Detecting patterns of correlational selection with sampling error: A simulation study.

The adoption of a multivariate perspective of selection implies the existence of multivariate adaptive peaks and pervasive correlational selection that promotes co-adaptation between traits. However, to test for the ubiquity of correlational selection in nature, we must first have a sense of how well can we estimate multivariate nonlinear selection (i.e., the γ-matrix) in the face of sampling error. To explore the sampling properties of estimated γ-matrices, we simulated inidividual traits and fitness under a wide range of sample sizes, using different strengths of correlational selection and of stabilizing selection, combined with different number of traits under selection, different amounts of residual variance in fitness, and distinct patterns of selection. We then ran nonlinear regressions with these simulated datasets to simulate γ-matrices after adding random error to individual fitness. To test how well could we detect the imposed pattern of correlational selection at different sample sizes, we measured the similarity between simulated and imposed γ-matrices. We show that detection of the pattern of correlational selection is highly dependent on the total strength of selection on traits and on the amount of residual variance in fitness. Minimum sample size needs to be at least 500 to precisely estimate the pattern of correlational selection. Furthermore, a pattern of selection in which different sets of traits contribute to different functions is the easiest to diagnose, even when using a large number of traits (10 traits), but with sample sizes in the order of 1000 individuals. Consequently, we recommend working with sets of traits from distinct functional complexes and fitness proxies less prone to effects of environmental and demographic stochasticity to test for correlational selection with lower sample sizes.

High-Resolution Linkage Map With Allele Dosage Allows the Identification of Regions Governing Complex Traits and Apospory in Guinea Grass (Megathyrsus maximus).

Forage grasses are mainly used in animal feed to fatten cattle and dairy herds, and guinea grass (Megathyrsus maximus) is considered one of the most productive of the tropical forage crops that reproduce by seeds. Due to the recent process of domestication, this species has several genomic complexities, such as autotetraploidy and aposporous apomixis. Consequently, approaches that relate phenotypic and genotypic data are incipient. In this context, we built a linkage map with allele dosage and generated novel information of the genetic architecture of traits that are important for the breeding of M. maximus. From a full-sib progeny, a linkage map containing 858 single nucleotide polymorphism (SNP) markers with allele dosage information expected for an autotetraploid was obtained. The high genetic variability of the progeny allowed us to map 10 quantitative trait loci (QTLs) related to agronomic traits, such as regrowth capacity and total dry matter, and 36 QTLs related to nutritional quality, which were distributed among all homology groups (HGs). Various overlapping regions associated with the quantitative traits suggested QTL hotspots. In addition, we were able to map one locus that controls apospory (apo-locus) in HG II. A total of 55 different gene families involved in cellular metabolism and plant growth were identified from markers adjacent to the QTLs and APOSPORY locus using the Panicum virgatum genome as a reference in comparisons with the genomes of Arabidopsis thaliana and Oryza sativa. Our results provide a better understanding of the genetic basis of reproduction by apomixis and traits important for breeding programs that considerably influence animal productivity as well as the quality of meat and milk.