Luyan Zhang

Performance prediction of F1 hybrids between recombinant inbred lines derived from two elite maize inbred lines

Selection of recombinant inbred lines (RILs) from elite hybrids is a key method in maize breeding especially in developing countries. The RILs are normally derived by repeated self-pollination and selection. In this study, we first investigated the accuracy of different models in predicting the performance of F1 hybrids between RILs derived from two elite maize inbred lines Zong3 and 87-1, and then compared these models through simulation using a wider range of genetic models. Results indicated that appropriate prediction models depended on genetic architecture, e.g., combined model using breeding value and genome-wide prediction (BV+GWP) has the highest prediction accuracy for high VD/VA ratio (>0.5) traits. Theoretical studies demonstrated that different components of genetic variance were captured by different prediction models, which in turn explained the accuracy of these models in predicting the F1 hybrid performance. Based on genome-wide prediction model (GWP), 114 untested F1 hybrids possibly having higher grain yield than the original F1 hybrid Yuyu22 (the single cross between Zong3 and 87-1) have been identified and recommended for further field test.

The statistical power of inclusive composite interval mapping in detecting digenic epistasis showing common F2 segregation ratios.

Epistasis is a commonly observed genetic phenomenon and an important source of variation of complex traits, which could maintain additive variance and therefore assure the long-term genetic gain in breeding. Inclusive composite interval mapping (ICIM) is able to identify epistatic quantitative trait loci (QTLs) no matter whether the two interacting QTLs have any additive effects. In this article, we conducted a simulation study to evaluate detection power and false discovery rate (FDR) of ICIM epistatic mapping, by considering F2 and doubled haploid (DH) populations, different F2 segregation ratios and population sizes. Results indicated that estimations of QTL locations and effects were unbiased, and the detection power of epistatic mapping was largely affected by population size, heritability of epistasis, and the amount and distribution of genetic effects. When the same likelihood of odd (LOD) threshold was used, detection power of QTL was higher in F2 population than power in DH population; meanwhile FDR in F2 was also higher than that in DH. The increase of marker density from 10 cM to 5 cM led to similar detection power but higher FDR. In simulated populations, ICIM achieved better mapping results than multiple interval mapping (MIM) in estimation of QTL positions and effect. At the end, we gave epistatic mapping results of ICIM in one actual population in rice (Oryza sativa L.).

On the use of mathematically-derived traits in QTL mapping

Mathematically-derived traits from two or more component traits, either by addition, subtraction, multiplication, or division, have been frequently used in genetics and breeding. When used in quantitative trait locus (QTL) mapping, derived traits sometimes show discrepancy with QTL identified for the component traits. We used three QTL distributions and three genetic effects models, and an actual maize mapping population, to investigate the efficiency of using derived traits in QTL mapping, and to understand the genetic and biological basis of derived-only QTL, i.e., QTL identified for a derived trait but not for any component trait. Results indicated that the detection power of the four putative QTL was consistently greater than 90% for component traits in simulated populations, each consisting of 200 recombinant inbred lines. Lower detection power and higher false discovery rate (FDR) were observed when derived traits were used. In an actual maize population, simulations were designed based on the observed QTL distributions and effects. When derived traits were used, QTL detected for both component and derived traits had comparable power, but those detected for component traits but not for derived traits had low detection power. The FDR from subtraction and division in the maize population were higher than the FDR from addition and multiplication. The use of derived traits increased the gene number, caused higher-order gene interactions than observed in component traits, and possibly complicated the linkage relationship between QTL as well. The increased complexity of the genetic architecture with derived traits may be responsible for the reduced detection power and the increased FDR. Derived-only QTL identified in practical genetic populations can be explained either as minor QTL that are not significant in QTL mapping of component traits, or as false positives.

The Genetic Basis of Natural Variation in Kernel Size and Related Traits Using a Four-Way Cross Population in Maize

Kernel size is an important component of grain yield in maize breeding programs. To extend the understanding on the genetic basis of kernel size traits (i.e., kernel length, kernel width and kernel thickness), we developed a set of four-way cross mapping population derived from four maize inbred lines with varied kernel sizes. In the present study, we investigated the genetic basis of natural variation in seed size and other components of maize yield (e.g., hundred kernel weight, number of rows per ear, number of kernels per row). In total, ten QTL affecting kernel size were identified, three of which (two for kernel length and one for kernel width) had stable expression in other components of maize yield. The possible genetic mechanism behind the trade-off of kernel size and yield components was discussed.

Genomic Dissection of Leaf Angle in Maize (Zea mays L.) Using a Four-Way Cross Mapping Population.

Increasing grain yield by the selection for optimal plant architecture has been the key focus in modern maize breeding. As a result, leaf angle, an important determinant of plant architecture, has been significantly improved to adapt to the ever-increasing plant density in maize production over the past several decades. To extend our understanding on the genetic mechanisms of leaf angle in maize, we developed the first four-way cross mapping population, consisting of 277 lines derived from four maize inbred lines with varied leaf angles. The four-way cross mapping population together with the four parental lines were evaluated for leaf angle in two environments. In this study, we reported linkage maps built in the population and quantitative trait loci (QTL) on leaf angle detected by inclusive composite interval mapping (ICIM). ICIM applies a two-step strategy to effectively separate the cofactor selection from the interval mapping, which controls the background additive and dominant effects at the same time. A total of 14 leaf angle QTL were identified, four of which were further validated in near-isogenic lines (NILs). Seven of the 14 leaf angle QTL were found to overlap with the published leaf angle QTL or genes, and the remaining QTL were unique to the four-way population. This study represents the first example of QTL mapping using a four-way cross population in maize, and demonstrates that the use of specially designed four-way cross is effective in uncovering the basis of complex and polygenetic trait like leaf angle in maize.

Inclusive Composite Interval Mapping of QTL by Environment Interactions in Biparental Populations

Identification of environment-specific QTL and stable QTL having consistent genetic effects across a wide range of environments is of great importance in plant breeding. Inclusive Composite Interval Mapping (ICIM) has been proposed for additive, dominant and epistatic QTL mapping in biparental populations for single environment. In this study, ICIM was extended to QTL by environment interaction (QEI) mapping for multi-environmental trials, where the QTL average effect and QEI effects could be properly estimated. Stepwise regression was firstly applied in each environment to identify the most significant marker variables which were then used to adjust the phenotypic values. One-dimensional scanning was then conducted on the adjusted phenotypic values across the environments in order to detect QTL with either average effect or QEI effects, or both average effect and QEI effects. In this way, the genetic background could be well controlled while the conventional interval mapping was applied. An empirical method to determine the threshold of logarithm of odds was developed, and the efficiency of the ICIM QEI mapping was demonstrated in simulated populations under different genetic models. One actual recombinant inbred line population was used to compare mapping results between QEI mapping and single-environment analysis.

Linkage Analysis and Map Construction in Genetic Populations of Clonal F1 and Double Cross

In this study, we considered four categories of molecular markers based on the number of distinguishable alleles at the marker locus and the number of distinguishable genotypes in clonal F1 progenies. For two marker loci, there are nine scenarios that allow the estimation of female, male, and/or combined recombination frequencies. In a double cross population derived from four inbred lines, five categories of markers are classified and another five scenarios are present for recombination frequency estimation. Theoretical frequencies of identifiable genotypes were given for each scenario, from which the maximum likelihood estimates of one or more of the three recombination frequencies could be estimated. If there was no analytic solution, then Newton-Raphson method was used to acquire a numerical solution. We then proposed to use an algorithm in Traveling Salesman Problem to determine the marker order. Finally, we proposed a procedure to build the two haploids of the female parent and the two haploids of the male parent in clonal F1. Once the four haploids were built, clonal F1 hybrids could be exactly regarded as a double cross population. Efficiency of the proposed methods was demonstrated in simulated clonal F1 populations and one actual maize double cross. Extensive comparisons with software JoinMap4.1, OneMap, and R/qtl show that the methodology proposed in this article can build more accurate linkage maps in less time.

GACD: Integrated Software for Genetic Analysis in Clonal F1 and Double Cross Populations

Clonal species are common among plants. Clonal F1 progenies are derived from the hybridization between 2 heterozygous clones. In self- and cross-pollinated species, double crosses can be made from 4 inbred lines. A clonal F1 population can be viewed as a double cross population when the linkage phase is determined. The software package GACD (Genetic Analysis of Clonal F1 and Double cross) is freely available public software, capable of building high-density linkage maps and mapping quantitative trait loci (QTL) in clonal F1 and double cross populations. Three functionalities are integrated in GACD version 1.0: binning of redundant markers (BIN); linkage map construction (CDM); and QTL mapping (CDQ). Output of BIN can be directly used as input of CDM. After adding the phenotypic data, the output of CDM can be used as input of CDQ. Thus, GACD acts as a pipeline for genetic analysis. GACD and example datasets are freely available from www.isbreeding.net.