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Simulation is a key tool in population genetics for both methods development and empirical research, but producing simulations that recapitulate the main features of genomic data sets remains a major obstacle. Today, more realistic simulations are possible thanks to large increases in the quantity and quality of available genetic data, and to the sophistication of inference and simulation software. However, implementing these simulations still requires substantial time and specialized knowledge. These challenges are especially pronounced for simulating genomes for species that are not well-studied, since it is not always clear what information is required to produce simulations with a level of realism sufficient to confidently answer a given question. The community-developed frameworkstdpopsimseeks to lower this barrier by facilitating the simulation of complex population genetic models using up-to-date information. The initial version ofstdpopsimfocused on establishing this framework using six well-characterized model species (Adrion et al., 2020). Here, we report on major improvements made in the new release ofstdpopsim(version 0.2), which includes a significant expansion of the species catalog and substantial additions to simulation capabilities. Features added to improve the realism of the simulated genomes include non-crossover recombination and provision of species-specific genomic annotations. Through community-driven efforts, we expanded the number of species in the catalog more than three-fold and broadened coverage across the tree of life. During the process of expanding the catalog, we have identified common sticking points and developed best practices for setting up genome-scale simulations. We describe the input data required for generating a realistic simulation, suggest good practices for obtaining the relevant information from the literature, and discuss common pitfalls and major considerations. These improvements tostdpopsimaim to further promote the use of realistic whole-genome population genetic simulations, especially in non-model organisms, making them available, transparent, and accessible to everyone.

BACKGROUND & AIMS: The sucrase-isomaltase (SI) c.273_274delAG loss-of-function variant is common in Arctic populations and causes congenital sucrase-isomaltase deficiency, which is an inability to break down and absorb sucrose and isomaltose. Children with this condition experience gastrointestinal symptoms when dietary sucrose is introduced. We aimed to describe the health of adults with sucrase-isomaltase deficiency.METHODS: The association between c.273_274delAG and phenotypes related to metabolic health was assessed in 2 cohorts of Greenlandic adults (n = 4922 and n = 1629). A sucrase-isomaltase knockout (Sis-KO) mouse model was used to further elucidate the findings.RESULTS: Homozygous carriers of the variant had a markedly healthier metabolic profile than the remaining population, including lower body mass index (β [standard error], -2.0 [0.5] kg/m2; P = 3.1 × 10-5), body weight (-4.8 [1.4] kg; P = 5.1 × 10-4), fat percentage (-3.3% [1.0%]; P = 3.7 × 10-4), fasting triglyceride (-0.27 [0.07] mmol/L; P = 2.3 × 10-6), and remnant cholesterol (-0.11 [0.03] mmol/L; P = 4.2 × 10-5). Further analyses suggested that this was likely mediated partly by higher circulating levels of acetate observed in homozygous carriers (β [standard error], 0.056 [0.002] mmol/L; P = 2.1 × 10-26), and partly by reduced sucrose uptake, but not lower caloric intake. These findings were verified in Sis-KO mice, which, compared with wild-type mice, were leaner on a sucrose-containing diet, despite similar caloric intake, had significantly higher plasma acetate levels in response to a sucrose gavage, and had lower plasma glucose level in response to a sucrose-tolerance test.CONCLUSIONS: These results suggest that sucrase-isomaltase constitutes a promising drug target for improvement of metabolic health, and that the health benefits are mediated by reduced dietary sucrose uptake and possibly also by higher levels of circulating acetate.

In genomics-scale datasets, loci are closely packed within chromosomes and hence provide correlated information. Averaging across loci as if they were independent creates pseudoreplication, which reduces the effective degrees of freedom (df’) compared to the nominal degrees of freedom, df. This issue has been known for some time, but consequences have not been systematically quantified across the entire genome. Here we measured pseudoreplication (quantified by the ratio df’/df) for a common metric of genetic differentiation (FST) and a common measure of linkage disequilibrium between pairs of loci (r2). Based on data simulated using models (SLiM and msprime) that allow efficient forward-in-time and coalescent simulations while precisely controlling population pedigrees, we estimated df’ and df’/df by measuring the rate of decline in the variance of mean FST and mean r2 as more loci were used. For both indices, df’ increases with Ne and genome size, as expected. However, even for large Ne and large genomes, df’ for mean r2 plateaus after a few thousand loci, and a variance components analysis indicates that the limiting factor is uncertainty associated with sampling individuals rather than genes. Pseudoreplication is less extreme for FST, but df’/df ≤0.01 can occur in datasets using tens of thousands of loci. Commonly-used block-jackknife methods consistently overestimated var(FST), producing very conservative confidence intervals. Predicting df’ based on our modeling results as a function of Ne, L, S, and genome size provides a robust way to quantify precision associated with genomics-scale datasets.

Knowledge of how individuals are related is important in many areas of research, and numerous methods for inferring pairwise relatedness from genetic data have been developed. However, the majority of these methods were not developed for situations where data are limited. Specifically, most methods rely on the availability of population allele frequencies, the relative genomic position of variants and accurate genotype data. But in studies of non‐model organisms or ancient samples, such data are not always available. Motivated by this, we present a new method for pairwise relatedness inference, which requires neither allele frequency information nor information on genomic position. Furthermore, it can be applied not only to accurate genotype data but also to low‐depth sequencing data from which genotypes cannot be accurately called. We evaluate it using data from a range of human populations and show that it can be used to infer close familial relationships with a similar accuracy as a widely used method that relies on population allele frequencies. Additionally, we show that our method is robust to SNP ascertainment and applicable to low‐depth sequencing data generated using different strategies, including resequencing and RADseq, which is important for application to a diverse range of populations and species.

The role of rare variants in complex traits remains uncharted. Here, we conduct deep whole genome sequencing of 1457 individuals from an isolated population, and test for rare variant burdens across six cardiometabolic traits. We identify a role for rare regulatory variation, which has hitherto been missed. We find evidence of rare variant burdens that are independent of established common variant signals (ADIPOQ and adiponectin, P = 4.2 × 10−8; APOC3 and triglyceride levels, P = 1.5 × 10−26), and identify replicating evidence for a burden associated with triglyceride levels in FAM189B (P = 2.2 × 10−8), indicating a role for this gene in lipid metabolism., Rare genetic variants can contribute to complex traits but this contribution is not well understood. Here, the authors analyse deep whole genome sequencing data across 1457 individuals from an isolated Greek population and find association of rare variant burdens with cardiometabolic traits.

Meiotic recombination is fundamental for generating new genetic variation and for securing proper disjunction. Further, recombination plays an essential role during the rediploidization process of polyploid-origin genomes because crossovers between pairs of homeologous chromosomes retain duplicated regions. A better understanding of how recombination affects genome evolution is crucial for interpreting genomic data; unfortunately, current knowledge mainly originates from a few model species. Salmonid fishes provide a valuable system for studying the effects of recombination in nonmodel species. Salmonid females generally produce thousands of embryos, providing large families for conducting inheritance studies. Further, salmonid genomes are currently rediploidizing after a whole genome duplication and can serve as models for studying the role of homeologous crossovers on genome evolution. Here, we present a detailed interrogation of recombination patterns in sockeye salmon (Oncorhynchus nerka). First, we use RAD sequencing of haploid and diploid gynogenetic families to construct a dense linkage map that includes paralogous loci and location of centromeres. We find a nonrandom distribution of paralogs that mainly cluster in extended regions distally located on 11 different chromosomes, consistent with ongoing homeologous recombination in these regions. We also estimate the strength of interference across each chromosome; results reveal strong interference and crossovers are mostly limited to one per arm. Interference was further shown to continue across centromeres, but metacentric chromosomes generally had at least one crossover on each arm. We discuss the relevance of these findings for both mapping and population genomic studies.

Salmonids are an important cultural and ecological resource exhibiting near worldwide distribution between their native and introduced range. Previous research has generated linkage maps and genomic resources for several species as well as genome assemblies for two species. We first leveraged improvements in mapping and genotyping methods to create a dense linkage map for Chinook salmon Oncorhynchus tshawytscha by assembling family data from different sources. We successfully mapped 14 620 SNP loci including 2336 paralogs in subtelomeric regions. This improved map was then used as a foundation to integrate genomic resources for gene annotation and population genomic analyses. We anchored a total of 286 scaffolds from the Atlantic salmon genome to the linkage map to provide a framework for the placement 11 728 Chinook salmon ESTs. Previously identified thermotolerance QTL were found to colocalize with several candidate genes including HSP70, a gene known to be involved in thermal response, as well as its inhibitor. Multiple regions of the genome with elevated divergence between populations were also identified, and annotation of ESTs in these regions identified candidate genes for fitness related traits such as stress response, growth and behaviour. Collectively, these results demonstrate the utility of combining genomic resources with linkage maps to enhance evolutionary inferences.

The original version of this Article contained an error in Fig. 2. In panel a, the two legend items “rare” and “common” were inadvertently swapped. This has been corrected in both the PDF and HTML versions of the Article.