P-value 8.4 × 10−18). Candidate genes were found for lipid synthesis, breakdown, transfer, and protection against peroxidation. The detected SNP-lipid associations could be grouped into associations with 1) individual lipids, 2) lipids from one biochemical class, and 3) lipids from several classes, suggesting a multilevel genetic control architecture. We further found a strong connection between the lipidome and agronomic traits in field-evaluated hybrid progeny. A cross-validated prediction model yielded correlations of up to 0.78 suggesting that the lipidome accurately predicts agronomic traits relevant in hybrid maize breeding.Although the plant lipidome show an enormous level of structural and functional diversity, our knowledge about its genetic control and its connection to whole-plant phenotypes is very limited. Here, we profiled 563 lipid species with UPLC-FT-MS in 289 field-grown inbred lines genotyped with 56,110 SNPs. Genome-wide association study identified 174 associations for 76 lipids explaining up to 31.4% of the genetic variance ( Living organisms produce a plethora of chemically distinct lipid species, commonly referred to as the lipidome5678Arabidopsis thaliana, most of the biochemical steps involved in lipid biosynthesis are known and the key genes have been identified, the regulation of the processes that results in the final lipid composition is only weakly understood. Quantitative genetic approaches have been followed more recently in Arabidopsis thalianai.e. triacylglycerides. Recently, we have described results obtained for mapping primary metabolites in maizeAlthough in Using the same mapping population we here extend this study to identify numerous loci involved in lipid metabolism. Using UPLC-FT-MS, we measured 563 distinct leaf lipids in 289 diverse maize inbred lines genotyped with 56,110 SNPs. In addition we asked the question whether or not lipid profiles can be used for predicting general combining ability (GCA) of the parent lines reflecting the average performance of their hybrid progeny as previously successfully shown for primary metabolites2) of 0.66 (2 > 0.8. The lipids showed a correlation pattern with intense substructuring (Using ultra-performance liquid chromatography separation coupled to a high-resolution Fourier transform mass spectrometer (UPLC-FT-MS), 563 distinct leaf lipids were measured in the 289 maize inbred lines from the diversity panel of worldwide sources . It is c of 0.66 , with a ucturing . NotablyQ10 + K mixed model and a FDR threshold of 2.5%, we detected 174 SNP-lipid associations for 76 lipids, explaining up to 31.6% of the genetic variance  at position 66.8 Mb on chromosome 5, nearby (1.07 Mb) a lipolytic GDSL esterase/lipase and very close (4.5 kb) to an invertase gene . All three showed their strongest associations .The biochemically closely related TAGs 56:4, 58:4, and 60:4 showed two associations leading to level 2 candidate genes. The first association was found at the beginning of chromosome 3 at 8.6 Mb, very close (268.3 kb) to a phospholipase, and the second at the end of the chromosome at 228.4 Mb, nearby a lipid transporter.−64 and 5 × 10−86) to a fatty acid desaturase in Arabidopsis. Nine out of these 13 lipids also showed a common association on chromosome 1 nearby (538.1 kb) a lipid transfer protein. In addition, eight lipids from three chemical classes mapped to the same position at 115.5 Mb on chromosome 8, in close vicinity (4.9 kb) of a 3-hydroxyacyl-CoA dehydratase, a key component in the elongase complex for synthesizing very-long-chained fatty acidsExamples for level 3 candidate genes include amongst others a sphingosine kinase, found 294.5 kb away from an association at position 33.56 Mb on chromosome 4, for which one PG, one MGDG, and three unknown lipids mapped together. One PG, one MGDG, one SQDG, and nine unknown lipids mapped to a region from 41.4 to 42.4 Mb on chromosome 10, harboring two genes, the products of which have a high similarity  suggest underlying causal genes regulating the lipidome at a higher hierarchical level.ArabidopsisCurrently, incomplete annotations limit our ability to distinguish perfectly the level 3 from level 2 candidate genes. Nevertheless, the suggested hierarchy levels largely corresponded with the specificity of the associated candidate genes. For example, the lipid signaling molecule sphingosine-1-phosphate has recently received great attention because of its involvement in many physiological processes in yeast, mammals, and plants15Arabidopsis SHN transcription factors which are involved in the regulation of a variety of lipid metabolism processes including lipid transport, fatty acid elongation, and the synthesis of wax, suberin, and cutin1822Arabidopsis which have been demonstrated to be involved in fatty acid synthesis and triacylglycerol biosynthesis processes2425Two candidate genes, GRMZM2G084264, GRMZM2G073258 , show hi−12) to CER2, a gene studied in detail in Arabidopsis, which is required for the elongation of fatty acids of exceptional lengthOn chromosome 4, a candidate gene showed a high similarity  and soybean (Glycine max) cotyledonsLipid synthesis occurs via sequential addition of two-carbon units derived from acetyl CoA. which is nicely reflected in the mapping of biochemically closely related lipids from specific lipid classes such as TAGs 56:4, 58:4, and 60:4 to just one locus . The DAGThe rich functional diversity of the lipidome and its association to a diversity of biological processes suggests that it might be used for the prediction of phenotypic performance. We tested this hypothesis by predicting agronomic relevant traits evaluated in hybrid progeny in multi-environment field trials. The high prediction accuracy confirmed by cross-validation showed that such an approach is very promising. In addition, the higher absolute correlation coefficients and cross-validated predictions for flowering time, and sugar and fat content compared to dry matter yield indicated that the leaf lipidome is in general more closely connected to quality or developmental traits than to biomass.In conclusion, our study demonstrates that lipidomics is not only a powerful tool for shedding light on the genetic control of the lipid metabolism in crop plants, but it also provides access to a novel layer of biochemical information exploitable for predicting agronomic traits relevant in hybrid maize breeding.The genetic material consisted of (i) a previously described diversity panel of 285 Dent inbred linesGenotyping was performed using the Illumina SNP chip Maize-SNP50 (Illumina Inc.) containing 56,110 unique SNPshttp://www.thermofisher.com) in positive ionization mode. Processing of chromatograms, peak detection and integration were performed using REFINER MS® 5.3 . Processing of mass spectrometry data included the removal of the fragmentation information, isotopic peaks, as well as chemical noise. Obtained features (m/z at a certain retention time) were queried against an in-house lipid database for further annotation. Remaining unknown features were crossed checked with a different, on-line database using an in-house developed database search tool  for putative annotation.Inbred lines in field trials were subject to lipid profiling. Genotypes were divided into three maturity groups and randomized as three adjacent 20 × 5 α-lattice designs with two replications and planted in two-row plots. Leaf samples of ≈ 5 cm were cut from the middle part of the fully developed third leaf of 10 plants per plot, bulked, and immediately frozen using dry ice. The five plots of every incomplete block were sampled within a period of 15 s to minimize within-block error due to metabolic changes over time. The 50 samples from 10 randomly chosen blocks of one field replication of one maturity group were subsequently processed together as one batch. With this blocking structure, we could account for systematic shifts among batches while keeping the field randomization intact. Samples on batches were lyophilized and stored for the subsequent analysis. Samples were processed using ultra-performance liquid chromatography coupled with Fourier transform mass spectrometry  in Germany, as described previously2 = σ2g/(σ2g + σ2e/2) where σ2g is the genotypic variance estimated by REML, by setting factor G as random and σ2e is the residual variance. Following established analysis, the testcross genotype was decomposed as G = GCA + T + SCA, where GCA is the main effect of the line, T is the main effect of the tester genotype, and SCA (specific combining ability) is the line × tester interaction. Including an effect for the environment (E), defined as the year × location combination, the final model for estimating GCA was therefore GCA + T + SCA + GCA·E + T·E + SCA·E + E + E·M + E·M·R + E·M·R·B.Mass intensities were scaled by the median of each sample. Linear mixed model analysis was used to obtain least squares means of lipid levels and GCA values. The model for the lipids was G + M + M·R + M·R·A + M·R·A·B, with the following effects: genotype (G), trial of maturity group (M), field replication (R), batch (A), and block (B). Random effects are underlined. To achieve homoscedasticity of the model residuals for the lipids, the flexible Box-Cox power transformation was applied. For each lipid, the optimum transformation parameter was determined using the maximum likelihood method described previously10Q), as well as pairwise relatedness by using an allele-sharing kinship matrix K as covariance matrix for the random genotype effectsP-values were obtained with the 1 degree of freedom fast association score test–based analysis (FASTA), implemented in the function mmscore of GenABEL. P-values were transformed to q-values and regarded significant if smaller than 0.025 to control for a false discovery rate (FDR) of 2.5%. The proportion of genetic variance explained by a certain SNP was calculated as ρ = R2LR/w2 using the likelihood-ratio (LR) statistic R2LR = 1 – exp(–LR/n) with LR = 2 × log(LSNP/L0), where L0 is the maximum likelihood of the baseline 10 + KQ model without considering the SNP, LSNP is the maximum likelihood of the full 10 + KQ model including the SNP as cofactor, and n is the number of genotypesThe GWA mapping with plant populations is often hampered by population structure and cryptic relatedness, resulting in spurious associationsWe applied ridge regression BLUP RR-BLUP, for preC.R. performed leaf sampling, phenotypic analysis, genome-wide association and whole-lipidome prediction. C.R. and Y.B. carried out the annotation of the candidate genes. M.M. and Y.B. performed lipid profiling and M.M. accomplished the chemical annotation. C.R. and Y.B. wrote the manuscript. L.W. and A.E.M. designed the experiments and supervised the research.Supplementary information


Recent studies suggest that variation in complex disorders  is explained by a large number of genetic variants with small effect size (Odds Ratio∼1.05–1.1). The statistical power to detect these genetic variants in Genome Wide Association (GWA) studies with large numbers of cases and controls  is still low. As it will be difficult to further increase sample size, we decided to explore an alternative method for analyzing GWA data in a study of schizophrenia, dramatically reducing the number of statistical tests. The underlying hypothesis was that at least some of the genetic variants related to a common outcome are collocated in segments of chromosomes at a wider scale than single genes. Our approach was therefore to study the association between relatively large segments of DNA and disease status. An association test was performed for each SNP and the number of nominally significant tests in a segment was counted. We then performed a permutation-based binomial test to determine whether this region contained significantly more nominally significant SNPs than expected under the null hypothesis of no association, taking linkage into account. Genome Wide Association data of three independent schizophrenia case/control cohorts with European ancestry  using segments of DNA with variable length (2 to 32 Mbp) was analyzed. Using this approach we identified a region at chromosome 5q23.3-q31.3 (128–160 Mbp) that was significantly enriched with nominally associated SNPs in three independent case-control samples. We conclude that considering relatively wide segments of chromosomes may reveal reliable relationships between the genome and schizophrenia, suggesting novel methodological possibilities as well as raising theoretical questions. The statistical power to detect genetic effects in Genome Wide Association (GWA) studies of complex disorders is hindered by multiple testing problems and small effect sizes of single SNPs Schizophrenia is a complex psychiatric disorder which is to a large extent influenced by genetic effects In this study, we will study the excess of nominally significant SNPs within large segments (2–32 Mbp) of the genome. This approach uses all genome-wide genotype data, searching for possible clusters in the distribution of disease-related variation over the genome without prior restrictions to gene locations. The rationale of this approach is the premise that genes contributing to schizophrenia may not be randomly distributed across the genome but may be clustered in coordinated expression domains The aim of this paper is to present a novel, data-driven approach for the analysis of GWA data based on the possibility that subsets of weakly associated SNPs are located within relatively broad segments of DNA, spanning both genic and intergenic locations. Tests performed on three independent data sets showed that a relatively broad region of chromosome 5 is significantly associated with schizophrenia. The result provides novel information on the genetic basis of schizophrenia and supports the assumption that at least part of the genetic variation underlying schizophrenia involves genetic information contained within contiguous segments of the genome.p-values, the segments for the 16 and 32 Mbp width survived stringent Bonferroni correction. In the 5q23.3-q31.3 (128–136 Mbp) region, the number of observed nominally significantly SNPs was higher than the expected number in the sample from the Netherlands , the GAIN sample , and the German sample . As described in the methods section, the significance in each cohort was determined based on permutation testing, to account for possible linkage disequilibrium in this region. The results of these tests are summarized in One region on chromosome 5q was found to be associated with schizophrenia in all three samples, for all segments widths above 2 Mbp: chromosome 5q23.3-q31.3 (128–136 Mbp). Different widths showed only small variations in the borders of the region: width 4 Mbp, 132–136 Mbp; width 8 Mbp, 128–136 Mbp; width 16 Mbp, 120–136 Mbp; width 32 Mbp, 128–160 Mbp. When testing the significance of the regions using Fisher’s combined probability test for combining http://genome.ucsc.edu/). Furthermore, we included information on the genes that were previously found to be associated with schizophrenia; this information was obtained from the Schizophrenia Research Forum (www.schizophreniaresearchforum.org). Finally, of all disease-related genes reported in the UCSC Genome Bioinformatics site, we denoted the phenotypes with which these genes have been associated.The region was found to contain various (disease-related) genes, as shown in Follow-up analyses were performed for the chromosome 5 (128–136 Mbp) segment width to test the sensitivity of the results to the parameters of the analysis . In the The segment-wise method decreases the number of statistical tests from around 500,000 SNPs to the order of hundreds or thousands of segments. The fact that broad regions of 4 to 32 Mbp widths (but not of 2 Mbp) were found to contain increased numbers of nominally significant associated single SNPs suggests that the combination of information over larger segments can increase the strength of the segment-wise association signal. Clearly, the current approach is only appropriate for hypotheses or methods aimed at regionally clustered genetic information. Further, the increased power to detect association using large segments of the genome comes at the cost of a reduction in regional specificity. Based on our results, we do not know which specific genetic polymorphisms are involved with schizophrenia. The current method could therefore be seen as a first-sweep method which identifies regions of interest which should then be studied more extensively, but it may also indicate a distribution of schizophrenia-related genetic information that involves clusters over the genome.There are at least three possible explanations for a finding of apparently clustered genetic information, measured via the combined effect of a large number of neighbouring SNPs. First, it is possible that the positive association arises due to multiple schizophrenia associated genes which are located in the same segment. A number of previously reported candidate genes for schizophrenia are indeed located in the chromosome 5 region which was highlighted in this study. Second, a more speculative but intriguing possibility is that associations between disease and the genome may exist on a different level of resolution; i.e., information may be present in the higher-order structure of the genome The fact that we have found regional clustering of association signal at chromosome 5q (128–136 Mbp) suggests that a relatively large proportion of the genes in this segment plays a role in schizophrenia. Whether the genes in this segment are involved in the same biological or functional pathway should be investigated in future studies. A further question for future research is whether our approach could be relevant for other psychiatric or non-psychiatric complex traits. If the clustering of association signal is indeed explained by the higher-order organization of the genome, we might expect similar findings for other complex traits in which many genes within multiple biological pathways are involved. Colocalization of genes might be advantageous for other complex phenotypes for which the coexpression of many different genes has to be regulated. It would therefore be interesting to apply this approach to other complex phenotypes, such as height. In addition, we could investigate the role of the 5q region in, for example, bipolar disorder which is partly influenced by the same genetic risk factors as schizophrenia IL3, IL4, IL9, NEUROG1). In addition, it is striking that many of the remaining genes are reported to be associated with immune diseases, including asthma, Crohn’s disease and type-I diabetes. The prevalence of asthma Genetic variants in the 5q region have previously been found to be associated with schizophrenia  reached genome-wide significance. A meta-analysis of the three GWA studies  identified a cluster of genome-wide significant SNPs in substantial LD in the MHC region on chromosome 6p22.1 Recently, the findings of three multicenter studies were reported In conclusion, we found an association between schizophrenia and a large (8 Mbp) chromosomal region, spanning multiple genes. The segment-wise method proposed here complements a single SNP analysis as it increases the chance of detecting association which is clustered in a region but does not detect association if it is actually limited to one or few single SNPs. Further studies  are needed to gain more knowledge on the mechanisms which explain the clustering of genetic information in large segments of the genome.All three schizophrenia GWA data sets have been described before. Briefly, the first set consists of 728 schizophrenia patients and 653 healthy controls from The Netherlands genotyped with the Illumina HumanHap550 BeadArrays The studies were approved by the standing ethics committee, and all subjects gave written informed consent in accordance with the committee’s guidelines.p-value for the number of nominally significant associated SNPs it contained: this p-value was termed the metasignificance. The method was explored using a range of segment widths: 2, 4, 8, 16, and 32 Mbp. The significance level was corrected for the number of tests in the worst-case scenario, i.e., the number of tests when using segments of 2 Mbp width. Thus the probability of a false positive was at least Bonferroni-controlled at 0.05 for each segment width, and analyses involving different segments widths (and hence a different number of metasignificance tests) did not differ in terms of statistical threshold. The worst-case number of tests was 2729 which required a Bonferroni corrected p-value of 1.8 E-5. A segment was considered significant if the p-value was below the nominal criterion of p = 0.0264 in all three samples, so that the chance of a false positive occurring in all three independent samples was the required 0.0264Λ3 = 1.8 E-5. Note that tests were one-sided: only an increased number of SNPs in a segment would be considered a significant result for a sample, regardless of the p-value. This allows the probability of a compound event of a segment being significant at 0.0264 in all three independent samples to be calculated as the 0.0264Λ3 = 1.8 E-5. Analyses were performed using custom software in Matlab .All chromosomes from one to 22 were divided into segments of varying width (see below), overlapping by 50%. For each SNP in the segment an adjusted Armitage trend test was performed . For each segment, it was tested whether the number of nominally (p<.05) significant SNPs in the segment was higher than expected by chance. Linkage disequilibrium was accounted for by using permutation testing, in which case-control status was randomized using 1000 permutations. In this way, each segment was associated with a We acknowledge that statistical power using this approach will depend on a number of factors, including the power of the individual SNP tests, the number of participants, the number of SNPs measured in each segment, and the correction for multiple testing. In the current study, the choice was made to be reasonably strict in terms of correction; in future work, the statistical power could be increased by considering only broader segments which would decrease the number of tests. Since we aimed to thoroughly explore the sensitivity of our method to the specific segments sizes used, we used a strict correction for the total number of tests at segment size 2 Mbp. We did not correct for the total number of segment sizes tested since the overlap between segment sizes would be considered to be problematic and the tests of different segment sizes in the same region can be expected to be highly correlated. Correction for the total number of segment sizes would therefore result in tests which are overly conservative.p-values were lower such that the tests survived Bonferroni correction for all tested segments summed over all segment widths.Further, the permutation procedure introduces noise in the results, which could present a problem for results that are close to the threshold for sample-wise significance. To address this, we also calculated Fisher’s combined probability test for the combination of p-values over the sample. This method is less sensitive to thresholding and may be more robust, but tests a somewhat different null hypothesis than the compound-significance test of replicability. We note that when testing all segments using Fisher’s combined probability test, highly similar results to the compound-significance test were found. No additional regions were found that could have been undetected due to power problems with the compound event approach. Furthermore, for the 16 and 32 Mbp segment widths, In follow-up analyses focussed on the 32 Mbp segment, potential artefacts due to population stratification were accounted for by adjusting case/control status for the first 10 EIGENSTRAT components http://genome.ucsc.edu/) was used to find genes previously found to be associated with disease in the region. The March 2006 (NCBI36/hg18) was used to identify Phenotype and Disease Associations using the GAD view. Furthermore, we have used the Schizophrenia Research Forum (www.schizophreniaresearchforum.org) The UCSC Genome Bioinformatics site (


Regression analyses revealed an involvement of the serotonergic system in positive emotionality. There was, however, no direct association between positive emotionality and cortisol responses to S-citalopram; rather 5-HTTLPR-genotype showed an association (p < 0.05). That is, positive emotionality scores increased with the number of s-alleles carried by the individuals. Most notable was the moderating role of 5-HTTLPR-genotype (p < 0.05) on the association between acute serotonergic responsivity and positive emotionality. Indeed, this association was only found in ss-homozygotes, in which the acute responsivity of the serotonergic system additionally seems to contribute to the level of positive emotionality . The findings correspond to previous research demonstrating that the 5-HTTLPR is not only involved in the negative-emotional aspects of behavior and temperament, but is associated, moreover, with positive affectivity—supporting the assumption of its valence-neutrality. In addition, our data are in line with the idea of possible influences of the 5-HTTLPR-genotype on early neuronal development. They also indicate the need for further studies in order to clearly elucidate the role of the serotonergic system and its subcomponents in the regulation of positive emotionality.According to the idea that the central serotonergic system has a modulatory function on behavior and personality in general, we aimed to highlight its association to habitual positive emotionality. In a placebo-controlled double-blind and randomized cross-over neuroendocrine challenge design ( The central serotonergic system controls a wide range of processes  within the promoter of the serotonin-transporter (5-HTT) gene (SCL6A4). The deletion/insertion of a repeat-element results in a short  and a long allele   and habitual positive emotionality, expecting a moderating effect of 5-HTTLPR-genotype. This was based on findings regarding G × E interactions concerning psychological outcomes as well as first indications that serotonergic activity is especially influenced by environmental factors in s-allele-carriers  and if they met the following criteria: Caucasian ethnicity, age between 18 and 33, right-handedness, non-smoker for at least 1 year, no current or history of psychiatric or physical disorders , no current use of any medication, no drug or alcohol abuse, coffee-consumption below 8 cups per day and a body mass index (BMI) between 19 and 25.4. The listed criteria were assessed via self-report. Additionally, subjects with a Beck Depression Inventory-score above 17 were excluded  or abnormal measurement results in cortisol samples (N = 3). Therefore, the final data analyses are based on 68 participants (mean age 24.2 ± 2.6 years).Four participants had to be excluded after study-participation because of missing data .Positive Emotionality (PEM) was assessed through the brief form of the Multidimensional Personality Questionnaire  during one testing-session and placebo during the other in a double-blind randomized crossover-design. We chose a dosage of 10 mg, because prior studies showed a lack of side effects as well as an equal proportion of responders and non-responders in the cortisol response—a desirable prerequisite to investigate interindividual differences in the responsivity of the serotonergic system  to ensure a sufficient wash-out period according to the pharmacokinetics of S-citalopram . Before the second sample after 50 min, participants completed one of two questionnaire-sets during each testing-session.Because of the long testing-interval and to prevent side effects, participants were offered 100 ml of water for the substance-intakes and, additionally, 50 ml after cortisol samples 2, 5, and 7 as well as 100 ml water and a soft pretzel after the third sample.Saliva samples were collected with Salivettes® according to the standard-procedure . After every test day saliva samples were centrifuged  and frozen immediately at −30°C for later use. Concentrations of salivary cortisol levels were determined through use of a commercial enzyme immunoassay . All analyses were performed in duplicates using a fully automated analyzer . All samples were analyzed within the same lot to avoid inter-assay-variation due to differences in different charges. The intra-assay-variation (CV) was lower than 5%.ps ≤ 0.008) and a trend for sample 7 (p = 0.066). Therefore, areas under the curves (AUCs) were calculated for samples 3–7, using sample 3 as baseline. Finally, the placebo corrected AUC-R was determined by subtracting the AUC for placebo from the AUC for S-citalopram  for the cortisol response  = 1.68, p = 0.20] nor a significant interaction between substance and time point . Therefore, there were no adverse side effects of S-Citalopram, which was expected, due to the very low dosage of 10 mg —ranging from 1  to 100 —with each saliva sample. The somatic symptom items comprised typical adverse side effects of S-citalopram, i.e., nausea, drowsiness, nasal congestion, dizziness, diarrhea, headache, mouth dryness and blurred vision. For statistical analysis regarding side effects, all somatic symptoms were summed up, separate for each time point (1–7). There was neither a significant effect of substance compared to placebo  or BMI   or BMI .Prior to the main analysis we tested whether 5-HTTLPR-genotype or AUC-R was associated with age or BMI. 5-HTTLPR-genotype-groups did not differ with respect to age . In order to exclude the possibility that differences between the genotype-groups were non-linear , an ANOVA was performed. But likewise, there were no significant differences regarding AUC-R values between 5-HTTLPR-genotype-groups  showed only the 5-HTTLPR-genotype to be a significant predictor of PEM-scores , whereas the AUC-RZ was non-significant and hence excluded . Inclusion of the interaction term lead to a second significant model , rendering the interaction term 5-HTTLPR × AUC-Rz significant  and thereby minimally reducing the weight of the 5-HTTLPR-genotype . The AUC-Rz still remained excluded .A hierarchical regression analysis was conducted to examine whether markers of serotonergic system activity  predict PEM. Since age and BMI were not associated with either predictor and, additionally, were not associated with PEM-scores themselves  = 2.1, p = 0.14], while the linear association can clearly be seen in the data  but no associations for either the sl-heterozygotes  or the ll-homozygotes   = 3.0], indicating only the 5-HTTLPR-genotype as a significant predictor of positive emotionality .Although the effect of the AUC-R was not significant, we repeated our analysis with keeping the AUC-R in the regression model. When entering both the AUC-R and the interaction term simultaneously, neither reached significance [AUC-R: β = 0.03, r = 0.18, p > 0.15). This clearly indicates that the aforementioned interaction term is based on the associations between AUC-R and positive emotionality within ss-homozygotes . We are convinced that this alternative strategy of data analysis turned out to confirm our previous regression analysis and the relevance of the interaction term.To get a clearer picture of the nature of associations depicted in our first regression model, however, we additionally analyzed the correlation between the AUC-R and positive emotionality only within the group of l-allele-carriers . Interestingly, we observed no association between the AUC-R and positive emotionality within this group  lead to an increase in plasma oxytocin levels  and supraoptic nuclei (SON) of the hypothalamus . The exact consequences of the influence of oxytocin are, however, hard to specify (because of a contribution of levels of both neurotransmitters as well as various involved receptors). One could only speculate, whether oxytocinergic influences also contribute to our association of 5-HTTLPR-genotype with positive emotionality, which cannot be ruled out; not least due to additional neurodevelopmental effects of serotonin on the oxytocinergic system . The regression analysis did not show the 5-HTTLPR-genotype, the AUC-R or the interaction term to be associated with negative emotionality . Though somewhat surprising, this result clarifies that the association of the serotonergic predictors of positive emotionality do not simply reflect an association with negative emotionality, but rather a distinct association with positive emotionality. Nevertheless, based on the above mentioned assumptions, one would rather have expected associations to both, positive and negative emotionality. Returning to our foregoing explanation for the results regarding positive emotionality and the 5-HTTLPR-genotype , this also seems to explain this possible lack of association. Following this explanation, we potentially excluded especially 5-HTTLPR s-allele-carriers with a high incidence of negative life events that display higher scores in neuroticism  foster the association between the 5-HTTLPR and positive emotionality and (b) contribute to the acute serotonergic functionality that in turn could also shape personality traits. Reasonable candidates would be life events or other genetic variations.Initial evidence for an influence of living conditions stems from a study by Manuck et al. . They deBeyond this, further research should investigate the stability of the responsivity of the serotonergic system, which seems to be influenced by life events.To our knowledge, this was the first study to specifically investigate the combined effects of both stable genetic variations and the acute responsivity of the serotonergic system on variations in positive emotionality. According to Depue, a diminished serotonergic functionality should ultimately amplify both positive and negative emotionality (Depue and Collins, The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


Drosophila melanogaster demonstrated that wild-type genetic background influences the effects of an allele of scalloped (sd), with respect to both its principal consequence on wing development and its interactions with a mutation in optomotor blind. In this study we address whether the background dependence of mutational interactions is a general property of genetic systems by performing a genome wide dominant modifier screen of the E3sd allele in two wild-type genetic backgrounds using molecularly defined deletions. We demonstrate that ∼74% of all modifiers of the E3sd phenotype are background-dependent due in part to differential sensitivity to genetic perturbation. These background dependent interactions include some with qualitative differences in the phenotypic outcome, as well as instances of sign epistasis. This suggests that genetic interactions are often contingent on genetic background, with flexibility in genetic networks due to segregating variation in populations. Such background dependent effects can substantially alter conclusions about how genes influence biological processes, the potential for genetic screens in alternative wild-type backgrounds identifying new loci that contribute to trait expression, and the inferences of the topology of genetic networks.The phenotypic outcome of a mutation cannot be simply mapped onto the underlying DNA variant. Instead, the phenotype is a function of the allele, the genetic background in which it occurs and the environment where the mutational effects are expressed. While the influence of genetic background on the expressivity of individual mutations is recognized, its consequences on the interactions between genes, or the genetic network they form, is largely unknown. The description of genetic networks is essential for much of biology; yet if, and how, the topologies of such networks are influenced by background is unknown. Furthermore, a comprehensive examination of the background dependent nature of genetic interactions may lead to identification of novel modifiers of biological processes. Previous work in  Examining the consequences of how one mutation behaves when in the presence of a second mutation forms the basis of our understanding of genetic interactions, and is part of the fundamental toolbox of genetic analysis. Yet the logical interpretation of such mutational interactions depends on the generality of such findings. A small number of studies have demonstrated that factors such as the wild-type background in which the mutations are studied can have a profound impact on the observed phenotype of both specific effects of the mutation and the interactions between mutations. However, whether such findings are a common property of genetic interactions was unknown. We tested the generality of the background dependence of interactions between mutations and observed that the vast majority of the interactions were highly dependent on the wild-type background in which they are observed. We demonstrate that the same regions of the genome that contribute to the differences observed in the degree of severity of the mutational effect appear to also be responsible for the background dependence of the interaction. Fundamental to the logic of genetic analysis is that the observed variation in a phenotype for a genetically mediated trait is causally linked to one or more DNA lesions/variants. However, it is well known that the phenotypic effects of many individual mutant alleles are context dependent, with respect to environmental influences, as well as the “wild-type” genetic background in which the mutation is observed. Indeed, genetic background has long been known to influence observed phenotypic expression across traits, organisms, and a range of allelic effects, including hypomorphs, amorphs/nulls and neomorphs scalloped gene (E3sd) in Drosophila melanogaster is profoundly influenced by wild-type genetic background (optomotor blind/bifid (omb/bi), was then examined in a double mutant combination with E3sd. We demonstrated that the phenotypic consequence of the interaction between these mutations was markedly influenced by wild-type genetic background. In one wild-type background the double mutant combination resembled the individual E3sd phenotype, while in the other wild-type background, the omb mutation behaved as a strong synthetic enhancer of sdAlthough the basic influence of genetic background on the expressivity of mutations is well documented, the wider consequences of such influences are poorly understood ckground , with efE3sd using two wild-type genetic backgrounds. Our results suggest that the majority (∼74%) of all modifiers are background-dependent. The background-dependence of the modifier alleles are in part due to the wild-type strains differing in overall sensitivity to mutational perturbations. Using a subset of the deletions spanning the range of phenotypic effects of modifiers, we observed that the interaction effects were consistent using an additional allele, ETX4sd. Furthermore, we show that the deletion effects are a result of the interaction with mutations at the sd locus, and not a simple consequence of haplo-insufficiency in the genomic region of the deletion. We also demonstrate that the background-dependent interactions of modifiers with E3sd are linked to the same genomic regions that contribute to the background-dependent effects of the allele itself. We argue that the phenotypic expressivity of mutations can be considered a quantitative trait, and a more comprehensive, context-dependent view of the effects of mutations needs to emerge.Our findings clearly demonstrate the influence of wild-type genetic background on this genetic interaction, but an important challenge is to determine whether such context dependent effects are widespread. To address this question we performed a genome wide-screen for dominant modifiers of sd and omb, is dependent on genetic background E3sd mutation and deletions generated in otherwise isogenic backgrounds spanning the autosomes of Drosophila.Genetic modifier screens are powerful tools to both identify interacting factors that contribute to signaling networks, as well as to infer their topology. This approach has shaped our understanding of the genetic basis of many traits, across numerous organisms. However little is known about how wild-type genetic background influences genetic interactions. We previously demonstrated that the genetic interaction between mutations in two genes, Dll, wg, vg) previously demonstrated to interact with sd modify the E3sd phenotype. In each of these instances the deletions confirmed previous expectations for the interaction  genetic background , by asking what proportion of all “significant” modifiers also had a “significant” interaction effect between genetic background and the deletion. Based upon these criteria ∼79% of modifiers demonstrated background dependence. While each cross was carried out independently, there were a large number of crosses performed, and each deletion bearing genotype was compared to a common set of controls from within each block of crosses (see methods). Therefore we utilized several methods that adjust for multiple comparisons. While these methods will decrease the number of deletions deemed modifiers using standard comparisons (i.e. α = 0.05), we are primarily interested in the proportion of such modifiers that are due to background dependent effects. Using False Discovery Rate (FDR) we observed a similar frequency (∼78%) as with unadjusted p-values, while with the sequential Bonferroni (Holm) it was ∼68%. Regardless of the exact approach used, it is clear that the vast majority of modifiers recovered are background dependent.We performed this screen using two different sets of deletions, each of which varied in the size of the deletion. We observed little association between deletion size and severity of phenotypic modification  were recovered as genetically interacting with E3sd  showed surprisingly mild enhancement of the phenotype, which may be a result of the particular wild-type backgrounds used in this study. These findings suggest that even for well-characterized interacting genes, the influence of genetic background can be substantial, consistent with the flexible nature of genetic interactions. An important caveat to this interpretation is that many of these deletions may contain more than one gene. This could potentially mean that the interaction is due to both the deletion of the focal gene as well as other loci nearby. Yet, as described above, we observed no evidence for a relationship between deletion size and magnitude of effect, suggesting that this may be a minor contributing factor.These key results suggest that at least in sensitization screens, and possibly for many studies of genetic interaction, wild-type genetic background will have profound influences on the range of phenotypes observed and the modifiers that are identified, with only a subset of modifiers being background-independent. Using Flymine and Droid ith sdE3 . HoweversdE3/Y; Deletion/+ combinations in the Oregon-R wild-type background demonstrated considerably more variation between deletion strains, compared to the same genotypes in Samarkand ) across the two wild-type backgrounds. These data indicate many of the modifiers are acting in the same direction, although vary for magnitude of effect. Interestingly, even the non-genetic component of phenotypic variation observed for Oregon-R E3sd/Y; +/+ in crosses to the wild-type deletion progenitor shows considerably greater phenotypic variation for wing size compared to Samarkand , a few putative modifier regions failed to replicate in the tertiary validation cross with quantitative measures. Similarly a few deletion lines that were expected to not have an effect , did have one with the quantitative measure. However these potential false positives and negatives are few, of similar numbers, and thus are not expected to influence the overall conclusions.per se, and the results were not a specific consequence of the interaction between sd and the deletion. To investigate this we quantitatively examined females who were heterozygous for the E3sd mutation and for the deletions  across each genetic background. These females have qualitatively “wild-type” wings, and previous work did not observe an effect of E3sd on wing size in females as heterozygotes sd mutant phenotype in hemizygous males, but as general modulators of size, then we would expect a strong positive correlation between the effects on size in males and females . The correlation between Samarkand and Oregon-R E3sd/+ ; Deletion/+ females was ∼0.8, suggesting that the effects of the deletions on overall wing size is similar across backgrounds. However the correlations within each background  were 0.22, , and 0.21,  respectively, and neither case was significantly different from 0. The lack of a correlation indicates that the influence of the deletions in E3sd hemizygous males is largely independent of any effects on overall wing size. More importantly the CV for wing size in females (across deletions) for both backgrounds was ∼0.03, which is 5× and 10× less than that observed for E3sd hemizygotes in Samarkand and Oregon-R respectively , where “long” wings were selected in the backcross to the Oregon-R background, and “short” wings in backcrosses to the Samarkand background . Similar logic prevails for the Samarkand and the “long” phenotype. However, even using semi-quantitative measures, it is clear that these are highly correlated; 0.82 (CI 0.66–0.91) and 0.86 (CI 0.73–0.93) respectively. These results are consistent with the loci influencing the background-dependent genetic interactions being the same as those influencing the background-dependent effects on the phenotypic expressivity of the focal sdE3 mutation.Are the loci influencing the background-specific genetic interactions the same as those that modulate phenotypic expressivity for wing size of the focal ckground . Using ∼sdE3 allele, or a more general function of perturbation at the sd locus. To address this, we retested a subset (29) of the deletions spanning the range of phenotypic effects with E3sd, using an additional allele ETX4sd, across each genetic background. The phenotypic consequences of ETX4sd, while background-dependent, are somewhat weaker than sdE3 , and 0.76 (CI 0.55–0.88). In addition the general pattern of greater sensitivity to mutational perturbation by modifiers of the sd phenotype appears to be generally maintained  confirmed that the F02736vg allele behaved as a background-dependent enhancer with strong enhancement in Samarkand, but very weak enhancement in Oregon-R. We followed this up by introgressing this allele into both the Samarkand and Oregon-R background. Again we observed background-specific enhancement of the sd phenotype. Other fine mapping regions suggest several candidate genes, although for at least one region, no obvious candidate gene could be determined  is a direct result of the alleles that differ across backgrounds uncovered by the deletion. While we expect that our results are a combination of both explanations, it is likely that without very high resolution mapping of the genomic regions, or test of specific polymorphisms will we be able to determine the relative contribution of each type of interaction. However the previous work that motivated this current study, namely the background dependent interaction between sd and Omb was clearly due to a third order effect There are outstanding questions that our study is unable to address. The background dependent nature of the genetic interactions could be the result of a “third-order” effect between the sd mutations, showed increased sensitivity to mutational perturbation (both enhancers and suppressors) as well as greater phenotypic variation within strain. Recent work has demonstrated that loci can influence trait variability (“noise”) directly Hsp90 gene of DrosophilaE3sd and ETX4sd) represented more severe perturbations to wing development, so this may not provide an adequate comparison. Regardless, this remains an unanswered question, and a potential link between so-called variance controlling genes and sensitivity to perturbation would have important implications for the genetic architecture of canalization and robustness One curious finding of our study was that the background (Oregon-R) that demonstrated the higher degree of phenotypic expressivity of the focal E3sd and segmental deletions in each wild-type genetic background were correlated when observed across another (weaker) allele, ETX4sd, suggesting that such effects are not due to a particular allele. We also demonstrated that the effects of these interactions are tightly linked to the same genomic regions that contribute to the primary background-dependent phenotypic effects of the mutations. Thus for our system, the genetic variants influencing the phenotypic expressivity of the focal mutation appear to be the same as those modulating both the magnitude, and potentially the sign of genetic interactions between mutations.One constraint of the current study is that we utilized a hypomorph of moderate phenotypic effect, as opposed to a null allele. While a formal definition of functional epistasis sensu  requiresWhile the positive and negative implications for modifier (and other genomic) screens is clear, the potential flexibility of genetic networks given segregating variation in a population needs to also be considered. In particular an allele entering a population  may not have a “fixed” effect on fitness; instead the genetically contingent effects of the allele result in a distribution of phenotypic effects, including a possible change in sign .http://dx.doi.org/10.5061/dryad.4dt7cData and scripts associated with this manuscript are located on DRYAD: E3sd mutant allele , used in this study is caused by a P{w[E] ry[1t7.2] = wE} transposon located in the third intron of the sd gene white (w), by repeated backcrosses involving homozygous mutant female and the wild type male for over 20 generations ETX4sd and F02736vg alleles were also obtained from the Bloomington stock center, and were introgressed for 20 generations into each wild-type strain.The Oregon-R strain was originally obtained from the Bloomington stock center, while Samarkand was obtained from the lab of Dr. Trudy Mackay. For both strains, we further inbred them to near isogenicity, and tested via a panel of 30 polymorphic markers to confirm there was no contamination or residual heterozygosity. A combination of sequencing and PCR-based genotyping suggests that these two strains have an approximately 2% divergence from one another, and that all sequenced regions examined to date are a subset of variation from natural populations. The X-linked l(2)gl could potentially confound the results of our screen l(2)gl. Thus it is unlikely that this is a confounding factor in our analysis.We utilized the DrosDel sd mutant hemizygotes who were heterozygous for the deletions. Deletion lines (see above), and their isogenic wild-type progenitor strains were crossed to homozygous E3sd mutant females  for enhancement or suppression of the E3sd phenotype , and for each block a simultaneous set of control crosses with the progenitor wild-type strains for DrosDel and Exelixis flies was also performed. Nevertheless, there was negligible variation in the wing phenotypes of the flies resulting from the control crosses across all the blocks (not shown). However, for appropriate inferences, phenotypic analysis for all crosses within a block were made with respect to specific sets of control crosses from within that same block. We screened between (5–20) flies for each cross (crosses with fewer than 5 progeny were re-tested), with a mean/median of 8.2/7 flies per cross. Any deletion that showed evidence for modification (see below) of E3sd was re-tested (new crosses) to verify the phenotypic effects. Crosses performed with DrosDel deletions on chromosome arm 3L showed a marked increase in the number of modifiers relative to other arms (22/59 compared to 37/228 for the rest of the chromosome arms for the DrosDel collection). Thus putative modifiers on 3L were re-tested 3 times each, with consistent results, suggesting that these modifiers are unlikely due to a sampling artefact. In total 723 deletions were tested, with 18,167 flies scored.To assess the influence of wild-type background on genetic interactions we used a dominant modifier screen, and examined  females . Flies whenotype . Thus weE3sd individuals were generally category D while Oregon-R E3sd individuals were category H, with relatively minor variation in these scores. The rationale for such a semi-quantitative approach was two-fold. First, we wished to mirror the genetic screen approaches used in many functional genetic studies , and second this allowed us to screen a much larger panel of lines. As discussed below, these semi-quantitative measures correlated well with quantitative measures of wing size.For initial assessment of phenotypic modification we performed a semi-quantitative analysis similar to that used by other investigators E3sd, it was done so that the observed effects were of an almost qualitative nature . As discussed above, all putative modifiers were verified at least once with an independent replication cross.To mimic a traditional genetic screen we assessed interactions based largely on non-overlap distributions of phenotypes, comparing genotypes bearing deletions to their co-isogenic wild-types. While this likely underestimates the number of true interactions of the deletions with lm function and p.adjust in R (V 2.12).In addition, we also utilized a more quantitative approach, fitting the data to the following linear model:E3w sd/Y; Deletion/+) was dissected and mounted in glycerol, for both backgrounds. For the isogenic wild-type control strain, 30 individuals were used from each background-specific set of crosses to better ascertain the degree of variability. Images of the wings were captured using an Olympus DP30BW camera mounted on an Olympus BW51 microscope. Six landmarks (lm function in R (V 2.12) and 95% confidence intervals were constructed using confint. Significance was determined by non-overlapping confidence intervals with controls.To validate the primary findings of this study, we repeated crosses, and quantified wing size for a subset of 44 deletions, spanning the direction and magnitude of effects  observed in the genome-wide screen. A single wing from each of 5 male flies . This suggests high repeatability of the initial screen, as well as the semi-quantitative measure of wing size.sd mutants), we quantified wing size in females heterozygous for the focal E3sd mutation with each deletion  digitizing the same 6 landmarks on the wing.To ascertain whether there was a commensurate effect of the genomic deletions in “wild-type” wings  that influence the genetic interaction between the deletions and ckground . A backcckground . Upon geTo narrow down several genomic regions to a set of a few candidate genes we utilized an additional set of overlapping deletions in DrosDel, Exelixis and BSC strains followed by use of P-element insertional mutations co-isogenic with the Exelixis panel of lines. We utilized this approach for four genomic regions  detailed in Figure S1E3sd modifier screen. A) The Semi-Quantitative Scoring Scheme used for the primary screen for the modifiers of E3sd. The semi-quantitative scoring scheme used for this study was similar to other ones previously used (see methods), allowing for rapid phenotyping of the wings. A comparison of quantitative and semi-quantitative methods with a test data set were highly correlated (not shown). B) Reaction norms from deletions uncovering known interacting genes with sd.Scoring scheme and positive controls for (TIF)Click here for additional data file.Figure S2Landmarks used to quantify wing size. To quantify wing size in this study we utilized the centroid size calculated from 6 landmarks. These landmarks could be unambiguously found in all specimens that we examined in this study. It is worth noting that for mutations (not used in this study) that influence wing development more severely, these 6 landmarks could not be scored (not shown).(TIF)Click here for additional data file.Figure S3E3sd to introgress “long” and “short” alleles. The alleles that contribute to the background dependence of the genetic interactions between E3sd and the autosomal deletions could potentially be the same as those that contribute to the variation in expressivity in the sd phenotype. If this hypothesis is false, then we would predict no association between the genomic regions that contribute to variation for sd expressivity and the nature of genetic interactions across backgrounds. To test this, we utilized a backcross-selection procedure to move the genomic regions conferring “long” wings into an otherwise “short” Oregon-R background. Individuals from the Samarkand and Oregon-R background bearing the E3sd allele were crossed together, and F1 flies were mated interse to produce an F2 population segregating alleles influencing the expressivity of the sd wing phenotypes. Flies with the largest wings (most Samarkand sdE3 like) were then crossed to Oregon-R E3sd individuals, as well as the reciprocal for the shortest wings (crossed to Oregon-R). This two generation procedure was repeated for 12 cycles for the flies being selected for “short” wings, and 19 cycles for those for the “long” wings. This approach allows for the introgression of the alleles influencing sd expressivity from one background to the other. A panel of 30 SNP markers known to be polymorphic between Oregon-R and Samarkand were then used to verify the extent of the introgressions.Backcross-selection procedure across wild-type backgrounds with (TIF)Click here for additional data file.Figure S4Distribution of modifiers on remaining chromosome arms. Figure legend and description as for (PDF)Click here for additional data file.Figure S5E3sd. To determine whether the deletions generally uncovered a single or multiple modifier alleles of E3sd, we examined the relationship between the magnitude of the effect of the deletion on the wing phenotype, and the size of the deletions (in kbp). As seen in these figures, there is no association between them, suggesting that across the set of screened lines, each deletion is likely only uncovering a single modifier allele. However particular individual deletions may have more than one modifier, and modifiers that act in opposite directions.No association between size of the genomic deletion and magnitude of effect as a modifier of (TIF)Click here for additional data file.Figure S6E3sd. To determine the extent of the phenotypic effects of the genomic deletions on wild-type wing sizes, we examined the effects of 44 of the deletions . Importantly, the coefficient of variation across strains was ∼10× smaller for wing size for wild-type wings, than for the wings of E3sd hemizygous males.The effects on wing size of 44 deletions in females heterozygous for (TIF)Click here for additional data file.Figure S7ETX4sd allele. To determine whether the findings observed for the background dependence of the genetic interactions of the E3sd allele with the deletions would hold across other alleles, we introgressed an additional allele, ETX4sd, into both Samarkand and Oregon-R, and re-examined a subset of the deletions. A) ETX4sd also shows profound background dependence with respect to the expressivity of the sd phenotype. As described in the text, the results were significantly correlated across alleles. Interestingly the background dependent expressivity of ETX4sd is substantially weakened in crosses with the Exelixis Deletion progenitor strain. However, the background dependence of the genetic interactions appears to be at least as extreme as that observed for E3sd (B).The background dependent effects on the (TIF)Click here for additional data file.Table S1Candidate modifier genes (.xls) from the deletion mapping.(CSV)Click here for additional data file.Table S2Results from fine deletion mapping.(DOCX)Click here for additional data file.


Vigna unguiculata) consistent across experiments conducted in Burkina Faso, Nigeria, Senegal, and the United States of America under limited water conditions. A panel of 383 diverse cowpea accessions and a recombinant inbred line population (RIL) were SNP genotyped using an Illumina 1536 GoldenGate assay. Phenotypic data from thirteen experiments conducted across the four countries were used to identify SNP-trait associations based on linkage disequilibrium association mapping, with bi-parental QTL mapping as a complementary strategy. We identified seven loci, five of which exhibited evidence suggesting pleiotropic effects (stay-green) between delayed senescence, biomass, and grain yield. Further, we provide evidence suggesting the existence of positive pleiotropy in cowpea based on positively correlated mean phenotypic values (0.34< r <0.87) and allele effects (0.07< r <0.86) for delayed senescence and grain yield across three African environments. Three of the five putative stay-green QTLs, Dro-1, 3, and 7 were identified in both RILs and diverse germplasm with resolutions of 3.2 cM or less for each of the three loci, suggesting that these may be valuable targets for marker-assisted breeding in cowpea. Also, the co-location of early vegetative delayed senescence with biomass and grain yield QTLs suggests the possibility of using delayed senescence at the seedling stage as a rapid screening tool for post-flowering drought tolerance in cowpea breeding. BLAST analysis using EST sequences harboring SNPs with the highest associations provided a genomic context for loci identified in this study in closely related common bean (Phaseolus vulgaris) and soybean (Glycine max) reference genomes.The stay-green phenomenon is a key plant trait with wide usage in managing crop production under limited water conditions. This trait enhances delayed senescence, biomass, and grain yield under drought stress. In this study we sought to identify QTLs in cowpea ( Delayed senescence, grain yield, and biomass yield under drought stress rank among the most important traits targeted for improvement in crop plants The recent advent of cost-effective high-throughput genotyping technologies which enable construction of dense genetic maps and rapid genotyping of large germplasm collections has facilitated genome-level understanding of these traits and is helping to revolutionize marker-assisted breeding The stay-green trait, a phenomenon where QTLs for delayed senescence, biomass, and grain yield are co-located has been described in several crop species and is considered to be an important trait for improvement of yield under drought stress for cereals such as sorghum and maize Dro-1– Dro-10, were identified on a genetic linkage map using both greenhouse and field-based phenotyping In cowpea, an important legume crop for subsistence farmers of semi-arid regions of sub-Saharan Africa, South America, and Asia, improvement of grain yield under drought stress is a key objective in breeding programs. Gwathmey et al. Glycine max) and barrel clover (Medicago truncatula) Despite the critical role of cowpea in sustaining the food systems of many parts of the developing world, until recently very limited genomic resources have been developed. However, a high-throughput SNP genotyping platform has been implemented for cowpea, enabling the construction of a high density consensus genetic linkage map In this study, a 1536-multiplex SNP genotyping platform was applied to cowpea germplasm originating from diverse geographical locations and breeding programs together with the consensus genetic map developed from six constituent RIL populations to (i) determine the extent of linkage disequilibrium in cowpea and (ii) detect QTL related to early and late-season delayed senescence, grain and biomass yield. Further, we compared map locations for QTL identified previously for seedling drought tolerance Based on the Illumina GoldenGate assay 1,080 SNP markers had robust performance across the entire panel of 383 genotypes. Of these 1,054 SNPs had call rates ≥99% and only 14 SNPs had missing data between 5 and 10%. From the collection of diverse genotypes evaluated for these SNPs, 98.5% had call rates ≥99%. The remaining genotypes had call rates between 91.4 and 96% for the 1,080 SNPs. Genotyping results for the RIL population were reported previously 2 = 0.25. This represented 0.26% of all possible correlations. Of these, only 30 exceeded r2 = 0.4 representing 0.01% of possible correlation and only two correlations were above r2 = 0.5 (r2 = 0.5001 and 0.561963). Therefore, 99.7% of r2 values for unlinked markers fell below the set threshold. These statistics excluded SNP marker 1_0069 that mapped at the 39.2 cM-locus on linkage group (VuLG11) of the consensus map. This marker had high r2 values with markers on VuLG3 (up to 0.70) but had negligible correlation with neighboring markers, which suggested a possible map error given that there was also >3 cM distance between the marker and adjacent markers on the map. Therefore, marker 1_0069 was excluded from all subsequent analyses.For inter-chromosomal LD, 722 of the 281,731 pairwise correlations between markers exceeded the observed threshold of r2 values for LGs 1, 5, 6, 8, 9, 10, and 11 decayed completely within 10 cM. LD for VuLG4 also decayed within 10 cM with the exception of 8 correlations that were above the 0.25 threshold up to distances of 50 cM. For VuLG7, LD decayed within 7 cM with the exception of 5 correlations that exceeded the threshold up to a distance of 17 cM. VuLGs 2 and 3 exhibited the highest level of LD persistence with 19 and 24 pairwise r2 values, respectively, exceeding the 0.25 threshold at distances greater than 10 cM. In total, 74 pairwise correlations had r2 values greater than the 0.25 threshold over 10 cM, representing 0.46% of all cases for all 11 LGs. The r2 values plotted against map distance for each linkage group are illustrated in With a few exceptions, intra-chromosomal r2 threshold intercept, LD decayed between 0 and 2 cM for VuLGs 1, 2, 3, 4, 6, 8, and 10. For VuLGs 5, 7, 9, and 11 there was no intercept between the LOESS curve and the 0.25 threshold, suggesting that LD generally decayed within sub-cM distances for these four LGs , had the strongest expression across experiments. QTL Dro-3 was identified in all experiments conducted in Burkina Faso, Senegal, and USA with significance levels reaching 0.001, 0.0001, and 0.001, respectively. This was followed by QTL Dro-7 on LG9 (corresponding to consensus VuLG1), which was identified in all locations with at least three experiments achieving the 0.01 significance level or higher. In general, LOD profiles for grain yield and delayed senescence at the seedling or early vegetative stage demonstrated similarity across entire linkage groups. Examples of LOD trace profiles for delayed senescence measured under greenhouse conditions and grain yield measured under field conditions are shown in Dro-3) and Dro-1). In each case the allele for delayed senescence was associated with increased grain yield and (or) biomass yield.In the RIL population (IT93K-503-1 × CB46), 6 QTL for grain yield were identified across locations, reaching the more stringent 0.005 Kruskal-Wallis significance level in at least one experiment . Althouglaration . QTL DroG) calculated for the MLM analysis using the SimpleM and FDR methods gave similar results. With a Meff of 203 determined by the SimpleM method, an adjusted significance threshold was calculated as: αG = 0.05/203 = 2.46×10−4. The highest FDR-based significance threshold was calculated as αG0.2 (1/856) = 2.34×10−4. Seven loci, comprising 16 SNPs, exceeded the significance thresholds in at least one experiment  was suggested by significant correlations among mean phenotypic values for delayed senescence, grain yield, grain yield components, and biomass yield in experiments conducted in Burkina Faso, Nigeria, and Senegal . However mapping  and 3 anen trait . Additioen trait  and 2. TDro-1 identified in both bi-parental and association mapping. In this case allele effects were determined based on the MLM analysis using 187 diverse genotypes in the Burkina Faso (BF_2008) experiment. The same trend was observed for data collected in Nigeria and Senegal where the three traits were evaluated in the same experiment. Allele effects for the 794 mapped SNPs were positively correlated in each of the three environments: Nigeria , Burkina Faso , and Senegal (0.1880≥ r ≥0.0748 (p≤0.0352).Significant positive correlations of allele effects obtained from the MLM analysis for each of the 794 mapped and 62 unmapped SNPs suggested that the predominant pleiotropic effect was positive in which delayed senescence conferred higher biomass and grain yield, although a single instance of significant negative correlation was observed between delayed senescence and biomass in Burkina Faso . Fig. 3 Dro-1 and Dro-3 had strongest support based on multiple cowpea ESTs mapping in the same contiguous regions in both common bean and soybean. QTL Dro-3 exhibited the most collinearity with all 3 ESTs mapping in the same order on common bean chromosome 10 and soybean chromosome 1  soybean genome assemblies . Based omosome 1  encompasThe general decay of LD within distances less than or equal to 2 cM in cowpea suggested that genome-wide association mapping is practical in cowpea with the potential to provide QTL resolution up to 10× greater than bi-parental QTL mapping. Given the total length of the cowpea genetic map distance (680 cM) and the conservative 1 cM distance for complete LD decay, the cowpea consensus map should provide sufficient coverage for QTL detection. Therefore, the cowpea consensus map and its recently improved version Using the genome-wide scan with 856 SNP markers, we were able to identify seven loci with significant marker-trait associations. Of these, five were associated with more than one trait and three of them mapped in QTL intervals that were associated with delayed senescence at the seedling stage as well grain and biomass yield in the RIL population. The co-location of QTLs for at least two of the three traits is suggestive of common genetic determinants as well as the existence of the pleiotropic stay-green phenomenon in cowpea. This was further supported by positively correlated allele effects, and largely correlated mean phenotypic values for delayed senescence, grain yield, grain yield components and biomass yield in experiments conducted in Burkina Faso, Nigeria, and Senegal. However, there was also evidence suggesting genotype × environment interaction for this trait with the Senegal location exhibiting the lowest levels of correlation between delayed senescence, biomass, and grain yield. These findings suggest the importance of the stay-green trait in cowpea as an adaptive trait in drought-prone environments. The stay-green trait must have been favored in the arid and semi-arid environments of sub-Saharan Africa where cowpea was domesticated and has been cultivated for thousands of years. The coupling of mechanisms conferring drought tolerance, grain yield, and biomass yield would confer fitness while minimizing energy usage by utilizing the same genetic pathways for survival and productivity.Dro-1, Dro-3, and Dro-7 are potential targets for selection given their robust detection in more than one agro-ecological zone within both the RIL population and the panel of diverse germplasm. In addition to biomass and grain yield phenotypes, all three QTLs were previously reported to mediate delayed senescence at the early vegetative stage in both field and greenhouse experiments Dro-1 QTL had the largest percent phenotypic variance explained among 10 putative QTLs. The same interval was associated with early vegetative delayed senescence under greenhouse and field conditions in the USA as well as post-flowering delayed senescence under field conditions in Burkina Faso. Although overall biomass yield was negatively correlated with the post-flowering delayed senescence in Burkina Faso_2008, allele effects for SNP markers within the Dro-1 interval were positively correlated for delayed senescence, biomass, and grain yield. This observation suggests that Dro-1 QTL still exhibited positive pleiotropy and is consistent with the putative stay-green effect, regardless of trends in the overall phenotypes. Building on the present findings, genotypes with the most comprehensive constitution of favorable QTLs can be identified, including those with complementary sets of QTLs, and can be used in ideotype-driven selection schemes for improvement of grain yield, biomass yield, and drought tolerance in cowpea. High-throughput SNP genotyping in marker-assisted recurrent selection or whole genome selection breeding approaches can facilitate the required multiple QTL selection Dro-1 and Dro-3 QTLs should facilitate translational genomics among these legumes for targeted candidate gene studies.Further, the fact that these QTLs could be identified across different agro-ecological environments in both West Africa and North America indicates that these loci represent viable targets for the genetic improvement of cowpea production in arid and semi-arid regions. The apparent positive pleiotropy observed here suggests that genomic selection-based approaches to introgress more than one locus at a time While recognizing that the ‘stay-green’ trait does not encompass all potential drought tolerance mechanisms contributing to drought adaptation in cowpea, this study demonstrated a robust association of expression of the stay-green trait with grain yield and biomass yield under drought stress. These findings indicate that assessments of delayed senescence could be useful for indirect selection of grain yield and biomass yield under drought, especially for cost-effective preliminary assessments of very large samples of germplasm or early generation screening of breeding lines in breeding programs targeting improved drought tolerance. Such a cost-saving approach would be important for breeding programs where the time and resource requirements for grain yield phenotyping in drought environments are currently high relative to seedling or early vegetative stage high-throughput phenotyping assays. These findings in cowpea are distinct from crops such as sorghum, maize, and rice where phenotyping for this trait has only been done at the post-flowering stage under field conditions From practical and basic genetics considerations, cowpea offers an important opportunity for the dissection of the stay-green trait in legume crops and perhaps more broadly as a model for indeterminate, non-cereal crops. With its relatively simple, small, diploid genome and short generation time, rapid advances can be made in understanding this trait including synteny and translational genomics-based analysis relevant to other important crops. Notably, the close evolutionary relationship of cowpea to soybean and common bean and the demonstrated high level of synteny between their genomes Our results also showed that a RIL population as small as 48 lines together with a diverse germplasm set of 96 genotypes provided enough resolution to detect the co-location of delayed senescence, grain yield and (or) biomass yield QTLs. This finding corroborates previous results where we demonstrated that subsets of 57 and 70 RILs provided nearly the same mapping resolution for the delayed senescence trait as populations of 124 and 127 RILs In conclusion, isolating and characterizing the genetic determinants of the stay-green trait hold tremendous opportunity for the improvement of crop productivity in light of contemporary challenges that include global warming, rapidly increasing population, the need to reduce inorganic nitrogen applications in farming systems, and increasingly constrained agricultural water resources, as well as the need to develop sustainable renewable energy sources 2∶9 RIL population with 113 lines developed from the cross IT93K-503-1 (drought tolerant) × CB46 (drought susceptible) and the parental genotypes were used.A selection of 383 inbred cowpea genotypes  represenEach of the 383 genotypes was SNP-genotyped using the Illumina GoldenGate 1536-SNP assay and data for allele calls at each SNP processed as described by Muchero et al. 2 values for all pairwise correlations between markers were plotted against map distance for each linkage group using Excel. The critical r2 value was determined by taking the square root of r2 values for all unlinked markers and taking the parametric 95th percentile of the distribution 2 scatter plots of each linkage group with the smoothing parameter set between 0.1 and 0.2 using Statistix 9.0 software 2 threshold was considered to be the extent of LD on the respective linkage group.LD decay along linkage groups was analyzed using the Graphical Genotyping (GGT2.0) software http://www.maizegenetics.net) based on 856 markers used in the association study.Population structure was evaluated using the software STRUCTURE 2.1 The delayed drought-induced senescence trait (early vegetative delayed senescence) was evaluated on 205 genotypes in two greenhouse (Senescence_Greenhouse 1 and Senescence_Greenhouse 2) and one field (Senescence_USA 2009) experiments in the USA. Four replicates with one plant of each genotype were evaluated in greenhouse experiments. Four replicates of 1-meter plots with ten plants each were used to evaluate genotypes under field conditions. In greenhouse experiments, ratings were taken for visual-based senescence and maintenance of stem greenness on 2–6 week-old cowpea plants as described by Muchero et al. Experiments in all locations were conducted under rainless or limited rain conditions to induce drought stress. Climatic conditions related to maximum and minimum temperatures, evapo-transpiration, and total rainfall during the course of each experiment are summarized in Briefly, 96 cowpea genotypes and 57 RILs from the IT93K503-1 × CB46 population were evaluated for grain yield at the University of California-Riverside CVARS station under three treatments: fully irrigated (USA 2007A); irrigation withheld for 15 days from the day of planting (USA 2007B); and irrigation withheld for 30 days from the day of planting (USA 2007C). Normal irrigation cycle was resumed after stress treatment. In 2008, 201 diverse genotypes and 84 RILs were evaluated for grain yield at CVARS (USA 2008).In 2007, 339 cowpea genotypes were planted at the International Institute of Tropical Agriculture (IITA) Kano station in Nigeria (Nigeria 2007), and evaluated for biomass, grain yield, 100-seed weight, and post-flowering delayed senescence. Three replicates of 3 m×75 cm plots were planted with 15 seeds of each genotype. Pre-planting irrigation was applied to soil saturation and two additional irrigation cycles were applied at 2 and 4 weeks after planting after which no additional irrigation was applied. A total of 4 mm rainfall was recorded over the course of the experiment . Ten plaIn Burkina Faso, 201 genotypes and 49 RILs were planted at the Institute of Environmental and Agricultural Research (INERA) Kamboinse experiment station (BF 2008) and were evaluated for biomass, grain yield, and delayed senescence. The same set of material excluding the RIL population was evaluated in Burkina Faso in 3 additional experiments, BF 2009A and BF 2009B at the Pobe-Mengao experiment station, and BF_2009C at the Kamboinse experiment station. In each experiment, 2–2 m-plots replicated three times were planted with a total of 40 seeds for each genotype. Experiment BF_2008 in Kamboinse was conducted entirely under natural rainfall conditions with 123 mm and 28 mm being received for last two months of the experiment, September and October, respectively. Similarly, experiment BF_2009C was conducted under natural rainfall conditions. Planting followed a 260 mm rainfall event in late August with 98 mm being received in the last six weeks of the experiment For experiments BF_2009A and BF_2009B at Pobe-Mengao, planting was carried out after a 58-mm rainfall at the end of July for BF_2009A, and a 43-mm rainfall event for BF_2009B. A total of 324 mm of rainfall was received within 4 weeks from the beginning of August until the end of September, and 20 mm was received during the whole month of October. No additional rainfall or supplemental irrigation was received or applied for the remainder of the experiment. Delayed senescence was scored as described above and ten plants per replicate were harvested for biomass, grain yield, and grain yield components evaluation .163 genotypes and 51 RILs were evaluated at the Senegalese Institute of Agricultural Research (ISRA-CNRA) Bambey station in Senegal . Three replicates, each with 2–4 m long rows were planted with a total of 68 seeds. Plots were planted after a 22.7-mm rainfall event that was followed by a total of 102.3 mm rainfall during the course of the experiment . No suppThe Kruskal-Wallis and Multiple-Model QTL Mapping (MQM) packages of MapQTL 4.0 Dro-1 to Dro-10 on current linkage groups and SNPs flanking these intervals were used to determine positions on the consensus map. LOD score profiles across linkage groups were drawn using MapChart 2.2 QTLs detected for the early vegetative delayed senescence trait in the RIL population had been described by Muchero et al. http://www.maizegenetics.net) was used to identify marker-trait association using a total of 856 SNPs with MAF >0.1. Of the 856, 62 SNPs were not part the consensus map and therefore their genomic position was not known. The mixed-linear model analysis (MLM) TASSEL 2.1 software  unmapped markers had high pair-wise correlations with mapped loci harboring a detected QTL. For non-adjacent loci as well as loci on different linkage groups, the locus showing the highest significance was accepted as the QTL peak.To avoid spurious association due to LD persistence, LD was determined for all loci with significant association. Two or more QTLs were considered the same when (i) the markers next to the adjacent QTLs had rPleiotropic effects between delayed senescence, grain yield, and biomass yield were accepted only when (1) QTLs coincided on the same map position, (2) statistically significant Spearman rank correlation of p-values, and (3) statistically significant Spearman rank correlation of allele effect of each marker. For unmapped markers, co-location was accepted when the same marker gave significant associations with at least two of the three traits. Further, remapping the QTLs for delayed senescence at the seedling stage was used to assess the incidence of markers within these QTL regions that also showed statistically significant association with grain yield and biomass in the association mapping study. Co-location was visualized by plotting –log  against map distance along individual linkage groups of the cowpea consensus map for each of the three traits. To rule out spurious correlations, correlation analysis was carried out using MLM-derived p-values of association for the three traits and flowering from the Nigeria 2007 dataset.M method M method accounts for the lack of independence in multiple tests resulting from extensive LD between SNP markers tested for association. The program calculates an effective number of independent tests (Meff) which is then used as the number of independent tests in the Bonferroni correction for multiple testing eff with PCA threshold of 0.95 and the SimpleM source code in R statistical program (R Development Core Team (2010). The formula αG = 0.05/Meff was used to calculate the p-value significance for association.The SimpleFor the FDR method, the p-values from the MLM analysis were subjected to the FDR method based on a false discovery rate, q* = 0.20 as described by Benjamini and Yekutieli Analysis of Variance (ANOVA) and Spearman rank correlations were calculated using Statistix 9 software http://www.phytozome.net). Cowpea ESTs (121 nucleotides long) with 60 bases flanking the SNP position were used in the analysis. Start positions of the most significant BLAST matches were reported.Cowpea ESTs harboring SNPs with significant associations to phenotypes were used in BLAST analysis to infer positions on the common bean and soybean reference genomes Click here for additional data file.Figure S2Plot of mean values of ln likelihood over k-estimate of the number of subpopulations (K) inference for 383 diverse cowpea genotypes.(TIF)Click here for additional data file.Table S1Traits analyzed in Burkina Faso (BF), Senegal, Nigeria, and the United States of America (USA) and results of ANOVA analysis.(XLSX)Click here for additional data file.Table S2Suggestive marker-trait associations based on MLM analysis.(XLSX)Click here for additional data file.Table S3List of cowpea genotypes evaluated for delayed senescence, grain yield, and biomass accumulation.(XLSX)Click here for additional data file.Table S4Details of experiments conducted to evaluated cowpea response to drought stress.(DOCX)Click here for additional data file.Table S5Summary of climatic conditions for field experiments conducted Burkina Faso (BF), Nigeria, Senegal, and the United States of America (USA).(DOCX)Click here for additional data file.


A better alternative could be to form clusters of markers with similar effects where markers in a cluster have a common variance. Therefore, the influence of each marker group of size th QTL-MAS workshop were analyzed such that SNP markers were ranked based on their effects and markers with similar estimated effects were grouped together. In step 1, all markers with minor allele frequency more than 0.01 were included in a SNP-BLUP prediction model. In step 2, markers were ranked based on their estimated variance on the trait in step 1 and each 150 markers were assigned to one group with a common variance. In further analyses, subsets of 1500 and 450 markers with largest effects in step 2 were kept in the prediction model.The simulated data from the 15Grouping markers outperformed SNP-BLUP model in terms of accuracy of predicted breeding values. However, the accuracies of predicted breeding values were lower than Bayesian methods with marker specific variances.Grouping markers is less flexible than allowing each marker to have a specific marker variance but, by grouping, the power to estimate marker variances increases. A prior knowledge of the genetic architecture of the trait is necessary for clustering markers and appropriate prior parameterization. The statistical methods for genomic selection introduced by Meuwissen et al ; i.e. SNp on the posterior distribution of the marker variances will be p df. Further, they suggested assigning noninformative priors to scale and df parameters of the distribution of marker variance or marker-group variance. In the present study, a strategy for grouping markers with similar effects together was applied to the simulated data from the 15th QTL-MAS workshop [In BayesA and BayesB the prior distribution of each marker effect is assumed normal with a marker specific variance a priori . Therefoa priori . In thisa priori . As a rea priori  suggesteworkshop . The anaFirst, an animal model BLUP using pedigree and phenotypes was performed to predict breeding values of all animals, and REML estimate of heritability was obtained . FurtherThe SNP-BLUP additive genomic model was used to estimate SNP effects in the first step as:μ is the general mean,j  βis the allele substitution effect of the jth SNP with ij xis the genotype covariate of the jth marker for the ith animal, associating marker effects j βto the phenotype iy, and ei is residual or environmental effect with where j pis the frequency of one of the alleles of the jth SNP. Then, all markers were sorted based on their explained variance and each 150 marker were grouped together. We tried a grid of different SNP-group sizes and among them a SNP-group size of 150 yielded the highest accuracy of PBV in the validation dataset (explained in results and discussion).The model for the second step (ALL-SNP) with grouped SNP and group specific SNP variance was:The estimated marker effect jk βdenotes the effect of the jth marker that belong to group k, and g is the total number of groups. A model was used with a variance parameter per group, and the group variances are jointly modeled to have an inverse chi-square distribution in which the scale is treated as a model parameter. The prior specification was as follows:where μ~uniform; scale~uniform(>0); and df: a fixed number as hyperparameter.The fully conditional posterior distributions were as follows:k nis the group size for SNP-group k. In this study k nwas equal to p for all SNP-groups;In further analyses, respectively, 1500 (SNP1500) and 450  markers with the largest effects from model (2) were selected and were allocated to groups of size 150 (for 1500 markers), and 75 or 50 (for 450 markers). Then, breeding values of animals without records were predicted using the marker effects from these subsets of markers.Gibbs sampling was used to sample from joint posterior distributions for all datasets. The chain length was 50.000 in all analyses where the first 20.000 samples were discarded as burn in and one of each 30 samples were saved to compute the posterior means for the parameters. Preliminary experience showed that a burn in of size 20.000 guarantees the convergence for different parameters.The challenge was to predict the breeding values of 1000 genotyped animals with no phenotypes. The available data comprised of 2000 animals with both genotype and phenotype. We validated the models using 200 animals such that the last progeny with genotype and phenotype record from each dam was taken out of the data and used for validation. The remaining 1800 animals were used to train the model. Based on this validation the size of the SNP-groups was chosen to be 150 and the scale and df of the prior distribution of marker variances were set to zero because this resulted in the highest accuracy for the validation animals. A scale and df setting of zero corresponds to the so-called Jeffreys or non-informative prior for variances. After the true breeding values of the other 1000 animals were provided, it turned out that other prior specification for the marker variances can give better predictive abilities. Perhaps, the reason was that the 200 validation animals were not enough to represent the whole population. Further, there was an imprinted QTL where the effect is expressed if it has been transmitted from one of the parents only. It is likely that among these 200 animals most of them or all of them have got the paternal  imprinted QTL. Further details of the impact of prior specification of the marker variances on estimation of SNP effects and breeding values will be discussed.The accuracies of predicted breeding values (PBV) from the two-step method were higher than PBV from animal model BLUP and SNP-BLUP Table . Among tTable Overestimation of the heritability in the two-step method was mainly due to the prior setting for the SNP-group variances . In order to investigate the effect of prior df, two other analyses with either 50 or 150 degrees of freedom were run with all markers (extensions of ALL-SNP), where, the scale parameter was updated using equation (3). Figure Grouping markers is less flexible than allowing each marker to have a specific marker variance but, by grouping, the power to estimate marker variances increases. A prior knowledge of the genetic architecture of the trait is necessary in order to clustering markers and appropriate prior parameterization. In the workshop data set, the presented approach to group SNPs gave better predictions than a SNP-BLUP model, but worse predictions than a mixture (BayesB type) model. However, the workshop data set had a limited amount of QTL, which may not be representative for many real data sets. In real data often little advantages are seen for mixture models compared to SNP-BLUP, and as our method clearly outperformed SNP-BLUP our method could be of interest for further study in real data.The authors declare that they have no competing interests.MMS analyzed the data and drafted the paper. All authors contributed in planning the study, discussing the results and reading and editing the paper.