Browsing by Author "Lian, Lian"

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    Genomewide prediction of genotypic values and genetic variances within 969 maize biparental populations
    (2014-09) Lian, Lian
    In plant breeding, selecting within biparental crosses and selecting parents to make new crosses are both important. My first study investigated the accuracy of genomewide selection (rMG) within 969 biparental maize populations (Zea mays L.). My objectives were to determine: (i) the mean and variability of rMG, (ii) if rMG can be predicted, and (iii) how training population size (N), heritability (h2), and number of markers (NM) affect rMG. I modified an equation for expected rMG [E(rMG)] to account for linkage disequilibrium (r2) between markers and quantitative trait loci. Across the 969 populations, the mean and range (in parentheses) of observed rMG was 0.45 (&minus0.59, 1.03) for yield, 0.59 (&minus0.34, 0.96) for moisture, and 0.55 (&minus0.24, 1.10) for test weight. The observed rMGvalues were centered around E(rMG) when r2 was accounted for, but had a large spread around E(rMG). The r2(Nh2)&half had the strongest association with the observed rMG. In the second study, my objective was to determine whether related populations could be used to predict the genetic variance (VG) of a segregating population from two parents (A and B). For each of 85 A/B populations, 2&ndash23 A/* and B/* populations were used as training populations, where * denotes a random parent. In the genomewide selection model, the testcross VG in A/B was predicted as the variance among the predicted genotypic values of progeny from the simulated A/B population. In the mean variance model, VG was estimated as the mean of VG in A/* and B/* populations. The correlations between observed and predicted VG were not significant (P = 0.05) for the genomewide selection model but were significant for the mean variance model (0.26 for yield, 0.46 for moisture, and 0.50 for test weight). The VG of A/B population could therefore be predicted as the mean of VG in A/* and B/* populations. Overall, the results indicated that genomewide selection can identify the best individuals within a cross, but it cannot reliably predict which parents would lead to the largest genetic variance.
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    Identifying novel sources of resistance to the soybean cyst nematode.
    (2012-01) Lian, Lian
    Soybean cyst nematode (SCN, Heterodera glycines Ichinohe) is the most serious yieldlimiting pathogen on soybean [Glycine max (L.) Merr.]. Utilizing genetic resistance is an effective method to control SCN. Most commercial SCN-resistant cultivars in the North Central USA are developed from two sources of resistance, PI 88788 and Peking. However, frequent use of a limited number of resistance sources has shifted virulence phenotypes of SCN populations (HG Types) and the new types seem to overcome originally resistant cultivars. The main purpose of this study is to search for new sources of SCN resistance that are different from Peking or PI 88788 and to identify genetic regions that are associated with novel resistance loci. Since Peking is not resistant to HG Type 1- (race 14) and PI 88788 is not resistant to HG Type 2- (race 1), 17 soybean cultivars and accessions that were reported resistant to HG Type 1- or/and HG Type 2- were tested against 13 different nematode populations including race 1, race 2, race 3, race 4 and race 14. Most of the lines tested had high or moderate resistance to race 1, race 2 and race 3 populations and can serve as an alternative resistance sources to PI 88788. However, most of the lines were susceptible to race 4 and the two race 14 nematode populations. Only PI 633736 has a high level of resistance to all the nematode populations used. PI 417091, PI 404166, PI 567516C, PI 629013 have moderate or high resistance to race 4 and at least one of the two race 14 populations. The different resistance spectrums of those lines indicate that there should be novel genes in PI resistance spectrums of those lines indicate that there should be novel genes in PI 633736, PI 417091, PI 404166, PI 567516C and PI 629013 that are different from Peking and PI 88788. QTLs conferring resistance to an HG Type 2.5.7 population (race 1) were sought with 92 MN0095 × PI 567516C F2:3 families from greenhouse (Experiment 1) and 92 F2:3 families from field (Experiment 2) using 1536 SNP markers. Altogether, 5 QTLs were declared for Experiment 1 and Experiment 2, including 2 significant QTLs (genome-wide type I error =0.05) and 3 suggestive QTLs (LOD > 3). The two significant QTLs were detected on chromosome 10 and chromosome 19 and the three suggestive QTLs were detected on chromosome 8, chromosome 18, and chromosome 20. The QTL with the highest LOD score, located on chromosome 10 was detected in both Experiment 1 and Experiment 2 and was recently reported by another group. This QTL has not been identified in other sources of SCN resistance. This QTL has significant additive effect, and explained 22.2% and 22.4% total variance in Experiment 1 and Experiment 2, respectively. The QTLs on chromosome 19 was detected only in Experiment 1. It had significant dominance effect, and explained 12.7% of total variance. The suggestive QTL mapped on chromosome 18 in Experiment 2 was at or near the rhg1 locus. Haplotype analysis of rhg1 and Rhg4 genes for the 17 resistant soybean germplasm lines revealed that PI 567516C and Peking share the same rhg1 allele. Markers closest to rhg1 and the QTL on chromosome 10 might be considered for use in marker assisted selection.