Next-generation data filtering in the genomics era.

Genomic data are ubiquitous across disciplines, from agriculture to biodiversity, ecology, evolution and human health. However, these datasets often contain noise or errors and are missing information that can affect the accuracy and reliability of subsequent computational analyses and conclusions. A key step in genomic data analysis is filtering - removing sequencing bases, reads, genetic variants and/or individuals from a dataset - to improve data quality for downstream analyses. Researchers are confronted with a multitude of choices when filtering genomic data; they must choose which filters to apply and select appropriate thresholds. To help usher in the next generation of genomic data filtering, we review and suggest best practices to improve the implementation, reproducibility and reporting standards for filter types and thresholds commonly applied to genomic datasets. We focus mainly on filters for minor allele frequency, missing data per individual or per locus, linkage disequilibrium and Hardy-Weinberg deviations. Using simulated and empirical datasets, we illustrate the large effects of different filtering thresholds on common population genetics statistics, such as Tajima's D value, population differentiation (FST), nucleotide diversity (π) and effective population size (Ne).

Population genetics of a recent range expansion and subsequent loss of migration in monarch butterflies.

Range expansions-whether permanent or transient-strongly influence the distribution of genetic variation in space. Monarch butterflies are best known for long-distance seasonal migration within North America but are also established as nonmigratory populations around the world, including on Pacific Islands. Previous research has highlighted stepwise expansion across the Pacific, though questions remain about expansion timing and the population genetic consequences of migration loss. Here, we present reduced-representation sequencing data for 275 monarchs from North America (n = 85), 12 Pacific Islands (n = 136) and three locations in Australia (n = 54), with the goal of understanding (i) how the monarch's Pacific expansion has shaped patterns of population genetic variation and (ii) how loss of migration has influenced spatial patterns of differentiation. We find support for previously described stepwise dispersal across the Pacific and document an additional expansion from Hawaii into the Mariana Islands. Nonmigratory monarchs within the Mariana Islands show strong patterns of differentiation, despite their proximity; by contrast, migratory North American samples form a single genetically panmictic population across the continent. Estimates of Pacific establishment timing are highly uncertain (~100-1,000,000 years ago) but overlap with historical records that indicate a recent expansion. Our data support (i) a recent expansion across the Pacific whose timing overlaps with available historical records of establishment and (ii) a strong role for seasonal migration in determining patterns of spatial genetic variation. Our results are noteworthy because they demonstrate how the evolution of partial migration can drive population differentiation over contemporary timescales.

snpR: User friendly population genomics for SNP data sets with categorical metadata.

The analysis of genomic data can be an intimidating process, particularly for researchers who are not experienced programmers. Commonly used analyses are spread across many programs, each requiring their own specific input formats, and so data must often be repeatedly reorganized and transformed into new formats. Analyses often require splitting data according to metadata variables such as population or family, which can be challenging to manage in large data sets. Here, we introduce snpR, a user-friendly data analysis package in R for processing SNP genomic data. snpR is designed to automate data subsetting and analyses across categorical metadata while also streamlining repeated analyses by integrating approaches contained in many different packages in a single ecosystem. snpR facilitates iterative and efficient analyses centred on a single R object for an entire analysis pipeline.

Population genomic monitoring provides insight into conservation status but no correlation with demographic estimates of extinction risk in a threatened trout.

The current extinction crisis requires effective assessment and monitoring tools. Genetic approaches are appealing given the relative ease of field sampling required to estimate genetic diversity characteristics assumed related to population size, evolutionary potential, and extinction risk, and to evaluate hybridization with non-native species simultaneously. However, linkages between population genetic metrics of diversity from survey-style field collections and demographic estimates of population size and extinction risk are still in need of empirical examples, especially for remotely distributed species of conservation concern where the approach might be most beneficial. We capitalized on an exceptional opportunity to evaluate congruence between genetic diversity metrics and demographic-based estimates of abundance and extinction risk from a comprehensive Multiple Population Viability Analysis (MPVA) in a threatened fish, the Lahontan cutthroat trout (LCT). We sequenced non-native trout reference samples and recently collected and archived tissue samples of most remaining populations of LCT (N = 60) and estimated common genetic assessment metrics, predicting minimal hybridization with non-native trout, low diversity, and declining diversity over time. We further hypothesized genetic metrics would correlate positively with MPVA-estimated abundance and negatively with extinction probability. We uncovered several instances of hybridization that pointed to immediate management needs. After removing hybridized individuals, cautious interpretation of low effective population sizes (2-63) suggested reduced evolutionary potential for many LCT populations. Other genetic metrics did not decline over time nor correlate with MPVA-based estimates of harmonic mean abundance or 30-year extinction probability. Our results demonstrate benefits of genetic monitoring for efficiently detecting hybridization and, though genetic results were disconnected from demographic assessment of conservation status, they suggest reduced evolutionary potential and likely a higher conservation risk than currently recognized for this threatened fish. We emphasize that genetic information provides essential complementary insight, in addition to demographic information, for evaluating species status.