Biology subjects

Colbran, L. L.

Publications and source records attributed to Colbran, L. L..

7 recordsLinked to original sources

bioRxiv2026

Global patterns of natural selection inferred using ancient DNA

Ancient DNA has revolutionized our understanding of human history, and is now yielding important insights into evolution and natural selection. However, studies of selection using ancient DNA have largely been limited to Europe, excluding populations in other parts of the world. While many selective pressures were local to specific populations others, for example those related to the development of agriculture, may have been universal. By studying a broader range of global populations, we can identify examples of local adaption but also more general principles of adaptation to climatic, social and technological changes. We therefore leverage ancient DNA to test for selection in 7244 individuals from 13 ancient and 19 present-day populations across five regions: Europe, East Asia, South Asia, Africa and the Americas. In each region, we tested for selection using multiple approaches that account for complex demographic histories. We identify 31 genome-wide significant signals of selection, including both known and novel loci. We find a high degree of shared signal across regions, suggesting extensive parallel or shared adaptation. Using a novel admixture-aware time series method, we find that the strength of selection on many variants changed over time, for example decreasing selection at LCT in Europe and increasing selection at ADH1B in East Asia over the past few thousand years. Finally, we developed a test for polygenic selection on complex traits by modeling the frequencies of trait-associated alleles identified in GWAS. We tested for selection jointly across regions, avoiding the confounding effect of population stratification by excluding the European or East Asian GWAS population from the selection test. We find evidence for directional selection on pigmentation and immune traits, and that strong stabilizing selection on female waist-hip ratio was universal across human populations suggesting a fundamental constraint on human morphology.

genomics↗

bioRxiv2023

Predicting functional consequences of recent natural selection in Britain

Ancient DNA can directly reveal the contribution of natural selection to human genomic variation. However, while the analysis of ancient DNA has been successful at identifying genomic signals of selection, inferring the phenotypic consequences of that selection has been more difficult. Most trait-associated variants are non-coding, so we expect that a large proportion of the phenotypic effects of selection will also act through non-coding variation. Since we cannot measure gene expression directly in ancient individuals, we used an approach (Joint-Tissue Imputation; JTI) developed to predict gene expression from genotype data. We tested for changes in the predicted expression of 17,384 protein coding genes over a time transect of 4500 years using 91 present-day and 616 ancient individuals from Britain. We identified 28 genes at seven genomic loci with significant (FDR < 0.05) changes in predicted expression levels in this time period. We compared the results from our transcriptome-wide scan to a genome-wide scan based on estimating per-SNP selection coefficients from time series data. At five previously identified loci, our approach allowed us to highlight small numbers of genes with evidence for significant shifts in expression from peaks that in some cases span tens of genes. At two novel loci (SLC44A5 and NUP85), we identify selection on gene expression not captured by scans based on genomic signatures of selection. Finally we show how classical selection statistics (iHS and SDS) can be combined with JTI models to incorporate functional information into scans that use present-day data alone. These results demonstrate the potential of this type of information to explore both the causes and consequences of natural selection.

evolutionary biology↗

bioRxiv2023

Neanderthal Introgression Shaped Human Circadian Traits

IntroductionWhen the ancestors of modern Eurasians migrated out of Africa and interbred with Eurasian archaic hominins, namely Neanderthals and Denisovans, DNA of archaic ancestry integrated into the genomes of anatomically modern humans. This process potentially accelerated adaptation to Eurasian environmental factors, including reduced ultra-violet radiation and increased variation in seasonal dynamics. However, whether these groups differed substantially in circadian biology, and whether archaic introgression adaptively contributed to human chronotypes remains unknown. ResultsHere we traced the evolution of chronotype based on genomes from archaic hominins and present-day humans. First, we inferred differences in circadian gene sequences, splicing, and regulation between archaic hominins and modern humans. We identified 28 circadian genes containing variants with potential to alter splicing in archaics (e.g., CLOCK, PER2, RORB, RORC), and 16 circadian genes likely divergently regulated between present-day humans and archaic hominins, including RORA. These differences suggest the potential for introgression to modify circadian gene expression. Testing this hypothesis, we found that introgressed variants are enriched among eQTLs for circadian genes. Supporting the functional relevance of these regulatory effects, we found that many introgressed alleles have associations with chronotype. Strikingly, the strongest introgressed effects on chronotype increase morningness, consistent with adaptations to high latitude in other species. Finally, we identified several circadian loci with evidence of adaptive introgression or latitudinal clines in allele frequency. ConclusionsThese findings identify differences in circadian gene regulation between modern humans and archaic hominins and support the contribution of introgression via coordinated effects on variation in human chronotype. SIGNIFICANCE STATEMENTInterbreeding between humans and Neanderthals created the potential for adaptive introgression as humans moved into environments that had been populated by Neanderthals for hundreds of thousands of years. Here we discover lineage-specific genetic differences in circadian genes and their regulatory elements between humans and Neanderthals. We show that introgressed alleles are enriched for effects on circadian gene regulation, consistently increase propensity for morningness in Europeans, and show evidence of adaptive introgression or associations between latitude and frequency. These results expand our understanding of how the genomes of humans and our closest relatives responded to environments with different light/dark cycles, and demonstrate a coordinated contribution of admixture to human chronotype in a direction that is consistent with adaptation to higher latitudes.

evolutionary biology↗

bioRxiv2022

A gene-level test for directional selection on gene expression

Most variants identified in human genome-wide association studies and scans for selection are non-coding. Interpretation of these variants effects and understanding of the way in which they contribute to phenotypic variation and adaptation in human populations is therefore limited by our understanding of gene regulation and by the difficulty in confidently linking non-coding variants to genes. To overcome this, we developed a gene-by-gene test for population-specific selection based on combinations of regulatory variants. We extended the QX test for polygenic selection to test for selection on regulatory variants for 17,388 protein-coding genes across 2,504 individuals. We identified 45 genes with significant evidence (FDR <0.1) for selection, including FADS1, KHK, SULT1A2, ITGAM, and genes in the HLA region. We further confirm that significant selection signals do correspond to plausible population-level differences in predicted expression. However, we find that very few (0.2%) genes have strong evidence for directional, population-specific selection on the component of their expression that is predicted by cis-regulatory variants. While this is consistent with most cis-regulatory variation evolving under genetic drift or stabilizing selection, it is also possible that any effects are smaller than we can detect, or that population-specific selection is driven by tissue-specific or trans effects. Our gene-level QX score is independent of other methods for detecting selection based on genomic variation, may therefore be useful when used in combination with more traditional selection tests to specifically identify selection on regulatory variation. Overall, our results demonstrate the utility of one approach to combining population-level information with functional data to understand the evolution of gene expression.

genomics↗

bioRxiv2022

Resurrecting the Alternative Splicing Landscape of Archaic Hominins using Machine Learning

Alternative splicing contributes to adaptation and divergence in many species. However, it has not been possible to directly compare splicing between modern and archaic hominins. Here, we unmask the recent evolution of this previously unobservable regulatory mechanism by applying SpliceAI, a machine-learning algorithm that identifies splice altering variants (SAVs), to high-coverage genomes from three Neanderthals and a Denisovan. We discover 5,950 putative archaic SAVs, of which 2,186 are archaic-specific and 3,607 also occur in modern humans via introgression (244) or shared ancestry (3,520). Archaic-specific SAVs are enriched in genes that contribute to many traits potentially relevant to hominin phenotypic divergence, such as the epidermis, respiration, and spinal rigidity. Compared to shared SAVs, archaic-specific SAVs occur in sites under weaker selection and are more common in genes with tissue-specific expression. Further underscoring the importance of negative selection on SAVs, Neanderthal lineages with low effective population sizes are enriched for SAVs compared to Denisovan and shared SAVs. Finally, we find that nearly all introgressed SAVs in humans were shared across Neanderthals, suggesting that older SAVs were more tolerated in modern human genomes. Our results reveal the splicing landscape of archaic hominins and identify potential contributions of splicing to phenotypic differences among hominins.

genomics↗

bioRxiv2021

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

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.

genomics↗

bioRxiv2017

Short DNA sequence patterns accurately identify broadlyactive human enhancers

Enhancers are DNA regulatory elements that influence gene expression. There is substantial diversity in enhancers activity patterns: some enhancers drive expression in a single cellular context, while others are active across many. Sequence characteristics, such as transcription factor (TF) binding motifs, influence the activity patterns of regulatory sequences; however, the regulatory logic through which specific sequences drive enhancer activity patterns is poorly understood. Recent analysis of Drosophila enhancers suggested that short dinucleotide repeat motifs (DRMs) are general enhancer sequence features that drive broad regulatory activity. However, it is not known whether the regulatory role of DRMs is conserved across species. We performed a comprehensive analysis of the relationship between short DNA sequence patterns, including DRMs, and human enhancer activity in 38,538 enhancers across 411 different contexts. In a machine-learning framework, the occurrence patterns of short sequence motifs accurately predicted broadly active human enhancers. However, DRMs alone were weakly predictive of broad enhancer activity in humans and showed different enrichment patterns than in Drosophila. In general, GC-rich sequence motifs were significantly associated with broad enhancer activity; consistent with this enrichment, broadly active human TFs recognize GC-rich motifs. Our results reveal the importance of specific sequence motifs in broadly active human enhancers, demonstrate the lack of evolutionary conservation of the role of DRMs, and provide a computational framework for investigating the logic of enhancer sequences.

genomics↗