Tracing the evolution of human gene regulation and its association with shifts in environment

Abstract

As humans spread throughout the world, they adapted to variation in many environmental factors, including climate, diet, and pathogens. Because many of these adaptations were likely mediated by multiple non-coding variants with small effects on gene regulation, it has been difficult to link genomic signals of selection to specific genes, and to describe the regulatory response to selection. To overcome this challenge, we adapted PrediXcan, a machine learning method for imputing gene regulation from genotype data, to analyze low-coverage ancient human DNA (aDNA). First, we used simulated genomes to benchmark strategies for adapting gene regulatory prediction to increase robustness to incomplete aDNA data. Applying the resulting models to 490 ancient Eurasians, we found that genes with the strongest divergent regulation among ancient populations with hunter-gatherer, pastoralist, and agricultural lifestyles are enriched for metabolic and immune functions. Next, we explored the contribution of divergent gene regulation to two traits with strong evidence of recent adaptation: dietary metabolism and skin pigmentation. We found enrichment for divergent regulation among genes previously proposed to be involved in diet-related local adaptation, and in many cases, the predicted effects on regulation provide explanations for previously observed signals of selection, e.g., at FADS1, GPX1, and LEPR. For skin pigmentation, we applied new models trained in melanocytes to a time series of 2999 ancient Europeans spanning ~38,000 years BP. In contrast to diet, skin pigmentation genes show little regulatory change over time, suggesting that adaptation mainly involved large-effect coding variants. This work demonstrates how aDNA can be combined with present-day genomes to shed light on the biological differences among ancient populations, the role of gene regulation in adaptation, and the relationship between ancient genetic diversity and the present-day distribution of complex traits.

Knowledge structure and emerging trends in the application of deep learning in genetics research: A bibliometric analysis [2000–2021]

“…For the #5 "gene regulation" cluster, an ML modality was adapted by Colbran et al to impute gene regulation information from genotype data and investigate 490 ancient Eurasian human DNA samples and explore divergent gene regulation mechanisms which contributed to skin pigmentation and metabolic and immune functions. The authors identified gene regulation roles in adaptation and associations between complex traits and genetic diversity Colbran et al, (2021). Atak et al devised a deep learning approach and integrative genomics strategy to analyze functional enhancer mutations with allelic imbalance of gene expression and chromatin accessibility and successfully interpreted and predicted the impact of a mutation on gene regulation Atak et al, (2021).…”

Highlight—Epigenetics Virtual Issue: New Findings Amid Old Controversies

“…As discussed in the Highlight feature on this article ( McGrath 2021b ), the study supports the idea that early developmental exposures can have persistent impacts on DNA methylation patterns in humans. In their study, John A. Capra and coauthors used a machine learning method to impute gene regulation patterns in DNA samples from ancient Eurasians ( Colbran et al 2021 ). They found differential regulation of genes involved in metabolism and the immune system among ancient populations with different diets and lifestyles.…”