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A major goal in evolutionary biology and biomedicine is to understand the complex interactions between genetic variants, the epigenome, and gene expression. However, the causal relationships between these factors remain poorly understood. mSTARR-seq, a methylation-sensitive massively parallel reporter assay, is capable of identifying methylation-dependent regulatory activity at many thousands of genomic regions simultaneously and allows for the testing of causal relationships between DNA methylation and gene expression on a region-by-region basis. Here, we develop a multiplexed mSTARR-seq protocol to assay naturally occurring human genetic variation from 25 individuals from 10 localities in Europe and Africa. We identify 6957 regulatory elements in either the unmethylated or methylated state, and this set was enriched for enhancer and promoter chromatin annotations, as expected. The expression of 58% of these regulatory elements is modulated by methylation, which is generally associated with decreased transcription. Within our set of regulatory elements, we use allele-specific expression analyses to identify 8020 sites with genetic effects on gene regulation; further, we find that 42.3% of these genetic effects vary in direction or magnitude between methylated and unmethylated states. Sites exhibiting methylation-dependent genetic effects are enriched for GWAS and EWAS annotations, implicating them in human disease. Compared with data sets that assay DNA from a single European ancestry individual, our multiplexed assay is able to uncover more genetic effects and methylation-dependent genetic effects, highlighting the importance of including diverse genomes in assays that aim to understand gene regulatory processes. 

Abstract Life history theory predicts that organisms allocate resources across physiological processes to maximize fitness. Under this framework, early life adversity (ELA)—which often limits energetic capital—could shape investment in growth and reproduction, as well as trade-offs between them, ultimately contributing to variation in evolutionary fitness. Using long-term demographic, behavioral, and physiological data for 2,100 females from a non-human primate population, we tested whether naturally-occurring ELA influences investment in the competing physiological demands of growth and reproduction. By analyzing ELA, growth, and reproduction in the same individuals, we also assessed whether adversity intensifies trade-offs between life history domains. We found that ELA influenced life history patterns, and was associated with modified growth, delayed reproductive maturity, and small adult body size. Different types of ELA sometimes had distinct reproductive outcomes—e.g., large group size was linked to faster reproductive rates, while low maternal rank predicted slower ones. Adversity also amplified trade-offs between growth and reproduction: small body size was a stronger predictor of delayed and reduced reproductive output in females exposed to ELA, compared to those not exposed. Finally, we examined how traits modified by ELA related to lifetime reproductive success. Across the population, starting reproduction earlier and maintaining a moderate reproductive rate conferred the greatest number of offspring surviving to reproductive maturity. These findings suggest that ELA impacts key life history traits as well as relationships between them, and can constrain individuals from adopting the most optimal reproductive strategy. Significance StatementEarly life adversity (ELA) can have lasting effects on evolutionary fitness (e.g., the number of surviving offspring an animal produces); however, the paths connecting ELA to fitness—for example by influencing growth, reproductive timing or rate, or trade-offs between these processes—remain unclear. Leveraging long-term behavioral, physiological, and demographic data from 2,100 female rhesus macaques, we found that ELA-exposed females exhibited growth and reproductive schedules associated with less-optimal lifetime fitness outcomes. Further, ELA intensified trade-offs between growth and reproduction, suggesting that affected individuals face steeper energetic constraints. Our findings highlight the long-lasting impacts of ELA on traits of evolutionary and biomedical importance in a non-human primate model with relevance to humans. 

ABSTRACT Early life environments can have long-lasting impacts on health and fitness, but the evolutionary significance of these effects remains debated. Two major classes of explanations have been proposed: developmental constraints (DC) explanations posit that early life adversity limits optimal development, leading to long-term costs, while predictive adaptive response (PAR) explanations posit that organisms use early life cues to predict adult conditions, resulting in detriments when adult environments do not match expectations. We tested these hypotheses using anthropological and biomedical data for the Orang Asli—the Indigenous peoples of Peninsular Malaysia—who are undergoing a rapid but heterogenous transition from non-industrial, subsistence-based livelihoods to more industrialized, market-integrated conditions. Using questionnaire data, we show that this shift creates natural variation in the degree of similarity between early life and adult environments. Using anthropometric and health data, we find that, more rural, subsistence-based early life environments are associated with shorter stature but better adult cardiometabolic health. Applying a quadratic regression framework, we find support for DC but not PAR in explaining adult cardiometabolic health, echoing findings and conclusions from other long-lived species. Overall, our results suggest that early life conditions can provide additive protection against common health issues associated with urban, industrialized lifestyle exposure. 

Characterizing DNA methylation patterns is important for addressing key questions in evolutionary biology, development, geroscience, and medical genomics. While costs are decreasing, whole-genome DNA methylation profiling remains prohibitively expensive for most population-scale studies, creating a need for cost-effective, reduced representation approaches (i.e., assays that rely on microarrays, enzyme digests, or sequence capture to target a subset of the genome). Most common whole genome and reduced representation techniques rely on bisulfite conversion, which can damage DNA resulting in DNA loss and sequencing biases. Enzymatic methyl sequencing (EM-seq) was recently proposed to overcome these issues, but thorough benchmarking of EM-seq combined with cost-effective, reduced representation strategies is currently lacking. To address this gap, we optimized the Targeted Methylation Sequencing protocol (TMS)—which profiles ~4 million CpG sites—for miniaturization, flexibility, and multispecies use. First, we tested modifications to increase throughput and reduce cost, including increasing multiplexing, decreasing DNA input, and using enzymatic rather than mechanical fragmentation to prepare DNA. Second, we compared our optimized TMS protocol to commonly used techniques, specifically the Infinium MethylationEPIC BeadChip (n = 55 paired samples) and whole genome bisulfite sequencing (n = 6 paired samples). In both cases, we found strong agreement between technologies (R² = 0.97 and 0.99, respectively). Third, we tested the optimized TMS protocol in three non-human primate species (rhesus macaques, geladas, and capuchins). We captured a high percentage (mean = 77.1%) of targeted CpG sites and produced methylation level estimates that agreed with those generated from reduced representation bisulfite sequencing (R² = 0.98). Finally, we confirmed that estimates of 1) epigenetic age and 2) tissue-specific DNA methylation patterns are strongly recapitulated using data generated from TMS versus other technologies. Altogether, our optimized TMS protocol will enable cost-effective, population-scale studies of genome-wide DNA methylation levels across human and non-human primate species. 

ABSTRACT Characterizing DNA methylation patterns is important for addressing key questions in evolutionary biology, geroscience, and medical genomics. While costs are decreasing, whole-genome DNA methylation profiling remains prohibitively expensive for most population-scale studies, creating a need for cost-effective, reduced representation approaches (i.e., assays that rely on microarrays, enzyme digests, or sequence capture to target a subset of the genome). Most common whole genome and reduced representation techniques rely on bisulfite conversion, which can damage DNA resulting in DNA loss and sequencing biases. Enzymatic methyl sequencing (EM-seq) was recently proposed to overcome these issues, but thorough benchmarking of EM-seq combined with cost-effective, reduced representation strategies has not yet been performed. To do so, we optimized Targeted Methylation Sequencing protocol (TMS)—which profiles ∼4 million CpG sites—for miniaturization, flexibility, and multispecies use at a cost of ∼$80. First, we tested modifications to increase throughput and reduce cost, including increasing multiplexing, decreasing DNA input, and using enzymatic rather than mechanical fragmentation to prepare DNA. Second, we compared our optimized TMS protocol to commonly used techniques, specifically the Infinium MethylationEPIC BeadChip (n=55 paired samples) and whole genome bisulfite sequencing (n=6 paired samples). In both cases, we found strong agreement between technologies (R² = 0.97 and 0.99, respectively). Third, we tested the optimized TMS protocol in three non-human primate species (rhesus macaques, geladas, and capuchins). We captured a high percentage (mean=77.1%) of targeted CpG sites and produced methylation level estimates that agreed with those generated from reduced representation bisulfite sequencing (R² = 0.98). Finally, we applied our protocol to profile age-associated DNA methylation variation in two subsistence-level populations—the Tsimane of lowland Bolivia and the Orang Asli of Peninsular Malaysia—and found age-methylation patterns that were strikingly similar to those reported in high income cohorts, despite known differences in age-health relationships between lifestyle contexts. Altogether, our optimized TMS protocol will enable cost-effective, population-scale studies of genome-wide DNA methylation levels across human and non-human primate species.