Genetic Architecture Of Quantitative Traits Research Articles

Mapping of quantitative trait loci controlling adaptive traits in coastal Douglas-fir. IV. Cold-hardiness QTL verification and candidate gene mapping

Quantitative trait locus (QTL) analyses are used by geneticists to characterize the genetic architecture of quantitative traits, provide a foundation for marker-aided-selection (MAS), and provide a framework for positional selection of candidate genes. The most useful QTL for breeding applications are those that have been verified in time, space, and/or genetic background. In this study, spring cold-hardiness of Douglas-fir foliar tissues was evaluated in two clonally replicated (n=170 and 383 clones) full-sib cohorts derived from the same parental cross in two different years (made 5 years apart). The cohorts were established in widely separated forest test sites and tissues were artificially freeze tested using different cold injury assessment methods. Four of six unique QTL detected for spring cold-hardiness in needles of Cohort 1 were tentatively verified in the second cohort. Four additional QTL were detected in Cohort 2, two on linkage groups (LGs) not previously represented in the smaller cohort. In total, 10 unique QTL were identified across both cohorts. Seventeen of twenty-nine putative cold-hardiness candidate genes (Douglas-fir ESTs) placed on the Douglas-fir linkage map locate within the 95% confidence intervals of spring needle cold-hardiness QTL from the two cohorts and thus represent priority targets for initiating association mapping in Douglas-fir.

Back to the future: genetic correlations, adaptation and speciation

Genetic correlations can affect the course of phenotypic evolution. Although genetic correlations among traits are a common feature of quantitative genetic analyses, they have played a very minor role in recent linkage-map based analyses of the genetic architecture of quantitative traits. Here, we use our work on host-associated races in pea aphids to illustrate how quantitative trait locus (QTL) mapping can be used to test specific hypotheses about how genetic correlations may facilitate ecological specialization and speciation.

A Regulatory Network Analysis of Phenotypic Plasticity in Yeast

Models for the evolution of phenotypic plasticity suggest when and why plasticity might evolve. However, relatively little is known about the genetic basis of plasticity. Molecular studies have recently demonstrated that gene networks can provide a powerful way to infer phenotype from genotype. Information on the structure of the yeast gene regulatory network was combined with data on variation in gene expression in yeast across multiple environments in order to explore the genetic basis of phenotypic plasticity. The phenotypic plasticity of a gene was positively correlated with the number of transcription factors regulating that gene and was significantly lower for transcription factors than for downstream, nonregulatory genes. Plasticity of a gene was also affected by the local substructure of the network in which it was found and by the gene’s function. These results illustrate how network analyses can be used to understand the complex genetic architecture of quantitative traits.

Hairy: A quantitative trait locus for drosophila sensory bristle number.

Advances in medicine, agriculture, and an understanding of evolution depend on resolving the genetic architecture of quantitative traits, which is challenging since variation for complex traits is caused by multiple interacting quantitative trait loci (QTL) with small and conditional effects. Here, we show that the key developmental gene, hairy (h), is a QTL for Drosophila sternopleural bristle number, a model quantitative trait. Near-isoallelic lines (NIL) for the h gene region exhibited significant variation in bristle number and failed to complement a hairy mutation. Sequencing 10 h alleles from a single population revealed 330 polymorphic sites in approximately 10 kb. Genotypes for 25 of these and 14 additional sites in the flanking regions were determined for the 57 NIL and associated with variation in bristle number in four genetic backgrounds. A highly significant association was found for a complicated insertion/deletion polymorphism upstream of the transcription start site. This polymorphism, present in 17.5% of the h alleles, was associated with an increase of 0.5 bristle and accounted for 31% of the genetic variance in bristle number in the NIL.

Precision and high-resolution mapping of quantitative trait loci by use of recurrent selection, backcross or intercross schemes.

Dissecting quantitative genetic variation into genes at the molecular level has been recognized as the greatest challenge facing geneticists in the twenty-first century. Tremendous efforts in the last two decades were invested to map a wide spectrum of quantitative genetic variation in nearly all important organisms onto their genome regions that may contain genes underlying the variation, but the candidate regions predicted so far are too coarse for accurate gene targeting. In this article, the recurrent selection and backcross (RSB) schemes were investigated theoretically and by simulation for their potential in mapping quantitative trait loci (QTL). In the RSB schemes, selection plays the role of maintaining the recipient genome in the vicinity of the QTL, which, at the same time, are rapidly narrowed down over multiple generations of backcrossing. With a high-density linkage map of DNA polymorphisms, the RSB approach has the potential of dissecting the complex genetic architecture of quantitative traits and enabling the underlying QTL to be mapped with the precision and resolution needed for their map-based cloning to be attempted. The factors affecting efficiency of the mapping method were investigated, suggesting guidelines under which experimental designs of the RSB schemes can be optimized. Comparison was made between the RSB schemes and the two popular QTL mapping methods, interval mapping and composite interval mapping, and showed that the scenario of genomic distribution of QTL that was unlocked by the RSB-based mapping method is qualitatively distinguished from those unlocked by the interval mapping-based methods.

The genetic architecture of quantitative traits.

Phenotypic variation for quantitative traits results from the segregation of alleles at multiple quantitative trait loci (QTL) with effects that are sensitive to the genetic, sexual, and external environments. Major challenges for biology in the post-genome era are to map the molecular polymorphisms responsible for variation in medically, agriculturally, and evolutionarily important complex traits; and to determine their gene frequencies and their homozygous, heterozygous, epistatic, and pleiotropic effects in multiple environments. The ease with which QTL can be mapped to genomic intervals bounded by molecular markers belies the difficulty in matching the QTL to a genetic locus. The latter requires high-resolution recombination or linkage disequilibrium mapping to nominate putative candidate genes, followed by genetic and/or functional complementation and gene expression analyses. Complete genome sequences and improved technologies for polymorphism detection will greatly advance the genetic dissection of quantitative traits in model organisms, which will open avenues for exploration of homologous QTL in related taxa.

Power of the joint segregation analysis method for testing mixed major-gene and polygene inheritance models of quantitative traits

Understanding the genetic architecture of quantitative traits can greatly assist the design of strategies for their manipulation in plant-breeding programs. For a number of traits, genetic variation can be the result of segregation of a few major genes and many polygenes (minor genes). The joint segregation analysis (JSA) is a maximum-likelihood approach for fitting segregation models through the simultaneous use of phenotypic information from multiple generations. Our objective in this paper was to use computer simulation to quantify the power of the JSA method for testing the mixed-inheritance model for quantitative traits when it was applied to the six basic generations: both parents (P-1 and P-2), F-1, F-2, and both backcross generations (B-1 and B-2) derived from crossing the F-1 to each parent. A total of 1968 genetic model-experiment scenarios were considered in the simulation study to quantify the power of the method. Factors that interacted to influence the power of the JSA method to correctly detect genetic models were: (1) whether there were one or two major genes in combination with polygenes, (2) the heritability of the major genes and polygenes, (3) the level of dispersion of the major genes and polygenes between the two parents, and (4) the number of individuals examined in each generation (population size). The greatest levels of power were observed for the genetic models defined with simple inheritance; e.g., the power was greater than 90% for the one major gene model, regardless of the population size and major-gene heritability. Lower levels of power were observed for the genetic models with complex inheritance (major genes and polygenes), low heritability, small population sizes and a large dispersion of favourable genes among the two parents; e.g., the power was less than 5% for the two major-gene model with a heritability value of 0.3 and population sizes of 100 individuals. The JSA methodology was then applied to a previously studied sorghum data-set to investigate the genetic control of the putative drought resistance-trait osmotic adjustment in three crosses. The previous study concluded that there were two major genes segregating for osmotic adjustment in the three crosses. Application of the JSA method resulted in a change in the proposed genetic model. The presence of the two major genes was confirmed with the addition of an unspecified number of polygenes.

Finding the molecular basis of quantitative traits: successes and pitfalls.

Understanding the molecular basis of quantitative genetic variation is a principal goal for biomedicine. Although the complex genetic architecture of quantitative traits has so far largely frustrated attempts to identify genes in humans by standard linkage methodologies, quantitative trait loci (QTL) have been mapped in plants, insects and rodents. However, identifying the molecular bases of QTL remains a challenge. Here, we discuss why this is and how new experimental strategies and analytical techniques, combined with the fruits of the genome projects, are beginning to identify candidate genes for QTL studies in several model organisms.

Quantitative trait loci in Drosophila.

Phenotypic variation for quantitative traits results from the simultaneous segregation of alleles at multiple quantitative trait loci. Understanding the genetic architecture of quantitative traits begins with mapping quantitative trait loci to broad genomic regions and ends with the molecular definition of quantitative trait loci alleles. This has been accomplished for some quantitative trait loci in Drosophila. Drosophila quantitative trait loci have sex-, environment- and genotype-specific effects, and are often associated with molecular polymorphisms in non-coding regions of candidate genes. These observations offer valuable lessons to those seeking to understand quantitative traits in other organisms, including humans.

Estimating the genetic architecture of quantitative traits.

Understanding and estimating the structure and parameters associated with the genetic architecture of quantitative traits is a major research focus in quantitative genetics. With the availability of a well-saturated genetic map of molecular markers, it is possible to identify a major part of the structure of the genetic architecture of quantitative traits and to estimate the associated parameters. Multiple interval mapping, which was recently proposed for simultaneously mapping multiple quantitative trait loci (QTL), is well suited to the identification and estimation of the genetic architecture parameters, including the number, genomic positions, effects and interactions of significant QTL and their contribution to the genetic variance. With multiple traits and multiple environments involved in a QTL mapping experiment, pleiotropic effects and QTL by environment interactions can also be estimated. We review the method and discuss issues associated with multiple interval mapping, such as likelihood analysis, model selection, stopping rules and parameter estimation. The potential power and advantages of the method for mapping multiple QTL and estimating the genetic architecture are discussed. We also point out potential problems and difficulties in resolving the details of the genetic architecture as well as other areas that require further investigation. One application of the analysis is to improve genome-wide marker-assisted selection, particularly when the information about epistasis is used for selection with mating.

Statistically robust approaches for sib-pair linkage analysis.

Many traits that distinguish one individual from another, such as height or weight, are clearly heritable and yet vary continuously in populations. Continuous, heritable variation in trait levels presumably reflects the segregation of multiple genes, but elucidation of the genetic architecture of quantitative traits has been limited. Haseman & Elston (1972) developed a genetically robust method (HE) for detecting linkage to quantitative trait loci using sib-pairs. The method is based on a simple linear regression of the squared sib-pairs trait difference on the proportion of alleles shared identical by descent at a marker locus. Linkage is detected by a negative slope which has been traditionally assessed by a standard t-test. Wan, Cohen & Guerra (1997) have shown that the standard t-test is robust to the violations of the stochastic assumptions underlying the test. In practice, however, the standard t-test, based on least-squares regression, is sensitive to outliers. The presence of outliers in the data can lead to false positive and false negative linkage results. Accordingly we have developed and evaluated a statistically robust procedure for the HE approach to linkage. The procedure is based on robust regression. Simulation studies show that this robust procedure has greater power than the standard t-test in the presence of outliers, and has similar power to the standard t-test in the absence of outliers. This robust procedure also shows greater power than rank-based approaches either in the absence or presence of outliers. To illustrate the methods using real data we reanalyse data from two lipoprotein systems that motivated this work.

Trait association of genetic markers in the growth hormone and the growth hormone receptor gene in a White Leghorn strain

Alleles of the growth hormone (GH) gene and GH receptor (GHR) gene were analyzed for association with juvenile body weight (HBWT), age at first egg (AFE), the hen-day rate of egg production (HDR), egg specific gravity (SPG), and egg weight (EWT) in a strain of White Leghorns. The particular strain segregated at near equal frequencies for two GH alleles defined by differences at three restriction fragment length polymorphisms (RFLP) and for two GHR alleles defined by a single RFLP. The GH genotype was significantly associated with AFE (P < or = 0.04) as well as HDR from 274 to 385 d (P < or = 0.04) and 386 to 497 d (P < or = 0.0003). The GHR genotype (haploid in female chickens) had trends for association with HBW (P < or = 0.06) and HDR from AFE to 273 d (P < or = 0.07). The effects on the egg quality traits SPG and EWT were not significant. Regression analysis revealed that HDR was associated negatively with AFE and positively with HBWT. The slope of the regression line of HDR on AFE varied with the GH genotype, with the effect that the differences in HDR between GH genotypes was relatively small in chickens with early AFE and large in chickens with late AFE. Similarly, the slope of the regression of HDR on HBWT varied between GHR genotypes, with the result that the effect of the GHR genotype on HDR in chickens with low HBWT was opposite to its effect in chickens with high HBWT. The complex relationship between genotypes and traits may reflect gene interaction and indicates that simple models based on additive gene effects may not be adequate for the dissection of the genetic architecture of quantitative traits.

Genetics of atherosclerosis: Some strategies for studies of apolipoprotein E.

Many genes are hypothesized to be involved in determining an individual's risk for coronary artery disease (CAD). Recent efforts have focused on the genetic architecture of quantitative traits related to risk for CAD. Studies relating genetic variation in the structural gene for apolipoprotein E to plasma levels of apolipoprotein E illustrate strategies to begin to understand the genetic architecture of plasma levels of apolipoprotein E. Studies using a measured gene approach suggest that variability in the isoforms of apolipoprotein E explain some, but not all, of the variability in plasma levels of apolipoprotein E. Products of other genes may be associated with plasma apolipoprotein E variability. No studies to date have presented findings from an unmeasured gene approach to plasma levels of apolipoprotein E. Models most often used in the unmeasured gene approach are not appropriate for studies of plasma levels of apolipoprotein E and perhaps are inappropriate for the study of other traits. Variations of the models are suggested to ask if a single unmeasured gene is the same gene defined by the apolipoprotein E isoforms. Another model can be used to ask if there is evidence for genes influencing levels of apolipoprotein E after accounting for the effects of the isoforms. Both the measured gene and unmeasured gene strategies have limitations. The failure of most models to allow for the complexity of genotype-phenotype relationships or the heterogeneity of genetic causes will slow the progress to understand the genetic architecture of quantitative traits associated with risk for CAD. © 1993 Wiley-Liss, Inc.