Genomewide Association Mapping of Flowering Time, Kernel Composition, and Disease Resistance in Historical Minnesota Maize Inbreds

Schaefer, Christopher M.; Bernardo, Rex

doi:10.2135/cropsci2013.02.0121

Cited by 25 publications

(56 citation statements)

References 61 publications

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“…These data are available on request. Marker effects were estimated for all markers via ridge regression-best linear unbiased prediction (RR-BLUP; Meuwissen et al, 2001;Bernardo and Yu, 2007) given the lack of evidence of major quantitative trait loci (QTL) among the inbreds studied (Schaefer and Bernardo, 2013). The inbreds were genotyped with 28,626 SNP markers.…”

Section: Virtual Populationsmentioning

confidence: 99%

Predicting Genetic Variance from Genomewide Marker Effects Estimated from a Diverse Panel of Maize Inbreds

Adeyemo

Bernardo

2019

Crop Science

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Predicting the genetic variance (VG) in a biparental population has been difficult. Our objective was to determine whether the population mean, VG, and mean of the top 10% of progeny in a cross can be predicted effectively from genomewide marker effects estimated from a diverse panel of inbreds. Eight maize (Zea mays L.) crosses that differed in their predicted mean and VG were evaluated for plant and ear height and growing degree days to silking. Each cross was represented by 120 to 144 random F3 lines that were evaluated in balanced experiments at three locations in Minnesota in 2017. Correlations between the observed and predicted means of each breeding population were significant (r ≥ 0.80, P = 0.05) for all three traits. However, correlations between the observed and predicted VG were nonsignificant (−0.24 to 0.14) for the three traits. Correlations between the observed and predicted mean of the top 10% of progeny in each cross were significant (P = 0.05) for plant height (0.72), but not for ear height and silking date. These results for predicting the mean of the top 10% of progeny reflected the ability to predict the mean but not VG. We concluded that the mean, but not the VG, of biparental crosses can be effectively predicted from genomewide marker effects estimated from a diverse panel of inbreds.

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Section: Virtual Populationsmentioning

confidence: 99%

Predicting Genetic Variance from Genomewide Marker Effects Estimated from a Diverse Panel of Maize Inbreds

Adeyemo

Bernardo

2019

Crop Science

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“…The association mapping results for anthesis date were therefore consistent with the δ ij values. However, association mapping in the Schaefer and Bernardo (2013b) study did not detect any significant marker–trait associations for plant height and protein concentration. These results suggested that analysis of germplasm architecture via genomewide marker effects provides information that is not revealed by association mapping.…”

Section: Resultsmentioning

confidence: 67%

“…As was done in this study, a simple precaution against overfitting is to compare the square of the correlation ( r 2 ) between marker‐predicted and observed inbred means against the coefficient of determination for inbred effects ( R 2 Inbred ) from an ANOVA of the phenotypic data. For the traits studied herein, the high R 2 Inbred values (≥0.92; Schaefer and Bernardo, 2013b) left little room for overfitting, and the similar R 2 Inbred and r 2 values (both ≥0.92) indicated that the genomewide markers did not capture more variation than the inbreds themselves captured. On the other hand, an r 2 that exceeds R 2 Inbred may indicate overfitting, and the chromosomal effects may need to be calculated by cross‐validation.…”

Section: Resultsmentioning

confidence: 88%

“…This analysis of population structure led to membership probabilities for five subpopulations of inbreds: B73 (Iowa Stiff Stalk Synthetic), Mo17, Oh43, A321 (mostly Minnesota 13), and PH207 (Iodent). Prior to calculating genomewide marker effects, a set of 28,626 SNP loci with reduced multicollinearity was identified by Schaefer and Bernardo (2013b) via PLINK software (Purcell et al, 2007). The 28,626 SNP loci were distributed as follows on the 10 maize chromosomes (in parentheses): (1) 4182, (2) 1972, (3) 3618, (4) 3343, (5) 3367, (6) 2518, (7) 2493, (8) 2682, (9) 2316, and (10) 2135.…”

Section: Methodsmentioning

confidence: 99%

“…The SNP genotypes at each locus were coded as 1 for one homozygote, 0 for the heterozygote, and −1 for the other homozygote. Genomewide marker effects (for the SNP allele coded as 1) for each trait were obtained by Schaefer and Bernardo (2013b) for the 28,626 SNP loci through RR‐BLUP analysis as implemented in the rrBLUP package (Endelman, 2011) in R software (R Development Core Team, 2012).…”

Section: Methodsmentioning

confidence: 99%

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Germplasm Architecture Revealed through Chromosomal Effects for Quantitative Traits in Maize

Bernardo

Thompson

2016

The Plant Genome

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Germplasm architecture refers to how favorable alleles for a given trait are distributed across the genome in a germplasm collection. Our objective was to assess germplasm architecture for quantitative traits among US maize (Zea mays L.) inbreds. A total of 271 inbreds were genotyped at 28,626 single nucleotide polymorphism (SNP) loci and phenotyped for anthesis date, plant height, starch and protein concentration, and resistance to northern corn leaf blight (NCLB, caused by Setosphaeria turcica). Chromosomal effects were calculated as the sum of the trait effects of SNP alleles carried on a specific chromosome by an inbred. The chromosomal effects were further decomposed into the mean effects of chromosomes, mean effects of inbreds, and chromosome × inbred effects. On average, none of the 10 maize chromosomes was particularly rich or poor in favorable quantitative trait locus (QTL) alleles. However, extreme values of chromosome × inbred effects often involved chromosomes 5 and 8 for anthesis date, chromosomes 1 and 5 for plant height, and chromosome 9 for protein concentration. Inbreds with one or two chromosomes deficient in favorable alleles were candidates for improvement via chromosome-substitution lines. Specific chromosomes for which each of five genetic backgrounds (B73, Mo17, Oh43, A321, and PH207) were rich or poor for unknown favorable alleles were also identified. Chromosomal effects varied widely even when prior association mapping in the same germplasm collection had failed to identify any QTL. Genomewide marker effects, particularly when partitioned into chromosomal effects, provide a simple way to dissect germplasm architecture for quantitative traits.

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Brown midrib mutant and genome‐wide association analysis uncover lignin genes for disease resistance in maize

et al. 2022

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Brown midrib (BMR) maize (Zea mays L.) harbors mutations that result in lower lignin levels and higher feed digestibility, making it a desirable silage market class for ruminant nutrition. Northern leaf blight (NLB) epidemics in upstate New York highlighted the disease susceptibility of commercially grown BMR maize hybrids.We found the bm1, bm2, bm3, and bm4 mutants in a W64A genetic background to be more susceptible to foliar fungal (NLB, gray leaf spot [GLS], and anthracnose leaf blight [ALB]) and bacterial (Stewart's wilt) diseases. The bm1, bm2, and bm3 mutants showed enhanced susceptibility to anthracnose stalk rot (ASR), and the bm1 and bm3 mutants were more susceptible to Gibberella ear rot (GER). Colocalization of quantitative trait loci (QTL) and correlations between stalk strength and disease traits in recombinant inbred line families suggest possible pleiotropies. The role of lignin in plant defense was explored using high-resolution, genome-wide association analysis for resistance to NLB in the Goodman diversity panel. Association analysis identified 100 single and clustered single-nucleotide polymorphism (SNP) associations for resistance to NLB but did not implicate natural functional variation at bm1-bm5. Strong associations implicated a suite of diverse candidate genes including lignin-related genes such as a β-glucosidase gene cluster, hct11, knox1, knox2, zim36, lbd35, CASP-like protein 8, and xat3. The candidate genes are targets for breeding quantitative resistance to NLB in maize for use in silage and nonsilage purposes.

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Genomewide Association Mapping of Flowering Time, Kernel Composition, and Disease Resistance in Historical Minnesota Maize Inbreds

Cited by 25 publications

References 61 publications

Predicting Genetic Variance from Genomewide Marker Effects Estimated from a Diverse Panel of Maize Inbreds

Predicting Genetic Variance from Genomewide Marker Effects Estimated from a Diverse Panel of Maize Inbreds

Germplasm Architecture Revealed through Chromosomal Effects for Quantitative Traits in Maize

Brown midrib mutant and genome‐wide association analysis uncover lignin genes for disease resistance in maize

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