Quantitative genetic variance associated with chromosomal markers in segregating populations

Summary Use of chromosomal markers can accelerate genetic progress for quantitative traits in pedigree selection programs by providing early information on Mendelian segregation effects for individual progeny. Potential effectiveness of selection using markers is determined by the amount of additive genetic variance traced from parents to progeny by the markers. Theoretical equations for the amount of additive genetic variance associated with a marker were derived at the individual level and for a segregating population in joint linkage equilibrium. Factors considered were the number of quantitative trait loci linked to the marker, their individual effects, and recombination rates with the marker. Subsequently, the expected amount of genetic variance associated with a marker in a segregating population was derived. In pedigree selection programs in segregating populations, a considerable fraction of the genetic variance on a chromosome is expected to be associated with a marker located on that chromosome. For an average chromosome in the bovine, this fraction is approximately 40% of the Mendelian segregation variance contributed by the chromosome. The effects of interference and position of the marker on this expectation are relative small. Length of the chromosome has a large effect on the expected variance. Effectiveness of MAS is, however, greatly reduced by lack of polymorphism at the marker and inaccuracy of estimation of chromosome substitution effects. The size of the expected amount of genetic variance associated with a chromosomal marker indicates that, even when the marker is not the active locus, large chromosome substitution effects are not uncommon in segregating populations.     

Toward understanding genetic mechanisms of complex traits in rice

Rice is the primary carbohydrate staple cereal feeding the world population. Many genes, known as quantitative trait loci (QTLs), con-trol most of the agronomically important traits in rice. The identification of QTLs controlling agricultural traits is vital to increase yield and meet the needs of the increasing human population, but the progress met with challenges due to complex QTL inheritance. To date,many QTLs have been detected in rice, including those responsible for yield and grain quality; salt, drought and submergence tolerance;disease and insect resistance; and nutrient utilization efficiency. Map-based cloning techniques have enabled scientists to successfully fine map and clone approximately seventeen QTLs for several traits. Additional in-depth functional analyses and characterizations of these genes will provide valuable assistance in rice molecular breeding.     

Mapping genetic loci that interact with myostatin to affect growth traits

Myostatin, or GDF8, is an inhibitor of skeletal muscle growth. A non-functional myostatin mutation leads to a double muscling phenotype in some species, for example, mice, cattle and humans. Previous studies have indicated that there are loci in the genome that interact with myostatin to control backfat depth and other complex traits. We now report a quantitative trait loci (QTL) mapping study designed to identify loci that interact with myostatin to impact growth traits in mice. Body weight and average daily gain traits were collected on F2 progeny derived from a myostatin-null C57BL/6 strain by M16i cross. In all, 44 main effect QTL were detected above a 5% genome-wide significance threshold when an interval mapping method was used. An additional 37 QTL were identified to significantly interact with myostatin, sex or reciprocal cross. A total of 12 of these QTL interacted with myostatin genotype. These results provide a foundation for the further fine mapping of genome regions that harbor loci that interact with myostatin.     

Strategies for genetic mapping of categorical traits

The search for efficient and powerful statistical methods and optimal mapping strategies for categorical traits under various experimental designs continues to be one of the main tasks in genetic mapping studies. Methodologies for genetic mapping of categorical traits can generally be classified into two groups, linear and non-linear models. We develop a method based on a threshold model, termed mixture threshold model to handle ordinal (or binary) data from multiple families. Monte Carlo simulations are done to compare its statistical efficiencies and properties of the proposed non-linear model with a linear model for genetic mapping of categorical traits using multiple families. The mixture threshold model has notably higher statistical power than linear models. There may be an optimal sampling strategy (family size vs number of families) in which genetic mapping reaches its maximal power and minimal estimation errors. A single large-sibship family does not necessarily produce the maximal power for detection of quantitative trait loci (QTL) due to genetic sampling of QTL alleles. The QTL allelic model has a marked impact on efficiency of genetic mapping of categorical traits in terms of statistical power and QTL parameter estimation. Compared with a fixed number of QTL alleles (two or four), the model with an infinite number of QTL alleles and normally distributed allelic effects results in loss of statistical power. The results imply that inbred designs (e.g. F₂ or four-way crosses) with a few QTL alleles segregating or reducing number of QTL alleles (e.g. by selection) in outbred populations are desirable in genetic mapping of categorical traits using data from multiple families. This revised version was published online in July 2006 with corrections to the Cover Date.     

Influences of rare copy-number variation on human complex traits

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Study on mapping Quantitative Trait Loci for animal complex binary traits using Bayesian-Markov chain Monte Carlo approach

It is a challenging issue to map Quantitative Trait Loci (QTL) underlying complex discrete traits, which usually show discontinuous distribution and less information, using conventional statistical methods. Bayesian-Markov chain Monte Carlo (Bayesian-MCMC) approach is the key procedure in mapping QTL for complex binary traits, which provides a complete posterior distribution for QTL parameters using all prior information. As a consequence, Bayesian estimates of all interested variables can be obtained straightforwardly basing on their posterior samples simulated by the MCMC algorithm. In our study, utilities of Bayesian-MCMC are demonstrated using simulated several animal outbred full-sib families with different family structures for a complex binary trait underlied by both a QTL and polygene. Under the Identity-by-Descent-Based variance component random model, three samplers basing on MCMC, including Gibbs sampling, Metropolis algorithm and reversible jump MCMC, were implemented to generate the joint posterior distribution of all unknowns so that the QTL parameters were obtained by Bayesian statistical inferring. The results showed that Bayesian-MCMC approach could work well and robust under different family structures and QTL effects. As family size increases and the number of family decreases, the accuracy of the parameter estimates will be improved. When the true QTL has a small effect, using outbred population experiment design with large family size is the optimal mapping strategy.     

Quantitative trait loci controlling plant architectural traits in cotton

Cotton plant architecture is an important characteristic influencing the suitability of specific cotton varieties in cultivation, fiber yield and quality. However, complex multigenic relationships and substantial genotype–environment interaction underlie plant architecture, and will hinder the efficient improvement of these traits in conventional cotton breeding programs. An enhanced understanding of the molecular-genetic regulation of plant morphological developmental can aid in the modification of agronomically relevant traits. In this study, an interspecific Gossypium hirsutum and Gossypium barbadense BC₁ population was used to identify QTL associated with plant architectural traits. Twenty-six single QTL were identified for seven plant architecture traits. The phenotypic variation explained by an individual QTL ranged from 9.56% to 44.57%. In addition, 11 epistatic QTL for fruit branch angle (FBA), plant height (PH), main-stem leaf size (MLS), and fruiting branch internode length (FBI) explained 2.28–15.34% of the phenotypic variation in these traits. The majority of the interactions (60%) occurred between markers linked to QTL influencing the same traits. The QTL detected in this study are expected to be valuable in future breeding programs to develop cultivars exhibiting desirable cotton architecture.     

Quantitative Genetics in the Genomics Era

The genetic analysis of quantitative or complex traits has been based mainly on statistical quantities such as genetic variances and heritability. These analyses continue to be developed, for example in studies of natural populations. Genomic methods are having an impact on progress and prospects. Actual relationships of individuals can be estimated enabling novel quantitative analyses. Increasing precision of linkage mapping is feasible with dense marker panels and designed stocks allowing multiple generations of recombination, and large SNP panels enable the use of genome wide association analysis utilising historical recombination. Whilst such analyses are identifying many loci for disease genes and traits such as height, typically each individually contributes a small amount of the variation. Only by fitting all SNPs without regard to significance can a high proportion be accounted for, so a classical polygenic model with near infinitesimally small effects remains a useful one. Theory indicates that a high proportion of variants will have low minor allele frequency, making detection difficult. Genomic selection, based on simultaneously fitting very dense markers and incorporating these with phenotypic data in breeding value prediction is revolutionising breeding programmes in agriculture and has a major potential role in human disease prediction.     

Study on mapping Quantitative Trait Loci for animal complex binary traits using Bayesian-Markov chain Monte Carlo approach

It is a challenging issue to map Quantitative Trait Loci (QTL) underlying complex discrete traits,which usually show discontinuous distribution and less information,using conventional statisti-cal methods. Bayesian-Markov chain Monte Carlo (Bayesian-MCMC) approach is the key procedure in mapping QTL for complex binary traits,which provides a complete posterior distribution for QTL parameters using all prior information. As a consequence,Bayesian estimates of all interested vari-ables can be obtained straightforwardly basing on their posterior samples simulated by the MCMC algorithm. In our study,utilities of Bayesian-MCMC are demonstrated using simulated several ani-mal outbred full-sib families with different family structures for a complex binary trait underlied by both a QTL and polygene. Under the Identity-by-Descent-Based variance component random model,three samplers basing on MCMC,including Gibbs sampling,Metropolis algorithm and reversible jump MCMC,were implemented to generate the joint posterior distribution of all unknowns so that the QTL parameters were obtained by Bayesian statistical inferring. The results showed that Bayesian-MCMC approach could work well and robust under different family structures and QTL effects. As family size increases and the number of family decreases,the accuracy of the parameter estimates will be im-proved. When the true QTL has a small effect,using outbred population experiment design with large family size is the optimal mapping strategy.     

High‐precision genetic mapping of behavioral traits in the diversity outbred mouse population

Historically our ability to identify genetic variants underlying complex behavioral traits in mice has been limited by low mapping resolution of conventional mouse crosses. The newly developed Diversity Outbred (DO) population promises to deliver improved resolution that will circumvent costly fine‐mapping studies. The DO is derived from the same founder strains as the Collaborative Cross (CC), including three wild‐derived strains. Thus the DO provides more allelic diversity and greater potential for discovery compared to crosses involving standard mouse strains. We have characterized 283 male and female DO mice using open‐field, light–dark box, tail‐suspension and visual‐cliff avoidance tests to generate 38 behavioral measures. We identified several quantitative trait loci (QTL) for these traits with support intervals ranging from 1 to 3 Mb in size. These intervals contain relatively few genes (ranging from 5 to 96). For a majority of QTL, using the founder allelic effects together with whole genome sequence data, we could further narrow the positional candidates. Several QTL replicate previously published loci. Novel loci were also identified for anxiety‐ and activity‐related traits. Half of the QTLs are associated with wild‐derived alleles, confirming the value to behavioral genetics of added genetic diversity in the DO. In the presence of wild‐alleles we sometimes observe behaviors that are qualitatively different from the expected response. Our results demonstrate that high‐precision mapping of behavioral traits can be achieved with moderate numbers of DO animals, representing a significant advance in our ability to leverage the mouse as a tool for behavioral genetics .     

Properties and power of the Drosophila Synthetic Population Resource for the routine dissection of complex traits

The Drosophila Synthetic Population Resource (DSPR) is a newly developed multifounder advanced intercross panel consisting of >1600 recombinant inbred lines (RILs) designed for the genetic dissection of complex traits. Here, we describe the inference of the underlying mosaic founder structure for the full set of RILs from a dense set of semicodominant restriction-site-associated DNA (RAD) markers and use simulations to explore how variation in marker density and sequencing coverage affects inference. For a given sequencing effort, marker density is more important than sequence coverage per marker in terms of the amount of genetic information we can infer. We also assessed the power of the DSPR by assigning genotypes at a hidden QTL to each RIL on the basis of the inferred founder state and simulating phenotypes for different experimental designs, different genetic architectures, different sample sizes, and QTL of varying effect sizes. We found the DSPR has both high power (e.g., 84% power to detect a 5% QTL) and high mapping resolution (e.g., ～1.5 cM for a 5% QTL).     

Detection and validation of EST-SSR markers associated with sugar-related traits in sugarcane using linkage and association mapping

Sugar-related traits are of great importance in sugarcane breeding. In the present study, quantitative trait loci (QTL) mapping validated with association mapping was used to identify expressed sequence tag-simple sequence repeats (EST-SSRs) associated with sugar-related traits. For linkage mapping, 524 EST-SSRs, 241 Amplified Fragment Length Polymorphisms, and 10 genomic SSR markers were mapped using 283 F₁ progenies derived from an interspecific cross. Six regions were identified using Multiple QTL Mapping, and 14 unlinked markers using single marker analysis. Association analysis was performed on a set of 200 accessions, based on the mixed linear model. Validation of the EST-SSR markers using association mapping within the target QTL genomic regions identified two EST-SSR markers showing a putative relationship with uridine diphosphate (UDP) glycosyltransferase, and beta-amylase, which are associated with pol and sugar yield. These functional markers can be used for marker-assisted selection of sugarcane.     

Genetic control of physiological traits associated to low temperature growth in sunflower under early sowing conditions

This study was conducted to identify physiological traits associated with cold tolerance in sunflower and to identify the genomic regions involved in their variation. A population of 98 recombinant inbred lines (RILs) and their two parents were sown in the field as usual sowing date (control) and one or two months earlier (long-term low temperature treatments). A trait commonly used to underlying cold tolerance related to the degree of membrane damage, as well as traits associated with growth capacity (chlorophyll content, potential photochemical efficiency of photosystem II and plant dry weight) and finally those reflecting acclimation mechanism to stress conditions (osmotic potential at full turgor, and specific leaf area) have been investigated at early development stages. Significant differences were observed among the three sowing dates for all traits. Chlorophyll content and specific leaf area are genetically associated with cold tolerance. Genetic gains were observed for chlorophyll content and osmotic potential traits in some of early sowing dates, which suggest that they could be used for cold tolerance in breeding programs. QTL analyses show that several putative genomic regions are involved in the variation of the physiological traits studied under low temperature. Major QTLs for cold tolerance associated with SSR markers such as ORS331_2 for the cell membrane stability should be checked in several environments to see if they can be used in marker-assisted selection programs.     

The genetic dissection of complex traits in a founder population

We estimated broad heritabilities (H(2)) and narrow heritabilities (h(2)) and conducted genomewide screens, using a novel association-based mapping approach for 20 quantitative trait loci (QTLs) among the Hutterites, a founder population that practices a communal lifestyle. Heritability estimates ranged from.21 for diastolic blood pressure (DBP) to.99 for whole-blood serotonin levels. Using a multipoint method to detect association under a recessive model we found evidence of major QTLs for six traits: low-density lipoprotein (LDL), triglycerides, lipoprotein (a) (Lp[a]), systolic blood pressure (SBP), serum cortisol, and whole-blood serotonin. Second major QTLs for Lp(a) and for cortisol were identified using a single-point method to detect association under a general two-allele model. The heritabilities for these six traits ranged from.37 for triglycerides to.99 for serotonin, and three traits (LDL, SBP, and serotonin) had significant dominance variances (i.e., H(2) > h(2)). Surprisingly, there was little correlation between measures of heritability and the strength of association on a genomewide screen (P>.50), suggesting that heritability estimates per se do not identify phenotypes that are influenced by genes with major effects. The present study demonstrates the feasibility of genomewide association studies for QTL mapping. However, even in this young founder population that has extensive linkage disequilibrium, map densities <5 cM may be required to detect all major QTLs.     

Quantitative trait loci analysis of a Duroc commercial population highlights differences in the genetic determination of meat quality traits at two different muscles

We performed a whole‐genome scan with 110 informative microsatellites in a commercial Duroc population for which growth, fatness, carcass and meat quality phenotypes were available. Importantly, meat quality traits were recorded in two different muscles, that is, gluteus medius (GM) and longissimus thoracis et lumborum (LTL), to find out whether these traits are determined by muscle‐specific genetic factors. At the whole‐population level, three genome‐wide QTL were identified for carcass weight (SSC7, 60 cM), meat redness (SSC13, 84 cM) and yellowness (SSC15, 108 cM). Within‐family analyses allowed us to detect genome‐wide significant QTL for muscle loin depth between the 3rd and 4th ribs (SSC15, 54 cM), backfat thickness (BFT) in vivo (SSC10, 58 cM), ham weight (SSC9, 69 cM), carcass weight (SSC7, 60 cM; SSC9, 68 cM), BFT on the last rib (SSC11, 48 cM) and GM redness (SSC8, 85 cM; SSC13, 84 cM). Interestingly, there was low positional concordance between meat quality QTL maps obtained for GM and LTL. As a matter of fact, the three genome‐wide significant QTL for colour traits (SSC8, SSC13 and SSC15) that we detected in our study were all GM specific. This result suggests that QTL effects might be modulated to a certain extent by genetic and environmental factors linked to muscle function and anatomical location.     

Quantitative Trait Loci (QTLs) mapping for growth traits in the mouse: A review

The attainment of a specific mature body size is one of the most fundamental differences among species of mammals. Moreover, body size seems to be the central factor underlying differences in traits such as growth rate, energy metabolism and body composition. An important proportion of this variability is of genetic origin. The goal of the genetic analysis of animal growth is to understand its "genetic architecture", that is the number and position of loci affecting the trait, the magnitude of their effects, allele frequencies and types of gene action. In this review, the different strategies developed to identify and characterize genes involved in the regulation of growth in the mouse are described, with emphasis on the methods developed to map loci contributing to the regulation of quantitative traits (QTLs).     

Genetic and molecular dissection of quantitative traits in rice

Recent progress in the generation of a molecular genetic map and markers for rice has made possible a new phase of mapping individual genes associated with complex traits. This type of analysis is often referred to as quantitative trait locus (QTL) analysis. Increasing numbers of QTL analyses are providing enormous amounts of information about QTLs, such as the numbers of loci involved, their chromosomal locations and gene effects. Clarification of genetic bases of complex traits has a big impact not only on fundamental research on rice plant development, but it also has practical benefits for rice breeding. In this review, we summarize recent progress of QTL analysis of several complex traits in rice. A strategy for positional cloning of genes at QTLs is also discussed.     

A SNP-based genetic dissection of versatile traits in bread wheat (Triticum aestivum L.)

The continuous increase in global population prompts increased wheat production. Future wheat (Triticum aestivum L.) breeding will heavily rely on dissecting molecular and genetic bases of wheat yield and related traits which is possible through the discovery of quantitative trait loci (QTLs) in constructed populations, such as recombinant inbred lines (RILs). Here, we present an evaluation of 92 RILs in a bi-parental RIL mapping population (the International Triticeae Mapping Initiative Mapping Population [ITMI/MP]) using newly generated phenotypic data in 3-year experiments (2015), older phenotypic data (1997–2009), and newly created single nucleotide polymorphism (SNP) marker data based on 92 of the original RILs to search for novel and stable QTLs. Our analyses of more than 15 unique traits observed in multiple experiments included analyses of 46 traits in three environments in the USA, 69 traits in eight environments in Germany, 149 traits in 10 environments in Russia, and 28 traits in four environments in India (292 traits in 25 environments) with 7584 SNPs (292 × 7584 = 2 214 528 data points). A total of 874 QTLs were detected with limit of detection (LOD) scores of 2.01–3.0 and 432 QTLs were detected with LOD > 3.0. Moreover, 769 QTLs could be assigned to 183 clusters based on the common markers and relative proximity of related QTLs, indicating gene-rich regions throughout the A, B, and D genomes of common wheat. This upgraded genotype–phenotype information of ITMI/MP can assist breeders and geneticists who can make crosses with suitable RILs to improve or investigate traits of interest.     

The relationship between parental genetic or phenotypic divergence and progeny variation in the maize nested association mapping population

Appropriate selection of parents for the development of mapping populations is pivotal to maximizing the power of quantitative trait loci detection. Trait genotypic variation within a family is indicative of the family's informativeness for genetic studies. Accurate prediction of the most useful parental combinations within a species would help guide quantitative genetics studies. We tested the reliability of genotypic and phenotypic distance estimators between pairs of maize inbred lines to predict genotypic variation for quantitative traits within families derived from biparental crosses. We developed 25 families composed of ~200 random recombinant inbred lines each from crosses between a common reference parent inbred, B73, and 25 diverse maize inbreds. Parents and families were evaluated for 19 quantitative traits across up to 11 environments. Genetic distances (GDs) among parents were estimated with 44 simple sequence repeat and 2303 single-nucleotide polymorphism markers. GDs among parents had no predictive value for progeny variation, which is most likely due to the choice of neutral markers. In contrast, we observed for about half of the traits measured a positive correlation between phenotypic parental distances and within-family genetic variance estimates. Consequently, the choice of promising segregating populations can be based on selecting phenotypically diverse parents. These results are congruent with models of genetic architecture that posit numerous genes affecting quantitative traits, each segregating for allelic series, with dispersal of allelic effects across diverse genetic material. This architecture, common to many quantitative traits in maize, limits the predictive value of parental genotypic or phenotypic values on progeny variance.     

Genetic mapping of QTLs affecting productivity and plant architecture in a full-sib cross from non-inbred parents in Cassava (<Emphasis Type="Italic">Manihot esculenta</Emphasis> Crantz)

An attempt was made to identify quantitative trait loci (QTLs) for several productivity and plant architecture traits in a full-sib progeny of 144 individuals from two non-inbred parents in cassava. A molecular linkage map of this cross constructed previously with over 250 markers was the source of molecular markers. The progeny were grown under field conditions at two locations (Palmira and Quilichao) in Colombia and evaluated in 2 years (1998 and 1999) for architecture and productivity traits. Architecture traits evaluated were plant height (PH), branching height (BH), branching levels (BL), branching index (BI), stem portion with leaves (SPL) and leaf area index (LAI). Productivity traits were those related to total dry matter production and distribution, namely fresh root yield (FRY), fresh shoot yield (FSY), harvest index (HI) and the number of storage roots (NR). Phenotypic evaluation of the traits in this population revealed continuous variation for all traits. Broad-sense heritability estimates, ranged from 36% (for NR) to 94% (for BH). Several significant phenotypic correlations were observed between architecture and productivity traits. Primary QTLs, using the single-QTL model, and secondary QTLs, by a primary QTL interaction model, were detected by interval mapping. A total of 30 primary QTLs and 84 secondary QTLs were detected. We identified 35% of detected QTLs in two or more trials, the other QTLs were environment-specific. These results underscore the significant genotype × environment interactions found for most of the traits. Several genomic segments affecting multiple traits were identified and were in agreement with correlation among traits. All QTLs identified for FRY were found associated with either component traits of productivity or architecture traits. This study suggests that QTLs for plant architecture can be used to improve productivity. However an exhaustive search and analysis of QTLs controlling architecture is required before marker-assisted selection (MAS) for increasing productivity can be initiated.Communicated by H. C. Becker