Recent advances in conservation and population genomics data analysis

Abstract

New computational methods and next‐generation sequencing (NGS) approaches have enabled the use of thousands or hundreds of thousands of genetic markers to address previously intractable questions. The methods and massive marker sets present both new data analysis challenges and opportunities to visualize, understand, and apply population and conservation genomic data in novel ways. The large scale and complexity of NGS data also increases the expertise and effort required to thoroughly and thoughtfully analyze and interpret data. To aid in this endeavor, a recent workshop entitled “Population Genomic Data Analysis,” also known as “ConGen 2017,” was held at the University of Montana. The ConGen workshop brought 15 instructors together with knowledge in a wide range of topics including NGS data filtering, genome assembly, genomic monitoring of effective population size, migration modeling, detecting adaptive genomic variation, genomewide association analysis, inbreeding depression, and landscape genomics. Here, we summarize the major themes of the workshop and the important take‐home points that were offered to students throughout. We emphasize increasing participation by women in population and conservation genomics as a vital step for the advancement of science. Some important themes that emerged during the workshop included the need for data visualization and its importance in finding problematic data, the effects of data filtering choices on downstream population genomic analyses, the increasing availability of whole‐genome sequencing, and the new challenges it presents. Our goal here is to help motivate and educate a worldwide audience to improve population genomic data analysis and interpretation, and thereby advance the contribution of genomics to molecular ecology, evolutionary biology, and especially to the conservation of biodiversity.

An evaluation of sequencing coverage and genotyping strategies to assess neutral and adaptive diversity

“…This fits more a probabilistic distribution as described by Fuentes‐Pardo and Ruzzante (). This is consistent with the findings of Hendricks et al () who reported that RAD‐seq data of coverage 1×, 2×, 5×, and 10× called using genotype likelihoods led to inconsistent affiliation of four North American passerine subspecies.…”

These aren’t the loci you’e looking for: Principles of effective SNP filtering for molecular ecologists

“…Despite attempts to limit the introduction of technical artefacts during library preparation and bioinformatic processing, SNP data sets require rigorous filtering because the inclusion of only a few incorrectly genotyped loci in a data set can create a significant, misleading signal (Davey et al, 2013;Li & Wren, 2014;Meirmans, 2015;Puritz, Matz, et al, 2014). This is especially important for Fst-outlier detection to determine loci potentially under selection because signal caused by genotyping error is likely to stand out in pattern and magnitude from the signal produced by the background SNP data (Hendricks et al, 2018;Xue et al, 2009 lane. In addition, every data set will be unique in terms of the number and quality of samples/sequencing runs, and differences in the protocols employed (e.g., enzyme combinations, targeted coverage) and this means that individual data sets will differ in terms of missing data, coverage, etc.…”

RAD‐sequencing for estimating genomic relatedness matrix‐based heritability in the wild: A case study in roe deer

“…One of the major advantages of RAD-seq genotyping is that it captures rare variants that are often not covered by SNP chips in appropriate proportions (Eynard et al, 2015). Previous work has shown that imposing a strict MAF filter may significantly affect the estimate of relatedness coefficients (Eynard et al, 2015) and measures of genomic differentiation among populations (F ST , Hendricks et al, 2018), and may lead to inaccurate demographic inference (Nielsen et al, 2011). Removing rare alleles from data sets may also impede our ability to detect fine-scale patterns of connectivity and local adaptation (O'Leary, Puritz, Willis, Hollenbeck, & Portnoy, 2018).…”