Four‐Parent Maize (FPM) Population: Development and Phenotypic Characterization

Mahan, Adam L.; Murray, Seth C.; Klein, Patricia E.

doi:10.2135/cropsci2017.07.0450

Cited by 13 publications

(18 citation statements)

References 29 publications

(36 reference statements)

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“…Furthermore, individuals with high heterozygosity (> 20%) were removed resulting in 1149 individuals including: 126 B73Olc1 × Tx903, 112 Tx772 × Tx906, 107 4way0sib, 212 4way1sib, 93 4way2sib, and 491 4way3sib; eight were 4way (based on the observed parental allele contribution frequencies) but their intermating level was misplaced. For detailed methods on genotyping the FPM population refer to Mahan (2015).…”

Section: Methodsmentioning

confidence: 99%

“…Meng et al (2016) demonstrated increased QTL identification power and faster LD decay distance in the 8‐way Indica rice MAGIC population compared to the 4‐way. Currently MAGIC populations (reviewed by Huang et al, 2015) have been developed in Arabidopsis (Kover et al, 2009), rice (Bandillo et al, 2013; Meng et al, 2016), wheat (Huang et al, 2012; Mackay et al, 2014), durum wheat (Milner et al, 2016), barley (Sannemann et al, 2015), dry bean (Lobaton et al, 2015), tomato (Pascual et al, 2015), sorghum (Higgins et al, 2014) and maize (Dell'Acqua et al, 2015; Mahan et al, 2018).…”

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confidence: 99%

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Four Parent Maize (FPM) Population: Effects of Mating Designs on Linkage Disequilibrium and Mapping Quantitative Traits

et al. 2018

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Core Ideas Intermating did not expand phenotypic variation of quantitative traits in FPM populations. Three intermating generations increased genetic resolution 4‐fold, reducing LD 2.5‐fold. Mating designs had little effect on mapping power, population size was more important. Some intermating is needed to minimize admixture LD in multi‐parent crosses. Surprisingly few QTL were detected, given high heritability and large population size. Multiparent advanced generation inter‐cross (MAGIC) populations can provide improved genetic mapping resolution by increasing allelic diversity and effective recombination. The Four Parent Maize (FPM; Zea mays L.) population implemented five different mating designs used in MAGIC and bi‐parental populations to compare empirical effects on genetic resolution and power of quantitative trait locus (QTL) detection; the combined population here comprised of 1149 individuals with 118,509 genetic markers. Measurements were recorded for plant height (PH), ear height (EH), days to anthesis (DTA) and silking (DTS) in seven environments, spanning three years. Linkage disequilibrium (LD) analysis of subpopulations indicated MAGIC population designs should incorporate generations of intermating to overcome initial LD increase caused by population admixture in a non‐intermated four parent population (4way0sib). A 3‐ to 4‐fold increase in genetic resolution (r2<0.8) and a 2.5‐fold decrease in the extent of LD decay (r2<0.2) compared to the biparental populations was found for the four parent cross at the third generation of intermating (4way3sib). Power of QTL detection was affected to a greater extent by sample size rather than by mating designs. The FPM power simulations indicated that MAGIC populations have the ability to meet or exceed the mapping power of nested association panels with fewer individuals and diversity inputs. Using association mapping software we identified 2, 5, 7, and 6 QTL for PH, EH, DTA, and DTS, respectively. The FPM population is a valuable resource for quantifying empirical improvements of parent number, intermating, and the number of progeny for QTL linkage mapping.

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Section: Methodsmentioning

confidence: 99%

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Four Parent Maize (FPM) Population: Effects of Mating Designs on Linkage Disequilibrium and Mapping Quantitative Traits

et al. 2018

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“…Genetic variation of terminal plant height (PHT TRML ) in maize ( Zea mays L.) is a highly heritable trait (Anderson et al, 2018; Li et al, 2016b; Mahan et al, 2018; Peiffer et al, 2014; Wallace et al, 2016) and is relatively easy to phenotype, for instance measuring from the ground to the tip of a tassel on a representative plant. However, the labor and time required to collect data is still resource constrained, and height measurements collected in maize research programs are generally taken only once, when the plants have reached maximum growth after the completion of flowering.…”

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Prediction of Maize Grain Yield before Maturity Using Improved Temporal Height Estimates of Unmanned Aerial Systems

Anderson

Murray

Malambo

et al. 2019

The Plant Phenome Journal

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UAS captured increased genetic variation compared with manual terminal height. There were small significant differences in ground filtering methods to extract plant structure. Higher resolution did not improve imagery informativeness with regard to plant height. Logistic function provides informative phenotypes for temporal maize growth. Correlation and prediction accuracy of grain yield increased by ∼20% with UAS heights. Weekly unmanned aerial system (UAS) imagery was collected over the College Station, TX, 2017 Genomes to Fields (G2F) hybrid trial, across three environmental stress treatments, using two UAS platforms. The high‐altitude (120‐m) fixed‐wing platform increased the fraction of variation attributed to genetics and had highly repeatable (R > 60%) height estimates, increasing the genetic variance explained (10–40%) over traditional terminal plant height measurement (PHTTRML ∼30%), as well as over the low‐altitude rotary‐wing UAS platform (10–20%). A logistic function reduced the dimensionality (>20 flights) of each UAS dataset to three parameters (inflection point, growth rate, and asymptote) and produced a more robust predictive model than independent flight dates, effectively summarizing (R2 > 0.98) the UAS flight dates. The logistic model overcame the need to use specific flight dates when comparing different environments. The UAS height estimates (r = 0.36–0.48) doubled the correlations to grain yield in this G2F experiment compared with PHTTRML (r = 0.23–0.28). Parameters of the logistical function achieved equivalent correlations (r = 0.30–0.46) to individual flight dates (r = 0.36–0.48), improving grain yield prediction by ∼400% (R2 = 0.25–0.34) over PHTTRML (R2 = 0.06–0.08). Incorporating other UAS‐derived parameters beyond plant height may allow yield to be accurately predicted before maturity, speeding breeding programs. A new public R function to generate ESRI shapefiles for plot research is also described.

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“…Quantitative variation of complex traits in maize ( Zea mays L.) have been challenging to dissect since they show strong environmental interactions and are generally inconsistent between populations and different screening environments (Beavis et al., 1991; Koester et al., 1993; Peiffer et al., 2014; Sari‐Gorla et al., 1999; Veldboom & Lee, 1996; Wang et al., 2006). Plant height, traditionally measured terminally at the end of the growing season with a ruler, is a prime example of a quantitative, complex trait; it is relatively easy to measure across many plots and it has high repeatability and heritability (Anderson et al., 2018,2019, 2020; Mahan et al., 2018; Peiffer et al., 2014; Rood & Major, 1981; Veldboom & Lee, 1996). Genetic mapping and theory suggest an omnigenic model supported by the genetically polygenic inheritances observed and the variable contributions of pedigree as a source of variation, consistent with a large number of loci with minor effects governing these traits (Boyle et al., 2017; Mackay, 2001; Peiffer et al., 2014; Wallace et al., 2016; Wang et al., 2006).…”

Section: Introductionmentioning

confidence: 99%

Unoccupied aerial systems discovered overlooked loci capturing the variation of entire growing period in maize

et al. 2021

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Traditional phenotyping methods, coupled with genetic mapping in segregating populations, have identified loci governing complex traits in many crops. Unoccupied aerial systems (UAS)-based phenotyping has helped to reveal a more novel and dynamic relationship between time-specific associated loci with complex traits previously unable to be evaluated. Over 1,500 maize (Zea mays L.) hybrid row plots containing 280 different replicated maize hybrids from the Genomes to Fields (G2F) project were evaluated agronomically and using UAS in 2017. Weekly UAS flights captured variation in plant heights during the growing season under three different management conditions each year: optimal planting with irrigation (G2FI), optimal dryland planting without irrigation (G2FD), and a stressed late planting (G2LA).Plant height of different flights were ranked based on importance for yield using a random forest (RF) algorithm. Plant heights captured by early flights in G2FI trials had higher importance (based on Gini scores) for predicting maize grain yield (GY) but also higher accuracies in genomic predictions which fluctuated for G2FD (−0.06∼0.73), G2FI (0.33∼0.76), and G2LA (0.26∼0.78) trials. A genomewide association analysis discovered 52 significant single nucleotide polymorphisms (SNPs), seven were found consistently in more than one flights or trial; 45 were flight or trial specific. Total cumulative marker effects for each chromosome's contributions to plant height also changed depending on flight. Using UAS phenotyping, this study showed that many candidate genes putatively play a role in the regulation of plant architecture even in relatively early stages of maize growth and development.

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Four‐Parent Maize (FPM) Population: Development and Phenotypic Characterization

Cited by 13 publications

References 29 publications

Four Parent Maize (FPM) Population: Effects of Mating Designs on Linkage Disequilibrium and Mapping Quantitative Traits

Four Parent Maize (FPM) Population: Effects of Mating Designs on Linkage Disequilibrium and Mapping Quantitative Traits

Prediction of Maize Grain Yield before Maturity Using Improved Temporal Height Estimates of Unmanned Aerial Systems

Unoccupied aerial systems discovered overlooked loci capturing the variation of entire growing period in maize

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