Identification of blossom-end rot loci using joint QTL-seq and linkage-based QTL mapping in tomato

Key message Blossom-End Rot is Quantitatively Inherited and Maps to Four Loci in Tomato. Abstract Blossom-end rot (BER) is a devastating physiological disorder that affects tomato and other vegetables, resulting in significant crop losses. To date, most studies on BER have focused on the environmental factors that affect calcium translocation to the fruit; however, the genetic basis of this disorder remains unknown. To investigate the genetic basis of BER, two F2 and F3:4 populations along with a BC1 population that segregated for BER occurrence were evaluated in the greenhouse. Using the QTL-seq approach, quantitative trait loci (QTL) associated with BER Incidence were identified at the bottom of chromosome (ch) 3 and ch11. Additionally, linkage-based QTL mapping detected another QTL, BER3.1, on ch3 and BER4.1 on ch4. To fine map the QTLs identified by QTL-seq, recombinant screening was performed. BER3.2, the major BER QTL on ch3, was narrowed down from 5.68 to 1.58 Mbp with a 1.5-LOD support interval (SI) corresponding to 209 candidate genes. BER3.2 colocalizes with the fruit weight gene FW3.2/SlKLUH, an ortholog of cytochrome P450 KLUH in Arabidopsis. Further, BER11.1, the major BER QTL on ch11, was narrowed down from 3.99 to 1.13 Mbp with a 1.5-LOD SI interval comprising of 141 candidate genes. Taken together, our results identified and fine mapped the first loci for BER resistance in tomato that will facilitate marker-assistant breeding not only in tomato but also in many other vegetables suffering for BER.

Variance of the Gait Parameters and Fraction of Double-Support Interval for Determining the Severity of Parkinson’s Disease

The aim of this study was to determine the gait features that are most suitable for the quantified assessment of the severity of Parkinson’s disease (PD). This study computed the mean and variance of the four phases of gait intervals, i.e., stride, swing, stance and double-support intervals, and lateral difference to determine the difference between three groups, i.e., control subjects and PD patients with two severity levels (early and advanced stage) of the disease, PD1 and PD2. Data from 31 subjects were used in the study. The data were obtained from the public database (16 control healthy subjects, 6 Parkinson’s disease patients with early stages, and 9 Parkinson’s disease patients with advanced stages based on the Hoehn and Yahr scale). The main outcome measure of the study was the group difference of the four gait interval parameters and the statistical significance of this difference. The results show that there was a significant increase in the variance of the four gait intervals with the severity of the disease. However, there was no significant difference in the mean values between the three groups. It was also observed that the fraction corresponding to the double-support interval was significantly higher for PD patients. This study has shown that the variance of the gait parameters and the fraction of double-support interval are associated with the severity of PD and may be suitable measures for a quantified evaluation of the disease.

The Support Interval

A frequentist confidence interval can be constructed by inverting a hypothesis test, such that the interval contains only parameter values that would not have been rejected by the test. We show how a similar definition can be employed to construct a Bayesian support interval. Consistent with Carnap’s theory of corroboration, the support interval contains only parameter values that receive at least some minimum amount of support from the data. The support interval is not subject to Lindley's paradox and provides an evidence-based perspective on inference that differs from the belief-based perspective that forms the basis of the standard Bayesian credible interval.

The Support Interval

A frequentist confidence interval can be constructed by inverting a hypothesis test, such that the interval contains only parameter values that would not have been rejected by the test. We show how a similar definition can be employed to construct a Bayesian support interval. Consistent with Carnap’s theory of corroboration, the support interval contains only parameter values that receive at least some minimum amount of support from the data. The support interval is not subject to Lindley’s paradox and provides an evidence-based perspective on inference that differ from the belief-based perspective that forms the basis of the standard Bayesian credible interval.

Interval mapping of high growth (hg), a major locus that increases weight gain in mice.

The high growth locus (hg) causes a major increase in weight gain and body size in mice. As a first step to map-based cloning of hg, we developed a genetic map of the hg-containing region using interval mapping of 403 F2 from a C57BL/6J-hghg x CAST/EiJ cross. The maximum likelihood position of hg was at the chromosome 10 marker D10Mit41 (LOD = 24.8) in the F2 females and 1.5 cM distal to D10Mit41 (LOD = 9.56) in the F2 males with corresponding LOD 2 support intervals of 3.7 and 5.4 cM, respectively. The peak LOD scores were significantly higher than the estimated empirical threshold LOD values. The localization of hg by interval mapping was supported by a test cross of F2 mice recombinant between the LOD 2 support interval and the flanking marker. The interval mapping and test-cross results indicate that hg is not allelic with candidate genes Igf1 or decorin (Dcn), a gene that was mapped close to hg in this study. The hg inheritance was recessive in females, although we could not reject recessive or additive inheritance in males. Possible causes for sex differences in peak LOD scores and for the distortion of transmission ratios observed in F2 males are discussed. The genetic map of the hg region will facilitate further fine mapping and cloning of hg, and allow searches for a homologous quantitative trait locus affecting growth in humans and domestic animals.