Yield gains were linked to physiologically simple traits (plant phenology and architecture) which indirectly affected reproductive development and light interception in all studied environments, marked by significant genomic signatures of selection. Conversely, studied physiological processes involved in stress adaptation remained phenotypically unchanged  and showed no signatures of selection. By selecting for yield, breeders indirectly selected traits with stable effects on yield, but not physiological traits whose effects on yield can be positive or negative depending on environmental conditions. Because yield stability under climate change is desirable, novel breeding strategies may be needed for exploiting alleles governing physiological adaptive traits.Combined phenomic and genomic approaches are required to evaluate the margin of progress of breeding strategies. Here, we analyze 65 years of genetic progress in maize yield, which was similar (101 kg ha Phenomic and genomic approaches are required to evaluate the progress of breeding strategies. Here, the authors analyse 65 years of genetic progress in maize, showing that breeders have selected traits with stable effects on yield whereas not for adaptive traits key for climate change adaptation. Breeding and the improvement of agronomic practices have increased yields of most species3. In maize, a major source of yield progress was the fine-tuning of plant cycle duration to the local environmental conditions, based on the well-documented genetic variability for flowering time5. However, the genetic ability of varieties to produce high yield has also rapidly increased in maize, for a given cycle duration and a given set of management practices, representing 50–75% of total yield gain14. For each class of crop cycle duration, this gain was essentially obtained by breeders via selection for yield and, more recently, genomic selection based on yield16.Yield progress under challenging environmental conditions is necessary to face the negative impacts of climate change and the limitations on irrigation and fertilization, in a context of high demand for agricultural products18, to evaluate the yield progress over time of released varieties in multi-site field experiments13, or to identify traits that appreciably changed with year of release20. To our knowledge, the whole path of examining causal links between the changes in alleles, traits, and yield in a range of environmental scenarios has never been analyzed in the same genetic material. This path is not straightforward because a vast majority of yield quantitative trait loci (QTLs), which necessarily act via underlying traits, have positive, negative, or neutral effects depending on environmental conditions22. This justified the fact that we considered trait effects on yield in a range of environments, but also that we characterized their changes across generations of selection.A crucial question is whether this rate of genetic progress can be sustained in the next decades. Evaluating the margin of the progress of breeding strategies requires a comprehensive analysis that links, in the same genetic material, yield progress to underlying traits and corresponding genomic regions. This would allow the evaluation of which traits can still be improved, in relation to likely physical limits, and the allele fixation achieved for these traits by breeding programs. Elements for this exist in the literature, either for identifying the genomic changes associated with selection23, so it is challenging to consider them in a breeding program (in addition to the intrinsic difficulty to phenotype them at high throughput24). We recently proposed that evolution has coordinated the numerous and complex adaptive mechanisms into strategies ranging from ‘growth-oriented’ to ‘conservative’, which favor yield in most favorable vs most adverse environmental conditions25. Constitutive traits  have long-term variations with the environment, so they are selectable in a breeding program23. Their effect on yield is less dependent on environmental scenarios than adaptive traits, with the notable exception of crop cycle duration , for which shortest durations allow escaping stress in driest or hottest scenarios, whereas longest durations compensate the effects of increased mean temperatures in the absence of water deficit5.Different categories of traits are potentially involved in genetic progress. Adaptive physiological traits  vary several-fold over minutes to hours as a response to environmental conditions for a given genotype27 and three equipped fields  measured physiological traits with novel phenomic methods, in particular stomatal conductance at the plant level, growth responses to environmental conditions, plant architecture, and reproductive development  to mid-late (FAO 490) hybrids.The genetic progress was analyzed by comparing traits and performances of the 66 most commercially successful European hybrids released from 1950 to 2015 .This genetic progress panel captured the elite breeding pools of European and American germplasm, as shown by projecting the genotypes onto a principal component biplot of 96 commercial varieties with expired Patent Variety Protection  representing 75% of on-farm yield increase in Europe for the same period3. It was as fast in favorable scenarios as in unfavorable ones with water deficit or high temperatures  was, on average, 101 kg hares Fig. . Indeed,38 , opposing changes were observed in the duration of vegetative  and grain filling  phenological phases  which acted independently on grain number, as shown by Bayesian network analysis . The prolonged vegetative phase observed in recent varieties allowed more ovary cohorts to be initiated41. Second, silk growth and the number of extruded silks, which determine the ASI42, increased with year of release in all fields, platform experiments, and treatments . The difference between the oldest and newest hybrids accounted for most of the genetic variability in rhPAD in the genetic progress and diversity panels , consistent with an essentially stable biosynthesis of the stress hormone abscisic acid50. Stomatal conductance was therefore similar in the oldest and newest hybrids in spite of a large genetic variability observed in both the genetic progress and diversity panels, with a high heritability30 . As in the case of stomatal conductance, the large genetic variability was not exploited by breeding as shown by close leaf elongation rates in modern vs old varieties . Finally, the relation of grain yield to cumulated transpiration at harvest simulated via a crop model52 showed a change in generational responses to transpiration that was a by-product of increased grain yield and not the consequence of better transpiration efficiency  for all studied hybrids, a regression analysis of reference (B73) allele counts on the year of release was performed  were detected Fig.  based on33. The analysis included QTLs for: (i) flowering time and related traits33, (ii) florigens (eQTLs)33, (iii) stomatal conductance and difference in biomass between well-watered and water deficit treatments30, (iv) radiation interception efficiency (RIE) and vertical distribution of leaf area26, (v) sensitivity of leaf growth to soil water potential, (vi) sensitivity of grain number to soil water potential, night temperature, and light intensity29, also characterized by QTLs of grain yield observed only in fields with high temperature or water deficit21.We performed a meta-analysis of the RUS loci together with QTLs for phenological, architectural, or adaptive physiological traits identified in the diversity panel Fig. , measure5 random iterations of genomic regions having the same number and length  as the QTL space for each trait. Regions under selection largely overlapped with QTLs for flowering time, florigens, and architectural traits  correspond to adaptive traits  with allelic effects affected by water deficit . These allelic effects would also oscillate in each field with the year-to-year variability of climatic conditions54. These oscillations may explain why breeding programs did not affect, for these QTLs, the frequencies of the allele with a positive effect on yield under favorable conditions. Indeed, these frequencies were stable or decreased between the 22 oldest and the 22 most recent hybrids , have not been affected by breeding in the considered elite pools of temperate varieties. In natural contexts, avoidance of soil water depletion via limitation of transpiration in the early stages of the plant cycle allows the production of at least a few fertile grains. In agricultural contexts, yield improvement in drought-prone areas was reached via ‘conservative’ agronomical practices that limit transpiration, such as reduced duration of crop cycle or skipping one plant row out of two30, in which all lines derive from landraces used by farmers in the past28. Genetic differences and high heritabilities were also observed for stomatal conductance, water-use efficiency, and ABA synthesis in several breeding populations57. Hence, we propose that the genetic variability within the temperate maize breeding pool was not exploited by breeding strategies in the last 60 years.A first possible interpretation might be that physiological adaptive traits bring yield advantage only in extreme conditions mostly incompatible with agriculture. However, a strong genetic variability and high heritabilities were observed for stomatal conductance and growth sensitivity in the diversity panel−1 on average over 30 years were observed at 32% of the sites (15% lower than 5 t ha−1). Breeding companies involved in this study state that their own multi-site breeding experiments include such low-yielding fields . Hence, the selection pressure that resulted in the hybrids studied here would have included both favorable and unfavorable conditions. Similarly, the ERA series, resulting from a breeding program in the USA, also shows essentially parallel yield progress in growing areas with or without irrigation16, although the latter is associated with low yield58. In the ERA program, the released varieties bred for drought tolerance showed similar yield increases in dry and wet experiments16. Similar results were also found in Argentina with another set of varieties, with a rate of genetic progress similar to that reported here14. A common rate of genetic progress was observed in water deficit and well-watered conditions and at different fertilization rates, although observed mean yields ranged from 5 to 11 t ha−1 in worst vs best conditions59. Interestingly, these authors observed an increased number of ovaries and ear growth rate but a stable vegetative shoot growth rate, consistent with results presented here60. Hence, our conclusion is likely valid for the temperate maize growing area. It remains to be tested in subtropical and tropical regions where maize is grown.A second hypothesis is that our conclusion is specific to the breeding process in Europe, which would not favor the selection of traits that confer adaptation to adverse conditions. However, the retrospective analysis of maize yield over Europe Fig.  shows th29, allelic frequencies in genomic regions governing these traits have likely oscillated with year-to-year climatic variability during the breeding process, without an overall trend54. Hence, sensitivity traits have not been exploited thus far, suggesting an untapped reservoir of favorable alleles.The interpretation we propose here is that genetic variabilities for stomatal control, for parsimonious leaf growth under water deficit, for water-use efficiency, or for more stable yield exist in the breeding pool for temperate dent maize, but that they were not exploited by the breeding processes over the last 60 years. This is backed by the examples in Figs. 15. While recognizing that this statement is fair, we propose two arguments against it. Firstly, climate change will bring increasingly frequent events of severe drought or high temperatures. Breeding for the resilience to such events requires exploration of physiological adaptive traits, which may have neutral or slightly negative effects on yield in favorable conditions but allow new varieties to cope with extreme events . Because the climate is rapidly changing and breeding for new varieties takes years, it would probably be safe to explore this avenue. Secondly, the constitutive traits associated with yield progress in this study may reach a limit for future genetic progress, because the duration of the vegetative period and the distribution of foliage in the canopy cannot be indefinitely optimized. The high and increasing frequencies of positive alleles at some QTLs for these traits suggest that a limit might be reached in a near future.However, it remains to be demonstrated that exploiting this reservoir will bring comparative advantages for yield. Indeed, it was stated that, if this pool was not exploited but a substantial yield improvement was still observed, breeding based on yield (directly or via genomic selection) may well remain the most promising strategy61. This would allow one to refine selection for each environmental scenario, regardless of the actual location of an experiment in considered regions. (iii) Using the genetic variability of response curves of yield to temperature, evaporative demand, and soil water status may also be achieved with big data strategies that integrate phenomics, modeling, and genomic prediction58. Selection would then be carried out, in addition to yield itself, on the response of yield to environmental conditions clustered into growth-oriented vs conservative strategies. This would allow one to fine-tune hybrids for different environmental scenarios and present farmers with a larger offer, for them to choose the most appropriate hybrids based on their knowledge of each field and on their attitude towards risk23.Advances in phenotyping and novel breeding methods may help exploit the genetic variability of physiological adaptive traits. (i) The most obvious of those would be to select for yield stability in addition to high yield. However, yield stability cannot be considered as an objective per se because it may select for the lower yield. (ii) A major emphasis on suboptimal environments may be reached by systematically exploring the genotype × environment × management landscape62. Data can be found and downloaded at 10.15454/KLD0GH ‘genotypic dataset’. After quality control, 465,621 polymorphic SNPs were retained for analysis . Missing values were otherwise imputed63. All physical positions referred to hereafter are based on the B73 reference genome64.The genetic progress panel used in this study consisted of 66 highly successful commercial hybrids  with 250 dent lines that maximized the diversity of temperate dent germplasm while keeping a restricted flowering window , with the same range of earliness as the genetic progress panel. The parental lines of the diversity panel were also genotyped with the 600k Affymetrix array, as well as 500k markers obtained by genotyping-by-sequencing28. It was phenotyped in four experiments in a phenotyping platform32 and in 29 field experiments29 carried out in the same regions as those presented in Fig. 2 = 0.70, Supplementary Fig. 68, using prior information on genetic groups  to estimate admixture proportions28. Pairwise identity-by-state (IBS) estimates69 ranged from 0.65 to 0.99 (av. 0.72), compared to IBS of 0.58–0.95 (av 0.66)29 in the diversity panel. Nei’s genetic diversity index70 was stable across old, intermediate, and recent groups of hybrids, thereby suggesting the maintenance of allelic diversity across years of release71.We tested the representativeness of the above panel of varieties (named ‘genetic progress panel’ below) by comparing it with other two panels. First, we considered a series of 96 lines with expired Plant Variety Protection, known as ex-PVPs. These represent the founding material and germplasm pool for maize breeding from 1970 to 2000 in America and Europe5 collected over 35 years in 59 locations representative of the European maize growing area and of typical soil types of these regions, representing the ten European countries with the highest maize growing. Briefly, soil data were obtained from the JRC European Soil Commission database and from the Crop Growth Monitoring System. Meteorological data represent 35 years of daily weather (1975–2010) obtained from (i) the AGRI4CAST database of the JRC or the INRAE CLIMATIK databases. Sowing dates were simulated in each field as the first day from February to May in which the frequency of frost was <5% in the following ten days. We showed that these rules captured the variability of sowing dates in European databases5. Two watering regimes were simulated in each site, either rainfed or watered every seventh day with a water volume equaling the difference between reference evapotranspiration and rainfall. Plant density was adjusted in each country based on the JRC database. Nitrogen supply at sowing was calculated as 70% of nitrogen needed to reach the maximum yield of the optimum genotype with the optimum sowing date. Yields presented in Fig. 5. For that, we used a modified version of the APSIM model72 parametrized for the B73×UH007 hybrid73.We tested the representativeness of the experiments presented here based on the information weather 975–2010 The seven platform experiments presented here are synthesized in Supplementary Table 26https://www6.montpellier.inrae.fr/lepse/Plateformes-de-phenotypage/Montpellier-Plant-Phenotyping-Platforms-M3P/PhenoArch. They followed an alpha-lattice design with seven, four, three and three plants per genotype in experiments a, c, and d, respectively  involved transferring four plants per hybrid to a greenhouse at 33 ± 3 and 21 ± 1 °C during day and night, from the 14th visible leaf stage until flowering time. Air temperature and humidity were measured at six positions on the platform every 15 min30. The temperature of the meristematic zone was measured with fine thermocouples in either twenty (Exp. a) or six (other experiments) plants per experiment. Daily incident photosynthetic photon flux density (PPFD) over each plant was estimated by combining a 2D map of light transmission in the greenhouse with the outside PPFD averaged every 15 min26. Supplemental light was provided either during daytime when external solar radiation was below 300 W m−2 or to extend the photoperiod (14 h) with 0.4 lamps m−2. The number of visible and ligulated leaves of each plant was recorded every week. This information was used in a model of progression of leaf stage for each hybrid as a function of thermal time, used to predict phenological stages for all hybrids in all field experiments29.Three experiments were carried out at the Phenoarch platform44. An index, rhPAD, represented the point in this distribution  where half of the cumulative leaf area was observed44. The radiation intercepted by the canopy layer including the ear was calculated in simulated canopies repeating each plant in the platform, at densities of 6, 8, or 10 plants m−244. Total leaf area and shoot biovolume (transformed into biomass via periodic measurements of plant water content) were obtained every day from 3D reconstructions (Exp. a) or estimated by regression with pixel number in Exp. c and d30. Radiation use efficiency (RUE) was calculated as the ratio of biomass to intercepted light, with the latter calculated from 3D plant reconstructions44.Architectural traits were calculated from 3D reconstructions of each plant in the platform (Exp. a), based on RGB (2056 × 2454) images taken from thirteen views  captured daily for each plant during the night. The distribution of leaf area along the stem was calculated from 3D reconstructions42 (silks are modified styles of each ovary that collect pollen) and (ii) the number of ovary cohorts per ear38 . Raw trait values were corrected for spatial disturbances by fitting a mixed model to daily measurements (R package SpATS75) with a fixed term for genotype and random effects for rows and columns as well as a smooth surface defined on row and column coordinates. Generalized heritabilities were calculated daily with the same R package, using the same model but with genotypes included as a random term75. Genotypic predictions for traits at individual time points, t, were obtained from a generalized additive model fitted to the spatially adjusted daily measurements, k of genotype imgcv R package by REML. Variance components were obtained by treating smooth terms as random effectsReproductive development was studied in Exp. a, c and d. Four plants per hybrid were sampled on the first day of pollen shedding, individually recorded for each plant. Ears were dissected to measure (i) silk length (from ovary insertion to tip) and the number of emerged silks76, https://www6.montpellier.inrae.fr/lepse/Plateformes-de-phenotypage/Montpellier-Plant-Phenotyping-Platforms-M3P. A completely randomized design was used involving three replicates per hybrid. Leaf elongation rate (LER), expressed per equivalent days at 20 °C77, was measured with a 15 min temporal definition on the sixth appeared leaf51. Three treatments were imposed, well-watered , water deficit in which soil was left to dehydrate from −0.05 to −1.2 MPa, and high evaporative demand. The latter treatment involved transferring well-watered plants to a growth chamber in which steps of air vapor pressure deficit (VPDair) were imposed from 0.8 to 3 kPa51. Air temperature and relative humidity were measured at plant level with nine sensors51, the temperature of the meristematic zone was measured as above in six plants, photosynthetic photon flux density (PPFD) was measured using nine sensors51. The transpiration rate was calculated as in the previous work51. The genotypic sensitivities to soil water potential and VPD were estimated as the slopes of the regression of leaf elongation rate on soil water potential or VPDair78. In Exp. g, transpiration rates were scaled by individual plant leaf area, and smoothed by Nadaraya-Watson kernel regression with an optimal span of 0.25, using the R package stats. Genotypic estimates of transpiration rate were then calculated for each time-point with a mixed model adjusting for spatial effects as above. They were used for the calculation of stomatal conductance every 15th minute by inversion of the Pennman-Monteith equation30. Generalized heritabilities were calculated as above.Three series of experiments were performed in the Phenodyn platformhttps://uclouvain.be/en/research-institutes/eli/elia/rootphair.html), with a completely randomized design involving 30 hybrids and 30 replicates per hybrid (Supplementary Table One experiment (Exp. h) was carried out in the RootPhAir platform , and wind speed were measured every hour with an on-field weather station. Soil water potential was measured every day with tensiometers at 30, 60, and 90 cm depths with three replicates per treatment. Plots were mechanically harvested, then grain yield was scaled to 15% moisture content after estimation of grain moisture at harvest. Individual grain weight was measured and used to calculate grain number per square meter from grain yield. The proportion of the green fraction was calculated from images taken horizontally above the canopy80.One intense field experiment (Exp. k) tested the effect of plant density in the Phenofield platormRH were measured in ventilated shelters for calculation of VPDair. Soil water potential was measured every day with tensiometers at 30, 60, and 90 cm depths in watered and rainfed microplots with three and two replicates, respectively. Each day was characterized by the VPDair for the three hours in the afternoon during which VPDair was maximum, referred to as VPDmax. The progression of the crop cycle was expressed in d20 °C after emergence. Visible leaf number was recorded twice during the vegetative phase for 10 plants per plot for all field experiments. The number of fertile ears per plant was measured at harvest. The number of cohorts and the number of grains per cohort were manually counted from ears harvested from ten plants per plot. Silk length at silking was measured on ten plants per plot. Traits were averaged per plot and spatially adjusted as in the multi-site experiment below. In experiment j, images were taken from a hexacopter Atechsys (http://atechsys.fr/) that carried a six-channel multispectral camera (Hi-Phen modèle V3 AirPhen 6 bands (450–532–568–675–730–850). The leaf area index was calculated as the projected leaf area per unit ground surface area, generated by inverting the radiative transfer model PROSAIL81. The fraction of intercepted photosynthetically active radiation was calculated from RGB images. The mean leaf angle was calculated by inverting the radiative transfer model PROSAIL81. Other variables calculated from these images are presented in the datafile 10.15454/KLD0GH but not used in this study.Two intensive field experiments  were carried out in the DIAPHEN platform of Mauguio, near Montpellier. Experiments followed an alpha-lattice design with three replicates of four-row plots. The light was measured with PPFD sensors at 2 m height in the immediate vicinity of experimental fields. Air temperature and air. Soil water potential was measured every day with tensiometers at 30, 60, and 90 cm depths in watered and rainfed microplots with three and two replicates, respectively. Each day was characterized by the VPDair for the 3 h in the afternoon during which VPDair was maximum, referred to as VPDmax. Reference evapotranspiration (ET0) was calculated based on the Penman-Monteith equation as revised in the FAO-56 estimation82. The progression of the crop cycle in each site was expressed in d20 °C after emergence. The amount of light intercepted by plants was simulated for each experiment using a modified APSIM crop model73 for the standard hybrid B73xUH007 under well-watered conditions but parametrized with the mean final leaf number of the panel73. As such, the amount of light intercepted is akin to an environmental variable. Visible leaf number was recorded twice during the vegetative phase for 10 plants per plot for all field experiments, to check the model established based on experiments in PhenoArch. Sowing, harvesting and emergence dates were recorded for each experiment. Anthesis and silking dates were recorded for each plot when half the plants reached these stages. The number of fertile ears per plant was measured at harvest. The number of grain cohorts was calculated in each experiment by dividing grain number by the mean ear number per plant (as measured in each experiment) and by the number of grains per cohort measured in platform and intensive field experiments, very stable for each hybrid  between 2010 and 2017 in 16 European locations spread along a climatic transect for temperature and evaporative demand in both rainfed and irrigated conditions . A Bayesian network was constructed (R package bnlearn) to evaluate the conditional dependencies between year of release, measured phenotypic traits and grain number. For each environmental scenario, acyclic-directed Bayesian networks were fitted to scenario-specific genotypic means of grain number and phenology, together with estimates for traits measured in phenotyping platforms, centered and scaled prior to analysis. Straightforward assumptions were made in the analysis. Firstly, the year of release was assumed to be a driver for the considered traits . Secondly, conditional dependencies between phenotypic traits followed the temporal order of plant development25, i.e., traits determined during the vegetative and flowering periods were assumed as not conditionally dependent on grain number, which is fixed at the beginning of the grain-filling period. Network learning was conducted with the TABU algorithm combined with the Bayesian gaussian equivalent score (BGE), which assumes non-informative flat priors over both the space of network structures and of parameters83. Scenario-specific models were fitted to the corresponding genotypic means of traits for 500 bootstrap samples, and subsequently averaged across all bootstrap iterations84. Model averaging was based on an empirical test, whereby significant arcs were maintained84. Scenario-specific predictive ability of averaged models was then evaluated by ten times repeated five-fold cross-validation. Correlation between predicted and observed values was 0.7 on average across traits and scenarios. A synthesis is presented in Fig. Genetic gain for a trait was defined by the slope of the linear regression of the trait value on the year of release in the genetic progress panel. Genotypic means for traits were obtained for each environmental scenario or experimental treatment with a linear mixed model including a fixed term for hybrids and a random term for experiments , and additional terms for environmental main effects and genotype-by-environment interaction. The inclusion of terms was tested by ANOVA-based forward selection and backward elimination with the R package stats . The grain number was modeled as a response to a genotypic main effect, an environmental main effect, and a residual term that included the genotype by environment interaction. The linear model was fitted with the R package stats85. Genotypic and environmental effects were further partitioned by including a fixed genotypic covariate term for the year of release ((Scen), respectively. After the introduction of a genotypic covariate and an environmental scenario effect, a residual genotypic main effect , number of extruded silks (Silks), and plant architecture (rhPAD). In addition, Gyear, was again included to assess the extent to which genetic gain was captured by those three covariates. Finally, a residual genotypic main effect was added. For the environmental main effect, again we included the partitioning of trial main effects into scenarios (Scen) and experiments within scenarios (Eres). The interaction between the genotypic covariate year of release and scenario was also included  for the regression coefficient. A threshold of 3.5 was used for identifying SNPs whose allelic values changed most with year of release, and classified as belonging to RUS  over the whole panel , based on two independent methods:permp in the R package statmod88). Because published QTLs were positioned on the V2 version of the genome32, the whole study was performed on this version. Colocalization of QTLs was tested for: (i) QTLs for days to anthesis, days to silking and final leaf number identified in the panel of lines33, (ii) eQTLs for the florigens ZCN8, ZCN12, and ZCN7 in the same panel33, (iii) QTLs for maximal stomatal conductance and difference in biomass between well-watered and water deficit treatments, measured in a series of experiments in PhenoArch30 and calculated in the hybrid panel28, (iv) QTLs for radiation interception efficiency (RIE) and vertical distribution of leaf area measured in PhenoArch and calculated as in ref. 44 in the hybrid panel, (v) QTLs for the slope of the response curve of leaf elongation rate to soil water potential measured in PhenoArch in the hybrid panel, (vi) QTLs for grain yield in the hybrid panel, observed in fields classified in scenarios with high temperature or water deficit, but not in well-watered conditions21 (vi) QTLs for the slope of the responses of grain number to soil water potential, night temperature and light intensity29 in the same panel.Finally, we tested for the enrichment of colocalization between RUS and QTLs for studied traits, compared to proportions expected for an equal number of random genomic regions having the same physical size as the studied QTL intervals, using a permutation-based approach. We sampled, 100,000 times, random regions from the genome with matching numbers and physical lengths compared with the studied QTLs while keeping the RUS positions fixed. At each iteration, we counted the number of random regions overlapping with RUS. A null distribution was built based on these 100,000 values, to which the observed number of overlaps was compared via a Chi2 test (function 21 using measured environmental conditions in each field of the multi-site experiment (Exp i,j,l,m as in Supplementary Table 21 in the 22 most ancient and 22 most recent hybrids of the genetic progress panel. Frequencies presented in Fig. We considered six QTLs identified previously in the diversity panel, whose effects depend on the environmental scenarios defined as a function of soil water status and air temperature Fig. . We ascrFurther information on research design is available in the Supplementary InformationDescription of Additional Supplementary FilesSupplementary Dataset 1Supplementary Dataset 2Supplementary Dataset 3Supplementary Dataset 4Supplementary Dataset 5Supplementary Dataset 6Supplementary Dataset 7Supplementary Dataset 8Supplementary Dataset 9Supplementary Dataset 10Supplementary Dataset 11Reporting Summary


Thailand is a tropical country affected by global climate change and has high temperatures and humidity that cause heat stress in livestock. A temperature–humidity index (THI) is required to assess and evaluate heat stress levels in livestock. One of the livestock types in Thailand experiencing heat stress due to extreme climate change is crossbred dairy cattle. Genetic evaluations of heat tolerance in dairy cattle have been carried out for reproductive traits. Heritability values for reproductive traits are generally low (<0.10) because environmental factors heavily influence them. Consequently, genetic improvement for these traits would be slow compared to production traits. Positive and negative genetic correlations were found between reproductive traits and reproductive traits and yield traits. Several selection methods for reproductive traits have been introduced, i.e., the traditional method, marker-assisted selection (MAS), and genomic selection (GS). GS is the most promising technique and provides accurate results with a high genetic gain. Single-step genomic BLUP (ssGBLUP) has higher accuracy than the multi-step equivalent for fertility traits or low-heritability traits. As global temperatures have risen, heat stress in dairy cattle has become a severe concern for the worldwide dairy sector, especially in tropical countries. The average temperature on Earth increased by 1.09 °C during 2011–2020 . Hence, Heat stress is caused by changes in the environment around the animal, including temperature, relative humidity, solar radiation, and wind speed . The higUnderstanding the genetic response to heat stress could help improve the management of feed or housing modifications for dairy cows. Ravagnolo and Misztal  proposedGenetic differences in thermal tolerance between animals provide clues for selecting animals that are more tolerant to heat stress . PreviouBos taurus  and Bos indicus . However, most (>95%) are crosses between Holstein and Sahiwal cattle or Thai Native breeds [In Thailand, dairy cattle are primarily crossbreeds of r, most >5% are cre breeds ,20. Moste breeds .The dairy cattle breeding improvement program in Thailand has been carried out for 60 years. In the beginning, both live purebred dairy cows and frozen semen were imported from subtropical countries, followed by an artificial insemination (AI) program in 1956. Thai Native cattle were inseminated with that semen, and now, they are being developed as crossbred Thai dairy cattle, with Holstein Friesian (HF) selected as the predominant breed . The purCurrently, the development of crossbred Thai–Holstein cattle is still being carried out to select cattle with good performance and suitable for tropical conditions. Genetic evaluation of their economic traits is performed through a progeny testing program by the Thai Department of Livestock Development (DLD) .AFC is the age at which heifers first calve and is also known as the start of the heifer’s productive life ,24. AFC The number of days between calving and the first artificial insemination or natural service in cows is defined as the CFI . The recThe IFL is defined as interval days between the first and last service. It is one of the indicators used by farmers to cull cows after a series of inseminations because it can increase production costs . The IFLDO is the time period between calving and successful conception. It is also the part of the calving interval (CI) that can be shortened by improved dairy management and genetics . As a reThe CI is divided into three periods: postpartum anestrus, service period, and gestation . The ideThe FSCR is the percentage of cows that become pregnant after calving at the first insemination 35). The values for the FSCR in Thai–Holstein crossbred cattle were reported to be 54% and 46% ,50, simi. The valp < 0.05) affected by the season of calving and parity. The CR of cows in the rainy season was higher than in dry rainy seasons; this might be because of moderate climatic conditions and the availability of forage.Most Interbull-participating countries divide the CR into three categories: the heifer conception rate (HCR), cow conception rate (CCR), and daughter pregnancy rate (DPR). The HCR is defined as the percentage of inseminated heifers that become pregnant at each service . The CCRThe successful insemination date within 90 days of the first insemination is referred to as P90. Previous studies showed that the values of the P90 in Thai Crossbred cattle were 75% and 82.17% ,50, loweThe number of services required for a successful conception is referred to as the NSPC . The NSPB. indicus), HF (B. taurus), crossbred with 75% and 87.5% HF, respectively, and the interaction effect between skin color and the genetic fraction of HF on skin morphology [2; 1.60 μ3 × 10−6) compared with pure HF , respectively (p < 0.01). However, the components of skin blood flow, such as the capillary diameter, capillary circumference, and capillary surface, were higher in the HF purebreds , 87.5% HF , and 75% HF  than in the Sahiwal  (p < 0.01). This suggests that skin morphology may influence cutaneous evaporative heat-loss capabilities and heat tolerance in crossbred cattle.Heat stress is one of the significant factors affecting fertility . Heat strphology . The stuThe level of heat tolerance in animals can be determined by the THI limit, where livestock performance begins to decline. The THI value is used to assess heat stress levels in dairy cattle by combining the effects of temperature and humidity . The THIThe effects of heat stress on livestock reproduction altered endocrine regulation in the hypothalamic–pituitary–ovarian axis (HPO axis), impaired ovarian activity, and impaired progesterone concentrations, which reduced estrous behavior and caused a prolonged number of days open and low conception rate ,67. KornA previous study stated that heat stress also affected the service period, FSCR, CFI, and CI in dairy cattle. The cows that calved under heat stress conditions had a longer service period, 299 ± 11 days, than in the normal condition, reported as 133 ± 7 days . The FSCHeritability is the difference in the phenotype between animals caused by genetic factors. Broad-sense heritability is defined as the proportion of phenotypic variation (VP) due to genetic values (VG), which may include dominance (VD) and epistasis (VI) effects. Narrow-sense heritability, however, only represents the proportion of genetic variation owing to additive genetic values (VA) . Narrow-2 might still have a lot of genetic diversity at the loci that contribute to the observed trait variance. When a variable has a heritability value of near zero (0), it means that all phenotypic variation in the population is attributable to non-additive genetic influences and environmental factors. As a result, (VA ≈ 0) does not mean that the trait has no genetic basis; rather, it implies that the observed trait variation within the population under consideration cannot be explained by additive genetic variance [Heritability ranges from 0 to 1. A higher value of heritability means that most of the differences between the phenotypes of animals are genetic . In contvariance . SeveralThe heritability values for reproductive traits in both Thai–Holstein crossbreeds and the other dairy cattle were low . The difFaster genetic gain might be achieved by enhancing the accuracy of the heritability estimate, especially for reproductive traits. Berry et al.  reportedAccording to Genetic correlations measure how closely two traits are related genetically . PleiotrGenetic correlations ranged from −1.0 to 1.0, and they can be positive or negative. The presence of positive or negative coefficients merely denotes whether the relationship is direct or inverse. Genetic correlations reveal how traits “covary” or change in tandem. When genetic correlations are near zero, each attribute is controlled by a distinct set of genes, and selection for one trait has minimal impact on the other. If the genetic correlation is positive, selection for one characteristic will increase the other, while selection for the other trait will reduce it if the genetic correlation is negative . GeneticGenetic correlations values in this study ranged from 0.36 to 1.00 and are included in the high category . Strong The high negative values of genetic correlations (>−0.53) in Thai–Holstein crossbred cattle were found between NSPC-FSCR, NSPC-P90, CFI-FSCR, CFI-P90, IFL-FSCR, FSCR-DO, FSCR-CI, DO-P90, and P90-CI . A negatA study of Thai–Holstein crossbred dairy cattle also found a positive genetic correlation between reproductive traits (P90) and milk yield (MY) of 0.69 . This reTHI is calculated from temperature (°C) and relative humidity. Several formulas in the previous studies reported by Nascimento et al.  were comon (BIC) ,89,90.Several models were applied to evaluate variance components for heat tolerance in dairy cattle . Sigdel Some software is used by researchers to support the data analysis of each model. Aguilar et al.  used GIBThe goal of livestock breeding is to select animals that will produce superior animals with higher yields . The traFurthermore, the new method was designed with the combination of the phenotypic and those of relatives into the estimated breeding value (EBV), called the best linear unbiased prediction (BLUP) . This meHowever, according to Ibtisham et al. , there aMany models have been utilized for the genetic evaluation of reproductive traits, especially for interval traits, including the sire model (SM), sire–dam model (SDM), and the animal model (AM). However, a study comparison between these models revealed that AM could improve stability and accuracy in evaluating fertility traits in dairy cattle . The SDMThe several traits of animal reproduction are observed as discrete outcomes. A threshold model has been proposed and applied to the problems of animal breeding which assumes that no observable normal variable is defined as a threshold. Evaluations based on threshold models for categorical traits showed that they could provide a greater response to selection than those derived from linear models .MAS is the combination between traditional breeding methods and molecular genetic methods. This method was introduced in 1900, with the concept of using specific markers on genes that affected economic traits in animals . MAS reqMAS has several advantages compared with the TAB method: (1) the process is faster since the phenotype of an individual may be forecasted at an early stage; (2) it is more profitable for sex-limited characteristics and difficult late-life traits; and (3) it is more useful for crossbreeding programmed by interrogating animals with superior genetics and better breeding value into local breeds . HoweverIdentifying and characterizing candidate genes and genetic variations associated with economically significant traits is essential in animal breeding. Each candidate gene has its own regulatory region or coding region . SeveralMany SNPs covering whole genomes in animals have been discovered with genome-sequencing technology. Based on the genotypic information from whole genomes, a genomic selection (GS) method was proposed by using markers to estimate breeding values with high accuracy. Genomic selection is a useful tool and has been proven to raise genetic gain for low-heritability traits or difficult-to-measure traits . Hence, In principle, GS predicts the breeding value of each genotyped individual using high-density (HD) markers that span the entire genome, which is subsequently referred to as genomic estimated breeding value (GEBV). Several approaches for determining the GEBV for cattle have been proposed, including a single-step Genomic Best Linear Unbiased Prediction (ssGBLUP) and a weighted ssGBLUP (WssGBLUP). A hybrid matrix (H) in the ssGBLUP is created by combining the pedigree-based relationship matrix (A) with the genomic relationship matrix (G), and when compared to multi-step genomic predictions, this advantage improves the accuracy and decreases the prediction bias of GEBVs . HoweverGS has the potential to overcome the limitations of MAS and more correctly forecast breeding values . In MAS,The application of GS in dairy cattle significantly increased the economic efficiency of animal breeding programs . The benThe data are modeled at two levels using Bayesian estimation. The first is a model at the data level, while the second is a model at the variances of the chromosomal segments . Several−2 , where S denotes the scale parameter, and v denotes the degree of freedom. This is a valuable option since the resultant posterior distribution is similarly a scaled inverted chi-square when the information from the previous distribution is joined with the information from the data [The Bayes A technique assumes that the variance-of-marker effect varies across loci. The scaled inverted chi-square distribution is used to simulate the variances, χthe data .The Bayes B approach assumes variances of SNP effects in a genomic assessment context, with many SNP contributing to zero effects and a few contributing to substantial impacts on the trait . MeuwissHabier et al.  expandeds et al. , assume s et al.  presentes et al. .Bayesian methods have some drawbacks. According to Legara et al. , the SNPSeveral strategies have been developed since genomic selection was initially proposed in animal breeding. A multi-step procedure was introduced by VanRaden et al.  for GS iulate PA . In thisulate PA . Furtherulate PA .The multi-step approach was well suited to situations where the datasets belong to separate organizations and are condensed into a limited number of animals to genotype . This inSingle-step genomic BLUP (ssGBLUP) was designed to solve possible issues by combining phenotypes, by merging phenotypic, pedigree, and genomic links into a combined relationship matrix. Furthermore, by substituting the connection matrix with the combined matrix, pedigree-only studies may be converted to genomic analyses, and pseudo-phenotypes are no longer required. This method also allows for the simultaneous evaluation of non-genotyped animals and genotyped animals. It is a simple and more accurate method that can reduce the number of steps in the evaluation .Compared to multi-step genomic predictions, the ssGBLUP combines the pedigree-based relationship matrix (A) and the genomic relationship matrix (G) into a hybrid matrix (H). This benefit boosts the accuracy and minimizes the prediction bias of GEBVs . In addiAll relatives of genotyped individuals are automatically accounted for, as are their performances.Fitting genetic data and estimating additional effects are performed at the same time . As a result, there will be no data loss.Feedback: all of a genotyped individual’s relatives benefit from the greater accuracy.Extensions are simple. Because this is a linear BLUP-like estimator, it can be immediately applied to more complex models . Any model that can be fitted with relationship matrices may also be fitted with combined relationship matrices.The previous study suggested that the ssGBLUP method is a better option than the WssGBLUP method for crossbreeding with polygenic traits . When coPreviously, the single step method has been used to evaluate the genetics for production traits of Thai–Holstein crossbred cattle. In a recent study, Sungkhapreecha et al. ,135 propGS could increase the accuracy of EBV testing at an early age . The squGS is now being used on a broad scale in cattle; however, it is mostly focused on production traits ,139. TheSeveral studies on GS were reported in dairy cattle regarding fertility traits, including AFC, the non-return rate, and calving ease . The accThailand has had a dairy cattle breeding development program for about 60 years. They have attempted to produce its tropical dairy breed based on Holstein Friesian (HF) and indigenous Thai cattle through selection and inter se mating. Improving reproductive performance and resistance to heat is one of the main objectives of this crossbreeding program. The central database system of the Bureau of Biotechnology for Livestock Production, Department of Livestock Development, hosts milk-production data, reproduction data, and pedigree information. Several studies on genetic evaluation of reproductive traits in Thai–Holstein crossbred cattle have been carried out using these data ,50,146. Most dairy farmers in Thailand (>80%) are smallholders with an average of 30 animals per farm . SmallhoGenetic evaluations for reproductive traits have been carried out in dairy cattle, especially in Thailand. The genetic variance for reproductive traits in livestock is generally low, indicated by low heritability values (<0.1). However, there were moderate to strong genetic correlations between reproductive traits and between reproductive traits and production traits. THI should be included in the model for genetics evaluation under heat stress. Several approaches have been used for the genetic selection of reproductive traits, and GS may be the appropriate genetic technique for Thai dairy cattle. For reproductive traits in dairy cattle, the single-step genomic BLUP (ssGBLUP) technique is more accurate than the multi-step method. The implementation of GS for reproductive traits in Thai–Holstein cattle is interesting to study in the future because it has not been widely adopted.


Zea mays L.) genotypes grown in agronomically relevant conditions under high N (+N) and low N (-N) treatments. We analyze the maize rhizobiome in terms of 150 abundant and consistently reproducible microbial groups and we show that the abundance of many root-associated microbes is explainable by natural genetic variation in the host plant, with a greater proportion of microbial variance attributable to plant genetic variation in -N conditions. Population genetic approaches identify signatures of purifying selection in the maize genome associated with the abundance of several groups of microbes in the maize rhizobiome. Genome-wide association study was conducted using the abundance of microbial groups as rhizobiome traits, and n=622 plant loci were identified that are linked to the abundance of n=104 microbial groups in the maize rhizosphere. In 62/104 cases, which is more than expected by chance, the abundance of these same microbial groups was correlated with variation in plant vigor indicators derived from high throughput phenotyping of the same field experiment. We provide comprehensive datasets about the three-way interaction of host genetics, microbe abundance, and plant performance under two N treatments to facilitate targeted experiments toward harnessing the full potential of root-associated microbial symbionts in maize production.The root-associated microbiome (rhizobiome) affects plant health, stress tolerance, and nutrient use efficiency. However, it remains unclear to what extent the composition of the rhizobiome is governed by intraspecific variation in host plant genetics in the field and the degree to which host plant selection can reshape the composition of the rhizobiome. Here, we quantify the rhizosphere microbial communities associated with a replicated diversity panel of 230 maize ( Symbiotic relationships between plant hosts and root-associated microbes have been shaped through natural selection over millions of years of coevolution , and havArabidopsis diversity panel  and DNA sequence analysis of microbial communities in diverse plant hosts is time-consuming, expensive, and poses significant logistical and technical challenges. It has been shown that plant genetics can explain variation in both root architecture  and exudty panel  or at orty panel . Howeverty panel . It was ty panel  that soity panel . A possity panel . This isty panel . As a coty panel . Thus, iIn the present study, we evaluate the role that selection on plant genetic factors has played in shaping the maize rhizobiome under different N conditions. We employ the Buckler-Goodman maize diversity panel, a set of maize lines selected for maximum representation of genetic diversity and growth in temperate latitudes . This po3,313 rhizosphere samples from 230 replicated genotypes of the maize diversity panel  were colSupplementary Methods). Of these rhizobiome traits, 79/150 (52.7%) groups were significantly more abundant in samples collected from the +N condition , 53/150 (35.3%) significantly more abundant in samples collected from the -N condition, and 18/150 (12.0%) showed no significant difference in abundance between the two treatments. In several cases, more closely related microbial groups exhibit shared patterns of differential abundance between N treatments  shows the same pattern of divergent microbial communities between N treatments, and in particular under the -N treatment there is evidence for the association of plant genomic loci and microbiome composition . To studeatments .2 >0.6 in both +N and -N conditions  and two groups in the Sphingomonadales order (Sphingobium herbicidovorans 1 and an unknown genus in the Sphingomonadaceae family). Notably, closely related microbial groups did not exhibit obvious patterns of shared high or low estimated heritabilities  for +N and r=0.39  for -N  was estimated using an approach following a previous study  Materia. We founnditions . These ibilities . As heri) for -N .Luteolibacter pohnpeiensis , -N: Blastococcus , Pseusomonas umsongensis , Chthoniobacter flavus ).Under the hypothesis that the rhizobiome traits have effects on plant fitness, we sought to estimate the selection differentials under different N treatments . To reduSelection acting on rhizobiome traits can happen either by purging deleterious alleles (purifying selection) or by elevating the frequencies of advantageous alleles (positive selection). To evaluate the mode of selection at the genomic level, a Bayesian-based method  was used to test for each rhizobiome trait . A set of n=834,975 independent SNPs was used to estimate their effects on 150 rhizobiome traits as well as 17 conventional plant traits collected from the same population in the same field experiments  showed positive S values indicating that this trait might have been a target of positive selection. Relative to rhizobiome traits, plant leaf traits and nutrient traits were both more likely to exhibit evidence of selection within this maize population. Three out of 15 plant leaf traits, that is leaf area (LA), leaf fresh weight (FW), and leaf dry weight (DW) , exhibited S>0 values under the +N condition, consistent with positive selection, while only one of the three exhibited a slightly negative S value in the -N condition and in that case exhibited a pattern consistent with weak purifying selection (Bacillus fumarioli), we observed a skew towards higher MAF and in the case of purifying selection (f_Comamonadaceae Unknown Genus), a skew towards lower MAF  GWAS signals in each window. This analysis revealed 622 genomic regions containing at least one significant GWAS signal, and we refer to these regions as microbe-associated plant loci (MAPLs) . We report these MAPLs alongside nearby genes in The observation that many rhizobiome traits are both under significant host genetic control and targets of selection suggests it may be possible to detect individual large effect loci controlling rhizobiome traits. To investigate this, we performed GWAS using each of the 150 rhizobiome traits. This analysis was done separately for the -N and +N conditions, as N deficiency induces dramatic changes in plant metabolism, including changes in root gene expression  and rootf_Chitinophagaceae Unknown Genus, Sphingoaurantiacus, and f_Vicinamibacteraceae) showed significant associations under both N treatments, albeit with different plant loci. No loci were found that had associations with rhizobiome traits under both N treatments, which is expected as GWAS analyses were done separately for different N treatments and the microbial groups studied here were partly distinguished based on differential abundance in response to N treatments.To reduce false discoveries, we decided to discuss a subset of 119 MAPLs here, that had at least two significant GWAS signals. Among these 119 MAPLs, 69 were observed under +N treatment and 50 under -N treatment . Of the We hypothesized that many plant genes underlying MAPL hotspots may exert control over the rhizosphere microbiome via changes in root physiology, architecture, and exudate composition  and may To complement the gene expression data provided by Kremling et. al, we selected four diverse and well characterized maize genotypes . Plants were grown in a controlled greenhouse environment under standard N and N deficient conditions and gene expression was analyzed in roots and shoots of two-week old seedlings  for +N and even more significant  under the -N condition  to identify potential plant phenotypic consequences of variation in the abundance of specific rhizosphere microbes. Several rhizobiome traits were significantly correlated (p<0.01) with measures of plant performance, such as leaf area, leaf dry weight and fresh weight, and with several measures of leaf micronutrients such as nitrogen, sulfur, and phosphorus . The traondition .Sphingoaurantiacus, Bacillus fumarioli, f_Comamonadaceae Unknown Genus, and Burkholderia pseudomallei are of particular interest as they overlap in all performed assays, showing evidence of heritability and selection, a strong GWAS signal in associated plant genomic loci, and positive correlation with canopy coverage. The complete summary data for all 150 microbial groups are available in We summarize the relationship of the analyses conducted in this study under either N treatment for the 62 microbial groups that are correlated with CC. 44/62 (71%) are heritable and 13/62 (21%) are under selection under either or both N treatments  . 56/62 Overall, our data show a clear trend that the 62 microbial groups associated with plant performance also tend to be associated with host genetics, and the datasets provided here can be used to design more targeted experiments to confirm associations of rhizosphere microbial groups with plant genetics and performance on a case-by-case basis.We identified several strong GWAS signals that link plant genomic loci to rhizobiome traits . Such siComamonadaceae family mentioned above, while unnamed and uncharacterized, shows high heritability under both N treatments  and +N   . A total × 10–5) . A linka × 10–5) . In acco × 10–5) . As descreatment . Next, wr allele . Howeverr allele .The example discussed here shows a three-way association of the abundance of a particular microbial group in the rhizosphere, a corresponding locus on the maize genome, and plant performance in the field. The datasets provided alongside this publication contain several such associations and may serve as the basis for more targeted experiments to establish a direction of causation between microbe abundance and plant performance, and to shed light on the genetic mechanisms that shape symbiotic relationships between the plant host and associated rhizosphere microbes.This study profiled the rhizosphere inhabiting microbiota of several hundred maize genotypes under agronomically relevant field conditions. Through a 16 S rDNA-sequencing based approach, we identified a set of 150 rhizobiome traits based on 3626 ASVs that were highly abundant and consistently reproducible in this maize diversity panel. The phylogenetic tree in We observed relatively higher heritability for rhizobiome traits quantified from plants grown in the -N treatment than under the +N treatment. This outcome is consistent with a model where the partnerships between microbiomes and plants were established in natural and early agricultural systems which were predominantly N limited . N insufPseudomonas , and genes linked to variation in rhizobiome traits via GWAS were highly expressed in roots across genotypes in multiple independent gene expression datasets. This suggests a number of potential mechanisms for host plant genotypes to influence the composition of the rhizobiome.Arabidopsis encode AUXILIN-LIKE1 and AUXILIN-LIKE2, and overexpression of auxilin-like proteins in Arabidopsis has been shown to inhibit endocytosis in root hair cells  lines from the maize diversity panel . DNA was isolated using the MagAttract PowerSoil DNA KF Kit  and the KingFisher Flex Purification System . DNA sequencing was performed using the Illumina MiSeq platform at the University of Minnesota Genomics Center . In brief, 2 × 350 bp stretches of 16 S rDNA spanning the V4 region were amplified using V4_515 F_Nextera and V4_806 R_Nextera primers, and the sequencing library was prepared as described by Gohl ; and in 2019, n=3009 samples were collected from 230 genotypes , listed in  by Gohl .Paired-end 16 S sequencing reads from 3,313 samples were processed in R 3.5.2 using the workflow described by Callahan , which e2) of rhizobiome traits was calculated separately for +N and -N conditions using maize genotypes in the 2019 dataset for which balanced data was available. For each of the 150 rhizobiome traits, combined ASV counts were normalized by converting to relative abundance and subsequent natural log transformation. Using these transformed values, h2 was estimated following 2 is the amount of variance explained by the genotype term (Vgenotype) divided by the variance of the genotype and the error (Vgenotype +Verror/n), where n=6 is the total number of samples  used in this dataset. Heritability was tested for significance using a permutation test. For each trait, the genotype labels of microbial abundance data were shuffled 1000 times, and the distribution of heritabilities calculated from these shuffled datasets were used to assess the likelihood of observing the heritabilities calculated from the correctly labeled data under a null hypothesis of no host genetic control.Heritability , to the abundance of the microbial groups with a generalized additive model (GAM). To reduce biases due to environmental covariances , we empl2r ≥0.1) using the PLINK1.9 software  of the natural log transformed relative abundance of ASV counts as the dependent variable for the GWAS analysis. Since only a fraction of genotypes were sampled from the 2018 field experiment, only sample data collected in 2019 was used for the BLUP calculation. A BLUP value was calculated for each microbial group and each treatment using R package lme4 . In the Gene expression was analyzed using two independent datasets. The first dataset was obtained from Kremling  and inclA total of 17 plant traits were measured in the 2019 field experiment. First, 15 leaf physiological traits were measured on the same days the rhizobiome samples were collected, and included leaf area (LA), chlorophyll content (CHL), dry weight (DW), fresh weight (FW), as well as concentrations of the elements B, Ca, Cu, Fe, K, Mg, Mn, N, P, S, and Zn. Measurement of the leaf traits was carried out as previously described . Two aerhttps://github.com/jyanglab/Maize_Rhizobiome_2022; The sequencing data reported in this publication (3313samples) can be accessed via the following five Sequence Read Archive (SRA) accession numbers: PRJNA771710, PRJNA771712, PRJNA771711, PRJNA685208, PRJNA685228 (summarized under the umbrella BioProject PRJNA772177). Scripts used to analyze the data are available on GitHub  feedback on the manuscript for the authors, including requests for revisions, shown below. We also include an acceptance summary that explains what the editors found interesting or important about the work.Our editorial process produces two outputs: (i) Decision letter after peer review:eLife. Your article has has been reviewed by three peer reviewers, including Rebacca Bart as the Reviewing Editor and Reviewer #1, and the evaluation has been overseen by Detlef Weigel as the Senior Editor.Thank you for submitting your article "Maize root-associated microbes likely under adaptive selection by the host to enhance phenotypic performance" for consideration by eLife will be a good home for the paper, assuming you are able to address the below points.The reviewers have discussed their reviews with one another, and we have drafted this to help you prepare a revised submission. Overall, we are all enthusiastic about this manuscript. We believe that Essential revisions:1) Strengthen the data/analysis to support the claim in the title, abstract and text or revise the title to more accurately reflect the findings. We all felt that currently, the findings are overstated based on the presented data. As is, we feel that a more appropriate conclusion would be: "loci under selection in maize genome affect rhizosphere composition" or similar. We are currently not convinced that the microbes are in-fact driving selection, opposed to an indirect effect. Please include additional analysis to support this claim, or revise the text to avoid overstating and include discussion of this important caveat. Please see specific comments throughout the reviews.2) Revise the text to more clearly describe the methods and logic behind the analyses. Please see specific comments throughout the reviews. Most importantly, first, we all feel that more care is needed in describing how the 150 microbial traits were defined. We did consider your previous paper, but that did not resolve our questions about how these specific 150 traits were defined from these specific data. Second, we have concern about FDR given the size of these datasets. Please revise the text to address these issues.Reviewer #1 (Recommendations for the authors):1. Please point to Support file 1 in line 143-146. I was confused about how this math worked out, until I look at the Supplementary file.2. Lines 191-193 appear to be a different font color.3. Please note the age of plants used for the previously available RNAseq data.4. Supp Figure 5. Is PLAM supposed to be MAPL?5. Consider revising lines 385-395 for clarity. I found this difficult to follow until I looked at Supplemental Figure 5. Given that this is a pretty important part of the paper, I think it should be understandable on its own . For example, from the text, it is not immediately obvious that the 30 traits that positively correlate with CC are from the 15 from +N and 29 from +N and -N.6. You might also consider specifically referring to the microbe traits as 'microbe traits' and plant traits as 'plant traits' as I found this confusing in a few places.7. Line 505: remove 'however'.8. The final figure and analyses are exciting and very promising! Yet, you seem to shy away from emphasizing this point. I appreciate caution in drawing conclusions here but think that another sentence or two after line 431 would be appropriate.Reviewer #2 (Recommendations for the authors):This manuscript presents one of the largest 16S rRNA amplicon datasets I have ever seen. The study reports on a field experiment done with 230 maize genotypes under 2 nitrogen fertilization regimens. The abundance of each taxon within the root microbiota of over 3000 samples were used to perform two separate analyses: (i) a GWAS, using the abundance of each taxon as a quantitative trait and (ii) a phenotypic study, identifying microbial taxons correlated with different plant phenotypes (mainly canopy coverage).This is an impressive manuscript which will help pave the way for a better understanding of heritability in the plant microbiota, which is a major open question in our field. In massive screening studies such as this one, it is often challenging to deliver a concise take-home message or a mechanistic insight. Here, the actual functions of "MAPL" genes are not looked at in detail, besides the looking at their expression profiles across the plants . I therefore urge the authors to make their data, in the form of supplementary tables, as accessible as possible for follow up studies on the loci that they detected.From what I can understand, and from some consultation with colleagues, the GWAS and related analysis were performed rigorously and with the appropriate normalizations and controls. However, I stress that I am not an expert in population genetics.Comments:One methodological aspect is unclear to me. How were the 150 "rhizobiome traits" defined? This seems like a taxonomic classification, but I am not clear how, or why, this was done? Also, the term "rhizobiome traits" is somewhat confusing, especially since these 150 vectors are referred to by different names throughout the manuscript. This is a key part of the analysis and should be explained clearly.A second and related comment, is why are the only "rhizobiome traits" that the authors consider taxon abundances? Other quantities can be extracted from this dataset: α diversity, β diversity (e.g the 1st and 2nd axes of the ordination). You could also try to extract a measure of absolute abundance from the ratio of bacterial to plastid/mitochondrial reads. These would provide a more holistic picture of how plant genotype influences the microbiome composition.I am perceiving a bit of a disconnect between the GWAS and the phenotypic comparisons. Indeed, the authors show that heritability is correlated with the correlation with CC, but I would assume that much of the phenotypic differences observed in the first place are a result of the genotypic variability, but I do not see a unified model that accounts for the relationship between plant genotype, microbiome composition and plant phenotype. Am I missing something here?The relative abundance and the prevalence of ASVs is essentially ignored, beyond the initial screening described in the appendix. It would be important to know for example if heritable taxa are also abundant ones?Line 37: study, singular.Line 250: could the results of the PERMANOVA from the supplementary figure be highlighted also in the text?Line 253: I do not understand how these 150 groups were classified. Also, why do you say that they are functionally distinct? All you have is their 16S sequence, there is no functional information (see comment above).Figure 1b: As far as I could tell, the phylogenetic reconstruction in this tree does not match the consensus phylogeny for bacteria. This is more likely an error in their tree building algorithm than in the consensus tree and should be corrected.Line 275: This could be measured (pagel's λ for example). This also raises the question why were all the ASVs binned to 150 groups?Line 285: you didn't really frame this as a hypothesis.Line 291: please explain the S parameter.Line 305: these 3 traits are not independent traits. More like 3 facets of the same trait.Figure 3: why wasn't this analysis done for plant traits as well?Line 505: "however" – I do not understand how this is contrary to the previous sentence.The terms "rhizosphere", "rhizobiome" and "root associated microbiome" seem to be used interchangeably here. Please sort this out.Line 779: was the DNA extraction kit substituted mid project? How did this affect the results?Reviewer #3 (Recommendations for the authors):Comparing patterns of selection in N+ and N- environments can be done using methods that have been long established in evolutionary genetics: for example, calculating genotypic selection gradients in each of the treatments In theory, the patterns of heritability in rhizobiome features could be compared between treatments using a paired t-test. Essential revisions:1) Strengthen the data/analysis to support the claim in the title, abstract and text or revise the title to more accurately reflect the findings. We all felt that currently, the findings are overstated based on the presented data. As is, we feel that a more appropriate conclusion would be: "loci under selection in maize genome affect rhizosphere composition" or similar. We are currently not convinced that the microbes are in-fact driving selection, opposed to an indirect effect. Please include additional analysis to support this claim, or revise the text to avoid overstating and include discussion of this important caveat. Please see specific comments throughout the reviews.Thanks for the suggestion. We agree with this point and revised the title and abstract to highlight that the results are largely driven by association analyses. Please see the revised abstract in the edited manuscript. The title has been changed to “Association analyses of host genetics, root-colonizing microbes, and plant phenotypes under different nitrogen conditions in maize”. And we also added additional discussions about the caveats of some of the analyses.2) Revise the text to more clearly describe the methods and logic behind the analyses. Please see specific comments throughout the reviews. Most importantly, first, we all feel that more care is needed in describing how the 150 microbial traits were defined. We did consider your previous paper, but that did not resolve our questions about how these specific 150 traits were defined from these specific data.We added a section “Clustering of ASVs into microbial groups” in the Materials and methods (See detailed responses for the reviewer #1). Additionally, we added a supplementary figure to better explain how microbial groups were clustered.Second, we have concern about FDR given the size of these datasets. Please revise the text to address these issues.We agree that FDR adjusted p-values allow us to identify associations between plant genomic loci and microbial groups with fewer false discoveries.In our first submission we chose an arbitrary cutoff of -Log10(p) = 5 to distinguish significant vs. non-significant GWAS signals. To improve on this, p-values were adjusted based on the effective SNP number . The methods section was updated to reflect this.Added Text:“To mitigate false discoveries of GWAS, Bonferroni corrections were applied based on the effective number of independent SNPs (or effective SNP number) . The effective SNP number for the genetic marker set and population employed in this study was determined to be N = 769,690 independent markers as described previously . Using aUsing the new significance threshold of 7.2, we re-calculated the number of significant GWAS signals across all microbial groups.With the more stringent criteria this yielded a greatly reduced number of 1,089 significant GWAS signals. Using this reduced set of GWAS signals we proceeded to re-evaluate 10kb genomic windows in which strong associations with one or more microbial groups are observed .To create the summary data for Figure 3A, our previous method of choice was to tally the number of SNPs above the significance threshold in every 10kb bin . This apFigure 3A was updated using the new stringency criteria and the resulting set of 119 MAPLs. We further annotated the microbial group(s) associated with each MAPL on the maize genome. Likewise, gene expression was calculated anew for the updated set of 119 MAPLs in Figure 3B and C and the same patterns were observed as before.Dataset 5 was updated to include all 622 microbe-associated plant loci (MAPLs) that contain at least one significant association with any of the 150 microbial groups.Reviewer #1 (Recommendations for the authors):1. Please point to Support file 1 in line 143-146. I was confused about how this math worked out, until I look at the Supplementary file.Thanks. We added the reference to Supplementary File 1.2. Lines 191-193 appear to be a different font color.We fixed this in the revised manuscript.3. Please note the age of plants used for the previously available RNAseq data.We specified the plant tissues were collected during germination and at flowering time.4. Supp Figure 5. Is PLAM supposed to be MAPL?PLAM represents plant locus-associated microbe. We removed this term to avoid confusion with MAPL (microbe-associated plant locus).5. Consider revising lines 385-395 for clarity. I found this difficult to follow until I looked at Supplemental Figure 5. Given that this is a pretty important part of the paper, I think it should be understandable on its own . For example, from the text, it is not immediately obvious that the 30 traits that positively correlate with CC are from the 15 from +N and 29 from +N and -N.We thank the reviewer for the feedback. To clarify the relevant section, we revised the text. In addition, positive and negatively correlated microbial groups were marked in Figure 4. See revised text below.“In total, 62 microbial groups – more than expected by chance (permutation test p < 0.001) – were, either positively or negatively correlated with CC . 15 microbial groups were associated with CC under +N treatment, 18 under -N treatment, and 29 showed a significant association under both N treatments . 30 traits under +N and 35 under -N were positively correlated with CC. 14 traits under +N and 12 under -N were negatively correlated with CC. Among the same microbial groups, 44/62 (71%) showed significant evidence of host plant genetic control under either or both N treatments and 56/62 (90%) are associated with 255/395 (65%) genes in 174/467 MAPLs (39%) identified across the maize genome . Under both N treatments, we observe an association between heritability and the correlation with CC, which was statistically significant  for +N and even more significant  under the -N regime .”6. You might also consider specifically referring to the microbe traits as 'microbe traits' and plant traits as 'plant traits' as I found this confusing in a few places.“microbial traits”, is indeed a confusing term, was replaced with “rhizobiome traits” and specified in the introduction.7. Line 505: remove 'however'.Thanks. We have removed “However” in the revised text.8. The final figure and analyses are exciting and very promising! Yet, you seem to shy away from emphasizing this point. I appreciate caution in drawing conclusions here but think that another sentence or two after line 431 would be appropriate.To reinforce the main message of the manuscript, a final paragraph was added to the Results section as below.“The example showcased here is a clear example of a three-way association between the abundance of a particular microbial group in the rhizosphere, a corresponding locus on the maize genome, and plant performance in the field. The datasets provided in this study contain several such associations and may serve as the basis for more targeted experiments to establish a direction of the causation chain from plant genotype to microbe abundance to plant performance, and to shed light on the genetic mechanisms that shape symbiotic relationships between the plant host and associated rhizosphere microbes.”Reviewer #2 (Recommendations for the authors):This manuscript presents one of the largest 16S rRNA amplicon datasets I have ever seen. The study reports on a field experiment done with 230 maize genotypes under 2 nitrogen fertilization regimens. The abundance of each taxon within the root microbiota of over 3000 samples were used to perform two separate analyses: (i) a GWAS, using the abundance of each taxon as a quantitative trait and (ii) a phenotypic study, identifying microbial taxons correlated with different plant phenotypes (mainly canopy coverage).This is an impressive manuscript which will help pave the way for a better understanding of heritability in the plant microbiota, which is a major open question in our field. In massive screening studies such as this one, it is often challenging to deliver a concise take-home message or a mechanistic insight. Here, the actual functions of "MAPL" genes are not looked at in detail, besides the looking at their expression profiles across the plants . I therefore urge the authors to make their data, in the form of supplementary tables, as accessible as possible for follow up studies on the loci that they detected.We thank the reviewer for this comment. The key data produced in this study is a list of associations between the abundance of microbial groups, corresponding plant genomic loci, and plant performance in the field. We provided five datasets as supplementary tables in the original submitted version, formatted for easy access, to enable and encourage more targeted experiments in the future. Additional metadata and associated phenotypic and genotypic information were annotated in the manuscript as clearly as possible. We also provided the datasets and scripts in the GitHub Repository (https://github.com/mandmeier/Maize_Rhizobiome_2022).From what I can understand, and from some consultation with colleagues, the GWAS and related analysis were performed rigorously and with the appropriate normalizations and controls. However, I stress that I am not an expert in population genetics.Comments:One methodological aspect is unclear to me. How were the 150 "rhizobiome traits" defined? This seems like a taxonomic classification, but I am not clear how, or why, this was done? Also, the term "rhizobiome traits" is somewhat confusing, especially since these 150 vectors are referred to by different names throughout the manuscript. This is a key part of the analysis and should be explained clearly.Thanks for the comments. A better explanation of the methodology used here was a point raised by all reviewers. We have added an additional section to the methods as well as an additional supplementary figure to explain the process in more detail (see response above for Reviewer #1 question 1).A second and related comment, is why are the only "rhizobiome traits" that the authors consider taxon abundances? Other quantities can be extracted from this dataset: α diversity, β diversity (e.g the 1st and 2nd axes of the ordination). You could also try to extract a measure of absolute abundance from the ratio of bacterial to plastid/mitochondrial reads. These would provide a more holistic picture of how plant genotype influences the microbiome composition.We agree that high-level rhizosphere microbial community metrics may be of interest to some readers. To this end, we performed additional GWAS analyses using four α diversity metrics  as well as the first 10 principal components. Key findings of this analysis are summarized in an additional supplementary figure:A reference to this high-level analysis was added to the results.“An initial analysis looking at high-level rhizobiome traits  shows the same pattern of divergent microbial communities between N treatments, and in particular under the -N treatment there is evidence for the association of plant genomic loci and microbiome composition . To study changes in rhizobiome composition more accurately, the final 3,626 ASVs were clustered into n = 150 distinct microbial groups (“rhizobiome traits”), spanning 19 major classes of rhizosphere microbiota  using a method previously described .”I am perceiving a bit of a disconnect between the GWAS and the phenotypic comparisons. Indeed, the authors show that heritability is correlated with the correlation with CC, but I would assume that much of the phenotypic differences observed in the first place are a result of the genotypic variability, but I do not see a unified model that accounts for the relationship between plant genotype, microbiome composition and plant phenotype. Am I missing something here?Thanks for this insightful suggestion. A similar question was raised by Reviewer #1. We agree that the ultimate goal is to explain the relationship between plant genotype, microbiome composition, and plant phenotype in a unified model. However, to our knowledge, there is no such model readily available, and to develop such a three-variable model to connect genotype, microbiome, and plant phenotype altogether is challenging. Our group has attempted to build such a model for several years. For example, we leveraged a classical causal inference method, termed mediation analysis, to establish a causal chain from genotype to intermediate mediator to plant phenotype. We found the initial model can detect large effect mediators through extensive simulation. Using publicly available RNA-seq data as the mediators, we conducted empirical mediation analysis and identified mediator genes consistent with their known biological functions. As a next step, we will fit the microbiome data as the mediator. To get meaningful results, however, additional simulations and model fine-tuning need to be done. We believe the simulation and empirical results by fitting microbiome features as the intermediate mediators in a three-variable causal analysis is beyond the scope of this study.For the current work, we opted to apply more established methods to investigate associations between microbe abundance and plant genetics, and between microbe abundance and plant performance.The relative abundance and the prevalence of ASVs is essentially ignored, beyond the initial screening described in the appendix. It would be important to know for example if heritable taxa are also abundant ones?We briefly reported the differential abundance of microbial groups in Supplementary Figure 2. It is an interesting consideration to test whether heritable taxa are also abundant ones. To do this, we plotted the heritability of each taxonomic group vs. the mean abundance and added an extra panel to Supplementary Figure 2. There is indeed a positive correlation between heritability and microbe abundance, suggesting that the low measured heritability of some low abundance microbes might result from the less precise quantification of abundance provided by smaller read counts for these taxa. Thank you for the suggestions!Line 37: study, singular.Corrected in the revised manuscript. Thanks.Line 250: could the results of the PERMANOVA from the supplementary figure be highlighted also in the text?We added the results of the PERMANOVA to the relevant section in the text.Line 253: I do not understand how these 150 groups were classified. Also, why do you say that they are functionally distinct? All you have is their 16S sequence, there is no functional information (see comment above).We understand this concern. To avoid confusion, we decided to refer to the groups identified here simply as “microbial groups”. The relevant sections in the manuscript were edited.Figure 1b: As far as I could tell, the phylogenetic reconstruction in this tree does not match the consensus phylogeny for bacteria. This is more likely an error in their tree building algorithm than in the consensus tree and should be corrected.This is a valid concern and more accurate phylogenetic clustering should be considered in future studies with emphasis on the evolution of plant-microbe associations. The phylogenetic tree used here serves only to illustrate the grouping of ASVs. Deviations from the consensus phylogeny were expected since only the 350bp ribosomal V4 region sequenced in this study was used to establish the relationship between groups. We added this caveat in the revised discussion.Line 275: This could be measured (pagel's λ for example). This also raises the question why were all the ASVs binned to 150 groups?The method used to bin ASVs into groups at low taxonomic ranks was explained in more detail . The resulting number of groups happened to be 150 and was not arbitrarily chosen.Line 285: you didn't really frame this as a hypothesis.Thanks. The wording was changed, as below to avoid confusion.“A Bayesian-based  method was used to test for signatures of selection for each rhizobiome trait .”Line 291: please explain the S parameter.We clarified the text as below.“Using the relationship between effects of non-zero SNPs and their minor allele frequencies (MAFs) as a proxy for the signature of selection , the S value, a free parameter in the BayesS model, was estimated for both rhizobiome traits  and plant traits .”Line 305: these 3 traits are not independent traits. More like 3 facets of the same trait.Thanks for the comments. We agree that the leaf area, leaf fresh weight, and leaf dry weight are correlated traits. We added the pairwise correlation coefficients of these three traits in the revised manuscript.Note that the three leaf-related traits are not independent. The pairwise correlation coefficients are 0.92, 0.91, and 0.94, for LA and FW, LA and DW, FW and DW, respectively.Figure 3: why wasn't this analysis done for plant traits as well?This analysis was done for both microbiome traits and plant traits. See Figure 2B for the results from the plant traits.Line 505: "however" – I do not understand how this is contrary to the previous sentence.The terms "rhizosphere", "rhizobiome" and "root associated microbiome" seem to be used interchangeably here. Please sort this out.We have removed “however” for clarity. “Root-associated microbiome”  was changed to “rhizobiome” throughout the manuscript for consistency.Line 779: was the DNA extraction kit substituted mid project? How did this affect the results?The MagAttract PowerSoil kit and the KingFisher Flex purification system are two components of the same DNA isolation protocol. The text in the methods was edited for clarity as below:“DNA was isolated from rhizosphere soil using the MagAttract PowerSoil DNA KF Kit  and purified using the KingFisher Flex Purification System .”Reviewer #3 (Recommendations for the authors):Comparing patterns of selection in N+ and N- environments can be done using methods that have been long established in evolutionary genetics: for example, calculating genotypic selection gradients in each of the treatments Thank you again for this insightful suggestion! According to the reviewer’s recommendations, we estimated selection gradients following the method developed by Lande and Arnold, 1983 and Rausher 1992 and recently implemented by Morrissey and Sakrejda.Briefly, we estimated the fitness function relating fitness related traits, i.e., CC collected on August 22, to the abundance of the microbial groups with a generalized additive model (GAM). To reduce biases due to environmental covariances (Rausher 1992), we employed the BLUP values for both the microbial traits and the fitness related traits. Then, we obtained linear and quadratic selection gradients from the fitted GAM models using an R package developed by Morrissey and Sakrejda, 2013. We ran a total of 300 univariate models .As a result, we found 58 microbial groups that showed significant selection gradients, summarized in a new supplementary Figure. To increase confidence in our results, we only reported selection data for 10 microbial groups that showed significant selection gradients AND significant selection coefficients in the GCTB analysis. Figure 2 was adjusted accordingly.In theory, the patterns of heritability in rhizobiome features could be compared between treatments using a paired t-test.We thank the reviewer for this idea. A paired Student’s t-test indeed shows a significant difference in heritability between the two N treatments. This finding was added to the results:Revised text:265: “Rhizobiome traits were comparatively more heritable under -N than +N conditions .”