Lolium spp.) and the closely related fescues (Festuca spp.) particularly with a view to combining high forage quality with reliability and enhanced environmental services. We have analysed a L. perenne  population containing the majority of a F. pratensis (meadow fescue) genome as introgressed chromosome segments to identify a) marker-trait associations for nutrient use and abiotic stress response across the family, and b) to assess the effects of introgression of F. pratensis genomic regions on phenotype. Using container-based assays and a system of flowing solution culture, we looked at phenotype responses, including root growth, to nitrogen and phosphorus status in the growing medium and abiotic stresses within this festulolium family. A number of significant marker/trait associations were identified across the family for root biomass on chromosomes 2, 3 and 5 and for heading date on chromosome 2. Of particular interest was a region on chromosome 2 associated with increased root biomass in phosphorus-limited conditions derived from one of the L. perenne parents. A genotype containing F. pratensis chromosome 4 as a monosomic introgression showed increased tiller number, shoot and root growth and genotypes with F. pratensis chromosome segment introgressions at different ends of chromosome 4 exhibited differential phenotypes across a variety of test conditions. There was also a general negative correlation between the extent of the F. pratensis genome that had been introgressed and root-related trait performances. We conclude that 1) the identification of alleles affecting root growth has potential application in forage grass breeding and, 2) F. pratensis introgressions can enhance quantitative traits, however, introgression can also have more general negative effects.Alien chromosome introgression is used for the transfer of beneficial traits in plant breeding. For temperate forage grasses, much of the work in this context has focused on species within the ryegrasses ( Lolium perenne , are the most important forages in grassland agriculture and they are the target of sustained and competitive commercial breeding and seed production activities. The popularity of L. perenne in the farming sector is due to its combined qualities of forming a reliable perennial pasture, its responsiveness to fertilizer inputs in terms of biomass and quality, and the generation of good livestock performance in the form of enhanced meat and milk production. In more northerly and continental climates, however, the reliability of L. perenne declines and Festuca pratensis (meadow fescue), while lacking some of the quality characteristics of L. perenne, is often used as the key grass component of the sward due to its greater tolerance to low-temperature and related abiotic stresses. Similarly, in more southerly climates ryegrasses can exhibit sub-optimal performance in the face of heat and water stresses and polyploid fescues, such as F. arundinacea , may be preferred. As a consequence, it has been an aim of forage breeders for a number of years to exploit the inter-fertility of ryegrass and fescue species to combine their complementary and favourable attributes in ‘festulolium’ varieties, either using an amphiploid or an introgression breeding approach [Within Northern Europe and temperate areas worldwide, ryegrasses, particularly approach –6. Additapproach ,7–10.L. perenne and F. pratensis in particular, also have karyotypic characteristics which have made them very useful tools in both molecular cytogenetics and comparative genomics of the grasses. These characteristics are that they combine a high degree of interfertility and interspecific chromosome recombination at meiosis with the ability of the analyst or breeder to distinguish the species-of-origin of chromosomes and chromosome segments in hybrids and their progeny using genomic in situ hybridisation (GISH). This has been exploited in analyses of interspecific recombination [Brachypodium distachyon and, more distantly, to rice. Diploid L. perenne and F. pratensis are also both intermediate genome size species . Subsequently, King et al. [F. pratensis. Across the whole of this population this covered the great majority of the complete F. pratensis genome. That report, along with the L. perenne GenomeZipper [L. perenne [In addition to their agronomic complementarities, ryegrasses and fescues in general, and bination –16, traibination ,5,17–20 bination –23. Ryeg. 2.5Gb; ) and rep. 2.5Gb;  reportedg et al.  reportedmeZipper , establi perenne ,27.e.g., [Hydroponic cultivation, using flowing solution culture (FSC), has been used in the evaluation of ryegrasses and other forage species for many years e.g., –40) ofte oftee.g.In the present study we have conducted a series of phenotype assays on this genetically and genomically characterised festulolium family to identify genomic regions associated with differential phenotypic responses to nutrient status in the growing medium and to abiotic stresses and to determine the degree to which the architecture of trait inheritance might be influenced by the presence of alien chromosomes or chromosome segments. The outcomes are discussed in terms of the potential and possible implications for forage grass breeding.L. perenne genotype from the variety Meltra (designated LpM) was crossed with a diploid F. pratensis genotype  to derive a triploid genotype containing 2 haploid genomes of LpM and one of Fp. This triploid genotype was crossed with a diploid L. perenne from the variety Liprior (designated LpL) and 6 monosomic substitution genotypes (S1-S6) were identified within the progeny through molecular cytogenetic and molecular marker screening. Each of S1-S6 contained a different chromosome from the Fp haploid set (2n = 2x = 13 L. perenne + 1 F. pratensis). For S7, the first genotype identified as containing a complete introgressed Fp chromosome 7 (C7) also contained another small Fp-derived terminal segment from a different chromosome. This latter segment was removed while retaining the complete monosomic C7 introgression following a second round of back-crossing to LpL.The development of this mapping population has been described previously in detail ,25. The L. perenne /F. pratensis recombinant genotypes from the whole Fp chromosome substitutions in S1-S7 were identified through further rounds of molecular cytogenetic and molecular marker screening . The entire Fp genome was present across S1fam1-S7fam with the exception of the central section of chromosome 6 (C6)  and Lpcon were 8, 13, 23, 16, 10, 13, 21 and 19, respectively giving a total of 123 genotypes in total (designated collectively as LpFpFam).Each of S1-S7 were then further back-crossed to LpL, and e 6 (C6) . A furthThe list of experimental conditions and traits evaluated are described in -2 s-1 PAR at canopy height). From day 25 onwards, natural light was excluded and the plants were grown with artificial illumination over a 12 h photoperiod (08:00–18:00 h), provided by paired 400 W HPI/T and SON-T lamps (Philips) above each unit (650 ± 50 μmol m-2 s-1 PAR at canopy height).The three trials comparing 1) optimum N and P, 2) optimum N and low P and 3) low N and optimum P were carried out consecutively in the FSC facility at Aberystwyth University. Six tillers of uniform size were taken from established genotypes of LpM, LpL, Fp, S1-S7 and LpFpfam. These were washed free of compost prior to transplantation into FSC  optimum N and P, 2) low N and optimum P or 3) optimum N and low P. For optimum N and P and low N and optimum P, the nursery concentrations of nutrients in the flowing solutions were (μM): NOribed in . For opti.e. bulked for six plants) were recorded non-destructively on days 0, 7, 14, 21 and 28; vessels were randomised weekly across the 8 culture units. All plants were harvested on day 28 and separated into shoot and root fractions. Tiller numbers, shoot and root fresh and dry weights were recorded as bulks of the 6 (or remaining) plants within each vessel. All fractions were oven dried at 80°C for 24 hours prior to measurements of dry weight.During the treatment periods, total plant fresh weights per culture vessel  the plants were cut back to c. 4 cm above the compost surface and the flooding treatment was started. Flooding treatment consisted of partial submergence, in which the water level was maintained at c. 2cm above the top of the compost. Flooding treatments were conducted at two different day temperatures of either 10°C or 20°C for 3 or 6 weeks with a single clone evaluated for each genotype in each treatment. After the end of the treatment period, plants were cut-back to c. 4 cm above the compost surface and tiller number was counted after 2 weeks of regrowth.Based on the outcomes of this preliminary trial, a second experiment was carried out just using the parental genotypes, S4 and S4fam (omitting a single S4fam genotype which was no longer available). The same general procedure was followed using partial submergence for 3 or 6 weeks, but using 4 replicates of each genotype with plants maintained in a growth room with 20°C/10°C day/night temperature and a 12 hour photoperiod. In addition to tiller number, shoot dry weight was also measured.Drought assays were carried out in drought bins constructed in an unheated glasshouse at ambient temperature. Each drought bin consisted of a 1m x 1.1m x 1.1m (w x l x h) container filled with drainage stone and natural soil to a depth of 40 cm with a top 65 cm layer of potting compost. Five clones of each parental, S1-S7 and LpFpfam genotype were transplanted to randomized positions 7cm apart over 10 drought bins in the autumn and allowed to establish and vernalize over the winter. Edge effects were avoided by using guard rows around the sides of each bin. The bins were irrigated as needed to avoid any water deficit over this period. The following June, after flowering, the soil water content was taken up to field capacity (c. 38%) for one week and the clones were cut-back to c. 4cm above soil level. Water was then withheld for the next 3 months. During this drought period, the night time temperature range varied between 10–20°C and day time temperature range between 15–39°C. After this period, the soil water was taken back up to field capacity and the plants were left to recover for a further 4 weeks. After the end of this period, shoot dry weight and number of surviving clones per genotype were recorded.Tillers of two replicates of each parental, S1-S7 and LpFpfam genotype were transferred to a mixture of 50:50 vermiculite:grit sand in 15cm square pots with a depth of 20 cm in a glasshouse compartment maintained at 20°C/10°C day/night temperature with supplemental lighting to maintain a 10 hour day length. After 3 months, tiller number, shoot and root dry weight and root length were ascertained.Four replicate tillers of each parental, S1-S7 and LpFpfam genotype were established in multipot trays in the autumn and allowed to vernalize in an unheated glasshouse over winter. The following March, each replicate was transplanted to a separate 15 cm pots containing a standard potting compost and randomised on an external, hard-standing area. Heading date (emergence of 3 heads), tiller number on the 1st May and number of heads on the 16th June were recorded for each genotype.LpM, LpL, Fp, S1-S7, S1fam1-S7fam  and LpCon family (n = 19) were genotyped using the Illumina Infinium SNP genotyping microarray . DNA samL. perenne pseudotestcross mapping population (AberMagic x Aurora) and a L. perenne F2 mapping population [Marker/trait associations were established using one-way analysis of variance (ANOVA) or a Kruskal-Wallis test (KW) on a marker-by-marker basis across all of the genotypes in LpFpfam, with Bonferroni correction for family-wise error rate, and implemented using Genstat for Windows 18th Edition. Associations were only considered to be significant if the p-value for the marker-trait association was less than this threshold. The chromosomal positions of markers were inferred from genetic maps previously constructed using the Lolium Illumina Infinium SNP genotyping array using a pulation .F. pratensis introgression, using the recombination bins previously described to define common F. pratensis introgressed segments shared between genotypes [Trait data were also analysed in terms of the mean scores for all of the genotypes containing a defined segment of enotypes .F. pratensis introgressions in this family based on GISH discrimination of metaphase chromosomes has been previously reported [L. perenne genome had been substituted by the F. pratensis genome  were scored across all 123 genotypes within LpFpFam (mean = 121.8). Across all 976 markers and 123 genotypes, of the 120048 possible genotype calls, c. 45.5% of the genotypes were scored as heterozygous, c. 53.5% as homozygous and c. 1% were null.From the total number of 3541 potential marker assays on the SNP array, 976 markers were identified that were clearly segregating across the 123 genotypes within LpFpFam and had been assigned map positions in either of the AberMagic x Aurora or FSignificant marker trait associations identified using ANOVA or KW on a marker-by-marker basis with Bonferroni correction are summarized in Significant marker trait associations were identified for root biomass and heading date. None were identified for tiller number, shoot biomass, plant height and root length or in response to any of the waterlogging or drought evaluations. Of particular note were: 1) root biomass was associated with regions on chromosome 3 (C3) in all FSC nutrient conditions and, 2) a region on chromosome 2 (C2) was also significantly associated with root biomass, but specifically in optimum N and low P FSC conditions. For this latter case, the alleles on C2 associated with enhanced performance in optimum N and low P were derived from the LpM parent with LpM-derived allelic variation for marker Contig38631_269 . While only this trait was significant after Bonferroni correction, this region  was associated significantly with a number of independently evaluated traits before multiple testing correction , S4 was the strongest genotype and LpM was the weakest  was present.  weakest . S4 and  weakest . To inve weakest . FocusinF. pratensis introgressions on C4, a further trial was carried out measuring tiller number and shoot dry weight under control and partial flooding conditions for parental, S4 and S4fam genotypes, measured in quadruplicate in controlled environment growth rooms. The results, illustrated in In order to confirm this trend concerning i.e., those genotypes for which the crossing scheme involved a cross followed by a single backcross to LpL–see F. pratensis introgression and all 21 of the measured vegetative growth-related phenotypes was negative, with p < 0.05 for 11 of these negative correlations  and both were non-significant (p = 0.08 and 0.12).Considering genotypes within S1fam-S6fam  genotypes were attempted, but low or absent fertility was a common feature. However, we were also aware that the cross represented a) an early x late heading date combination and b) a genetic background that had been selected in the development of a tetraploid variety with a genetic background that had been selected in the development of a diploid variety. So, this might be an interesting cross per se in terms of trait expression.In terms of the specific choice of the LpM and LpL genotypes from the varieties Meltra and Liprior, this was partly due to the experimental tractability of these genotypes for developing systematic introgression populations–L. perenne F2 family, also an early x late heading date cross; [Meltra is a late flowering variety and Liprior is an early flowering variety, therefore it is not surprising that significant marker-trait associations with heading date were identified, all located on C2. C2 has been reported as containing heading-date QTL in a number of previous bi-parental and GWAS studies –49 and we cross;   and LpL (Liprior), LpM produced markedly less root growth in FSC under all nutrient conditions than LpL, though performed relatively better under optimum N and low P than under optimum N and P or low N and optimum P. Comparable differences were apparent in the measurements of root growth made under glasshouse conditions, where LpM produced approximately a third of the total root biomass of LpL and approximately one half of the total root length . It is possible to distinguish the genetic effects influencing root dry weight identified on C2 and C3. The C2 region (most significant marker Contig38631_269) was only detected in FSC under optimum N and low P nutrient conditions and increased root dry weight was associated with the presence of an allele derived from LpM. For the C3 region  increased root dry weight was associated with the presence of alleles derived from the LpL parent. The fact that the root growth region on C2 was specific to the P-limited conditions is of particular interest. It is well documented that future sources of P for plant fertilizer are potentially limited [e.g., high affinity phosphate transporters are known to be upregulated in L. perenne roots in response to P-limitation [Comparing the parental  limited  and it i limited . Thus, tmitation  and impomitation –84) shoumitation ; S3 TablF. pratensis genotype chosen for the present study was derived from a seed accession of a Russian landrace. At the time when the initial crosses were made to derive S1-S7, a combination of the experimental usefulness of this genotype, in terms of its utility within the crossing scheme, along with glasshouse and field-based observations that it was a robust genotype in the circumstances in which it was to be grown, suggested that specific F. pratensis introgressions from Fp might have significant influences on phenotype within the Meltra/Liprior L. perenne background. This can be placed in the context of a number of studies where it has been possible to link Festuca spp. introgressions with defined traits [F. pratensis monosomic introgression lines, S1-S7 [The d traits ,21,88–90s, S1-S7  used in i.e., Fp, LpM, LpL, S1-7 and LpFpfam) S4 was ranked 4th and S3 21st out of 138  and  segment  and whilneral in ,96).F. pratensis introgression. The relative sizes of the individual F. pratensis introgressions in this family based on GISH discrimination of metaphase chromosomes has been previously reported [L. perenne background has been substituted by F. pratensis. Considering just genotypes within S1fam-S6fam  F. pratensis-derived alleles, the number proportional to the relative size of the introgressed F. pratensis segment. Following from this, in the absence of any major and counteracting beneficial effects of introgression, this might manifest in marginally reduced vigour. It would be very interesting to know if this low-level ‘debilitating’ effect is generic in terms of L. perenne/F. pratensis introgression or family specific, as this may have implications for the selection of parental genotypes and the design of crossing schemes in festulolium plant breeding programmes. As in exploiting heterosis in inbreeding species, evaluations of combining ability between prospective L. perenne and F. pratensis parents may come to play an important role in festulolium improvement.A secondary observation can be made relating to just the relative percentage of the whole genome represented by reported . Extrapot growth . If all L. perenne background on C2, C3 and C5. The effect on C2, inherited from the LpM parent, was specific to P-limited conditions. These results suggest avenues for developing experimental populations and germplasm resources for understanding and improving root growth and P-use-efficiency in forage grasses.We have identified genetic effects on root growth segregating in the Meltra x Liprior F. pratensis-derived introgressions evaluated across this family: 1) the monosomic introgression for F. pratensis C4 and a derived sub-chromosomal region at one end of the chromosome were associated with enhanced plant growth across a number of the test conditions; and 2) there was a negative correlation between the general extent of F. pratensis genome introgression and trait expression–particularly for root growth. These results, indicate that F. pratensis introgression into the L. perenne background can have positive effects on quantitative traits but there may be co-incident challenges associated with predicting progeny performances from parental introgression genotypes and, more generally, low-level negative consequences associated with the extent of alien chromosome introgression.Two major effects were associated with the genome-spanning S1 FigA. Crossing scheme for pairs of genotypes used to generate LpFpfam. B. Diagrammatic representation of the single chromosomes carrying the F. pratensis introgressed segments in the L. perenne background.(PPTX)Click here for additional data file.S2 Fig(PPTX)Click here for additional data file.S3 Fig(PPTX)Click here for additional data file.S1 Table(XLSX)Click here for additional data file.S2 Table(XLSX)Click here for additional data file.S3 Table(DOCX)Click here for additional data file.S4 Table(XLSX)Click here for additional data file.S5 Table(XLSX)Click here for additional data file.


Motivation: Researchers in genomics are increasingly interested in epigenetic factors such as DNA methylation because they play an important role in regulating gene expression without changes in the sequence of DNA. Abnormal DNA methylation is associated with many human diseases. Results: We propose two different approaches to test for differentially methylated regions (DMRs) associated with complex traits, while accounting for correlations among CpG sites in the DMRs. The first approach is a nonparametric method using a kernel distance statistic and the second one is a likelihood-based method using a binomial spatial scan statistic. The kernel distance method uses the kernel function, while the binomial scan statistic approach uses a mixed-effects model to incorporate correlations among CpG sites. Extensive simulations show that both approaches have excellent control of type I error, and both have reasonable statistical power. The binomial scan statistic approach appears to have higher power, while the kernel distance method is computationally faster. The proposed methods are demonstrated using data from a chronic lymphocytic leukemia (CLL) study. Genetic variations from genome-wide association studies can explain only a small proportion of the phenotypic variation for most diseases . It has Methylation data from next-generation sequencing (NGS) such as Methyl-seq have been used to detect aberrant DNA methylation . NGS couMethylation rates are treated as continuous when measured across a large number of cells . The ratMany statistical methods have been developed to detect DMRs, including some general approaches for bump detection, such as bump-hunting techniques . Other mt-test at each CpG site. DMRs are defined as adjacent CpG sites with observed values of the t-statistic above a pre-defined threshold, and with the significance of the DMRs evaluated using permutation test. However, this method depends on the pre-defined threshold for the t-statistic, which would hinder automated analysis and, possibly, lead to biased conclusions. BSmooth tests the group differences via a test that is similar to a p-values for the CpG sites are transformed into normalized z-scores, and then the average is calculated for a given region, and compared to those obtained from resampling data. In order to make improvements, BiSeq uses a False Discovery Rate (FDR) procedure to control the expected proportion of incorrectly rejected regions. BiSeq also has the advantage of taking spatial dependence into account. Besides that, BiSeq can improve power with a hierarchical procedure in which it starts with a beta-binomial model to account for biological variation between replicates, and then tests significance at each CpG site in all target regions for methylation differences, with a triangular kernel to capture the step-like changes observed in their data. The resulting Ryu et al.  suggesteIt has been shown that methylation rates could be strongly associated with relevant predictors and other covariates such as age ,11 and gA is a pre-defined matrix of a measure of closeness between two points, was first introduced by Tango [A is referred to as the kernel matrix by Schaid et al. [A can serve as a smoother, so that smoothed fitted values can be computed and then plotted versus chromosome positions. The peaks in smoothing plot would then be used to detect and locate DMRs.In this paper, we propose two methods for DMR detections, one based on a kernel distance statistic (KDM) and the other based on a binomial scan statistic method (SSM). A kernel distance statistic, by Tango , to deted et al. . The benA is used to determine how rapid similarity decreases to 0 as the distance between the variants increases, since the association decreases as the distance of two SNPs increases. Schaid et al. [j and l. This function has similar shape as a popular non-compact Gaussian function In order to detect clustering of risk variants for case-control data, Schaid et al.  used Q= study , with thWe modified the KDM, proposed by , to modeBinom stands for binomial distribution, To facilitate the discussion of the kernel distance method, let To adjust for confounding factors and linear predictors such as age and gender, we first use logistic regression to fit all data from both groups, using the model,The difference between the observed and expected methylated counts at CpG site Define Generally, the correlation of methylation rates decreases as the distance between the two CpG sites increases. Therefore, the kernel matrix therwise , where dSince the lengths and number of DMRs are unknown and difficult to predict, and the lengths of DMRs vary across the genome, it is difficult to determine the scaling factor that represents the cluster size. When an appropriate size of clusters cannot be predicted and many clusters are expected, it is common to repeat the procedure using different values of τ. Tango  suggestsd et al. , we consWhen a single test statistic is computed based on one scaling factor, the distribution of the kernel distance statistic can be approximated by a scaled chi-square distribution . HoweverWhen the null hypothesis is rejected, the scaling factor, Scan statistic method can be used as an alternative to KDM for detecting DMRs associated with the disease status. SSM is a likelihood-based approach that uses the likelihood ratio to test whether the methylation rates are different between groups. We use moving windows along the genome, with multiple window sizes, allowing more accurate evaluation of the location and sizes of DMRs. k is calculated, using Equations (2) and (3). The mixed-effect logistic regression model also allows us to account for relevant covariates. Since the methylation rate at each CpG site is correlated with those at the adjacent CpG sites, these correlations are first adjusted by using mixed-effect logistic regression model  size approach across the whole genome. DMR is defined as the window with the highest value of the scan statistic. Thus, for each window W of size w, the binomial scan statistic is be calculated, and the one with highest value denoted by LR calculation is unstable if the frequency of methylated counts within a given window is 0 for either cases or controls. To overcome this issue, a pseudo-count of 1 is added to the adjusted methylated and unmethylated counts at each CpG site, these additions implicitly assume that the null hypothesis of no differential methylation is true at all sites. Since the distribution of scan statistic is unknown, an approximate p-value for the window with the largest wLR is calculated using permutation method. The For case-control studies, SSM is expected to have higher power than the KDM, since SSM using moving window with variable window sizes overcomes the difficult problem of determining the value of scaling factor We conducted extensive simulation studies to evaluate the performances of both SSM and KDM. They were compared with respect to the empirical type I error, empirical power and computational efficiency.Since we used logistic regression for both methods to adjust for covariates, for simplicity, we did not include any covariates in the simulation. Although there are many DMRs along the genome, for the power comparisons for various alternate hypotheses at various significant levels, we assumed that there was only one DMR, so that we only simulated a small genome region around the DMR. We simulated two different scenarios with respect to number of CpG sites in the region, 24 and 30, and all CpG sites within the region were equally spaced.We considered Methylation counts at each CpG site for every individual were assumed to be distributed as lysis by . The corposed by  in orderFirst, independent random samples For each individual in each group, the vector of independent random variables Simulations were conducted at significance levels of 0.05 and 0.01, total sample sizes of 48 and 60 with equal sample sizes in each group, and regions of 24 and 30 CpG sites with 6 sites in the middle constituting the DMR. We assumed correlation coefficients of p-values and the empirical type I errors at significant levels 0.05 and 0.01, in order to evaluate the statistical validity of the two approaches. The results are presented in p-values for SSM and KDM in p-values must be uniformly distributed between 0 and 1 under the null hypothesis. As evident from p-value distributions are very close to uniform in both the cases, thus asserting the statistical validity of both our proposed methods. Also, the empirical type I errors are very close to the significant levels, confirming that both methods have excellent control of type I errors. First of all, we generated 10,000 simulated samples using Because of the massive computing time needed for simulations under the alternate hypotheses, only 1000 simulations were conducted to evaluate the power of SSM and KDM under various alternate scenarios. The plots of power versus different values of effect sizes and correlations at 5% significance level are presented in The conclusions on power at 1% significant level are very similar to and consistent with that at 5% significance level, showing consistently higher power for SSM compared to KDM.We applied our proposed methods to the methylation data from a genome-wide study of chronic lymphocytic leukemia (CLL), which manifests as a result of clonal expansion of malignant B cells. Research in CLL has identified several molecular alternations that are associated with prognostic values. These include specific cytogenetic patterns , mutatioCD19+ B cells from peripheral blood were collected from 40 subjects [Illumina reduced representation bisulfate sequencing  was usedThe percentage contribution of each CpG site to the kernel distance statistic is plotted at the top of The wedge shapes in both middle and bottom of The SSM approach detected a total of 66 DMRs with varying window sizes, that containing different number of CpG sites, with a total of 1355 CpG sites . The top 20 DMRs with highest scan statistic are presented in http://genome.ucsc.edu/) to find the genes in the regions. Some of the genes detected in our study include the apolipoprotein gene cluster , which are shown to have tight linkage with a chronic lymphocytic leukemia-associated translocation breakpoint [CATSPERD, PRR22, RFX2, and MILT1, which have been shown to be associated with leukemia [MILT1 with myeloid lymphoid leukemia could result in potent oncogenic activity [The start and end positions of base pairs for each detected DMR were used in the UCSC genome browser  may have a role in the pathogenesis of human acute myeloid leukemia (AML) and other cancers [C3 is detected whose expression has been reported to have a direct correlation with CREB in AML cell lines, as well as in the AML cells from the patients [C3 may have a role in neoplastic myelopoiesis by promoting the G1/S progression. Another detected gene, LAIR1, also has been found to have a correlation with CREB [LAIR1, activates downstream CREB in AML cells, and sustains the survival and self-renewal of AML stem cells. As a result, inhibition of expression of the immunoreceptor tyrosine-based inhibition motif (ITIM)-containing receptor LAIR1 does not affect normal hematopoiesis but abolishes leukemia development [Several studies have suggested that the transcription factor  cancers ,34. In opatients . It is sith CREB . A pathwelopment . Results from our simulation studies and the analysis of CLL data indicate that both methods, SSM and KDM, are valid approaches to detect DMRs. Both methods detect DMRs, while allowing for covariates as well as correlation between CpG sites. The tri-weight function used in KDM allows for a correlation structure in which the correlation decreases as the distance between CpG sites increases, while SSM use a mixed effects model to incorporate the correlation structure. Although compound symmetry assumption used in SSM may not truly represent actual correlation structure, the sandwich estimates of the fixed effects are appropriate even when the correlation structure is mis-specified, with some trade off of the flexibility for robustness of inference. Our simulation results also show that the mixed effects model is able to adjust for correlation when the simulated correlations decrease as the distances between CpG sites increase. Since the correlation structure can be complicated for methylation data, it may not be easy to find a statistical model that incorporates the correlation structure in its fully complex form.Both SSM and KDM have reasonable power and good controls of type I error in detecting DMRs between two groups, though SSM performs better in this respect compared to KDM. One reason might be that SSM is a likelihood-based method while KDM is a non-parametric method. Another reason for increased power for SSM could be the use of moving windows with multiple window sizes, which eliminates the difficulty of determining the value of In addition, SSM accounts for within cluster correlation by incorporating the method proposed by Xu et al. . SSM alsThe uncertainty of variants . Use of Another reason for the lower power for KDM compared to SSM may be that KDM is not able to adjust for unequal sequencing coverage for all individuals at each CpG site, while SSM incorporates the method proposed by Xu et al.  to adjusWe have only focused on DNA methylation data in developing both our methods. However, large-scale cancer genomics projects such as TCGA (The Cancer Genome Atlas Research Network) are currently generating multiple layers of genomics data for early tumor, including DNA copy number, methylation, and mRNA expression. Statistical methods for integrated analyses and systematic modeling of such genomics data deserve more attention.


Helianthus annuus and H. debilis) that form a natural hybrid in the wild (H. annuus ssp. texanus). We established separate control and hybrid populations and allowed them to evolve naturally in a field evolutionary experiment. In a final common-garden, we measured fitness and a suite of key traits for these lineages. We show that hybrid fitness evolved in just seven generations, with fitness of the hybrid lines exceeding that of the controls by 14% and 51% by the end of the experiment, though only the latter represents a significant increase. More traits evolved significantly in hybrids relative to controls, and hybrid evolution was faster for most traits. Some traits in both hybrid and control lineages evolved in an adaptive manner consistent with the direction of phenotypic selection. These findings show a causal pathway from hybridization to rapid adaptation and suggest an explanation for the frequently noted association between hybridization and adaptive radiation, range expansion, and invasion.Hybridization is a common phenomenon, yet its evolutionary outcomes remain debated. Here, we ask whether hybridization can speed adaptive evolution using resynthesized hybrids between two species of Texas sunflowers ( Arguing from theory, researchers have hypothesized for decades that hybridization can act as an evolutionary stimulus13. Evolution is constrained by the availability of standing genetic variation15. Novel genetic material can arise via two main mechanisms: either by new alleles from de novo mutations17 or via introgression of alleles from other populations or species (reviewed in20). In fact, hybridization has been shown to provide sources of new genetic material  and there is mounting evidence in numerous systems that hybridization is associated with adaptation, speciation, and radiation24. For instance, in a meta-analysis, naturally-occurring hybrids had increased invasion potential (measured via proxies such as fecundity and size) relative to their progenitor nonhybrid species25. However, most of this evidence is correlational in nature, thus the causal nature of these relationships needs to be investigated empirically with field experiments.Although historically regarded as a transitory or rare phenomenon28. Conducting these experiments in a field setting allows for the assessment of changes under realistic conditions that include multidimensional selective pressures and relevant genotype-by-environment interactions29. Such studies  on the effects of hybridization have so far been carried out exclusively in crop-wild systems. These studies have documented that natural selection favors wild alleles and phenotypes in some Helianthus crop-wild hybrids30, and in Raphanus, crop-wild hybrids outperform nonhybrids in terms of survival and fecundity34. However, evolutionary outcomes of hybridization in crop systems may differ from those in wild systems, as environments are homogenized in the former35 and population genetic parameters and loci under selection differ between agricultural and wild settings36.Evolutionary experiments allow for the observation and characterization of evolutionary change in real time using baseline conditions that are known with a high degree of certainty. They can address many questions, including those related to adaptation, evolutionary tradeoffs, population genetic parameters, and other lineage-specific evolutionary hypothesesHelianthus annuus ssp. annuus, the hybrid-derived subspecies H. annuus ssp. texanus, and the cucumber-leaved sunflower H. debilis ssp. cucumerifolius. All taxa are annuals that are wild and native to North America: H. a. annuus is geographically widespread across nearly the entire continent, while H. a. texanus and H. debilis are both centered in Texas. The subspecies H. a. texanus is locally-adapted to the environmental conditions in Texas (see fitness comparisons in37) and has long been considered the product of natural introgression of Texas-adapted H. debilis alleles into the widespread species H. a. annuus39.Here, we present a unique field experimental evolution study investigating whether natural hybridization can speed adaptation in the wild. We focus on the rate of adaptation (as well as the phenotypic endpoints) as this component of hybrid evolution has been little studied. As a model system, we used three taxa of annual Texas sunflowers, including the annual sunflower H. debilis × H. a. annuus backcrossed to H. a. annuus) populations in a common-garden setting. Examination of intermediate generations allows for fine-scale temporal resolution of evolutionary rates. We measure evolutionary changes in fitness and morphology, focusing on a suite of 27 ecophysiological, phenological, architectural, resistance/palatability, and herbivore damage traits to obtain a comprehensive picture of phenotypic evolution. We further use genomic data to detect allelic changes due to local gene flow. We ask: (1) does hybrid fitness evolve compared to controls? (2) do key traits evolve more rapidly in hybrids relative to controls?, and (3) can trait evolution be predicted by initial phenotypic distance from the locally-adapted phenotype?We used an eight-year field experiment to examine adaptive evolution in initial, multiple intermediate, and final generations of control (nonhybrid) and resynthesized hybrid (1 hybrid (H. a. annuus × H. debilis) and back-crossing it to H. a. annuus, resulting in BC1 individuals with approximately 75% H. a. annuus and 25% H. debilis genetic backgrounds to mimic the hypothesized genetic composition of the ancestors of the natural hybrid lineage38  and control (H. a. annuus) lines (separated by 260 m), but due to space limitations only a hybrid line was established at BFL. For all experimental lineages, the initial allelic composition was derived from sources hundreds of kilometers distant from the study site . This helped to reduce, but not eliminate, gene flow from local sources  and evolve significantly, they significantly exceeded control fitness in one of the cases. The larger increase in performance of LBJ vs. BFL hybrids over controls may reflect local adaptation in the former to the common garden site, since the final common-garden was planted at home site for LBJ control and hybrid lines but was 14.5 km distant from the home site of the BFL hybrids.We tested our fundamental hypothesis, that hybridization can increase the rate of adaptation, by comparing the rate of fitness change across seven generations between hybrid and control populations. We estimated fitness as the total seed output of each individual (see Methods for details). We built Bayesian linear models in JAGS Plummer 2003) to regress standardized fitness on generation and interpret significant positive slopes (β) as evolution of increased fitness. Control fitness did not change across generations  and for those with both strong and moderate support . The number of traits evolving in controls and BFL hybrids did not differ significantly for traits evolving with strong support  or traits evolving with both strong and moderate support . See Supplementary Table We tracked evolution of ecophysiological, phenological, architectural, resistance/palatability, and herbivore damage traits  were significant and in the same direction, we interpreted this as evidence for adaptive evolution . Conversely, we examined how many of the traits that evolved significantly also evolved adaptively. In controls two out of five traits that evolved did so in an adaptive manner, while four out of 16 did so for LBJ hybrids and three out of six did so for BFL hybrids . Note that not all traits measured in 2017 were included in the 2003 phenotypic selection analysis.Although traits evolved in both controls and hybrids, not all evolution was necessarily adaptive in nature, as it could have been driven by demographic factors such as genetic drift or by genetic correlations with other traits under selection; we note that many traits were correlated  and four additional traits showed moderate support for a steeper slope  and those with both strong and moderate support . Differences in trait evolution for the BFL hybrids versus controls were not significant when examining those with strong support  or those with both strong and moderate support . As an additional metric, we measured the rate of trait evolution in haldanes 44. Control traits evolved faster than LBJ hybrids in only three traits , while the LBJ hybrids evolved faster in the remaining 24 traits , a significant difference . Controls evolved faster than BFL hybrids in ten traits, while the BFL hybrids evolved faster in the remaining 17 traits , again a significant difference   could predict the rate and direction of trait evolution. To do so, we computed the distance from the standardized average trait value of our experimental plants in generation 1 (hybrids and controls separately) to the average trait value of H. a. texanus planted in the garden. We used a simple linear regression to relate distance to the slopes from the Bayesian trait evolution analyses. Control and hybrid (both LBJ and BFL) evolutionary rates were both positively and significantly correlated with distance from the locally-adapted phenotype, meaning that traits with initial values far from those of the local phenotype tended to evolve faster than traits starting with trait values similar to the local phenotype, and in a direction toward the values of H. a. texanus . We asked whether the initital phenotypic distance to the locally-adapted taxon , to 78% in generation 3 and 100% by generation 6 . Although the proportion of outside alleles varied between individuals, a fraction of hybrid samples (14 LBJ and 17 BFL) had virtually no outside alleles (<0.5%), suggesting these samples had no outside ancestry and supporting the validity of the test statistic. For the LBJ experimental hybrid population, the percentage of admixed individuals increased with generation, from 0% in generation 1 , including this system. Heiser originally proposed that H. a. texanus is the product of natural hybridization between H. debilis and H. a. annuus, with incorporation of genetic material from H. debilis allowing for the southward range expansion of H. annuus into Texas.Hybridization can result in adaptive introgression of fitness-enhancing alleles from one species into another42 and herbivore resistance37 and identified putative QTL underlying these traits47. Our experiment finds rapid evolution in both traits and fitness in resynthesized hybrids, potentially due to the introgression of traits and alleles from H. debilis. Traits in all broad categories  tended to evolve faster in hybrids than in controls , but briefly we note that the damage done by seed predators  is a strong selective pressure, apparently resulting here in decreased rates of damage in later generations. Other changes in ecophysiology or architecture may be associated with adaptation to the local abiotic environment, although we did not test for specific causation here. For instance, in both hybrid lines, specific leaf area (SLA) evolved higher values, perhaps in response to the warmer conditions in the study area relative to those experienced by the more northern H. a. annuus parent. Additionally, phenotypic distance from the locally-adapted phenotype (H. a. texanus) is correlated with the speed of evolution, indicating that this experiment may be “replaying” the natural history of hybridization, introgression, and adaptation in this system.Previous work in this system found evidence for adaptive introgression of traits related to abiotic toleranceH. a. texanus in the local environment. While we reduced local gene flow by removing wild Helianthus individuals near our experimental plots, we did not eliminate it, and novel alleles began to accumulate in the hybrid populations, stabilizing at around 5% of loci by generation 6 at both LBJ and BFL  alleles, as has been found in Senecio squalidus in the British Isles48. In this scenario, a large fraction of the incoming pollen from local sunflowers would have had unique SI alleles relative to experimental hybrids, and thus would have had fertilization advantage. An alternative mechanism for increased porosity posits that hybrids may have had relatively high genetic load (relative to controls) via outbreeding depression . We posit that historical formation of the wild hybrid H. a. texanus may have been shaped by this process as well, with high porosity and allele-sharing between multiple, independently formed early-generation hybrid populations contributing to rapid spread of advantageous alleles.A competing  explanation for the observed rapid evolution in hybrids is that, relative to the control genome, the hybrid genome may have permitted more rapid introgression of local alleles from wild 51 compiled microevolutionary rates in plants across 23 studies, 15 of which include estimates in haldanes. Rates ranged from 0–0.808, with a mean of 0.158 haldanes. Most rates fell at the lower end of the distribution  with a median of 0.047 haldanes and only four studies with rates higher than 0.5 haldanes51. The rates we estimated in our study , are well within this wide range.We found higher rates of trait evolution in hybrids relative to controls, both in terms of the slope of change over time Fig.  and in eHelianthus hybrids rapidly evolved higher fitness than non-hybrid controls. Further, key traits associated with ecophysiology , phenology , architecture , and herbivore resistance (seeds killed by midges or holes) evolved faster in hybrids relative to controls, perhaps enabling hybrids to acquire resources and produce viable offspring more effectively. Our results provide experimental support for the long-hypothesized connection between hybridization and more rapid adaptive evolution and provide a set of potential mechanisms for the long-noted association of hybridization with diverse evolutionary phenomena including adaptive radiations, invasions, and range expansions. However, an important caveat is that we have been able to examine only one control and two hybrid populations, given logistical constraints inherent in conducting a large field experiment over eight years. Replication in studying the evolutionary outcomes of hybridization will need to be built up across (rather than within) studies, as has been done for other evolutionary questions requiring intensive field-based investigation. For instance, a series of experiments transplanted populations of the Trinidadian guppy Poecilia reticulata between environments, there by manipulating exposure to predators55. Each study was conducted using either microcosms or field systems in different rivers or tributaries, building on each other and on previous observational work to form a more complete picture of life history and color pattern evolution in response to predators (reviewed in28). Replication of field experiments within our Helianthus system and other plant and animal systems should provide additional evidence on the degree to which hybridization can speed adaptive evolution, while elucidating details regarding the context-dependent nature of hybrid evolution.Here we show that, despite initial low fitness values, H. a. annuus) to the study area from the north, followed either by hybridization with the local H. debilis (BC1 hybrid lines) or not (H. a. annuus control line). These populations then evolve in, and exchange genes with, a matrix of other hybrid populations, similar to the expected scenario during the historical formation of H. a. texanus. Note that the parental species H. debilis is native to the study area, and thus would not be an informative non-hybrid control for the rate of local adaptation, since it is presumably already locally adapted. The BC1 generation was obtained by first mating H. debilis from Texas to wild H. a. annuus from Oklahoma to produce F1 progeny in the greenhouse (Supplementary Table 37). To produce enough BC1 seed for replicate field populations, a single progeny from the F1 generation was selected and propagated vegetatively to produce 14 F1 clones. A single H. a. annuus pollen donor from north Texas (the recurrent parent) was then mated to the F1 clones. Controls were field-collected H. a. annuus from the recurrent parent population “RAR59” (see Table 1 in37), consisting of roughly 10 seeds from each of 50 maternal sibships.Our design simulated the arrival of a non-locally-adapted taxon (H. a. annuus) and hybrid (BC1) populations were established at Lady Bird Johnson Wildflower Center , with plots separated 260 meters and a dense copse of trees to limit gene flow between plots. To assess the generality of hybrid responses in different parts of the H. a. texanus range, a second hybrid population was established approximately 14.5 km away at the Brackenridge Field Laboratory . Space limitations prevented the establishment of a control population at BFL. All three populations were initiated with 500 individuals in late March 2003. The source populations for the BC1 line are far away from LBJ  to maintain low humidity. The experiment had to be terminated after eight generations because of a change in land use at the host site.H. a. texanus , followed by roughly one month of growth in the UNM greenhouses. Over ten thousand seeds were scarified for this study, with an average germination rate  of 36%. The final common garden was planted at LBJ between April 2 and April 4, 2017. One-month old seedlings were transported to Texas and planted 90 cm apart in rows 1 m apart. We aimed to plant 60 individuals per lineage for final generation and wild H. a. texanus lines and 30 per lineage intermediate generations, using approximately equal numbers of seeds per maternal line to avoid overrepresenting any individual half-sibling families. Due to variable germination rates, the actual number of individuals planted varied (Supplementary Table Stored seeds were germinated at the University of New Mexico (UNM). These included seeds from hybrid and control lineages, as well as seeds from wild 42; see Table Traits were measured as in2 ∙ g−1) is the ratio of leaf area to mass and a measure of overall leaf construction costs . Leaf succulence (Succ) is calculated as (leaf wet mass − dry mass)/wet mass, while leaf dry matter content (LDMC) is calculated as leaf dry mass/wet mass. Leaf length to width ratio (LWR) is an estimate of the narrowness of the leaf and is calculated as leaf length/leaf width. Leaf water use efficiency (WUE) is the rate of carbon gained via photosynthesis per unit of water lost via transpiration, measured using δ13C. Leaf longevity (LeafLong) is the length of time that a leaf remains on the plant, measured in days. Two fully expanded leaves per plant were selected in the period before first flowering, one for WUE and one for the remaining leaf traits. An expanding leaf of approximately 4 cm was selected and tagged with a jewelry tag to track leaf longevity. Chlorophyll content (Chloro) was estimated using a SPAD meter , averaged across five measurements. Wet mass was measured on a microbalance, leaves were scanned using a flatbed scanner, and leaves were dried in a drying oven until constant mass was reached, and dry mass was then measured. One ca. 3 mg sample from dried leaf disk tissue (taken using a #7 cork borer) per plant was weighed in tin and analyzed for Carbon, Nitrogen, 13C, and 15N content at the University of New Mexico Center for Stable Isotopes. To reduce costs, only a subset of individuals was submitted for isotope analysis and leaf Carbon:Nitrogen ratio (CNratio)  as the number of days between transplanting and the first appearance of the immature apical flowering head, seed maturation time (SMT) as the number of days between the end of stigma receptivity and achene maturity for the apical head, and plant longevity (Longevity) as the number of days between transplanting and mortality. For a small number of plants remaining alive after the last census date in November, we added one or two weeks to longevity based on the plant’s appearance . Eliminating these outliers from the analyses did not affect results.56, where heads originating on the main stem have a branch position of 1, heads from a primary branch have a branch position of 2, and heads from a secondary branch have a position of 3. Relative branch diameter (RelBrDiam) is a measure of investment in branches relative to the main stem and is estimated as the average branch diameter across all primary branches with diameters at the base > = 3 mm, divided by the stem diameter. Plant volume  is an estimate of overall plant size and is calculated using the equation for the volume of a cylinder, V = π × r2 × l, where r is half the diameter of the primary stem and l is the height of the plant.Disk diameter  is the diameter of the central disk of the apical flowering head measured during stigma receptivity. Height of the lowest branch  is the height of the lowest branching point on the primary stem. Bushiness  was estimated as the mean branch position of all flowering heads on the plantNeolasioptera helianthis; Diptera: Cecidomyiidae combined with parasitoid damage), hole damaged , and gray seed weevil damaged . In total, 310,378 seeds were scored for this study (mean = 536 seeds per plant). Damage scores were calculated as fractions . Viable seed production was chosen as the measure of fitness in these annual plants. Seed production was estimated by multiplying the total number of heads produced (bagged + unbagged) by the average number of viable seeds per bagged head.For seed count and seed predation measurements, individual seed heads were enclosed with bags made of plastic mesh  after pollination but before seed release and collected throughout the season, with a goal of >4 heads per plant. At plant senescence, all remaining heads (both bagged and unbagged) were collected and counted. Damage to receptacles (the structures subtending the inflorescence) by head-feeding Lepidoptera was measured by counting the number of larval holes in a sample of one to eight mature receptacles per plant and taking the average (RecepDam). Seed damage was visually assessed under a dissecting microscope . Seeds were sorted into categories including viable, parasitoid attacked (ParaDam), midge damaged . The ratio of leaf carbon to nitrogen (CNratio) was estimated using values from the Center for Stable Isotopes (see above).Densities of glandular (GlandDens) and nonglandular (HairDens) trichomes were measured on a single dried leaf disk per plant. Trichomes were counted using a Leica MZ 125 compound light microscope  under 5x magnification with a 1 cm × 1 cm reticle and converted to densities  and for leaf chewers  were constructed by summing D scores for each of the component taxa. Stem and petiole damage were assessed in mid-June as a continuous trait, counting the number of lesions on all stems and petioles caused by Rhodobaenus weevils (Coleoptera: Curculionidae) (WeevilDam), and number of holes per plant caused by stem-boring larvae .Insect damage to leaves was scored twice for each plant, once in mid-June and once in late-July. Briefly, we scored percent cover of damage on three of the oldest leaves per plant caused by different types of herbivores and calculated a damage index 57. Prior to analysis, of the 615 original plants, we filtered out early transplant deaths (n = 9), plants apparently damaged during seed scarification that never developed a root system (n = 10), and plants that lived less than 75 days because the majority of traits could not be measured on these plants . Additionally, when analyzing architectural traits, we excluded plants with weevil damage to the primary bud, as these resulted in abnormal growth architectures. See Supplementary Table All analyses were carried out in R v3.3.1βj0 is the intercept term for treatment j (control or hybrid) and βj is the regression coefficient for treatment j. For the slope and intercept terms, we used normal priors with a mean of zero and variance of one, while the error term was assigned a gamma prior with shape and scale both equal to one. We implemented the models in JAGS v 4.3.058 and report results from five MCMC (Markov Chain Monte Carlo) chains run for 100,000 iterations , thinned every 25th iteration, with 10,000 iterations saved for the posterior sample. We used traceplots to check for convergence, and verified that Rhat values were under 1.01 for all parameters59. Regression coefficients were deemed significant if the 95% credible intervals did not overlap zero. We used all 10,000 posterior samples to create posterior distributions for the difference between treatments by subtracting the values for the hybrid coefficient samples from the control coefficient samples and taking the absolute values of these differences. From these new distributions, we estimated means and 95% credible intervals, where intervals that did not overlap zero indicate that coefficients differ between treatments.We used Bayesian linear regression models to estimate the evolutionary change in phenotypic values of traits through time. We ran separate models for each trait , with grstanarm60 using standardized fitness values as a response with a random terms for lineage. We increased adapt_delta to 0.999 to avoid divergent transitions and used N priors. We ran each model using four chains for 2000 iterations each including a 1000 iteration burnin and checked Rhat values (<1.01) to ensure that the chains converged. We compared the posterior distributions by subtracting the samples of control fitness from the samples of the hybrid fitness, producing a mean and full distribution of the differences.To explicitly compare fitness values for controls against hybrids for each generation (LBJ) or the final generation (BFL), we ran Bayesian models using the stan_glmer function in the R library H, Eq. .We calculated the allochronic evolutionary rates in haldanes, the proportional change over generational time elapsed , for bos′) and selection gradients (β) from 2003 trait and fitness data separately for controls and hybrids at each home site (BFL and LBJ)42. Predictor variables were transformed to approach normality then standardized to a mean of 0 and standard deviation of 1 within each treatment. Linear selection differentials were estimated from the covariance of relative fitness within treatment (hybrid or control). Linear selection gradients were estimated from the partial regression coefficients from a multiple regression using the lm function in R  will necessarily differ somewhat from previously reported results. Full results are presented in Supplementary Table To determine whether evolution was adaptive for individual traits, we determined if the direction of trait evolution aligned with trait – fitness associations during 2003, the first field generation. We estimated selection differentials  using the lm function in R.For each trait, we calculated the distance from the mean value of the initial (2003) generation hybrids and controls to that of H. a. texanus individuals) over the course of the study. We sequenced 90 samples from the original BC1 generation, plus 229 LBJ and 264 BFL samples from subsequent generations 3 to 8; material from generation 2 was not available. See Supplementary Methods for details on DNA extraction, library preparation, sequencing, and bioinformatics. Since our hybrid population is entirely derived from a single backcross, all variants should be found in the BC1 population at approximately 25%, 50% or 75% frequency. We did not attempt to examine the control population in this way, as its much higher starting level of allelic diversity would have made identification of outside alleles difficult or impossible. In our hybrid populations, variants that only appear in subsequent generations are likely to be the result of outside gene flow. To quantify this gene flow, we filtered for sites sequenced in at least 20 BC1 samples and catalogued all alleles present. We then looked in all subsequent generation samples and calculated the percentage of called variants that were not in our catalogue from the BC1s. See Supplementary Methods for full explanation of genetic work.We used genotyping-be-sequencing to determine if our experimental hybrid populations experienced local gene flow from outside the experiment (e.g., from naturally occurring Supplementary Information


To identify genetic risk loci for major depressive disorder (MDD), two broad study design approaches have been applied: (1) to maximize sample size by combining data from different phenotype assessment modalities  and (2) to reduce phenotypic heterogeneity through selecting more homogenous MDD subtypes. The value of these strategies has been debated. In this review, we summarize the most recent findings of large genomic studies that applied these approaches, and we highlight the merits and pitfalls of both approaches with particular attention to methodological and psychometric issues. We also discuss the results of analyses that investigated the heterogeneity of MDD. We conclude that both study designs are essential for further research. So far, increasing sample size has led to the identification of a relatively high number of genomic loci linked to depression. However, part of the identified variants may be related to a phenotype common to internalizing disorders and related traits. As such, samples containing detailed clinical information are needed to dissect depression heterogeneity and enable the potential identification of variants specific to a more restricted MDD phenotype. A balanced portfolio reconciling both study design approaches is the optimal approach to progress further in unraveling the genetic architecture of depression. Diagnostic and Statistical Manual of Mental Disorders , defined as psychiatric diagnosis established according to the criteria based on the , et al.  summarizet al., et al., The first suggestion was to maximize the number of cases and controls, a strategy that has been successfully applied to schizophrenia   and clear evidence for sex-specific genetic effects with an estimated genetic correlation in liability to MD in men and women at approximately 0.55 and 0.63, respectively . A high correlation implies that, on average, SNPs have directionally similar effects on the two subgroups  with BMI  that is genetically similar to the second trait . A formal test of heterogeneity between rg estimates was found to be statistically non-significant, and the authors interpreted this result as an indication of a strong overlap in the common genetic architecture of the different phenotypes supporting the comparability of assessment via different measurement methods. Furthermore, Wray et al.  reflecting the percentage of SNP effects on one phenotype that can be explained by the SNP effects on other phenotypes  might not capture the same quantitative or qualitative psychopathological entity  or a diagnosis based on a questionnaire (e.g. a self-reported symptom score based on a cut-off). Similarly, the self-report of the indication for antidepressant use may represent a poor proxy for actual MDD. These concerns are strengthened by the results of some studies, showing for instance poor agreement between self-report measurement invariance, meaning that the perception of the (severity) of the symptoms might depend on factors other than the severity of the illness-like properties of the questionnaire (e.g. choice of items or wording) or characteristics of the respondent that are not relevant for the disease   by focusing on stable trait variance.Researchers who have applied this strategy argue that narrowing the phenotype and increasing phenotypic homogeneity may tag higher underlying genetic homogeneity and, hence, heritability, compensating for the related drop in sample size. This approach has been shown to be effective in the CONVERGE study that focused on recurrent severe depression in Han Chinese women and resulted in two significant hits. Furthermore, polygenic analyses provide evidence for the existence of heterogeneity in the depression phenotype. Nevertheless, applying this strategy can be challenging. First, the best criterion for stratification, allowing to accurately identify more homogenous subtypes, is a matter of debate. Second, few large genomic datasets include complete measures of the potential features of interest, such as specific symptom profiles. Overall, while different studies showed an increase in point-estimates for , et al.  estimateet al. . To forward the field and increase power to find meaningful genetic associations, Levinson et al.  proposedIn the light of the discussion to which extent the different depression phenotypes measure the same entity, it is important to acknowledge that we cannot directly observe MDD as we can other human characteristics (such as a person's height or someone's hair color). Consequently, in order to make MDD measurable, we need to specify an underlying (psychometric) model to operationalize it. As for every other latent (unobservable) psychiatric trait, the ‘correct’ model is unknown and we can adopt different frameworks. Those in favor of broadening the phenotype adopt a model that suggests that the difference in MDD among people is a matter of degree (e.g. MDD being represented by a dimension on which people can be ordered). On the other hand, those in favor of decreasing heterogeneity adopt a model where the difference in MDD is a matter of ‘type’ one belongs to or not (e.g. MDD being a different entity than other depressive disorders or subthreshold). This is an enduring issue in psychology  that further complicates choosing between the two strategies. The findings that are discussed in this review provide evidence for both frameworks: strong and consistent genetic correlations across studies using different depression phenotypes provide empirical support for a common underlying internalizing trait and specific polygenic signatures for subgroups of depressed patients provide empirical support for the presence of distinct pathophysiological processes acting under the same diagnostic label. Overall, results from large-scale genetic studies draw a composite picture of the underlying liability of depression.The aforementioned debate is closely related to what has been referred to as the ‘reification problem’ by Kendler  stating et al., et al., Despite indications of detectable heterogeneity, so far the studies applying the strategy aimed at maximizing sample sizes identified a higher number of genome-wide replicated loci compared to those that focused on increasing homogeneity. The number of significantly associated genetic variants has been steadily increasing with increasing sample sizes, indicating that combining different depression phenotypes increases power to detect a large number of common genetic variants shared across similar but distinct phenotypes. In contrast, with the exception of CONVERGE, no other GWAS so far identified specific genetic variants reliably associated with MDD phenotypes defined more strictly. Given the high co-morbidity between MDD and other psychiatric disorders, such as anxiety disorders  has been shown to substantially increase statistical power to detect genetic association for each trait in a joint analysis of multiple traits using GWAS summary statistics (Turley To conclude, so far increasing the sample size by carefully and thoughtfully adding samples with non-traditional diagnostic approaches has enabled the identification of a large number of genetic variants for depression, but this must be done with great care. Part of these variants probably also influence other internalizing disorders and related traits. As sample sizes increase, further increases in the number of genetic loci robustly associated with depression will likely be achieved.At the same time, it is also important to collect samples with genotype data and detailed clinical information. These samples will be essential to dissect clinical and biological heterogeneity using genetic instruments. Eventually, the achievement of larger sample sizes as compared to those available nowadays may allow to reliably identify genetic loci specifically linked to depression phenotypes defined more strictly. Adoption of a balanced portfolio reconciling the two main strategies discussed in the present review is probably the optimal approach to progress further in unraveling depression genetic architecture.


Genome wide association study was conducted using a mixed linear model (MLM) approach that accounted for family structure to identify single nucleotide polymorphisms (SNPs) and candidate genes associated with body weight (BW) and feed efficiency (FE) traits in a broiler chicken population. The results of the MLM approach were compared with the results of a general linear model approach that does not take family structure in to account. In total, 11 quantitative trait loci (QTL) and 21 SNPs, were identified to be significantly associated with BW traits and 5 QTL and 5 SNPs were found associated with FE traits using MLM approach. Besides some overlaps between the results of the two GWAS approaches, there are considerable differences in the detected QTL. Even though the genomic inflation factor (λ) values indicate that there is no strong family structure in this population, using models that account for the existing family structure may reduce bias and increase accuracy of the estimated SNP effects in the association analysis. The SNPs and candidate genes identified in this study provide information on the genetic background of BW and FE traits in broiler chickens and might be used as prior information for genomic selection. GWAS have revealed important regions associated with production, reproduction and disease resistance traits in chickens8. Methods that model population structure by estimating the covariance due to genetic relatedness between individuals has been reported to perform better in terms of detecting true associations than models that ignore population structure12.One of the essential elements in GWAS is a powerful statistical method that can be employed to identify genetic associations in unbiased fashionet al.13 reported that using general linear model (GLM) analysis when relations between animals exist, results in an inflated F-test. Consequently it is likely to find an excess of spurious genotype effects when actually no genotype effect exists13. In this situation, the use of mixed-model procedures under an animal model treating single-gene effects as fixed effect and accounting for family relations can provide an exact F-test of associated hypotheses and unbiased estimates of genotype effects14.Kennedy 15. Population stratification is the presence of systematic differences in allele frequencies between subpopulations in a population due to different ancestry between study subjects16. Unrecognized population stratification can lead to both false positive and falsenegative findings and can obscure the true association signals if not appropriately corrected18.Population structure mainly refers to population stratification and cryptic relatedness19. Because population based association studies assume individual independence of study samples, cryptic relatedness may make these statistical tests less reliable and reduce the robustness and efficiencies of the studies17.Cryptic relatedness refers to the phenomenon that some members of a study sample (population) might be related beyond what can be inferred from the pedigree, in which case their genotypes are not independent of the population frequencies15. Mixed models offer a practical and comprehensive approach for simultaneously addressing confounding due to population stratification, family structure and cryptic relatedness19.Methods modeling population structure, family structure and cryptic relatedness are expected to perform better than models that ignore these complexitiesThis study aims to identify potential loci and candidate genes associated with body weight (BW) and feed efficiency (FE) traits in a commercial broiler line genotyped with 60 k SNP chip using mixed linear model (MLM) approach that accounts for family structure and compare the results with general linear model (GLM) approach that does not take family structure in to account.Samples were collected from a commercial flock under the guidance of the local committees for the care and use of animals following the Cobb-Vantress Inc. Animal Welfare Policy. In addition, the experimental protocol was carried out in accordance with the approved guidelines for safeguarding good scientific practice at the institutions in the Leibniz Association.Phenotypic data were obtained from Cobb-Vantress broiler breeding company. A total of 5000 male broilers fed standard commercial broiler chicken diet based mainly on maize were raised from hatch to five weeks of age. Only males were studied in this experiment since selection intensity in males is considerably higher than that of females in broiler breeding programs. At the age of 36 days, birds were weighed (BW36) and the heaviest 1000 birds were selected for feed efficiency (FE) experiment and put in individual cages. At the age of 39 days (BW39), birds entered to a 7 day FE experiment and final body weight (BW46) was recorded at the end of the experiment. Total feed intake (FI) was recorded from individually caged birds during the experiment. Body weight gain (Gain) was calculated as the difference between final body weight (BW49) and start weight (BW39). Feed conversion ratio (FCR) was calculated as the ratio of feed intake to body weight gain. After data cleaning, 848 BW and FE records were used for further analysis.20. SNPs with low call rates (<95%), minor allele frequency (MAF < 0.03) and Hardy Weinberg equilibrium (HWE) P-value (<0.0001) were excluded. After quality control a total of 43914 SNPs were retained for GWAS analysis.Blood samples from the branchial vein were collected in anticoagulant tubes for DNA extraction. Extraction of genomic DNA was performed using Qiagen 96-well extraction kit . DNA from a total of 864 samples were genotyped using the Illumina 60 K SNP chip . The Illumina 60 K SNP chip contains 57636 SNPs that are distributed across 29 autosomes (chromosome 1 to 28 and chromosome 32), two linkage groups (LGE 64 and LGE 22 C19 W28_E50C23), and two sex chromosomes (Z and W) using chicken genome assembly Galgal4. In this study only male broilers were examined so only genotypes from the Z sex chromosome were included. Plink software was used for quality control of the genotypic data21. The genomic relationship matrix (GRM) was constructed using methods from Yang et al.22. The P-values were adjusted by Bonferroni correction based on linkage disequilibrium23. The effective number of independent SNPs of autosomes were defined by the independent pairwise option in plink20. A total of 19416 independent SNPs were identified and the 5% genome wide significance threshold was adjusted to -log10  = 5.60. The threshold P-value for suggestive significant association that allows one false positive association per GWAS was adjusted to -log10  = 4.3. Genomic inflation factor (λ), was calculated using the R package GenABEL with “median” option24.Genome wide association analysis was performed for BW traits  and for traits from the FE test  using GCTA25. The annotated genes that were closest to the top SNPs were identified using Ensembl and NCBI. The reported top SNPs or “lead SNPs” are SNPs which have the highest -log 10  among the significant SNPs which are in linkage disequilibrium (LD) with each other in 1 Mb windows. Base pair positions of SNP markers were updated to the latest version of the chicken genome assembly Gallus-gallus-5 .Manhattan plots of genome wide association analysis and quantile-quantile (QQ) plots were created using the qqman package in R softwareFor FI and Gain, the following linear mixed model was used:th SNP genotype indicator variable coded as 0, 1 or 2, β1 is the regression coefficient (the effect of start weight on FI and Gain that accounts for differences in start weight), x is a vector of start weights (BW39), u is a vector of random polygenic effects i.e. The effect of all QTL except those on the chromosome where the candidate SNP is located, e is a vector of random residuals. The variance of u was re-estimated each time when a chromosome was excluded from calculating the genetic relationship matrix. Z is the incidence matrix for the random effect. The variance co-variance structure for the random effects were assumed to be normally distributed with mean 0 and variance; var (u) = 26, therefore maternal effects were not included in the model.For FCR and BW traits the following linear mixed model was used:et al.5 which does not take family structure in to account, using the following model.th SNP genotype indicator variable coded as 0, 1 or 2, β1 is the regression coefficient (the effect of start weight on FI and Gain) that accounts for differences in start weight, x is a vector of start weights (BW39) for FI and Gain, and, e is the random residual. The variance of e is Iet al.5 were updated to the latest version of the chicken genome assembly, Gallus-gallus-5  for easy comparison of the results with the current study.The results of the present study were compared with the results of the GLM analysis by Reyer Using MLM approach, the present study revealed 3 QTL which have suggestive significance association with BW36, 6 QTL with BW39 and 2 QTL with BW46 Table . A total27. Moreover, SNPs, rs14073523 and rs16617885, located on chromosome 14 and 8, are top SNPs associated with BW36. CACNA1H ; a protein coding gene and LOC107053920 (uncharacterized gene) are the proximal genes to the top SNPs, rs14073523, and rs16617885, respectively. Wang et al.2 identified QTL located on chromosome 14 associated with abdominal fat weight in chickens. The detected QTL for BW traits in the current study suggest that the genetic variance of BW was not exhausted after the pre-selection rather there are many genes with small effects left in the population. This is a remarkable finding relative to our expectation given the fact that this broiler line has been pre-selected for BW. Mebratie et al.26 also noted an increase in genetic variance of BW after several generations of selection in a commercial broiler chicken population.SNP, rs14042911, is one of the top SNPs  = 5.15) associated with BW36 which is located on chromosome 12. Due to the proximal position to the top SNP, PTPRG  is proposed as candidate gene associated with BW36 . SNPs, rs316610173, and, rs15652523, which are located on chromosome 12 and SNPs, rs14073523, rs13880135, and, rs16190017, located on chromosome 14, 1 and 23, respectively, are also found associated with BW 39  in laying hens. In the chicken QTL database28 different authors have reported different QTL on chromosome 1 to be associated with FI. Similarly QTL on chromosomes 6 and 17 were reported to be associated with FCR in the QTL database. The reported chromosomes are overlapping with the chromosomes associated with FI and FCR in the current study. However, the QTL regions are different except the QTL reported by Reyer et al.5 for FCR located on chromosome 6 which were identified using Bayesian method of analysis for the same broiler line.Two QTL located on chromosome 17 and 6 showed suggestive significance association with FCR. SNPs, rs14098962 and rs14568465, located on chromosome 17 and 6, respectively are found associated with FCR  and three FE traits  using the same data. Table et al.5. The GLM analysis was replicated in this study to compute the λ values reported in Table et al.5  ≥ 4.3). No QTL reached genome wide significance level  ≥ 5.6) except QTL on chromosome 17 which is associated with FCR  and three FE traits (8 QTL and 9 SNPs), were reported by Reyer et al.5 using the GLM approach  and residual feed intake32.Body weight and feed efficiency traits are the most important economic traits in the poultry industry. Body weight is the live weight of birds at a given age and feed efficiency (FE) is the ability of birds to convert a certain input to a certain output (e.g. Kg of feed in to Kg of meat)We have performed GWAS for body weight and feed efficiency traits in a commercial broiler chicken population using a MLM approach, taking family structure into account. The results were compared with the results of a GLM approach, which does not take family structure into account.et al.5 also used Bayesian (Multi-marker) approach, which is more robust to population stratification33 and reported more significant SNPs associated with BW and FE traits than the GLM approach. However, in this paper the results  were only compared with the single marker GLM approach which is comparable to the single marker MLM approach in the present study.Reyer et al.6 reported that chromosome 1 and 4 are the two critical chromosomes influencing growth traits particularly body weight in chickens. In this study, SNPs on chromosome 1 were found to be associated with BW39 and BW46 while no significant SNP were found on chromosome 4 for any of the BW traits under study. Podisi et al.34 also reported two significant QTL for body weight at 12 weeks of age on chromosome 1 in broiler-layer cross female chickens.Xu et al.26 have reported that the genetic correlation between BW at different ages increased as the distance between BW measurements decreased. This might be due to changes in the physiological system of the chickens with age. Indeed, Schaeffer35 states that there might be genes that “switch on” and “off” at a certain age of an animal which could lead to changes in physiology and performance. Similarly, Carlborg et al.36 concluded that there are different genes and gene actions involved in growth at different developmental stages.Some of the BW and FE traits share consistent QTL and candidate genes. BW36 and BW39 share consistent region on chromosome 14 and candidate genes, PTPRG and CACNA1H, while BW46 does not share those candidate genes with BW36 and BW39. The effects of the lead SNPs with standard errors in parenthesis for BW36 and BW39 were found to be 0.036 (0.008) and 0.041 (0.010), respectively. This might indicate that the effects of these genes are smaller on BW46 due to increasing importance of other genes for the trait, suggesting that the identified genes are age dependent and the two traits (BW36 and BW39) might be genetically correlated. Mebratie BW36 and body weight gain share an overlapping region on chromosome 8 and consistent candidate gene, LOC107053920, with SNP effects −0.051 (0.012) and −0.058 (0.013), respectively  and FE traits (0.001–0.006 kg2) in this broiler population is very small with high standard error and among others, one of the reasons for the reported very small estimates of genetic variance was pre-selection of the broiler line for BW.This broiler line is pre-selected for BW (only heaviest birds were entered to the FE experiment) and undergone several generations of selection for feed efficiency. This phenomenon might affect the detection power of our GWAS and estimated SNP effect sizes since the genetic variance of BW and FE traits might be reduced due to the pre-selection and several generations of intense selection. Mebratie et al.5 which does not take family structure in to account. A number of studies39 have shown that methods that model family structure perform better than models that ignore family structure. A widely used approach to evaluate whether confounding due to population stratification, family structure and cryptic relatedness exists is to compute the genomic inflation factor (λ), which is defined as the median χ2 (1 degree of freedom) association statistic across SNPs divided by its theoretical median under the null distribution40. Values of genomic inflation factor (λ) > 1 generally indicate population stratification or other confounders, such as family structure or cryptic relatedness19. Values of λ < 1.05 are considered “benign” regarding power and type I error41, although inflation in λ is proportional to sample size.We have conducted GWAS using MLM approach, which takes in to account family structure and compared the results with the GLM approach by Reyer et al.5. The genomic inflation factor values suggest that population structure is not a strong concern in our data and the values are not significantly different from each other in the two methods. Moreover, the genomic relationship matrix of individuals shows that there are only few half sib and full sib relations in the current data38 suggesting that family structure is not a strong concern. However, there is a slightly higher inflation of λ values in the results of Reyer et al.5 compared to the results of the present study. This may suggest that although there is no strong family structure in the population, using MLM analysis that takes in to account the existing family structure may increase power to detect true associations than ignoring this kind of sample structure which may result spurious associations.Table et al.42 have noted that in the absence of markers with unusual allele frequency differences  using the genomic relationship matrix to account for both population and family structure can effectively control spurious associations under a variety of settings. Price et al.19 have also suggested that in studies where population stratification is not a very serious concern, an appealing and simple approach is to use mixed models.In a simulation study, Thornton 42, we have used a MLM approach that takes in to account the existing family structure and revealed differences in the identified top SNPs and candidate genes associated with BW and FE traits compared with the GLM approach by Reyer et al.5. This strengthens the suggestion that, even though there is no strong family structure in the data, MLM approach that uses the genomic relationship matrix to account for the existing family structure may decrease bias and improve accuracy of the association analysis.For the commercial broiler chicken data used in this study, family structure is not a strong concern. However, as suggested by different authorsThe results of this study might provide insight about the genetic background of body weight and feed efficiency traits. Furthermore, the study emphasizes that GWAS using the two approaches (GLM and MLM) does not necessarily give similar results even with the absence of strong family structure in the data.GWAS for BW and FE traits was performed in a commercial broiler chicken population. The present study has identified 11 QTL and 21 SNPs associated with BW traits and 5 QTL and 5 SNPs associated with FE traits. The results of this study provide insight on QTL and genes that are involved in the genetics of BW and FE traits in broiler chickens and can be used as fundamental information for genomic selection. Moreover, the MLM approach, which takes in to account the existing family structure by using the genomic relationship matrix, resulted in different QTL for most of the analyzed BW and FE traits compared to the GLM approach that ignored the existing family structure. Although, there is no strong family structure in this population, the use of MLM approach may increase power to detect true associations compared to the GLM approach that does not take family structure into account as suggested by previous studies.


Perissodus microlepis is a textbook example of bilateral asymmetry due to its left or right‐bending heads and of negative frequency‐dependent selection, which is proposed to maintain this stable polymorphism. The mechanisms that underlie this asymmetry remain elusive. Several studies had initially postulated a simple genetic basis for this trait, but this explanation has been questioned, particularly by reports observing a unimodal distribution of mouth shapes. We hypothesize that this unimodal distribution might be due to a combination of genetic and phenotypically plastic components. Here, we expanded on previous work by investigating a formerly identified candidate SNP associated to mouth laterality, documenting inter‐individual variation in feeding preference using stable isotope analyses, and testing their association with mouth asymmetry. Our results suggest that this polymorphism is influenced by both a polygenic basis and inter‐individual non‐genetic variation, possibly due to feeding experience, individual specialization, and intraspecific competition. We introduce a hypothesis potentially explaining the simultaneous maintenance of left, right, asymmetric and symmetric mouth phenotypes due to the interaction between diverse eco‐evolutionary dynamics including niche construction and balancing selection. Future studies will have to further tease apart the relative contribution of genetic and environmental factors and their interactions in an integrated fashion.The scale‐eating cichlid fish  Yet, ths Figure  from Laks Figure . Its asys Figure . This pos Figure . The sugFutuyma, . HoweverFutuyma, , totallyFutuyma, , 2010, aFutuyma, , which aP. microlepis has a sizable genetic component  of a 146 bp RAD locus (ID: 56537) placed on a scaffold  containing three genes and one pseudogene for immunoglobulins. More specifically, this RAD locus is located in the non‐coding region between two of these genes  and nitrogen (δ15N) stable isotope. The first provides information on the original source of carbon to the food web. In lacustrine animals, planktonic primary producers are depleted in δ13C compared to benthic primary producers and their respective predators  mouth polymorphism.2A total of 239 adult fish were collected in April 2010 from four locations on the Zambian coast of Lake Tanganyika Figure , accordiThe degree of mouth asymmetry was measured as reported in Raffini et al. . BrieflyP. microlepis suggested that this species is not an exception. Therefore, we used body size as a proxy for age , and the 34 most symmetric (“symmetric” phenotype) samples Table . Similar13C, did not follow a normal distribution, similarly to what observed in other cichlids  and the mouth phenotype . To allow for heteroscedasticity, we also used generalized least squares models  was statistically analyzed in R. Prior to these analyses, we performed the Shapiro–Wilk test . This analysis was performed to test if the asymmetric  fish have a more specialized diet when compared to the symmetric individuals. If this were true, it would lend support to the idea of a significant relationship between individual specialization and the level of asymmetry.Variance in stable isotopes was used as a measure of niche width .To verify a previously identified SNP and its relationship with the degree of mouth asymmetry, we explored the association between the candidate SNP 56537‐113 and mouth bending angle. 168 samples were successfully genotyped at this locus Table . These iF1,166 = 10.66, p = .001329, R‐squared = .06035; PERMANOVA F1,166 = 10.662, p = .002, R‐squared = .06035). This relationship remained significant when sampling sites  were included in the model , that, together with our SNP, explains about 7% of the phenotypic variation.The three quantitative genetic models  were all statistically significant for our genetic data; the G dominant model was the one that best characterizes this SNP Table . The res3.2Variation in individual feeding behavior and its association with mouth phenotypes  was investigated through stable isotope analysis of muscle (medium term) ad bone (long term) tissue , sampling location and body size (a proxy for age) best explain our data Table , while i13C compared to the symmetric pool. This association was not significant for the nitrogen marker  in both medium (muscle) and long (bone) term diet . The asymmetric group was on average depleted  in δr Figure .The asymmetric and symmetric phenotypes showed a different niche width  in the medium term (muscle). It is larger in the symmetric group, indicative of a broader diet including more various kinds of food, compared to the asymmetric group, which appeared to be more specialized. This difference was not observed for the longer‐term niche width  of phenotypic variation, and provide further support for the hypothesis that head asymmetry in P. microlepis has a complex genetic basis. They also suggest that individual specialization and variation in feeding habits could, in addition to such a complex genetic architecture, contribute to the unimodal distribution of this trait. We introduce a comprehensive hypothesis potentially explaining how these genetic and non‐genetic cues jointly influence the direction and the degree of mouth asymmetry as well as the maintenance of intraspecific variation.We analyzed the relationship between genetic and ecological  variation and morphological asymmetry in the scale‐eating fish 4.1P. microlepis’ mouth asymmetry . Both groups mainly feed on primary consumers  showed a different niche width in the medium term, and it was smaller in asymmetric individuals compared to symmetric ones. This could be interpreted as a hallmark of a more specialized, narrow ecological niche in the more asymmetric fish compared to a more generalist diet in the more symmetrical individuals. On a longer timescale, these two phenotypes did not exhibit significant differences in their variance of isotopic composition. This is likely because bones provide an averaged information about assimilated nutrients over several months/years  that can also vary within lakes at small spatial scales  relative abundance  according to their mouth phenotype, which is partly genetically determined, allowing the coexistence and maintenance of different morphs. Importantly, our study highlights the promise of considering inter‐individual variation when aiming to understand how this polymorphism is produced and maintained, and how an integrative view can help reconcile previously distinct observations.This study clarified the relative importance of genetic and environmental factors affecting mouth asymmetry in None declared.FR, CF, and AM designed the study. Morphological data were collected by FR and analyzed by CF. Stable isotopes samples were preprocessed by FR and analyzed in the Limnological Institute of the University of Konstanz. Genetic data was generated by FR. Raw data were analyzed by FR and CF. FR drafted the manuscript. All authors edited and agreed to the manuscript.The datasets generated during this study are included in this published article and its  Click here for additional data file.