Dataset Information

Population phenomena inflate genetic associations of complex social traits.

ABSTRACT: Heritability, genetic correlation, and genetic associations estimated from samples of unrelated individuals are often perceived as confirmation that genotype causes the phenotype(s). However, these estimates can arise from indirect mechanisms due to population phenomena including population stratification, dynastic effects, and assortative mating. We introduce these, describe how they can bias or inflate genotype-phenotype associations, and demonstrate methods that can be used to assess their presence. Using data on educational achievement and parental socioeconomic position as an exemplar, we demonstrate that both heritability and genetic correlation may be biased estimates of the causal contribution of genotype. These results highlight the limitations of genotype-phenotype estimates obtained from samples of unrelated individuals. Use of these methods in combination with family-based designs may offer researchers greater opportunities to explore the mechanisms driving genotype-phenotype associations and identify factors underlying bias in estimates.

SUBMITTER: Morris TT

PROVIDER: S-EPMC7159920 | biostudies-literature | 2020 Apr

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Population phenomena inflate genetic associations of complex social traits.

Morris Tim T TT Davies Neil M NM Hemani Gibran G Smith George Davey GD

Science advances 20200415 16

Heritability, genetic correlation, and genetic associations estimated from samples of unrelated individuals are often perceived as confirmation that genotype causes the phenotype(s). However, these estimates can arise from indirect mechanisms due to population phenomena including population stratification, dynastic effects, and assortative mating. We introduce these, describe how they can bias or inflate genotype-phenotype associations, and demonstrate methods that can be used to assess their pr ...[more]

PMID: 32426451

Similar Datasets

Project description:Despite a widespread global distribution and highly variable disease phenotype, there is little DNA sequence diversity among isolates of Mycobacterium tuberculosis. In addition, many regional population genetic surveys have revealed a stereotypical structure in which a single clone, lineage, or clade makes up the majority of the population. It is often assumed that dominant clones are highly adapted, that is, the overall structure of M. tuberculosis populations is the result of positive selection. In order to test this assumption, we analyzed genetic data from extant populations of bacteria circulating in Aboriginal communities in Saskatchewan, Canada. Demographic parameters of the bacterial population were estimated from archival epidemiological data collected over approximately 130 years since the onset of epidemic tuberculosis in the host communities. Bacterial genetic data were tested against neutral theory expectations and the local evolutionary history of M. tuberculosis investigated by phylogenetic analysis. Our findings are not consistent with positive selection on the bacterial population. Instead, we uncovered founder effects persisting over decades and barriers to gene flow within the bacterial population. Simulation experiments suggested that a combination of these neutral influences could result in the stereotypical structure of M. tuberculosis populations. Some aspects of population structure were suggestive of background selection, and data were on the whole consistent with combined effects of population bottlenecks, subdivision, and background selection. Neutral phenomena, namely, bottlenecks and partitions within populations, are prominent influences on the evolution of M. tuberculosis and likely contribute to restricted genetic diversity observed within this species. Given these influences, a complex evolutionary model will be required to define the relative fitness of different M. tuberculosis lineages and, ultimately, to uncover the genetic basis for its success as a pathogen.

Project description:BackgroundAn important contributor to the decreased life expectancy of individuals with schizophrenia is sudden cardiac death. Arrhythmic disorders may play an important role herein, but the nature of the relationship between schizophrenia and arrhythmia is unclear.AimsTo assess shared genetic liability and potential causal effects between schizophrenia and arrhythmic disorders and electrocardiogram (ECG) traits.MethodWe leveraged summary-level data of large-scale genome-wide association studies of schizophrenia (53 386 cases, 77 258 controls), arrhythmic disorders (atrial fibrillation, 55 114 cases, 482 295 controls; Brugada syndrome, 2820 cases, 10 001 controls) and ECG traits (heart rate (variability), PR interval, QT interval, JT interval and QRS duration, n = 46 952-293 051). We examined shared genetic liability by assessing global and local genetic correlations and conducting functional annotation. Bidirectional causal relations between schizophrenia and arrhythmic disorders and ECG traits were explored using Mendelian randomisation.ResultsThere was no evidence for global genetic correlation, except between schizophrenia and Brugada syndrome (rg = 0.14, 95% CIs = 0.06-0.22, P = 4.0E-04). In contrast, strong positive and negative local correlations between schizophrenia and all cardiac traits were found across the genome. In the most strongly associated regions, genes related to immune and viral response mechanisms were overrepresented. Mendelian randomisation indicated that liability to schizophrenia causally increases Brugada syndrome risk (beta = 0.14, CIs = 0.03-0.25, P = 0.009) and heart rate during activity (beta = 0.25, CIs = 0.05-0.45, P = 0.015).ConclusionsDespite little evidence for global genetic correlation, specific genomic regions and biological pathways emerged that are important for both schizophrenia and arrhythmia. The putative causal effect of liability to schizophrenia on Brugada syndrome warrants increased cardiac monitoring and early medical intervention in people with schizophrenia.

Dataset Information

Population phenomena inflate genetic associations of complex social traits.

Publications

Population phenomena inflate genetic associations of complex social traits.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets