Maternal half-sibling families with discordant fathers: a contrastive design assessing cross-generational paternal genetic transmission of alcohol use disorder, drug abuse and major depression

Annual Research Review: Towards a deeper understanding of nature and nurture: combining family-based quasi-experimental methods with genomic data

Distinguishing between the effects of nature and nurture constitutes a major research goal for those interested in understanding human development. It is known, for example, that many parent traits predict mental health outcomes in children, but the causal processes underlying such associations are often unclear. Family-based quasi-experimental designs such as sibling comparison, adoption and extended family studies have been used for decades to distinguish the genetic transmission of risk from the environmental effects family members potentially have on one another. Recently, these designs have been combined with genomic data, and this combination is fuelling a range of exciting methodological advances. In this review we explore these advances - highlighting the ways in which they have been applied to date and considering what they are likely to teach us in the coming years about the aetiology and intergenerational transmission of psychopathology.

Time trends and associated factors of global burden due to drug use disorders in 204 countries and territories, 1990-2019

BACKGROUND

Drug use disorders (DUDs) have been a public health crisis which strongly impacted community health and socio-economic development. However, there are few studies based on the latest global data to show changes in the disease burden due to DUDs, specifically investigating associations between the country-level socio-economic factors and the burden of DUDs.

METHODS

Data of DUDs were extracted from the Global Burden of Disease Study 2019 database to explore the trends of the disease burden due to DUDs from 1990 to 2019. Univariate linear regression and stepwise multiple linear regression analysis were performed to analyze the correlations between burden due to DUDs and country-level socio-economic factors.

RESULTS

Globally, the number of disability-adjusted life-years (DALYs) caused by DUDs approximately increased by 2.6% yearly from 1990 to 2019, though the age-standardized DALY rate has not changed significantly in the past 30 years. The age-standardized DALY rate of opioid use disorders showed an upward trend during the past 30 years and was highest among 5 types of DUDs in 2019. Inequality-adjusted human development index (β = 15.9, 95% confidence interval [CI]: 12.9-18.9, P < 0.001) was identified as the key risk factor associated with square-root transformed age-standardized DALY rate of DUDs.

CONCLUSIONS

Global burden due to DUDs has increased significantly over the past 30 years. More effective targeted public health policies should be formulated to manage the public health challenge of DUDs, especially in developed countries and territories.

Within family Mendelian randomization studies

Mendelian randomization (MR) is increasingly used to make causal inferences in a wide range of fields, from drug development to etiologic studies. Causal inference in MR is possible because of the process of genetic inheritance from parents to offspring. Specifically, at gamete formation and conception, meiosis ensures random allocation to the offspring of one allele from each parent at each locus, and these are unrelated to most of the other inherited genetic variants. To date, most MR studies have used data from unrelated individuals. These studies assume that genotypes are independent of the environment across a sample of unrelated individuals, conditional on covariates. Here we describe potential sources of bias, such as transmission ratio distortion, selection bias, population stratification, dynastic effects and assortative mating that can induce spurious or biased SNP-phenotype associations. We explain how studies of related individuals such as sibling pairs or parent-offspring trios can be used to overcome some of these sources of bias, to provide potentially more reliable evidence regarding causal processes. The increasing availability of data from related individuals in large cohort studies presents an opportunity to both overcome some of these biases and also to evaluate familial environmental effects.

Population phenomena inflate genetic associations of complex social traits

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.