Estimating Trait Heritability in Highly Fecund Species. Issue 12 (1st December 2015)

• Record Type: Journal Article
• Authors: Davies, Sarah W; Scarpino, Samuel V; Pongwarin, Thanapat; Scott, James; Matz, Mikhail V
• Is Part Of: G3. Volume 5:Issue 12(2015)
• Page Start: 2639
• Page End: 2645
• Publication Date: 2015-12-01
• Subjects: heritability -- nonmodel organisms -- common garden -- binary variable traits -- coral settlement; Genetics -- Research -- Periodicals; Genomics -- Periodicals; Genetics; Genomics; Genes; Genetics -- Research; Genomics; Electronic journals; Periodical; Periodicals; Fulltext; Internet Resources; Periodicals; 572.8
• DOI: 10.1534/g3.115.020701
• Languages: English
• ISSNs: 2160-1836

Abstract

Abstract: Increasingly, researchers are interested in estimating the heritability of traits for nonmodel organisms. However, estimating the heritability of these traits presents both experimental and statistical challenges, which typically arise from logistical difficulties associated with rearing large numbers of families independently in the field, a lack of known pedigree, the need to account for group or batch effects, etc. Here we develop both an empirical and computational methodology for estimating the narrow-sense heritability of traits for highly fecund species. Our experimental approach controls for undesirable culturing effects while minimizing culture numbers, increasing feasibility in the field. Our statistical approach accounts for known issues with model-selection by using a permutation test to calculate significance values and includes both fitting and power calculation methods. We further demonstrate that even with moderately high sample-sizes, the p-values derived from asymptotic properties of the likelihood ratio test are overly conservative, thus reducing statistical power. We illustrate our methodology by estimating the narrow-sense heritability for larval settlement, a key life-history trait, in the reef-building coral Orbicella faveolata . The experimental, statistical, and computational methods, along with all of the data from this study, are available in the R package multiDimBio.