Mapping of quantitative trait loci controlling lifespan in the short‐lived fish Nothobranchius furzeri– a new vertebrate model for age research

Abstract

The African annual fish Nothobranchius furzeri emerged as a new model for age research over recent years. Nothobranchius furzeri show an exceptionally short lifespan, age-dependent cognitive/behavioral decline, expression of age-related biomarkers, and susceptibility to lifespan manipulation. In addition, laboratory strains differ largely in lifespan. Here, we set out to study the genetics of lifespan determination. We crossed a short- to a long-lived strain, recorded lifespan, and established polymorphic markers. On the basis of genotypes of 411 marker loci in 404 F2 progeny, we built a genetic map comprising 355 markers at an average spacing of 5.5 cM, 22 linkage groups (LGs) and 1965 cM. By combining marker data with lifespan values, we identified one genome-wide highly significant quantitative trait locus (QTL) on LG 9 (P < 0.01), which explained 11.3% of the F2 lifespan variance, and three suggestive QTLs on LG 11, 14, and 17. We characterized the highly significant QTL by synteny analysis, because a genome sequence of N. furzeri was not available. We located the syntenic region on medaka chromosome 5, identified candidate genes, and performed fine mapping, resulting in a c. 40% reduction of the initial 95% confidence interval. We show both that lifespan determination in N. furzeri is polygenic, and that candidate gene detection is easily feasible by cross-species analysis. Our work provides first results on the way to identify loci controlling lifespan in N. furzeri and illustrates the potential of this vertebrate species as a genetic model for age research.

The African turquoise killifish: A research organism to study vertebrate aging and diapause

“…The turquoise killifish strain that is most frequently used is the GRZ strain, an inbred line that originates from a sample collected in 1970 in the Gona‐Re‐Zhou National Park of Zimbabwe (Parle, 1970). Since then, several additional samples have been collected in Mozambique and Zimbabwe, and wild strains or strains derived from these samples (e.g., the MZM0403 and 0410 strains which were collected in 2004) have also been broadly used for a variety of studies (Bartakova et al., 2013; Baumgart et al., 2016; Blazek et al., 2017; Kirschner et al., 2012; Reichard, Polacik & Sedlacek, 2009; Reichwald et al., 2015; Terzibasi et al., 2008; Valenzano et al., 2009, 2015). In particular, these strains were used to identify the genetic architecture underlying phenotypic differences in these strains, such as color (Valenzano et al., 2009) and survival under specific laboratory environments (Baumgart et al., 2016; Blazek et al., 2017; Reichwald et al., 2015; Valenzano et al., 2015).…”

“…In 2015, two independent groups de novo assembled and annotated the turquoise killifish genome: Anne Brunet's laboratory at Stanford University (African Turquoise Killifish Genome Browser: http://africanturquoisekillifishbrowser.org/; Valenzano et al., 2015) and Matthias Platzer's laboratory in Jena, Germany ( Nothobranchius furzeri Genome Browser: http://nfingb.leibniz-fli.de/; Reichwald et al., 2015). Additionally, other genomic resources have also been established through the years, including genetic linkage maps, quantitative trait loci, over 150 microsatellite markers (Blazek et al., 2017; Kirschner et al., 2012; Valenzano et al., 2009), and numerous transcriptomic and epigenomic datasets of different tissues at different ages using various strains (Baumgart et al., 2012, 2014, 2016, 2017; D'Angelo et al., 2014; Ng'oma, Groth, Ripa, Platzer & Cellerino, 2014; Petzold et al., 2013). A reference genome from the NCBI pipeline was made available online in 2016 (NCBI Genome ID: 2642, URL: https://www.ncbi.nlm.nih.gov/genome/2642).…”

Correction

“…Although these did not directly reveal genes involved in longevity, they suggested possible candidates. From this study, the scientists estimated that about 32% of variation in lifespan among turquoise killifish results from genetics, a figure comparable to the 20-35% estimated genetic contribution in mice 6 . From then on, the killifish's transformation into a valid research model accelerated.…”

Fifteen years of quantitative trait loci studies in fish: challenges and future directions

“…For example, less than half of the studies (63 of 146) reported QTL regions in cMs (Fig 3C) and those that did record the width of the region employed a number of different methods. Methods used included the allele drop-off (Bras et al 2011), bootstrapping (Kirschner et al 2012), Bayesian credibility intervals (Sauvage et al 2012), set LOD limit from the peak of the QTL (Gagnaire et al 2013) or distance between markers above the significance level cut-off method (Jin et al 2012). The finding that QTL regions are described using a wide array of methods is surprising, given that simulation studies have clearly indicated that some approaches are more accurate than others (Visscher et al 1996;Manichaikul et al 2006).…”