GPR126 gene, has been previously associated with the forced expiratory volume/forced vital capacity ratio. The other novel missense variant mapped to the EPAS1 gene encoding the hypoxia inducible factor 2α. EPAS1 is known to be the major selection candidate gene in Tibetans. The derived allele of GPR126 is associated with lung function in our sample of highlanders (p < 0.05). These variants may contribute to the physiological adaptations to hypobaric hypoxia, possibly by altering lung function. The new statistical approach might be a useful tool to detect selected variants in population studies.The aim of this study is to identify genetic variants that harbour signatures of recent positive selection and may facilitate physiological adaptations to hypobaric hypoxia. To achieve this, we conducted whole genome sequencing and lung function tests in 19 Argentinean highlanders (>3500 m) comparing them to 16 Native American lowlanders. We developed a new statistical procedure using a combination of population branch statistics (PBS) and number of segregating sites by length (nSL) to detect beneficial alleles that arose since the settlement of the Andes and are currently present in 15–50% of the population. We identified two missense variants as significant targets of selection. One of these variants, located within the  Each of these populations, faced with ongoing environmental pressure, has developed their unique array of physiological adaptations. In Andeans, these include an enlarged chest, increased lung capacities2, only slightly increased ventilation rate and an elevated haematocrit3.High altitude represents an extreme environment characterised by low concentrations of atmospheric oxygen (hypoxia), arid climate, high solar radiation and other environmental stressors. Populations have resided at high elevations in Ethiopia, the Himalayas and the Andes for several millennia4. These groups could trace back to the beginning of human settlement in the Andes, which archaeological evidence places between 12,000 and 9,000 years before present7.In this study we focus on the Colla population living in the Northwest Argentinean highlands. Collas also inhabit Southern Bolivia and Northern Chile and are considered to be related to other Andean groups such as Quechua, Aymara and Atacameño10. Using genotype data genes such as VEGFB, PRKAA1, NOS2A and FAM213A have been suggested to be under selection in Andeans10. These genes play a role in a vast array of processes such as cardiac function, oxygen sensing, vasodilation and oxidative stress reduction. In Tibetans, a particular gene involved in the response to hypoxia, the hypoxia inducible factor 2α (HIF-2α) or EPAS1, has been repeatedly shown to have a signature of selection14 and to be associated with the almost absent increase of haemoglobin concentration in native Himalayan populations14. A gene involved in the oxygen sensitive expression of HIF itself, EGLN1, was selected in both Tibetans and Andeans12. A first subset of Andean whole genome sequences was published comparing 10 healthy Peruvian highlanders with highlanders suffering from chronic mountain sickness15. The authors detected variants in an erythropoiesis regulator gene SENP1, and an oncogene, ANP32D in association with cross-population tests of selection and a higher transcriptional response to hypoxia in individuals with chronic mountain sickness relative to those without.The genetic component of high altitude adaptation in Andeans has been the subject of a number of recent studiesCompared to other high altitude populations, such as Tibetans, Andeans have been living for less time at elevations above 3500 m. Given that there has been shorter duration for selection to act, rather than being close to fixation, it is likely that proportionally more advantageous gene variants exist at intermediate frequencies. To this end, we complement the scans for hard sweep variants with scans for signatures of incomplete selective processes. These tests are applied on high coverage whole genome sequence data for healthy Andean highlanders from Northwest Argentina together with sequence data from Native American lowlanders and pinpoint to variants advantageous in the adaptation to high altitude.The sequencing of the whole genome of 19 Collas living above 3500 m resulted in an average genome-wide call rate of 97.4% (min. 97.1%). Coverage of 30x was reached for 95% of the genome with a minimum of 89% in one individual, while the minimum exonic coverage was 93%. On average 280 Gb of sequence data could be mapped for each genome with a minimum of 205 Gb in a single individual. Around 3.3 million SNPs could be identified in each highlander genome. Of these 1.7% were novel SNPs. Within the whole exome only 20,900 SNPs were identified with 2% representing newly discovered SNPs . About 10,200 synonymous SNPs had no effect on the amino acid sequence while 9,350 were non-synonymous. These were further classified as: mainly missense mutations , about 75 nonsense, 10 nonstop, 18 misstart and 70 disruptive mutations.2. The divergence time of Collas and lowland Wichí was estimated to be 5,000 years possibly indicating the onset of permanent high altitude settlement 18 200 kb windows, and then filtered these for variants in the top 1% of population branch statistic (PBS)14 scores. PBS is a statistic that detects variants that have changed substantially in frequency in a target population, using a related population and an outgroup to determine which population the allele frequency change occurred in. The nSL test is a haplotype based statistic identifying haplotypes that are at high frequency but haven’t yet been broken up by recombination, suggesting that the increase in frequency was recent. Both statistics seek to identify a recent change in the frequency of an allele or haplotype, which can reflect the impact of selection in a genomic region. PBS was calculated for 19 Collas in comparison to 19 Siberians20 and 16 lowlanders from Central and South America2. The lowland group represented a conservative grouping since it included Calchaquíes, who live at an altitude of 2300 m in Argentina and bear overall genomic similarities to the nearby Collas2. Hence, any genomic signatures shared by Calchaquíes and Collas would be missed by our approach. These filters yielded 72 SNPs. After excluding SNPs with frequency >10% in the 1000 Genomes database, those without gene annotation, and with combined annotation dependent depletion (CADD) scores ≤5 only 9 SNPs remained  test in Collas21, we hypothesize that ongoing selective sweeps acting on intermediate frequency selected variants may contribute a substantial fraction of adaptive genetic changes among the Collas. However, detecting a selective sweep in its early stages poses particular challenges. When selection has not yet driven an allele to high frequency, the impact on linked genetic variation is also low. Consequently, most statistics designed to detect positive selection only have high power towards the final stages of a selective sweep24. Using simulations to inform our targeted approach, we chose to combine several aspects of the population genetic data and genome annotation information. To search for these signals, we focused on derived allele frequency (DAF) ≥ 15% and ≤50% to identify alleles at the beginning of selection which are overlooked in the traditional positive selection tests. To this end, we obtained population branch statistic (PBS) and number of segregating sites by length (nSL) scores for exonic variants with a DAF ≥ 15% and ≤50% in Collas, corresponding to 17147 SNPs of which 2831 (16.5%) were classified as missense variants in dbSNP build 141. Calculating nSL + PBS scores as described in the methods  statistics used alone yielded 25, 31 and 31 hits respectively . Assuming missense variants are more likely to have an adaptive phenotypic effect, the data supports simulations in suggesting that combining p(PBS|DAFColla) and nSL scores increases power to detect early-stage selection. Based on the degree of enrichment, we expect approximately 14/42 top missense hits to be caused by non-neutral processes.Given the short time-frame of human occupation in the Andesods Fig.  yielded ods Fig. ; these sods Fig. . This apods Fig. , as compWe found that 23/42 (54.8%) missense hits had evidence of a deleterious effect (as indicated by a deleterious SIFT or PolyPhen2 categorisation), compared to 960/2733 (35.1%) over all scored missense variants. This enrichment  suggests that PBS + nSL outliers are likely to be damaging. Given that we would expect 42 * 0.344 ≈ 14.7 damaging hits in 42 random missense variants, finding 23 suggests an excess 8–9 damaging missense variants that may be non-neutral.The enrichment of selection signatures among deleterious missense variants with outlier PBS + nSL scores prompted us to define a set of variants with especially high-probability of being damaging. We supplemented SIFT and PolyPhen2 scores with CADD scores for our 42 missense hits and defined a high-probability damaging variants as . This identified 11 SNPs  is located in AKAP6, which is involved in cardiac myocyte reaction to stresses such as hypoxia, hypertrophic remodelling of the heart25 and stabilises the hypoxia master regulator HIF-1α during hypoxia while enhancing its degradation at normoxia26. Furthermore, of the 11 shortlisted SNPs, rs17280293, located in GPR126, is the variant with the second most extreme nSL + PBS score. During the course of our data analysis, a study of a European cohort was published identifying a strong association between lung function and SNP rs148274477 27. In that cohort, the GPR126 SNP rs17280293 that we identify here is mentioned as being in strong LD with rs148274477 (r2 = 0.85). While the same level of LD is observed in our Colla population (r2 = 0.848) the intriguing possibility exists that it, rather than rs148274477, is driving a strong lung function association in a population unrelated to the Colla. The derived alleles at both SNPs in GPR126 rs148274477 and rs17280293 are associated with increased forced expiratory volume in the first second/forced vital capacity (FEV1/FVC). As we have FEV1/FVC measurements for our 19 Colla individuals2, we were able to speculatively test for a similar association in our small Colla sample. We could identify a positive association  are not found in 1000 Genome Phase 1 data and are polymorphic only in Peruvians from Lima in the extended 1000 Genome Phase 3 data set; this was only true for one other variant in our list of 42 missense hits. Tellingly, one of these two variants (rs570553380) is located in the EPAS1 and GPR126 as high-probability selection candidates.Based on a wide range of evidence - low worldwide frequency DAF (1000 Genome) and local population differentiation (PBS), relatively extended derived-allele haplotype homozygosity (nSL) and evidence for disruption of the protein  – as well as evidence based on prior selection signals and gene function, we consider the two missense variants in GPR126 and EPAS1. The first gene has been previously associated with lung function measurements; the second, with the main adaptive phenotype in Tibetans. In contrast, no functional variant could be identified in Collas by scans of late-stage selective sweeps.In this study, we have detected missense variants as novel candidates for local positive selection in the genomes of Andean highlanders with possible functional consequences. By combining different selection statistics, along with genome annotation information, we were able to identify intermediate frequency variants that may contribute to high altitude adaptation in two very prominent candidate genes, GPR126  found at 2.8% in 1000 Genomes Project data. This mutation causes an amino acid change (p.S123G) classed as damaging by SIFT and possibly damaging by PolyPhen2 within the seven transmembrane domain of the protein. Several previous studies have shown a strong association of this gene with lung function28. Furthermore, this SNP has been identified as in strong linkage disequilibrium with a variant (rs148274477) associated with FEV1/FVC. We found rs17280293 to be associated with FEV1/FVC in our own phenotype data, suggesting that Andeans with a derived allele may have better equipped lungs for life at high altitude. This is the first time a phenotype in Andeans could be correlated with genomic data. Taken together, the extreme PBS + nSL score, evidence of damaging effect, and association with lung function situate rs17280293 as an interesting selection candidate.Our first main finding is the second strongest hit in the intermediate frequency analysis originated from a missense SNP (rs17280293) in EPAS1) at intermediate frequencies in the Collas. While the HIF-1α is activated during short-term hypoxia, HIF-2α is rather expressed as a chronic hypoxia response29. Both transcription factors regulate a plethora of genes involved in energy metabolism and differential oxygen use during hypoxic conditions. EPAS1 has already been repeatedly shown to have a signature of selection in Tibetan populations30, but this is the first study to indicate selection also in Andeans. Since this gene was highlighted with a test investigating variants at intermediate levels it may indicate that a potentially advantageous allele of EPAS1 is on its way to higher frequencies also in Andeans but has not yet reached levels equivalent to Tibetans. Interestingly, the selected haplotype in Sherpas and Tibetans was suggested to be an introgression from the Denisovan genome31 and its frequency increased with altitude of populations in the Himalayas32. The Tibetan genotype has been associated with an inhibition of haemoglobin concentrations rise at high altitude14. However, the specific functional mechanism has not yet been identified in Tibetans. This might be due to the fact that all highlighted SNPs so far fall within non-coding regions of the gene33. In contrast, the highlighted variant in Andeans (rs570553380) encodes a missense mutation predicted to have a damaging effect on protein function. Knock-out studies in mice underlined the essential role of HIF-2α for normal erythropoiesis. Complete knock-out leads to a drastically reduced haematocrit34. Heterozygous knock-out mice had a similar haematocrit compared to wild type mice but showed a resistance to hypoxia induced pulmonary hypertension and thus no subsequent right ventricular hypertrophy35. This functional advantage under hypoxic conditions suggests a protective role of at least some loss of function mutations in EPAS1 for life at high altitude. In contrast, human patients with gain of function mutations were characterised by pulmonary hypertension and erythrocytosis showing a continuous activation of the HIF response36. Hypoxia induced pulmonary hypertension is a universal physiological response to lowlanders going to high altitude37 and is constantly present in Andeans38. While in healthy highlanders pressures are only moderately increased39 they are exaggerated in individuals suffering from chronic mountain sickness, which is more common in the Andean population than in other high altitude dwellers40. Pulmonary hypertension leads to a remodelling of the pulmonary vasculature, inducing changes which may result in right heart failure. Thus, a protective allele in EPAS1 might not only reduce the red blood cell count as seen in Tibetans but also protect from extensive pulmonary hypertension in the Andean population.Our second main finding highlighted a missense SNP in the hypoxia inducible factor 2 α  derived variants in Andean high altitude populations as potential targets of recent selection. Among exonic variants with functional annotation we identify two missense variants in the 9. Native American lowland control samples for selection analyses originated from 5 Calchaquí and 4 Wichí from neighbouring regions in Argentina2, plus publicly available sequences from the Complete Genomics database including 5 Mexican  and 2 Puerto Rican  samples20. Additional North-East Siberian samples served as controls for threesided population statistics including 2 Chukchi, 4 Eskimo and 13 Koryak individuals20. Two additional Chukchi individuals were added in the demographic analyses.All participants provided their written informed consent. The study was approved by the by the Ethics Committee at the University of East Anglia and the Ministry of Health of the Province of Salta . Ethical approval for the whole genome sequencing of the samples was obtained from University of Cambridge Human Biology Research Ethics Committee (HBREC.2011.01). All methods were performed in accordance with the relevant guidelines and regulations. High altitude samples were collected from an Andean population, the Colla, living in Northern Argentina at an altitude >3500 m20. The newly sequenced Colla genomes were deposited in the European Nucleotide Archive: PRJEB14828. Data are also freely available through the Estonian Biocentre website. All genomes were annotated against the Ensembl GRCh37 database and compared to dbSNP Human Build 141 and Phase 1 of the 1000 Genomes Project dataset41 as described previously20. For 1000 Genomes variant frequency Phase 3 data42 was used. We focused on variants annotated as “exonic” in Ensembl occurring in Hardy-Weinberg equilibrium in our sample resulting in 402,227 autosomal variants. Variants with a combined annotation dependent depletion (CADD) score >5 were investigated further. The CADD score was calculated to estimate impact of synonymous and non-synonymous variable alike43. Missense variants were assessed with the in silico prediction programs SIFT44 and Polyphen245.Whole genome sequences were obtained with the Complete Genomics platform and quality filtered as described elsewhere14 and the number of segregating sites by length (nSL)18. The PBS score was based on pairwise FST values for the three populations using a weighted analysis of variance46 with the program GENEPOP’00747. PBS was estimated as in Yi and colleagues14. The nSL scores were calculated only on the Colla sample.Two statistics were calculated for each SNP – the population branch statistic (PBS)17. A window-based nSL score was also calculated as the average proportion of common  SNPs with |nSL| > 2.0 in a 200 kb window.Two statistics were window based. We calculated Tajima’s D on the Colla sample using non-overlapping 200 kb windowsIn scans for late-stage hard sweeps we first identified the 200 kb windows that showed genome-wide the lowest 1% of Tajima’s D or nSL scores. The top 1% PBS scoring variants in these windows were further filtered for SNPs with less than 10% frequency in the 1000 Genomes Phase 3 data. We only retained those with a CADD score higher than 5.21, we supplemented our selection scans with an attempt to detect positive selection acting on intermediate frequency variants. To this end, we first chose to focus on SNP-by-SNP selection statistics, considering each SNP a potential target of selection. Although this vastly increases the number of tests we performed, early-stage selective sweeps have not yet had a substantial impact on variation far from the selected locus such that they may be difficult to detect using average statistic values or the number of outliers over large genomic windows. We first calculated the two statistics PBS and nSL for each SNP in the Colla sample. For nSL approximate p-values were generated, based on nSL scores that were standardised in derived allele frequency (DAF) bins assuming unstandardised SL follows a roughly normal distribution in frequency bins. PBS values were calculated using lowland Americans as the comparative closely related control population and Siberian populations as outgroup. These were then converted into empirical p-values given DAF in Collas based on 4% DAF bins  would be 0.01). nSL has been shown to have power to detect early-stage selective sweeps18, while PBS has been an effective test-statistic for detecting local selection. Simulations suggest that combining these two statistics using Fisher’s method provides a composite statistic with high power to detect early-stage sweeps , and only considered SNPs with DAF in the Colla of 15% ≤ DAF ≤ 50% to focus on early-stage selective sweeps. We considered the set of top 1% most extreme statistic value SNPs as enriched for SNPs subject to or impacted by selection. We filtered this set for missense variants and obtained CADD, SIFT and Polyphen2 scores for each SNP.e) and population split times between Collas and other Native Americans were estimated using multiple sequentially Markovian coalescent model (MSMC)48 on up to four phased genomes per population, as described elsewhere20. To provide a comprehensive picture of the changes in effective population size over time of each of the analysed populations, the MSMC curves obtained using 1, 2 or 4 genomes were joined together at points of their overlap. The joining was performed to maximise the MSMC accuracy within a given time interval, as suggested by Schiffels and Durbin48 and implemented by Clemente, Cardona and colleagues49.Effective population sizes  and the combined nSL + PBS statistic by generating 250 neutral simulations of the Koryak, Wichí and Colla populations, taking population sizes and population split times from our MSMC analysis. Here, the Colla and Wichí split 5478 years before present (ybp), and the Koryak and ancestral Wichí/Colla population 19451 ybp. These split times are inferred based on cross-coalescence, and no migration is included in the model. We assumed a generation time of 25 years, and started the model at 1 million years ago . In each simulation, we generated 1 MB of genetic data, using a mutation rate of 1.7e−8/generation and human-realistic variable recombination rate by randomly sampling from non-centromeric regions of the chromosome 2 of the HapMap combined genetic map51. At the end of each simulation we retrieved samples according to our study , and then randomly chose 50 mutations with DAF 0.15–0.75 in the Colla sample from the central region (450 kb-550 kb) of the data. We calculated SL and PBS for each of these mutations.Simulations were performed using the forward-time population genetic simulator SLiMTo approximate the distribution of these statistics under selection, we performed 160 simulations following the neutral model until the time of the Colla/Wichí split. At this point, for 20 times in each replicate, we randomly chose a variant in the central region (450 kb-550 kb) and applied positive selection  for the next 5478 years in the Colla population. We restarted any of the 20 ‘sub-replicate’ routines if the selected allele did not have 0.15 ≤ DAF ≤ 0.75 at sampling time in the Colla sample. On successful completion of a sub-replicate, SL and PBS were calculated for the selected variant.For both neutral and selected results, SL was normalised to nSL and PBS to p(PBS|DAF) using 5% frequency bins and the neutral distribution of SL and PBS. The nSL score was converted to an approximate p-value by assuming a standard normal distribution of SL in frequency bins, and this was combined with p(PBS|DAF) using Fisher’s method. Each frequency bin included at least 440 neutral results and 100 selected results. Power was estimated using the Fisher-combined p-values with a p-value cut off of 0.0064  driving this from the expected value of 0.01).2. A ratio of FEV1/FVC was calculated and linear regression was used to assess effects of the derived allele with a one-sided p-value.Lung function measurements of forced vital capacity (FVC) and forced expiratory volume 1 second (FEV1) for Collas were reported previously Supplementary Information


Increasing marker density was proposed to have potential to improve the accuracy of genomic prediction for quantitative traits; whole-sequence data is expected to give the best accuracy of prediction, since all causal mutations that underlie a trait are expected to be included. However, in cattle and chicken, this assumption is not supported by empirical studies. Our objective was to compare the accuracy of genomic prediction of feed efficiency component traits in Duroc pigs using single nucleotide polymorphism (SNP) panels of 80K, imputed 650K, and whole-genome sequence variants using GBLUP, BayesB and BayesRC methods, with the ultimate purpose to determine the optimal method to increase genetic gain for feed efficiency in pigs.Phenotypes of average daily feed intake (ADFI), average daily gain (ADG), ultrasound backfat depth (FAT), and loin muscle depth (LMD) were available for 1363 Duroc boars from a commercial breeding program. Genotype imputation accuracies reached 92.1% from 80K to 650K and 85.6% from 650K to whole-genome sequence variants. Average accuracies across methods and marker densities of genomic prediction of ADFI, FAT, LMD and ADG were 0.40, 0.65, 0.30 and 0.15, respectively. For ADFI and FAT, BayesB outperformed GBLUP, but increasing marker density had little advantage for genomic prediction. For ADG and LMD, GBLUP outperformed BayesB, while BayesRC based on whole-genome sequence data gave the best accuracies and reached up to 0.35 for LMD and 0.25 for ADG.Use of genomic information was beneficial for prediction of ADFI and FAT but not for that of ADG and LMD compared to pedigree-based estimates. BayesB based on 80K SNPs gave the best genomic prediction accuracy for ADFI and FAT, while BayesRC based on whole-genome sequence data performed best for ADG and LMD. We suggest that these differences between traits in the effect of marker density and method on accuracy of genomic prediction are mainly due to the underlying genetic architecture of the traits.The online version of this article (10.1186/s12711-018-0387-9) contains supplementary material, which is available to authorized users. Feed is of major economic importance in pig production, accounting for 60 to 70% of total costs. The grow-finish phase accounts for the largest proportion of total feed, at about 75% . Thus, iGS has been widely applied in livestock breeding programs, using medium-to-high density single nucleotide polymorphism (SNP) panels . The mosTherefore, this study aimed at evaluating the accuracy of genomic prediction of feed efficiency component traits of average daily feed intake, average daily gain, ultrasound backfat depth, and loin muscle depth, using 80K and imputed 650K SNPs, as well as imputed whole-genome sequence variants. Three methods, GBLUP , BayesB Data were collected at the Prairie Sun Research and Development Facility . All animals used in this study were raised under commercial production-like conditions and fed standard diets designed to exceed the pig’s requirements, as described previously . The prolongissimus muscle (LMD) were measured approximately 7 cm off the midline over the last three ribs using ultrasound  and Biotronics Toolbox Software .A total of 1363 Duroc boars (from 63 sires and 439 dams) tested in 2014 were used for this study. At weaning, on average, two boars per litter were selected to create a group of 24 or 48 boars, depending on the number of litters weaned in a given week. The average genetic relationship among these 1363 individuals was about 0.12 based on pedigree information. The boars were placed in nursery pens at a stocking density of 24 per pen, with littermates split between the two pens when groups of 48 were stocked. At completion of the nursery phase (approximately 9 weeks of age), each group of boars was put into a single test pen (22 to 24 boars per pen) that was fitted with two electronic feeders per pen . Boars from a nursery pen were kept together in the test pen. Following a 7-d acclimation period, feed intake was recorded in a test period of 14 weeks. Body weights were recorded at the beginning (~ 45 kg) and end (~ 110 kg) of the test, with an intermediate weight of ~ 80 kg. In addition, when average weight in the pen was near 110 kg , boars were individually weighed and depths of backfat (FAT) and Individual meal events were edited to remove outliers and obvious errors using adapted procedures recommended by Casey et al. , as desc® 650K SNP Array, including: (1) the 94 sires, maternal grand-sires/great-grandsires of the 1363 animals, (2) the 29 sequenced animals, (3) 19 sons of the sequenced animals, and (4) the next 29 ancestors (19 boars and 10 dams), which cumulatively contributed about 20% of the genetics of the studied population. In order to test the accuracy of imputed genotypes across three different genotyping platforms, the 29 sequenced animals and 67 of the animals with 650K genotypes were also genotyped with the 80K SNP chip. All genotyping and sequencing analyses were conducted by Delta Genomics . Library construction for next-generation sequencing was performed with 1 μg of genomic DNA according to library preparation protocols (Bio-O Scientific NEXTflex™ DNA Sequencing Kit). The Illumina 100 paired-end sequencing kit was used for sequencing on an Illumina HiSeq 2000 PE100. Variant calling was performed according to GATK Best Practices work flow [Sscrofa 10.2) using BWA [Genomic DNA was isolated from tail tissue samples following the DNA Extraction instruction manual . Samples from all animals (1363) with phenotypic records were genotyped using the Geneseek-Neogen GPPHD 80K SNP chip. A deep pedigree for these animals was traced back ~ 8 generations. The common ancestors and their genetic contribution to the studied population (1363) were calculated using the PEDIG program . On the ork flow , 33. Morsing BWA . Then, dsing BWA  and basesing BWA , 33.Sscrofa 10.2), with the reference-based allele denoted 0 and the alternate allele denoted 1. SNPs or variants for each genotyping platform were filtered for analysis according to the following criteria: SNP or variant call rate higher than 95%, SNP or variant with map information on autosomes (Sscrofa 10.2), Chi square of Hardy–Weinberg equilibrium test less than 600, and minor allele frequency (MAF) in the genotyped animals higher than 5%. Stepwise imputation from 80K to 650K and then to the whole-genome sequence was performed by Fimpute v2.2 [A total of 16,560,854 autosomal variants were detected in the 29 sequenced animals, including 2,576,543 Indels and 13,984,543 SNPs. Before imputation, alleles for all SNPs on the 80K and 650K panels were converted to the standard reference  and covariate (age), a is a vector of random additive genetic effects [P < 0.01) fixed effects were included in the final model to estimate the variance components and residuals of the traits. The effects of contemporary group and animal age were significant for all traits, and ultrasonic test machine was significant for FAT and LMD. The interaction between contemporary group and ultrasonic test machine was not significant. Corrected phenotypes were calculated as the sum of the estimated breeding value and the estimated residuals from the above univariate pedigree-based animal model.Significance of all possible systematic effects on phenotype, including the fixed effects of contemporary group (78 levels) consisting of ultrasonic test date and grow-finish pen, ultrasonic test machine , and the covariate of animal age at the end of the test (140 to 170 days), were tested using the following univariate animal model in ASREML :1\documeThen, the 1363 Duroc boars were split into training (n = 1167) and prediction datasets (n = 196) based on birthdate, before and after June 10, 2014, respectively. The 196 youngest animals for prediction were from 19 sires and 88 dams, and almost all had half-sibs in the training dataset. The genetic relationship between individuals in the training and prediction datasets averaged 0.11 based on pedigree data. First, a full animal model  was used to obtain estimated breeding values (EBV), i.e. EBV1, and corrected phenotypes  or excluded (δj = 0) in the model for a given Markov chain Monte Carlo (MCMC) iteration. A total of 50,000 iterations were run for each analysis, with the first 5000 iterations used as burn-in. The prior probability of a SNP to have no effect was set equal to π = 0.9995 based on the posterior value obtained from BayesCπ. Due to the computational demands associated with testing the very large number of sequence variants (~ 4.8 × 106) simultaneously, an alternative split-and-merge method was used, similar to Calus et al. [The top variants (SNPs and Indels) were selected from the imputed whole-genome sequence data based on their effects on phenotype, as estimated in the training dataset n = 1167) using method BayesB in GenSel , 28. The167 usings et al. . BrieflySscrofa 10.2 assembly of the swine genome using NGS-SNP [r2 > 0.99) in a 5000-kb sliding-window with 50 variants. LD pruning was carried out first independently within each category  and then any REG or CHIP variant that was in complete LD with an NSC variant was removed. Finally, all CHIP variants that were in complete LD with a REG variant were removed. The remaining 2,154,844 variants, henceforth referred to as “SEQ”, were used for genomic prediction. They included 13,642 NSC, 157,809 REG and 1,983,393 CHIP variants.The imputed whole-genome variants were annotated based on the  NGS-SNP . All var NGS-SNP . The fir NGS-SNP  by excluGenomic predictions for the validation animals were estimated based on their genotypes  and the marker effects estimated in the training dataset using three methods: GBLUP , BayesB ) based on each of the three sets of genotypes was calculated using PLINK. The GBLUP approach was applied to the model of Eq. . Final estimates were derived from the means of the five replicate chains. Using the resulting posterior means of marker effects, the genomic breeding value for the validation animals were calculated using Eq. .The average genotype imputation accuracy for individual SNPs was 92.1% from 80K to 650K and 85.6% from 650K to whole-genome sequence, with the complete range from 0 to 100% across SNPs. Most SNPs had an imputation accuracy higher than 90%, 77% of SNPs for imputation from 80K to 650K and 57% for imputation from 650K to sequence. About 12.6 and 25.9% of SNPs had an imputation accuracy lower than 80% for imputation of 80K–650K and of 650K to sequence imputation, respectively (Table The accuracy and bias of (G)EBV for the studied traits are in Table Improvement in the accuracy of genomic predictions based on BayesB compared with GBLUP is shown in Fig. The change in the accuracy of genomic predictions with increasing SNP density is in Fig. R2) between imputed and observed genotypes ranged from 0.80 to 0.96 for imputation from different low densities to 50K [In the current study, we obtained high imputation accuracies, which reached 0.92 for imputation from 80K to 650K SNPs. In pigs, there are several reports on the accuracy of genotype imputation from lower densities to 60K SNPs, with correlations between observed and imputed genotypes ranging from 0.952 to 0.995 for imputation from 6K to 60K , from 0.s to 50K  and froms to 50K . For seqs to 50K . Imputats to 50K .Many factors can influence the accuracy of genotype imputation. MAF is one important factor, especially when imputing to sequence data, since the number of SNPs with a very low MAF is usually limited in SNP panels but large in sequence data  was not uniform across traits. Compared to pedigree-BLUP, using genomic data increased prediction accuracy and decreased prediction bias for ADFI and FAT but not for ADG and LMD. Similar results were reported in cattle  and sheeSus scrofa chromosomes (SSC) SSC1 and SSC18 for ADFI and FAT, using BayesB in the full dataset (data not shown). With this method, the effects of SNPs surrounding large QTL, such as those on SSC1 and SSC18 for ADFI and FAT, are easier to detect and more accurately estimated. This could be the main reason why BayesB gave higher accuracy for ADFI and FAT than GBLUP. The advantage of BayesB for genomic prediction for traits that are, at least in part, determined by QTL of large effect was also recognized by Meuwissen et al. [Genetic architecture and the statistical method used for genomic prediction are two interrelated factors that have a large influence on the accuracy of genomic prediction. Usually, higher accuracy can be achieved when the model assumptions more closely represent the underlying genetic architecture of the traits. We found that BayesB outperformed GBLUP in the accuracy of genomic prediction for ADFI and FAT. BayesB assumes that only a small proportion of SNPs have a large effect on the trait, which is in agreement with our GWAS results, where relatively large QTL were detected on n et al.  and demon et al. , 59, 60.We also implemented the BayesRC method for the imputed whole-genome sequence data. Compared to BayesB and GBLUP, BayesRC gave higher accuracy for ADG and LMD, improving accuracy from 0.12 to 0.25 for ADG and from 0.32 to 0.35 for LMD. For ADFI and FAT, the accuracy from BayesRC was between those obtained with GBLUP and BayesB. The advantage of BayesRC compared with GBLUP and BayesB is that it can incorporate prior biological information by defining classes of variants that are likely enriched for causal mutations and by fitting a mixture distribution for the effects of variants in each class , 19, 61,Increasing marker density has the potential to improve the accuracy of genomic prediction and the use of whole-genome sequence data is expected to give the best accuracy, as the causative mutations are expected to be included in the genotype data , 64. HowResults from using sequence data to improve the accuracy of genomic prediction have been inconsistent. Simulation studies suggested that including whole-sequence data could improve the accuracy of genomic prediction by as much as 40%, depending on the trait, statistical method, and MAF of the causal mutations affecting the trait , 25, 68.A significant challenge for genomic prediction using whole-genome sequence data is the computational requirement due to the large number of markers. Preselecting the most important markers and/or filtering out the uninformative ones can address this problem. The split-and-merge approach, which splits one large computational task into many smaller ones, was first proposed by Calus et al.  to pre-sAccording to Goddard , predictRelatedness between individuals is also very important for both genotype imputation and genomic prediction. Ideally, having less related animals in a large reference population is helpful to break down high levels of LD, thus making it easier to identify the causal mutations and to capture all the genetic variance. For example, use of multiple breeds in the training population to reduce their average relatedness gave more accurate genomic predictions than using the same single breed for training, especially in simulated datasets , 76, 77.A small effective population size, which contributes to high LD , is anotIn conclusion, although the reference population used was small, the genotype imputation accuracies were as high as 92.1% from 80K to 650K, and 85.6% from 650K to whole sequence. Increasing marker density, however, had no or little advantage for genomic prediction for FAT and ADFI, such that the available 80K SNP panel is sufficient for these traits. BayesB resulted in higher prediction accuracy than the other methods tested for these two traits. For LMD and ADG, GBLUP gave higher genomic prediction accuracies than BayesB, and BayesRC in SEQ data gave the best prediction accuracies. However, pedigree-based BLUP outperformed all genomic methods and produced the highest prediction accuracies for ADG and LMD, likely because the SNPs captured less genetic variance for these traits than pedigree data. In the future, with decreasing costs for whole-genome sequencing, a better understanding of the functional annotation of the genome and variants , and larAdditional file 1: Fig. S1. Histograms of MAF distribution for the variants from final 80K, 650K and SEQ data.Additional file 2: Table S1. Variance component and heritability estimates using different information. The data provided presented the genetic variance, total phenotypic variance and estimated heritability for the traits using different information and methods.


Genetic research utilizes a decomposition of trait variances and covariances into genetic and environmental parts. Our software package biMM is a computationally efficient implementation of a bivariate linear mixed model for settings where hundreds of traits have been measured on partially overlapping sets of individuals.www.iki.fi/mpirinen.Implementation in R freely available at Bioinformatics online. Decomposing phenotypic variance and covariance into genetic and environmental parts is important for designing genetic studies and understanding relationships between traits and diseases. The two main approaches are linear mixed model (LMM) implementations, such as GCTA  or BOLT-n individuals is n-vector of mean-centered phenotype values from which the covariates, such as age, sex and principal components of population structure have been regressed out, n-vector of genetic random effects and n-vector of environmental random effects. The n × n block matrices. i, j of the genetic relationship matrix (GRM) ikg is the genotype of individual i at variant k, coded as 0, 1 or 2 copies of the minor allele and We consider settings where individual-level data are available, and hence use LMM. The bivariate LMM for GtV approximates additive genetic variance of each trait  explained by the variants included in the calculation of From this model, an estimate of The existing bivariate LMM implementations have not been designed for a case where hundreds of traits have been measured on 10 000s of individuals. Our software package biMM combines a fast likelihood computation (similar in speed to GEMMA) with an algorithm that optimizes the sample overlap between consecutive pairs of traits analyzed and therefore efficiently reuses the computationally expensive matrix decompositions. biMM allows user to control how much missing data are tolerated for a single analysis and automatically executes both phenotype imputation and matrix decompositions required to achieve that tolerance.Once an eigendecomposition of function . Similarit missing values and/or dropping at most dt non-missing values for a trait pair to make it match the available eigendecomposition   and prepTo assess scaling to larger datasets, we consider 20 000 individuals simulated by HapGen2  with pheIn all examples we used a desktop computer with an Intel Quad-Core i7-3770 CPU @ 3.40 GHz and 16 Gb of RAM.To assess scaling to larger datasets, we evaluated how much time each additional trait pair would require for a dataset of 20 000 individuals after the eigendecomposition is available and phenotype data are completely observed. The resulting times in CPU seconds are 1.6 for biMM, 75 for GEMMA and 2670 for BOLT-REML. GCTA was unable to run with 16 Gb of RAM. The difference between biMM and GEMMA in this example with no missing data is that biMM holds the eigendecomposition in RAM while GEMMA reads it from a file for each pair of traits. The eigendecomposition itself took 70 CPU minutes with biMM and 450 CPU minutes with GEMMA. Hence, with a desktop computer, an analysis of completely observed or imputed omics data for 500 traits (124 750 trait pairs) measured on 20 000 individuals would take less than 2.5 days with biMM, over 100 days with GEMMA (although with a multivariate analysis strategy GEMMA could finish in 14 days, Our freely available biMM software package makes a bivariate linear mixed model analysis of high-dimensional phenotypes on cohorts of a few tens of thousands of individuals practical using a desktop computer. For even larger cohorts, where explicit matrix decompositions are impractical on current desktop computers, BOLT-REML may be the only available option to analyze a pair of traits, but it cannot utilize sharing of individuals across trait pairs to efficiently analyze tens of thousands of trait pairs.Supplementary DataClick here for additional data file.


The associations and causal relations revealed between 76 metabolites from 135 winter wheat lines and 17 372 SNP markers allow the genetic interventions on sets of metabolites to be dissected. Triticum aestivum) lines, and 17 372 single nucleotide polymorphism (SNP) markers. Metabolite profiles were generated from flag leaves of plants from three different environments, with average repeatabilities of 0.5–0.6. The average heritability of 0.25 was unaffected by the heading date. Correlations among metabolites reflected their functional grouping, highlighting the strict coordination of various routes of the citric acid cycle. Genome-wide association studies identified significant associations for six metabolic traits, namely oxalic acid, ornithine, L-arginine, pentose alcohol III, L-tyrosine, and a sugar oligomer (oligo II), with between one and 17 associated SNPs. Notable associations with genes regulating transcription or translation explained between 2.8% and 32.5% of the genotypic variance (pG). Further candidate genes comprised metabolite carriers (pG 32.5–38.1%), regulatory proteins (pG 0.3–11.1%), and metabolic enzymes (pG 2.5–32.5%). The combinatorial use of genomic and metabolic data to construct partially directed networks revealed causal inferences in the correlated metabolite traits and associated SNPs. The evaluated causal relationships will provide a basis for predicting the effects of genetic interferences on groups of correlated metabolic traits, and thus on specific metabolic phenotypes.We investigated associations between the metabolic phenotype, consisting of quantitative data of 76 metabolites from 135 contrasting winter wheat ( Despite its economic relevance, only one study based on 233 doubled haploid lines has performed genome-wide mQTL mapping . The suspension was centrifuged  and the two supernatants pooled. For phase separation, 1000 μl of water and 500 μl of chloroform were added. After centrifugation , 30 µl of the upper phase was transferred to a glass vial and dried in a vacuum centrifuge.A mixed sample was assembled composed of equal amounts from each individual sample in order to capture systematic shifts during extraction and measurement. The mixed sample was processed in the same way as all other samples and its data were used to normalize for shifts during metabolite profile analysis across the data set for all three locations. All analyses were performed in duplicate. Polar metabolites were extracted twice with 500 μl of 70% methanol per 20 mg dry weight (DW), with 50 µM m/z range of 65–650 in centroid mode and applying the dynamic enhancement (DRE) mode. Compound annotation was performed by running authentic standards, determining retention indices and specific fragment spectra, and by comparing to information in the NIST database implemented in MassLynx 4.1 software and the Golm Metabolome Database . Data processing and peak integration were performed using the QuanLynx tool implemented in MassLynx 4.1 software, utilizing specific masses for metabolite quantification (Supplementary Table S1 at JXB online).Derivatization was performed using a MultiPurposeSampler  connected to a GC-MS system by first adding 40 μl of methoxyamine hydrochloride (20 mg/ml in pyridine) for 2 h at 37°C, and then adding 40 μl of N-methyl-N-(trimethylsilyl) trifluoroacetamide containing the alkane retention time standard  for 30 min at 37°C (JXB online) as outlined in detail by P values were used as an approximation  and by all mQTL (adjusted R2) was estimated using a standard multiple regression approach (pG) was determined as a proportion of the explained phenotypic variance standardized by broad-sense heritability. Association mapping analyses were performed using the ASReml-R 3.0 software  were added into the undirected network and the QPSO algorithm applied.Causal phenotype networks among the metabolomic data were inferred using the heuristic QTL+phenotype supervised orientation (QPSO) search algorithm . In the JXB online). The level of repeatability of metabolite abundance did not vary between the three sites, with a mean value of 0.52 at Böhnshausen, 0.60 at Hadmersleben, and 0.59 at Hohenheim , resulting in variances of genotypes that were on average only half the size of the variances of genotype × location interactions. As a result, the broad-sense heritabilities were low, with a global mean of 0.25 (P < 0.001) larger than zero (Supplementary Table S2). For 17 metabolite abundances, non-significant (P > 0.9) variation of genotypic effects was observed.The analyses of variance revealed a strong linear effect of the environments on the metabolite profiles (Supplementary Table S2). Individual metabolite profiles varied substantially across the three sites . This implies that differences in heading date did not lead to a decrease in the heritability of flag leaf metabolite profiles in the total population of 135 lines.To evaluate the influence of the genetic differences in the 135 lines with respect to their developmental stage at the time the flag leaves were sampled, we estimated heritabilities for subgroups of early-, intermediate-, and late-maturing lines. The heritability estimates were similar for all three classes of lines  , 1124 (39.44%) were moderately significantly correlated (P < 0.01), and 1462 (51.30%) were just significantly correlated (P < 0.05) (Supplementary Table S3 at JXB online). A number of regions with tightly correlated metabolites were identified, often reflecting functional relationships. Some examples are highlighted in JXB online. One such group comprised sugars and sugar alcohols (box 1), one was a set of organic acids associated with the citric acid cycle (boxes 2 and 4), and another included various small organic acids and amino acids (box 3). Pathway analysis showed that metabolites representing the main entry points of glycolysis and the citric acid cycle clustered within box 1  were highly significantly correlated (P < 0.34). A cluster analysis based on the genome-wide SNP marker data revealed the absence of a major population structure but the presence of a family structure among the 135 parental lines . Therefore, a linear mixed model was applied for the GWAS to correct for relatedness, modelling a kinship matrix.We estimated distance matrices among the 135 lines based on the metabolite abundances or the genomic profiles. These matrices were only weakly associated, with a correlation coefficient of r = 0.02  were queried against available wheat genome sequence data to define a set of candidate genes, and against available plant gene and protein sequences  to gather any known functional information. Only 21 of the sequences could be functionally assigned. The candidate genes encoded several transporters, catalytic enzymes, proteins involved in molecular signalling processes, transcriptional and translational regulators, and one defence-related protein. Most of the annotated genes are thought to generate just a single message, but one generates 12 splicing variants , and the eukaryotic translation initiation factor 3A. Among the more weakly associated SNPs were one encoding for the transcription activator DEMETER, an E3 ubiquitin-protein ligase SINA-like protein 4, an unmatched SNP at the same chromosome position, and an unmapped SNP encoding for a putative calcium-binding protein. Overall, we concluded that the associations found for pentose alcohol III involved a strong transcriptional and translational regulation.Two L-arginine mQTL were identified, the larger one explaining 17.2% of the variance. Both informative SNPs lay within genes encoding a bZIP protein, with both yet to be chromosomally mapped.A high proportion of the variance was explained by the sequences associated with the L-tyrosine (47.69%) and oligo II (44.77%) mQTL. The oligo II mQTL was associated with a gene encoding an ABC-transporter protein.Causal relationships were inferred from the reconstructed partially directed networks . The fivIn the second cluster , ornithiHordeum vulgare)   had impaired nuclear division, germination, and virulence, and a reduced ability to secrete oxalic acid  has only recently beenWith respect to the four sequences associated with oxalic acid abundance, one putatively encodes TFIIS (a transcription elongation factor), one a GTP-binding protein, and one a galactose oxidase/kelch repeat protein. TFIIS enhances the activity of RNA polymerase II by reactivating transcription elongation complexes in which transcription has been arrested . In the a priori experimental evidence for this has been found in the literature. reported  is identical to Traes_5BL_F367A99A7.1. This gene was differentially transcribed during wheat anther development, but its function is not known.The two genes associated with L-arginine abundance both encoded bZIP proteins. Members of the bZIP superfamily of eukaryotic DNA-binding transcription factors act as master regulators of a large number of developmental and physiological processes , identified oxalic acid as a key regulator or precursor of various branches of amino acid synthesis. Oxalic acid is hydrolysed via three pathways that involve glyoxylate/glycolic acid, ascorbic acid, and oxaloacetate , it often neglects the underlying metabolic and genetic networks  . Using rhia coli . FurtherThe negative relationship between the abundance of L-arginine and its breakdown product putrescine with a side-link to upstream L-tyrosine  has not Overall, the outcome supports an extensive co-regulation of central pathways in both N and C metabolism, as previously postulated  and riceA number of new gene–metabolite associations and their causal relationships have been revealed in this study, confirming the potential of the metabolite GWAS approach even in a polyploid species. The limitations of this approach relate in part to the complex inheritance of metabolite abundance and in part to the limited availability of sets of genetic markers and analysable metabolites. These constraints could be overcome by additional genomic marker development and fine-mapping based on new deep-sequencing and transcriptomics technologies (AM, H-PM, JCR, EE, VK, FL, RS, EK, and JS designed the research; AM, GL, YJ, YZ, and AH performed the research and analysed data; EE, RS, EK, and JS performed field trials and provided plant material; AM, GL, and JCR wrote the paper; HW provided support for the network analysis. The authors declare no conflict of interest.Supplementary DataClick here for additional data file.


Small RNA (sRNA) sequences are known to have a broad impact on gene regulation by various mechanisms. Their performance for the prediction of hybrid traits has not yet been analyzed. Our objective was to analyze the relation of parental sRNA expression with the performance of their hybrids, to develop a sRNA-based prediction approach, and to compare it to more common SNP and mRNA transcript based predictions using a factorial mating scheme of a maize hybrid breeding program.Correlation of genomic differences and messenger RNA (mRNA) or sRNA expression differences between parental lines with hybrid performance of their hybrids revealed that sRNAs showed an inverse relationship in contrast to the other two data types. We associated differences for SNPs, mRNA and sRNA expression between parental inbred lines with the performance of their hybrid combinations and developed two prediction approaches using distance measures based on associated markers. Cross-validations revealed parental differences in sRNA expression to be strong predictors for hybrid performance for grain yield in maize, comparable to genomic and mRNA data. The integration of both positively and negatively associated markers in the prediction approaches enhanced the prediction accurary. The associated sRNAs belong predominantly to the canonical size classes of 22- and 24-nt that show specific genomic mapping characteristics.Expression profiles of sRNA are a promising alternative to SNPs or mRNA expression profiles for hybrid prediction, especially for plant species without reference genome or transcriptome information. The characteristics of the sRNAs we identified suggest that association studies based on breeding populations facilitate the identification of sRNAs involved in hybrid performance.The online version of this article (10.1186/s12864-018-4708-8) contains supplementary material, which is available to authorized users. A key objective of modern crop breeding is to generate hybrids to increase yield by exploiting heterosis, as well as take advantage of a uniform F1 population. The generation of large numbers of inbred lines does not constitute a bottleneck through the application of doubled-haploid technology . Thus, tArabidopsis ecotypes and rice subspecies showed substantial epigenetic variations at the level of DNA methylation, histone modifications and small RNAs (sRNAs) [Arabidopsis hybrids of near-isogenic but epigenetically diverse parents exhibit substantial heterosis for various traits [Epigenetic variations have been suggested to be important components for complex traits such as crop yield . Genome- (sRNAs) , 16. Aras traits . Artifics traits . These rs traits , 19. Nons traits –22. The s traits . These rOur objective was to investigate the predictiction of hybrid performance (HP) for grain yield (GY) using parental sRNA expression profiles. We used next-generation sRNA sequencing data of whole 7-day-old seedlings of 21 elite maize inbred lines from which 98 (7 × 14) hybrid crosses were generated. A previous study revealed high prediction accuracy with distance measures based on trait-associated mRNAs . Here, w− 1 with adjustment to 155 g kg− 1 grain moisture  from the breeding program of the University of Hohenheim, Germany, with 98 hybrids resulting from the factorial mating scheme of Dent by Flint lines . The setFor transcriptome expression analysis and sRNA sequencing all 21 inbred lines were grown under controlled conditions  for seven days, the whole seedlings including roots were flash-frozen in liquid nitrogen. Five biological individuals of the same genotype were pooled before RNA isolation.SNP data were generated with the Illumina MaizeSNP50 chip  in the sMicroarray gene expression data was generated on the 46 k maize oligo nucleotide array . RNA-proSmall RNA isolation, sequencing experiments as well as sequencing data processing and normalization are described in the study of Seifert et al. . All seqPolymorphic SNPs, where nucleotides of at least one line differed from the remaining lines, were considered as discriminative markers. For mRNA, differential expression is defined as described for microarray analysis for at least one inbred line combination. For sRNA, differential expression between inbred lines is defined as a minimum expression of the lower expressed parent of 0.5 rpmqn and a two-fold expression change towards the higher expressed parent. In case the expression of the lower parent is below 0.5 rpmqn, the higher parent needs to have at least an expression of 1 rpmqn to consider a sRNA as differentially expressed.e (1) were calculated for all three data types  as the sum of marker differences for all markers that are differential in at least one inbred line combination. The differences for the data types are calculated for the combination of the inbred lines i and j, with ds being the expression difference for a specific sRNA, mRNA, or SNP.The euclidean distances Ds for sRNA and mRNA expression data cs between the lines i and j is calculated as follows:The expression difference ds for SNP data with cs being the actual sequence between the lines i and j is calculated as follows:The difference dL and oH respectively. With the null hypothesis that differential expression occurs with the same probability for both classes, the probability Ps (4) of a marker being associated to the trait was estimated via the binomial distribution probability function. This function depends on the number of hybrids whose inbred lines exhibit differential expression for the given sRNA in the classes L and H:Associations of markers with the traits HP for GY were established analogous to Frisch et al.  by separp = 0.5. The parameter kmin depending on positive (6) or negative (7) association:p-values lower than the probability threshold after adjustment for multiple testing via FDR correction at 0.05 [All markers with  at 0.05  were conWe calculated trait-associated marker binary distances for two inbred lines i and j and a defined set of n markers as follows:s being set to 1 for differential markers between the two inbred lines and 0 otherwise.pos positively associated markers Db,pos and nneg binary distance for negatively associated markers Db,neg for the two inbred lines i and j as follows:To integrate the opposing binary distances for positively and negatively associated markers in one distance measure, we developed the combined binary distance which integrates the binary distance for nThe prediction of HP was performed after Frisch et al.  using a Additionally a HP prediction based using multivariate linear regression (MLR) was performed as follows:b,pos, as well as negatively associated markers Db,neg. The third predictor mf represents the fraction of differential positively associated markers npos of all differential associated markers, given by the sum of npos and differential negatively associated markers nneg, between the two lines the two lines i and j:Four predictors were included in the MLR, including the binary distances of positively associated markers Dd represents the dominance of positively or negatively differential associated markers between the lines i and j, given as npos and nneg, to the difference of the positively and negatively associated markers  defined as follows:The fourth predictor mThe denominator is incremented by 0.1 to avoid division by zero, if the sets of positively and negatively associated markers are equally large. This predictor includes information about the number of differential associated markers in relation to all associated markers.We performed three different prediction scenarios as described by Schrag et al. . In the We used DESeq2  to call We analyzed the differentially expressed sRNAs for known miRNA sequences as well as overlap with hybrid performance associated-sRNAs (hpa-sRNAs) that we identified by marker-trait association analysis. The fractions of differentially expressed hpa-sRNAs of the two sets were compared to the fractions of threshold-based differential hpa-sRNAs from the 9 corresponding inbred line combinations.p-values for enrichment and depletion of HP for GY associated sRNAs of specific sequence length were computed by bootstrap analysis as described in Seifert et al. [The t et al. .The mapping of sRNAs to the B73 reference genome   as well r = 0.474, p = 8.110 × 10− 7, Fig. r = 0.343, p = 5.5 × 10− 4, Fig. r = − 0.518, p = 4.675 × 10− 8, Fig. For all three data types  we correlated the sum of differences between inbred line combinations with HP for GY to test for relations of parental differences and HP for GY. All individual features with differences between at least one inbred line combination of Flint and Dent lines were included in the analysis. In total, 32,330 (55.9%) SNPs, 12,414 (28.6%) of the mRNAs and 178,753 (0.6%) of the sRNAs were included in the analysis. The correlation of SNP differences and mRNA differences between inbred line combinations with HP for GY in their hybrids resulted in moderate  and negatively (45.7%) associated SNPs. There were 729 mRNAs significantly associated with HP for GY, representing 5.9% of all candidate mRNAs. In contrast to the associated SNPs, there is a clear major fraction of 82.17% transcripts with positive association with HP for GY. The distribution of 7142 sRNAs associated with HP for GY, representing a subset of 4.0% of the candidate sRNAs, showed a larger fraction of positively associated sRNAs (61.9%). We named the sRNAs, which showed a significant association with HP for GY based on the full set of inbred lines , hybrid performance associated-sRNAs (hpa-sRNAs). The numbers as well as fractions of positively and negatively associated markers for all three data types  are listed in Table b,pos), negatively associated markers , the combined binary distance  and the two additional predictors mf and md, which represent additional information about the size of Db,pos and Db,neg. In addition, the correlation strength of the MLR was tested. For both SNPs and sRNAs the binary distance based on Db,neg result in stronger correlations (r = − 0.801 and r = − 0.831) than on Db,pos. The opposite result was obtained for mRNA data, where the correlation for Db,pos was stronger (r = 0.819) than for Db,neg (− 0.794). The predictor mf results in equally strong correlations as for the positively associated markers for SNP and sRNA markers but stronger correlations for mRNA markers (r = 0.846). In comparison md does not results in superior correlations to the stronger binary distance Db,neg for SNPs and sRNAs or Db,pos for mRNAs. Whereas the combined binary distance  performs for SNPs and mRNAs equally good as the best correlation of the other four predictors, the correlation for the MLR outperforms all five predictors with a slight increase for SNPs (r = 0.804) but distinct increases for both mRNAs (r = 0.861) and sRNAs (r = 0.857). The results show that the negatively associated markers as binary distance  are highly related to HP for GY and the combination of information on positively and negatively associated markers in Db,com or the MLR for all three data types always outperform correlations based on Db,pos alone. All correlation results are listed in Table We performed separate correlations for all predictors with HP for GY. The five predictors are the binary distances for positively associated markers  or negatively  associated markers, the combined binary distance  as well as a MLR-based prediction.We evaluated marker-based predictions in 100 cross-validation runs by randomly selecting a subset of 5 Dent and 3 Flint inbred lines as estimation sets. Three prediction schemes were distinguished to evaluate the prediction accuracy of sRNA vs. SNP or mRNA marker-based prediction of HP. For type-2 prediction both parents, type-1 one parent and type-0 none of the parents have been evaluated in test-crosses. The three prediction types are schematically shown in Fig. Overall the type-2 prediction performs best for all three data types. The drop of prediction accuracy is mild from type-2 to type-1 prediction, but drastically from type-1 to type-0 prediction for all three data types  revealed the highest mean prediction accuracies. The type-0 prediction for mRNAs performed best using the binary distance of positively associated mRNAs  using sRNAs as markers performed best  and showed the least variation. The type-0 predictions based on positively associated  SNPs or the combined binary distance  generated from positively and negatively associated SNPs revealed higher mean prediction accuracies, but had a remarkably higher variation and did not result in predictions in all cross-validation runs  transcribed, protein coding sequences (gene); 2) TE or repeats (repeats); and 3) sequences without one of the previous annotations (intergenic). Whereas the majority of the 24-nt sRNAs map solely to intergenic regions of the genome, the 22-nt sRNAs map predominantly to multiple annotations Fig. .Fig. 7ReWe tested the correlation of genomic (SNP) and expression  differences between inbred lines with HP for GY in their hybrid offspring. It has already been shown that the genetic distance per se is not a sufficient predictor to determine HP or the extent of heterosis of an inbred line combination . This fiTo identify SNP, mRNAs or sRNAs with strong relation of parental differences and HP for GY, we employed an association approach based on the method described in Frisch et al.  with a mThe large numbers of associated individual markers of all three data types likely reflect HP for GY being a highly complex trait, which is most likely affected by many genomic loci each with small contributions to the phenotype. Considering as many components as possible that have an effect on HP for GY should thereby increase the prediction of this trait. In terms of breeding we assume that actively selecting against constraining elements, which are likely represented by negatively associated SNPs, mRNAs or sRNAs, might result in higher HP for GY.The correlation of marker-based distances for associated markers with HP for GY resulted in strong and very strong correlations ranging in absolute values from 0.79 to 0.86. In general, we observed stronger correlations for mRNA and sRNA than for SNP based distances. We assume these higher correlations are caused by more information being integrated in mRNA and sRNA expression patterns in contrast to the pure genomic information from SNPs. mRNAs contain SNP information which is located in coding regions and might have effects on the protein function, but in contrast to SNPs provide additional information about transcriptional differences between the inbred lines. sRNAs are genome-wide regulators of the epigenetic landscape and themselves subject to the transcriptional activity regulated by the epigenetic state of their region of origin . It is eb,pos) largely resemble the correlations with transcriptome-based binary distance in of Frisch et al. [p-value threshold was set to p < =0.05 instead of p < =0.01. This relaxation of the conditions for marker selection by binomial exact tests resulted in a slightly decreased correlation coefficient (0.82 instead of 0.86). However, we applied the threshold of p < =0.05 to allow for comparison of SNP, mRNA and sRNA data in the present study. Within our analyses we demonstrated that integrating negatively associated mRNAs increase the correlation coefficients. Positively associated mRNAs alone  resulted in r of 0.82, the combined binary distance for both negatively and positively associated mRNAs  in r of 0.84, and the MLR in r of 0.86. With increased correlation coefficients higher predictive power is indicated.In a previous study, correlations of transcriptome data with HP for GY were performed based on the same mRNA dataset as in this study . Our corh et al. , but in Correlations of the observed with predicted HP for GY by 100 cross-validation runs, resulted in the expected behavior for all three data types  with stronger prediction accuracies for type-2 predictions than type-1 predictions and the least performance for type-0 predictions, for which test-cross data for both parents were lacking. The results show for all three data types that having one of the parents evaluated in test-crosses (type-1) results in considerably higher prediction accuracy with lower variability. The gained prediction accuracy by having both parents evaluated in test-crosses (type-2) is not as pronounced as it is between type-0 and type-1 prediction  are identical to the statistics of the approach using the transcriptome-based binary distance of Frisch et al. [b,pos) resulted in highest prediction accuracies  or the MLR-based prediction resulted in considerably less variation of the prediction accuracy  outperformed the binary distance for positively associated mRNAs . This again highlights that information on negative contributions to HP for GY are important for the prediction accuracy. That type-0 predictions based solely on the binary distance of positively associated mRNAs  could not be improved by adding additional information about negatively associated mRNAs  suggests that information from related crosses, with shared parental lines, are important to select most informative individual markers.We compared our prediction approaches with those from a previous study that used the same mRNA dataset, but considered positively associated mRNAs only . The stah et al.  and bothb,com) based predictions outperformed all other approaches for type-1 predictions. Clearly, like for predictions using mRNA expression profiles, the integration of negatively associated markers facilitates a more precise selection of the best inbred line combinations. This holds true, as long as test-crosses for at least one of the parents have been supplied in the estimation set of the prediction model. In the case the estimation set is composed solely by unevaluated lines (type-0), the MLR prediction approach showed a poor performance. Thus nor the information added in the two predictor variables mf and md neither the separate binary distances for positively and negatively associated markers  provide a gain of precision anymore. We assume that a lack of germplasm in the estimation set with genomic relation to the inbred line combinations that are supposed to be predicted hampers the identification of the predictors needed for accurate prediction. Although the prediction accuracies for type-0 predictions were low and highly variable for the negatively associated markers , the predictions using the combined binary distance  performed similar or better than predictions based on positively associated markers  alone. The prediction approach using the combined binary distance  as predictor was thus shown to be less susceptible to the composition of the estimation set.Using SNP and sRNA as markers the MLR-based predictions resulted in highest prediction accuracies for type-2 predictions. The combined binary distance  and the MLR-based predictions . The genomic characteristics of the hpa-sRNAs we identified indicate a functional contribution of these sRNAs to the formation of hybrid performance.Additional file 1:Table S1: Hybrid performance for grain yield of 7x14 Flint/Dent crosses measured in Mg ha-1 adjusted to 155 g kg-1 grain moisture. (XLS 16 kb)Additional file 2:Table S2: Summary of sRNA sequencing data. (XLS 20 kb)Additional file 3:Figure S1: Comparison of prediction accuracies of different prediction types. (A) SNP, (B) mRNA, (C) sRNA based predictions. (TIF 9480 kb)Additional file 4:Table S3: Number and percentage of hpa-sRNAs with homology to known miRNA, tRNA and rRNA. (XLS 27 kb)


Sweet cherry is consumed widely across the world and provides substantial economic benefits in regions where it is grown. While cherry breeding has been conducted in the Pacific Northwest for over half a century, little is known about the genetic architecture of important traits. We used a genome-enabled mixed model to predict the genetic performance of 505 individuals for 32 phenological, disease response and fruit quality traits evaluated in the RosBREED sweet cherry crop data set. Genome-wide predictions were estimated using a repeated measures model for phenotypic data across 3 years, incorporating additive, dominance and epistatic variance components. Genomic relationship matrices were constructed with high-density SNP data and were used to estimate relatedness and account for incomplete replication across years.High broad-sense heritabilities of 0.83, 0.77, and 0.76 were observed for days to maturity, firmness, and fruit weight, respectively. Epistatic variance exceeded 40% of the total genetic variance for maturing timing, firmness and powdery mildew response. Dominance variance was the largest for fruit weight and fruit size at 34% and 27%, respectively. Omission of non-additive sources of genetic variance from the genetic model resulted in inflation of narrow-sense heritability but minimally influenced prediction accuracy of genetic values in validation. Predicted genetic rankings of individuals from single-year models were inconsistent across years, likely due to incomplete sampling of the population genetic variance.Predicted breeding values and genetic values revealed many high-performing individuals for use as parents and the most promising selections to advance for cultivar release consideration, respectively. This study highlights the importance of using the appropriate genetic model for calculating breeding values to avoid inflation of expected parental contribution to genetic gain. The genomic predictions obtained will enable breeders to efficiently leverage the genetic potential of North American sweet cherry germplasm by identifying high quality individuals more rapidly than with phenotypic data alone.The online version of this article (10.1186/s12863-018-0609-8) contains supplementary material, which is available to authorized users. Prunus avium L.) is a lucrative fresh market horticultural crop whose monetary worth is directly and indirectly determined by several horticultural and fruit traits. Worldwide, more than 2.8 million tons of sweet cherry fruit were produced in 2014 .Where Influential data points were those with a value greater than 2 times the average value of the diagonal of the hat matrix excluding zeros.h2) and as H2); both were divided by the sum of the variance components for final heritability estimates. Genetic values were computed as the sum of main effects for a, d and i for an individual, following the methodology of Kumar et al. [ay, dy, and iy when all years were used in the estimation.The statistical significance of main effects and interactions were tested by first generating reduced models and then performing log-likelihood ratio tests between full and reduced models. To account for positively-bound variance component estimates, a mixture of Chi-square distributions as implemented in the R package asremlPlus  was usedr et al. . GenotypFive-fold cross validation was used where the data set was randomly divided into 5 equal-sized parts (“folds”), a single fold  was removed across all years, and the remaining observations were used for variance component estimation and prediction of genetic values. The resultant model was used to predict genetic values of those removed individuals. This process was repeated for all 5 folds. Observations lacking phenotypic information for a specific year and trait were excluded from the model-building and validation. Because predictions can be affected by sampling variance, 5-fold cross validation was repeated 25 times using different randomly generated folds for each iteration. In addition, cross validation was performed, omitting each of the 66 full-sib families or a year as validation populations. These latter situations were intended to reflect the situation of predicting genetic performance for previously unphenotyped individuals that are related to the training population, and for predicting performance for an unobserved year. Prediction accuracy was assessed by computing correlation coefficients between predicted genetic values and observed data adjusted for fixed effects.n = 48), using 8 independent traits: bloom time, harvest time, pedicel-fruit retention force, fruit weight, firmness, SSC, TA, and powdery mildew incidence. The first and second principal components were graphed on a biplot [The statistical significance of year on the models was checked with the Wald test. Genetic-by-year effects were further explored by estimating genetic values and genetic variance components using a single year of data. Spearman’s rank-order correlations were conducted to evaluate changes in rank of genetic values of individuals across years. Pairwise Pearson (r) and Spearman (ρ) correlations between traits were assessed for the multi-year ADI model predictions. Principal component analyses were conducted on correlation matrix of genetic values calculated from (1) all individuals used in this study, and (2) only the cultivars and ancestors .All trait distributions (consisting of 600–755 data points for each trait) were influenced by the year of data collection Fig. . Wald teThe 2010 data visually differed most from the other years, particularly for bloom date, fruit dimensions, fruit weight, firmness, and SSC. Data in 2010 were also the most sparse compared to data from other years . Population structure was observed among the individuals. In a principal component analysis of the correlation matrix of the SNP data, the first two components summarized 14% of the variation. There was distinct grouping of the wild accessions and offspring derived from those wild accessions along the second principal component (data not shown). Visual inspection of the diagonals and off-diagonals from the realized relationship implies a single Gaussian distribution of the matrix elements. Thus, the population structure likely had minimal impact on the genomic additive relationship matrix . The extended hat matrix revealed no influential data points for any of the models. Appropriate residual patterns were observed for all models and traits (results not shown), demonstrating no major departures from the assumption of homoscedasticity. Moderate correlations were observed between the additive, dominance and epistatic effects within a trait for the full model r = 0.–0.7. Popp-values < 0.05) for all focus traits, except for powdery mildew incidence and SSC, which had notable p-values defined as less than 0.10. Reduced models consisting of single main effects  or single main effects plus their year interaction term  were highly significant for all traits. This demonstrates that the reduced models did not adequately capture variation compared to the full model. For most focus traits, genetic models that included additive, epistatic, additive-by-year and epistasis-by-year effects were not statistically different from the full model. Thus, dominance and dominance-by-year could be dropped from their genetic models without significant loss of information. Traits that were exceptions to the above were fruit weight, fruit dimensions, and bloom date, for which optimal fit was obtained by including dominance in the model. For all traits, dominance-by-year and epistasis-by-year effects could be removed from the model without much loss of information. Additive-by-year effects had a statistically significant effect on bloom date, bloom time, and PFRF (p < 0.01).Log likelihood ratio tests comparing reduced models with the full ADI model demonstrated that the full model was not necessary to describe trait variance for any focus trait Table . The maiaY = 11%) and TA (iY = 14%). Residual variance of most traits was less than 25% of phenotypic variance, except for PFRF (45%) and SSC (48%). Variances and standard errors for all components and traits, and variance percentages, are provided in Additional files Variance component estimates from the full model indicated moderate to high broad-sense heritabilities across the focus traits, ranging from 0.47 for pedicel-fruit retention force to 0.83 for harvest date Table . Narrow-r > 0.65). The lowest prediction accuracies were observed for SSC and TA, which never exceeded 0.50.Correlations between adjusted phenotypic data and genetic values from the ADI model were high, 0.82–0.97 for all focus traits  for all focus traits Fig. . Narrow-r2, was consistent between the ADI model and the AI model for all traits  and small changes in the A model compared to ADI model (r = 0.91–0.96) for genetic values and breeding values   between fruit firmness and fruit dimensions among the unreleased progeny suggests genetic linkage among loci influencing these traits. This outcome was in contrast to that of a multi-year QTL study, in which the Pearson correlations between fruit firmness and fruit weight ranged from − 0.64 to − 0.67 for Regina × Lapins and − 0.40 to − 0.15 for Regina × Garnet F1 families [The high broad-sense heritability for firmness 0.77) Table  was cons to 0.85 . In our families . Those c7 Table  2 was most often between 0.2 and 0.3 (Table H2 = 0.48) was similar to that of other stone fruit: approximately 0.50 for apricot [H2 = 0.60 and h2 = 0.27. However, the population used in Dirlewanger et al. [H2.Expectations for genetic improvement in SSC were moderately positive. Narrow-sense heritability was estimated at 0.22, typical of the other focus traits in this study, where h apricot , 0.72 fo apricot , and 0.4 apricot . Previou apricot , 87. Tit apricot . These Qr et al.  was creaH2 and h2 estimated for foliar powdery mildew incidence indicated excellent potential for genetic improvement, but the lack of genome-wide dominance effects was surprising (Table In Prep). Haploblock analysis might be required to detect dominance effects, which appeared to be absorbed by the other relationship matrices. The large epistatic component (42%) determined for this trait in sweet cherry was consistent with resistance to other plant diseases such as soybean to sudden death disease (causative agent Fusarium virguliforme) and rice to rice blast disease (Pyricularia oryzae) [The large  oryzae) –93.The improvement in prediction accuracy when incorporating epistasis into the genetic model is consistent with studies on apple, Eucalyptus, wheat, cassava and maize , 94–99. Epistasis has not typically been targeted for parental selection in genetic improvement programs, although it can be captured indirectly with additive effects if epistatic alleles are fixed through inbreeding or drift , 101. AlDistributions of genome-estimated breeding values from the ADI model Additional file 2:Number of observations (N) and Spearman rank correlations (ρ) between genetic values derived from a single year and the multi-year genetic values using the ADI model (Panel A) or the phenotypic data (Panel B), genetic values derived from the reduced models and the full model (Panel C), and breeding values derived from the reduced models and the full model (Panel D). (XLSX 11 kb)Additional file 3:Histogram of the diagonals and off-diagonals from the additive relationship matrix. (PNG 268 kb)Additional file 4:Variance component estimates and standard errors for all RosBREED sweet cherry traits. (CSV 5 kb)Additional file 5:Variance component percentages for all RosBREED sweet cherry traits. (CSV 3 kb)Additional file 6:Biplot of genetic values among sweet cherry cultivars and their ancestors using the correlation matrix of eight traits. Trait rotations were scaled by the first eigenvalue. (PNG 9522 kb)Additional file 7:https://www.rosbreed.org/breeding/cherry/gebv. (XLSX 791 kb)Breeding values, dominance values, epistatic values and genetic values of all individuals for all traits in the RosBREED sweet cherry Crop Reference Set. An online app for exploring the breeding and genetic values presented in Additional file


The study of epigenomic variation at the landscape-level in plants may add important insight to studies of adaptive variation. A major goal of landscape genomic studies is to identify genomic regions contributing to adaptive variation across the landscape. Heritable variation in epigenetic marks, resulting in transgenerational plasticity, can influence fitness-related traits. Epigenetic marks are influenced by the genome, the environment, and their interaction, and can be inherited independently of the genome. Thus, epigenomic variation likely influences the heritability of many adaptive traits, but the extent of this influence remains largely unknown. Here, we summarize the relevance of epigenetic inheritance to ecological and evolutionary processes, and review the literature on landscape-level patterns of epigenetic variation. Landscape-level patterns of epigenomic variation in plants generally show greater levels of isolation by distance and isolation by environment then is found for the genome, but the causes of these patterns are not yet clear. Linkage between the environment and epigenomic variation has been clearly shown within a single generation, but demonstrating transgenerational inheritance requires more complex breeding and/or experimental designs. Transgenerational epigenetic variation may alter the interpretation of landscape genomic studies that rely upon phenotypic analyses, but should have less influence on landscape genomic approaches that rely upon outlier analyses or genome–environment associations. We suggest that multi-generation common garden experiments conducted across multiple environments will allow researchers to understand which parts of the epigenome are inherited, as well as to parse out the relative contribution of heritable epigenetic variation to the phenotype. Understanding the ecological and evolutionary processes governing landscape patterns of genetic diversity and adaptive variation is important to predicting and managing the impacts of climate change on plant species distributions and function . GenomicEpigenetic modifications are changes in phenotype that are mediated by the regulation of gene expression rather than alterations in the DNA sequence. Epigenetic modifications may be reset within an organism’s lifespan or during meiosis, or they may be passed to offspring . These mLandscape-level investigations of patterns of genomic variation in plants have examined both adaptive and neutral loci and have employed a wide variety of methods. Genomic regions influencing adaptive traits are routinely discovered via genome–phenotype associations, phenotype-free approaches, and common gardens . Genome-Examination of gene flow within a species, based on neutral markers, typically shows isolation by distance (IBD) and/or isolation by environment (IBE). Genetic drift is the primary driver of IBD, where genetic differentiation increases with spatial distance . IBE is Experiments combining an assessment of adaptive traits, molecular genetic loci underlying traits, genetic correlations, and gene flow barriers  have thus far provided the most mechanistic understanding of landscape-level patterns of genetic variation . Often tArabidopsis thaliana, revealed epigenetic quantitative trait loci that account for 60–90% of the heritability in two ecologically relevant traits, flowering time and primary root length  across a north–south transect from Luxembourg to central Sweden, where no gradient in DNA methylation was found , significant correlations were found between epigenetic variation and habitat in S. alterniflora, but not B. frutescens  are not able to disentangle the effects of transgenerational epigenetic inheritance, relative to Mendelian genetic inheritance, on the phenotype.Genome–phenotype association methods such as quantitative trait loci mapping and GWAS studies detect relationships between adaptive traits and genomic variation through the association of phenotypic and genomic data in individuals from crosses or natural populations . The incCommon garden studies are crucial for disentangling environmental and genetic influences on adaptive traits. Common garden and quantitative genetic designs rarely cover a landscape in as much detail as methods such as GWAS. However, in combination with genomic data, these studies can be used to more fully understand patterns of adaptive variation across the landscape .Multi-generation common garden experiments conducted across multiple environments will allow researchers to understand adaptive epigenomic inheritance . In planFigure 1 shows a design for distinguishing between genetic and non-genetic inheritance mechanisms for adaptive traits. In this design, natural populations with contrasting environments are used in a reciprocal transplant context to test the adaptive nature of variation. The use of a clonal plant species allows for greater control of genetic background across treatments, and clonal replicates can help researchers minimize the effects of somatic mutation during the experiment. Multiple generations and reciprocal crosses between environments and sources in the parental generation enable separation of the various inheritance patterns  where the same clone is distributed across a wide geographic area. The clonal identity controlled for genetic variation . Generations in different environments resulted in epigenetic differentiation among locations. When plants were grown in a common environment, traits of clonal replicates from different environments remained distinct, showing differentiation was not caused by the environmental exposure in the current generation.Thus far, only a few studies have taken advantage of common garden approaches for studying epigenetic inheritance. In two examples, clonal systems also helped narrow down potential sources of phenotypic variation. Studies in pinaster  used seeStrong evidence exists for epigenetic inheritance and its potential to influence adaptive traits in plants. At the landscape level, studies have identified genomic variation that affects adaptation, but the genetic basis of additional phenotypic variation remains unaccounted for. A number of recent investigations of epigenetic variation across the landscape show patterns consistent with epigenetic inheritance contributing to adaptation. However, carefully designed common garden studies are needed to partition the contributions of genetic variation, phenotypic plasticity, and transgenerational epigenetic inheritance to adaptive phenotypes.Both AW and LH contributed to the ideas, writing and editing of this manuscript.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.