights into the Shared and Distinct Genetic Architecture of Cognitive Function and Schizophrenia

Human social complexity: Evolutionary and methodological considerations

Contact Info

Product

Resources

About