The wild barley HsDry2.2 allele confers a reproductive advantage that is independent of flowering time under drought conditions and is manifested through modifications in shoot morphology, reduced senescence, and longer grain filling. per se (i.e. regardless of the environment) with a study for quantitative trait loci (QTLs) × environment interactions (Q×E), indicates the prevalence of Q×E mostly for reproductive traits. One Q×E locus on chromosome 2, Hordeum spontaneum Dry2.2 (HsDry2.2), showed a positive and conditional effect on PGY and grain number (GN). The wild allele significantly reduced HEA; however, this earliness was not conditioned by water deficit. Furthermore, BC2F1 lines segregating for the HsDry2.2 locus showed that the wild allele conferred an advantage over the cultivated allele in PGY, GN, and harvest index, as well as modified shoot morphology, a longer grain-filling period, and reduced senescence (only under drought). This suggests the presence of an adaptation mechanism against water deficit rather than an escape mechanism. The study highlights the value of evaluating wild relatives in search of novel alleles and provides clues to resilience mechanisms underlying crop adaptations to abiotic stress.Increasing crop productivity under conditions of climate change requires the identification, selection, and utilization of novel alleles for breeding. In this study, we analysed the genotype and field phenotype of the barley HEB-25 multi-parent mapping population under well-watered and water-limited environments for two years. A genome-wide association study (GWAS) for genotype × environment interactions was performed for 10 traits including flowering time  and plant grain yield (PGY). Comparison of the GWAS for traits  Hordeum vulgare ssp. vulgare L.) is ranked the fourth most-produced cereal worldwide, providing fodder, human food, and substrate for malting  retains water in the cells (i.e. osmotic adjustment) and thus sustains metabolic activity . Plant sAdvanced backcross QTL analysis (AB-QTL) enables the introduction of beneficial alleles into the modern gene pool by crossing a wild donor accession with a modern elite cultivar , 2010, fHordeum vulgare ssp. vulgare) and 25 highly divergent exotic wild barley genotypes (H. vulgare ssp. spontaneum). The population has been used to study the genetic architecture of flowering , and Vrn-H1/H2/H3 . The strongest exotic haplotype identified accelerated flowering time by 11 d, as compared to Barke (denso/sdw1 (3H) and the Ppd-H1 loci, respectively. In a more recent use of this genetic resource, the plants were placed under two regimes of salinity and GWAs of the two treatments were compared (The barley nested association mapping (NAM) population termed ‘Halle Exotic Barley 25’ (HEB-25) originated from interspecific crosses between the spring barley elite cultivar Barke  and their corresponding parents were genotyped using the barley Infinium iSelect 9K chip  segregating for the wild donor allele in the HsDry2.2 locus were genotyped for the peak marker BOPA2_12_30265 using high-resolution melting analysis. This marker was previously found to show strong divergent selection in winter versus spring barleys according to N=22 and N=7, respectively) or cultivated  allele for statistical analyses.BC1S3:4) and 2015–16 (BC1S3:5). During 2014–15, the entire population was phenotyped in the field under both conditions, and in 2015–16 1320 lines were included in the experiment. Seeds from each line were sown in germination trays and at the 3-leaf stage a total of 16 plants were transplanted into troughs measuring 0.4 × 0.3 m , with each trough containing eight plants. The soil in the troughs was composed of two layers of volcanic soil . The two troughs for each line were kept together in pairs and watering was regulated throughout the growth of the plants (see Results). The pairs of troughs for each line were placed in a completely randomized design within an insect-proof net house, roofed by polyethylene, at the experimental farm of The Hebrew University of Jerusalem in Rehovot, Israel . The unique arrangement of the net house enabled approximately 3000 experimental units of eight plants to be accommodated  was applied via irrigation in 2014–15  as well as in 2015–16 (15.4 l for both treatments).The HEB-25 lines were evaluated for their drought responses under well-watered (WW) and water-limited (WL) conditions during the winters of 2014–15 , followed by 3 d of acclimation at room temperature (22 °C), and then planted into small plastic pots  in a standard commercial soil mix , defined as the time between sowing to the time at which the first spikes of 50% of the plants in a plot have first awns visible , was recorded based on daily inspections. Days from sowing to stage BBCH 87  was recorded as maturity (MAT). At maturity, mean plant height (HEI) per plot was measured from the soil surface to the base of the three first spikes.At full grain maturity and after plants were fully dried, all above-ground biomass was harvested and weighed to determine total dry matter (TDM). In particular, all the free-threshing material (approximately one quarter of the genotypes) was caged with netting bags between stages BBCH49 and BBCH87 to avoid loss of spikes. Spikes were then threshed and the grains were weighed to determine plant grain yield (PGY). Finally, grains were counted to estimate grain number (GN) per 8-plant trough and average grain weight (GW). Harvest index (HI) was calculated as the ratio between PGY and TDM. Vegetative dry weight (VDW) was calculated by subtracting PGY from TDM. The grain-filling period (GFP) was calculated by subtracting HEA from MAT. Trait values were adjusted based on the ratio between population mean values in the two years, with each trait value being multiplied a factor equal to 1/. The adjusted means of the HEB family traits were then averaged across the two years and were used in the GWAS (see below).Heading time, defined as the date at which the awns of the first three spikes were first visible (spikes were tagged), was recorded daily and used to score HEA. MAT was recorded when the three first (tagged) spikes had dried, which was followed by the rest of the plant drying out. The GFP was calculated by subtracting HEA from MAT. All above-ground biomass per plant was harvested at full grain maturity; fertile spikes were counted to assess the number of spikes per plant and were separated from the vegetative organs (stems and leaves), both were oven-dried  and weighed to determine spike dry matter and TDM. Spikes were threshed and the total grain weight was determined. PGY, HI, and GN per plant were determined. The mean GW for the tagged spikes was recorded, and the mean GW of the other spikes was determined separately. At 64 DAP, the flag leaf (FL) sheath and the blade length and width of the preceding leaf (termed here as ‘–1FL’) were measured. Stem diameter (adjacent to the spike) was measured after harvest. For analysis of leaf senescence, we used two sets of photos taken with a Canon EOS1200 camera at 66 and 78 DAP against a background of a standard white sheet (80 × 120 cm). Custom-made software (unpublished data) was used to calculate the ratio of yellow/brown areas to the total leaf area. Shades covering the range of green and yellow/brown were selected by sampling from several images, and were then used for all analyses. For each photo, the green and the yellow/brown areas were calculated in percentages, using the YCbCr method , with same tolerance level for all analyses, while taking into account the white background for standardization. Then, the percentage of yellow/brown out of the total leaf area was calculated. Senescence was calculated as the increase of yellow/brown to the total between 66 and 78 DAP for each plant.per se (i.e. regardless of environment), under WW and WL conditions, and to assess SNP interactions with the environment  using a mixed linear model (MLM) as implemented in the software package TASSEL 5.0 (A genome-wide association study (GWAS) was conducted to identify trait variations, Fi is the family effect (i=1..25), Gj denotes the marker effect , K represents the relative kinship matrix, and εij denotes the error.where µ denotes the population mean for the trait, http://cran. r-project.org/web/packages/GWAF/). To test associations between each of the continuous traits and each SNP, we applied the linear mixed effects model (LME) implemented in lmekin.int.batch in the GWAF package based on the kinship computed by the kinship2 package  with addition of the WW/WL conditions as follows:A genome scan for SNP × environment (Q×E) interactions was conducted with the GWAF package in the R software (Ek denotes the watering (environment) effect and Gj × Ek denotes the effect of the interaction between the marker and the environment. The in-house R script that utilizes the GWAF and kinship2 R-packages can be found in where, P<0.05) in at least 30% of subsamples were accepted as putative QTLs. The designation of the QTL was based on linkage disequilibrium with the major SNP that showed the maximal significance level in a genomic interval . The experimental set-up under a rain-sheltered net house was designed to allow whole-plant phenotyping of plants together with controlled irrigation with measurable water deficit in the plants . AssessmOverall, we observed wide variation for all traits across the HEB-25 population, and increases in the coefficient of variation under WL conditions, with means of 28.3 versus 16.8 for all traits under WL versus WW, respectively . WL condr=0.1, P<0.005), whereas under WL this correlation was negative .Pair-wise correlation analysis between all traits found similar relationships under both treatments, with a few exceptions . One sucHvELF3, Ppd-H1, HvCEN, denso, Vrn-H1, and Vrn-H3, were identified. In general, at loci associated with PGY and GN the wild alleles were associated with a reduction of the traits under WW conditions. Under this trait analysis, the mean reductions of the three loci significantly associated with GN or PGY variation were –3.4% and –9.2%, respectively, under WW conditions (Ppd-H1 on chromosome 2 (–19.7% effect with probability of association –LogP=7.9) for GN.Initially, we performed a genome scan for all ten traits under both watering treatments, and we identified 69 loci with a significant contribution to trait variation . The mainditions . SimilarNext, the GWAS model was modified to identify QTL × environment interactions . No significant Q×E loci were identified for two of the phenological traits, HEA and MAT. For the vegetative traits, only two Q×E loci were identified in VDW and no significant loci were identified for plant height . In contHordeum spontaneum Dry 2.2 (HsDry2.2). The peak of the Q×E interaction in this locus was identified at 57 cM. Plotting the mean values of PGY measured for the HEB-25 population under WW and WL conditions, using BOPA2_12_30265 as a representative marker, illustrates the conditioned effect of HsDry2.2 on the trait  originated from at least three wild donors. Performing the genotype-to-phenotype analysis in the HEB-16 family showed that plants carrying the wild HID219 allele  allele were further analysed to validate effects on reproductive traits, as well on possible related traits. A pot experiment was carried out to evaluate the wild allele effects under optimal as well as early and late water limitation, i.e. starting at transplanting or at the stem elongation stage (52 DAP), respectively. The type of traits examined were plant productivity, phenology, canopy structure, and leaf senescence.Plants derived from one of the HEB lines that carry the HsDry2.2 For most of the measured variables the effect of the drought treatment was found significant . TDM undHvCEN gene that resides between them , for example mean spike dry weights were 30.8, 32.8, and 35.6 g for Hv/Hv, Hs/Hs, and Hv/Hs, respectively. We therefore decided to group the carriers of one or two wild alleles as single groups (hereafter Hs/_) to increase the number of replicates. TDM did not differ between Hs/_ and the cultivated (Hv/Hv) groups averaged across treatments and under each treatment separately , although we still consider this to be relevant as it is typical for such agricultural reproductive traits. In addition, while the wild allele was associated with lower GW of the first three spikes as compared to the cultivated one  is a region of suppressed recombination, so markers are linked. It includes large number of markers in full linkage, e.g. SCRI_RS_208320, BOPA2_12_30265, and SCRI_RS_1249,2 and the c groups , they weparately . Howeverparately , and thiparately . Spike nparately . The firparately . The genparately . HoweverThe first three spikes to emerge were used to determine plant phenology. The Hs/_ group emerged significantly earlier than the Hv/Hv plants  as compared to plants homozygous for the cultivated allele (Hv/Hv). This approach was different from recent Q×E studies in Arabidopsis, in which analyses were performed in two stages: initially performing a genome scan for loci that are associated with the trait variation ese loci  for reprnditions , S5. Howper se and 22 for Q×E  variance, as compared to 35% of the genotype component.The most reproducible and significant Q×E locus was identified on the long arm of chromosome 2H , 3. Intearieties . Cuesta-HvCEN (HvCEN as the candidate gene) on all measured traits; however, they did not measure yield. It would therefore be interesting to further characterize these differences at the metabolic and developmental levels, both in the sink and source tissues, under gradients of water availability for isogenic lines for this Q×E locus. This may lead to the identification of hitherto unknown pathways related to drought tolerance.Both field trials and pot experiment showed that the wild allele had no advantage over the cultivated one in vegetative or TDM production; however, it showed superiority for reproductive traits (PGY and HI). Recently, loci regulating developmental characteristics were found to be co-located with flowering-time genes, including Despite the similarity in reduced PGY for the early and late WL treatments, it was quite clear than the reduction was obtained via different pathways. As expected, while spikes per plant was significantly reduced by early WL, in the late WL treatment it was not. Total GW  under late WL was reduced by only half of that of early WL, and GN appeared to be reduced only in the early WL treatment, although not significantly .As temperatures in the greenhouse were high throughout the entire season , it is rWhereas under the WW treatment the benefit of the wild allele on PGY may be attributed to higher GN, under both the early and late WL treatments the wild allele presented advantages in GW, ripening period, and reduced senescence rate. In that respect, the condensed canopy structure of the drought-adapted wild allele under early WL may also have had an effect on the reduced senescence rate as a ‘by-product’ of this phenotype.Overall, the Hs/_ plants presented a phenotype that matched the desired ideotype of future climate-resilient barley for Mediterranean climatic zones based on crop models  is responsible.Overall, the current study provides data from large-scale genome-wide association scanning to a finer-resolution scale, and sheds some more light on the promising HsDry2.2 locus and its mode of action. This appears to be a rare case of a wild allele with direct beneficial effects on grain yield. Finer mapping of this locus should determine whether the causal gene for these multiple differences between the wild and cultivated alleles does indeed correspond to JXB online.Supplementary data are available at Table S1. Primers used for high-resolution melt (HRM) and HvCEN sequencing.Table S2. Trait distributions and heritability values under WW and WL treatments.Table S3. Pairwise correlations between traits under WW and WL treatments in the field.per se.Table S4. GWAS results for traits Table S5. Q×E loci for the different traits.Table S6. Pot experiment ANOVA for measured traits.2F1 pot experiment.Fig. S1. Genotyping for the BCFig. S2. Details of the irrigation management in the pot experiment.Fig. S3. Reaction norms for vegetative dry matter in the field experiment.Fig. S4. Reaction norms for grain number for HEB-05 and HEB-16 families in the field experiment.Fig. S5. Shoot morphology in the pot experiment for Hv/Hv and Hs/_.Fig. S6. Temperature profile for the pot experiment.HvCEN.Fig. S7. Sequencing results for File S1. The in-house R script utilizing the GWAF and kinship2 R-packages.Supplementary Fig S1-S4Click here for additional data file.Supplementary Fig S5-S7Click here for additional data file.supplementary TablesClick here for additional data file.Supplementary figure legendsClick here for additional data file.RS, LOM, EL, and EF designed the experiments, collected the data, conducted the data analysis and interpretations, and wrote the manuscript. RS, AF, and LOM were involved in the data analysis and its interpretation, and assisted in writing the manuscript. KP and AM supplied the plant material and genotype data.


With case–parent triad data, one can frequently deduce parent of origin of the child's alleles. This allows a parent‐of‐origin (PoO) effect to be estimated as the ratio of relative risks associated with the alleles inherited from the mother and the father, respectively. A possible cause of PoO effects is DNA methylation, leading to genomic imprinting. Because environmental exposures may influence methylation patterns, gene–environment interaction studies should be extended to allow for interactions between PoO effects and environmental exposures . One should thus search for loci where the environmental exposure modifies the PoO effect.We have developed an extensive framework to analyze PoOxE effects in genome‐wide association studies (GWAS), based on complete or incomplete case–parent triads with or without independent control triads. The interaction approach is based on analyzing triads in each exposure stratum using maximum likelihood estimation in a log‐linear model. Interactions are then tested applying a Wald‐based posttest of parameters across strata. Our framework includes a complete setup for power calculations. We have implemented the models in the R software package Haplin.To illustrate our PoOxE test, we applied the new methodology to top hits from our previous GWAS, assessing whether smoking during the periconceptional period modifies PoO effects on cleft palate only. A PoO effect refers to the situation where the effect of a particular allele in the child depends on whether it is inherited from the mother or the father  may also play an important role in complex traits. In our context, this means that the observed PoO effect may vary across environmental strata, which is plausible from a biologic perspective. A known cause of imprinting is DNA methylation in the germline. It is possible that maternal environmental exposures influencing methylation patterns might also influence the effects of maternally and paternally inherited alleles in unequal measures.Conceivably, PoOxE effects may appear in different ways. The allele in question might increase risk only when transmitted from exposed mothers. A PoOxE effect may also be observed if the allele is protective to the child only when inherited from unexposed mothers but with no particular effect in the other situations. In principle, there might even be a “qualitative” interaction where the genetic effect is reversed. For instance, an allele might increase risk when inherited from exposed mothers and decrease risk when inherited from unexposed mothers, and concurrently decrease risk when inherited from exposed fathers and increase risk when inherited from unexposed fathers.in the child, depending on whether they were inherited from the mother or the father  = . For notational convenience, we assume that the second allele from the mother and the second allele from the father are transmitted to the child; that is, the full triad  can thus be described by the mating type  = .We consider a single, multi‐allelic locus with M,F,C), conditional on the child being a case. If D denotes the event that the child is a case, Bayes' theorem allows our sampling model to be written asP(D), cannot be observed directly from the case triad distribution and serves as a normalizing constant only. Assuming a population in Hardy–Weinberg equilibrium (HWE) with random mating and Mendelian transmission, we haveThe sampling model should describe the distribution of , describes the probability of a child having the disease, conditional on the triad genotype. Assigning different effects to the alleles depending on parental origin, a penetrance model for PoO effects isRRM,j and RRF,j are the risk increase (or decrease) associated with allele Aj, relative to the baseline risk level B, depending on whether the allele is transmitted from the mother or the father. The fraction RRM,j/RRF,j is then a measure of the extent of the risk associated with allele Aj, depending on parental origin. The parameter RRjl∗ is included to allow homozygous individuals to have a risk that deviates from what would be expected from a multiplicative model . To incorporate this deviation, we have that RRjl∗=RRj∗ when j=l and that RRjl∗=1 when j≠l. Thus, if RRj∗=1 for all j, the penetrance model is purely multiplicative. Note that B is typically associated with the reference allele and functions only as a normalizing constant. Moreover, this model also applies to multi‐allelic markers. The full sampling model (1) can then be parameterized asRR=1 for a reference allele. Alternatively, population or reciprocal references can be used  is the relative risk associated with allele Ai carried by the mother, and RRij(M)∗ is interpreted analogously to RRij∗. We thus assume that the maternal alleles have a multiplicative effect on top of the fetal alleles. Note specifically that in a combined model, the PoO effect is estimated essentially by contrasting allele frequencies of transmitted alleles, depending on parental origin, whereas the maternal effect is estimated by contrasting the frequencies of nontransmitted alleles in case mothers with that of nontransmitted alleles in case fathers.An important feature of the log‐linear model is the possibility to incorporate and adjust for maternal effects. Specifically, PoO and maternal genetic effects can be addressed simultaneously by the modelNote that the PoO model requires information on parental origin, which is not available for ambiguous (uninformative) triads. However, the EM algorithm is implemented in our software and uses maximum likelihood to account for unknown parental origin in ambiguous triads. Additionally, it will account for missing information on individuals, such as when some triads are reduced to mother–child dyads due to missing data on the father. The basic model relates to a single multi‐allelic locus. In combination with the EM algorithm it extends directly to haplotypes over multiple loci by statistically reconstructing unknown haplotype phase  if it is inherited from the mother or from the father, respectively. We define the PoO effect as the relative risk ratio RRR=RRM/RRF. This fraction is a measure of the magnitude of the risk associated with the allele under study, depending on whether it is maternally or paternally derived. A ratio larger than one indicates a higher risk when the variant allele is inherited from the mother versus the father. If it is equal to 1, the variant allele increases (or decreases) the risk by the same amount regardless of parental origin, and there is no PoO effect. For instance, if the variant allele doubles the risk of disease independently of parental origin, this is a standard fetal association; as such, it would have been identified in a traditional search for fetal gene effects. Note that one can assume a priori that, for instance, the paternal allele has no effect  and try to detect a “pure” imprinting effect RRM. This effect is, however, confounded with a standard fetal effect whenever the assumption RRF=1 does not hold. Accordingly, we prefer to define our PoO test as a contrast between maternally and paternally derived allele risks.For a single SNP, let RR1 and RR2 as the relative risks in the unexposed and exposed strata, respectively. The relative risk ratio RRR=RR2/RR1 is a measure of the extent of the risk associated with the allele, depending on the exposure status of the case. For instance, a ratio larger than 1 implies that an exposed child carrying the variant allele has a higher risk than the unexposed child carrying the variant allele.Under the weak assumption of independence between exposure and child genotype conditional on parental mating type  depends on which allele and exposure group are chosen as reference.The PoO effect can be seen as a statistical interaction between the transmitted allele and its parental origin, whereas the GxE effect is an interaction between a main fetal effect with an external environment. It is thus natural to consider a PoOxE effect as a two‐way interaction that takes into account both parent of origin and environmental exposure in the same estimate. At a locus with two alleles and a dichotomous environmental exposure, the ratio2.2.1βM,s and βF,s are the log relative risks within stratum s, depending on whether the allele is derived from the mother or the father. Within each mutually exclusive exposure stratum, s=1,2,…,S, we calculate β^s=β^M,s−β^F,s, the difference between parental relative risks estimated on a log‐scale. From the asymptotic theory of log‐linear models .The Wald test can then be used to conduct post‐hoc inference on the β parameters, based on the asymptotic normality Agresti, , Ch. 1.3s=2,…,S against the first stratum s=1; that is, the test takes the form2 distribution with r=S−1 degrees of freedom under the null hypothesis of no PoOxE effect. This is an overall test for any difference in PoO effects across strata when measured on a log risk scale.In the PoOxE test, our null hypothesis can be seen as a test of all strata Interactions with a continuous exposure variable can be incorporated in our framework by categorizing the variable into an appropriate number of categories and testing for a trend‐type association of the resulting ordinal variable. This approach is outlined for GxE effects in Skare et al. , and a t2.2.2S1.Genetic association analyses of X‐linked markers are especially relevant if the prevalence of a complex trait differs systematically for males and females. Various penetrance models in Haplin address different causal scenarios that apply to an X‐linked disease locus. The models depend on the assumptions made regarding allele‐effects in males versus females, and might include sex‐specific baseline risks, shared or distinct relative risks for males and females, and X‐inactivation in females. A detailed description of parameterization models is provided in a previous study  is a common craniofacial birth defect in humans, occurring with (nonisolated) or without (isolated) other congenital anomalies or identifiable malformation syndromes. The prevalence rate for isolated CPO is 5 per 10,000 births worldwide , this was evaluated as a simple yes/no response to ever having smoked during this period. The GWAS data set is available at the dbGAP database (http://www.ncbi.nlm.nih.gov/gap) under accession ID phs000094.v1.p1. Information on quality control and detailed characterizations of study participants and environmental exposure have been provided elsewhere  on chromosome 18, and the re‐analyzed GxE results are presented in Table C, allele G is associated with a decreased risk of CPO among smokers and an increased risk among nonsmokers. Consequently, RRR=0.42, and this qualitative effect has a P‐value of 4.5−4. It is important to note that although maternal smoking appears to be beneficial at first sight, this apparent risk‐reducing effect of smoking is contingent on the choice of reference allele. Switching the reference and variant allele inverts the estimated value of the RRR. Obviously, the main effect of smoking cannot be assessed from case‐triad designs alone, without independent controls. Therefore, the GxE RRR measures only how smoking modifies the estimated fetal genetic effects. For rs470563, we did not detect any significant PoO or PoOxE effects , had one of the strongest signals in our search for PoOxE effects, and the PoO, GxE, and PoOxE results are shown in Table RRR=0.09, with P‐value 6.5×10−7 . Altogether 424,401 SNPs passed the quality controls and were included in our PoOxE analysis. We used eight CPU cores with 2.5 GHz per core, and the approximate run time of Haplin was 58 hours.3.2We investigated the performance of our PoOxE test by evaluating its power in various settings. Power and sample size can be computed from the asymptotic variance–covariance structure underlying the Wald test; this approach is implemented in Haplin. The Haplin framework also includes a complete setup for power calculations through simulations, which is a robust way of checking software implementations, power, small‐sample behavior, and attained significance level. A detailed derivation of our asymptotic approximation formulae is given in Appendix RRM,1= RRF,1 = RRF,2 = 1 in all scenarios so that the value of RRR in Equation RRM,2. Moreover, we assumed equally sized exposed and unexposed groups. The left panel of Figure We examined the power of the PoOxE test using the above‐mentioned asymptotic approximations. We first analyzed the power for a single SNP at the 5% nominal significance level. Power calculations for increasing relative risk ratios, RRRs, are shown in Figure MAF<0.2 or if the ratio of exposed versus unexposed is highly skewed (the latter result is not shown). Because the PoOxE test stratifies on both parent of origin and exposure, detecting a PoOxE effect requires a larger sample size than detecting a PoO effect or a GxE effect. Naturally, greatest power is achieved in a search for fetal effects.The power to detect PoOxE effects for a single SNP is sufficient for RRRs above 1.6–1.7 and a total sample size of 1500 case–parent triads with equally sized exposure groups. Nevertheless, larger sample sizes are needed if the RRM,2) with nominal significance levels 10−4 (left panel) and 5×10−8 (right panel). The power is demonstrated for an increasing number of case–parent triads using equally sized exposure groups and a MAF of 0.2. With a nominal significance level of 10−4, approximately 5000 case–parent triads are required to detect RRRs of 1.6–1.7 with 80% power. With a nominal significance level of 5×10−8, a sample size of 10,000 case‐parent triads suffices for RRRs above 1.6.We also examined the power using nominal significance levels more relevant to GWAS settings. Figure Our PoOxE test is asymptotically unbiased. However, the asymptotic approximations underlying log‐linear models may be suboptimal when the number of cases or controls is too small in one or more strata. When testing for GxE and PoOxE effects, one may occasionally encounter highly skewed exposure distributions. For example, in our CPO example, only 8 women of Asian ancestry answered “yes” to the question of maternal smoking during pregnancy, whereas the remaining 245 answered “no.” In such situations, the nominal significance level of the tests may be incorrect; the actual significance level is most easily assessed through simulations.P‐values from 100,000 simulated data sets under the null hypothesis . The P‐values should be uniformly distributed when the null hypothesis is true. Hence, if no bias is present, the P‐values would fall close to the diagonal line. Throughout, a total of 1000 case–parent triads were divided into two exposure groups, and an MAF of 0.2 was assigned to both strata. Two scenarios were investigated according to the distribution of exposed and unexposed triads. In the first scenario (100–900), the smallest stratum comprised 100 case–parent triads. In the second scenario (300–700), the smallest stratum comprised 300 case–parent triads.In Figure P‐values than expected. At the 0.05 nominal level, the attained significance level is 0.045 in the 100–900 setting. For lower significance levels, typically occurring in genome wide analyses, this bias might become substantial. Each exposure group should be large enough so that the asymptotic approximation of the estimator, β^, is sufficiently precise. Hence, the bias would be less pronounced for skewed exposure distributions at larger sample sizes (such as in a 1000–9000 setting). In other words, the unbalanced exposure design itself is not the cause of the observed deflation. The bias is negligible in the 300–700 setting, verifying that our PoOxE test attains the nominal significance level when the sample size of the smallest stratum increases.As expected, we observed a small bias for the PoOxE test when the number of cases in one exposure group was low, obtaining larger 4RRM=RRF=1.5>1, this would trigger a test for imprinted genes but not for PoO.In this study, we have proposed a statistical method for detecting PoOxE effects. Postestimation in the log‐linear framework, incorporated into the Haplin software, allows us to combine the theory on PoO and GxE effects to test for the second‐order PoOxE effect. Although PoO and GxE studies abound, the combination has hardly been analyzed, in spite of its obvious biological relevance. Wang et al.  proposedWang et al.  use condDetailed study planning typically requires calculating the sample sizes needed to obtain sufficient power. Because statistical power depends on multiple factors including haplotype frequencies, penetrance model, and so on, published power tables for genetic studies are typically too restrictive, and software often covers only basic genetic models. As illustrated in S1, Haplin provides extensive power simulations, even covering the complex setup of PoOxE analyses. By entering the necessary parameters, the user can easily perform either “raw” simulations of power or use a very fast power calculation based on the asymptotic distribution of the parameter estimates.In a GWAS analysis, the power to detect PoOxE effects is generally low. However, a candidate gene approach would reduce the complexity of multiple comparisons and enable a search for PoOxE effects when the sample size is limited. Specific environmental exposures that relate directly to the putative cause of the PoO effect of a candidate gene should be used in a PoOxE test. For example, one might assume that a detected PoOxE effect has a better chance of revealing a causal relationship involving genomic imprinting due to methylation than the standard PoO or GxE searches. A selection of relevant candidate genes might therefore be based on a GWAS screen for PoO or GxE effects.Tracking the different etiologic mechanisms underlying complex diseases is crucial in improving diagnosis, prognosis, and prevention. The test for PoOxE effects and the comprehensive framework for assessing statistical power for genetic association analyses presented in this article are thus important contributions in advancing our understanding of the different etiologic mechanisms that underlie complex traits.5R . Our website (http://folk.uib.no/gjessing/genetics/software/haplin) provides further information.Haplin is implemented as a standard package in the statistical software The authors thank Prof. Ivar Heuch for his valuable comments.The authors declare that they have no competing interests.Supporting Data S1Click here for additional data file.Table S1: Values of ω2, the asymptotic variance of the log‐parameter for a) a complete case‐parent triad; and b) a complete case‐mother dyad.Click here for additional data file.


The method is particularly helpful in adjustment for winner's curse effects when the initial gene-based test has low power and for relatively more common, non-causal variants. Adjustment for winner's curse is recommended for all post-hoc estimation and ranking of variants after a gene-based test. Further work is necessary to continue seeking ways to reduce bias and improve inference in post-hoc analysis of gene-based tests under a wide variety of genetic architectures.To date, gene-based rare variant testing approaches have focused on aggregating information across sets of variants to maximize statistical power in identifying genes showing significant association with diseases. Beyond identifying genes that are associated with diseases, the identification of causal variant(s) in those genes and estimation of their effect is crucial for planning replication studies and characterizing the genetic architecture of the locus. However, we illustrate that straightforward single-marker association statistics can suffer from substantial bias introduced by conditioning on gene-based test significance, due to the phenomenon often referred to as “winner's curse.” We illustrate the ramifications of this bias on variant effect size estimation and variant prioritization/ranking approaches, outline parameters of genetic architecture that affect this bias, and propose a bootstrap resampling method to correct for this bias. We find that our correction method significantly reduces the bias due to winner's curse (average two-fold decrease in bias,  In recent years, numerous gene-based rare variant tests of association (hereafter GBTs) have been proposed that seek to aggregate genotype-phenotype association signals across rare variants within a gene to improve the overall evidence of genotype-phenotype association within a gene of interest  sample approaches propose that parameter estimates be obtained from a different sample than was used to test significance. These approaches yield unbiased parameter estimates by looking at an independent replication sample .Qburden, weighted, or Qbw for short) and one variance components test  in our analysis, selected in order to represent these two broad classes of testing methods. We used the SKAT R package to conduct all tests  or joint  , we considered single-marker, variant level statistics, Ti, are functions of the observed minor allele frequency in the cases . In the Q) followed by a single-marker statistic Ti leads to the following relationship for i:Single-marker statistics at Step 2, which are a function of observed single-marker minor allele frequencies, often perform less than optimally , and due to winner's curse will often be quite large when we perform post-hoc estimation only after a significant Step 1 test. This in turn will result in a large post-hoc bias in single-marker statistics This relationship demonstrates that the bias of the naïve Step 2 statistic can be decomposed into an overall gene bias/variance term, and then a unique term for each variant in the gene. The variant-specific term depends on the minor allele frequency and relative risk of the variant, the choice of weights for the GBT, and the strength of LD between the variant of interest and other variants in the gene. The overall gene bias term in Equation (1) is the Recently, a bootstrap resampling approach was proposed to estimate and adjust for bias in single-marker tests on common variants in GWAS studies . If the test yields a significant association statistic (pQ < α) continue to Step 2. Otherwise, stop: there is no need for post-hoc single-marker analysis.Part 1. Calculate the GBT of interest, i = 1, …, m. These statistics will frequently be biased toward showing stronger association than is actually present in the population (due to winner's curse).Part 2. Calculate naïve single-marker association statistics b = 1, …, B bootstrap resamples of the N subjects, separately resampling cases and controls so that each bootstrap sample again has N+ cases and N− controls. This will yield bth bootstrap sample and bth bootstrap sample . Note that, on average, Q as in Part 1. If the GBT is significant on the bootstrap sample, compute the same single-marker post-hoc statistics used in Part 2 on the bootstrap : (a) All 10 SNPs are “rare” , (b) one common neutral variant and nine rare variants  or (c) two common  variants and eight rare variants . For each distribution of minor allele frequencies we considered five different relative risk distributions : (1) 20:80:0, (2) 50:50:0, (3) 80:20:0, (4) 20:60:20, and (5) 0:100:0, for a total of 50 different combinations. Relative risk λ for all risk variants is determined by the minor allele frequency, f. Namely, for risk increasing variants we used the following combinations of : , ,  and . For risk reducing variants we used , ,  and . For all simulations we used 1,500 cases and 1,500 controls, and simulated all variants independently. We performed 10,000 simulations for each of the 50 simulation settings  to each simulated data set. In particular, we computed p-values for each GBT Qbw and Qsw. Step 1 power was computed as the proportion of the 10,000 simulated data sets yielding p-values less than the significance level. We considered two different significance levels (α): 0.01 and 0.0001. If the Step 1 test was significant we performed bootstrap resampling (Step 2) for that test: We computed adjusted and unadjusted versions of the post-hoc statistics , 161 of these 200 situations yielded more than one simulation out of 10,000 with a significant Step 1 result. Averaging across each of the 10 variants in each of these 161 situations showed improvements in the overall bias and MSE of −4 vs. average bias after adjustment = 3.1 × 10−4, with paired t-test p < 2.0 × 10−16; MSE: 9.7 × 10−6 (unadjusted) vs. 5.5 × 10−6 (adjusted) with paired t-test p < 2.0 × 10−16] and −5 (unadjusted) vs. 3.7 × 10−6 (adjusted) with paired t-test p < 2.0 × 10−16; MSE: 2.7 × 10−9 (unadjusted) vs. 1.1 × 10−9 (adjusted) with paired t-test p = 2.2 × 10−16]. In short, the bias and MSE are, on average, halved across all settings. Thus, on average we have reduced, but not eliminated, the bias in these statistics [both 3.1 × 10−4 . The times that ranking based on the adjusted statistics is not better tend to be settings when the percent of times the top SNP is causal was already quite high before adjustment, and the ranking after adjustment is often not that much worse in these cases. Supplementary Figure We have focused primarily on improvements to the bias and MSE of post-hoc single-marker statistics after our proposed post-hoc adjustment strategy. However, we note that a reasonable approach taken to identify causal variants after a significant GBT involves ranking of individual markers within the gene. Figure Revisiting the simulation setting illustrated in Figure We have documented widespread bias in post-hoc single-marker statistics performed after a significant GBT. This bias is due to winner's curse: namely, that the naïve post-hoc single-marker association statistics  on the post-hoc inference. Furthermore, the choice of post-hoc statistic (Step 2) is important to align with the GBT statistic (Step 1). As others have noted . Further work is needed to explore these extreme cases since all post-hoc, variant-level association statistics will struggle to yield robust estimates in these cases. We anticipate that there may be ways to combine extremely rare variants in an ad hoc manner to yield post-hoc association statistics for sets of variants which are more robust and reliable in these situations. Further work is necessary here as well.A number of extensions of the method are possible and worth further exploration including alternative variant weights when conducting post-hoc ranks , conditioning on known causal variants and approaches which attempt to predict the most likely subset of causal variants vs. a ranked list based on single-marker statistics. An extensive effort will be needed to fully address all of the many issues presented by this setting of post-hoc inference.Winner's curse is a widespread and well-understood phenomenon for common variants in genetic association studies. However, the use of GBTs for sequence data in order to incorporate rare variants into resulting association tests still leads to substantial winner's curse effects, especially when attempting to prioritize and identify causal variants using single-marker methods (Step 2) after a significant GBT (Step 1). We have shown that low powered Step 1 tests, and relatively more common, non-causal variants are particularly prone to winner's curse effects and can yield substantial bias in resulting estimates of genotype–phenotype association, which can in turn lead to cases where the top ranked variant is non-causal and causal variants show less evidence of association. We have proposed a preliminary method of adjusting for winner's curse when generating post-hoc ranks of variants. Our approach reduces bias and leads to improved variant prioritization. Further work is needed to continue to improve the performance of the proposed method, and to develop further methods which address the plethora of issues presented by this setting of post-hoc inference.All authors participated in the design of the study, methods development, and results summarization. KG and NT drafted the initial manuscript. JW led the implementation of the simulations. All authors read and approved the final 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.


The new generation of whole genome sequencing platforms offers great possibilities and challenges for dissecting the genetic basis of complex traits. With a very high number of sequence variants, a naïve multiple hypothesis threshold correction hinders the identification of reliable associations by the overreduction of statistical power. In this report, we examine 2 alternative approaches to improve the statistical power of a whole genome association study to detect reliable genetic associations. The approaches were tested using the Genetic Analysis Workshop 19 (GAW19) whole genome sequencing data. The first tested method estimates the real number of effective independent tests actually being performed in whole genome association project by the use of an extreme value distribution and a set of phenotype simulations. Given the familiar nature of the GAW19 data and the finite number of pedigree founders in the sample, the number of correlations between genotypes is greater than in a set of unrelated samples. Using our procedure, we estimate that the effective number represents only 15 % of the total number of independent tests performed. However, even using this corrected significance threshold, no genome-wide significant association could be detected for systolic and diastolic blood pressure traits.PFH14, associated with systolic blood pressure, interacts directly with genes involved in calcium-channel formation and the second gene, MAP4, encodes a microtubule-associated protein and had already been detected by previous genome-wide association study experiments conducted in an Asian population. Our results highlight the necessity of the development of alternative approached to improve the efficiency on the detection of reasonable candidate associations in whole genome sequencing studies.The second approach implements a biological relevance-driven hypothesis tested by exploiting prior computational predictions on the effect of nonsynonymous genetic variants detected in a whole genome sequencing association study. This guided testing approach was able to identify 2 promising single-nucleotide polymorphisms (SNPs), 1 for each trait, targeting biologically relevant genes that could help shed light on the genesis of the human hypertension. The first gene,  The new generation of sequencing platforms has dramatically altered the genetics field by allowing a cost-effective approach to completely explore the information stored in a genome of interest. Large genetic and epidemiologic projects are beginning to utilize whole genome sequencing (WGS) to identify genetic variants  that could explain the missing heritability paradox . The parThe astronomical number of independent hypotheses tested on a WGS study, above 10 million tests, incurs substantial costs in terms of statistical power. Clearly, only a small subset of sequence variants is molecularly functional and even fewer are phenotypically functional with regard to a focal trait of interest. The assessment of the LD (linkage disequilibrium) between single-nucleotide polymorphisms (SNPs) in a WGS project is challenging because of the immense number of rare variants identified, the potential for cross-chromosome gametic disequilibrium, and, consequently, the immense number of possible pairwise correlations. A reduction in the number of relevant hypotheses tested is possible by incorporating prior knowledge of functional potential or localization of genetic variants on a target gene of interest. Many different computational approaches have been proposed for a fast and accurate prediction of the functional outcome of nonsynonymous variants. The prior selection of variants with a common functional classification of interest can directly reduce the number of tests carried out. It leads to a biologically informed hypothesis-driven exploration that should yield simpler interpretation of observed results. The addition of biologically relevant databases such as GO (Gene Ontology) or KEGG (Kyoto Encyclopedia of Genes and Genomes) greatly improve the interpretation of results gathered in a WGS association project [p values to a beta distribution following the method proposed by Dudbridge and Gusnanto [e) where ne is the effective number of tests [ne is the parameter β in the beta distribution and the best parameter is defined by a maximum likelihood approach. This method empirically controls for the redundancy that is inherent in correlated hypotheses, such as the testing of sequence variants that are in linkage/gametic disequilibrium without explicitly requiring estimation of the pairwise correlations. The second general tested approach was the reduction of the hypothesis state space using computational mapping to identify molecularly functional nonsynonymous variants. Using this subset of variants, 2 new significance thresholds were defined: the Bonferroni multiple hypothesis correction, and the second a LD corrected multiple hypothesis correction using the data generated for the first approach. Both methods were tested using the set of real systolic and diastolic phenotypes provided for Genetic Analysis Workshop 19 (GAW19) and 2 very promising associations were identified. Those nonsynonymous variants have the potential to disrupt the proteins coded by the genes PFH14 and MAP4. Both genes are promising candidates and support our method to improve the statistical power of a highly dimensional WGS association project.In this report, we applied 2 distinct methods to increase the statistical power of our analysis by reducing the hypothesis state space of the association analysis performed. The first estimates the number of effective independent tests performed in WGS (or any subset of the genome) by fitting the observed extreme of tests . The estAll our analyses were performed using the GAW19 WGS family data set. A group of 6538591 SNPs with more than 5 copies of the minor allele counts (MACs) had been filtered from the initial set of 8,348,674 genetic variants. This minimum number of MACs is required to achieve approximate asymptotic test statistic expectations under the null hypothesis and was chosen based on a previously performed computer simulation. The large set of variants filtered was subdivided in groups of 500 SNPs to allow an efficient process parallelization and the files generated organized locally in our server.2 = 0). For each simulated phenotype, we performed full WGS-based association analyses for all single-nucleotide variants (SNVs) filtered in the last item. All association analyses were performed using the linear mixed model implemented in SOLAR , allowing for residual nonindependence between relatives [p value) extracted. The distribution of 1000 extreme p values was fit to a theoretical beta distribution and maximum likelihood estimation was used to estimate the β parameter that is directly related to the effective number of tests. This corrected number of effective tests is used to define a new multiple hypothesis Bonferroni correction in the form of target nef is the number of effective tests defined by the β parameter.We simulated 1000 quantitative phenotypes under the null hypothesis of no genetic effects , the corresponding reference genome, and the alternative allele of each variant were jointly used as an input for the ANNOVAR (Annotate Variation) annotation tool . All ann2, sex*age, sex*age2, and 3 principal components extracted from the genotypic dosage information (using unrelateds only) to account for latent population structure.The measurements of systolic and diastolic blood pressure (SBP_1 and DBP_1) were normalized using an inverse Gaussian procedure. In all analyses, we included the covariate effects of smoking status, blood pressure medication usage, sex, age, agep values were sorted and the most extreme association was determined. Using the approach proposed by Dudbridge and Gusnanto in 2008 [p value to a theoretical beta distribution. Based on this procedure, we estimated that there are only 947,109 effectively independent tests performed, corresponding to an astounding reduction of 85 % in the number of performed tests. The relatively small number of founder individuals strongly influences this reduction. This new estimate was employed to calculate a statistically appropriate multiple hypothesis significance threshold of 5.27 × e−8. Given the large number of complete WGS association analyses (n = 1000) performed, we also checked if the effective number of tests was consistent across the totality of the empirical null distribution. The fit appeared to be excellent suggesting that a theoretical beta distribution recapitulate the most important parameters features from the null distribution of extreme p values.The GAW19 WGS was obtained and initially we filtered 6,538,591 SNPs with 5 or more copies of the alternative allele. A set of 1000 simulated phenotypes were generated under a null hypothesis of no genetic association with no heritability estimate. The filtered SNVs had their association with the simulated phenotypes determined using a linear mixed-model approach implemented in SOLAR. For each phenotypic simulation, the set of 2, sex*age, sex*age2, and 3 principal components as covariates. All associations were obtained using the linear mixed model implemented in SOLAR and interpreted using our previous simulation results  located in a nonconserved region 10 kb upstream of the gene PHF14 in chromosome 7. The association results for DBP are shown in the Fig. p value <4.70 e−07), a rare variant with only 16 copies of the minor allele. That genetic variant is located in an intronic sequence of the gene MAP4 in chromosome 3.Using the provided real SBP and DBP data, we tested the association of each of the 6.5 M SNVs using smoking status, blood pressure medication usage, sex, age, agelts Fig. . The set−6, an approximately 100 times higher statistical threshold than the one required for a whole genome association. The nonsynonymous SNVs were a subset from the simulation results and the p values were fitted into an extreme value distribution, leading to the estimate of 13,399 independent tests. This reduced set of independent tests required a corrected new multiple hypotheses significance threshold of 3.73e−6. These thresholds were applied to the association results for SBP and DBP. Two SNVs, 1 for each trait, would be considered associated  and identify a set of 28,292 nonsynonymous SNVS. This specific type of genetic variation is the cream of the crop in an association study because it directly pinpoints a candidate gene, simplifying the interpretation of the results. As expected, this reduced hypothesis test state space requires less statistical evidence for association. The Bonferroni multiple hypothesis correction in this set requires a target alpha for a genome-wide significance of only 1.76 × esee Fig.  and d. IPHF14 on chromosome 7 and shows a significant association with SBP . This gene encodes a zinc finger class transcription factor that controls the expression of the PCBD2 gene that had been involved in the calcium channels formation [MAP4, and is strongly associated with DBP . The SNV is located in an alternative use exon of the MAP4 gene and the genetic variant is present in 2 of the 8 known gene isoforms. The MAP4 gene encodes a microtubule-associated protein that is central to the control of cellular cycle. SNVs in this gene had already been associated with pulse pressure and mean arterial pressure in Asian individuals [The first SNV, rs218966, leads to a missense mutation (K115R) located in the second exon of the gene ividuals . The ass2/2 computational complexity. The approximation of the extreme value distribution to a theoretical beta distribution was proposed by Šidák in 1967 and their applicability in association studies was proposed by Dudbridge and Gusnanto in 2008 [−6 was defined. This procedure led to the identification of 2 missense SNVs significantly correlated with human blood pressure. The gene MAP4 was previously associated with blood pressure alterations in Asian populations and the identified nonsynonymous SNV requires further functional validation. Our analyses highlight the necessity of the development of alternative methods favoring the detection of reasonable candidate genes in a WGS paradigm.WGS platforms are becoming more cost effective and their application in large human pedigree studies, like San Antonio Family Studies (SAFS), allows the assessment of rare variants that are population, or even family, specific. The number of genetic variants identified in such projects is astronomical, and the required multiple hypothesis threshold correction reduces the statistical power. In this report, we used the WGS data provided by the GAW19 organization to test 2 alternative approaches to improve the statistical power of a WGS association study and favor the detection of reliable candidate genes. The first approach efficiently eliminates the redundancy of linkage/gametic disequilibrium among SNVs by the use of the extreme value theory. The estimation of LD in a dense panel of genetic marker is challenging due to a result of the required n


As next-generation sequencing data become increasingly available for non-model organisms, a shift has occurred in the focus of studies of the geographic distribution of genetic variation. Whereas landscape genetics studies primarily focus on testing the effects of landscape variables on gene flow and genetic population structure, landscape genomics studies focus on detecting candidate genes under selection that indicate possible local adaptation. Navigating the transition between landscape genomics and landscape genetics can be challenging. The number of molecular markers analyzed has shifted from what used to be a few dozen loci to thousands of loci and even full genomes. Although genome scale data can be separated into sets of neutral loci for analyses of gene flow and population structure and putative loci under selection for inference of local adaptation, there are inherent differences in the questions that are addressed in the two study frameworks. We discuss these differences and their implications for study design, marker choice and downstream analysis methods. Similar to the rapid proliferation of analysis methods in the early development of landscape genetics, new analytical methods for detection of selection in landscape genomics studies are burgeoning. We focus on genome scan methods for detection of selection, and in particular, outlier differentiation methods and genetic-environment association tests because they are the most widely used. Use of genome scan methods requires an understanding of the potential mismatches between the biology of a species and assumptions inherent in analytical methods used, which can lead to high false positive rates of detected loci under selection. Key to choosing appropriate genome scan methods is an understanding of the underlying demographic structure of study populations, and such data can be obtained using neutral loci from the generated genome-wide data or prior knowledge of a species' phylogeographic history. To this end, we summarize recent simulation studies that test the power and accuracy of genome scan methods under a variety of demographic scenarios and sampling designs. We conclude with a discussion of additional considerations for future method development, and a summary of methods that show promise for landscape genomics studies but are not yet widely used. Understanding the spatial distribution of adaptive genetic variation is at the very core of evolutionary biology and population genetics. Recent advances in next-generation sequencing make studies of the genomic basis of local adaptation now possible for virtually any organism. Simultaneously, spatial data for nearly every corner of the Earth are available due to dramatic increases in GIS and mapping technologies. These technological developments have led to the rapid proliferation of studies that integrate geographic and genomic data to test for spatial patterns of genes under selection, collectively termed “landscape genomics” . However, except for candidate gene approaches, where a priori information about the function of specific genes is known, dozens of markers are generally insufficient for tests of selection; such tests commonly rely on orders of magnitude more loci to have appropriate statistical power to conduct outlier analyses  brings about new computational challenges. Whereas landscape genetics relies upon a rich history of spatial statistics dating back to the 1950s and 1960s, genome-wide selection analyses have primarily been developed in the last decade. New methods are rapidly being developed, and embarking on a landscape genomics study may seem like a daunting task for some researchers. Here, we aim to disentangle some of the complexity involved in conducting a landscape genomics study and associated downstream analyses, and we hope to offer some perspective for novice and experienced researcher alike. We focus primarily on marker-based studies of non-model organisms, as it is in these systems that landscape genomics studies are most rapidly expanding. Additionally, inference in non-model organisms is particularly challenging as they lack the genomic tools such as reference genomes and transcriptomes, which are typically available for model systems . One way to identify false positives is that loci in close proximity do not show a signature of selection. Even if loci detected in such analyses are “true” positives, the function of the candidate loci remains unknown, particularly when lacking a reference genome and thus the ability to map a candidate locus . Transcriptome sequencing, however, will only capture a subset of all coding genes, as gene expression is tissue-specific  regions. Genes will contain much of the functional genetic variation that underlies adaptation, and such regions are also in linkage with promoter regions also under selection  across a large number of loci can be used to infer the process of selection acting on a subset of loci  values than the distribution of genome-wide FST values are presumed to be under diversifying or local selection or linked to those under selection  differentiation outlier methods  could be determined using a Bayesian clustering algorithm such as FastSTRUCTURE  to be employed. First, if attainable, knowledge of underlying demographic structure can be used to choose the most powerful methods that are least prone to Type I errors for that specific demographic history. For example, phylogeographic analyses can be used to assess whether there have been recent geographic range expansions from glacial refugia. To parameterize the number of latent factors , the number of genetic clusters  in the human populations; those with high starch diets have higher numbers of copies than populations with diets lower in starch  likely due to selection for high salt tolerance necessary in desert environments  accounting for the effects of coding and non-coding regions of genomes and overall genomic architecture, combined with protein expression levels, on phenotypic variation; (2) coding for genomic features such as copy number, chromosome inversions or transposable element composition or location in our population genetic models ; (3) constructing hierarchical models to integrate sources of error from different data types. Then, the challenge is compounded further with the necessity to integrate these complex genomic models with multiple types of spatial environmental data and habitat models in ways that optimize sampling while avoiding potential biases. Mapping the genotype-phenotype relationship has been a key challenge for evolutionary biology for over a century, and landscape genomics will provide the analytical framework to do so across spatially variable ecological environments. A long road may lie ahead, but it is certainly an exciting time for landscape genomics to unravel the complexity of the genomic architecture that underlies local adaptation.Landscape genomics has emerged as a prominent framework for studying the genomic basis of local adaptation. Using large genomic data sets, researchers scan the genome for loci that exhibit signatures of selection across heterogeneous environments (Haasl and Payseur, AS conceived of, and wrote most of the paper. AP and AF contributed to the writing, as well as gathered information for, and assembled Table 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.


Glycine max) is the most widely grown oilseed in the world and is an important source of protein for both humans and livestock. Soybean is widely adapted to both temperate and tropical regions, but a changing climate demands a better understanding of adaptation to specific environmental conditions. Here, we explore genetic variation in a collection of 3,012 georeferenced, locally adapted landraces from a broad geographical range to help elucidate the genetic basis of local adaptation. We used geographic origin, environmental data and dense genome-wide SNP data to perform an environmental association analysis and discover loci displaying steep gradients in allele frequency across geographical distance and between landrace and modern cultivars. Our combined application of methods in environmental association mapping and detection of selection targets provide a better understanding of how geography and selection may have shaped genetic variation among soybean landraces. Moreover, we identified several important candidate genes related to drought and heat stress, and revealed important genomic regions possibly involved in the geographic divergence of soybean.Soybean ( Glycine max) is the leading legume crop produced in the world1, accounting for nearly half of the total world production of vegetable oils2 and a large majority of the oilseed meal included in livestock feed3. Further uses of soybean vary widely, ranging from various traditional food products such as soy milk and natto, to protein isolates included in processed foods4. Global soybean production has been steadily increasing for the last 50 years or more, but growth has been strongest in places far outside the original region of soybean cultivation, including diverse regions such as South America , northern North America , and India1. This is a testament to soybeans adaptability, which will be crucially needed for agriculture in the face of a changing climate.Soybean (Glycine soja (Sieb. and Zucc.) in China 3,000 to 5,000 years ago, a multitude of soybean landraces was created by ancient farmers through both artificial and natural selection5. These landraces were first cultivated in China for circa 3000 years before being dispersed to the neighboring regions of Japan, India, Nepal, and Russia around the first century C.E6. Distinct landraces emerged by the 15th century C.E and ranged in areas from modern-day Japan, Korea, Vietnam, India, and Indonesia. By the 20th century, it has been estimated that as many as 20,000–45,000 distinct landraces were grown by farmers in China alone5. Several factors, such as the self-pollinating nature of soybean and its adaptation to specific latitudinal clines due to photoperiodism led to diverged pockets of landraces that are both morphologically and genetically diverse, even from within small areas in China7. These landraces, each adapted to its specific locale and corresponding environmental conditions, have largely been replaced by modern high yielding varieties, but fortunately some of the wealth of this genetic diversity has been maintained through the storage of these landraces in germplasm banks across the world8.Following domestication from its wild progenitor 9, as well as the J locus controlling length of juvenility10, allows soybean to maximize yield and avoid frost within very narrow latitudinal ranges5. Beyond maturity adaptation and genetically controlled photoperiodism, information on contributors to soybean adaptation to abiotic conditions is limited, but known cases include adaptation to cold tolerance11, heat and drought tolerance12, acid soils13 and saline soils14. To our knowledge a comprehensive study on the forces shaping the genetic variation of landraces has not been performed.The most important factor in soybean adaptation is its photoperiod response. The right combination of alleles at the “E loci”16. Environmental association analysis (EAA) has emerged as a core part of landscape genomics, the goal of which is to find associations between genetic variants and environmental conditions corresponding to the locale of adaptation15. Studies in Arabidopsis have shown that loci implicated as being adaptive through an EAA can predict relative fitness in a common garden experiments grown across wide-ranging environments18. Recent landscape genomics and EAA studies on some of the world’s most important crops such as sorghum19, maize20, and barley21 provide insights into the genetic architecture and genes underlying local adaptation. No such studies have been performed in soybean, but a recent study has been performed on soybean’s wild ancestor, Glycine soja. Anderson et al.22 performed an EAA on 533 G. soja accessions and identified SNPs associated with environmental variables such as precipitation and percent sand in the subsoil. Such associations, if taken beyond this exploratory analysis and validated, could provide a useful source of variation to improve soybean given the ease in which G. soja can be crossed to G. max.Landscape genomics is a framework that aims to identify environmental factors shaping adaptive genetic variation and genetic variants that drive local adaptationHere, we explore the genetic variation in a georeferenced collection of locally adapted landraces from a broad geographical range to help elucidate the genetic basis of local adaptation in soybean. We used geographic origin, environmental data and genome-wide SNP data to perform an environmental association analysis and discover loci displaying steep gradients in allele frequency across geographical distance and between landrace and modern cultivars. Our results provide a better understanding of how geography, environment, and selection may have shaped the distribution of genetic variation among soybean landraces, and begins to identify loci possibly associated with adaptation to diverse environmental conditions.24. An analysis of population structure shows subpopulation membership is largely explained by the country from which the seeds were originally collected , with the Chinese landraces diverged from both Japanese (FST = 0.27) and Korean landraces (FST = 0.22).We analyzed a collection of 3,012 georeferenced soybean landraces from the geographic range of Asia  spanning the regions of China, Japan, North Korea and South Korea Fig. . Genomicted Fig. . About 7ted Fig. . After d25 determined using a TreeMix analysis on the SNP data largely recapitulates the known relationships among countries and known migration patterns in the history of soybean trade , we compiled the biophysical/bioclimatic data for each landrace collection site, which varied widely in precipitation, temperature, soil, and altitude . Sites within China are generally colder and drier  compared to Korea and Japan (more maritime climate)  .We aimed to determine how environmental factors have shaped genetic diversity among soybean landraces. Because soybean adapts to latitudinal zones, largely via genetic alteration of photoperiodism, genomic variation is likely to be strongly correlated with latitude. To condition the effects of spatial variation, we performed a partial redundancy analysis3%) Fig. . Altoget3%) Fig. . Genomic3%) Fig. . Our res15 to identify candidate loci that could contribute to local adaptation. Four mixed linear models were fitted to correct for confounding effects due to population structure , required for appropriate root growth31 37, which gives a comparable measure where high values are consistent with selection for adaptation  and shattering resistance (Pdh139)  Fig. .ST/SPA analysis. Five associated regions of overlap between environmental association, FST and SPA were found  while dt1dt1 genotypes are determinate . Indeterminate genotypes, mostly from China, appear to be more adapted to drier climates  and 2OG-Fe (II) oxygenase family protein  oxygenase is important for early iron deficiency chlorosis signaling in soybean43 and is associated with resistance to downy mildew44. A chromosome 17 region associated with longitude, soil and temperature variables  the weather data stored in the WorldClim database does not necessarily perfectly represent the weather conditions during the time of soybean landrace adaptation, and (2) the geographical coordinates associated with each landrace held in the Collection likely do not represent the exact original location each landrace was adapted to.Pdh1 and Dt1 had divergent allele frequencies across locations with differing temperature and precipitation profiles. While it is expected that landraces with different shattering and stem termination phenotypes would be found in different environments, this is the first time this has been quantified and demonstrated using genomic, environmental, and phenotypic data. This observation helps to reinforce the validity of our associations on other, yet-to-be characterized loci.We did find that the well-known loci of DREB2A, AtCLB, and CAMTA1 are all important genes related to drought and cold tolerance. Notably, we found a putative novel genomic region for soil adaptation which encodes for RHD3 that plays an important role for root growth and development31. These narrowed candidate genes are essential to achieve a better understanding of drought and heat tolerance in soybean.Overall, 73 distinct genomic regions were identified across 78 environmental and spatial variables, nearly 40% of which were associated with at least two environmental variables. We noted important abiotic-stress responsive genes very near or directly underneath SNP associations. For example, 19. Future common garden experiments should also be conducted to help validate associations, followed by confirmation through gene editing. This research framework could greatly contribute to our understanding of soybean adaptation and eventually help to discover and extract useful alleles from germplasm collections.Increasing soybean productivity will require adapting the crop to new locations and changing weather patterns. The results of this study can assist soybean breeders in identifying germplasm accessions that can be used as donor parents for breeding soybean cultivars for a changing climate. This SNP-level knowledge on adaptation could also be used as prior information for genomic prediction of yield under specific environmental conditions24, or G. max lines added to the USDA collection prior 1945 sourced from China, Japan, North Korea, and South Korea. This threshold was meant to eliminate elite lines developed through modern breeding practices. We then omitted those accessions determined to be genotypic duplicates and accessions that were potential geographic outliers. Filtering left a total of 3,012 landrace accessions that were collected within the geographic range of 22–50°N and 113–143°E. Landrace accessions were distributed in China (N = 625), Japan (N = 587), South Korea  and North Korea (N = 63)  for all available G. max landrace and elite accessions24. Ambiguous and heterozygous SNP calls were treated as missing data due to the low outcrossing rate in G. max5. The physical map positions of the SoySNP50K SNPs23 were mapped into the second genome assembly ‘Glyma.Wm82.a2’22. Any SNP with minor allele frequency (MAF) <0.01 was removed from the genotype dataset for subsequent analyses. The SNP genotype data set is publicly available at http://www.soybase.org/dlpages/index.php.Genotype data from the SoySNP50K platform were downloaded from SoyBase (Grant G. max accessions were used to query the WorldClim database (see http://www.worldclim.org/) for 67 environmental variables . The unit used for downloaded temperature data are in °C * 10. This means that a value of 231 represents 23.1 °C. Temperature data was converted into °C by dividing the temperature value by 10. The unit used for the precipitation data is millimeter (mm).The latitude and longitude coordinates of 3,012 51 (World Soil Information database) for seven biophysical variables  in permilles) at a resolution of 30 arc-seconds (Supplementary Table http://www.isric.org/). Available data for seven biophysical variables were taken at six soil depths: 2.5 cm, 10 cm, 22.5 cm, 45 cm, 80 cm, and 150 cm51. Because of high correlation and less variability in soil variables across depths, we grouped the six measurements per variable into one class by taking the average value across soil depths, leaving us with seven soil variables.The sampling locations of 3,012 landrace accessions were also used to query the ISRIC databaseG. max localities to examine the distribution for each variable  was conducted using the prcomp function in R. Pearson correlation coefficients between bioclimatic and biophysical variables were calculated in R. Boxplots for each scaled bioclimatic and biophysical variable were created based on prcomp function in R. The Bayesian clustering program fastSTRUCTURE52 was used to calculate varying levels of K (2–10) and the command chooseK.py was used to identify the model complexity that maximized the marginal likelihood (K = 2–6). The population structure was visualized using barplot based-function in R. Genome-wide and intra-chromosomal linkage disequilibrium (LD) were estimated using pairwise r2 between SNPs, which was calculated using PLINK version 1.0753. All geographical maps in this study were created with the RgoogleMaps54 package in R (https://cran.r-project.org/web/packages/RgoogleMaps/RgoogleMaps.pdf).Principal component analysis using SNPs present in all landrace accessions was conducted using the vegan. The RDA is an eigenanalysis ordination to assess the explanatory power of multivariate predictors  for multivariate responses29 . The variance components explained by environmental variables were partitioned by fitting different models. The first model considered all environmental and geographic variables as explanatory variables and the SNP data as response variables. Because geographic effects are correlated with the SNP data, we fit a partial model in which the SNP data were conditioned on the effects of geographic coordinates. For both models, significance testing was conducted using Monte Carlo permutations test with 500 runs and α = 0.01.We calculated the proportion of genome-wide SNP variation explained by environmental variables. We used variance partitioning of redundancy analysis (RDA) implemented in the R package FST outlier analyses and Spatial Ancestry Analysis (SPA) were used to identify loci with differential allele frequency across groups or geographical space, which could be caused by selection. To identify loci that had been selected locally, FST analysis was conducted between elite and landrace populations within each country (FST within). Theta (Ѳ), the variance-based FST estimate55 was estimated using the R hierfstat package56. For visualization, FST was averaged in sliding windows, with a window size of five and a step of 3 SNPs22. SNPs with FST values above the 99.9th percentile were identified as outliers. A Mantel test was conducted to explore isolation by distance utilizing great circle distance between geographic locations and pairwise genetic distance using the vegan package in R.37. The SPA incorporates geographic and genetic gradients in identifying local clines. This type of analysis is particularly compelling for species with a continuous distribution and relationship among individuals driven by isolation-by-distance37. SNPs with SPA scores above the 99.9th percentile were identified as outliers.SPA was used to detect loci showing steep gradients in allele frequency57 was used to test for associations between individual SNPs and bioclimatic and biophysical variables. The following models were explored: K, Q + K, P + K and L + K. The Q + K model was fitted using the equation y = Xβ + Cγ + Zu + e, where y is a vector of environmental variable; β is a vector of fixed marker effects; γ is a vector of subpopulation effects; u is a vector of polygenic effects caused by relatedness, i.e., e is a vector of residuals, i.e., X is a marker matrix; C is an incidence matrix containing membership proportions to each of the three genetic clusters identified by the fastSTRUCTURE analysis; Z is the corresponding design matrix for u; and K is the realized relationship matrix estimated internally in the FaST-LMM. The K model was the same with the Q + K model except that the term Cγ was removed in the model. In the P + K, the incidence matrix C of the Q + K model was replaced with a matrix that contained the first three PCs identified from PCA. In the L + K model, the incidence matrix C of the Q + K model was replaced with a matrix that contained the latitude information corresponding to each accession’s collection site.Mixed-model association as implemented in the Factored Spectrally Transformed Linear Mixed Models (FaST-LMM)58. Multiple linear regression was used to estimate the proportion of phenotypic variance accounted for by significant SNPs after accounting for population structure effects.A comparison-wise error rate of P < 0.0000143 was used to control the experiment-wise error rate determined by calculating the effective number of SNPs tested59 implemented in the software Haploview60. The 4gamete method creates block boundaries where there is evidence of recombination between adjacent SNPs based on the presence of all four gametic types. A cutoff of 1% was used, meaning that if addition of a SNP to a block resulted in a recombinant allele at a frequency exceeding 1%, then that SNP was not included in the block.Haplotype blocks were constructed using the four gamete method (4gamete)FST approaches were examined for functional annotation using SoyBase (www.soybase.org). A sliding window-approach  was used to search for functional genes implemented in bedtools61. The prediction of candidate genes was based on (a) genes of known function in soybean related to the trait under study, and (b) genes with function-known sequence homologs in Arabidopsis related to the trait. For each significant SNP, we collected additional information on genic context, nearby annotated genes, and the inferred Arabidopsis ortholog (TAIR10 best hit provided by Soybase). We performed enrichment analysis to determine if euchromatin, 3′ UTR, 5′ UTR, coding sequence (CDS), and intronic regions were over or under represented among outliers. Significance of enrichment was assessed by creating a 99% confidence interval around the proportion of SNPs that were found in each category as calculated by bootstrap sampling the number of SNPs in each category 1000 times.SNPs identified as outliers through the environmental association mapping, SPA, or Supplementary InformationSupplementary Table 1Supplementary Table 2Supplementary Table 3Supplementary Table 4Supplementary Table 5Supplementary Table 6Supplementary Table 7Supplementary Table 8Supplementary Table 9