Drosophila simulans population to a new temperature regime and uncovered a polygenic architecture of an adaptive trait with high genetic redundancy among beneficial alleles. We observed convergent responses for several phenotypes—e.g., fitness, metabolic rate, and fat content—and a strong polygenic response . However, each of these selected alleles increased in frequency only in a subset of the evolving replicates. We discerned different evolutionary paradigms based on the heterogeneous genomic patterns among replicates. Redundancy and quantitative trait (QT) paradigms fitted the experimental data better than simulations assuming independent selective sweeps. Our results show that natural D. simulans populations harbor a vast reservoir of adaptive variation facilitating rapid evolutionary responses using multiple alternative genetic pathways converging at a new phenotypic optimum. This key property of beneficial alleles requires the modification of testing strategies in natural populations beyond the search for convergence on the molecular level.The genetic architecture of adaptive traits is of key importance to predict evolutionary responses. Most adaptive traits are polygenic—i.e., result from selection on a large number of genetic loci—but most molecularly characterized traits have a simple genetic basis. This discrepancy is best explained by the difficulty in detecting small allele frequency changes (AFCs) across many contributing loci. To resolve this, we use laboratory natural selection to detect signatures for selective sweeps and polygenic adaptation. We exposed 10 replicates of a  Replicate populations of the fruit fly Drosophila simulans exposed to a novel temperature environment showed convergent evolution on the phenotypic level. On the genetic level, however, no convergence was observed. These results support the “trait optimum” paradigm as the predominant mode of adaptation. Drosophila simulans exposed to a high temperature regime, we identified a polygenic response with many (99) selected alleles having pronounced allele frequency changes (AFCs). Despite phenotypic convergence across the 10 replicates, the genomic response was highly heterogeneous, with different subsets of the selected alleles contributing to the convergent phenotype in each of the replicate populations. The observed genetic redundancy not only indicates lack of genetic constraint but also demonstrates that multiple genetic pathways lead to convergent phenotypic outcomes.It is widely assumed that adaptation is mainly polygenic, with the underlying frequency changes being so small that they are difficult to detect and characterize. Taking advantage of laboratory-based natural selection of replicated populations of the fruit fly  Despite a long-standing interest, it is surprising how limited the understanding of the genetic architecture of adaptation is. The best characterized adaptive traits have a simple genetic basis such as pigmentation –3, lactoDrosophila simulans populations to identify selected alleles. We distinguish between the allele frequency dynamics of selective sweeps and polygenic adaptation by testing the redundancy of selected alleles during evolution in a new thermal environment. Genetic redundancy [Due to these limitations, genomic selection signatures of polygenic adaptation to a new trait optimum have not received as much attention as selective sweep signatures. Once reliable alternative approaches are available to identify loci contributing to polygenic adaptive traits, it will be possible to address their importance for adaptive processes. Here, we demonstrate that replicated populations evolving in the same environment provide a novel approach to characterize adaptive loci. Specifically, we use whole-genome sequencing data from multiple time points in 10 replicated dundancy –25 is a dundancy .Drosophila involves many contributing loci [D. simulans isofemale lines to a new hot temperature regime that cycled every 12 hours between 18 and 28°C, mimicking night and day. After more than 100 generations, we assessed the adaptive response of the 10 evolving replicates. We contrasted fecundity of the ancestral population with each of the evolved replicates after rearing all of them in the hot temperature regime. In agreement with previous results in D. melanogaster [simulans [p < 0.0001, Temperature is a key environmental factor for all ectotherms, and the associated adaptive response in ing loci . To undesimulans , the evoTo characterize the genomic signature of adaptation in the evolved replicates, we generated replicated time series data by sequencing pools of individuals (Pool-Seq ) from thTo determine the selected phenotypes driving the genomic selection signatures, we performed gene ontology (GO) enrichment analyses of genes overlapping with the SNP markers of the selected alleles. Consistent with there being many selected genes contributing to similar phenotypes, we detected significant enrichment in several GO categories, including oxidative phosphorylation, mitochondrial respiratory chain, ATP synthesis coupled electron transport, melanin biosynthesis process, monosaccharide transportation activity, DNA repair, and endopeptidase activity . MoreoveDrosophila populations from Africa and Europe also reveal metabolic differences [p = 0.0007, p < 0.0001, p > 0.7 for both traits, p > 0.05, The GO enrichment analysis suggests that several phenotypes changed in response to the hot environment. We chose resting metabolic rate and fat content as high-level phenotypes for experimental validation because (1) they both reflect the enrichment for metabolic pathways (oxidative phosphorylation pathway), (2) fat content has been shown to respond to temperature , and (3)ferences ,35. Conss) for beneficial alleles. Furthermore, the highly convergent phenotypic response across the replicates allowed us to investigate the fundamental question of whether adaptation to a specific stress is driven by the same alleles in all replicates or whether multiple alternative genetic routes are possible.Our experimental setting allowed us to empirically quantify several fundamental variables in adaptation genetics, including the starting frequency and distribution of selection coefficients  remain(s) unknown. Hence, we estimated the frequency of each selected allele using the median frequency of all its marker SNPs. Most of the selected alleles started from a low frequency in the ancestral population, but several alleles were rather common with frequencies up to 0.75 . The strWe used the heterogeneous genomic pattern among replicates  to disceNe) of the experimental populations  but without linkage and epistasis. Using replicate-specific Ne estimates  was used  in 1,000 iterations with allele-specific starting frequencies and s  than for randomly combined alleles of the redundancy paradigm  with stabilizing selection after a shift in trait optimum. We assumed equal effect size for all 99 alleles in 10 replicates with starting frequencies matching the experimental data  and simus = 0.059) do occur at intermediate frequencies , and locus size . Because only the starting frequency (p < 1.11 × 10−12) and replicate frequency (p < 2.2 × 10−16) were significant, our data do not support a model with many randomly distributed targets of selection. These conclusions are robust to the choice of frequency increase threshold for determining replicate frequency and estimating s  from a natural  Florida . Each isGenomic DNA was extracted for all replicates at generation 0  and all evolved replicates in 10 generation intervals until generation 60 (mixed sexes). The replicates at generation 0 will be referred to as “ancestral population” hereafter. Details of DNA extraction and library preparation are provided in identify-genomic-indel-regions.pl with options—indel-window 5—min-count 167). The minimum read count cutoff corresponds to 2% of the average coverage across all ancestral and evolved replicates. We further masked 200-bp flanking the SNPs specific to autosomal genes translocated to the Y chromosome [SNPs were called from replicates of the ancestral population; in brief, SNPs with base quality of 40 in at least one of the 10 ancestral replicates were selected for further analyses. To improve the reliability of the pipeline, the polymorphic sites in the upper 1% and lower 1% tails of the coverage distribution . Second, the pronounced AFCs specific to each replicate were determined by contrasting each ancestral replicate with the corresponding evolved replicate at generation 60  using Fisher’s exact test . For the parameters of fisher-test.pl, the minimum SNP coverage for each replicate was set to 5% of the average coverage of ancestral and evolved (generation 60) samples. For the maximum coverage, the upper 1% tail of the coverage distribution in the sample with the highest sequencing depth was chosen  assuming independence among SNPs. As a first step for the neutral simulations, Ne was estimated for each of the replicates. We used windows of 1,000 SNPs based on AFCs between the ancestral and evolved replicates at generation 60 for autosomes (each chromosome separately) and the X chromosome using Nest . We averaged the medians of the Ne estimates across replicates; the estimated Ne was 291 for autosomes and 262 for the X chromosome  matched the experimental data. Candidate SNPs were inferred based on an empirical CMH/Fisher’s exact test cutoff (q ≤ 0.05) using neutral simulations. After correction, we obtained 47,532 candidate SNPs across replicates (CMH test), and 4,667 additional SNPs deviated from neutral expectations in at least one of the replicates (Fisher’s exact test). This number of candidate SNPs is still heavily inflated due to the considerable linkage expected in our experiment.Neither the CMH nor the Fisher’s exact test accounts for drift. Thus, to determine the candidate SNPs whose AFCs were higher than expected under drift, neutral Wright-Fisher simulations were performed with Nest   in e blocks ,57. While blocks ,58,59, te blocks . We rease blocks . Thus, wnumpy.arcsin and numpy.sqrt functions in Python) and standardized, i.e., centered to mean and scaled to unit variance . The left and right arms of Chromosomes 2 and 3 were concatenated for this analysis, because some haplotype blocks overlap the centromere. All pairwise Pearson’s correlation coefficients were computed among SNPs in sliding windows of 1 Mb with a step size of 500 kb. Each window should have at least a minimum of 20 candidate SNPs. The correlation matrix was converted to Euclidean distance, d = √2(1 − r), and entered in a distance matrix , which was used for hierarchical clustering (using function scipy.cluster.hierarchy.linkage in Python). SNPs with the minimum average Pearson’s correlation coefficient of 0.75 were classified into a cluster. Clusters obtained from overlapping windows were merged if they shared at least five SNPs. Only clusters with more than 20 SNPs were retained.First, we grouped candidate SNPs together with stringent clustering (minimum average Pearson’s correlation coefficient of 0.75 among SNPs). Candidate SNPs (q ≤ 0.05) inferred from both CMH and Fisher’s exact tests were combined and used for the selected haplotype block reconstruction; the candidate SNPs from the CMH test were polarized based on the rising allele, and only SNPs with a frequency increase of ≥0.2 (between any time points from F0 to F60) in at least two replicates were retained. Additionally, all candidate SNPs from Fisher’s exact tests were polarized based on the rising allele and used for block reconstruction. The allele frequencies of these candidate SNPs were transformed .Typically, multiple haplotypes are associated with a putative selection target, in particular when it occurs at higher frequency in the ancestral population. Because we cannot identify the selected mutation, we use the term “selected allele” in the subsequent analyses to refer to selected haplotype blocks , i.e., ds) and the results of computer simulations under the different selection regimes critically depend on the allele frequency of the selection target, we used six different methods to estimate the frequency of the selected allele. In the ancestral and evolved populations, the frequency of selection targets is estimated by the median frequency of all SNPs characterizing a selected allele . Furthermore, our analyses require the distinction between selection targets increasing in frequency  and those not contributing to adaptation.Despite selected haplotype blocks being reconstructed with high confidence, the selected sequence variant remains unknown. Because estimates of the selection coefficient (s (see below: “Inference of selection coefficient”).Method 1 and method 2 use an ad hoc cutoff for AFC in the contrast between the ancestral population and the evolved replicates at generation 60. Thus, replicates with at least a 0.1 (method 1) or 0.2 (method 2) frequency increase of selected alleles after 60 generations were considered to have alleles contributing to adaptation and consequently were included in the calculation of s was computed for all 10 replicates (“Inference of selection coefficient”), and the highest s was used for forward Wright-Fisher simulations. A total of 1,000 simulations were performed for each allele using PoolSeq [wf.traj) using replicate-specific Ne  of selection: each selected allele was divided into several regions (clustering with stringent correlation coefficient of 0.75), and for each region, the median s across the replicates with a frequency increase ≥0.1 was computed, similar to method 1. The region with the largest s was chosen as the “core region” of the selected allele, and s  were estimated assuming codominance (h = 0.5) with PoolSeq [estimateSH), which uses time series allele frequency data to infer s. To account for the uncertainty about the selection targets, we used six different approaches to determine whether an allele contributed to adaptation in a replicate . For replicates with frequency increase ≥threshold after 60 generations (determined by methods 1–6), s was estimated, and the median s across the corresponding replicates is reported as s of the selected allele (Selection coefficients ( PoolSeq  (using ts of those replicates with frequency increase ≥threshold (methods 1–6) results in a biased s estimate. Thus, rather than reporting the median s, we also reported s separately for each replicate. We show the replicate-specific s for all approaches of estimating s  and interaction between replicate frequency and p0. We estimated s using methods 1, 3, and 4 . s and replicate frequency were log10-transformed (log10 function in R), and the square root of p0 was arcsin-transformed (asin and sqrt functions in R). We accounted for nonlinearity of p0 by adding a quadratic term (squared p0) to the model. The interaction between replicate frequency and p0 was not significant and was therefore dropped from the model (sijklm ~ μ + p0i + replicate frequencyj + p02k + sizel + errorijklm). The data met the assumptions of normality of the residuals and homogeneity of variance. Moreover, we used the D. simulans recombination map (Dsim_recombination_map_LOESS_100kb_1.txt in [size) into genetic distance: genetic distance. We then performed the regression model (sijklm ~ μ + p0i + replicate frequencyj + p02k + genetic distancel + errorijklm). We did not find a significant contribution of locus size or genetic distance (s estimation (We identified factors influencing 1.txt in ) to convdistance . Similartimation .We observed a highly heterogeneous response across the 10 evolved replicates; most of the 99 selected alleles increase in only four to six replicates . We usedTo compare different evolutionary paradigms 2 of different simulations using one-way ANOVA with Tukey’s honest significant difference (HSD).In each evolutionary scenario summarized in section A–E, 1,000 sets of 10 replicates  were simulated, RFS was determined for each set, and mean and 95% CI were computed for each category of RFS . For each simulation, we compared RFS of the empirical data (observed) and the mean RFS of simulations (expected) using Σ(obs − exp)We also estimated the similarity among replicates by calculating the Jaccard index of all pairwise combinations in evolved replicates using the same three thresholds used for QT simulations . Pairwise Jaccard indices were also computed for each simulation set of 10 replicates for all evolutionary scenarios in A–E using the same three frequency change thresholds: methods 1, 3, and 4 Figs  and S8.wf.traj). In each set, 99 independent alleles  were simulated for 60 generations in 10 replicates using the allele-specific starting frequency, replicate-specific Ne . To exclude the possibility that an overestimate of s for alleles that increased in frequency only in a small number of replicates affects the outcome of simulations, we repeated the simulations  in 1,000 iterations for 60 generations with the recombination rate estimated from the haplotypes (Dsim_recombination_map_LOESS_100kb_1.txt in [s). We also repeated the simulations (w in MimicrEE2 (version mim2-v193), which uses haplotype information and also accounts for the differences between the X chromosome and autosomes [We simulated a sweep paradigm assuming linkage among selected alleles to rule out that Hill-Robertson interference caused the genetic heterogeneity among the replicates. We used 189 individually sequenced haplotypes from the ancestral population  for the 1.txt in ). Similaulations  with theulations  using onulations . The posutosomes . SimulatSimilar to sweep simulations in section A, the RFS difference between empirical and simulated data was high, and we did not obtain a good fit to the empirical RFS  Figs.One prediction of the QT paradigm with stabilizing selection is that the same trait optimum can be obtained by different combinations of contributing alleles (genetic redundancy). Before simulating the trajectories of the selected alleles, we first tested genetic redundancy.d jackknifing. In each set, the number of selected alleles for each replicate matched our observations , a different number of selected alleles was detected among the replicate populations . Neverth 3 and 4 .frequencyAt-pheno-quantitative.py).To determine whether the genomic heterogeneity among the replicates of the empirical data are compatible with the QT paradigm, we simulated frequency trajectories of alleles contributing to a QT after a change in trait optimum D in . Using fD. simulans recombination map (Dsim_recombination_map_LOESS_100kb_1.txt in [qff of MimicrEE2 (version mim2-v193) [We simulated adaptation of a QT to a new trait optimum in a diploid population of 300 individuals assuming linkage among the selected alleles. Similar to the QT paradigm without linkage, the phenotypic values of the trait were mapped to fitness using a Gaussian fitness function. We performed 1,000 iterations of forward simulations of a QT in 10 replicates with 99 contributing alleles with equal effects E in . The pos1.txt in ). Simulam2-v193) .Because the ancestral population experienced the invasion of a P-element ,40, it mbwasw algorithm). The reference genome consists of the repeat-masked reference genome and TE sequences as described in [se2pe), and a ppileup file was generated using PoPoolationTE2 [subsamplePpileup), and TE insertions were identified with PoPoolationTE2 (functions identifySignatures and frequency). The identified TE insertions were filtered (filterSignatures with parameters—max-otherte-count 2—max-structvar-count 2), and the final set of insertions was identified (function pairupSignatures).The raw reads of evolved replicates at generation 60 were separately mapped to a TE-merged reference genome using bwa  version ribed in . The paiationTE2 . All evoWe used different frequency thresholds for identification of selected alleles . We filtered the identified P-elements by selecting those with frequencies ≥threshold that have insertion sites in the genomic regions corresponding to selected alleles. Furthermore, the P-elements with frequency increase must be present in at least one of the replicates that have frequency increase of ≥threshold for any given selected allele. Then, for each selected allele with an available P-element in the filtered data set, we computed delta (absolute frequency difference) using the frequency of the allele in any of the two replicates with highest frequency at generation 60 and the frequency of the P-element in those replicates.We used Gowinda  to assocNe of isofemale lines is very small, very limited adaptation is expected to have occurred during their maintenance in the lab [All phenotypic assays were performed in a common garden setting (with temperature fluctuating between 18°C and 28°C) to eliminate the possibility that uncontrolled environmental variation is affecting the phenotypic measurements. We reconstituted the ancestral population from the isofemale lines that were used to seed the original ancestral population. Because  the lab . Even mu2 anesthesia, we only started measuring fecundity in two-day-old flies. For the next four days, which corresponds to the egg-laying period during the experimental evolution, the flies were daily transferred to new bottles without CO2 anesthesia, and the eggs in each bottle were counted. After the fourth day, females and males were separated, and the females were counted, dried, and weighed. Fecundity—measured as the total number of eggs laid per female during four days (between day two to five after eclosion)—was log10-transformed and analyzed using a linear model.At generation 103, between three to six subreplicates were set up for each of the 10 evolved replicates to ensure reliable fecundity estimates. Ten subreplicates were set up for the ancestral population. Immediately after eclosion, around 250 flies  were put in a bottle. Because the flies were collected under COanova. Therefore, following the principle of parsimony, we present results from the linear fixed effects model (Fecundityijk ~ μ + populationi + average body weightj + errorijk). The data met the assumptions of normality of the residuals and homogeneity of variance.To test for the significance between fecundity of the ancestral and evolved  populations, we initially fitted a linear mixed model with a fixed categorical effect (population) with two levels  and a fixed continuous effect (average body weight). The subreplicates of the ancestral and evolved populations were included as a random effect. The mixed model was not significantly different from the linear fixed effects model tested using the function lsmeans [The model to test for differences between the 10 evolved replicates was the same as above, but with population being a fixed categorical effect with 10 levels. The mixed effects model including a random effect to model the covariance between the subreplicates of each evolved replicate was not significantly better than the fixed effects model and hence we dropped it in favor of the simpler fixed effects model. Significance of the fixed effects was tested using ANOVA F-tests. We present effect sizes as lsmeans calculated with package lsmeans  and usedDrosophila medium (150 flies in a bottle). After 48 hours of CO2 anesthesia recovery, flies were used for metabolic rate measurement. Females and males were four to five and six to seven days old, respectively, during resting metabolic measurements. Our preliminary assays identified no significant difference in the metabolic rate of four- to five- and six- to seven-day-old males when groups of 25 males were tested in RC respirometry chambers . For measuring metabolic rate, 150 flies were transferred to a 250-mL bottle without CO2 anesthesia. To avoid desiccation and starvation, 50 mL of Drosophila medium was placed in each bottle and sealed with a stopper. The resting metabolic rate was measured by repeated CO2 emission measurements of stop-flow respirometry (Sable Systems). Flies of different replicates were randomly assigned to each bottle, and one bottle with only Drosophila medium was used as an empty control in each run. CO2 measurement was conducted at 18°C in the dark, overnight for at least 12 hours. During the measurement assay, an 8-channel multiplexer (RM8 Intelligent Multiplexer) controlled the sequential flushing and closing of eight bottles. Each bottle was flushed for 15 minutes at a constant flow rate of 75 μL/min. After the flush phase, the bottle was closed while the rest of bottles continued to be flushed. Therefore, CO2 in each bottle was measured every two hours. The flushed air passed through a magnesium perchlorate column to remove water, and CO2 was measured with a CA-10A carbon dioxide analyzer (Sable Systems). At the end of each run, the flies were counted, dried, and weighed. An in-house macro was used for computing the total CO2 emission during each flushing time and the mean flow rate by ExpeData software. The resting metabolic rate for each bottle was computed as the average of the three lowest data points [CO2 μL h−1 mg−1. A total of 32 subreplicates (16 for each sex) were measured for the ancestral population. At least six subreplicates were measured for each evolved replicate (three for each sex).The evolved populations were at generation 113 when phenotyped for resting metabolic rate. The flies used for measurement of metabolic rate were maintained at controlled density as described above . Flies were collected immediately after eclosion and mated for 24 hours, and then females and males were separated and maintained in bottles with a points . The metijk ~ μ + populationi + sexj + populationi: sexj + errorijk). The data met the assumptions of normality of the residuals and homogeneity of variance.To test for the significance between the metabolic rate of the ancestral and evolved  populations, we initially fitted a linear mixed model with two fixed categorical effects (population and sex) each with two levels  and interaction between the fixed categorical effects. Subreplicates of the ancestral population and subreplicates of the evolved replicates were included as a random effect. Similar to fecundity, the mixed model was not significantly different from the linear fixed effects model. Therefore, we present results from the linear fixed-effects model . After eclosion, flies were mated for 1 day, and females and males were separated and placed in vials . After 48 hours, the body fat was measured in four-day-old flies three hours after the start of the daily 28°C cycle. Homogenates were prepared as described in , and lipijk ~ μ + populationi + sexj + populationi: sexj + errorijk). The data met the assumptions of normality of the residuals and homogeneity of variance.The measured fat content  was log-transformed and analyzed using a linear model. To test for the significance between the fat content of the ancestral and evolved  populations, we initially fitted a linear mixed model with two fixed categorical effects (population and sex) each with two levels , a fixed continuous effect (average body weight), and interaction between the fixed categorical effects. The subreplicates of the ancestral population and subreplicates of the evolved replicates were included as a random effect. Due to error during weighing, for six subreplicates, we did not provide weight values in the linear model. The average body weight was not significant and was therefore dropped from the model. The mixed model was also not significantly different from the linear fixed effects model. Therefore, we present results from the linear fixed effects model  and R version 3.3.1 .S1 Figp < 0.0001). The number of eggs laid over four days (two to five days after eclosion) were counted, (B) Females of 10 evolved replicates are equally fecund . Similar fat content (C) and metabolic rate (D) were measured among males of the evolved replicates . The bars show least-squares means of the linear model, and error bars depict 95% confidence levels of least-squares means. The dark khaki horizontal bar shows the 95% confidence levels of least-squares means of the ancestral population. Data deposited in the Dryad Repository: https://doi.org/10.5061/dryad.rr137kn. HSD, honest significant difference; TAG, triglyceride.(A) Evolved females are more fecund than the ancestral population Click here for additional data file.S2 FigFifty percent of the haplotype blocks were smaller than 100 Kb, but approximately 25% were larger than 1 Mb. Data available in (PNG)Click here for additional data file.S3 Fighttps://doi.org/10.5061/dryad.rr137kn. AFC, allele frequency change; ASFC, allele-specific frequency change; RFS, replicate frequency spectrum.The RFS shows the frequency distribution of replicates in which selected alleles increase in frequency. Different thresholds were used to identify an allele as selected in each replicate; Top panel: ≥0.1 (method 1) and ≥0.2 AFC (method 2): an allele with ≥0.1/0.2 frequency change, bottom panel: ≥5% (method 3) and ≥10% (method 4) ASFC: lower 5%/10% tail of AFC in selective sweep simulations . Regardless of the threshold used to determine a selected allele in a given replicate, a heterogeneous pattern among replicates is observed. Data deposited in the Dryad Repository: (PNG)Click here for additional data file.S4 Figs) was estimated using the median frequency trajectories of selected alleles in replicates with ≥0.1  and ≥0.2  AFC. s was also estimated using the median frequency change in replicates with ≥5%  and ≥10%  ASFC. s and starting frequency of the selected alleles were estimated using either all SNP characteristic of a given selected allele  or only the “core region” (methods 5 and 6). See We compare different adaptive sweep and QT scenarios to the empirical data: selective sweep simulations of alleles without (panel A) and with (panel B) linkage were studied, as well as different aspects of a QT paradigm: genetic redundancy (panel C) and simulations of AFCs assuming a QT with stabilizing selection without (panel D) and with (panel E) linkage among alleles. Sweep simulations (panel A and B) were performed for 99  and 79 alleles . The selection coefficient ((PDF)Click here for additional data file.S5 Figs estimation (A–C). ASFC thresholds of ≥5% (method 3) and ≥10% (method 4) were used to determine selected alleles in each replicate, s in replicates with selected alleles was estimated and median is reported (E and H). Panels K and N show the estimated s for the region with the highest estimated s in each allele (methods 5 and 6). See s was estimated for replicates with a selected allele using different frequency increase thresholds (methods 2–6) and the median s across the replicates is reported in B, E, H, K, and N , ≥5% ASFC (G), ≥10% ASFC (J), and ≥0.1 AFC (M) and ≥0.2 AFC (P) for the core regions of selected alleles is shown. The estimated s using all approaches agrees (similar mean and median), but frequency trajectories of replicates with ≥0.1 AFC  was computed, and the frequency trajectory of replicates with ≥0.1 (method 1) and ≥0.2 (method 2) AFC until generation 60 were used for ≥0.1 AFC  and ≥5% (PDF)Click here for additional data file.S6 Figs across replicates and no linkage”) and is indicated in light gray (mean: black line). Simulations performed with median s estimated from frequency trajectories of replicates with (A) ≥0.2 AFC for an allele  in ≥4 replicates in experimental data. Note that alleles identified in only 1–3 replicates had high s and low starting frequency  is indicated by salmon dots. The expected distribution of RFS was obtained by computer simulations  ≥5%n allele , and (C)n allele .   ≥0.1 D,  and ≥0.2 ≥0.1 D, . (F) Simrequency  and wererequency . All sim(PDF)Click here for additional data file.S7 Figs across replicates”) and is indicated in dark gray (mean in black line). The same selection coefficients and starting frequencies were used as in https://doi.org/10.5061/dryad.rr137kn. RFS, replicate frequency spectrum.RFS shows the frequency distribution of replicates in which selected alleles increase in frequency. RFS of experimental data (observed) is indicated by salmon dots. The expected distribution of RFS was obtained by computer simulations (see (PDF)Click here for additional data file.S8 FigComparison of the genetic heterogeneity (A–B) and replicate similarity (C–D) of the selective sweep and QT paradigm simulations to the observed data.  The difference between RFS of empirical (observed) and the simulated (expected) data. For 1,000 iterations of each simulation, the difference between empirical and simulated RFS, Σ(obs − exp)2, is shown.  Pairwise Jaccard indices among 10 replicates in empirical data and in 1,000 iterations of each simulation. The threshold to determine selected alleles in empirical and simulated data in each replicate is ≥5% ASFC in A, C and ≥10% ASFC in B, D . Data in panels A and C were simulated under QT paradigm without Click here for additional data file.S9 Fighttps://doi.org/10.5061/dryad.rr137kn. ASFC, allele-specific frequency change; RFS, replicate frequency spectrum.The RFS shows the frequency distribution of replicates in which selected alleles increase in frequency . The RFS of experimental data (observed) is indicated by salmon dots. The expected distribution of RFS was obtained by 1,000 iterations of delete-d jackknifing computer simulations  and is indicated in blue (mean: black line). The number of randomly drawn alleles from 99 alleles in each set of simulations was equal to the number of selected alleles in each replicate with ≥5% and ≥10% ASFC in A and B, respectively. Data deposited in the Dryad Repository: (PDF)Click here for additional data file.S10 Figs. Boxplots show the first and third quartile of the distribution, and horizontal bars in each box shows the median in each category. The data of individual selected alleles are shown as scattered dots in each boxplot. Data deposited in the Dryad Repository: https://doi.org/10.5061/dryad.rr137kn.Starting frequency (top panel) and selection coefficient (bottom panel) of the selected alleles classified by the number of replicates in which a given selected allele has ≥0.1 frequency increase at generation 60 . The selected alleles that increased in frequency (≥0.1) in only one to three replicates have low starting frequencies and the highest estimated (PDF)Click here for additional data file.S11 Fighttps://doi.org/10.5061/dryad.rr137kn. ASFC, allele-specific frequency change; QT, quantitative trait; RFS, replicate frequency spectrum.The RFS shows the frequency distribution of replicates in which selected alleles increase in frequency . The RFS of experimental data (observed) is indicated by salmon dots. The expected distribution of RFS was obtained by 1,000 iterations of computer simulations  in 10 replicates for 60 generations with 99 contributing alleles having the same starting frequency as the selected alleles in the empirical data  and is i(PDF)Click here for additional data file.S12 Fighttps://doi.org/10.5061/dryad.rr137kn. ASFC, allele-specific frequency change; QT, quantitative trait; RFS, replicate frequency spectrum.RFS shows the frequency distribution of replicates in which selected alleles increase in frequency . The RFS of experimental data (observed) is indicated by salmon dots. The expected distribution of RFS was obtained by 1,000 iterations of computer simulations  in 10 replicates for 60 generations with 99 contributing alleles having the same starting frequency as the selected alleles in the empirical data  and is i(PDF)Click here for additional data file.S13 Fig(A) Gaussian fitness function used in QT paradigm without linkage D in  optimum (PDF)Click here for additional data file.S1 TableIn cases in which the block spans both arms of the chromosome, both chromosome arms are specified. chr, chromosome; num, an arbitrary number given to the haplotype block of each chromosome; pos (bp), the genomic position of block in the chromosome; size (kb), length of the block in kb; SNP nums, the number of SNP in the haplotype block; rising reps. num., the number of replicates in which a haplotype block has at least 0.1 frequency increase; rising reps., the replicates with at least 0.1 frequency increase; columns rep1 to rep10 show the number of haplotypes that share at least 80% of the SNPs characteristic to the haplotype block in replicates 1, 3, 4, 7, and 10, respectively, for which evolved haplotypes were sequenced. The number of available haplotypes is given in parentheses; validated haplos. num. evolved, total number of haplotypes across all five replicates in which the haplotype block is validated by at least 80% of the SNPs characteristic to the block; validated haplos. num. ancestral, total number of haplotypes from 189 ancestral haplotypes in which the haplotype block is validated by at least 80% of the SNPs characteristic to the block. SNP, single nucleotide polymorphism.(XLSX)Click here for additional data file.S2 Table“*” indicates uncorrected for multiple testing. “$” indicates p-value after adjustment for multiple testing.(XLSX)Click here for additional data file.S3 Table“*” indicates uncorrected for multiple testing. “$” indicates p-value after adjustment for multiple testing.(XLSX)Click here for additional data file.S4 TableChr.: left and right arms of chromosomes are concatenated as some haplotype blocks span the centromere. No.: an arbitrary number given to the haplotype block of each chromosome (similar to the numbers in (XLSX)Click here for additional data file.S5 TableNe, effective population size.(XLSX)Click here for additional data file.S6 Tables was computed based on the frequency trajectory of a selected allele in replicates with ≥5% and ≥10% ASFC . Standard errors are shown in parentheses. The table has been made using stargazer . stargazer: Well-Formatted Regression and Summary Statistics Tables. R package version 5.2.2. https://CRAN.R-project.org/package=stargazer). ASFC, allele-specific frequency change.(A) The size of haplotype block is used as physical or genetic distance. (B) The estimated (DOCX)Click here for additional data file.S7 TableDetails of DNA extraction and library preparation for Pool-Seq (A) and haplotype samples (B). (A) For the founder population, nomenclature is as follows: species_population_selectionRegime_replicate , and for the evolved populations, nomenclature is as follows: species_population_selectionRegime_generation_replicate . (B) Nomenclature of the samples is as follows: species_population_selectionRegime_generation_replicate_cross .(XLSX)Click here for additional data file.S8 Table(XLSX)Click here for additional data file.


Genome-wide association studies have identified hundreds of loci that influence a wide variety of complex human traits; however, little is known regarding the biological mechanism of action of these loci. The recent accumulation of functional genomics (“omics”), including metabolomics data, has created new opportunities for studying the functional role of specific changes in the genome. Functional genomic data are characterized by their high dimensionality, the presence of (strong) statistical dependency between traits, and, potentially, complex genetic control. Therefore, the analysis of such data requires specific statistical genetics methods.To facilitate our understanding of the genetic control of omics phenotypes, we propose a trait-centered, network-based conditional genetic association (cGAS) approach for identifying the direct effects of genetic variants on omics-based traits. For each trait of interest, we selected from a biological network a set of other traits to be used as covariates in the cGAS. The network can be reconstructed either from biological pathway databases (a mechanistic approach) or directly from the data, using a Gaussian graphical model applied to the metabolome (a data-driven approach). We derived mathematical expressions that allow comparison of the power of univariate analyses with conditional genetic association analyses. We then tested our approach using data from a population-based Cooperative Health Research in the region of Augsburg (KORA) study  with measured data for 151 metabolites.We found that compared to single-trait analysis, performing a genetic association analysis that includes biologically relevant covariates can either gain or lose power, depending on specific pleiotropic scenarios, for which we provide empirical examples. In the context of analyzed metabolomics data, the mechanistic network approach had more power compared to the data-driven approach. Nevertheless, we believe that our analysis shows that neither a prior-knowledge-only approach nor a phenotypic-data-only approach is optimal, and we discuss possibilities for improvement. Genome-wide association studies (GWASs) are a highly popular method for identifying alleles that affect complex traits in humans, including the risk of common diseases. In the past decade, GWASs have enabled the identification of thousands of loci, significantly increasing our understanding of the genetic basis underlying the control of complex human traits . On the Metabolomics is an emerging field that has been studied extensively in the past decade. A number of GWASs of metabolites have been performed using various platforms –8, reveaN-glycomics. Indeed, not only did our multivariate analysis double the number of loci identified in the analysis sample but also all five novel loci were strongly replicated. With respect to metabolomics, Inouye et al. [P-value < 5 × 10−11 for at least one metabolite . These findings provide empirical evidence supporting the value of using multivariate methods to analyze the genomics of metabolic traits, at least in the context of locus discovery.However, analyzing metabolomics data requires specialized statistical methods due to their characteristically high dimensionality and the presence of statistical dependencies that reflect biological relationships between different variables. Conventional univariate GWAS (uGAS) approaches ignore any possible dependencies between different omics traits, which can confound the biological interpretation of the results and may lead to a loss of statistical power. On the other hand, utilizing multivariate phenotype information increases the statistical power of the association tests compared to univariate analysis –12. Despe et al.  performee et al. . We founIt should be noted that these multivariate methods and tests were developed by statistical geneticists to specifically increase the power of gene identification. In such “gene-centric” tests, the model that includes the effects of genotype on multiple traits is contrasted with the null model in which the gene has no effect on any trait analyzed. Although useful and powerful for genetic mapping, this approach may have limited interpretability in a context in which one is interested in the genetic control and biology of a specific trait or a subset of traits (the “trait-centered” view). Several statistical methods have been suggested to address the question of which specific traits are affected in an analyzed ensemble . OnHere, we present a statistical model in which a given trait depends on a genetic polymorphism and in which a number of related traits are included in the model as covariates. In this model, the relationship between the genotype and the trait of interest is our primary focus. Analyzing such a model allows us to identify the direct effect of genetics on the trait of interest. Mathematically, the model is equivalent to the model used by Deng and Pan . We firsAs might be expected, and as demonstrated here, the choice of covariates plays a critical role in conditional analyses. First, we use the assumption that the covariates  are known. Second, we explore the problem of selecting appropriate covariates, and we test the approaches by performing a proof-of-principle study using metabolomics data consisting of 151 metabolites (Biocrates assay) obtained from the KORA  F4 study . Specifically, we select covariates based on existing knowledge from metabolite biochemical networks (BN-cGAS) and use a data-driven approach based on Gaussian graphical modeling (GGM-cGAS). Finally, we compare and discuss the obtained results, and we discuss possible applications for this analysis based on biologically and/or statistically relevant traits.We start with the theoretical substantiation and identification of specific scenarios in which adjusting for biologically relevant covariates can modify the power of an association analysis.y, covariate c, and genotype g. We can formulate this problem in terms of a linear regression as follows: y, g, and c can be specified using a set of three correlation coefficients, y and g, we use the Wald test, which is defined as the square of the ratio between the effect estimate and its standard error, with the latter estimated under the alternative hypothesis  and y. Note that under the null hypothesis, when n is large, both n is thousands or orders of magnitude more.Let us consider a trait of interest sis (see ). The vaFor the conditional model, Because the first term in Equation  is depeny and covariate c, which is induced by the variation in the genotype g. This quantity takes a central place in a Mendelian randomization analysis, which uses a genetic variation to anchor the causality arrow and consequently infers a causal relation between various traits . Note tIn general, the genetically induced covariance and the residual covariance are expected to have a concordant sign (see Discussion for details and relevant references). Thus, we conclude somewhat surprisingly that when genotype-induced and environmental correlations are similar in sign , the product mentclass2pt{minimmentclass2pt{minimk covariates are included in the conditional model. Denoting the estimated coefficients of correlation between g and covariate i as y and covariate i as We can readily extend Equation  to a sitWhen appropriate covariates are selected, performing cGAS using individual-level data becomes rather trivial and can be achieved using standard statistical and software tools in which one estimates the effects of a SNP and covariates. However, cGAS becomes somewhat less trivial if one chooses to use summary-level univariate GWAS data such as data available from previously published studies. The formalization of cGAS in terms of summary univariate GWAS statistics is described in The ability to select appropriate covariates is extremely important, as it can have direct implications regarding the outcome of the analysis. If the biological/biochemical relationships between traits of interest are known and are summarized in a database(s), this knowledge can be used directly, e.g., by using all direct neighbors as covariates. We refer to this approach as a biochemical network-driven cGAS (BN-cGAS). Alternatively, the network can be reconstructed in a hypothesis-free, empirical manner from the data, e.g., using a GGM . We refeWe compared cGAS and uGAS by performing a genome-wide analysis of genetic effects using summary-level data obtained from the KORA F4 study. This study included 151 metabolites measured in 1,784 individuals using the Biocrates assay and imputed at 1,717,498 SNPs.First, we examined the potential of using cGAS when the covariates are selected based on a known biochemical network . Thus, our analysis was restricted to a subset of 105 metabolites for which at least the one-reaction-step immediate biochemical neighbors are known . This biETFDH) but identified two more (ACSL1 for PC ae C42:5 and PKD2L1 for lyso-PC a C16:1). It is interesting to note that for ACSL1, the effect of SNP rs4862429 on PC ae C42:5 was highly significant (P = 7e-11) with BN-cGAS but was not significant (P = 0.7) with uGAS; this outcome is to be expected under the model of unexpected pleiotropy.Table P = 0.123)  see .SLC22A4 locus, the SNP-trait pairs for which BN-cGAS had increased power are the pairs in which the second term in Equation (For the SNPs listed in Table FADS1) and a locus in which the pleiotropic component's contribution to power was negative (rs8396 at ETFDH). Figure FADS1 , which corresponds to a cutoff at p ≤ 8.83 × 10−7. The network used in our analysis is shown in Although using a known biochemical network to select covariates has many advantages, it may be somewhat impractical and perhaps even harmful, as our biochemical knowledge is still relatively incomplete. Therefore, we explored the potential of performing a cGAS in which the covariates are selected using a data-driven approach (GGM-cGAS). The network of metabolites was reconstructed using GGMs based on partial correlations. For a given metabolite, we selected covariates based on significant partial correlations. Specifically, we used the following threshold as proposed previously : a P-valTo compare GGM-cGAS with BN-cGAS, we used the same set of metabolites that we used for BN-cGAS to run our GGM-cGAS analysis; these results are presented in P = 6 × 10−5). Moreover, the second “pleiotropic” component in Equation (P = 0.015). Nevertheless, for 3 of 15 GGM-cGAS SNP-trait pairs, the pleiotropic component was positive. The average chi-square value was 25% smaller for GGM-cGAS than for BN-cGAS, indicating an average loss of power .For the best SNP-trait pairs detected by GGM-cGAS or BN-cGAS, we computed the components in Equation  and compEquation  was genep ≤ 5 × 10−8/151 . On the other hand, GGM-cGAS identified 19 significant loci using the same threshold. As expected, the standard errors of the genetic effect estimates were smaller for GGM-cGAS than for uGAS  or rs17112944 (LOC728755). Taking into account that this association was not found in (much) bigger meta-analysis, we conclude the observed associations with rs17112944 and rs2943644 as likely false positives, and these two loci were excluded from further consideration.Finally, we searched the available literature for the loci listed in Table We report a new “trait-centric” approach for analyzing genetic determinants of multivariate “omics” traits by performing a network-based cGAS analysis. In the context of metabolomics, for each trait we selected a set of other metabolites to be used as covariates in our genetic association analysis. The selection of covariates can be either mechanistic  or data driven . Importantly, this approach can use either individual-level or summary-level data. First, we mathematically compared the power of conditional and standard single-trait genetic association analyses (uGAS), and we identified scenarios in which these analyses are expected to produce different results. Next, we applied cGAS to 151 metabolomics traits (Biocrates panel) in a large  population-based KORA cohort.We found that the log-ratio between the cGAS and uGAS test statistic can be decomposed in a “noise” component  and a “pleiotropic” component. The pleiotropic component is negative in cases in which genetically induced covariance (between the trait of interest and the trait used as the covariate) and the residual covariance have the same sign . The pleiotropic component is positive in cases in which the genetically induced covariance and residual covariance act in opposite directions.Should one expect that genetically induced covariance and residual covariance act in the same or opposite directions? In essence, this is a question about the architecture of pleiotropy: is a pleiotropic genetic variant expected to induce the same covariance as would be induced by non-genetic mechanisms? It has been reasoned that in randomly bred populations, the genetic correlations are expected to arise primarily from pleiotropic gene action . In suchConsequently, for complex traits, one may expect that the sign of the pleiotropic component of the log-ratio between the cGAS and uGAS tests  is geneNevertheless, in the case of metabolomic traits, genetic and environmental sources do not necessarily generate consistent covariance. Moreover, for a given locus that affects the activity of an enzyme involved in a biochemical reaction, the unexpected inconsistency between genetically induced covariance and residual covariance may not be so unexpected after all. Indeed, consider an allele associated with an increased activity of an enzyme that converts substrate A into product B. One would expect that the levels of A and B are positively correlated. One would also expect that the allele is positively correlated with the level of product B and negatively correlated with the level of substrate A. This is precisely the scenario that yields a positive value for the second term in Equation , thus prETFDH gene, which was prioritized by Data-driven Expression-Prioritized Integration for Complex Traits (DEPICT) software  as the best candidate in this region , encodes the enzyme electron transfer flavoprotein (ETF) dehydrogenase, which plays a role in mitochondrial fatty acid oxidation. During this process, the acyl group is transferred from a long-chain acylcarnitine to a long-chain acetyl-CoA, which is then catabolized. ETF dehydrogenase participates in the catabolic process by transferring electrons from acyl-CoA dehydrogenase to the oxidative phosphorylation pathway. Thus, the ETFDH gene should affect all forms of long-chain acylcarnitines in the same way, and we can expect that the pleotropic effect of this gene on the acylcarnitines in our example  will be unidirectional. The presence of unidirectional genetic effects and the positive correlation between these acylcarnitines makes the second term in Equation . It The proper selection of sets of biologically related traits is extremely important for the conditional genetic association analysis method described here, as well as for multivariate methods that model the joint effects of genotype on an ensemble of traits. Here, we considered two alternative approaches—knowledge based and data driven—to finding the networks of related traits, with a subnetwork centered around a trait of interest used as the analyzed set. In principle, in the context of analyzed metabolomics data, the knowledge-based network approach has slightly higher power in the context of trait-centric genetic association analysis. However, we believe that our analysis revealed that both approaches are suboptimal. The knowledge-based network reconstruction has many advantages, but it may be somewhat unpractical, as our biochemical knowledge is still relatively incomplete. Secondly, by reconstructing the network while relying only on current knowledge, we may be missing new knowledge that may be revealed by the data. Finally, by including neighbors that are based only on biochemical information, we may miss covariance induced by technical confounders; adjusting for this may increase the power of analysis . LearninThe KORA study  is a series of population-based studies in the region of Augsburg in southern Germany . KORA F4The concentration of 163 metabolites were measured in 3,061 serum samples obtained from KORA F4 participants using flow injection electrospray ionization tandem mass spectrometry and the AbsoluteIDQ p150 Kit  . After aGenotyping was performed using the Affymetrix 6.0 SNP array , with further imputation using HapMap2 (release 22) as a reference panel, resulting in 1,717,498 SNPs . Both tP-values were calculated using the “ppcor” package [P < 0.01/(151*150/2) .Partial correlation coefficients and their  package  in R. Gr package  package  package , we cons2≥0.3, Hardy–Weinberg equilibrium p≥10−6, and , minor allele frequency (MAF) ≥0.1  were included in the analysis. The genomic control method was used to correct for any possible inflation of the test statistics. The genomic control [For the GWAS analysis, we used OmicABEL software . Prior t control  lambda vP-value) was selected to represent this locus. The cGAS and uGAS results were considered to reflect different loci if the strongest associations were in loci that were separated by at least 500 kb. The threshold for the genome-wide significance for 151 traits was set to P = 5 × 10−8/151 .In a specific analysis , we defined independent loci as groups of genome-wide significant associations that were separated by at least 500 kb or were located on different chromosomes. The strongest association  test statistics into the noise and pleiotropic components . Requests can be submitted online and are subject to approval by the KORA board.The code produced in relation to this work and all summary statistics and association data that are necessary to reproduce our results are stored in the GigaDB database  and on tSupplementary Note 1: cGAS using summary level dataSupplementary Note 2: Literature search for loci identified by cGAS and uGASSupplementary Table S1: BN- cGAS and GGM- cGAS for 105 metabolitesSupplementary Table S2: GGM-cGAS and uGAS for 151 metabolitesSupplementary Table S3: List of metabolites measured using the AbsoluteIDQ p150 KitSupplementary Figure S1: Partial correlations networkSupplementary Figure S2: Manhattan plots for cGAS and uGAS for 151 metabolitesSupplementary Figure S3: Comparison of effect estimates and their standard errors for SNPs from Table 2BN: biochemical network; BN-cGAS: cGAS based on biochemical networks; cGAS: conditional GWAS; DEPICT: Data-driven Expression-Prioritized Integration for Complex Traits; ETF: electron transfer flavoprotein; GGM-cGAS: Gaussian graphical modeling cGAS based on partial correlations network; GWAS: genome-wide association study; SNP: single-nucleotide polymorphism; uGAS: univariate GWAS (trait-by-trait); KORA study: Cooperative Health Research in the region of Augsburg; MAF - minor allele frequency.Y.S.A. is the founder and co-owner of PolyOmica, a private research organization that specializes in computational and statistical (gen)omics.The KORA study was initiated and financed by the Helmholtz Center Munich—German Research Center for Environmental Health, which is funded by the German Federal Ministry of Education and Research and by the State of Bavaria. The KORA study was supported by the Munich Center of Health Sciences , Ludwig Maximilian University of Munich, as part of the LMUinnovativ project.This work was supported by the European Union FP7 framework project Pain-Omics (grant 602736).S.Z.S. was supported by the Russian Ministry of Science and Education under the 5–100 Excellence Programme. Y.S.A.was supported by the Federal Agency of Scientific Organisations via the Institute of Cytology and Genetics (project 0324-2018-0017). Y.A.T. was supported by the Federal Agency of Scientific Organisations via the Institute of Cytology and Genetics (project 0324-2018-0017) and by the Russian Foundation for Basic Research (project 16-04-00360).Y.A.T., C.G., and Y.S.A. designed and supervised the study; C.P., J.A., K.G., and R.W.-S. collected the data; C.G. and K.S. contributed data for the analysis; Y.A.T., O.O.Z., and S.Z.S. analyzed the data; Y.A.T., Y.S.A., C.G., O.O.Z., J.K., and K.S. discussed and interpreted the results; Y.A.T., O.O.Z., C.G., and Y.S.A. wrote the manuscript. All authors contributed to and approve the final version of the manuscript.giga-d-17-00337_original_submission.pdfClick here for additional data file.giga-d-17-00337_revision_1.pdfClick here for additional data file.giga-d-17-00337_revision_2.pdfClick here for additional data file.giga-d-17-00337_revision_3.pdfClick here for additional data file.response_to_reviewer_comments_original_submission.pdfClick here for additional data file.response_to_reviewer_comments_revision_1.pdfClick here for additional data file.response_to_reviewer_comments_revision_2.pdfClick here for additional data file.reviewer_1_report_ -- Simina Boca1/22/2018 ReviewedClick here for additional data file.reviewer_1_report_revision_1 -- Simina Boca5/25/2018 ReviewedClick here for additional data file.reviewer_2_report_ -- Tim Ebbels2/2/2018 ReviewedClick here for additional data file.Supplemental FilesClick here for additional data file.


ABCB1-rs1045642 and CYP3A4-rs2242480, had a significant effect on AFP pharmacokinetics (P < 0.05) and were the predictor factors in a log-linear regression model. The ABCB1 and CYP3A4 gene polymorphisms were associated with a fast metabolizer phenotype. These results suggest that metabolism of AFP in the Mexican population is variable. In addition, the genetic variants ABCB1-rs1045642 and CYP3A4-rs2242480 may partially explain the AFP pharmacokinetic variability.Amfepramone (AFP) is an appetite-suppressant drug used in the treatment of obesity. Nonetheless, studies on interindividual pharmacokinetic variability and its association with genetic variants are limited. We employed a pharmacokinetic and pharmacogenetic approach to determine possible metabolic phenotypes of AFP and identify genetic markers that could affect the pharmacokinetic variability in a Mexican population. A controlled, randomized, crossover, single-blind, two-treatment, two-period, and two sequence clinical study of AFP (a single 75 mg dose) was conducted in 36 healthy Mexican volunteers who fulfilled the study requirements. Amfepramone plasma levels were measured using high-performance liquid chromatography mass spectrometry. Genotyping was performed using real-time PCR with TaqMan probes. Four AFP metabolizer phenotypes were found in our population: slow, normal, intermediate, and fast. Additionally, two gene polymorphisms,  Today, 36.3% of adolescents aged 12–19 years, and 72.5% of adults are overweight or obese2. Strictly, obesity is abnormal fat accumulation that can compromise health and be associated with other clinical conditions such as cardiovascular diseases, diabetes, musculoskeletal disorders, and cancer3.Currently, obesity is a major health problem worldwide. According to the organization for economic co-operation and development (OECD), in 2017, Mexico was among the countries with the highest obesity rates worldwide3. Therefore, most treatments are focused on calorie-restricted diets and an increase in physical activity. Only when aforementioned treatments yield unsatisfactory results, pharmacological treatment is recommended5. Anti-obesity pharmacotherapy is limited to a few options that either suppress appetite or increase energy expenditure4. At present, the Food and Drug Administration (FDA) has approved the short-term use of appetite suppressants such as amfepramone (AFP) and phentermine (PHE) to treat obesity7; however, adverse drug reactions have been reported for some of them9.Obesity is mainly caused by an energy surplus between calories consumed and calories expended10, where it not only induces the release of the neurotransmitters noradrenaline (NA) and dopamine (DA) but also suppresses their reuptake. This mechanism of action results in less appetite12. However, owing to the psychostimulant effects of AFP, cautions should be taken regarding the dose and in patients with a psychiatric or cardiovascular history15. Specifically, in a Mexican population, AFP has been proven to be effective and safe for long-term treatment16. Nonetheless, some mild to moderate adverse events have been associated with AFP use, such as dry mouth, insomnia, anxiety, irritability, constipation, headache, dizziness, and polydipsia16.The drug AFP, is classified as a schedule IV controlled substance in the United States and Canada because it acts on the central nervous system18 and, therefore, pharmacokinetic parameters19. For example, recent studies in a Mexican population have shown that genetic variation may explain 30–90% of the pharmacokinetic variability21 of Statins. Here, the most common genetic variation responsible for these pharmacokinetic discrepancies are polymorphisms in genes encoding metabolizing enzymes and transporters21. Nevertheless, no pharmacogenetic data is available for AFP.The wide range of pharmacological effects of drugs, from being safe and effective to the risk of adverse reactions, may involve genetic factors that control absorption, distribution, metabolism, and excretion (ADME) processes24. Two metabolic pathways seem to be involved: 1) N-dealkylation and reduction23 and 2) N-deethylation24. The enzymes encoded by CYP3A4 and CYP3A5 are the most important because they metabolize a significant percentage of drugs. These enzyme isoforms have similar substrate selectivity, and therefore metabolize the same compounds25. Previous studies reported that CYP3A4 performs N-dealkylation and N-deethylation27. Therefore, CYP3A4 and CYP3A5 may have an important role in AFP metabolism. However, other CYP isoforms25 and other enzymes of phase II metabolism, such as glutathione S-transferases (GSTs), may also be involved28.Therefore, to date, although after oral administration, AFP is extensively biotransformed to secondary metabolites; until now, the responsible metabolic enzymes are unknown24. Transporter proteins known to be involved in drug removal, such as the ones encoded by ABCB1, ABCG2, and SLCO1B1, may affect AFP plasma levels in the Mexican population21.Amfepramone and its metabolites are excreted via the kidneyBecause variations in genes involved in the ADME processes may affect the pharmacokinetic profile and, therefore, the effectivity of a drug, the aims of this study were: (1) to classify the AFP metabolism, (2) to evaluate the impact of genetic polymorphism related to drug metabolism in the Mexican population on AFP pharmacokinetics, and (3) to assess a possible association between genotypes and metabolizer phenotypes.P ≤ 6.97 × 10−5); the Body Mass Index (BMI) was not significantly different. No other features displayed significant variability  = 7.04 ± 2.86 ng/mL, area under the plasma concentration-time curve from 0 to the time of last measurement (AUC0-t) = 29.87 ± 12.39 ng/mL/h, AUC from time 0 extrapolated to infinity (AUC0-∞) = 36.59 ± 13.66 ng/mL/h, elimination rate constant in the terminal drug phase (Ke) = 0.1923 ± 0.0539, half-life (T1/2) = 7.04 ± 2.86 h, total drug clearance (Cl) = 2.32 ± 0.90 L/h/kg, volume of distribution (Vd) = 7.04 ± 2.86 L/kg. No sex differences were found in pharmacokinetic parameters , intermediate metabolizers (n = 9), normal metabolizers (n = 12), and fast metabolizers (n = 8)–as shown in Fig. P ≤ 0.026, Table 0-t values to validate the pharmacokinetic variability. The regression analysis suggests that the four metabolizer phenotypes offer a better prediction of the variability of AUC0-t . There was a > 6-fold difference in AFP pharmacokinetic parameters between the slowest metabolizer (Cmax = 17.80 ng/mL and AUC0-t = 78.85 ng/mL/h) and the fastest metabolizer (Cmax = 2.94 ng/mL and AUC0-t = 10.31 ng/mL/h). The pharmacokinetic profiles of the different metabolizer phenotypes are presented in Fig. Based on the multivariate analysisers n = 1, and fasP > 0.05) and passed the quality control tests of genotyping. Genotype frequencies are presented in Table All gene polymorphisms were in Hardy Weinberg Equilibrium (HWE)  in carriers of genotype C/C were significantly higher than those in carriers of the homozygous variant allele (T/T). The regression analysis confirmed our findings. Regarding CYP3A4-rs2242480, AUC0-t was significantly lower in carriers of the heterozygous genotype (C/T) than in subjects with the C/C genotype (P = 0.033). Moreover, the C/T genotype was associated with lower Cmax values than the T/T genotype (P = 0.021). In addition, the Cmax and AUC0-t values of the C/T genotype were significantly lower than those in the combined homozygous subjects .The association between genotype and phenotype was evaluated under the various genetic models and combinations of genotypes. We found two polymorphisms associated with the fast metabolizer phenotype Table . The C/C1; however, only a small fraction of the population goes to professional health services for diagnosis and treatment32. Pérez-Salgado et al. suggest that this could be because the government institutions in Mexico focus mainly on raising awareness among the population about diseases that derive from obesity32 and, therefore, research focused on improving the effectiveness of treatments is needed.In Mexico, the issue of obesity is alarming because of its high prevalence33 and, therefore, influence the pharmacokinetic variability34. Nevertheless, this variability could be eliminated if normalization by weight is performed33. Accordingly, in our findings, no sex differences were observed in pharmacokinetic parameters among males and females  with significant differences in pharmacokinetic parameters among the metabolizer phenotype groups . One polymorphic variant involved a gene that encodes a transporter protein (ABCB1-rs1045642), and the other located polymorphism involved genes encoding drug-metabolizing enzymes (CYP3A4-rs2242480). It is known that ABCB1 interacts with a multitude of structurally and biochemically unrelated substrates37. Therefore, ABCB1 affects plasma concentrations of and, consequently, the pharmacological response to several drugs, with inconsistent results. The latter depends on the population as well as the substrate analyzed44, for example, in a Chinese study population, the T/T genotype was associated with lower plasma concentrations of clopidogrel and its active metabolites45. However, in Korean subjects, the T/T genotype was associated with higher Cmax and AUC values of fexofenadine hydrochloride46. These discrepancies may be due to the fact that polymorphisms with silent effects can affect the time of protein translation and, therefore, its folding, causing changes in substrate specificity47. Regarding anti-obesity drugs, Lloret et al. conducted a study on the role of ABCB1-rs1045642 gene variant on oral morphine pharmacokinetics; however, they did not find an association between the two48. Nevertheless, there is no information on the effect of ABCB1 on AFP pharmacokinetics. For the Mexican population, the C/C genotype of the rs1045642 polymorphism has been associated with the atorvastatin fast metabolizer phenotype because of a significant effect on Cmax and AUC0-t values among 60 healthy volunteers20. The results of the present study are consistent with the previous report; carriers of the C/C genotype showed significantly lower Cmax, AUC0-t, and AUC0-∞ values  found in our study population was different from those reported in populations from the United Kingdom (0.09)53, Poland (0.08)50, and Asia (0.29)54.max, AUC0-t, and Cl can be predicted  of AFP  at Ipharma S.A. The study complies with the guidelines of the Declaration of Helsinki, the Good Clinical Practice Standards of Tokyo, and the Mexican regulations for studies of bioavailability and bioequivalence. The clinical protocol was approved by the Research and Ethics Committee of the Clinical and Experimental Pharmacology Center, Ipharma S. A. , and the pharmacogenetic procedure was approved by the Ethics, Research, and Biosecurity Committees of the University of Monterrey . The study was registered in the National Registry of Clinical Trials  at the Federal Commission for Protection Against Health Risks (COFEPRIS) and Australian New Zealand Clinical Trials Registry . Written informed consent was obtained from all subjects or their parents/legal tutors.2, non-smokers, and willing to use adequate methods of contraception throughout the study. Exclusion criteria included electrocardiographic, clinical, biochemical, haematological, coagulation, or urinalysis test abnormalities; a positive test for drug or alcohol abuse, human immunodeficiency virus, hepatitis B and C viruses, and rapid plasma reagin; a medical history of serious adverse reaction or serious hypersensitivity to the test drug or to chemically related drugs; glaucoma, hypertension, anorexia and thyroid problems or existence of concurrent disease; a history of smoking, alcohol or drug abuse; the use of prescription or over-the-counter medication 3 weeks before enrolment; and participation in a clinical research study in the previous 3 months.From February 2017 to March 2017, 36 healthy volunteers were enrolled and randomized using R statistical software version 2.15.2. Inclusion criteria for this study were as follows: healthy males and non-pregnant, non-breastfeeding females, aged 18–55 years, with a BMI in the range 18–27 kg/mThe volunteers who fulfilled the requirements were selected by the clinical research coordinator of Ipharma, S.A. Before the start of the clinical study, the statistical division of Ipharma S.A. performed randomization for the allocation of drugs . The randomization was done in two blocks in a balanced design using the R statistical software version 2.15.2. and the Mersenne Twister algorithm. The participants were blinded to treatment allocation.2EDTA-coated Vacutainers  at pre-dose (time 0) and seventeen times after drug administration . Plasma was separated by centrifugation (10 min at 1600 × g at 4 °C) and stored at −65 ± 15 °C for subsequent analysis.A single dose of 75 mg of AFP extended-release capsule  was administered orally after overnight fasting. The volunteers were under medical supervision throughout the protocol. Blood samples (4 mL) were collected in BD K55 and guidelines of European Medicines Agency (EMA) regarding bioanalytical method validation56.Plasma AFP concentration was measured using a validated method developed by Ipharma S.A., according to Mexican regulations (NOM-177-SSA1-2013)g, 10 min, 10 °C). The supernatant (200 µL) was injected into a high-performance liquid chromatography (HPLC) system coupled to a tandem mass spectrometer 6410B (MS/MS) with a triple quadrupole detector . The chromatographic separation was performed at 45 °C with a Gemini C18 pre-column  and a Zorbax Eclipse XDB C-18 column  . The injection sample volume was 5 µL. The mobile phase had a flow rate of 0.6 mL/min and consisted of 5.0 mM ammonium formate/0.1% formic acid and acetronitrile (15:85). The detection system used an ESI MS/MS precursor ion (+) 206.2 m/z and a (+) product ion 105.0 m/z. The interday linearity was assessed with calibration curves . The intraday quality control was evaluated by control samples of 1.7, 7.5, 35, and 75 ng/mL each. The validated method had a coefficient of variation (CV) ≤5% for the precision and an error ≤5% for the accuracy.In brief, the plasma proteins were eliminated by acetonitrile precipitation. Then, 300 µL acetonitrile was added to a 50 µL plasma sample, vortexed , and centrifuged (9600 × max) and time to reach Cmax (Tmax) were obtained from the concentration-time data of the plasma. The area under the plasma AFP concentration-time curve (AUC) from time 0 to the time of last measurement (AUC0-t), AUC from time 0 extrapolated to infinity (AUC0-∞) were calculated by log-linear trapezoidal rule. Ke was estimated by log-linear regression from the terminal portion of the log-transformed concentration-time plots. T1/2 was estimated by dividing 0.693 by Ke. The total drug clearance was calculated by dividing dose by AUC0-∞ and adjusting for weight. Volume of distribution was calculated as Cl divided by Ke. The AUC and Cmax values were adjusted for dose and weight (AUC/dW and Cmax/dW). The pharmacokinetic analysis was performed using R statistical software version 2.15.2.The pharmacokinetic parameters were calculated by non-compartmental methods. The maximum plasma concentration 29. Since the clustering algorithms are dependent on the data size, we used a modified method to estimate the ideal number of clusters31. First, we introduced the data set of the unadjusted Cmax and AUC0-t values together with the adjusted data set. Second, Cmax and AUC0-t were standardized to minimize the effect of scale differences, and a distance matrix was made from the combined Cmax and AUC0-t values. Third, hierarchical cluster analysis (HCA) using the Ward linkage method was performed on individual Cmax and AUC0-t values. Finally, the interindividual Manhattan distances were computed. Minitab 16 software  was used for standardization and HCA. We identified the subjects of each cluster and calculated the means of all adjusted pharmacokinetic parameters by cluster. According to the mean values of the pharmacokinetic parameters of the clusters, they were classified into metabolizer phenotypes20.The metabolizer phenotypes were determined according to a multivariate analysis of the combined pharmacokinetic parameters CABCB1-rs1045642, ABCG2-rs2231142, SLCO1B1-rs4149056, DRD3-rs6280, CYP3A4-rs2242480, CYP3A5-rs776746, CYP2B6-rs3745274, and GSTM3-rs179973557 using real-time polymerase chain reaction and Taqman probes  according to the manufacturer’s protocol. Three quality control thresholds were applied: a genotype call rate equal to 1, an HWE test with P > 0.05, and minor allele frequency >0.01.The Wizard Genomic DNA Purification kit  was used to isolate DNA according to manufacturer’s instructions. Genomic DNA was quantified by UV absorbance using Nanodrop . The DNA purity was evaluated with the A260/280 and A260/230 ratios, and the samples were stored at −20 °C until use. The DNA samples were subjected to genotyping for the polymorphisms max and AUC. Considering a confidence level of 95%, a significance level of 5%, and a minimum power of 80%, a sample size of 30 would suffice. One-way analysis of variance (ANOVA); Kruskal Wallis test; automatic linear modelling using forward stepwise method, Akaike Information Criterion (AICC) and Overfit Prevention Criterion (ASE); and linear and logistic regression analysis model were applied to validate the phenotyping model. The HWE was determined by comparing the genotype frequencies with the expected values using the maximum likelihood method58. Differences between males and females regarding genotype frequencies were determined using a corrected χ2 test. To assess the effects of polymorphisms on the AFP pharmacokinetic parameters, comparisons between two and three groups were made. The Student’s t-test and one-way analysis of variance were used for parametric distributions, whereas Mann-Whitney U and Kruskal-Wallis H tests were used for nonparametric distributions. Post-hoc tests (Bonferroni correction and Tamhane’s T2 test) were used for pairwise comparisons. To confirm the contribution of genetic factors to the variability of pharmacokinetic parameters, automatic linear modelling using forward stepwise method, AICC and ASE as well as linear regression analysis using enter, stepwise, remove, backward, and forward methods were performed. Possible associations of genotypes or combinations of genotypes with phenotypes were evaluated using χ2 and Fisher’s exact tests and validated by logistic regression analysis. The evaluation effects of polymorphisms and associations were assessed under three different models . The odds ratio (OR) was estimated with a 95% confidence interval (95% CI). All P values were two-tailed. Corrected P values (Pc) were obtained using the Bonferroni correction for exclusion of spurious associations. P < 0.05 was interpreted as statistically significant. The statistical analyses were performed with SPSS for Windows, V.20 .For sample size calculation, the intrasubject CV obtained in a previous pilot study (Amfepramone/A359-16P) was considered. It was assumed that CV was 25.77% for both CAustralian New Zealand Clinical Trials Registry: ACTRN12619000391178, date registered: 12/03/2019.


Brassica napus population from a commercial breeding programme was analysed by automated non‐invasive phenotyping. Time‐resolved data for early growth‐related traits, including estimated biovolume, projected leaf area, early plant height and colour uniformity, were established and complemented by fresh and dry weight biomass. Genome‐wide SNP array data provided the framework for genome‐wide association analyses. Using time point data and relative growth rates, multiple robust main effect marker–trait associations for biomass and related traits were detected. Candidate genes involved in meristem development, cell wall modification and transcriptional regulation were detected. Our results demonstrate that early plant growth is a highly complex trait governed by several medium and many small effect loci, most of which act only during short phases. These observations highlight the importance of taking the temporal patterns of QTL/allele actions into account and emphasize the need for detailed time‐resolved analyses to effectively unravel the complex and stage‐specific contributions of genes affecting growth processes that operate at different developmental phases.A major challenge of plant biology is to unravel the genetic basis of complex traits. We took advantage of recent technical advances in high‐throughput phenotyping in conjunction with genome‐wide association studies to elucidate genotype–phenotype relationships at high temporal resolution. A diverse  Brassica napus L., AACC, 2n = 38) is the leading oilseed crop in Canada, Australia, China and Europe and second globally. Its oils have diverse uses, including food, industrial feedstock and as an environmentally friendly renewable energy source  offering a non‐invasive, image‐based method to analyse complex traits  Which key genomic regions are associated with growth‐related traits and relative growth rates in the early phase of vegetative plant development? (ii) To what extent do identified regions contribute to trait variance? (iii) Can we resolve dynamic, stage‐specific contributions of loci for early growth by a time course analysis? and (iv) Are we able to nominate candidate genes that might be causal for the observed marker–trait associations?In summary, the studies mentioned above highlight the need to investigate QTL/allele effects by time‐series data to efficiently elucidate growth processes and to detect stage‐specific loci that would likely be missed by analysing single or end‐point data only. Hence, here we investigated a genetically diverse population of 477 spring‐type B. napus canola with double‐low seed quality . Plants were cultivated under controlled environmental conditions in an incomplete randomized block design  n = 12 and the two male sterile testers ‘M1’ and ‘M2’ each n = 6 per experiment, respectively) in all cultivations. Plants were grown in large 25‐litre square containers  in red substrate 2  to provide enough space for the plants to grow and to avoid pot size effects . Before sowing, seeds were stratified for three days at 4 °C on moist filter paper in Petri dishes to trigger uniform germination. At 14 DAS, four plants per container were sampled to provide enough material for subsequent molecular/biochemical analyses. The remaining five plants were grown until 28 DAS. Watering was performed with an automated balance/watering station by target weight of the containers to maintain 80% field capacity, pH 5.5. Plants were shuffled each day by one row and every second day by one block (11 neighbouring carriers in one row) in the system to minimize position effects.The experimental materials consisted of a total of 477 genotypes Data  from a dign Data  in four et al. (Over a duration of three weeks (between 6 DAS and 28 DAS), plants were subjected to a daily imaging routine involving automated capturing of top and side view images. Two types of illumination and camera systems in the IPK automated non‐invasive plant phenotyping system for large plants were used as described in Junker et al. . Visibleet al., et al., 2)’ was derived from VIS top view, early plant height ‘side geometry fluo height (px)’ from FLUO side view, and plant colour uniformity ‘side intensity vis lab a stddev’ from VIS side view images, respectively. Colour uniformity is given as the standard deviation of the a‐values in the L*a*b* colour space of the plant pixels. The lower this value, the more uniform is the plant colour. Leaf colour differs between young and old leaves, and therefore, this trait may act as a proxy for the range of maturation stages of leaves within a given plant and thus of its rate of development. Traits represent information obtained from the analysis of whole containers, including nine plants at early and five plants at later stages, respectively.Automated image analysis was performed using the IPK Integrated Analysis Platform, IAP version 2.07 . Broad‐sense heritabilities (H2) for each trait were estimated by Eq. σG2 and σe2 denote the variance components of the genotype and the residual variance, respectively, and n0 the number of experiments or in case of the end‐point biomass data the number of plant replicates per genotype  and relative growth rates (RGRs) were calculated using previously published procedures Hunt, . RelativBrassica napus cv. Darmor‐bzh v4.1 reference genome assembly  the B. napus Darmor‐bzh v.4.1, (ii) a concatenated Brassica AC‐genome assembly comprising the B. rapa v1.5 . Transcripts were counted as hit if they reached a minimum similarity of 80% over 40% of the target transcript. If annotations in multiple genomes were obtained, they were prioritized in the order B. napus, B. oleracea/rapa, A. thaliana. The B. napus Darmor‐bzh v.4.1, B. rapa Chiifu‐401–42 and B. oleracea TO1000 transcriptomes were functionally annotated using Blast2GO .To ensure the unique positioning of as many markers as possible, an enhanced version of the Brassica Infinium 60k genotyping array  as described previously by Jan et al.  with 95% similarity over a length of 50 bp. Markers showing multiple BLAST hits in the genome were removed. For genome‐wide association studies (GWAS), SNPs were coded in numerical format  using R {GAPIT} ] and population clustering for K = 2–5 are shown in Figure All 477 genotypes were genotyped using the n et al. . Raw datet al. (n = 13 201) and CNV (n = 3110) markers. Analyses were performed in RStudio on a CentOS 7.2 Linux server . As the programme does not allow for missing marker information in the numeric genotype input file, missing data were replaced by the heterozygous allele. Kinship was calculated using the FARM‐CPU algorithm. Principal component analysis was performed on the centred genotype data using the pca function in R {pcaMethods}  by a significant marker was estimated in R , has been proposed by Liu et al. , which cet al., Pairwise linkage disequilibrium (LD) was analysed for each chromosome in R {LDheatmap} , and were extended to the left and right unrelated marker, respectively. All genes within the respective LD block were considered for candidate gene identification. For significant markers outside of LD blocks, the 100 kb flanking regions on either side were searched for candidate genes as suggested by Zhou et al.  were estimated  and 0. 874 (projected leaf area at 26 DAS). High H2 values of 0.895 and 0.878 were also obtained for fresh weight and dry weight, respectively, facilitating the temporal analysis of trait relationships and forming a solid basis for genetic analyses.In the present study, a diverse spring‐type canola breeding population consisting of 477 genotypes with ‘double‐low’ seed quality  was investigated at an early vegetative growth phase. We applied automated high‐throughput phenotyping daily using the previously described IPK phenotyping platform for large plants  as described previously by Jan et al.  were calculated for each chromosome and LD decay derived for both subgenomes (A & C) separately based on the SNP data  were detected, especially on the C‐subgenome chromosomes  at 28 DAS, were used for genome‐wide association studies using Fixed and random model Circulating Probability Unification, R {FarmCPU}  ≤ 0.1 , these traits are not redundant, as similar FW values may break down into different contributions of FW components. Despite the high phenotypic correlation, only three shared MTAs for FW and DW, one on chromosome A10 and two on C02, were identified. We compared the 22 significant MTAs for fresh weight and dry weight to a list of 771 previously described QTL obtained from 13 publications analysing 45 growth, yield and quality‐related traits  were detected at P‐value(FDR) ≤ 0.1) revealed a total of 787 MTAs, including 191 associations for estimated biovolume, 200 MTAs for projected leaf area, 182 MTAs for early plant height and 192 MTAs for colour uniformity, respectively. There were no substantial differences in the number of associations between the A and the C subgenomes. The majority of detected associations could be attributed to unique, single‐copy SNP markers . A substantial number of CNVs (deletions and duplications) also showed trait associations independently of the two SNP alleles  for projected leaf area, estimated biovolume and early plant height, as well as absolute change rates (ACRs), for colour uniformity were calculated over 15 intervals of three day durations to integrate the effects over longer periods Figure e,f. Highet al., et al.  as exemplarily shown for candidate region 5 on chromosome C08 , Longifolia1 (BnaA10g18650D), Squamosa promoter binding 4 (BnaC03g18800D) and Shatterproof1 (BnaC08g29530D). Several other genes are putatively involved in flowering time or cell wall biogenesis and modification, or were annotated as transcription factors. Although candidate genes need to be further analysed and validated in follow‐up studies involving temporally and spatially resolved assessment of gene activity, the described dynamic QTL represent a well exploitable resource to deepen our knowledge of early plant growth and biomass accumulation.A comprehensive list of all thus identified candidate genes for all evaluated traits can be found in Data Further examination of the associated markers of the five candidate regions revealed that the allele distribution differs between the three breeding pools: For example, for ‘Bn‐A04‐p2218115’, the minor allele is underrepresented in breeding pool 2 and completely absent in breeding pool 3. In contrast, the minor allele of ‘Bn‐scaff_21312_1‐p895326’ is nearly absent in breeding pool 1 and 2, but although only present in the heterozygous state, is highly overrepresented in breeding pool 3. The introgression of beneficial alleles underlying dynamic QTL, absent or underrepresented in conventional breeding pools, on the one side, as well as selection and stacking of beneficial alleles on the other side might help to enhance genetic gain for complex traits towards further improvement of growth performance in canola breeding. Moreover, it broadens the selection basis by introducing the factor temporal dynamics, facilitating marker‐assisted selection to breed high vigour cultivars.TA, RCM and DK designed the experiments. AA provided seed material. DK performed the experiments and analysed the data. FG and CRW analysed and provided genotype data. BS and RJS provided the reference assembly and transcript annotations. DK wrote the manuscript. RJS, RCM and TA managed the project, advised on interpretation and obtained the funding. All authors read and edited the manuscript.Figure S1 Example for acquired raw image data.Figure S2 Overview of phenotypic data.Figure S3 Biomass distribution and correlation with image‐derived traits.Figure S4 Heritability of phenotypic traits.Figure S5 LD‐decay in the A and C subgenomes.Figure S6 Phenotypic variance explained (PVE%) by detected MTAs.Figure S7 Allele effects of dynamic associations.Figure S8 Allele effects of dynamic associations for relative growth rates.Figure S9 Population structure analysis.Figure S10 Manhattan plots for representative associations in the candidate regions with selected candidate genes and correlations between markers.Data S1 List of canola lines utilized in this study.Data S2 Overview of experimental design.Data S3 Phenotypic data (BLUEs), heritabilities and coefficients of variation.Data S4 Genotype dataset (SNP and CNV markers).Data S5 List of genes in candidate regions.Data S6 Correlations of FW, DW and biomass‐related traits.Data S7 Pairwise LD matrices for all chromosomes.Data S8 List of all associations detected during the cultivation from 6 to 28 DAS.Click here for additional data file.