Nanopore sequencing of 1000 Genomes Project samples to build a comprehensive catalog of human genetic variation

Less than half of individuals with a suspected Mendelian condition receive a precise molecular diagnosis after comprehensive clinical genetic testing. Improvements in data quality and costs have heightened interest in using long-read sequencing (LRS) to streamline clinical genomic testing, but the absence of control datasets for variant filtering and prioritization has made tertiary analysis of LRS data challenging. To address this, the 1000 Genomes Project ONT Sequencing Consortium aims to generate LRS data from at least 800 of the 1000 Genomes Project samples. Our goal is to use LRS to identify a broader spectrum of variation so we may improve our understanding of normal patterns of human variation. Here, we present data from analysis of the first 100 samples, representing all 5 superpopulations and 19 subpopulations. These samples, sequenced to an average depth of coverage of 37x and sequence read N50 of 54 kbp, have high concordance with previous studies for identifying single nucleotide and indel variants outside of homopolymer regions. Using multiple structural variant (SV) callers, we identify an average of 24,543 high-confidence SVs per genome, including shared and private SVs likely to disrupt gene function as well as pathogenic expansions within disease-associated repeats that were not detected using short reads. Evaluation of methylation signatures revealed expected patterns at known imprinted loci, samples with skewed X-inactivation patterns, and novel differentially methylated regions. All raw sequencing data, processed data, and summary statistics are publicly available, providing a valuable resource for the clinical genetics community to discover pathogenic SVs.

Out to sea: ocean currents and patterns of asymmetric gene flow in an intertidal fish species

Passive dispersal via wind or ocean currents can drive asymmetric gene flow, which influences patterns of genetic variation and the capacity of populations to evolve in response to environmental change. The mangrove rivulus fish (Kryptolebias marmoratus), hereafter "rivulus," is an intertidal fish species restricted to the highly fragmented New World mangrove forests of Central America, the Caribbean, the Bahamas, and Florida. Mangrove patches are biological islands with dramatic differences in both abiotic and biotic conditions compared to adjacent habitat. Over 1,000 individual rivulus across 17 populations throughout its range were genotyped at 32 highly polymorphic microsatellites. Range-wide population genetic structure was evaluated with five complementary approaches that found eight distinct population clusters. However, an analysis of molecular variance indicated significant population genetic structure among regions, populations within regions, sampling locations within populations, and individuals within sampling locations, indicating that rivulus has both broad- and fine-scale genetic differentiation. Integrating range-wide genetic data with biophysical modeling based on 10 years of ocean current data showed that ocean currents and the distance between populations over water drive gene flow patterns on broad scales. Directional migration estimates suggested some significant asymmetries in gene flow that also were mediated by ocean currents and distance. Specifically, populations in the center of the range (Florida Keys) were identified as sinks that received migrants (and alleles) from other populations but failed to export individuals. These populations thus harbor genetic variation, perhaps even from extirpated populations across the range, but ocean currents and complex arrangements of landmasses might prevent the distribution of that genetic variation elsewhere. Hence, the inherent asymmetry of ocean currents shown to impact both genetic differentiation and directional migration rates may be responsible for the complex distribution of genetic variation across the range and observed patterns of metapopulation structure.

The practice and promise of temporal genomics for measuring evolutionary responses to global change

Understanding the evolutionary consequences of anthropogenic change is imperative for estimating long-term species resilience. While contemporary genomic data can provide us with important insights into recent demographic histories, investigating past change using present genomic data alone has limitations. In comparison, temporal genomics studies, defined herein as those that incorporate time series genomic data, leverage museum collections and repeated field sampling to directly examine evolutionary change. As temporal genomics is applied to more systems, species, and questions, best practices can be helpful guides to make the most efficient use of limited resources. Here, we conduct a systematic literature review to synthesize the effects of temporal genomics methodology on our ability to detect evolutionary changes. We focus on studies investigating recent change within the past 200 years, highlighting evolutionary processes that have occurred during the past two centuries of accelerated anthropogenic pressure. We first identify the most frequently studied taxa, systems, questions, and drivers, before highlighting overlooked areas where further temporal genomic studies may be particularly enlightening. Then, we provide guidelines for future study and sample designs while identifying key considerations that may influence statistical and analytical power. Our aim is to provide recommendations to a broad array of researchers interested in using temporal genomics in their work.

The Biological Hierarchy, Time, and Temporal 'Omics in Evolutionary Biology: A Perspective

Sequencing data-genomics, transcriptomics, epigenomics, proteomics, and metabolomics-have revolutionized biological research, enabling a more detailed study of processes, ranging from subcellular to evolutionary, that drive biological organization. These processes, collectively, are responsible for generating patterns of phenotypic variation and can operate over dramatically different timescales (milliseconds to billions of years). While researchers often study phenotypic variation at specific levels of biological organization to isolate processes operating at that particular scale, the varying types of sequence data, or 'omics, can also provide complementary inferences to link molecular and phenotypic variation to produce an integrated view of evolutionary biology, ranging from molecular pathways to speciation. We briefly describe how 'omics has been used across biological levels and then demonstrate the utility of integrating different types of sequencing data across multiple biological levels within the same study to better understand biological phenomena. However, single-time-point studies cannot evaluate the temporal dynamics of these biological processes. Therefore, we put forward temporal 'omics as a framework that can better enable researchers to study the temporal dynamics of target processes. Temporal 'omics is not infallible, as the temporal sampling regime directly impacts inferential ability. Thus, we also discuss the role the temporal sampling regime plays in deriving inferences about the environmental conditions driving biological processes and provide examples that demonstrate the impact of the sampling regime on biological inference. Finally, we forecast the future of temporal 'omics by highlighting current methodological advancements that will enable temporal 'omics to be extended across species and timescales. We extend this discussion to using temporal multi-omics to integrate across the biological hierarchy to evaluate and link the temporal dynamics of processes that generate phenotypic variation.