Clarifying the causes of consistent and inconsistent findings in genetics

Abstract

As research in genetics has advanced, some findings have been unexpected or shown to be inconsistent between studies or datasets. The reasons these inconsistencies arise are complex. Results from genetic studies can be affected by various factors including statistical power, linkage disequilibrium, quality control, confounding and selection bias, as well as real differences from interactions and effect modifiers, which may be informative about the mechanisms of traits and disease. Statistical artefacts can manifest as differences between results but they can also conceal underlying differences, which implies that their critical examination is important for understanding the underpinnings of traits. In this review, we examine these factors and outline how they can be identified and conceptualised with structural causal models. We explain the consequences they have on genetic estimates, such as genetic associations, polygenic scores, family-and genome-wide heritability, and describe methods to address them to aid in the estimation of true effects of genetic variation. Clarifying these factors can help researchers anticipate when results are likely to diverge and aid researchers' understanding of causal relationships between genes and complex traits.

Interaction of social support and depressive symptoms on antiretroviral therapy adherence among people living with HIV in South Africa

“…Although the study found no significant association between depressive symptoms and poor ART adherence, it is imperative to note that this study has become outdated. The inconsistencies in findings between this study and the previous study can be attributed to variations in sample characteristics, sample size, methodological approaches, and geographical location (Dattani et al 2022 ), given their potential influence on the relationship between depressive symptoms and ART adherence.…”

Analysis of Genetic Diversity in Patients with Major Psychiatric Disorders versus Healthy Controls: A molecular-genetic study of 1,698 subjects genotyped for 100 candidate genes (549 SNPs)

“…Existing GWAs of psychiatric patients appear to be a good choice to evaluate genetic diversity without much effort in the proposed way. However, GWAs typically have relatively high error rates along with high percentages of missing data which might become an unmanageable obstacle for multidimensional methods [34]. Another problem could arise from the fact that the SNPs within genes cannot be freely selected, so that sufficiently high information contents for the resulting SNP combinations (genotypic patterns) are not necessarily given.…”