Genomics advances the study of inbreeding depression in the wild

Abstract

Inbreeding depression (reduced fitness of individuals with related parents) has long been a major focus of ecology, evolution, and conservation biology. Despite decades of research, we still have a limited understanding of the strength, underlying genetic mechanisms, and demographic consequences of inbreeding depression in the wild. Studying inbreeding depression in natural populations has been hampered by the inability to precisely measure individual inbreeding. Fortunately, the rapidly increasing availability of high‐throughput sequencing data means it is now feasible to measure the inbreeding of any individual with high precision. Here, we review how genomic data are advancing our understanding of inbreeding depression in the wild. Recent results show that individual inbreeding and inbreeding depression can be measured more precisely with genomic data than via traditional pedigree analysis. Additionally, the availability of genomic data has made it possible to pinpoint loci with large effects contributing to inbreeding depression in wild populations, although this will continue to be a challenging task in many study systems due to low statistical power. Now that reliably measuring individual inbreeding is no longer a limitation, a major focus of future studies should be to more accurately quantify effects of inbreeding depression on population growth and viability.

From microsatellites to single nucleotide polymorphisms for the genetic monitoring of a critically endangered sturgeon

“…Inbreeding is another important parameter to measure in captive breeding programs because inbred individuals have lower fitness than the offspring of unrelated parents (Hedrick & García-Dorado, 2016;Kardos, Luikart, & Allendorf, 2015;Kardos, Taylor, Ellegren, Luikart, & Allendorf, 2016). Inbreeding is another important parameter to measure in captive breeding programs because inbred individuals have lower fitness than the offspring of unrelated parents (Hedrick & García-Dorado, 2016;Kardos, Luikart, & Allendorf, 2015;Kardos, Taylor, Ellegren, Luikart, & Allendorf, 2016).…”

Inferring Individual Inbreeding and Demographic History from Segments of Identity by Descent inFicedulaFlycatcher Genome Sequences

“…It is clearly crucial to account for recombination rate variation across the genome when using IBD information to infer historical demography (Browning and Browning 2015), and when estimating individual inbreeding (Kardos et al 2016). Recombination rate may also influence the dynamics of inbreeding depression and the efficiency of natural selection at purging of deleterious recessive alleles (Bersabé et al 2016).…”

“…Following early observations of ROH in humans (Broman and Weber 1999), the ability to genotype large numbers of mapped single nucleotide polymorphisms (SNPs) sparked interest in ROH as a means to precisely estimate individual inbreeding, i.e., the proportion of the genome that is IBD (McQuillan et al 2008;Keller et al 2011;Kardos et al 2015aKardos et al , 2016. Identification of ROH also implied new possibilities to infer effective population size (N e ) and demographic history (Kirin et al 2010;Pemberton et al 2012;MacLeod et al 2013;Browning and Browning 2015;Kardos et al 2016), to map loci contributing to recessive disorders (Lander and Botstein 1987;Alkuraya 2010), and to explain variation in the abundance of ROH across the genome (Pemberton et al 2012).…”

“…Identification of ROH also implied new possibilities to infer effective population size (N e ) and demographic history (Kirin et al 2010;Pemberton et al 2012;MacLeod et al 2013;Browning and Browning 2015;Kardos et al 2016), to map loci contributing to recessive disorders (Lander and Botstein 1987;Alkuraya 2010), and to explain variation in the abundance of ROH across the genome (Pemberton et al 2012).…”

Recent advances in conservation and population genomics data analysis

“…A recent study testing the impact of data processing on population genetic inferences using RAD‐seq data observed large differences between reference‐based and de novo approaches in population genetic summary statistics, particularly those based on the site frequency spectrum (Shafer et al., 2016). In addition, the recent debate over the effectiveness of RAD‐seq for discovering loci under selection (Catchen et al., 2017; Lowry et al., 2016; McKinney, Larson, Seeb, & Seeb, 2017) has highlighted the importance of testing the extent of linkage disequilibrium (LD) over the genome, whenever possible, in order to assess the power of genome scans to detect selected loci (e.g., Kardos, Taylor, Ellegren, Luikart, & Allendorf, 2016). …”