Phenotypic analysis of intermated B73 x Mo17 (IBM) populations

Intermating within a population creates more opportunities for recombination, thereby increasing the probabilities of observing recombination events between linked loci. Thus, more reliable genetic maps can be constructed. IBM and IBM-10 are two intermated recombinant inbred line populations that have been developed to facilitate high resolution genetic mapping. Both populations were derived from the cross between maize inbreds B73 and Mo17. The populations differ in the number of cycles of intermating of the F2 generation before the initiation of line derivation. IBM was created after 4 cycles of intermating. IBM-10 was created after 6 additional cycles of intermating of the F2 generation, and show increased recombination fraction between adjacent loci compared with IBM. Recombination and subsequent change in allele configuration between linked loci may produce change in population parameters such as variances and covariance between traits. This study was designed to determine whether the six additional cycles of intermating in the development of the IBM-10 population had effects on the genetic variances, means and phenotypic correlations for quantitative traits. Two hundred and forty four lines of each population were grown in two separate but adjacent experiments in Ames in 2006. Sixteen traits were measured in each population. The estimate of genetic variance for kernel oil concentration (OIL) and number of kernels per ear row (EKR) were significantly larger in the IBM-10 than in the IBM population (1.5 and 1.3 times larger for OIL and EKR, respectively). There was no difference in phenotypic correlations between populations. The mean for plant height and ear rows were significantly larger for IBM-10 than IBM. The differences in genetic variance for OIL and EKR between populations could be due to recombination and the subsequent change in allele configuration between linked loci affecting each of these traits. This could be favorable for the estimation of location and effect of QTL controlling these traits. The phenotypic characterization of the IBM-10 lines would be useful for research purposes such as QTL detection as a prelude to finer-scale genetic mapping and sequence discovery

The Impact of Genetic Relationship and Linkage Disequilibrium on Genomic Selection

Genomic selection is a promising research area due to its practical application in breeding. In this study, impact of realized genetic relationship and linkage disequilibrium (LD) on marker density and training population size required was investigated and their impact on practical application was further discussed. This study is based on experimental data of two populations derived from the same two founder lines (B73, Mo17). Two populations were genotyped with different marker sets at different density: IBM Syn4 and IBM Syn10. A high-density marker set in Syn10 was imputed into the Syn4 population with low marker density. Seven different prediction scenarios were carried out with a random regression best linear unbiased prediction (RR-BLUP) model. The result showed that the closer the real genetic relationship between training and validation population, the fewer markers were required to reach a good prediction accuracy. Taken the short-term cost for consideration, relationship information is more valuable than LD information. Meanwhile, the result indicated that accuracies based on high LD between QTL and markers were more stable over generations, thus LD information would provide more robust prediction capacity in practical applications

Phenotypic analysis of intermated B73 x Mo17 (IBM) populations

Intermating within a population creates more opportunities for recombination, thereby increasing the probabilities of observing recombination events between linked loci. Thus, more reliable genetic maps can be constructed. IBM and IBM-10 are two intermated recombinant inbred line populations that have been developed to facilitate high resolution genetic mapping. Both populations were derived from the cross between maize inbreds B73 and Mo17. The populations differ in the number of cycles of intermating of the F2 generation before the initiation of line derivation. IBM was created after 4 cycles of intermating. IBM-10 was created after 6 additional cycles of intermating of the F2 generation, and show increased recombination fraction between adjacent loci compared with IBM. Recombination and subsequent change in allele configuration between linked loci may produce change in population parameters such as variances and covariance between traits. This study was designed to determine whether the six additional cycles of intermating in the development of the IBM-10 population had effects on the genetic variances, means and phenotypic correlations for quantitative traits. Two hundred and forty four lines of each population were grown in two separate but adjacent experiments in Ames in 2006. Sixteen traits were measured in each population. The estimate of genetic variance for kernel oil concentration (OIL) and number of kernels per ear row (EKR) were significantly larger in the IBM-10 than in the IBM population (1.5 and 1.3 times larger for OIL and EKR, respectively). There was no difference in phenotypic correlations between populations. The mean for plant height and ear rows were significantly larger for IBM-10 than IBM. The differences in genetic variance for OIL and EKR between populations could be due to recombination and the subsequent change in allele configuration between linked loci affecting each of these traits. This could be favorable for the estimation of location and effect of QTL controlling these traits. The phenotypic characterization of the IBM-10 lines would be useful for research purposes such as QTL detection as a prelude to finer-scale genetic mapping and sequence discovery.</p

Dissecting the genetic basis of physiological processes determining maize kernel weight using a RIL population.

Knowledge on the genetic bases of physiological processes determining maize kernel weight (KW) is relevant for maize yield improvement. However, little is known about the genetic control of KW and its component traits: kernel growth rate (KGR) and grain-filling duration (GFD). We phenotyped several grain-filling traits in 245 RILs from the IBM Syn4 population (B73×Mo17) under two environments, and a multi-trait multi-environment quantitative trait loci (QTL) analysis was conducted. We were specifically interested inseeking genetic links of knowncorrelated traits atthe phenotypic level, like kernelmaximum water content(MWC) and KGR. Our specific objectives were (i)to conduct a QTL analysis over grain-filling traits to determine their genetic complexity,(ii)to study the relationships between kernel developmental traits at phenotypic and genetic levels, and (iii) to suggest possible candidate genes for each specific trait using detected QTL and B73 sequence data. All traits showed significant genotype × environment interactions (p < 0.001) and large phenotypic variability. KW variability was positively associated (p < 0.01) with variations in KGR (r = 0.79) and GFD (r = 0.32). As expected, KGR was positively correlated to MWC, while GFD was negatively correlated to the kernel moisture concentration at physiological maturity (MCPM). A total of 10 joint QTL were detected under both environments, located on chromosomes 1, 2, 4, 5, 6, 7, 9 and 10. Most QTL showed inconsistent effects underlying genotype × environment interactions. However, the multi-trait multi-environment approach helped understand genetic correlations between traits, where positive and consistent genetic correlations were observed between KW, KGR and MWC on chromosomes 2, 6, 9 and 10. Only one consistent QTL for KW, GFD and kernel desiccation rate (KDR) was detected. KGR and GFD showed no common consistent QTL, supporting previous observations on independent physiological control. Several detected QTL co-localized with previous mapping studies. With the use of B73 sequence data we described genes within QTL marker intervals, and discussed relevant candidate ones for future dissection. Results showing the co-localization of consistent QTL for KW, KGR and MWC suggest a common genetic basis for these critical secondary traits measured under field conditions.Fil: Alvarez Prado, Santiago. Universidad Nacional de Rosario. Facultad de Ciencias Agrarias; Argentina;Fil: Lopez, Cesar Gabriel. Universidad Nacional de Lomas de Zamora. Facultad de Ciencias Agrarias; Argentina;Fil: Gambin, Brenda Laura. Universidad Nacional de Rosario. Facultad de Ciencias Agrarias; Argentina; Universidad Nacional de Rosario. Facultad de Ciencias Agrarias. Departamento de Biologia. Cátedra de Fisiología Vegetal; Argentina;Fil: Abertondo, Victor. Advanta Semillas S.A.I.C.;Fil: Borras, Lucas. Universidad Nacional de Rosario. Facultad de Cs.agrarias; Universidad Nacional de Rosario. Facultad de Ciencias Agrarias. Cátedra de Cultivo Extensivos Cereales y Oleaginosas; Argentina

Correction: The Impact of Genetic Relationship and Linkage Disequilibrium on Genomic Selection.

[This corrects the article DOI: 10.1371/journal.pone.0132379.]

The Impact of Genetic Relationship and Linkage Disequilibrium on Genomic Selection

Genomic selection is a promising research area due to its practical application in breeding. In this study, impact of realized genetic relationship and linkage disequilibrium (LD) on marker density and training population size required was investigated and their impact on practical application was further discussed. This study is based on experimental data of two populations derived from the same two founder lines (B73, Mo17). Two populations were genotyped with different marker sets at different density: IBM Syn4 and IBM Syn10. A high-density marker set in Syn10 was imputed into the Syn4 population with low marker density. Seven different prediction scenarios were carried out with a random regression best linear unbiased prediction (RR-BLUP) model. The result showed that the closer the real genetic relationship between training and validation population, the fewer markers were required to reach a good prediction accuracy. Taken the short-term cost for consideration, relationship information is more valuable than LD information. Meanwhile, the result indicated that accuracies based on high LD between QTL and markers were more stable over generations, thus LD information would provide more robust prediction capacity in practical applications.This article is published as Liu, Hongjun, Huangkai Zhou, Yongsheng Wu, Xiao Li, Jing Zhao, Tao Zuo, Xuan Zhang et al. "The impact of genetic relationship and linkage disequilibrium on genomic selection." PloS one 10, no. 7 (2015): e0132379. doi: 10.1371/journal.pone.0132379. Posted with permission.</p