Molecular Quantitative Genetics Research Articles

Genomic prediction in the wild: A case study in Soay sheep.

Genomic prediction, the technique whereby an individual's genetic component of their phenotype is estimated from its genome, has revolutionised animal and plant breeding and medical genetics. However, despite being first introduced nearly two decades ago, it has hardly been adopted by the evolutionary genetics community studying wild organisms. Here, genomic prediction is performed on eight traits in a wild population of Soay sheep. The population has been the focus of a >30year evolutionary ecology study and there is already considerable understanding of the genetic architecture of the focal Mendelian and quantitative traits. We show that the accuracy of genomic prediction is high for all traits, but especially those with loci of large effect segregating. Five different methods are compared, and the two methods that can accommodate zero-effect and large-effect loci in the same model tend to perform best. If the accuracy of genomic prediction is similar in other wild populations, then there is a real opportunity for pedigree-free molecular quantitative genetics research to be enabled in many more wild populations; currently the literature is dominated by studies that have required decades of field data collection to generate sufficiently deep pedigrees. Finally, some of the potential applications of genomic prediction in wild populations are discussed.

Estimation of genetic variance contributed by a quantitative trait locus: correcting the bias associated with significance tests.

The Beavis effect in quantitative trait locus (QTL) mapping describes a phenomenon that the estimated effect size of a statistically significant QTL (measured by the QTL variance) is greater than the true effect size of the QTL if the sample size is not sufficiently large. This is a typical example of the Winners' curse applied to molecular quantitative genetics. Theoretical evaluation and correction for the Winners' curse have been studied for interval mapping. However, similar technologies have not been available for current models of QTL mapping and genome-wide association studies where a polygene is often included in the linear mixed models to control the genetic background effect. In this study, we developed the theory of the Beavis effect in a linear mixed model using a truncated noncentral Chi-square distribution. We equated the observed Wald test statistic of a significant QTL to the expectation of a truncated noncentral Chi-square distribution to obtain a bias-corrected estimate of the QTL variance. The results are validated from replicated Monte Carlo simulation experiments. We applied the new method to the grain width (GW) trait of a rice population consisting of 524 homozygous varieties with over 300 k single nucleotide polymorphism markers. Two loci were identified and the estimated QTL heritability were corrected for the Beavis effect. Bias correction for the larger QTL on chromosome 5 (GW5) with an estimated heritability of 12% did not change the QTL heritability due to the extremely large test score and estimated QTL effect. The smaller QTL on chromosome 9 (GW9) had an estimated QTL heritability of 9% reduced to 6% after the bias-correction.

Comparative analysis of the GBLUP, emBayesB, and GWAS algorithms to predict genetic values in large yellow croaker (Larimichthys crocea).

BackgroundThe advances of sequencing technology accelerate the development of theory of molecular quantitative genetics such as QTL mapping, genome-wide association study and genomic selection. This paper was designed to study genomic selection in large yellow croaker breeding. The aims of this study were: (i) to estimate heritability values of traits in large yellow croaker; (ii) to assess feasibility of genomic selection in the traits of growth rate and meat quality; (iii) to compare predictive accuracies affected by different algorithms and training sizes, and to find what training sizes could reach ideal accuracies; (iv) to compare results of GWAS with genomic prediction, and to assess feasibility of pre-selection of significant SNPs in genomic selection. 500 individuals were tested in the trait of body weight and body length, while 176 were tested in the percentage of n-3 highly unsaturated fatty acids (n-3HUFA) in muscle. GBLUP and emBayesB were used to perform genomic prediction.ResultsGenotyping-By-Sequencing method was used to construct the libraries for the NGS sequencing and find ~30,000 SNPs. Heritability estimates were 0.604, 0.586 and 0.438 for trait of body weight, body length and n-3HUFA, respectively. The predictive abilities estimated by GBLUP showed higher than that by emBayesB in traits of body weight and body length. However, the result was just the opposite in n-3HUFA. According to fit the curve of predictive accuracy, we estimated that at least 1000 individuals in training set could reach an accuracy of 0.8 in body weight and body length. GBLUP, emBayesB and GWAS could not always find significant SNPs associated with phenotypes consistently. Significant SNPs were selected by emBayesB could obtain the largest proportions to explain total additive genetic variances.ConclusionsThis research showed that genomic selection was feasible in large yellow croaker breeding. We suggest doing a test before deciding to use which algorithm in specific trait in genomic prediction. We estimated required training sizes to reach ideal predictive accuracies and assessed feasibility of pre-selection of SNPs successfully. Because of high mortality rate of fish and high cost in genomic sequencing, genomic selection may be more suitable for applying on some traits which cannot be measured on candidates directly.

English

Large amounts of polysaccharides, polyphenols, tannins, proteins, and other secondary metabolites in recalcitrant longan leaves make it difficult to obtain high quality genomic DNA during extraction. To obtain good quality of nucleic acids from local longan leaves and for its downstream applications, a new protocol was developed. It consists of rapid isolation of stable nuclei, which hinders covalent interactions with phenolics, followed by DNA extraction. The yield and quality of the resulting DNA were satisfactory and suitable for PCR analysis and digestion with a restriction enzyme. Here, a valid combination measure (β-mercaptoethanol, PVP40 and PVPP were used at different stages) was created to eliminate the influence of polysaccharides and polyphenols in recalcitrant longan during DNA extraction, which will facilitate the development of molecular quantitative genetics of longan.   Key words: Longan, extraction buffer, DNA isolation, PCR products. †These authors contributed equally to this work.

A rapid and effective method for silver staining of PCR products separated in polyacrylamide gels.

With the development of molecular quantitative genetics, particularly, genetic linkage map construction, quantitative trait loci mapping or genes fine mapping and association analysis etc., more and more PCR products separated in polyacrylamide gels need to be silver-stained. However, conventional silver-staining procedures are complicated and time-consuming as they require a lot of preparation and handling of several solutions prior to use. In this study, a simple and rapid protocol for silver staining of PCR products was developed. The number of steps was reduced compared to conventional protocols, thus achieving detection of PCR products in 7 min, saving time and resources. Fixation and staining solution and developing solution in present staining procedure allowed a reutilization for 12 and 8 times, respectively, reducing the cost greatly. Meanwhile, the sensitivity was significantly improved with the improved method and the minimum of 0.097 ng/μL of DNA amount can be detected in denaturing polyacrylamide gel. The protocol developed in this study will facilitate the development of molecular quantitative genetics.

WormQTLHD—a web database for linking human disease to natural variation data inC. elegans

Interactions between proteins are highly conserved across species. As a result, the molecular basis of multiple diseases affecting humans can be studied in model organisms that offer many alternative experimental opportunities. One such organism—Caenorhabditis elegans—has been used to produce much molecular quantitative genetics and systems biology data over the past decade. We present WormQTLHD (Human Disease), a database that quantitatively and systematically links expression Quantitative Trait Loci (eQTL) findings in C. elegans to gene–disease associations in man. WormQTLHD, available online at http://www.wormqtl-hd.org, is a user-friendly set of tools to reveal functionally coherent, evolutionary conserved gene networks. These can be used to predict novel gene-to-gene associations and the functions of genes underlying the disease of interest. We created a new database that links C. elegans eQTL data sets to human diseases (34 337 gene–disease associations from OMIM, DGA, GWAS Central and NHGRI GWAS Catalogue) based on overlapping sets of orthologous genes associated to phenotypes in these two species. We utilized QTL results, high-throughput molecular phenotypes, classical phenotypes and genotype data covering different developmental stages and environments from WormQTL database. All software is available as open source, built on MOLGENIS and xQTL workbench.

Three genetic systems controlling growth, development and productivity of rice (Oryza sativa L.): a reevaluation of the ‘Green Revolution’

The Green Revolution (GR-I) included worldwide adoption of semi-dwarf rice cultivars (SRCs) with mutant alleles at GA20ox2 or SD1 encoding gibberellin 20-oxidase. Two series of experiments were conducted to characterize the pleiotropic effects of SD1 and its relationships with large numbers of QTLs affecting rice growth, development and productivity. The pleiotropic effects of SD1 in the IR64 genetic background for increased height, root length/mass and grain weight, and for reduced spikelet fertility and delayed heading were first demonstrated using large populations derived from near isogenic IR64 lines of SD1. In the second set of experiments, QTLs controlling nine growth and yield traits were characterized using a new molecular quantitative genetics model and the phenotypic data of the well-known IR64/Azucena DH population evaluated across 11 environments, which revealed three genetic systems: the SD1-mediated, SD1-repressed and SD1-independent pathways that control rice growth, development and productivity. The SD1-mediated system comprised 43 functional genetic units (FGUs) controlled by GA. The SD1-repressed system was the alternative one comprising 38 FGUs that were only expressed in the mutant sd1 backgrounds. The SD1-independent one comprised 64 FGUs that were independent of SD1. GR-I resulted from the overall differences between the former two systems in the three aspects: (1) trait/environment-specific contributions; (2) distribution of favorable alleles for increased productivity in the parents; and (3) different responses to (fertilizer) inputs. Our results suggest that at 71.4 % of the detected loci, a QTL resulted from the difference between a functional allele and a loss-of-function mutant, whereas at the remaining 28.6 % of loci, from two functional alleles with differentiated effects. Our results suggest two general strategies to achieve GR-II (1) by further exploiting the genetic potential of the SD1-repressed and SD1-independent pathways and (2) by restoring the SD1-mediated pathways, or 'back to the nature' to fully exploit the genetic diversity of those loci in the SD1-mediated pathways which are virtually inaccessible to most rice-breeding programs worldwide that are exclusively based on sd1.

WormQTL—public archive and analysis web portal for natural variation data in Caenorhabditis spp

Here, we present WormQTL (http://www.wormqtl.org), an easily accessible database enabling search, comparative analysis and meta-analysis of all data on variation in Caenorhabditis spp. Over the past decade, Caenorhabditis elegans has become instrumental for molecular quantitative genetics and the systems biology of natural variation. These efforts have resulted in a valuable amount of phenotypic, high-throughput molecular and genotypic data across different developmental worm stages and environments in hundreds of C. elegans strains. WormQTL provides a workbench of analysis tools for genotype–phenotype linkage and association mapping based on but not limited to R/qtl (http://www.rqtl.org). All data can be uploaded and downloaded using simple delimited text or Excel formats and are accessible via a public web user interface for biologists and R statistic and web service interfaces for bioinformaticians, based on open source MOLGENIS and xQTL workbench software. WormQTL welcomes data submissions from other worm researchers.

Caenorhabditis elegansas a platform for molecular quantitative genetics and the systems biology of natural variation

Over the past 30 years, the characteristics that have made the nematode Caenorhabditis elegans one of the premier animal model systems have also allowed it to emerge as a powerful model system for determining the genetic basis of quantitative traits, particularly for the identification of naturally segregating and/or lab-adapted alleles with large phenotypic effects. To better understand the genetic underpinnings of natural variation in other complex phenotypes, C. elegans is uniquely poised in the emerging field of quantitative systems biology because of the extensive knowledge of cellular and neural bases to such traits. However, perturbations in standing genetic variation and patterns of linkage disequilibrium among loci are likely to limit our ability to tie understanding of molecular function to a broader evolutionary context. Coupling the experimental strengths of the C. elegans system with the ecological advantages of closely related nematodes should provide a powerful means of understanding both the molecular and evolutionary genetics of quantitative traits.

Methods comparison for microsatellite marker development: Different isolation methods, different yield efficiency

Microsatellite markers have become one kind of the most important molecular tools used in various researches. A large number of microsatellite markers are required for the whole genome survey in the fields of molecular ecology, quantitative genetics and genomics. Therefore, it is extremely necessary to select several versatile, low-cost, efficient and time- and labor-saving methods to develop a large panel of microsatellite markers. In this study, we used Zhikong scallop (Chlamys farreri) as the target species to compare the efficiency of the five methods derived from three strategies for microsatellite marker development. The results showed that the strategy of constructing small insert genomic DNA library resulted in poor efficiency, while the microsatellite-enriched strategy highly improved the isolation efficiency. Although the mining public database strategy is time- and cost-saving, it is difficult to obtain a large number of microsatellite markers, mainly due to the limited sequence data of non-model species deposited in public databases. Based on the results in this study, we recommend two methods, microsatellite-enriched library construction method and FIASCO-colony hybridization method, for large-scale microsatellite marker development. Both methods were derived from the microsatellite-enriched strategy. The experimental results obtained from Zhikong scallop also provide the reference for microsatellite marker development in other species with large genomes.

Understanding quantitative genetics in the systems biology era.

Biology is now entering the new era of systems biology and exerting a growing influence on the future development of various disciplines within life sciences. In early classical and molecular periods of Biology, the theoretical frames of classical and molecular quantitative genetics have been systematically established, respectively. With the new advent of systems biology, there is occurring a paradigm shift in the field of quantitative genetics. Where and how the quantitative genetics would develop after having undergone its classical and molecular periods? This is a difficult question to answer exactly. In this perspective article, the major effort was made to discuss the possible development of quantitative genetics in the systems biology era, and for which there is a high potentiality to develop towards "systems quantitative genetics". In our opinion, the systems quantitative genetics can be defined as a new discipline to address the generalized genetic laws of bioalleles controlling the heritable phenotypes of complex traits following a new dynamic network model. Other issues from quantitative genetic perspective relating to the genetical genomics, the updates of network model, and the future research prospects were also discussed.

Quantitative genetics, version 3.0: where have we gone since 1987 and where are we headed?

The last 20 years since the previous World Congress have seen tremendous advancements in quantitative genetics, in large part due to the advancements in genomics, computation, and statistics. One central theme of this last 20 years has been the exploitation of the vast harvest of molecular markers--examples include QTL and association mapping, marker-assisted selection and introgression, scans for loci under selection, and methods to infer degree of coancestry, population membership, and past demographic history. One consequence of this harvest is that phenotyping, rather than genotyping, is now the bottleneck in molecular quantitative genetics studies. Equally important have been advances in statistics, many developed to effectively use this treasure trove of markers. Computational improvements in statistics, and in particular Markov Chain Monte Carlo (MCMC) methods, have facilitated many of these methods, as have significantly improved computational abilities for mixed models. Indeed, one could argue that mixed models have had at least as great an impact in quantitative genetics as have molecular markers. A final important theme over the past 20 years has been the fusion of population and quantitative genetics, in particular the importance of coalescence theory with its applications for association mapping, scans for loci under selection, and estimation of the demography history of a population. What are the future directions of the field? While obviously important surprises await us, the general trend seems to be moving into higher and higher dimensional traits and, in general, dimensional considerations. We have methods to deal with infinite-dimensional traits indexed by a single variable (such as a trait varying over time), but the future will require us to treat much more complex objects, such as infinite-dimensional traits indexed over several variables and with graphs and dynamical networks. A second important direction is the interfacing of quantitative genetics with physiological and developmental models as a step towards both the gene-phenotype map as well as predicting the effects of environmental changes. The high-dimensional objects we will need to consider almost certainly have most of their variation residing on a lower (likely much lower) dimensional subspace, and how to treat these constraints will be an important area of future research. Conversely, the univariate traits we currently deal with are themselves projections of more complex structures onto a lower dimensional space, and simply treating these as univariate traits can result in serious errors in understanding their selection and biology. As a field, our future is quite bright. We have new tools and techniques, and (most importantly) new talent with an exciting international group of vibrant young investigators who have received their degrees since the last Congress. One cloud for concern, however, has been the replacement at many universities of plant and animal breeders with plant and animal molecular biologists. Molecular tools are now an integral part of breeding, but breeding is not an integral part of molecular biology.

Proteomics: a link between genomics, genetics and physiology.

Thanks to spectacular advances in the techniques for identifying proteins separated by two-dimensional electrophoresis and in methods for large-scale analysis of proteome variations, proteomics is becoming an essential methodology in various fields of plant biology. In the study of pleiotropic effects of mutants and in the analysis of responses to hormones and to environmental changes, the identification of involved metabolic pathways can be deduced from the function of affected proteins. In molecular quantitative genetics, proteomics can be used to map translated genes and loci controlling their expression, which can be used to identify proteins accounting for the variation of complex phenotypic traits. Linking gene expression to cell metabolism on the one hand and to genetic maps on the other, proteomics is a central tool for functional genomics.

Dissecting complex physiological functions through the use of molecular quantitative genetics

Testing possible associations between physiological and biochemical traits by comparing plant phenotypes and looking for correlations between them is unreliable. The development of molecular marker technologies offers powerful alternative methods to examine the relationships between traits. This review describes the genetical methods required to analyse possible associations between traits that are inherited in a quantitative manner using quantitative trait locus (QTL) analysis. The regulation of carbohydrate metabolism is chosen as an example of how QTL analysis can be used to identify key control factors in a series of processes, by identifying possible candidate genes for QTL effects on sucrose and starch metabolism. Methods are also described to study the association between physiological traits such as abscislc acid concentrations and stomata1 conductance. Advantages and some limitations of QTL analysis over other methods currently in use by physiologists to test associations between traits are discussed.

Characterizing Molecular Quantitative Variability for Inbreeding Depression

Abstract Molecular quantitative genetics seeks to map and characterize the effects of individual genes underlying traits (quantitative trait loci, or QTLs). Classically, it uses genetic differences between distinct strains or taxa and saturated marker maps to infer the location and effects of specific genes. Little attention has been devoted to characterizing QTL variability within populations, as higher heterozygosity and smaller allele effects require much larger experimental effort to detect specific QTLs. An alternative approach for within‐population studies is to utilize multiple crosses, each assayed for relatively few genetic markers. The genome regions surveyed for QTLs are regarded as random samples, with the objective of characterizing variability of QTLs. This approach is particularly appropriate for genetically heterogeneous traits such as inbreeding depression and life history (e.g., fitness) traits. In this paper, we explore this approach for QTLs affecting inbreeding depression, based upon a method using selfed progeny arrays, and illustrated with an experiment involving the common monkeyflower, Mimulus guttatus.