The analysis of quantitative trait loci in multi-environment trials using a multiplicative mixed model

2003 ◽  
Vol 54 (12) ◽  
pp. 1395 ◽  
Author(s):  
A. P. Verbyla ◽  
P. J. Eckermann ◽  
R. Thompson ◽  
B. R. Cullis
Keyword(s):  
Program Analysis ◽  
Quantitative Trait ◽  
Mixed Model ◽  
Genetic Model ◽  
Random Effect ◽  
Doubled Haploid Population ◽  
Environment Interaction ◽  
Factor Analytic Model ◽  
Factor Analytic ◽  
Trait Locus

A new approach for multi-environment quantitative trait locus (QTL) analysis based on an appropriate genetic model is presented. To accommodate a multi-environment analysis, the size of a QTL effect is assumed to be a random effect. The approach results in a multiplicative mixed model for QTL × environment interaction of the factor analytic type. The full genetic model may also include a factor analytic model for the residual genotype × environment interaction, whereas the environmental model for the non-genetic variation involves local, global, and extraneous variation. The approach is used to determine QTLs for yield in the Arapiles × Franklin doubled haploid population of the National Barley Molecular Marker Program. Analysis leads to the determination of 8 QTLs. Many of these QTLs are associated with other traits.

scholarly journals Quantitative Trait Locus-by-Environment Interaction for Milk Yield Traits onBos taurusAutosome 6

Genetics ◽  
2008 ◽  
Vol 179 (3) ◽  
pp. 1539-1546 ◽  
Author(s):  
Marie Lillehammer ◽  
Mike E. Goddard ◽  
Heidi Nilsen ◽  
Erling Sehested ◽  
Hanne Gro Olsen ◽  
...  
Keyword(s):  
Quantitative Trait Locus ◽  
Milk Yield ◽  
Quantitative Trait ◽  
Environment Interaction ◽  
Yield Traits ◽  
Trait Locus

scholarly journals Mapping Quantitative Trait Loci for 1000-Grain Weight in a Double Haploid Population of Common Wheat

2020 ◽  
Vol 21 (11) ◽  
pp. 3960 ◽  
Author(s):  
Tao Liu ◽  
Lijun Wu ◽  
Xiaolong Gan ◽  
Wenjie Chen ◽  
Baolong Liu ◽  
...  
Keyword(s):  
Quantitative Trait ◽  
Genetic Distances ◽  
Snp Markers ◽  
Grain Weight ◽  
High Yield ◽  
Doubled Haploid Population ◽  
Phenotypic Variance ◽  
Yield Trait ◽  
Haploid Population ◽  
Trait Locus

Thousand-grain weight (TGW) is a very important yield trait of crops. In the present study, we performed quantitative trait locus (QTL) analysis of TGW in a doubled haploid population obtained from a cross between the bread wheat cultivar “Superb” and the breeding line “M321” using the wheat 55-k single-nucleotide polymorphism (SNP) genotyping assay. A genetic map containing 15,001 SNP markers spanning 2209.64 cM was constructed, and 9 QTLs were mapped to chromosomes 1A, 2D, 4B, 4D, 5A, 5D, 6A, and 6D based on analyses conducted in six experimental environments during 2015–2017. The effects of the QTLs qTgw.nwipb-4DS and qTgw.nwipb-6AL were shown to be strong and stable in different environments, explaining 15.31–32.43% and 21.34–29.46% of the observed phenotypic variance, and they were mapped within genetic distances of 2.609 cM and 5.256 cM, respectively. These novel QTLs may be used in marker-assisted selection in wheat high-yield breeding.

scholarly journals Genetic Mapping with Background Control for Quantitative Trait Locus (QTL) in 8-Parental Pure-Line Populations

2019 ◽  
Vol 110 (7) ◽  
pp. 880-891 ◽  
Author(s):  
Jinhui Shi ◽  
Jiankang Wang ◽  
Luyan Zhang
Keyword(s):  
Quantitative Trait Locus ◽  
Genetic Variation ◽  
Linear Model ◽  
Quantitative Trait ◽  
Genetic Model ◽  
Pure Line ◽  
Interval Mapping ◽  
Unbiased Estimation ◽  
Trait Locus ◽  
Marker Variables

Abstract Multiparental advanced generation intercross (MAGIC) populations provide abundant genetic variation for use in plant genetics and breeding. In this study, we developed a method for quantitative trait locus (QTL) detection in pure-line populations derived from 8-way crosses, based on the principles of inclusive composite interval mapping (ICIM). We considered 8 parents carrying different alleles with different effects. To estimate the 8 genotypic effects, 1-locus genetic model was first built. Then, an orthogonal linear model of phenotypes against marker variables was established to explain genetic effects of the locus. The linear model was estimated by stepwise regression and finally used for phenotype adjustment and background genetic variation control in QTL mapping. Simulation studies using 3 genetic models demonstrated that the proposed method had higher detection power, lower false discovery rate (FDR), and unbiased estimation of QTL locations compared with other methods. Marginal bias was observed in the estimation of QTL effects. An 8-parental recombinant inbred line (RIL) population previously reported in cowpea and analyzed by interval mapping (IM) was reanalyzed by ICIM and genome-wide association mapping implemented in software FarmCPU. The results indicated that ICIM identified more QTLs explaining more phenotypic variation than did IM; ICIM provided more information on the detected QTL than did FarmCPU; and most QTLs identified by IM and FarmCPU were also detected by ICIM.

Limitations of molecular-marker-aided selection in forest tree breeding

1992 ◽  
Vol 22 (7) ◽  
pp. 1050-1061 ◽  
Author(s):  
S. H. Strauss ◽  
R. Lande ◽  
G. Namkoong
Keyword(s):  
Quantitative Trait Locus ◽  
Quantitative Trait ◽  
Tree Breeding ◽  
Forest Tree ◽  
Genotype By Environment Interaction ◽  
Quantitative Trait Locus Allele ◽  
Environment Interaction ◽  
Linkage Equilibrium ◽  
Genotype By Environment ◽  
Trait Locus

The advances to date with quantitative trait locus identification in agronomic crops, which have mostly been with studies of inter- and intra-specific hybrids, are of little relevance to assessing the potential for marker-aided selection in nonhybrid forest tree populations. Although molecular markers provide great opportunities for dissection of quantitative traits in experimental populations, we expect that their near-term usefulness in most operational tree breeding programs will be limited. In addition to cost, this limitation results from quantitative trait locus–marker associations being limited to specific genetic backgrounds as a result of linkage equilibrium, interactions of quantitative trait locus effects with genetic backgrounds, genotype by environment interaction, and changes of quantitative trait locus allele frequencies among generations. Marker-aided selection within individually mapped full-sib families can substantially aid phenotypic selection, but only where large restrictions of genetic base are tolerated, trait heritabilities are low, markers are able to explain much of the additive variance, selection intensities within families are high compared with that among families, and very large numbers of progeny are examined. Broad use of marker-aided selection in the longer term will require substantial technical advances in a number of areas, including means for precise quantitative trait locus identification; reduction of large-scale mapping and genotyping costs; and changes in breeding and propagation systems. Consideration of trait characteristics suggests that marker-aided selection will be most efficient in direct selection with high-value, low-heritability traits such as height and diameter growth. These traits, however, often show genotype by environment interaction and unfavorable genetic correlations with other desirable traits, and are likely to be controlled by a large number of minor genes rather than relatively few major ones. Traits with the most potential for marker-aided selection in nonhybrid tree populations will therefore be strongly inherited ones for which phenotypic assay is difficult; examples might include wood quality, resistance to biotrophic pathogens, and resistance to air pollutants. Because of the large disequilibrium generated during hybridization and the great phenotypic variance that segregates in F2 and backcross generations, interspecific hybrid programs lend themselves much more readily to marker-aideed selection. Segregation distortion and related meiotic aberrations, however, may substantially hamper precise estimation of quantitative trait locus locations and phenotypic effects. Nonadditive quantitative trait locus effects will likely be greater in hybrid populations than in intraspecific populations. Rapid decay of disequilibrium due to recombination, and allele frequency shifts due to selective breeding and natural selection during early generations after hybridization, are likely to cause instability for quantitative trait locus - marker associations and quantitative trait locus phenotypic effects. Finally, interspecific hybridization of highly heterozygous individuals from species in linkage equilibrium will impede marker-aided selection.

Quantitative trait locus analysis of late maturity a-amylase in wheat using the doubled haploid population Cranbrook Halberd

2001 ◽  
Vol 52 (12) ◽  
pp. 1267 ◽  
Author(s):  
K. Mrva ◽  
D. J. Mares
Keyword(s):  
Quantitative Trait Locus ◽  
Doubled Haploid ◽  
Quantitative Trait ◽  
Triticum Aestivum L ◽  
Temperature Treatment ◽  
Doubled Haploid Population ◽  
Cool Temperature ◽  
Haploid Population ◽  
Late Maturity ◽  
Trait Locus

Mapping of the late maturity α-amylase (LMA) gene using quantitative trait locus (QTL) analysis represents an important step in identification of potential molecular markers that would greatly improve efficiency and accuracy of screening for LMA. QTL controlling the expression of LMA in wheat were detected in a doubled haploid (DH) cross/population derived from wheat (Triticum aestivum L. em. Thell) cultivars Cranbrook (LMA source) and Halberd (non-LMA). The DH population and parents were sown in replicated trials at Narrabri with sowing times differing by 2 weeks. Cool temperature treatment of detached tillers was used to induce expression of LMA in lines carrying the defect. The number of grains in ripe, treated tillers that contained high pI (malt, germination type) α-amylase isozymes was measured using an ELISA antibody kit highly specific for high pI isozymes. QTL analyses were conducted separately for each sowing, but results from both sowings were consistent and indicated that there was a highly significant (P < 0.001) QTL on the long arm of chromosome 7B (accounting for 31% of the variation in the first experiment), with Cranbrook contributing the higher value allele. A second QTL that accounted for 13% of the variation was found close to the centromere on chromosome 3B. Although it was less important than the QTL on 7B it was nevertheless still significant (P < 0.05).

scholarly journals A classical setting for associations between markers and loci affecting quantitative traits

1999 ◽  
Vol 74 (3) ◽  
pp. 271-277 ◽  
Author(s):  
DAHLIA M. NIELSEN ◽  
B. S. WEIR
Keyword(s):  
Genetic Marker ◽  
Quantitative Trait ◽  
Quantitative Traits ◽  
Genetic Variance ◽  
Genetic Model ◽  
Marker Locus ◽  
Multiple Alleles ◽  
Classical Setting ◽  
Tests Of Association ◽  
Trait Locus

We examine the relationships between a genetic marker and a locus affecting a quantitative trait by decomposing the genetic effects of the marker locus into additive and dominance effects under a classical genetic model. We discuss the structure of the associations between the marker and the trait locus, paying attention to non-random union of gametes, multiple alleles at the marker and trait loci, and non-additivity of allelic effects at the trait locus. We consider that this greater-than-usual level of generality leads to additional insights, in a way reminiscent of Cockerham's decomposition of genetic variance into five terms: three terms in addition to the usual additive and dominance terms. Using our framework, we examine several common tests of association between a marker and a trait.

scholarly journals Climatic drivers of genotype–environment interactions in lodgepole pine based on multi-environment trial data and a factor analytic model of additive covariance

2018 ◽  
Vol 48 (7) ◽  
pp. 835-854 ◽  
Author(s):  
Nicholas K. Ukrainetz ◽  
Alvin D. Yanchuk ◽  
Shawn D. Mansfield
Keyword(s):  
Climate Change ◽  
Mixed Model ◽  
Linear Mixed Model ◽  
Lodgepole Pine ◽  
Future Climate Change ◽  
Climate Modelling ◽  
Mixed Model Analysis ◽  
Factor Analytic Model ◽  
Factor Analytic ◽  
Breeding Groups

The optimum deployment of select material from tree breeding programs is affected by the presence of genotype–environment interactions (G × E) and further complicated by future climate change. Here, we analyzed tree height data from 28 progeny test sites in a multi-environment trial (MET) dataset for two testing cycles of the lodgepole pine (Pinus contorta Douglas ex Loudon) breeding program in British Columbia to characterize one approach to investigating the climatic variables influencing G × E and the potential impacts of climate change. Linear mixed model analysis was conducted using an approximate reduced animal model with a factor analytic (FA) variance model to estimate the complex additive (co)variance structure. Test sites were grouped according to patterns of G × E, and climate modelling was employed to project historical and future deployment zones for each group. Based on these findings, it appears that breeding groups with historically wide deployment zones from northern environments will become less important as the climate warms, and therefore investment should be directed toward southern breeding groups, which will be useful across a very wide geographic range in the near to mid-term future.

scholarly journals Deciphering the Genetic Architecture of Plant Height in Soybean Using Two RIL Populations Sharing a Common M8206 Parent

Plants ◽  
2019 ◽  
Vol 8 (10) ◽  
pp. 373 ◽  
Author(s):  
Yongce Cao ◽  
Shuguang Li ◽  
Guoliang Chen ◽  
Yanfeng Wang ◽  
Javaid Akhter Bhat ◽  
...  
Keyword(s):  
Plant Height ◽  
Quantitative Trait ◽  
Composite Interval Mapping ◽  
Mixed Model ◽  
Interval Mapping ◽  
Interaction Effects ◽  
Epistatic Effect ◽  
Environment Interaction ◽  
Additive Effects ◽  
Yield And Quality

Plant height (PH) is an important agronomic trait that is closely related to soybean yield and quality. However, it is a complex quantitative trait governed by multiple genes and is influenced by environment. Unraveling the genetic mechanism involved in PH, and developing soybean cultivars with desirable PH is an imperative goal for soybean breeding. In this regard, the present study used high-density linkage maps of two related recombinant inbred line (RIL) populations viz., MT and ZM evaluated in three different environments to detect additive and epistatic effect quantitative trait loci (QTLs) as well as their interaction with environments for PH in Chinese summer planting soybean. A total of eight and 12 QTLs were detected by combining the composite interval mapping (CIM) and mixed-model based composite interval mapping (MCIM) methods in MT and ZM populations, respectively. Among these QTLs, nine QTLs viz., QPH-2, qPH-6-2MT, QPH-6, qPH-9-1ZM, qPH-10-1ZM, qPH-13-1ZM, qPH-16-1MT, QPH-17 and QPH-19 were consistently identified in multiple environments or populations, hence were regarded as stable QTLs. Furthermore, Out of these QTLs, three QTLs viz., qPH-4-2ZM, qPH-15-1MT and QPH-17 were novel. In particular, QPH-17 could detect in both populations, which was also considered as a stable and major QTL in Chinese summer planting soybean. Moreover, eleven QTLs revealed significant additive effects in both populations, and out of them only six showed additive by environment interaction effects, and the environment-independent QTLs showed higher additive effects. Finally, six digenic epistatic QTLs pairs were identified and only four additive effect QTLs viz., qPH-6-2MT, qPH-19-1MT/QPH-19, qPH-5-1ZM and qPH-17-1ZM showed epistatic effects. These results indicate that environment and epistatic interaction effects have significant influence in determining genetic basis of PH in soybean. These results would not only increase our understanding of the genetic control of plant height in summer planting soybean but also provide support for implementing marker assisted selection (MAS) in developing cultivars with ideal plant height as well as gene cloning to elucidate the mechanisms of plant height.

scholarly journals Langzeitselektion bei asymmetrischer Merkmalsverteilung

2000 ◽  
Vol 43 (5) ◽  
pp. 421-430
Author(s):  
N. Mielenz ◽  
L. Schüler
Keyword(s):  
Quantitative Trait ◽  
Mixed Model ◽  
Major Effect ◽  
Polygenic Effect ◽  
Phenotypic Variance ◽  
Environment Interaction ◽  
Term Selection ◽  
Wide Range ◽  
Skewness And Kurtosis

Abstract. Title of the paper: Long-term selection by asymmetric trait distribution A quantitative trait is assumed to be genetically affected by a polygenic effect and a major effect of a Single dialellic locus. Such a model is called mixed model of inheritance and the identified gene is denoted as quantitative trait locus (QTL). By choosing the part of the QTL-variance, the degree of dominance and the frequency of the favourable allele it is possible to generate distributions with given level of asymmetry characterised by skewness and kurtosis. If the ratio of QTL- and phenotypic variance is small (less than 8%), then genotype-environment interaction can be used in the mixed inheritance model to explain values of skewness and kurtosis estimated with poultry data. The Situation is considered where the environmental variances given for the three QTL-genotypes show a wide ränge. In most of these cases the short- and long-term selection does not effects the high values for skewness and kurtosis over multiple generations. Further the ratio of response to selection and the difference of selection depends on the selection intensity.

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