Geographic Confounding in Genome-Wide Association Studies

Abdellaoui, Abdel; Kj, Verweij; Nivard, Michel G.

doi:10.21203/rs.3.rs-362358/v1

Cited by 5 publications

(3 citation statements)

References 41 publications

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“…Seventh, a key limitation of MLM approaches is that endogenous, omitted variables associated with the higher-level contexts can reduce generalisability. If the aim of an analysis is solely to adjust for higher level context, then a fixed effects approach might be more appropriate (Abdellaoui et al, 2021).…”

Section: Limitationsmentioning

confidence: 99%

Examining the association between genetic risk for depression, wellbeing and schizophrenia, and proximity to greenspace

Reed

Morris

Davis

et al. 2022

Preprint

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It is unclear whether associations between between greenspace and mental health reflects those with better mental health seeking out greener environments.To examine this we 1) test associations between depression, wellbeing and schizophrenia polygenic scores (PGS) with two greenspace measures in UK Biobank (N=238,306 and 293,922), 2) estimate multilevel-models (MLM), clustering individuals by local geography and, 3) conduct one-sample Mendelian randomisation (MR) to estimate causal effects.Depression and schizophrenia PGS were associated with lower greenspace, whilst wellbeing PGS was associated with higher greenspace. Locally-clustered MLM demonstrated attenuation for the individual wellbeing PGS association and a reversal of effect for the schizophrenia PGS association. MR revealed evidence of a causal effect of increased depression liability on decreased greenspace.Therefore, greater liability to depression may cause selection into less green neighbourhoods. Our results also highlight how individual-level PGS associations can be biased by contextual, between-area differences in outcome.

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Section: Limitationsmentioning

confidence: 99%

Examining the association between genetic risk for depression, wellbeing and schizophrenia, and proximity to greenspace

Reed

Morris

Davis

et al. 2022

Preprint

View full text Add to dashboard Cite

show abstract

“…A major issue of current interest is the transferability of the scores between di↵erent scenarios. In particular, the scores may not transfer easily between human populations [1,24,30], mainly due to di↵erences in allele frequencies, LD-structure, and e↵ect size. Moreover, scores may show reduced accuracy even within a single population where most above di↵erences are negligible [21], including in prediction of within-family variation [27], with changes in covariates such as socioeconomic status, age and sex leading to decreased accuracy, possibly due to Gene-by-Environment interactions.…”

Section: Introductionmentioning

confidence: 99%

Selecting Covariates for Genome-Wide Association Studies

Dor

Margaliot

Brandes

et al. 2023

Preprint

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The choice of which covariates to include in a Genome-Wide Association Study (GWAS) is important since it affects the ability to detect true association signal of variants, to correct for confounders and avoid false positives, and the running time of the analysis. Commonly used covariates include age, sex, genotyping batches, genotyping array type, as well as an arbitrary number of Principal Components (PCs) used to adjust for population structure. Despite the importance of this issue, there is no consensus or clear guidelines for the right choice of covariates. Therefore, studies typically employ heuristics for their choice with no clear justification. Here, we explore the dependence of the GWAS analysis results on the choice of covariates for a wide range of quantitative and binary human phenotypes. We propose guidelines for covariates choice based on the phenotype's type (quantitative vs. disease), the heritability, and the disease prevalence, with the goal of maximizing the statistical power to detect true associations and fit accurate polygenic scores while avoiding spurious associations and minimizing computation time. We analyze 36 traits in the UK-Biobank dataset. We show that the genotype batch and assessment center can be safely removed as covariates, thus significantly reducing the GWAS computational burden for these traits.

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“…First, recent studies show uncorrected fine-scale population structure in UK Biobank causes geographic and socioeconomic confounding that results in spurious associations with disease traits [14][15][16] This issue may be especially pertinent for the MSY, which is uniquely sensitive to population structure. 17,18 Eales et al 13 only corrected for 5 genetic principal components, which may not be adequate to rule out such confounding.…”

mentioning

confidence: 99%

Limited Effect of Y Chromosome Variation on Coronary Artery Disease and Mortality in UK Biobank—Brief Report

Timmers

Wilson

2022

ATVB

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Background: The effect of genetic variation in the male-specific region of the Y chromosome (MSY) on coronary artery disease and cardiovascular risk factors has been disputed. In this study, we systematically assessed the association of MSY genetic variation on these traits using a kin-cohort analysis of family disease history in the largest sample to date. Methods: We tested 90 MSY haplogroups against coronary artery disease, hypertension, blood pressure, classical lipid levels, and all-cause mortality in up to 152 186 unrelated, genomically British individuals from UK Biobank. Unlike previous studies, we did not adjust for heritable lifestyle factors (to avoid collider bias) and instead adjusted for geographic variables and socioeconomic deprivation, given the link between MSY haplogroups and geography. For family history traits, subject MSY haplogroups were tested against father and mother disease as validation and negative control, respectively. Results: Our models find little evidence for an effect of any MSY haplogroup on cardiovascular risk in participants. Parental models confirm these findings. Conclusions: Kin-cohort analysis of the Y chromosome uniquely allows for discoveries in subjects to be validated using family history data. Despite our large sample size, improved models, and parental validation, there is little evidence to suggest cardiovascular risk in UK Biobank is influenced by genetic variation in MSY.

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Geographic Confounding in Genome-Wide Association Studies

Cited by 5 publications

References 41 publications

Examining the association between genetic risk for depression, wellbeing and schizophrenia, and proximity to greenspace

Examining the association between genetic risk for depression, wellbeing and schizophrenia, and proximity to greenspace

Selecting Covariates for Genome-Wide Association Studies

Limited Effect of Y Chromosome Variation on Coronary Artery Disease and Mortality in UK Biobank—Brief Report

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