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Abstract The adoption of agriculture triggered a rapid shift towards starch-rich diets in human populations¹. Amylase genes facilitate starch digestion, and increased amylase copy number has been observed in some modern human populations with high-starch intake², although evidence of recent selection is lacking^3,4. Here, using 94 long-read haplotype-resolved assemblies and short-read data from approximately 5,600 contemporary and ancient humans, we resolve the diversity and evolutionary history of structural variation at the amylase locus. We find that amylase genes have higher copy numbers in agricultural populations than in fishing, hunting and pastoral populations. We identify 28 distinct amylase structural architectures and demonstrate that nearly identical structures have arisen recurrently on different haplotype backgrounds throughout recent human history.AMY1andAMY2Agenes each underwent multiple duplication/deletion events with mutation rates up to more than 10,000-fold the single-nucleotide polymorphism mutation rate, whereasAMY2Bgene duplications share a single origin. Using a pangenome-based approach, we infer structural haplotypes across thousands of humans identifying extensively duplicated haplotypes at higher frequency in modern agricultural populations. Leveraging 533 ancient human genomes, we find that duplication-containing haplotypes (with more gene copies than the ancestral haplotype) have rapidly increased in frequency over the past 12,000 years in West Eurasians, suggestive of positive selection. Together, our study highlights the potential effects of the agricultural revolution on human genomes and the importance of structural variation in human adaptation. 

Abstract Over the past few decades, there has been an explosion in the amount of publicly available sequencing data. This opens new opportunities for combining data sets to achieve unprecedented sample sizes, spatial coverage or temporal replication in population genomic studies. However, a common concern is that nonbiological differences between data sets may generate patterns of variation in the data that can confound real biological patterns, a problem known as batch effects. In this paper, we compare two batches of low‐coverage whole genome sequencing (lcWGS) data generated from the same populations of Atlantic cod (Gadus morhua). First, we show that with a “batch‐effect‐naive” bioinformatic pipeline, batch effects systematically biased our genetic diversity estimates, population structure inference and selection scans. We then demonstrate that these batch effects resulted from multiple technical differences between our data sets, including the sequencing chemistry (four‐channel vs. two‐channel), sequencing run, read type (single‐end vs. paired‐end), read length (125 vs. 150 bp), DNA degradation level (degraded vs. well preserved) and sequencing depth (0.8× vs. 0.3× on average). Lastly, we illustrate that a set of simple bioinformatic strategies (such as different read trimming and single nucleotide polymorphism filtering) can be used to detect batch effects in our data and substantially mitigate their impact. We conclude that combining data sets remains a powerful approach as long as batch effects are explicitly accounted for. We focus on lcWGS data in this paper, which may be particularly vulnerable to certain causes of batch effects, but many of our conclusions also apply to other sequencing strategies. 

Abstract Low‐coverage whole genome sequencing (lcWGS) has emerged as a powerful and cost‐effective approach for population genomic studies in both model and nonmodel species. However, with read depths too low to confidently call individual genotypes, lcWGS requires specialized analysis tools that explicitly account for genotype uncertainty. A growing number of such tools have become available, but it can be difficult to get an overview of what types of analyses can be performed reliably with lcWGS data, and how the distribution of sequencing effort between the number of samples analysed and per‐sample sequencing depths affects inference accuracy. In this introductory guide to lcWGS, we first illustrate how the per‐sample cost for lcWGS is now comparable to RAD‐seq and Pool‐seq in many systems. We then provide an overview of software packages that explicitly account for genotype uncertainty in different types of population genomic inference. Next, we use both simulated and empirical data to assess the accuracy of allele frequency, genetic diversity, and linkage disequilibrium estimation, detection of population structure, and selection scans under different sequencing strategies. Our results show that spreading a given amount of sequencing effort across more samples with lower depth per sample consistently improves the accuracy of most types of inference, with a few notable exceptions. Finally, we assess the potential for using imputation to bolster inference from lcWGS data in nonmodel species, and discuss current limitations and future perspectives for lcWGS‐based population genomics research. With this overview, we hope to make lcWGS more approachable and stimulate its broader adoption. 

Abstract Genomic methods are becoming increasingly valuable and established in ecological research, particularly in nonmodel species. Supporting their progress and adoption requires investment in resources that promote (i) reproducibility of genomic analyses, (ii) accessibility of learning tools and (iii) keeping pace with rapidly developing methods and principles.We introduce marineomics.io, an open‐source, living document to disseminate tutorials, reproducibility tools and best principles for ecological genomic research in marine and nonmodel systems.The website's existing content spans population and functional genomics, including current recommendations for whole‐genome sequencing, RAD‐seq, Pool‐seq and RNA‐seq. With the goal to facilitate the development of new, similar resources, we describe our process for aggregating and synthesizing methodological principles from the ecological genomics community to inform website content. We also detail steps for authorship and submission of new website content, as well as protocols for providing feedback and topic requests from the community.These web resources were constructed with guidance for doing rigorous, reproducible science. Collaboration and contributions to the website are encouraged from scientists of all skill sets and levels of expertise.