Detection of agriculturally important QTLs in chickens and analysis of the factors affecting genotyping strategy

Three single cross populations were generated in order to analyze factors affecting the ability to detect true linkage with minimum false positive or false negative associations, and to detect associations between markers and quantitative traits. The three populations are: (1) a broiler x broiler cross of a single sire and 34 dams, resulting in 266 progeny; (2) a broiler x broiler cross of a single sire and 41 dams resulting in 360 progeny; and (3) a broiler x layer cross of a single sire with 56 dams resulting in 1180 progeny. Based on these three resource populations we show that: a) gradient selective genotyping was more effective than the random selective genotyping; b) selective genotyping was significant at a selected proportion less than 62% of the cumulative truncation point; c) as few as 10% of selected individuals (5% of each of the two tails) were sufficient to show significant association between markers and phenotypes; d) a gradient slices approach was more powerful than using replicates of the extreme groups; and e) in resource populations resulting from crosses between lines of different backgrounds, most of the microsatellite markers used are polymorphic. We also used simulation to test factors affecting power to detect true associations between markers and traits that are hard to detect in experimental resource populations. Using defined populations in the simulation, we concluded that the following guidelines provide reliable detection of linked QTLs: 1) the resource population size should be larger than 100; 2) a QTL effect larger than 0.4 SD is detectable with a reasonable number of markers (>100) and resource population size (>200 subjects); 3) the DNA pool from each tail of the trait distribution should contain at least 10% of the resource family; 4) each of the two DNA pools should include more than 35 individuals. Some of these guidelines that were deduced from the simulation analysis have been confirmed in the experimental part of this study.     

The future is now - will the real disease gene please stand up?

The transmission/disequilibrium test (TDT) [Spielman et al.: Am J Hum Genet 1993;52:506-516] has been postulated as the future of gene mapping for complex diseases, provided one is able to genotype a dense enough map of markers across the genome. Risch and Merikangas [Science 1996;273:1516-1517] suggested a million-marker screen in affected sibpair (ASP) families, demonstrating that the TDT is a more powerful test of linkage than traditional linkage tests based on allele-sharing when there is also association between marker and disease alleles. While the future of genotyping has arrived, successes in family-based association studies have been modest. This is often attributed to excessive false positives in candidate gene studies. This problem is only exacerbated by the increasing numbers of whole genome association (WGA) screens. When applied in ASPs, the TDT statistic, which assumes transmissions to siblings are independent, is not expected to have a constant variance in the presence of variable linkage. This results in generally more extreme statistics, hence will further aggravate the problem of having a large number of positive results to sort through. So an important question is how many positive TDT results will show up on a chromosome containing a disease gene due only to linkage, and will they obfuscate the true disease gene location. To answer this question we combined theory and computer simulations. These studies show that in ASPs the normal version of the TDT statistic has a mean of 0 and a variance of 1 in unlinked regions, but has a variance larger than 1 in linked regions. In contrast, the pedigree disequilibrium test (PDT) statistic adjusts for correlation between siblings due to linkage and maintains a constant variance of 1 at unassociated markers irrespective of linkage. The TDT statistic is generally larger than the PDT statistic across linked regions. This is true for unassociated as well as associated markers. To compare the two tests we ranked both statistics at the disease locus, or an associated marker, among statistics at all other markers. The TDT did better job than PDT placing the score of the associated marker near the top. Though, strictly speaking, the TDT in ASPs should be interpreted as a test of linkage and not a test of association, there is a good chance that if a marker stands out, the marker is associated as well as linked. In conclusion, our results suggest that TDT is an effective screening tool for WGA studies, especially in multiplex families.     

Novel rank-based approaches for discovery and replication in genome-wide association studies

In recent years, genome-wide association studies (GWAS) have uncovered a large number of susceptibility variants. Nevertheless, GWAS findings provide only tentative evidence of association, and replication studies are required to establish their validity. Due to this uncertainty, researchers often focus on top-ranking SNPs, instead of considering strict significance thresholds to guide replication efforts. The number of SNPs for replication is often determined ad hoc. We show how the rank-based approach can be used for sample size allocation in GWAS as well as for deciding on a number of SNPs for replication. The basis of this approach is the "ranking probability": chances that at least j true associations will rank among top u SNPs, when SNPs are sorted by P-value. By employing simple but accurate approximations for ranking probabilities, we accommodate linkage disequilibrium (LD) and evaluate consequences of ignoring LD. Further, we relate ranking probabilities to the proportion of false discoveries among top u SNPs. A study-specific proportion can be estimated from P-values, and its expected value can be predicted for study design applications.     

Case-control studies of association in structured or admixed populations

Case-control tests for association are an important tool for mapping complex-trait genes. But population structure can invalidate this approach, leading to apparent associations at markers that are unlinked to disease loci. Family-based tests of association can avoid this problem, but such studies are often more expensive and in some cases--particularly for late-onset diseases--are impractical. In this review article we describe a series of approaches published over the past 2 years which use multilocus genotype data to enable valid case-control tests of association, even in the presence of population structure. These tests can be classified into two categories. "Genomic control" methods use the independent marker loci to adjust the distribution of a standard test statistic, while "structured association" methods infer the details of population structure en route to testing for association. We discuss the statistical issues involved in the different approaches and present results from simulations comparing the relative performance of the methods under a range of models.     

A simple method for analyzing microsatellite allele image patterns generated from DNA pools and its application to allelic association studies.

Allelic association studies provide the most powerful method for locating genes of small effect contributing to complex diseases and traits. However, in outbred populations, allelic association is usually maintained only over distances of <=1 cM. Therefore, systematic searches over large regions are costly. Here we present a method involving DNA pooling that can be used as a rapid preliminary screen for allelic association with the most common class of polymorphic markers, single-sequence repeats. Patient and control samples are pooled separately, and markers are typed in the two pools. By use of primers with fluorescent 5'' ends, PCR products can be analyzed on an automated sequencing apparatus. Allele image patterns (AIPs) produced for the two groups are overlaid and differences in pattern area between pools computed. From this, a DeltaAIP statistic is calculated from the difference in areas between the two AIPs expressed as a fraction of the total shared and nonshared area. AIPs of a range of different-sized pools were generated by computer simulation for markers with a range of allele sizes and frequencies. DeltaAIPs from pools and chi2 values for individual genotypings were compared, with both simulated and real data from microsatellite markers. The results demonstrated a high correlation between DeltaAIP and chi2 values. DeltaAIP analysis of real microsatellite data indicated the feasibility of using this method in systematic searches for allelic association and generated a small number of false positives but few false negatives. We conclude that DeltaAIP analysis of DNA pools can be used effectively and efficiently as a rapid screen for allelic association in case-control studies.     

Will the real disease gene please stand up?

A common dilemma arising in linkage studies of complex genetic diseases is the selection of positive signals, their follow-up with association studies and discrimination between true and false positive results. Several strategies for overcoming these issues have been devised. Using the Genetic Analysis Workshop 14 simulated dataset, we aimed to apply different analytical approaches and evaluate their performance in discerning real associations. We considered a) haplotype analyses, b) different methods adjusting for multiple testing, c) replication in a second dataset, and d) exhaustive genotyping of all markers in a sufficiently powered, large sample group. We found that haplotype-based analyses did not substantially improve over single-point analysis, although this may reflect the low levels of linkage disequilibrium simulated in the datasets provided. Multiple testing correction methods were in general found to be over-conservative. Replication of nominally positive results in a second dataset appears to be less stringent, resulting in the follow-up of false positives. Performing a comprehensive assay of all markers in a large, well-powered dataset appears to be the most effective strategy for complex disease gene identification.     

复杂疾病全基因组关联研究进展——遗传统计分析

2005年, Science杂志首次报道了有关人类年龄相关性黄斑变性的全基因组关联研究, 此后有关肥胖、2型糖尿病、冠心病、阿尔茨海默病等一系列复杂疾病的全基因组关联研究被陆续报道, 这一阶段被称为人类全基因组关联研究的第一次浪潮。文章分别介绍了全基因组关联研究统计分析的方法、软件和应用实例; 比较了关联分析中多重检验的P值调整方法, 包括Bonferroni、递减的Bonferroni校正法、模拟运算法和控制错误发现率的方法; 还讨论了人群混杂对关联分析结果可能产生的影响及原理, 以及全基因组关联研究中控制人群混杂的方法的研究进展和应用实例。在全基因组关联研究的第一次浪潮中, 应用经典的遗传统计方法发现了许多基因-表型之间的关联并且能够对这些关联做出解释, 其中包括许多基因组中的未知基因和染色体区域。然而, 全基因组关联研究的继续发展需要进一步阐述基因组内基因之间相互作用、基因-基因之间的复杂作用网络与环境因素的相互作用在复杂疾病发生中的作用, 现有的统计分析方法肯定不能满足需要, 开发更为高级的统计分析方法势在必行。最后, 文章还给出了全基因组关联研究统计分析软件的相关网站信息。     

A Clinically Orientated Approach for Combining Discordant and Concordant Sib Pairs

The use of extreme discordant sib pairs (EDSP) or extreme concordant sib pairs (ECSP) has recently been proposed to increase power for mapping quantitative traits in humans (RISCH and ZHANG, 1995, 1996). In this paper we propose a test statistic to jointly analyze EDSP and ECSP based on a clinical sampling procedure. This test statistic does not fulfill any optimality criteria. However, this approach is useful for quantitative traits of clinical significance for which EDSP are rare and/or expensive to ascertain. We show how sample size calculations can be adjusted for recombination using single markers, multipoint analysis, incompletely polymorphic markers and varying proportions of ECSP. If the true genetic model is unknown, the combined approach appears to be more robust than sampling based on only EDSP or only ECSP. We discuss how to find the optimal proportion of EDSP and ECSP to be included in an analysis under power considerations.     

Use of parents, sibs, and unrelated controls for detection of associations between genetic markers and disease.

Detecting the association between genetic markers and complex diseases can be a critical first step toward identification of the genetic basis of disease. Misleading associations can be avoided by choosing as controls the parents of diseased cases, but the availability of parents often limits this design to early-onset disease. Alternatively, sib controls offer a valid design. A general multivariate score statistic is presented, to detect the association between a multiallelic genetic marker locus and affection status; this general approach is applicable to designs that use parents as controls, sibs as controls, or even unrelated controls whose genotypes do not fit Hardy-Weinberg proportions or that pool any combination of these different designs. The benefit of this multivariate score statistic is that it will tend to be the most powerful method when multiple marker alleles are associated with affection status. To plan these types of studies, we present methods to compute sample size and power, allowing for varying sibship sizes, ascertainment criteria, and genetic models of risk. The results indicate that sib controls have less power than parental controls and that the power of sib controls can be increased by increasing either the number of affected sibs per sibship or the number of unaffected control sibs. The sample-size results indicate that the use of sib controls to test for associations, by use of either a single-marker locus or a genomewide screen, will be feasible for markers that have a dominant effect and for common alleles having a recessive effect. The results presented will be useful for investigators planning studies using sibs as controls.     

Association study highlights the influence of ELOVL fatty acid elongase 6 gene region on backfat fatty acid composition in Large White pig breed

Dietary fatty acid (FA) composition has an impact on human health. There is an increasing request from consumers for healthier food and pork industry must respond to it without worsening performance and the technological properties of pork products. The inclusion of genetic markers for carcass FA composition in pig selection schemes could be a useful tool to reach the right balance between unsaturated and saturated FAs to satisfy market demands. With the aim of finding genomic regions associated with porcine backfat FA composition, a genome-wide association study was performed on 798 Italian Large White pigs genotyped using Illumina PorcineSNP60 k. The strongest associations with backfat contents of palmitic, palmitoleic, oleic, medium-chain and long-chain FAs were found for the Sus scrofa chromosome (SSC) 8 region located at 119 to 122 Mb, where the gene ELOVL FA elongase 6 is mapped. Palmitic, palmitoleic, stearic and oleic acid contents were also found associated with SSC14, in particular with the genomic region at 121 to 124 Mb, where stearoyl-CoA desaturase Δ9 gene lies. On the other hand, the genomic regions associated with backfat contents of arachidic, arachidonic, n-6 and n-3 FAs showed to harbour mainly genes involved in dietary lipids and carbohydrates digestion, absorption and utilisation. To our knowledge, this is the first study performed in Large White pigs identifying markers and genomic regions associated with backfat FA composition. The results validate in Large White some associations previously detected in other pig breeds and indicate the involvement of distinct metabolic pathways in the deposition pattern of essential and non-essential FAs.     

Case-control association studies in mixed populations: correcting using genomic control

OBJECTIVE: Case-control association studies in mixed populations can result in spurious disease-marker associations if subpopulation disease prevalence and marker frequencies both differ. Genomic control (GC) uses neutral loci to correct for spurious association (due to population stratification), but how well this works remains undetermined. METHODS: We simulated and mixed populations with different disease and marker frequencies but without marker-disease association. We generated case-control datasets, calculated the chi2 for disease association with each marker, and applied two GC procedures, dividing by the mean chi2 or median-chi2/0.456. RESULTS: Corrections became conservative (false positive rate [FPR] <5%) with increasing subpopulation prevalence and marker differences. The mean correction resulted in FPRs close to 5% at average subpopulation allele frequency differences <0.26, but inclusion of just a few markers with large frequency differences resulted in conservative FPRs. FPRs from the median correction were mostly conservative but became anticonservative when a few markers with large frequency differences were included. CONCLUSION: GC can both lead to a notable loss of power to detect a true association (conservative) in many circumstances or may fail to eliminate the spurious associations (anticonservative). The mean correction factor is useful in certain situations to correct population stratification, but it is difficult to know when those situations exist.     

A test statistic to detect errors in sib-pair relationships.

Several authors have proposed algorithms to detect Mendelian errors in human genetic linkage data. Most currently available methods use likelihood-based methods on multiplex family data to identify typing or pedigree errors. These algorithms cannot be applied in many sib-pair collections, because of lack of parental-genotype information. Nonetheless, misspecifying the relationships between individuals has serious consequences for sib-pair linkage studies: false relationships bias the statistics designed to identify linkage with disease phenotypes. To test the hypothesis that two individuals are sibs, we propose a test statistic based on the summation, over a large number of genetic markers, of the number of alleles shared identical by state by a pair of individuals, for each marker. The test statistic has an approximately normal distribution under the null hypothesis, and extreme negative values correspond to nonsib pairs. Power and significance studies show that the test statistic calculated by use of 50 unlinked markers has 96% power to detect half-sibs and has 100% power to detect unrelated individuals as not full-sib pairs, with a 5% false-positive rate. Furthermore, extreme positive values of the test statistic identify sibs as MZ twins.     

Genome-wide search for markers associated with bovine spongiform encephalopathy

A genome-wide search for markers associated with BSE incidence was performed by using Transmission-Disequilibrium Tests (TDTs). Significant segregation distortion, i.e., unequal transmission probabilities of alleles within a locus, was found for three marker loci on Chromosomes (Chrs) 5, 10, and 20. Although TDTs are robust to false associations owing to hidden population substructures, it cannot distinguish segregation distortion caused by a true association between a marker and bovine spongiform encephalopathy (BSE) from a population-wide distortion. An interaction test and a segregation distortion analysis in half-sib controls were used to disentangle these two alternative hypotheses. None of the markers showed any significant interaction between allele transmission rates and disease status, and only the marker on Chr 10 showed a significant segregation distortion in control individuals. Nevertheless, the control group may have been a mixture of resistant and susceptible but unchallenged individuals. When new genotypes were generated in the vicinity of these three markers, evidence for an association with BSE was confirmed for the locus on Chr 5. Subscribers can view a supplementary table for this article at url:http:// link.springer-ny/link/service/journals/OO335/contents/01/3068/paper/ index.html.     

Predicting signatures of "synthetic associations" and "natural associations" from empirical patterns of human genetic variation

Genome-wide association studies (GWAS) have in recent years discovered thousands of associated markers for hundreds of phenotypes. However, associated loci often only explain a relatively small fraction of heritability and the link between association and causality has yet to be uncovered for most loci. Rare causal variants have been suggested as one scenario that may partially explain these shortcomings. Specifically, Dickson et al. recently reported simulations of rare causal variants that lead to association signals of common, tag single nucleotide polymorphisms, dubbed "synthetic associations". However, an open question is what practical implications synthetic associations have for GWAS. Here, we explore the signatures exhibited by such "synthetic associations" and their implications based on patterns of genetic variation observed in human populations, thus accounting for human evolutionary history -a force disregarded in previous simulation studies. This is made possible by human population genetic data from HapMap 3 consisting of both resequencing and array-based genotyping data for the same set of individuals from multiple populations. We report that synthetic associations tend to be further away from the underlying risk alleles compared to "natural associations" (i.e. associations due to underlying common causal variants), but to a much lesser extent than previously predicted, with both the age and the effect size of the risk allele playing a part in this phenomenon. We find that while a synthetic association has a lower probability of capturing causal variants within its linkage disequilibrium block, sequencing around the associated variant need not extend substantially to have a high probability of capturing at least one causal variant. We also show that the minor allele frequency of synthetic associations is lower than of natural associations for most, but not all, loci that we explored. Finally, we find the variance in associated allele frequency to be a potential indicator of synthetic associations.     

Leveraging GWAS data to identify metabolic pathways and networks involved in maize lipid biosynthesis

Maize (Zea mays mays) oil is a rich source of polyunsaturated fatty acids (FAs) and energy, making it a valuable resource for human food, animal feed, and bio‐energy. Although this trait has been studied via conventional genome‐wide association study (GWAS), the single nucleotide polymorphism (SNP)‐trait associations generated by GWAS may miss the underlying associations when traits are based on many genes, each with small effects that can be overshadowed by genetic background and environmental variation. Detecting these SNPs statistically is also limited by the levels set for false discovery rate. A complementary pathways analysis that emphasizes the cumulative aspects of SNP‐trait associations, rather than just the significance of single SNPs, was performed to understand the balance of lipid metabolism, conversion, and catabolism in this study. This pathway analysis indicated that acyl‐lipid pathways, including biosynthesis of wax esters, sphingolipids, phospholipids and flavonoids, along with FA and triacylglycerol (TAG) biosynthesis, were important for increasing oil and FA content. The allelic variation found among the genes involved in many degradation pathways, and many biosynthesis pathways leading from FAs and carbon partitioning pathways, was critical for determining final FA content, changing FA ratios and, ultimately, to final oil content. The pathways and pathway networks identified in this study, and especially the acyl‐lipid associated pathways identified beyond what had been found with GWAS alone, provide a real opportunity to precisely and efficiently manipulate high‐oil maize genetic improvement.     

An Arabidopsis example of association mapping in structured samples

A potentially serious disadvantage of association mapping is the fact that marker-trait associations may arise from confounding population structure as well as from linkage to causative polymorphisms. Using genome-wide marker data, we have previously demonstrated that the problem can be severe in a global sample of 95 Arabidopsis thaliana accessions, and that established methods for controlling for population structure are generally insufficient. Here, we use the same sample together with a number of flowering-related phenotypes and data-perturbation simulations to evaluate a wider range of methods for controlling for population structure. We find that, in terms of reducing the false-positive rate while maintaining statistical power, a recently introduced mixed-model approach that takes genome-wide differences in relatedness into account via estimated pairwise kinship coefficients generally performs best. By combining the association results with results from linkage mapping in F2 crosses, we identify one previously known true positive and several promising new associations, but also demonstrate the existence of both false positives and false negatives. Our results illustrate the potential of genome-wide association scans as a tool for dissecting the genetics of natural variation, while at the same time highlighting the pitfalls. The importance of study design is clear; our study is severely under-powered both in terms of sample size and marker density. Our results also provide a striking demonstration of confounding by population structure. While statistical methods can be used to ameliorate this problem, they cannot always be effective and are certainly not a substitute for independent evidence, such as that obtained via crosses or transgenic experiments. Ultimately, association mapping is a powerful tool for identifying a list of candidates that is short enough to permit further genetic study.     

A strategy for using multiple linked markers for genetic counseling.

A strategy for using multiple linked markers for genetic counseling is to test sequentially individual markers until a diagnosis can be made. We show that in order to minimize the number of tests performed per case while diagnosing all informative cases the order in which the markers are to be tested is critical. We describe an algorithm to obtain this order using the parameter "I," the frequency of informative cases. The I value for a specific locus used depends on the marker frequency, association with the disease locus, and also on the informativeness of the marker loci already tested. Realizing that a direct assay for the beta S gene already exists, and that most cases of beta-thalassemia in Mediterraneans can be directly diagnosed using synthetic oligonucleotide probes, we illustrate the above technique by examining nine DNA polymorphisms in the human beta-globin cluster for their ability to diagnose sickle-cell anemia in American blacks and beta-thalassemia in Mediterraneans. This analysis shows that 95.39% of all sickle-cell pregnancies can be diagnosed by testing a subset of only six markers chosen by our algorithm. Furthermore, six markers can also diagnose 88.03% of beta-thalassemia in Greeks and 83.56% of beta-thalassemia in Italians. The test set is different from that suggested by the individual informative frequencies due to nonrandom associations between the restriction sites.     

Monte Carlo pedigree disequilibrium test for markers on the X chromosome

Because of the need for fine mapping of disease loci and the availability of dense single-nucleotide-polymorphism markers, many forms of association tests have been developed. Most of them are applicable only to triads, whereas some are amenable to nuclear families (sibships). Although there are a number of methods that can deal with extended families (e.g., the pedigree disequilibrium test [PDT]), most of them cannot accommodate incomplete data. Furthermore, despite a large body of literature on association mapping, only a very limited number of publications are applicable to X-chromosomal markers. In this report, we first extend the PDT to markers on the X chromosome for testing linkage disequilibrium in the presence of linkage. This method is applicable to any pedigree structure and is termed "X-chromosomal pedigree disequilibrium test" (XPDT). We then further extend the XPDT to accommodate pedigrees with missing genotypes in some of the individuals, especially founders. Monte Carlo (MC) samples of the missing genotypes are generated and used to calculate the XMCPDT (X-chromosomal MC PDT) statistic, which is defined as the conditional expectation of the XPDT statistic given the incomplete (observed) data. This MC version of the XPDT remains a valid test for association under linkage with the assumption that the pedigrees and their associated affection patterns are drawn randomly from a population of pedigrees with at least one affected offspring. This set of methods was compared with existing approaches through simulation, and substantial power gains were observed in all settings considered, with type I error rates closely tracking their nominal values.     

Assessing the Probability that a Finding Is Genuine for Large-Scale Genetic Association Studies

Genetic association studies routinely involve massive numbers of statistical tests accompanied by P-values. Whole genome sequencing technologies increased the potential number of tested variants to tens of millions. The more tests are performed, the smaller P-value is required to be deemed significant. However, a small P-value is not equivalent to small chances of a spurious finding and significance thresholds may fail to serve as efficient filters against false results. While the Bayesian approach can provide a direct assessment of the probability that a finding is spurious, its adoption in association studies has been slow, due in part to the ubiquity of P-values and the automated way they are, as a rule, produced by software packages. Attempts to design simple ways to convert an association P-value into the probability that a finding is spurious have been met with difficulties. The False Positive Report Probability (FPRP) method has gained increasing popularity. However, FPRP is not designed to estimate the probability for a particular finding, because it is defined for an entire region of hypothetical findings with P-values at least as small as the one observed for that finding. Here we propose a method that lets researchers extract probability that a finding is spurious directly from a P-value. Considering the counterpart of that probability, we term this method POFIG: the Probability that a Finding is Genuine. Our approach shares FPRP''s simplicity, but gives a valid probability that a finding is spurious given a P-value. In addition to straightforward interpretation, POFIG has desirable statistical properties. The POFIG average across a set of tentative associations provides an estimated proportion of false discoveries in that set. POFIGs are easily combined across studies and are immune to multiple testing and selection bias. We illustrate an application of POFIG method via analysis of GWAS associations with Crohn''s disease.     

Association mapping of drought tolerance-related traits in barley to complement a traditional biparental QTL mapping study

Key message

Association mapping of drought-related traits in barley was used to increase the density of existing QTL maps without recreating mapping populations.

Abstract

We used 109 spring barley genotypes exhibiting high or low drought tolerance to elucidate the associations between diversity array technology sequencing (DArTseq) and single nucleotide polymorphism (SNP) markers and various physiological parameters related to plant responses to drought conditions. The study was performed in controlled conditions (growth chambers), drought tolerance was phenotyped in the four-leaf seedlings. We identified 58 associations including 34 new markers (i.e., 16 DArTseq and 18 SNP markers). The results for three markers were consistent with the data obtained in an earlier traditional biparental QTL mapping study. The regions neighboring markers on linkage group 2H contained the highest number of significant marker–trait associations. Five markers related to the photosynthetic activity of photosystem II were detected on chromosome 4H. The lowest number of associations were observed for the sequences neighboring DArT markers on linkage group 6H. A chromosome 3H region related to water use efficiency and net photosynthesis rate in both biparental QTL, and association study, may be particularly valuable, as these parameters correspond to the ability of plants to remain highly productive under water deficit stress. Our findings confirm that association mapping can increase the density of existing QTL maps without recreating mapping populations.