The Genetic Architecture of Quantitative Traits

Mackay, Trudy F. C.

doi:10.1146/annurev.genet.35.102401.090633

Cited by 952 publications

(465 citation statements)

References 236 publications

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“…The genes underlying QTLs are generally sensitive to genetic background and environmental factors (Mackay 2001). To accurately compare the eVects of individual QTLs, it is necessary to minimize the inXuence of these factors.…”

Section: Discussionmentioning

confidence: 99%

The phenotypic expression of QTLs for partial resistance to barley leaf rust during plant development

Wang

et al. 2010

Theor Appl Genet

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Partial resistance is generally considered to be a durable form of resistance. In barley, Rphq2, Rphq3 and Rphq4 have been identiWed as consistent quantitative trait loci (QTLs) for partial resistance to the barley leaf rust pathogen Puccinia hordei. These QTLs have been incorporated separately into the susceptible L94 and the partially resistant Vada barley genetic backgrounds to obtain two sets of near isogenic lines (NILs). Previous studies have shown that these QTLs are not eVective at conferring disease resistance in all stages of plant development. In the present study, the two sets of QTL-NILs and the two recurrent parents, L94 and Vada, were evaluated for resistance to P. hordei isolate 1.2.1 simultaneously under greenhouse conditions from the Wrst leaf to the Xag leaf stage. EVect of the QTLs on resistance was measured by development rate of the pathogen, expressed as latency period (LP). The data show that Rphq2 prolongs LP at the seedling stage (the Wrst and second leaf stages) but has almost no eVect on disease resistance in adult plants. Rphq4 showed no eVect on LP until the third leaf stage, whereas Rphq3 is consistently eVective at prolonging LP from the Wrst leaf to the Xag leaf. The changes in the eVectiveness of Rphq2 and Rphq4 happen at the barley tillering stage (the third to fourth leaf stages). These results indicate that multiple disease evaluations of a single plant by repeated inoculations of the fourth leaf to the Xag leaf should be conducted to precisely estimate the eVect of Rphq4. The present study conWrms and describes in detail the plant development-dependent eVectiveness of partial resistance genes and, consequently, will enable a more precise evaluation of partial resistance regulation during barley development.

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Section: Discussionmentioning

confidence: 99%

The phenotypic expression of QTLs for partial resistance to barley leaf rust during plant development

Wang

et al. 2010

Theor Appl Genet

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“…Many researchers estimate interaction only between loci that have statistically significant marginal effects (Fedorowicz et al 1998;Blangero et al 2000;Mackay, 2001). Recent theoretical work by Culverhouse et al (2002) shows that it is possible to have a statistically significant component of genetic variance attributable to epistatic effects between variable loci that have no statistically significant marginal effects.…”

Section: Intragenic Non-additivitymentioning

confidence: 99%

Evidence for Non‐additive Influence of Single Nucleotide Polymorphisms within the Apolipoprotein E Gene

Hamon

Stengård

Clark

et al. 2004

Annals of Human Genetics

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SummaryWe analyzed 13 single nucleotide polymorphisms (SNPs) within the apolipoprotein E (APOE) gene, to identify pairs of SNPs that interact in a non-additive manner to influence genotypic mean levels of the ApoE protein in blood. An overparameterized general linear model of two-SNP genotype means was applied to data from 456 female and 398 male unrelated European Americans from Rochester, MN, USA. We found statistically significant evidence for non-additivity between SNPs within the male sample, but not within the female sample. We observed nine pairs of SNPs with evidence of non-additivity at the α = 0.05 level of statistical significance within the male sample, when approximately three were expected by chance. Five of the nine pairs involved three SNPs (560, 624 and 1163) that did not have a statistically significant influence when considered separately in a single-site analysis. Three of the nine pairs involving four SNPs (832, 1998, 3937 and 4951) showed significant evidence for non-additivity in at least one of two other male samples from Jackson, MS, USA and North Karelia, Finland. Although all four of these SNPs had a statistically significant influence in Rochester when considered separately, only SNP 3937 gave a significant result in the other male samples. The four SNPs are located in the promoter, intronic and exonic regions, and 3' to the polyadenylation signal in the APOE gene. Our study suggests that analyses that only consider SNPs located in exons and ignore contexts such as those indexed by gender and population, and disregard non-additivity of SNP effects, may inappropriately model the contribution of a gene to the genetic architecture of a trait that has a complex multifactorial etiology.

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“…Or alternatively do they show some form of epistasis that complicates modeling and prediction. Comparing epistasis versus additive interactions is largely impossible in GWA studies do to the non-random structure of the populations and instead requires more randomly structured population like recombinant inbred lines [38,42,43]. Analysis of secondary metabolite pathways using RILs has shown a high level of epistatic interactions amongst causal genes [29 ,44,45].…”

Section: Quantitative Trait Locus Mapping To Reverse Engineer the Shamentioning

confidence: 99%

Synthetic biology of metabolism: using natural variation to reverse engineer systems

Kliebenstein

2014

Current Opinion in Plant Biology

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A goal of metabolic engineering is to take a plant and introduce new or modify existing pathways in a directed and predictable fashion. However, existing data does not provide the necessary level of information to allow for predictive models to be generated. One avenue to reverse engineer the necessary information is to study the genetic control of natural variation in plant primary and secondary metabolism. These studies are showing that any engineering model will have to incorporate information about 1000s of genes in both the nuclear and organellar genome to optimize the function of the introduced pathway. Further, these genes may interact in an unpredictable fashion complicating any engineering approach as it moves from the one or two gene manipulation to higher order stacking efforts. Finally, metabolic engineering may be influenced by a previously unrecognized potential for a plant to measure the metabolites within it. In combination, these observations from natural variation provide a beginning to help improve current efforts at metabolic engineering. Introduction A significant goal of synthetic biology or biological engineering is to take an existant organism and alter it in a directed and predictive fashion to introduce a new phenotype that had not previously existed in that organism. Some of these efforts start with introducing a new metabolic pathway into an organism to provide a better source of a commercially important chemical or to make new chemicals entirely [1][2][3]. Other efforts are focused at taking agronomically important traits such as defensive chemicals, nitrogen fixation or perennial growth from one species and transferring these traits into other species that do not contain them [4 ,5,6]. However these efforts while exciting frequently run into great difficulty and for this review article, I will focus on one the potential of using information from natural variation studies to facilitate these efforts for metabolic engineering.To predictively engineer a new metabolic pathway into a plant with optimal efficiency requires a base level of knowledge that likely does not exist to any full level. To generate full predictive ability we would need much deeper understanding of the following questions. First, how many genes within the plant need to be manipulated to facilitate the integration of the new pathway into the organisms metabolic and regulatory network. How do these genes interact, are they solely additive or is there evidence of more complex epistasis that complicates predictive capacity? Finally, are these genes solely in the nuclear genome or is there causal variation also in the organellar genomes? Most efforts to answer these questions focus largely on forward or single-gene reverse genetics approaches which are highly time consuming and difficult to scale up to the necessary level.An alternative approach to understanding a system is to use the concept of reverse engineering [7,8]. The goal of reverse engineering is to take a functioning system, say an existing metabolic pathway, ...

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The Genetic Architecture of Quantitative Traits

Cited by 952 publications

References 236 publications

The phenotypic expression of QTLs for partial resistance to barley leaf rust during plant development

The phenotypic expression of QTLs for partial resistance to barley leaf rust during plant development

Evidence for Non‐additive Influence of Single Nucleotide Polymorphisms within the Apolipoprotein E Gene

Synthetic biology of metabolism: using natural variation to reverse engineer systems

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