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Multi-trait mimicry and the relative salience of individual traits.

ABSTRACT: Mimicry occurs when one species gains protection from predators by resembling an unprofitable model species. The degree of mimic-model similarity is variable in nature and is closely related to the number of traits that the mimic shares with its model. Here, we experimentally test the hypothesis that the relative salience of traits, as perceived by a predator, is an important determinant of the degree of mimic-model similarity required for successful mimicry. We manipulated the relative salience of the traits of a two-trait artificial model prey, and subsequently tested the survival of mimics of the different traits. The unrewarded model prey had two colour traits, black and blue, and the rewarded prey had two combinations of green, brown and grey shades. Blue tits were used as predators. We found that the birds perceived the black and blue traits to be similarly salient in one treatment, and mimic-model similarity in both traits was then required for high mimic success. In a second treatment, the blue trait was the most salient trait, and mimic-model similarity in this trait alone achieved high success. Our results thus support the idea that similar salience of model traits can explain the occurrence of multi-trait mimicry.

SUBMITTER: Kazemi B

PROVIDER: S-EPMC4650165 | biostudies-literature | 2015 Nov

REPOSITORIES: biostudies-literature

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Multi-trait mimicry and the relative salience of individual traits.

Kazemi Baharan B Gamberale-Stille Gabriella G Leimar Olof O

Proceedings. Biological sciences 20151101 1818

Mimicry occurs when one species gains protection from predators by resembling an unprofitable model species. The degree of mimic-model similarity is variable in nature and is closely related to the number of traits that the mimic shares with its model. Here, we experimentally test the hypothesis that the relative salience of traits, as perceived by a predator, is an important determinant of the degree of mimic-model similarity required for successful mimicry. We manipulated the relative salience ...[more]

PMID: 26511051

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Project description:BACKGROUND:Little is known about the genetic architecture of economically important traits in Brown Swiss cattle because only few genome-wide association studies (GWAS) have been carried out in this breed. Moreover, most GWAS have been performed for single traits, thus not providing detailed insights into potentially existing pleiotropic effects of trait-associated loci. RESULTS:To compile a comprehensive catalogue of large-effect quantitative trait loci (QTL) segregating in Brown Swiss cattle, we carried out association tests between partially imputed genotypes at 598,016 SNPs and daughter-derived phenotypes for more than 50 economically important traits, including milk production, growth and carcass quality, body conformation, reproduction and calving traits in 4578 artificial insemination bulls from two cohorts of Brown Swiss cattle (Austrian-German and Swiss populations). Across-cohort multi-trait meta-analyses of the results from the single-trait GWAS revealed 25 quantitative trait loci (QTL; P?<?8.36?×?10-?8) for economically relevant traits on 17 Bos taurus autosomes (BTA). Evidence of pleiotropy was detected at five QTL located on BTA5, 6, 17, 21 and 25. Of these, two QTL at BTA6:90,486,780 and BTA25:1,455,150 affect a diverse range of economically important traits, including traits related to body conformation, calving, longevity and milking speed. Furthermore, the QTL at BTA6:90,486,780 seems to be a target of ongoing selection as evidenced by an integrated haplotype score of 2.49 and significant changes in allele frequency over the past 25?years, whereas either no or only weak evidence of selection was detected at all other QTL. CONCLUSIONS:Our findings provide a comprehensive overview of QTL segregating in Brown Swiss cattle. Detected QTL explain between 2 and 10% of the variation in the estimated breeding values and thus may be considered as the most important QTL segregating in the Brown Swiss cattle breed. Multi-trait association testing boosts the power to detect pleiotropic QTL and assesses the full spectrum of phenotypes that are affected by trait-associated variants.

Project description:Genomic prediction is a promising approach for accelerating the genetic gain of complex traits in wheat breeding. However, increasing the prediction accuracy (PA) of genomic prediction (GP) models remains a challenge in the successful implementation of this approach. Multivariate models have shown promise when evaluated using diverse panels of unrelated accessions; however, limited information is available on their performance in advanced breeding trials. Here, we used multivariate GP models to predict multiple agronomic traits using 314 advanced and elite breeding lines of winter wheat evaluated in 10 site-year environments. We evaluated a multi-trait (MT) model with two cross-validation schemes representing different breeding scenarios (CV1, prediction of completely unphenotyped lines; and CV2, prediction of partially phenotyped lines for correlated traits). Moreover, extensive data from multi-environment trials (METs) were used to cross-validate a Bayesian multi-trait multi-environment (MTME) model that integrates the analysis of multiple-traits, such as G × E interaction. The MT-CV2 model outperformed all the other models for predicting grain yield with significant improvement in PA over the single-trait (ST-CV1) model. The MTME model performed better for all traits, with average improvement over the ST-CV1 reaching up to 19, 71, 17, 48, and 51% for grain yield, grain protein content, test weight, plant height, and days to heading, respectively. Overall, the empirical analyses elucidate the potential of both the MT-CV2 and MTME models when advanced breeding lines are used as a training population to predict related preliminary breeding lines. Further, we evaluated the practical application of the MTME model in the breeding program to reduce phenotyping cost using a sparse testing design. This showed that complementing METs with GP can substantially enhance resource efficiency. Our results demonstrate that multivariate GS models have a great potential in implementing GS in breeding programs.

Dataset Information

Multi-trait mimicry and the relative salience of individual traits.

Publications

Multi-trait mimicry and the relative salience of individual traits.

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