Multiple trait analysis of genetic mapping for quantitative trait loci

Sex Chromosome Evolution, Heterochiasmy, and Physiological QTL in the Salmonid Brook Charr Salvelinus fontinalis

Whole-genome duplication (WGD) can have large impacts on genome evolution, and much remains unknown about these impacts. This includes the mechanisms of coping with a duplicated sex determination system and whether this has an impact on increasing the diversity of sex determination mechanisms. Other impacts include sexual conflict, where alleles having different optimums in each sex can result in sequestration of genes into nonrecombining sex chromosomes. Sex chromosome development itself may involve sex-specific recombination rate (i.e., heterochiasmy), which is also poorly understood. The family Salmonidae is a model system for these phenomena, having undergone autotetraploidization and subsequent rediploidization in most of the genome at the base of the lineage. The salmonid master sex determining gene is known, and many species have nonhomologous sex chromosomes, putatively due to transposition of this gene. In this study, we identify the sex chromosome of Brook Charr Salvelinus fontinalis and compare sex chromosome identities across the lineage (eight species and four genera). Although nonhomology is frequent, homologous sex chromosomes and other consistencies are present in distantly related species, indicating probable convergence on specific sex and neo-sex chromosomes. We also characterize strong heterochiasmy with 2.7-fold more crossovers in maternal than paternal haplotypes with paternal crossovers biased to chromosome ends. When considering only rediploidized chromosomes, the overall heterochiasmy trend remains, although with only 1.9-fold more recombination in the female than the male. Y chromosome crossovers are restricted to a single end of the chromosome, and this chromosome contains a large interspecific inversion, although its status between males and females remains unknown. Finally, we identify quantitative trait loci (QTL) for 21 unique growth, reproductive, and stress-related phenotypes to improve knowledge of the genetic architecture of these traits important to aquaculture and evolution.

The roles of pleiotropy and close linkage as revealed by association mapping of yield and correlated traits of wheat (Triticum aestivum L.)

Grain yield (GY) of bread wheat (Triticum aestivum L.) is quantitatively inherited. Correlated GY-syndrome traits such as plant height (PH), heading date (HD), thousand grain weight (TGW), test weight (TW), grains per ear (GPE), and ear weight (EW) influence GY. Most quantitative genetics studies assessed the multiple-trait (MT) complex of GY-syndrome using single-trait approaches, and little is known about its underlying pleiotropic architecture. We investigated the pleiotropic architecture of wheat GY-syndrome through MT association mapping (MT-GWAS) using 372 varieties phenotyped in up to eight environments and genotyped with 18 832 single nucleotide polymorphisms plus 24 polymorphic functional markers. MT-GWAS revealed a total of 345 significant markers spread genome wide, representing 8, 40, 11, 40, 34, and 35 effective GY-PH, GY-HD, GY-TGW, GY-TW, GY-GPE, and GY-EW associations, respectively. Among them, pleiotropic roles of Rht-B1 and TaGW2-6B loci were corroborated. Only one marker presented simultaneous associations for three traits (i.e. GY-TGW-TW). Close linkage was difficult to differentiate from pleiotropy; thus, the pleiotropic architecture of GY-syndrome was dissected more as a cause of pleiotropy rather than close linkage. Simulations showed that minor allele frequencies, along with sizes and distances between quantitative trait loci for two traits, influenced the ability to distinguish close linkage from pleiotropy.

High-density ddRAD linkage and yield-related QTL mapping delimits a chromosomal region responsible for oil content in rapeseed (Brassica napus L.)

Rapeseed (Brassica napus L.) is one of the most important oil crops almost all over the world. Seed-related traits, including oil content (OC), silique length (SL), seeds per silique (SS), and seed weight (SW), are primary targets for oil yield improvement. To dissect the genetic basis of these traits, 192 recombinant inbred lines (RILs) were derived from two parents with distinct oil content and silique length. High-density linkage map with a total length of 1610.4 cM were constructed using 1,329 double-digestion restriction site associated DNA (ddRAD) markers, 107 insertion/deletions (INDELs), and 90 well-distributed simple sequence repeats (SSRs) markers. A total of 37 consensus quantitative trait loci (QTLs) were detected for the four traits, with individual QTL explained 3.1-12.8% of the phenotypic variations. Interestingly, one OC consensus QTL (cqOCA10b) on chromosome A10 was consistently detected in all three environments, and explained 9.8% to 12.8% of the OC variation. The locus was further delimited into an approximately 614 kb genomic region, in which the flanking markers could be further evaluated for marker-assisted selection in rapeseed OC improvement and the candidate genes targeted for map-based cloning and genetic manipulation.

The genetic architecture of shoot-root covariation during seedling emergence of a desert tree, Populus euphratica

The coordination of shoots and roots is critical for plants to adapt to changing environments by fine-tuning energy production in leaves and the availability of water and nutrients from roots. To understand the genetic architecture of how these two organs covary during developmental ontogeny, we conducted a mapping experiment using Euphrates poplar (Populus euphratica), a so-called hero tree able to grow in the desert. We geminated intraspecific F₁ seeds of Euphrates Poplar individually in a tube to obtain a total of 370 seedlings, whose shoot and taproot lengths were measured repeatedly during the early stage of growth. By fitting a growth equation, we estimated asymptotic growth, relative growth rate, the timing of inflection point and duration of linear growth for both shoot and taproot growth. Treating these heterochronic parameters as phenotypes, a univariate mapping model detected 19 heterochronic quantitative trait loci (hQTLs), of which 15 mediate the forms of shoot growth and four mediate taproot growth. A bivariate mapping model identified 11 pleiotropic hQTLs that determine the covariation of shoot and taproot growth. Most QTLs detected reside within the region of candidate genes with various functions, thus confirming their roles in the biochemical processes underlying plant growth.

QUANTITATIVE GENETIC ANALYSIS OF DIVERGENCE IN MALE SECONDARY SEXUAL TRAITS BETWEEN DROSOPHILA SIMULANS AND DROSOPHILA MAURITIANA

The sibling species Drosophila simulans and D. mauritiana differ significantly in a number of male secondary sexual traits, providing an ideal system for genetic analysis of interspecific morphological divergence. In the experiment reported here, F₁ hybrids from a cross of two inbred lines were backcrossed in both directions and about 200 flies from each backcross were scored for several traits (bristle numbers and cuticle areas), as well as 18 markers distributed throughout the genome. Each trait was analyzed by composite interval mapping to identify quantitative trait loci (QTL) and estimate their effects. For each trait, from one to eight loci were detected, with more divergent traits showing evidence for greater numbers of QTL. Estimates of additive effects varied widely, with a range of 0.4 to 4.1 environmental standard deviation units and an average of 2.2 units. There was substantial evidence for nonadditive effects, since the magnitude of estimates often differed significantly between the two backcrosses. The sign of the estimated effect differed among QTL for bristle traits, but not for cuticle area traits, suggesting that these two types of trait may have undergone different types of selection. Finally, several similarities were found between different traits in the estimated positions of QTL, suggesting that pleiotropy and/or linkage of QTL may have been important in the evolution of these traits.

A rare-variant test for high-dimensional data

Genome-wide association studies have facilitated the discovery of thousands of loci for hundreds of phenotypes. However, the issue of missing heritability remains unsolved for most complex traits. Locus discovery could be enhanced with both improved power through multi-phenotype analysis (MPA) and use of a wider allele frequency range, including rare variants (RVs). MPA methods for single-variant association have been proposed, but given their low power for RVs, more efficient approaches are required. We propose multi-phenotype analysis of rare variants (MARV), a burden test-based method for RVs extended to the joint analysis of multiple phenotypes through a powerful reverse regression technique. Specifically, MARV models the proportion of RVs at which minor alleles are carried by individuals within a genomic region as a linear combination of multiple phenotypes, which can be both binary and continuous, and the method accommodates directly the genotyped and imputed data. The full model, including all phenotypes, is tested for association for discovery, and a more thorough dissection of the phenotype combinations for any set of RVs is also enabled. We show, via simulations, that the type I error rate is well controlled under various correlations between two continuous phenotypes, and that the method outperforms a univariate burden test in all considered scenarios. Application of MARV to 4876 individuals from the Northern Finland Birth Cohort 1966 for triglycerides, high- and low-density lipoprotein cholesterols highlights known loci with stronger signals of association than those observed in univariate RV analyses and suggests novel RV effects for these lipid traits.

An Analytic Solution to the Computation of Power and Sample Size for Genetic Association Studies under a Pleiotropic Mode of Inheritance

Our motivation here is to calculate the power of 3 statistical tests used when there are genetic traits that operate under a pleiotropic mode of inheritance and when qualitative phenotypes are defined by use of thresholds for the multiple quantitative phenotypes. Specifically, we formulate a multivariate function that provides the probability that an individual has a vector of specific quantitative trait values conditional on having a risk locus genotype, and we apply thresholds to define qualitative phenotypes (affected, unaffected) and compute penetrances and conditional genotype frequencies based on the multivariate function. We extend the analytic power and minimum-sample-size-necessary (MSSN) formulas for 2 categorical data-based tests (genotype, linear trend test [LTT]) of genetic association to the pleiotropic model. We further compare the MSSN of the genotype test and the LTT with that of a multivariate ANOVA (Pillai). We approximate the MSSN for statistics by linear models using a factorial design and ANOVA. With ANOVA decomposition, we determine which factors most significantly change the power/MSSN for all statistics. Finally, we determine which test statistics have the smallest MSSN. In this work, MSSN calculations are for 2 traits (bivariate distributions) only (for illustrative purposes). We note that the calculations may be extended to address any number of traits. Our key findings are that the genotype test usually has lower MSSN requirements than the LTT. More inclusive thresholds (top/bottom 25% vs. top/bottom 10%) have higher sample size requirements. The Pillai test has a much larger MSSN than both the genotype test and the LTT, as a result of sample selection. With these formulas, researchers can specify how many subjects they must collect to localize genes for pleiotropic phenotypes.

Novel Resampling Improves Statistical Power for Multiple-Trait QTL Mapping

Multiple-trait analysis typically employs models that associate a quantitative trait locus (QTL) with all of the traits. As a result, statistical power for QTL detection may not be optimal if the QTL contributes to the phenotypic variation in only a small proportion of the traits. Excluding QTL effects that contribute little to the test statistic can improve statistical power. In this article, we show that an optimal power can be achieved when the number of QTL effects is best estimated, and that a stringent criterion for QTL effect selection may improve power when the number of QTL effects is small but can reduce power otherwise. We investigate strategies for excluding trivial QTL effects, and propose a method that improves statistical power when the number of QTL effects is relatively small, and fairly maintains the power when the number of QTL effects is large. The proposed method first uses resampling techniques to determine the number of nontrivial QTL effects, and then selects QTL effects by the backward elimination procedure for significance test. We also propose a method for testing QTL-trait associations that are desired for biological interpretation in applications. We validate our methods using simulations and Arabidopsis thaliana transcript data.

MARV: a tool for genome-wide multi-phenotype analysis of rare variants

BACKGROUND

Genome-wide association studies have enabled identification of thousands of loci for hundreds of traits. Yet, for most human traits a substantial part of the estimated heritability is unexplained. This and recent advances in technology to produce high-dimensional data cost-effectively have led to method development beyond standard common variant analysis, including single-phenotype rare variant and multi-phenotype common variant analysis, with the latter increasing power for locus discovery and providing suggestions of pleiotropic effects. However, there are currently no optimal methods and tools for the combined analysis of rare variants and multiple phenotypes.

RESULTS

We propose a user-friendly software tool MARV for Multi-phenotype Analysis of Rare Variants. The tool is based on a method that collapses rare variants within a genomic region and models the proportion of minor alleles in the rare variants on a linear combination of multiple phenotypes. MARV provides analyses of all phenotype combinations within one run and calculates the Bayesian Information Criterion to facilitate model selection. The running time increases with the size of the genetic data while the number of phenotypes to analyse has little effect both on running time and required memory. We illustrate the use of MARV with analysis of triglycerides (TG), fasting insulin (FI) and waist-to-hip ratio (WHR) in 4,721 individuals from the Northern Finland Birth Cohort 1966. The analysis suggests novel multi-phenotype effects for these metabolic traits at APOA5 and ZNF259, and at ZNF259 provides stronger support for association (P TG+FI = 1.8 × 10-9) than observed in single phenotype rare variant analyses (P TG = 6.5 × 10-8 and P FI = 0.27).

CONCLUSIONS

MARV is a computationally efficient, flexible and user-friendly software tool allowing rapid identification of rare variant effects on multiple phenotypes, thus paving the way for novel discoveries and insights into biology of complex traits.

Genome-Wide Association Study and QTL Mapping Reveal Genomic Loci Associated with Fusarium Ear Rot Resistance in Tropical Maize Germplasm

Fusarium ear rot (FER) incited by Fusarium verticillioides is a major disease of maize that reduces grain quality globally. Host resistance is the most suitable strategy for managing the disease. We report the results of genome-wide association study (GWAS) to detect alleles associated with increased resistance to FER in a set of 818 tropical maize inbred lines evaluated in three environments. Association tests performed using 43,424 single-nucleotide polymorphic (SNPs) markers identified 45 SNPs and 15 haplotypes that were significantly associated with FER resistance. Each associated SNP locus had relatively small additive effects on disease resistance and accounted for 1-4% of trait variation. These SNPs and haplotypes were located within or adjacent to 38 candidate genes, 21 of which were candidate genes associated with plant tolerance to stresses, including disease resistance. Linkage mapping in four biparental populations to validate GWAS results identified 15 quantitative trait loci (QTL) associated with F. verticillioides resistance. Integration of GWAS and QTL to the maize physical map showed eight colocated loci on chromosomes 2, 3, 4, 5, 9, and 10. QTL on chromosomes 2 and 9 are new. These results reveal that FER resistance is a complex trait that is conditioned by multiple genes with minor effects. The value of selection on identified markers for improving FER resistance is limited; rather, selection to combine small effect resistance alleles combined with genomic selection for polygenic background for both the target and general adaptation traits might be fruitful for increasing FER resistance in maize.

An Adaptive Fisher's Combination Method for Joint Analysis of Multiple Phenotypes in Association Studies

Currently, the analyses of most genome-wide association studies (GWAS) have been performed on a single phenotype. There is increasing evidence showing that pleiotropy is a widespread phenomenon in complex diseases. Therefore, using only one single phenotype may lose statistical power to identify the underlying genetic mechanism. There is an increasing need to develop and apply powerful statistical tests to detect association between multiple phenotypes and a genetic variant. In this paper, we develop an Adaptive Fisher’s Combination (AFC) method for joint analysis of multiple phenotypes in association studies. The AFC method combines p-values obtained in standard univariate GWAS by using the optimal number of p-values which is determined by the data. We perform extensive simulations to evaluate the performance of the AFC method and compare the power of our method with the powers of TATES, Tippett’s method, Fisher’s combination test, MANOVA, MultiPhen, and SUMSCORE. Our simulation studies show that the proposed method has correct type I error rates and is either the most powerful test or comparable with the most powerful test. Finally, we illustrate our proposed methodology by analyzing whole-genome genotyping data from a lung function study.

Genetics of Skeletal Evolution in Unusually Large Mice from Gough Island

Organisms on islands often undergo rapid morphological evolution, providing a platform for understanding mechanisms of phenotypic change. Many examples of evolution on islands involve the vertebrate skeleton. Although the genetic basis of skeletal variation has been studied in laboratory strains, especially in the house mouse Mus musculus domesticus, the genetic determinants of skeletal evolution in natural populations remain poorly understood. We used house mice living on the remote Gough Island-the largest wild house mice on record-to understand the genetics of rapid skeletal evolution in nature. Compared to a mainland reference strain from the same subspecies (WSB/EiJ), the skeleton of Gough Island mice is considerably larger, with notable expansions of the pelvis and limbs. The Gough Island mouse skeleton also displays changes in shape, including elongations of the skull and the proximal vs. distal elements in the limbs. Quantitative trait locus (QTL) mapping in a large F₂ intercross between Gough Island mice and WSB/EiJ reveals hundreds of QTL that control skeletal dimensions measured at 5, 10, and/or 16 weeks of age. QTL exhibit modest, mostly additive effects, and Gough Island alleles are associated with larger skeletal size at most QTL. The QTL with the largest effects are found on a few chromosomes and affect suites of skeletal traits. Many of these loci also colocalize with QTL for body weight. The high degree of QTL colocalization is consistent with an important contribution of pleiotropy to skeletal evolution. Our results provide a rare portrait of the genetic basis of skeletal evolution in an island population and position the Gough Island mouse as a model system for understanding mechanisms of rapid evolution in nature.

Genetic basis of nitrogen use efficiency and yield stability across environments in winter rapeseed

Background

Nitrogen use efficiency is an important breeding trait that can be modified to improve the sustainability of many crop species used in agriculture. Rapeseed is a major oil crop with low nitrogen use efficiency, making its production highly dependent on nitrogen input. This complex trait is suspected to be sensitive to genotype × environment interactions, especially genotype × nitrogen interactions. Therefore, phenotyping diverse rapeseed populations under a dense network of trials is a powerful approach to study nitrogen use efficiency in this crop. The present study aimed to determine the quantitative trait loci (QTL) associated with yield in winter oilseed rape and to assess the stability of these regions under contrasting nitrogen conditions for the purpose of increasing nitrogen use efficiency.

Results

Genome-wide association studies and linkage analyses were performed on two diversity sets and two doubled-haploid populations. These populations were densely genotyped, and yield-related traits were scored in a multi-environment design including seven French locations, six growing seasons (2009 to 2014) and two nitrogen nutrition levels (optimal versus limited). Very few genotype × nitrogen interactions were detected, and a large proportion of the QTL were stable across nitrogen nutrition conditions. In contrast, strong genotype × trial interactions in which most of the QTL were specific to a single trial were found. To obtain further insight into the QTL × environment interactions, genetic analyses of ecovalence were performed to identify the genomic regions contributing to the genotype × nitrogen and genotype × trial interactions. Fifty-one critical genomic regions contributing to the additive genetic control of yield-associated traits were identified, and the structural organization of these regions in the genome was investigated.

Conclusions

Our results demonstrated that the effect of the trial was greater than the effect of nitrogen nutrition levels on seed yield-related traits under our experimental conditions. Nevertheless, critical genomic regions associated with yield that were stable across environments were identified in rapeseed.

Electronic supplementary material

The online version of this article (doi:10.1186/s12863-016-0432-z) contains supplementary material, which is available to authorized users.

Animal breeding in the (post-) genomic era

One of the benefits of the genomics revolution for animal production will be knowledge of genes that can be used to select more profitable livestock. Although it is possible to use genetic markers linked to genes of economic importance, tests for the genes themselves will be much more successful. Consequently finding genes of economic importance to livestock will be a major research aim for the future. Most traits of economic importance are quantitative traits affected by many genes. Mutations at many genes (e.g. 500) and at many positions within a gene (e.g. 1000 coding and non-coding bases) can affect a typical quantitative trait. The effect of these mutations on phenotype is usually small (e.g. 0·1 standard deviation) but occasionally large. Many mutations are lost from the population through genetic drift and selection, so that polymorphisms exist at only a subset of the relevant genes (e.g. 100 genes). Finding these genes, that have relatively small effects, is more difficult than finding genes for a classical Mendellian trait but, as the genomic tools become more powerful, it is becoming feasible and some successes have already occurred. The standard approach is to map a quantitative trait loci (QTL) to a chromosome region using linkage and linkage disequilibrium. Then test polymorphisms in positional candidate genes for an effect on the trait. Tools such as genomic sequence, EST collections and comparative maps make this approach feasible. Candidate genes can be selected based on functional data such as gene expression obtained from microarrays. At present the gain in rate of genetic improvement from use of DNA-based tests for QTL is small, because selection without them is already quite accurate, not enough QTL have been identified and genotyping is too expensive. However, in the future, with many QTL identified and inexpensive genotyping combined with decreased generation intervals, large gains are possible.

Genomic architecture of phenotypic divergence between two hybridizing plant species along an elevational gradient

Knowledge of the genetic basis of phenotypic divergence between species and how such divergence is caused and maintained is crucial to an understanding of speciation and the generation of biodiversity. The hybrid zone between Senecio aethnensis and S. chrysanthemifolius on Mount Etna, Sicily, provides a well-studied example of species divergence in response to conditions at different elevations, despite hybridization and gene flow. Here, we investigate the genetic architecture of divergence between these two species using a combination of quantitative trait locus (QTL) mapping and genetic differentiation measures based on genetic marker analysis. A QTL architecture characterized by physical QTL clustering, epistatic interactions between QTLs, and pleiotropy was identified, and is consistent with the presence of divergent QTL complexes resistant to gene flow. A role for divergent selection between species was indicated by significant negative associations between levels of interspecific genetic differentiation at mapped marker gene loci and map distance from QTLs and hybrid incompatibility loci. Within-species selection contributing to interspecific differentiation was evidenced by negative associations between interspecific genetic differentiation and genetic diversity within species. These results show that the two Senecio species, while subject to gene flow, maintain divergent genomic regions consistent with local selection within species and selection against hybrids between species which, in turn, contribute to the maintenance of their distinct phenotypic differences.

The Genetic Basis of Natural Variation in Kernel Size and Related Traits Using a Four-Way Cross Population in Maize

Kernel size is an important component of grain yield in maize breeding programs. To extend the understanding on the genetic basis of kernel size traits (i.e., kernel length, kernel width and kernel thickness), we developed a set of four-way cross mapping population derived from four maize inbred lines with varied kernel sizes. In the present study, we investigated the genetic basis of natural variation in seed size and other components of maize yield (e.g., hundred kernel weight, number of rows per ear, number of kernels per row). In total, ten QTL affecting kernel size were identified, three of which (two for kernel length and one for kernel width) had stable expression in other components of maize yield. The possible genetic mechanism behind the trade-off of kernel size and yield components was discussed.

Partially repeatable genetic basis of benthic adaptation in threespine sticklebacks

The extent to which convergent adaptation to similar ecological niches occurs by a predictable genetic basis remains a fundamental question in biology. Threespine stickleback fish have undergone an adaptive radiation in which ancestral oceanic populations repeatedly colonized and adapted to freshwater habitats. In multiple lakes in British Columbia, two different freshwater ecotypes have evolved: a deep-bodied benthic form adapted to forage near the lake substrate, and a narrow-bodied limnetic form adapted to forage in open water. Here, we use genome-wide linkage mapping in marine × benthic F2 genetic crosses to test the extent of shared genomic regions underlying benthic adaptation in three benthic populations. We identify at least 100 Quantitative Trait Loci (QTL) harboring genes influencing skeletal morphology. The majority of QTL (57%) are unique to one cross. However, four genomic regions affecting eight craniofacial and armor phenotypes are found in all three benthic populations. We find that QTL are clustered in the genome and overlapping QTL regions are enriched for genomic signatures of natural selection. These findings suggest that benthic adaptation has occurred via both parallel and nonparallel genetic changes.

Linkages and Interactions Analysis of Major Effect Drought Grain Yield QTLs in Rice

Quantitative trait loci conferring high grain yield under drought in rice are important genomic resources for climate resilient breeding. Major and consistent drought grain yield QTLs usually co-locate with flowering and/or plant height QTLs, which could be due to either linkage or pleiotropy. Five mapping populations used for the identification of major and consistent drought grain yield QTLs underwent multiple-trait, multiple-interval mapping test (MT-MIM) to estimate the significance of pleiotropy effects. Results indicated towards possible linkages between the drought grain yield QTLs with co-locating flowering and/or plant height QTLs. Linkages of days to flowering and plant height were eliminated through a marker-assisted breeding approach. Drought grain yield QTLs also showed interaction effects with flowering QTLs. Drought responsiveness of the flowering locus on chromosome 3 (qDTY_3.2) has been revealed through allelic analysis. Considering linkage and interaction effects associated with drought QTLs, a comprehensive marker-assisted breeding strategy was followed to develop rice genotypes with improved grain yield under drought stress.

Quantitative trait loci analysis and genome-wide comparison for silique related traits in Brassica napus

BACKGROUND

Yield of rapeseed is determined by three components: silique number, seed number per silique and thousand seed weight. Seed number per silique and thousand seed weight are influenced by silique length, seed density, silique breadth, silique thickness and silique volume. Some QTLs for silique traits have been reported in B. napus, however, no studies have focused on the six agronomic traits (seed number per silique, silique length, silique breadth, silique thickness, seed density and silique volume) simultaneously, and the genetic determinism of such complex traits have not been fully elucidated.

RESULTS

In this study, the six silique traits were evaluated using 348 lines of a doubled haploid population, the KN population. The results showed that 2, 4, 1, 1 and 2 QTLs explaining > 10 % of phenotypic variation were obtained for silique length, silique breadth, silique thickness, seed number per silique and silique volume, respectively. Notably, three major effect QTLs (cqSB-C6-1, cqSB-C6-2 and cqSV-C6-3) were identified in at least three environments, and 17 unique QTLs controlling at least two traits were obtained. A high-density consensus map containing 1225 markers was constructed for QTL comparison by combining the KN map with other five published maps. The comparative results revealed that 14, 13 and 11 QTLs for silique breadth, silique thickness and silique volume might be the potential new QTLs because few QTLs for these traits were reported in B. napus. In addition, potential new QTLs for silique length (11), seed number per silique (6) and seed density (5) were also identified. Twenty-five candidate genes underlying 27 QTLs for silique related traits were obtained.

CONCLUSIONS

This study constructed QTL analysis in B. napus, and obtained 60 consensus QTLs for six silique related traits. The potential new QTLs will enhance our understanding of the genetic control of silique traits, and the stable QTLs provided the targets for improving seed yield in future. These findings provided comprehensive insights into the genetic network affecting silique traits at QTL level in B. napus.

Improvement of Rice Biomass Yield through QTL-Based Selection

Biomass yield of rice (Oryza sativa L.) is an important breeding target, yet it is not easy to improve because the trait is complex and phenotyping is laborious. Using progeny derived from a cross between two high-yielding Japanese cultivars, we evaluated whether quantitative trait locus (QTL)-based selection can improve biomass yield. As a measure of biomass yield, we used plant weight (aboveground parts only), which included grain weight and stem and leaf weight. We measured these and related traits in recombinant inbred lines. Phenotypic values for these traits showed a continuous distribution with transgressive segregation, suggesting that selection can affect plant weight in the progeny. Four significant QTLs were mapped for plant weight, three for grain weight, and five for stem and leaf weight (at α = 0.05); some of them overlapped. Multiple regression analysis showed that about 43% of the phenotypic variance of plant weight was significantly explained (P < 0.0001) by six of the QTLs. From F2 plants derived from the same parental cross as the recombinant inbred lines, we divergently selected lines that carried alleles with positive or negative additive effects at these QTLs, and performed successive selfing. In the resulting F6 lines and parents, plant weight significantly differed among the genotypes (at α = 0.05). These results demonstrate that QTL-based selection is effective in improving rice biomass yield.

Next Generation Mapping of Enological Traits in an F2 Interspecific Grapevine Hybrid Family

In winegrapes (Vitis spp.), fruit quality traits such as berry color, total soluble solids content (SS), malic acid content (MA), and yeast assimilable nitrogen (YAN) affect fermentation or wine quality, and are important traits in selecting new hybrid winegrape cultivars. Given the high genetic diversity and heterozygosity of Vitis species and their tendency to exhibit inbreeding depression, linkage map construction and quantitative trait locus (QTL) mapping has relied on F₁ families with the use of simple sequence repeat (SSR) and other markers. This study presents the construction of a genetic map by single nucleotide polymorphisms identified through genotyping-by-sequencing (GBS) technology in an F₂ mapping family of 424 progeny derived from a cross between the wild species V. riparia Michx. and the interspecific hybrid winegrape cultivar, ‘Seyval’. The resulting map has 1449 markers spanning 2424 cM in genetic length across 19 linkage groups, covering 95% of the genome with an average distance between markers of 1.67 cM. Compared to an SSR map previously developed for this F₂ family, these results represent an improved map covering a greater portion of the genome with higher marker density. The accuracy of the map was validated using the well-studied trait berry color. QTL affecting YAN, MA and SS related traits were detected. A joint MA and SS QTL spans a region with candidate genes involved in the malate metabolism pathway. We present an analytical pipeline for calling intercross GBS markers and a high-density linkage map for a large F₂ family of the highly heterozygous Vitis genus. This study serves as a model for further genetic investigations of the molecular basis of additional unique characters of North American hybrid wine cultivars and to enhance the breeding process by marker-assisted selection. The GBS protocols for identifying intercross markers developed in this study can be adapted for other heterozygous species.

Estimation and testing for multiple regulation of multivariate mixed outcomes

Considerable interest has recently been focused on studying multiple phenotypes simultaneously in both epidemiological and genomic studies, either to capture the multidimensionality of complex disorders or to understand shared etiology of related disorders. We seek to identify multiple regulators or predictors that are associated with multiple outcomes when these outcomes may be measured on very different scales or composed of a mixture of continuous, binary, and not-fully observed elements. We first propose an estimation technique to put all effects on similar scales, and we induce sparsity on the estimated effects. We provide standard asymptotic results for this estimator and show that resampling can be used to quantify uncertainty in finite samples. We finally provide a multiple testing procedure which can be geared specifically to the types of multiple regulators of interest, and we establish that, under standard regularity conditions, the familywise error rate will approach 0 as sample size diverges. Simulation results indicate that our approach can improve over unregularized methods both in reducing bias in estimation and improving power for testing.

The Dissection of Expression Quantitative Trait Locus Hotspots

Studies of the genetic loci that contribute to variation in gene expression frequently identify loci with broad effects on gene expression: expression quantitative trait locus hotspots. We describe a set of exploratory graphical methods as well as a formal likelihood-based test for assessing whether a given hotspot is due to one or multiple polymorphisms. We first look at the pattern of effects of the locus on the expression traits that map to the locus: the direction of the effects and the degree of dominance. A second technique is to focus on the individuals that exhibit no recombination event in the region, apply dimensionality reduction (e.g., with linear discriminant analysis), and compare the phenotype distribution in the nonrecombinant individuals to that in the recombinant individuals: if the recombinant individuals display a different expression pattern than the nonrecombinant individuals, this indicates the presence of multiple causal polymorphisms. In the formal likelihood-based test, we compare a two-locus model, with each expression trait affected by one or the other locus, to a single-locus model. We apply our methods to a large mouse intercross with gene expression microarray data on six tissues.

Genome-Wide Identification of QTL for Seed Yield and Yield-Related Traits and Construction of a High-Density Consensus Map for QTL Comparison in Brassica napus

Seed yield (SY) is the most important trait in rapeseed, is determined by multiple seed yield-related traits (SYRTs) and is also easily subject to environmental influence. Many quantitative trait loci (QTLs) for SY and SYRTs have been reported in Brassica napus; however, no studies have focused on seven agronomic traits simultaneously affecting SY. Genome-wide QTL analysis for SY and seven SYRTs in eight environments was conducted in a doubled haploid population containing 348 lines. Totally, 18 and 208 QTLs for SY and SYRTs were observed, respectively, and then these QTLs were integrated into 144 consensus QTLs using a meta-analysis. Three major QTLs for SY were observed, including cqSY-C6-2 and cqSY-C6-3 that were expressed stably in winter cultivation area for 3 years and cqSY-A2-2 only expressed in spring rapeseed area. Trait-by-trait meta-analysis revealed that the 144 consensus QTLs were integrated into 72 pleiotropic unique QTLs. Among them, all the unique QTLs affected SY, except for uq.A6-1, including uq.A2-3, uq.C1-2, uq.C1-3, uq.C6-1, uq.C6-5, and uq.C6-6 could also affect more than two SYRTs. According to the constructed high-density consensus map and QTL comparison from literatures, 36 QTLs from five populations were co-localized with QTLs identified in this study. In addition, 13 orthologous genes were observed, including five each gene for SY and thousand seed weight, and one gene each for biomass yield, branch height, and plant height. The genomic information of these QTLs will be valuable in hybrid cultivar breeding and in analyzing QTL expression in different environments.

Dynamic Quantitative Trait Locus Analysis of Plant Phenomic Data

Advanced platforms have recently become available for automatic and systematic quantification of plant growth and development. These new techniques can efficiently produce multiple measurements of phenotypes over time, and introduce time as an extra dimension to quantitative trait locus (QTL) studies. Functional mapping utilizes a class of statistical models for identifying QTLs associated with the growth characteristics of interest. A major benefit of functional mapping is that it integrates information over multiple timepoints, and therefore could increase the statistical power for QTL detection. We review the current development of computationally efficient functional mapping methods which provide invaluable tools for analyzing large-scale timecourse data that are readily available in our post-genome era.

Confirmation of delayed canopy wilting QTLs from multiple soybean mapping populations

KEY MESSAGE

QTLs for delayed canopy wilting from five soybean populations were projected onto the consensus map to identify eight QTL clusters that had QTLs from at least two independent populations. Quantitative trait loci (QTLs) for canopy wilting were identified in five recombinant inbred line (RIL) populations, 93705 KS4895 × Jackson, 08705 KS4895 × Jackson, KS4895 × PI 424140, A5959 × PI 416937, and Benning × PI 416937 in a total of 15 site-years. For most environments, heritability of canopy wilting ranged from 0.65 to 0.85 but was somewhat lower when averaged over environments. Putative QTLs were identified with composite interval mapping and/or multiple interval mapping methods in each population and positioned on the consensus map along with their 95% confidence intervals (CIs). We initially found nine QTL clusters with overlapping CIs on Gm02, Gm05, Gm11, Gm14, Gm17, and Gm19 identified from at least two different populations, but a simulation study indicated that the QTLs on Gm14 could be false positives. A QTL on Gm08 in the 93705 KS4895 × Jackson population co-segregated with a QTL for wilting published previously in a Kefeng1 × Nannong 1138-2 population, indicating that this may be an additional QTL cluster. Excluding the QTL cluster on Gm14, results of the simulation study indicated that the eight remaining QTL clusters and the QTL on Gm08 appeared to be authentic QTLs. QTL × year interactions indicated that QTLs were stable over years except for major QTLs on Gm11 and Gm19. The stability of QTLs located on seven clusters indicates that they are possible candidates for use in marker-assisted selection.

Inclusive Composite Interval Mapping of QTL by Environment Interactions in Biparental Populations

Identification of environment-specific QTL and stable QTL having consistent genetic effects across a wide range of environments is of great importance in plant breeding. Inclusive Composite Interval Mapping (ICIM) has been proposed for additive, dominant and epistatic QTL mapping in biparental populations for single environment. In this study, ICIM was extended to QTL by environment interaction (QEI) mapping for multi-environmental trials, where the QTL average effect and QEI effects could be properly estimated. Stepwise regression was firstly applied in each environment to identify the most significant marker variables which were then used to adjust the phenotypic values. One-dimensional scanning was then conducted on the adjusted phenotypic values across the environments in order to detect QTL with either average effect or QEI effects, or both average effect and QEI effects. In this way, the genetic background could be well controlled while the conventional interval mapping was applied. An empirical method to determine the threshold of logarithm of odds was developed, and the efficiency of the ICIM QEI mapping was demonstrated in simulated populations under different genetic models. One actual recombinant inbred line population was used to compare mapping results between QEI mapping and single-environment analysis.

Joint prediction of multiple quantitative traits using a Bayesian multivariate antedependence model

Predicting organismal phenotypes from genotype data is important for preventive and personalized medicine as well as plant and animal breeding. Although genome-wide association studies (GWAS) for complex traits have discovered a large number of trait- and disease-associated variants, phenotype prediction based on associated variants is usually in low accuracy even for a high-heritability trait because these variants can typically account for a limited fraction of total genetic variance. In comparison with GWAS, the whole-genome prediction (WGP) methods can increase prediction accuracy by making use of a huge number of variants simultaneously. Among various statistical methods for WGP, multiple-trait model and antedependence model show their respective advantages. To take advantage of both strategies within a unified framework, we proposed a novel multivariate antedependence-based method for joint prediction of multiple quantitative traits using a Bayesian algorithm via modeling a linear relationship of effect vector between each pair of adjacent markers. Through both simulation and real-data analyses, our studies demonstrated that the proposed antedependence-based multiple-trait WGP method is more accurate and robust than corresponding traditional counterparts (Bayes A and multi-trait Bayes A) under various scenarios. Our method can be readily extended to deal with missing phenotypes and resequence data with rare variants, offering a feasible way to jointly predict phenotypes for multiple complex traits in human genetic epidemiology as well as plant and livestock breeding.

Positive genetic correlation between brain size and sexual traits in male guppies artificially selected for brain size

Brain size is an energetically costly trait to develop and maintain. Investments into other costly aspects of an organism's biology may therefore place important constraints on brain size evolution. Sexual traits are often costly and could therefore be traded off against neural investment. However, brain size may itself be under sexual selection through mate choice on cognitive ability. Here, we use guppy (Poecilia reticulata) lines selected for large and small brain size relative to body size to investigate the relationship between brain size, a large suite of male primary and secondary sexual traits, and body condition index. We found no evidence for trade-offs between brain size and sexual traits. Instead, larger-brained males had higher expression of several primary and precopulatory sexual traits--they had longer genitalia, were more colourful and developed longer tails than smaller-brained males. Larger-brained males were also in better body condition when housed in single-sex groups. There was no difference in post-copulatory sexual traits between males from the large- and small-brained lines. Our data do not support the hypothesis that investment into sexual traits is an important limiting factor to brain size evolution, but instead suggest that brain size and several sexual traits are positively genetically correlated.

Genetic mapping of QTL for maize weevil resistance in a RIL population of tropical maize

A tropical RIL maize population was subjected to phenotypic and genotypic analysis for maize weevil resistance during four seasons, and three main genomic areas were detected as main QTLs. The maize weevil (Sitophilus zeamais) (MW) is a common and important pest of stored maize (Zea mays) worldwide, especially in tropical areas. Quantitative trait loci (QTLs) associated with the MW have been analyzed previously in an F2 maize population. In this work, new germplasm-based F6 recombinant inbred line (RIL) families, derived from the cross of Population 84 and Kilima, were analyzed using insect bioassays during four seasons. The parameters analyzed for MW resistance were grain weight losses (GWL), adult progeny (AP), and flour production (FP). Composite interval mapping identified a total of 15 QTLs for MW parameters located on six chromosomes, explaining between 14 and 51 % of phenotypic variation (σ p (2) ) and 27 and 81 % of genotypic variation (σ g (2) ). The QTL obtained for GWL was located in bin 2.05, which explained 15 % of σ p (2) . For AP and FP, the QTLs were located on regions 1.09 and 2.05, explaining 7 and 15 % of σ p (2) , respectively. Comparative analysis between F2 and F6 families showed similarities in QTL localization; three main regions were co-localized in chromosomes 4.08, 10.04, and 10.07, where no resistance-associated genes have been reported previously. These regions could be used for a marker-assisted selection in breeding programs for MW resistance in tropical maize.

Diversity trends in bread wheat in Italy during the 20th century assessed by traditional and multivariate approaches

A collection of 157 Triticum aestivum accessions, representative of wheat breeding in Italy during the 20(th) century, was assembled to describe the evolutionary trends of cultivated varieties throughout this period. The lines were cultivated in Italy, in two locations, over two growing seasons, and evaluated for several agronomical, morphological and qualitative traits. Analyses were conducted using the most common univariate approach on individual plant traits coupled with a correspondance multivariate approach. ANOVA showed a clear trend from old to new varieties, leading towards earliness, plant height reduction and denser spikes with smaller seeds. The average protein content gradually decreased over time; however this trend did not affect bread-making quality, because it was counterbalanced by a gradual increase of SDS sedimentation volume, achieved by the incorporation of favourable alleles into recent cultivars. Correspondence analysis allowed an overall view of the breeding activity. A clear-cut separation was observed between ancient lines and all the others, matched with a two-step gradient, the first, corresponding roughly to the period 1920-1940, which can be ascribed mostly to genetics, the second, from the 40s onward, which can be ascribed also to the farming practice innovations, such as improvement of mechanical devices and optimised use of fertilizers.

Pleiotropic locus for emotion recognition and amygdala volume identified using univariate and bivariate linkage

OBJECTIVE

The role of the amygdala in emotion recognition is well established, and amygdala volume and emotion recognition performance have each been shown separately to be highly heritable traits, but the potential role of common genetic influences on both traits has not been explored. The authors investigated the pleiotropic influences of amygdala volume and emotion recognition performance.

METHOD

In a sample of randomly selected extended pedigrees (N=858), the authors used a combination of univariate and bivariate linkage to investigate pleiotropy between amygdala volume and emotion recognition performance and followed up with association analysis.

RESULTS

The authors found a pleiotropic region for amygdala volume and emotion recognition performance on chromosome 4q26 (LOD score=4.40). Association analysis conducted in the region underlying the bivariate linkage peak revealed a variant meeting the corrected significance level (Bonferroni-corrected p=5.01×10(-5)) within an intron of PDE5A (rs2622497, p=4.4×10(-5)) as being jointly influential on both traits. PDE5A has been implicated previously in recognition-memory deficits and is expressed in subcortical structures that are thought to underlie memory ability, including the amygdala.

CONCLUSIONS

This study extends our understanding of the shared etiology between the amygdala and emotion recognition by showing that the overlap between amygdala volume and emotion recognition performance is due at least in part to common genetic influences. Moreover, this study identifies a pleiotropic locus for the two traits and an associated variant, which localizes the genetic signal even more precisely. These results, when taken in the context of previous research, highlight the potential utility of PDE5 inhibitors for ameliorating emotion recognition deficits in individuals suffering from mental or neurodegenerative illness.

Bayesian Partition Models for Identifying Expression Quantitative Trait Loci

Expression quantitative trait loci (eQTLs) are genomic locations associated with changes of expression levels of certain genes. By assaying gene expressions and genetic variations simultaneously on a genome-wide scale, scientists wish to discover genomic loci responsible for expression variations of a set of genes. The task can be viewed as a multivariate regression problem with variable selection on both responses (gene expression) and covariates (genetic variations), including also multi-way interactions among covariates. Instead of learning a predictive model of quantitative trait given combinations of genetic markers, we adopt an inverse modeling perspective to model the distribution of genetic markers conditional on gene expression traits. A particular strength of our method is its ability to detect interactive effects of genetic variations with high power even when their marginal effects are weak, addressing a key weakness of many existing eQTL mapping methods. Furthermore, we introduce a hierarchical model to capture the dependence structure among correlated genes. Through simulation studies and a real data example in yeast, we demonstrate how our Bayesian hierarchical partition model achieves a significantly improved power in detecting eQTLs compared to existing methods.

Genotype–phenotype modeling considering intermediate level of biological variation: a case study involving sensory traits, metabolites and QTLs in ripe tomatoes

We integrate Gaussian graphical modelling and causal inference to infer dependency networks from multilevel phenotypic and omics data.

Multi-trait multi-environment quantitative trait loci mapping for a sugarcane commercial cross provides insights on the inheritance of important traits

Breeding trials typically consist of phenotypic observations for various traits evaluated in multiple environments. For sugarcane in particular, repeated measures are obtained for plant crop and one or more ratoons, such that joint analysis through mixed models for modeling heterogeneous genetic (co)variances between traits, locations and harvests is appropriate. This modeling approach also enables us to include molecular marker information, aiding in understanding the genetic architecture of quantitative traits. Our work aims at detecting QTL and QTL by environment interactions by fitting mixed models with multiple QTLs, with appropriate modeling of multi-trait multi-environment data for outcrossing species. We evaluated 100 individuals from a biparental cross at two locations and three  years for fiber content, sugar content (POL) and tonnes of cane per hectare (TCH). We detected 13 QTLs exhibiting QTL by location, QTL by harvest or the three-way interaction. Overall, 11 of the 13 effects presented some degree of pleiotropy, affecting at least two traits. Furthermore, these QTLs always affected fiber and TCH in the same direction, whereas POL was affected in the opposite way. There was no evidence in favor of the linked QTL over the pleiotropic QTL hypothesis for any detected genome position. These results provide valuable insights into the genetic basis of quantitative variation in sugarcane and the genetic relation between traits.

Dissecting dynamic genetic variation that controls temporal gene response in yeast

Inter-individual variation in regulatory circuits controlling gene expression is a powerful source of functional information. The study of associations among genetic variants and gene expression provides important insights about cell circuitry but cannot specify whether and when potential variants dynamically alter their genetic effect during the course of response. Here we develop a computational procedure that captures temporal changes in genetic effects, and apply it to analyze transcription during inhibition of the TOR signaling pathway in segregating yeast cells. We found a high-order coordination of gene modules: sets of genes co-associated with the same genetic variant and sharing a common temporal genetic effect pattern. The temporal genetic effects of some modules represented a single state-transitioning pattern; for example, at 10-30 minutes following stimulation, genetic effects in the phosphate utilization module attained a characteristic transition to a new steady state. In contrast, another module showed an impulse pattern of genetic effects; for example, in the poor nitrogen sources utilization module, a spike up of a genetic effect at 10-20 minutes following stimulation reflected inter-individual variation in the timing (rather than magnitude) of response. Our analysis suggests that the same mechanism typically leads to both inter-individual variation and the temporal genetic effect pattern in a module. Our methodology provides a quantitative genetic approach to studying the molecular mechanisms that shape dynamic changes in transcriptional responses.

A new simple method for improving QTL mapping under selective genotyping

The selective genotyping approach, where only individuals from the high and low extremes of the trait distribution are selected for genotyping and the remaining individuals are not genotyped, has been known as a cost-saving strategy to reduce genotyping work and can still maintain nearly equivalent efficiency to complete genotyping in QTL mapping. We propose a novel and simple statistical method based on the normal mixture model for selective genotyping when both genotyped and ungenotyped individuals are fitted in the model for QTL analysis. Compared to the existing methods, the main feature of our model is that we first provide a simple way for obtaining the distribution of QTL genotypes for the ungenotyped individuals and then use it, rather than the population distribution of QTL genotypes as in the existing methods, to fit the ungenotyped individuals in model construction. Another feature is that the proposed method is developed on the basis of a multiple-QTL model and has a simple estimation procedure similar to that for complete genotyping. As a result, the proposed method has the ability to provide better QTL resolution, analyze QTL epistasis, and tackle multiple QTL problem under selective genotyping. In addition, a truncated normal mixture model based on a multiple-QTL model is developed when only the genotyped individuals are considered in the analysis, so that the two different types of models can be compared and investigated in selective genotyping. The issue in determining threshold values for selective genotyping in QTL mapping is also discussed. Simulation studies are performed to evaluate the proposed methods, compare the different models, and study the QTL mapping properties in selective genotyping. The results show that the proposed method can provide greater QTL detection power and facilitate QTL mapping for selective genotyping. Also, selective genotyping using larger genotyping proportions may provide roughly equivalent power to complete genotyping and that using smaller genotyping proportions has difficulties doing so. The R code of our proposed method is available on http://www.stat.sinica.edu.tw/chkao/.

Whole-genome analysis of multienvironment or multitrait QTL in MAGIC

Multiparent Advanced Generation Inter-Cross (MAGIC) populations are now being utilized to more accurately identify the underlying genetic basis of quantitative traits through quantitative trait loci (QTL) analyses and subsequent gene discovery. The expanded genetic diversity present in such populations and the amplified number of recombination events mean that QTL can be identified at a higher resolution. Most QTL analyses are conducted separately for each trait within a single environment. Separate analysis does not take advantage of the underlying correlation structure found in multienvironment or multitrait data. By using this information in a joint analysis—be it multienvironment or multitrait — it is possible to gain a greater understanding of genotype- or QTL-by-environment interactions or of pleiotropic effects across traits. Furthermore, this can result in improvements in accuracy for a range of traits or in a specific target environment and can influence selection decisions. Data derived from MAGIC populations allow for founder probabilities of all founder alleles to be calculated for each individual within the population. This presents an additional layer of complexity and information that can be utilized to identify QTL. A whole-genome approach is proposed for multienvironment and multitrait QTL analysis in MAGIC. The whole-genome approach simultaneously incorporates all founder probabilities at each marker for all individuals in the analysis, rather than using a genome scan. A dimension reduction technique is implemented, which allows for high-dimensional genetic data. For each QTL identified, sizes of effects for each founder allele, the percentage of genetic variance explained, and a score to reflect the strength of the QTL are found. The approach was demonstrated to perform well in a small simulation study and for two experiments, using a wheat MAGIC population.

The genetic architecture of coordinately evolving male wing pigmentation and courtship behavior in Drosophila elegans and Drosophila gunungcola

Many adaptive phenotypes consist of combinations of simpler traits that act synergistically, such as morphological traits and the behaviors that use those traits. Genetic correlations between components of such combinatorial traits, in the form of pleiotropic or tightly linked genes, can in principle promote the evolution and maintenance of these traits. In the Oriental Drosophila melanogaster species group, male wing pigmentation shows phylogenetic correlations with male courtship behavior; species with male-specific apical wing melanin spots also exhibit male visual wing displays, whereas species lacking these spots generally lack the displays. In this study, we investigated the quantitative genetic basis of divergence in male wing spots and displays between D. elegans, which possesses both traits, and its sibling species D. gunungcola, which lacks them. We found that divergence in wing spot size is determined by at least three quantitative trait loci (QTL) and divergence in courtship score is determined by at least four QTL. On the autosomes, QTL locations for pigmentation and behavior were generally separate, but on the X chromosome two clusters of QTL were found affecting both wing pigmentation and courtship behavior. We also examined the genetic basis of divergence in three components of male courtship, wing display, circling, and body shaking. Each of these showed a distinct genetic architecture, with some QTL mapping to similar positions as QTL for overall courtship score. Pairwise tests for interactions between marker loci revealed evidence of epistasis between putative QTL for wing pigmentation but not those for courtship behavior. The clustering of X-linked QTL for male pigmentation and behavior is consistent with the concerted evolution of these traits and motivates fine-scale mapping studies to elucidate the nature of the contributing genetic factors in these intervals.

A new method to infer causal phenotype networks using QTL and phenotypic information

In the context of genetics and breeding research on multiple phenotypic traits, reconstructing the directional or causal structure between phenotypic traits is a prerequisite for quantifying the effects of genetic interventions on the traits. Current approaches mainly exploit the genetic effects at quantitative trait loci (QTLs) to learn about causal relationships among phenotypic traits. A requirement for using these approaches is that at least one unique QTL has been identified for each trait studied. However, in practice, especially for molecular phenotypes such as metabolites, this prerequisite is often not met due to limited sample sizes, high noise levels and small QTL effects. Here, we present a novel heuristic search algorithm called the QTL+phenotype supervised orientation (QPSO) algorithm to infer causal directions for edges in undirected phenotype networks. The two main advantages of this algorithm are: first, it does not require QTLs for each and every trait; second, it takes into account associated phenotypic interactions in addition to detected QTLs when orienting undirected edges between traits. We evaluate and compare the performance of QPSO with another state-of-the-art approach, the QTL-directed dependency graph (QDG) algorithm. Simulation results show that our method has broader applicability and leads to more accurate overall orientations. We also illustrate our method with a real-life example involving 24 metabolites and a few major QTLs measured on an association panel of 93 tomato cultivars. Matlab source code implementing the proposed algorithm is freely available upon request.

Selection on crop-derived traits and QTL in sunflower (Helianthus annuus) crop-wild hybrids under water stress

Locally relevant conditions, such as water stress in irrigated agricultural regions, should be considered when assessing the risk of crop allele introgression into wild populations following hybridization. Although research in cultivars has suggested that domestication traits may reduce fecundity under water stress as compared to wild-like phenotypes, this has not been investigated in crop-wild hybrids. In this study, we examine phenotypic selection acting on, as well as the genetic architecture of vegetative, reproductive, and physiological characteristics in an experimental population of sunflower crop-wild hybrids grown under wild-like low water conditions. Crop-derived petiole length and head diameter were favored in low and control water environments. The direction of selection differed between environments for leaf size and leaf pressure potential. Interestingly, the additive effect of the crop-derived allele was in the direction favored by selection for approximately half the QTL detected in the low water environment. Selection favoring crop-derived traits and alleles in the low water environment suggests that a subset of these alleles would be likely to spread into wild populations under water stress. Furthermore, differences in selection between environments support the view that risk assessments should be conducted under multiple locally relevant conditions.

Multiple-trait genome-wide association study based on principal component analysis for residual covariance matrix

Given the drawbacks of implementing multivariate analysis for mapping multiple traits in genome-wide association study (GWAS), principal component analysis (PCA) has been widely used to generate independent 'super traits' from the original multivariate phenotypic traits for the univariate analysis. However, parameter estimates in this framework may not be the same as those from the joint analysis of all traits, leading to spurious linkage results. In this paper, we propose to perform the PCA for residual covariance matrix instead of the phenotypical covariance matrix, based on which multiple traits are transformed to a group of pseudo principal components. The PCA for residual covariance matrix allows analyzing each pseudo principal component separately. In addition, all parameter estimates are equivalent to those obtained from the joint multivariate analysis under a linear transformation. However, a fast least absolute shrinkage and selection operator (LASSO) for estimating the sparse oversaturated genetic model greatly reduces the computational costs of this procedure. Extensive simulations show statistical and computational efficiencies of the proposed method. We illustrate this method in a GWAS for 20 slaughtering traits and meat quality traits in beef cattle.

Quantitative genetic analysis of chlorophyll a fluorescence parameters in maize in the field environments

Chlorophyll fluorescence transient from initial to maximum fluorescence ("P" step) throughout two intermediate steps ("J" and "I") (JIP-test) is considered a reliable early quantitative indicator of stress in plants. The JIP-test is particularly useful for crop plants when applied in variable field environments. The aim of the present study was to conduct a quantitative trait loci (QTL) analysis for nine JIP-test parameters in maize during flowering in four field environments differing in weather conditions. QTL analysis and identification of putative candidate genes might help to explain the genetic relationship between photosynthesis and different field scenarios in maize plants. The JIP-test parameters were analyzed in the intermated B73 × Mo17 (IBM) maize population of 205 recombinant inbred lines. A set of 2,178 molecular markers across the whole maize genome was used for QTL analysis revealing 10 significant QTLs for seven JIP-test parameters, of which five were co-localized when combined over the four environments indicating polygenic inheritance and pleiotropy. Our results demonstrate that QTL analysis of chlorophyll fluorescence parameters was capable of detecting one pleiotropic locus on chromosome 7, coinciding with the gene gst23 that may be associated with efficient photosynthesis under different field scenarios.

Exploring the function of genetic variants in the non-coding genomic regions: approaches for identifying human regulatory variants affecting gene expression

Understanding the genetic basis of human traits/diseases and the underlying mechanisms of how these traits/diseases are affected by genetic variations is critical for public health. Current genome-wide functional genomics data uncovered a large number of functional elements in the noncoding regions of human genome, providing new opportunities to study regulatory variants (RVs). RVs play important roles in transcription factor bindings, chromatin states and epigenetic modifications. Here, we systematically review an array of methods currently used to map RVs as well as the computational approaches in annotating and interpreting their regulatory effects, with emphasis on regulatory single-nucleotide polymorphism. We also briefly introduce experimental methods to validate these functional RVs.