Genetic mapping of simply inherited categorical traits, including anthocyanin accumulation profiles and fruit appearance, in eggplant (Solanum melongena).

BACKGROUND: The appearance quality of the eggplant (Solanum melongena L.) fruit is an important trait that influences its commercial value. It is known that quality traits such as anthocyanin composition and fruit surface pattern are categorical and are inherited simply. However, research examples of gene mapping for the composition (anthocyanin accumulation profile) and the surface pattern in eggplant fruit are limited. METHODS AND RESULTS: To map loci for these traits including the accumulation profiles of two anthocyanins, a widely spreading anthocyanin, delphinidin 3-(p-coumaroyl) rutinoside-5-glucoside (nasunin), and the relatively rare delphinidin 3-glucoside (D3G), we used two F2 intracrossed populations (LWF2 and N28F2). For the LWF2 population, mapping was achieved by reconstructing the linkage map created by Fukuoka et al. [1]. In the case of the N28F2 population, we constructed a linkage map consisting of 13 linkage groups using 238 simple sequence repeats, 75 single-nucleotide polymorphisms. Using the two F2 populations, the nasunin accumulating profile, the striped pattern on the fruit surface, the colors of flowers, fruit, and calyxes, and the D3G accumulating profile were genetically mapped. Furthermore, by utilizing the eggplant reference genome information, mutations in the causative candidate genes for those loci were identified. CONCLUSION: Overall, the results of this study suggest that inactivation of key enzymes of anthocyanin metabolism and the gene orthologous to the tomato u gene are potential causes of observed variety in eggplant appearance traits.

Validation of single-step GBLUP genomic predictions from threshold models using the linear regression method: An application in chicken mortality.

The objective of this study was to determine whether the linear regression (LR) method could be used to validate genomic threshold models. Statistics for the LR method were computed from estimated breeding values (EBVs) using the whole and truncated data sets with variances from the reference and validation populations. The method was tested using simulated and real chicken data sets. The simulated data set included 10 generations of 4,500 birds each; genotypes were available for the last three generations. Each animal was assigned a continuous trait, which was converted to a binary score assuming an incidence of failure of 7%. The real data set included the survival status of 186,596 broilers (mortality rate equal to 7.2%) and genotypes of 18,047 birds. Both data sets were analysed using best linear unbiased predictor (BLUP) or single-step GBLUP (ssGBLUP). The whole data set included all phenotypes available, whereas in the partial data set, phenotypes of the most recent generation were removed. In the simulated data set, the accuracies based on the LR formulas were 0.45 for BLUP and 0.76 for ssGBLUP, whereas the correlations between true breeding values and EBVs (i.e. true accuracies) were 0.37 and 0.65, respectively. The gain in accuracy by adding genomic information was overestimated by 0.09 when using the LR method compared to the true increase in accuracy. However, when the estimated ratio between the additive variance computed based on pedigree only and on pedigree and genomic information was considered, the difference between true and estimated gain was <0.02. Accuracies of BLUP and ssGBLUP with the real data set were 0.41 and 0.47, respectively. This small improvement in accuracy when using ssGBLUP with the real data set was due to population structure and lower heritability. The LR method is a useful tool for estimating improvements in accuracy of EBVs due to the inclusion of genomic information when traditional validation methods as k-fold validation and predictive ability are not applicable.

Utilizing mutual information for detecting rare and common variants associated with a categorical trait.

Background. Genome-wide association studies have succeeded in detecting novel common variants which associate with complex diseases. As a result of the fast changes in next generation sequencing technology, a large number of sequencing data are generated, which offers great opportunities to identify rare variants that could explain a larger proportion of missing heritability. Many effective and powerful methods are proposed, although they are usually limited to continuous, dichotomous or ordinal traits. Notice that traits having nominal categorical features are commonly observed in complex diseases, especially in mental disorders, which motivates the incorporation of the characteristics of the categorical trait into association studies with rare and common variants. Methods. We construct two simple and intuitive nonparametric tests, MIT and aMIT, based on mutual information for detecting association between genetic variants in a gene or region and a categorical trait. MIT and aMIT can gauge the difference among the distributions of rare and common variants across a region given every categorical trait value. If there is little association between variants and a categorical trait, MIT or aMIT approximately equals zero. The larger the difference in distributions, the greater values MIT and aMIT have. Therefore, MIT and aMIT have the potential for detecting functional variants. Results.We checked the validity of proposed statistics and compared them to the existing ones through extensive simulation studies with varied combinations of the numbers of variants of rare causal, rare non-causal, common causal, and common non-causal, deleterious and protective, various minor allele frequencies and different levels of linkage disequilibrium. The results show our methods have higher statistical power than conventional ones, including the likelihood based score test, in most cases: (1) there are multiple genetic variants in a gene or region; (2) both protective and deleterious variants are present; (3) there exist rare and common variants; and (4) more than half of the variants are neutral. The proposed tests are applied to the data from Collaborative Studies on Genetics of Alcoholism, and a competent performance is exhibited therein. Discussion. As a complementary to the existing methods mainly focusing on quantitative traits, this study provides the nonparametric tests MIT and aMIT for detecting variants associated with categorical trait. Furthermore, we plan to investigate the association between rare variants and multiple categorical traits.