Background

Genome-Wide Association Studies (GWAS) have been a powerful tool for elucidating the relationship between specific genetic variants and human diseases or behavioral traits. Researchers worldwide have identified tens of thousands of genetic associations using this method, but its limitations are also evident. Traditional analysis methods primarily rely on data from a large number of unrelated individuals, making it difficult to separately explain the individual effects of genetic factors inherited from parents and environmental influences within the household.

When parents do not directly pass on genes to their children, but their genetic makeup influences the parenting environment, which in turn indirectly affects the child, this phenomenon is known as genetic nurture. For example, parents with a genetic predisposition for reading may create a home environment rich in books, leading to higher academic achievement in their children, even if the children do not inherit the same genes. These indirect effects, along with assortative mating between parents and population structure distortions, have been identified as factors that overestimate genetic influences in traditional analyses. To overcome these issues, family-based designs that analyze the genomes of parents and children together are required, but obtaining trio data has been challenging, making such research difficult to conduct.

Key Findings

An international research team addressed this challenge by utilizing data from the Norwegian Mother, Father and Child Cohort Study (MoBa). The team obtained genomes from 77,634 mothers, 53,358 fathers, and 76,577 children, forming 44,017 complete trio families where all three family members were included. Based on this family genomic data, the researchers precisely analyzed four traits in children: height (at age 7), sleep duration (at age 7), depressive symptoms (at age 8), and academic achievement (at age 10).

When applying a family-based model, the genetic influences were significantly reduced compared to previous population-level analyses. The heritability of academic achievement and height decreased from 31% and 35%, respectively, to approximately 22% after adjusting for parental genotypes. The heritability of sleep duration also dropped sharply from 5% to 1%, revealing the dominant role of environmental factors. In contrast, the heritability of depressive symptoms increased from 6% to 11%, indicating a stronger direct genetic effect.

The effects of genetic variants associated with academic achievement, previously reported in studies, decreased by 23% after controlling for parental genotypes, and the effects of height-related genetic variants also reduced by 11%. Similar distortion patterns were observed in polygenic score (PGS) analyses. The correlation between a child's height PGS and actual height was inflated by about 9% when parental genotypes were not adjusted. Notably, the negative correlation between academic achievement PGS and child depressive symptoms increased by more than double (-0.03 to -0.06) after adjusting for parental genotypes. Most of these suppression effects and distortions in the depressive symptom correlation were attributed to the mother's genetic factors rather than the father's.

Implications and Future Outlook

This analysis successfully separated direct genetic effects from indirect environmental effects using large-scale family data. It marks a milestone in moving beyond the limitations of population-based genetic studies and in uncovering the true genetic causal relationships in disease and development. However, family-based analyses involve complex statistical processes and standard errors that are, on average, 38% larger than those in traditional analyses, making ultra-large sample sizes essential. The current analysis is primarily based on European populations, limiting its direct applicability to diverse global ethnicities and environments. Complementary studies using multi-ethnic family cohorts are expected to become more active in the future.