Road traffic congestion continues to manifest and propagate in cities around the world. The recent technological advancements in intelligent traveler information have a strong influence on the route choice behavior of drivers by enabling them to be more flexible in selecting their routes. Measuring traffic congestion in a city, understanding its spatial dispersion, and investigating whether the congestion patterns are stable  are critical to developing effective traffic management strategies. In this study, with the help of Google Maps API, we gather traffic speed data of 29 cities across the world over a 40-day period. We present generalized congestion and network stability metrics to compare congestion levels between these cities. We find that (a) traffic congestion is related to macroeconomic characteristics such as per capita income and population density of these cities, (b) congestion patterns are mostly stable on a day-to-day basis, and (c) the rate of spatial dispersion of congestion is smaller in congested cities, i.e. the spatial heterogeneity is less sensitive to increase in delays. This study compares the traffic conditions across global cities on a common datum using crowdsourced data which is becoming readily available for research purposes. This information can potentially assist practitioners to tailor macroscopic network congestion and reliability management policies. The comparison of different cities can also lead to benchmarking and standardization of the policies that have been used to date. Growing metropolitan cities with strong economies continue to sprawl into suburbs with the desire for private transportation and on-time access to goods and services placing pressure on road networks. Traffic congestion and its repercussions are particularly alarming in developing countries as the existing transport infrastructure struggles to keep up with rapidly increasing productivity . CongestBased on a day-to-day scenario, congestion can be classified into two types: i) recurrent, and ii) non-recurrent. Recurrent congestion is predictable, where the number of users on the road exceeds the road’s design capacity in a sustained manner, such as peak hour. Recent technological advances have influenced the driver’s route choice behavior and have enabled them to be much more flexible in selecting their routes to the destination ,10. On tA variety of metrics have been used in the past to quantify the impact of traffic congestion in a road network. For example, Long et al. (2008) studied the severity of traffic congestion through traffic propagation and congestion bottleneck identification . SaeedmaUnderstanding the traffic patterns and congestion in multiple cities at once and be able to compare and contrast has been challenging since it is reliant on access to efficient and accurate traffic data. Current standard practice to collect traffic data is by using “in-situ” methods which require physical sensing apparatus such as pneumatic tubes, inductive loops, weigh-in-motion (WIM) sensors, Video Image Processing System (VIPS) and radar detectors ,16. EvenThe utilization of crowdsourced traffic data has recently emerged as a viable alternative to the traditional means of traffic data collection. Crowdsourcing involves obtaining information by enlisting or engaging with crowds of people to better understand a process or solve a problem . CrowdsoCommercial traffic data providers such as TomTom , Here 2, INRIX , and GooNevertheless, these studies  demonstrate the feasibility of using Maps APIs to reflect traffic patterns that are recorded by the traditional techniques . Furthermore, the volume of data available through these applications grows bigger as the scale of the road network of interest increases which can become a storage issue. However, this shortcoming is rapidly diminishing with the advances in computer technology such as large storage devices (servers) and cloud storage. Lastly, factors such as lack of awareness (about the benefits of crowdsourced data), data privacy and more phone battery consumption among smartphone users can also result in the users avoiding from keeping the location services “on”. While crowdsourced data platforms maintain the confidentiality of collected data by only sharing the aggregated information through the APIs , improveIn the literature, congestion index is usually defined in terms of traffic parameters such as travel time, traffic speed, traffic flow, etc. where traditional data collection techniques such as loop detectors, pneumatic tubes, sensors, video detection, etc., have served as the primary resources of data. Despite the robust methodologies and meticulous implementation of those congestion matrices, the high costs associated with obtaining the data have proven to be the bottleneck in traffic congestion mitigation and management where most of the rapidly growing cities are looking for monitoring the traffic conditions around the clock. Also, using these congestion metrics to measure congestion, in not a single city but many at once has been a particular challenge in this line of research. In this study, we define a generalized real-time congestion index using just one traffic parameter that can be collected using the crowd-sourced data most cost-effectively. We then analyze the spatial dispersion of traffic congestion in the network and discuss the stability of network equilibrium under various congestion levels and traffic conditions.th March and 19th April 2017) using Google Maps. Due to the enormous size of these cities, only the road network surrounding the Central Business Districts (CBD) is selected for analysis. Google aggregates speed data from smartphone users to estimate speed and travel times. The data is recorded from devices which use the “Google Maps” feature or have their “My Location” feature turned on [In this study, we gather traffic speed data for 29 major cities around the world for 40 days  is defined as the maximum speed during the off-peak period, that usually occur during the night in most of the cities . Then the congestion index of a link i at time t (CIit) is computed as the ratio of its delay . Most of them are the cities in developing economies of the world, and all of them are densely populated cities that are expected to have highly congested traffic conditions. All other cities except the ones from the Middle East , exhibit moderate congestion with an average CI between 0.5 and 0.7. Although the average congestion is moderate, some cities like Rome, Bangkok, Hong Kong, New York, Los Angeles, London, Hyderabad, and Paris show a high level of congestion (closer to 100%) during the peak hours. Most of them are tourist cities with a high global destination index, which would add extra traffic to the city network. As shown, different cities have different CI that varies across time.r = 0.68) and emissions (r = 0.57). This observation is intuitive because as the number of individuals increases in a small area (for example the CBD of a city), it leads to more usage of automobiles for commute activities. The emissions PM2.5 concentration corresponds to the fine particles (less than 2.5 μm) in a city’s atmosphere. The concentration is lower than 50μm/m3 limit given by WHO for the cities in developed economies [Further, the relation between the proposed CI and different macroeconomic features of cities are explored in this study. conomies . The resr = −0.45) per capita of a city. This result is intuitive as well given that the low-income countries (the ones with lower GDP per capita) typically have mixed traffic conditions, encompassing a wide variety of vehicles competing for available road space. Moreover, traffic in such countries is characterized by poor lane keeping behavior among drivers, loose traffic regulations and enforcement, side friction, and complex interactions between vehicles [The CI shows a negative relationship with the GDP  is developed for the given dataset in STATA . The selθ1) and dissipation (θ2) for each city as shown in θ1 and θ2 values  and dissipation (θ2) for each city can be interpreted as the fractional congestion index increase and decrease experienced in one hour, respectively.The rate of congestion formation  is comparatively low and dissipation (θ2 = 0.04 to 0.13) is comparatively high. This cluster mainly includes the Middle Eastern cities whose average speeds are high (Cluster (a) includes cities with a low congestion index. The congestion starts forming around 6AM in the morning and dissipates only around 12PM and the maximum rate of congestion formation . This cluster predominantly includes developing cities of the Asian continent that are highly populated and show an extended peak (single peak) pattern.Cluster (b) includes cities with moderate to high congestion index and they exhibit moderate to high θ1 (0.12 to 0.28) observed during the evening peak is higher and θ2 (0.03 to 0.08) is comparatively lower than during the morning peak. It includes most of the developed countries that have a moderate congestion index.Cluster (c) includes cities that have a proper morning and evening peak system that last for around 2 to 4 hours. Generally, θ1 (0.13 to 0.18) and a high θ2 (0.08 to 0.16). They are highly populated metropolitan cities that exhibit a late morning peak and evening peak unlike cities from other clusters.Finally, Cluster (d) is an exception where the rate of dissipation is found to increase as the congestion increases. It includes Indian cities that exhibit a high average congestion index, a moderate σ′(CI) is the standard deviation of congestion index across multiple days, μ′(δ) is the mean value of the congestion index across multiple days ε′ is the random error. A greater Travelers often plan their trips accommodating the expected regular delays in congestion and get frustrated by unexpected delays. The day-to-day variation in delay  signifies the sensitivity of the city towards the congestion induced decline in reliability. A small value for the congestion index often prevails together with low variance and the variance in mean congestion increases as the congestion index increases. The theoretical relationship between mean congestion index and its standard deviation across days can be expressed by n 5% see . The comn period .σ(δ) is the standard deviation of delay, μ(δ) is the mean value of the delay γ1, γ2 are coefficients and ε is the random error  is different for different cities and has a negative correlation with the congestion index. That is, we find that the rate of spatial dispersion is lower in highly congested cities. In other words, congestion is not limited to certain areas of these cities, but widespread across the network.A small value for the delay which corresponds to the less congested traffic conditions often prevails together with low standard deviation. As congestion increases, the average speed of the network decreases due to the interaction of the vehicles, which causes a fluctuation in the speed and thus affect the average delay and the variation in delay increases. The magnitude of The notion of equilibrium in urban transportation networks stems from the dependence of link travel times on the link flows. The problem of interest here is how the users (at congested or free flow conditions) will be dispersed among the possible paths when every driver tries to minimize their own travel time. It is reasonable to assume that the travel time and thereby the speed of each link changes with the inflow and outflow of users and the travel time of several of the network’s paths change as the link flows change. Since each user can be expected to behave independently, a true equilibrium condition will be reached when no user can improve his travel time or perceived travel time by unilaterally changing routes.t or a duration of time Δt, the network equilibrium and the expected average delay of the network will be stable across days, if the distribution of delay across the network is the same. We obtain the delay profiles (delay distributions) of different cities at every hour for all the working days during the observation period and plot the kernel density estimates at every one hour to empirically analyse the network stability by examining the delay distribution plots. Inspection of the kernel density plots reveals that the distributions are homogeneous and stable across days for almost all the cities. An example (Auckland) is provided in Network equilibrium has been the cornerstone of transport planning. Multiple equilibrium theories have been explored with mixed results . Howeverp) closer to 1 indicates that the delay profiles are indifferent. That is, at a given time t or a duration of time Δt, the network equilibrium and the average expected delay of the network is stable across days, if the mismatch in probability (α) is minimal.Further, to be able to compare and quantify the homogeneity and the spread of the delay distributions, we estimate the overlap probability for each pair of dates . An overt and a given day d be represented by n speed bins δ1,δ2….δn having probabilities p(δ1),p(δ2)….p(δn). According to the law of sum of probabilities Let the probability delay distribution plot for a duration of time Δd′ which has an overlap probability of (1−α). The maximum deviation in the expected delay can be obtained by adjusting α/2 in the least and the highest delay bin. The new expected delay Assume a new probability distribution for another day The average overlap probability values of each city are shown in In general, the overlap probabilities are observed to be higher during the off-peak (low demand) hours compared with the peak (high demand) hours. This finding is unsurprising because less traffic activity happens during the off-peak hours and hence traffic speed distribution across the city will be more stable. Furthermore, we found that the congested cities like New Delhi, Mumbai, Tokyo, Cairo, London, Paris, New York, and Rome are relatively more stable than the other cities.Road traffic congestion has significant adverse impacts on the economy, environment, and society as a whole. Thus, measuring congestion and understanding its spatial and temporal patterns are critical to developing sustainable transport networks. In recent years, crowdsourced data has become increasingly popular among transport agencies as it provides easy accessibility to the way individuals travel in a road network. This study shows the utility of crowdsourced data to determine traffic conditions and, to the best of our knowledge, proposes a standard data source which facilitates comparison of traffic congestion across multiple cities of the world. This study proposed generalized congestion and stability metrics to compare and contrast speed variation patterns, congestion levels, its degree of formation and dissipation, spatial dispersion, and the stability of network equilibrium.In this study, we defined a congestion index in terms of the proportion of travel time lost in congestion and showed that the congestion index of different cities is strongly related to emissions, population density, and GDP per capita. Then we showed the congestion formation and dissipation profiles of different cities and further clustered the cities with similar profiles. We then discussed the theoretical relationship between the mean spatial delay of the network and its standard deviation. We found that the standard deviation of delay, a measure of spatial heterogeneity, monotonically increases with the increase in the average delay and the heterogeneity level is different for different cities which has a negative correlation with their congestion index. We empirically analyzed the existence of a stable equilibrium condition in the network across days. We found that the distribution of speed and the expected average speed of the network is stable across days for a given time period and the stability increases as the number of segments in the network increases.


Moderate heat stress accompanied by short episodes of extreme heat during the post-anthesis stage is common in most US wheat growing areas and causes substantial yield losses. Sink strength (grain number) is a key yield limiting factor in modern wheat varieties. Increasing spike fertility (SF) and improving the partitioning of assimilates can optimize sink strength which is essential to improve wheat yield potential under a hot and humid environment. A genome-wide association study (GWAS) allows identification of novel quantitative trait loci (QTLs) associated with SF and other partitioning traits that can assist in marker assisted breeding. In this study, GWAS was performed on a soft wheat association mapping panel (SWAMP) comprised of 236 elite lines using 27,466 single nucleotide polymorphisms (SNPs). The panel was phenotyped in two heat stress locations over 3 years. GWAS identified 109 significant marker-trait associations (MTAs) (p ≤ 9.99 x 10−5) related to eight phenotypic traits including SF (a major component of grain number) and spike harvest index . MTAs detected on chromosomes 1B, 3A, 3B, and 5A were associated with multiple traits and are potentially important targets for selection. More than half of the significant MTAs (60 out of 109) were found in genes encoding different types of proteins related to metabolism, disease, and abiotic stress including heat stress. These MTAs could be potential targets for further validation study and may be used in marker-assisted breeding for improving wheat grain yield under post-anthesis heat stress conditions. This is the first study to identify novel QTLs associated with SF and SHI which represent the major components of grain number and grain weight, respectively, in wheat. Triticum aestivum L.) is one of the major food crops worldwide and is grown on more than 218 million hectares of land with an average grain yield (GY) of 3.3 t ha−1 —the GY over the total produced above-ground biomass . The sysng wheat  and wintng wheat , thus cong wheat . One of Association mapping is a powerful approach that utilizes genetic diversity and historical recombination events to provide a high resolution of trait-linked loci . CurrentThe present study used a soft red facultative wheat association panel consisting of 236 elite lines that are well adapted to the warm and humid south and southeastern regions of the USA. These lines were developed by public and private soft wheat breeding programs in the south and southeastern USA and this collection is hereafter referred to as the Soft Wheat Association Mapping Panel (SWAMP). The SWAMP was evaluated in five yield trials at two heat stress locations in Florida: Citra and Quincy. In Citra, the SWAMP was evaluated in three growing seasons: 2015–2016 , 2016–2017 , and 2017–2018 . In Quincy, the SWAMP was evaluated in two growing seasons: 2015–2016  and 2016–2017 . Citra had moderate precipitation (212–447 mm), high humidity, and frequent episodes of high temperatures (>30˚C) during the grain filing stages Table S3−1. The SWAMP was planted in a randomized augmented block design  at the rate of 100 kg hk design  in all t−1. Days to heading (GS 59), days to anthesis (GS65), and days to physiological maturity (GS90) were determined using the Zadoks scale (−2 (SPK) was determined by counting number of spikes in a 0.5 m2 area from middle rows and converted to m2. HI was determined as the ratio of grain weight m−2 to above ground dry matter m−2. Ten spikes were sampled randomly from each plot at physiological maturity, dried for 72 h at 60°C, and threshed to determine chaff weight (the non-grain part of a spike), calculated as the difference between total spike dry weight and spike grain weight. SF was measured as a ratio of GN m−2 to chaff weight m−2  was conducted assuming a mixed linear model. The “lme4” package  and the Eij), kth block (Bk) nested within the jth environment (Ej), and the residual error (εijk).where the phenotypic response (Yijk) is a function of the overall mean (µ), ith genotype (Gi), jth environment, genotype-environment interaction , BLUEQ , and BLUEA . Broad sense heritability was calculated assuming random genotypic effect  and was obtained by:2G, genetic variance; σ2G × E, genotype-by-environmental variance; σ2E, residual variance; and n, number of environments.where H2, broad-sense heritability estimate; σPearson’s correlations were calculated from BLUEs in R using the “corrplot” package . Pearson's correlation coefficient (r) was used to determine the direction and magnitude of measured traits. Associations between traits were also explored in PC biplot analysis using the package “factoextra” in R .High quality DNA was isolated from freeze-dried, powdered leaf tissue (∼100 mg) collected from 2-week-old plants using a modified cetyltrimethylammonium bromide (CTAB) protocol . The GBSSNP calling was performed using the TASSEL v5.0 GBS v2.0 discovery pipeline . Reads w2) were plotted against a range of physical distance (bp) for the whole genomes in R  using the “LDcorSV” package  and LOESS line was considered as the distance beyond which linkage disequilibrium (LD) starts to decay. Population structure was observed using discriminant analysis of principal components  was used as the cut-off to define significant MTAs by looking at Q-Q plots to identify the deviation of the observed test statistics values from the expected test statistics values (A genome-wide association study was conducted using the FarmCPU (fixed and random model circulating probability unification) model executed in the Genome Association Prediction Integrated Tool (GAPIT) in R . FarmCPUs values . In addis values . Candidas values . Potenti−1 with mean yield of 3,518 kg h−1 in Citra, and 1,780–8,639 kg h−1 with mean yield of 5,025 kg h−1 in Quincy  analysis showed that PC1 and PC2 explained 72.9, 66, and 71.5% of the total variation for phenotypic trait data from Citra, Quincy, and the combined analysis (2 = 0.2) was estimated at 1,182, 1,920, and 2,916 bp for ranges of 30,000, 40,000, and 50,000 bp, respectively, across the whole genome , B , and D  genomes Figure 2e genome . PC analThe GWAS identified novel MTAs for all measured traits and explained high phenotypic variances. The FarmCPU model with kinship and PC scores was used to identify MTAs for each trait using 27,466 GBS-derived SNPs. The SNP markers were uniformly distributed throughout the chromosome of each genome 3. GWAS We identified 15 MTAs for SF across three datasets on chromosomes 1B, 3B, 4A, 6B, and 7D for which PVEs ranged from 8 to 11%  Table 1.We found significant genotypic variation for SHI across all environments with moderate broad sense heritability (0.38) Table 1.We detected 12 MTAs associated with GY distributed on four chromosomes: 2A, 3A, 3B, and 5D with PVEs ranging from 14 to 30% , 3A Neu, 3B Suk, and 5D For GN, we identified 10 significant MTAs distributed on chromosomes 1D, 2B, 3A, 4B, 4D, 5A, 6A, and 6B , 3A Gro, 4B Gro, 5A Suk, 6A Gro, and 6B We reported 12 MTAs associated with HI on chromosomes 3A, 5A, and 5B , 5A Awa, and 5B Twenty-eight MTAs were associated with TGW on chromosomes 1B, 2B, 3A, 4A, 5A, 6B, 7A, and 7D with PVEs ranging from 24 to 31% , 2B Neu, 3A Zan, 4A Bha, 5A Suk, 6B Suk, 7A Suk, and 7D For GFR, we identified five MTAs on chromosomes 2A, 3A, 3B, and 5D with phenotypic variation explained (PVEs) ranging from 19 to 25% .The SNP data generated for this study can be found in the NCBI using accession number PRJNA578088 (MAB conceived and designed the study. SP completed the study and data analysis and wrote the manuscript. JG, KR, and MB contributed to statistical analysis and editing of the manuscript. GB, PA, and AB performed marker analysis and SNP calling. RM, EM, MM, SA, SG, B-KB, and ARB edited the manuscript. JK, MH, DS, MA, and JG collected the phenotypic data on association panel and edited the manuscript.This research was funded by UF/IFAS early career award.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


Availability of whole-genome sequence data for a large number of cattle and efficient imputation methodologies open a new opportunity to include rare and low-frequency variants (RLFV) in genomic prediction in dairy cattle. The objective of this study was to examine the impact of including RLFV that are within genes and selected from whole-genome sequence variants, on the reliability of genomic prediction for fertility, health and longevity in dairy cattle.All genic RLFV with a minor allele frequency lower than 0.05 were extracted from imputed sequence data and subsets were created using different strategies. These subsets were subsequently combined with Illumina 50 k single nucleotide polymorphism (SNP) data and used for genomic prediction. Reliability of prediction obtained by using 50 k SNP data alone was used as reference value and absolute changes in reliabilities are referred to as changes in percentage points. Adding a component that included either all the genic or a subset of selected RLFV into the model in addition to the 50 k component changed the reliability of predictions by − 2.2 to 1.1%, i.e. hardly no change in reliability of prediction was found, regardless of how the RLFV were selected. In addition to these empirical analyses, a simulation study was performed to evaluate the potential impact of adding RLFV in the model on the reliability of prediction. Three sets of causal RLFV  that were randomly selected from different numbers of genes were generated and accounted for 10% additional genetic variance of the estimated variance explained by the 50 k SNPs. When genic RLFV based on mapping results were included in the prediction model, reliabilities improved by up to 4.0% and when the causal RLFV were included they improved by up to 6.8%.Using selected RLFV from whole-genome sequence data had only a small impact on the empirical reliability of genomic prediction in dairy cattle. Our simulations revealed that for sequence data to bring a benefit, the key is to identify causal RLFV.The online version of this article (10.1186/s12711-018-0432-8) contains supplementary material, which is available to authorized users. Due to the progress in sequencing technology, whole-genome sequence data has become available for large numbers of dairy cattle –3. UsingThe SNP chips that are routinely used in genomic prediction in dairy cattle include mostly SNPs with a relatively high minor allele frequency (MAF) that can efficiently tag common variants. However, the ability of these chips to tag rare and low-frequency variants (RLFV) is limited. There are several indications that RLFV make an important contribution to genetic variance. For instance, nonsynonymous SNPs are expected to make a larger contribution to genetic variance than synonymous SNPs . In the There are various ways to select a subset of RLFV from whole-genome sequence for inclusion in genomic prediction , 13. OneThe objective of this study was to test the above hypothesis and to empirically examine the impact of using selected RLFV from imputed whole-genome sequence data on the reliability of genomic prediction in dairy cattle. We also undertook a simulation study to evaluate what are the critical factors that increase the reliability of genomic prediction as a result of using RLFV in the model, depending on the amount of genetic variance explained by causal RLFV.run4 of the 1000 bull genomes project [2 value) at imputed markers lower than 0.9 were removed from the original sequence data [In total, 6337 Holstein sires with de-regressed proofs (DRP) were genotyped using the Illumina BovineSNP50 BeadChip (50 k) version 1 or 2  . The fol project  and from project –27. The nce data . The subnce data . Imputedby-gene option in VEP [All called variants were annotated using Variant Effect Predictor (VEP) and ENSEMBL (v.67) databases . Any sitn in VEP  and annon in VEP . VEP detn in VEP .RLFV in all 23,431 genes including non-coding genes;RLFV with annotations of ‘high impact’ from VEP and SIFT scores  according to how much the amino acid can be changed;RLFV from set (2) in addition to those with annotations of ‘medium impact’  according to how much the amino acids can be changed;p value lower than 0.01 from famSKAT approach were selected and the RLFV within these genes were extracted. Briefly, the model is:RLFV in genes that were associated with the analyzed index trait by considering only the individuals included in the training population using the MONSTER software . The detRLFV were defined as the imputed sequence variants that had a MAF lower than 0.05 and were selected based on their genic or intergenic localization, annotation and association mapping. Depending on the amino acid change that results from having the rare instead of the alternative allele, rare variants are assigned to a different class of annotations (see classifications below). We selected four sets of RLFV in this study:Finally, we examined a scenario that randomly sampled a similar number of RLFV when RLFV with medium-to-high impact annotations were included in the prediction model. This analysis was done for the health index because it showed the largest improvement in reliability of prediction compared to when 50 k SNPs were used in the prediction model.http://www.nordicebv.info/ntm-and-breeding-values.Three fitness-related index traits  were studied, for which the phenotypes were DRP. In total, 5043, 4926 and 4673 bulls with DRP, imputed sequence data, 50 k data and pedigree information were available for fertility, health and longevity, respectively. The fertility index is a linear combination of breeding values for interval from first to last insemination, number of inseminations for heifers and cows and interval from calving to first insemination for cows. The health index is a linear combination of breeding values for diseases other than clinical mastitis, which describe the genetic risk of reproductive, metabolic and feet-and-leg diseases. These breeding values are based on recorded treatments by veterinarians during the first three lactations. The longevity index describes the genetic potential for the cows’ productive longevity. For details regarding the index traits, recording procedures and models used to estimate their breeding values, see SQTN : seven to ten genes were randomly selected per chromosome and the RLFV from these genes were simulated as QTN.MQTN (QTN simulated with a medium effect): one gene per chromosome was randomly selected and the RLFV within these genes were simulated as QTN.LQTN (QTN simulated with a large effect): nine genes were randomly selected across the whole genome and the RLFV in these genes were simulated as QTN.Simulations were undertaken to evaluate the  impact of including selected RLFV in the prediction model on the reliability of genomic prediction, assuming that rare causal variants explained a small proportion of the genetic variance. In these simulations, quantitative trait nucleotides (QTN) were simulated by randomly drawing RLFV from genes based on the assumption that the power of detecting RLFV can be improved when combining RLFV, each with a supposedly small effect, within a gene or genomic region. Three scenarios were simulated:From scenario (1) to (3), the number of simulated QTN decreased, while the variance explained by each QTN increased. Therefore, we expected a gradual increase in the power of detection of the simulated rare QTN from scenario (1) to (3).α is the column vector of QTN effects. Then, the values in Effects for the rare QTN were sampled from a normal distribution Genotypes of the simulated QTN were used to compute the second GRM used in prediction model, and thus we assumed that the QTN were known without error.The RLFV from 10 randomly selected genes per chromosome were added to the simulated QTN to construct the second GRM in the prediction model, with none of these variants having a simulated effect. The second strategy mimicked real situations more closely, i.e. in which false positive associations add noise in the prediction model.The RLFV were selected based on significance of association mapping (see subset (4) in “For each of the three simulation scenarios, we tested three strategies for selecting RLFV to include in the subsequent genomic prediction step:Each simulation scenario was replicated 10 times, thus the reported reliabilities and the measure of bias estimated by the slope of the regression of the sum of DRP and The GBLUP model was used to predict genomic breeding values using the following model:method 1 :\documenAn alternative GBLUP model is:Model (1) with the GRM built using 50 k genotype data was used as the reference model for comparisons with other approaches. Genomic prediction with selected RLFV were done using Model (2) in which the first GRM was based on 50 k data and the second GRM was built using the RLFV selected by one of the strategies as described above. Estimation of variance components and prediction of breeding values were carried out using the DMU software .N is the size of the training data [For each index trait, the 1000 youngest bulls with a birth date between 23/07/2005 and 14/01/2009 were used as validation bulls. The remaining bulls  born before 23/07/2005 were used as training data. The reliability of genomic prediction was measured as the squared correlation between GEBV and DRP divided by the mean reliability of DRP for validation individuals, i.e. ing data . A measuThe total number of genic RLFV ranged from 1,585,116 to 1,605,553 across the three index traits . However, it is unlikely that the decrease in prediction reliability using the high compared to the medium-to-high impact annotated RLFV is due to either this small difference in imputation accuracy or to a difference in MAF (the MAF for high impact annotation variants was on average 0.0162 and the MAF for medium-to-high impact variants was on average 0.0166). The main difference between the medium-to-high and high impact variants, is that the former group contained ~ 19 times more variants compared to the latter group. The fact that the reliabilities achieved with the medium-to-high impact variants were closer to those obtained in the scenario using all genic RLFV suggests that the benefit of this scenario may be due simply to the inclusion of many more RLFV. This is also in line with results of Hayes et al.  and Do eLow MAF levels imply that RLFV are only observed in a relatively small number of individuals, and some RLFV may be segregating only in either the training or validation population. Thus, such RLFV may contribute only slightly to the predictive ability at the population level. However, when we investigated this issue, we found that, in all cases, more than 92% of all genic RLFV were segregating in both the training and validation populations . Several reasons can explain these results, including low imputation accuracies for RLFV, a limited power to map genic RLFV with the existing sample size, and the fact that common variants on the 50 k chip are able to capture a large proportion of variance due to genic RLFV. However, we showed by using simulations that prediction reliability increased slightly when significant genic RLFV from association mapping were included in the prediction model and increased substantially when the known rare QTN were added as a separate genetic component in the model, but this added benefit was much reduced when adding additional random genic RLFV. This indicates that prediction reliability can be improved by using both 50 k data and selected genic RLFV, provided that the RLFV that explain a sizable variance in the index traits of interest are identified, without adding too many false positives that cause noise in the model.Additional file 1: Table S1. Number of rare and low-frequency variants (RLFV) selected for inclusion in the genomic prediction model.Additional file 2: Table S2. Characteristics for each simulation scenario across 10 replicates.Additional file 3: Table S3. Reliabilities of genomic prediction using various marker sets for the index traits affected by different rare QTN sets, averaged over 10 replicates.aAdditional file 4: Table S4. Bias of the GEBV measured by regression slope using various marker sets for the index trait affected by different rare QTN sets, averaged over 10 replicates.Additional file 5: Table S5. Proportion of rare or low-frequency variants (RLFV)a segregating both in the validation and training populations from different models using various marker sets for fertility, health and longevity.Additional file 6: Table S6. The additive genetic variances explained in the models for one replicate  in each simulation scenario and different strategies for selection of rare and low-frequency variants (RLFV).


Local adaptation in tree species has been documented through a long history of common garden experiments where functional traits  are used as proxies for fitness. However, the ability to identify genes or genomic regions related to adaptation to climate requires the evaluation of traits that precisely reflect how and when climate exerts selective constraints.Pinus strobus trees growing in a common garden experiment representing 38 populations from the majority of its range.We combine dendroecology with association genetics to establish a link between genotypes, phenotypes and interannual climatic fluctuations. We illustrate this approach by examining individual tree responses embedded in the annual rings of 233 We found that interannual variability in growth was affected by low temperatures during spring and autumn, and by summer heat and drought. Among‐population variation in climatic sensitivity was significantly correlated with the mean annual temperature of the provenance, suggesting local adaptation. Genotype–phenotype associations using these new tree‐ring phenotypes validated nine candidate genes identified in a previous genetic–environment association study.Combining dendroecology with association genetics allowed us to assess tree vulnerability to past climate at fine temporal scales and provides avenues for future genomic studies on functional adaptation in forest trees. https://doi.org/10.1111/nph.15094.See also the Commentary on this article by  Polewaret al., et al., et al., et al., Local adaptation in tree species has been documented through a long history of common garden experiments composed of georeferenced genetic material (provenances), where dendrometric traits such as diameter or height growth were used as proxies for fitness  common garden experiment located in Quebec that includes provenances from a wide range of environments across the species’ distribution area in eastern North America. This species is representative of many temperate and boreal tree species for which the magnitude and speed of climate change are expected to induce widespread range shifts by 2060  to search for local adaptation in growth responses to climate (e.g. genetic–environment clines). Third, GPA analyses were performed on those tree‐ring traits to identify putative loci involved in growth responses to climatic constraints and to further cross‐validate candidate genes that had been identified in a previous GEA study  in block 1, and 2.0 × 2.0 m in block 2. On average, three trees (SD = 0.96) per population were sampled in each block. A total of 233 trees were sampled, representing two to ten trees per population , depending on the population survival rate , mean monthly precipitation (P) and monthly means of daily drought code (DC) time‐series at the Valcartier experimental site were obtained for the 1993–2014 period using the BioSIM v.10 software . Monthly values were computed from the average of the daily quantities. DC is an index for the amount of moisture stored in deep organic layers and soils and takes into account snow arrival in autumn and thawing in spring based on modeled snow accumulation  is generally low according to pairwise Spearman rank correlation analysis  were also computed and averaged over the 1961–1990 period (i.e. the period corresponding to seed collection) using BioSIM v.10.Monthly means of daily temperature  and cofecha  from each tree at 1.3 m above ground using a Pressler increment borer. Cores were kept frozen until being air‐dried, with no risk of developing mold. They were later sawn to a 1.7‐mm thickness longitudinally using a twin blade sleeve to obtain a smooth, thin surface. The same sample was used for dendrochronological and densitometric analyses. Annual rings of each core were visually cross‐dated with skeleton‐plots and pointer‐year identification under 4.5× magnification. Samples were then conditioned at a temperature of 20°C and relative humidity of 40% to maintain an equilibrium moisture content of 8%. For dendrochronological analyses, ring‐widths were measured from high‐resolution scanned images of the cores using the software Coo‐Recorder  at a precision of 10 Holmes, . The rin.3 Bunn, . Averagearstan v.41d XP . Cores were scanned in 0.04‐mm steps, producing high‐resolution density profiles. Average annual density was determined for each tree ring and the correct identification of annual ring borders was verified using the previously cross‐dated ring‐width measurements. Mean ring density was calculated for each population by averaging the annual ring density of all trees in a population for the 1993–2014 period. Then, for each core, average ring‐density time‐series were detrended using modified generalized exponential functions and temporal autocorrelation was removed as described above. Finally, annual averages of these standardized residual density scores were computed for each population as described above.1993–2014) and average density (avDens1993–2014) over the 1993–2014 period, corresponding to classic measures used in forestry; (B) growth variation for a specific year following an extreme climatic event; and (C) climatic sensitivity (CS) traits. Type A traits are long‐term averages that probably result from many climatic events or constraints on growth throughout a tree's lifespan, whereas type B and type C traits reflect growth responses to climate on a year‐to‐year basis. To illustrate type B traits, we analyzed the standardized residual BAI scores for the year 2003 (BAI2003) because we observed large among‐population variation during that year : when the 95% confidence interval included zero, the null hypothesis of ‘no relationship’ could not be rejected. CS traits were also calculated at the individual‐tree level to be used as phenotypes in GPAs (see below). As is routinely done in dendroecology, a response function analysis was also used as a complement to quantify the influence of climate on tree growth  traits were determined as follows. For each population, correlation analyses were computed between the annual standardized residual BAI scores, averaged at the population level, and the matrix of monthly climatic data (from 1993 to 2014) interpolated at the location of the common garden , which was found to be the best predictor of genetic variation in growth potential among P. strobus populations  was ‘no relationship with MATp’. Regarding the effect of MATp on CS traits (type C), a linear regression model provided the best fit. To reduce the risk of generating spurious results, we only tested the CS traits for which growth–climate correlations were significant in at least five populations. This threshold was arbitrarily set to allow sufficient interpopulation variance in the CS traits for the detection of genetic associations.We screened tree‐ring traits  exhibiting among‐population variation that could be correlated with the climate of the provenance, an indication of local adaptation in growth responses to climate . The 233 trees were genotyped on the same set of 153 SNP markers as in Nadeau et al. , including 21 of the candidate SNPs identified by Nadeau et al.  and ϵ ~ N, where σa2 is the additive genetic variance, σϵ2 is the residual variance, K is the kinship matrix and I is the identity matrix. The kinship matrix K was computed from the 128 SNPs using the method of Ritland ; correction for population structure ; and correction for population structure and for kinship . To obtain the population membership matrix (Q), we performed a discriminant analysis of principal components  as the Q matrix to correct for population structure in the tassel analyses , each SNP–phenotype association was classified as follows: ‘very likely’ when significant (P < 0.05) for all three models; ‘likely’ when significant for two out of the three models; ‘uncertain’ when significant for only one model; and ‘no evidence’ when not significant in the three models  gave similar results . Nevertheless, when population structure covaries with adaptive genetic variation as in , et al. . DAPC mases Fig. b. The th. et al. : a south. et al. c. To sumu et al. , which uts Table .blastx against the RefSeq database (https://www.ncbi.nlm.nih.gov/refseq) using a threshold E‐value of 1 e−10. Given that two P. strobus sequences had no significant hit on the RefSeq database, blast analyses were performed against the P. glauca gene catalog , personal communication). Complementary information regarding their putative function was then deduced from P. glauca and P. strobus best‐ortholog TAIR annotations . SNP annotations  were deduced as previously described by Nadeau et al.  and smaller MATp differences .High variability was observed for annual averages of raw‐absolute BAI among ons Fig. a. GlobalBAI–Temp.Octt−1), and with May temperature of the year concurrent with ring formation (CSBAI–Temp.Mayt)  and in June of the year of ring formation (CSBAI–Freeze.Junt) ( and CSBAI‐Drought.Augt) were also observed, highlighting the influence of summer drought stress on BAI ) Fig. a. Furthet)) Fig. a. In addBAI Fig. a. In par1993–2014, type A trait), expressed as a function of MATp, exhibited a second‐order curve . This revealed a high degree of variation among populations and suggested local adaptation to the mean annual temperature of the provenance. According to that model, the best‐performing population was nonlocal. The MATp of the population that had the highest growth performance was 6.6°C, which is higher than the MAT in the Valcartier common garden experiment (3.95°C). Populations from both warmer and colder provenances exhibited lower growth. No significant relationship was observed between absolute avDens1993–2014 and MATp  was stronger for the populations from warmer provenances and absent in some cold‐climate provenances ( and CSBAI–Freeze.Junt). The reduction in growth in 2003 (BAI(t) Fig.  and CSBA(t) Fig.  were pos(t) Fig.  was nega(t) Fig.  were neg−1) Fig.  was posiP < 0.01) correlation with MATp: meanBAI1993–2014 (type A), BAI2003 (type B) and five CS traits (type C): CSBAI–Drought.Augt)( and CSBAI–Freeze.Junt)(. Diameter at breast height (DBH21) and height measured at age 21 (H21), referred to as dendrometric phenotypic traits in the present study, were also tested. GPA analysis revealed significant associations (P < 0.05) with dendrometric traits, as well as with the three types of tree‐ring traits for both candidate and noncandidate SNPs (Table 21 and H21) and type A tree‐ring traits (meanBAI1993–2014), the proportion of significant SNPs was similar between candidate and noncandidate SNPs (Table 2003) and type C traits (CS traits), we found a greater proportion of significant candidate SNPs as compared to noncandidate SNPs, although the proportion of SNPs classified as very likely was greater for noncandidate SNPs for CSBAI–Prec.Jult)( and CSBAI‐FreezeOctt−1) were associated with tree‐ring traits . As many as 10 SNPs (eight genes) were associated with the climate‐related type B and type C traits . Traits reflecting responses to specific freezing events  were associated with SNPs G‐001, G‐004, M‐016, M‐017, N‐029, N‐033, N‐040 and O‐002. CS traits reflecting heat and drought tolerance (CSBAI–Temp.Jult) were associated with SNPs G‐014, M‐015, M‐016, N‐029, N‐033 and N‐040.Among the 21 candidate SNPs tested  and interannual climatic fluctuations. Indeed, the new class of phenotypes obtained provided useful information to identify climatic constraints for a given tree species. We illustrated this approach by an examination of the CS traits shaping the growth of an important conifer in eastern North America's temperate forests. We quantified the sensitivity of populations to climatic‐related constraints across their lifespan and identified the critical times of the year (or previous year) when those constraints affected growth. We then highlighted the clinal variation along MATet al., The dendroclimatic analysis pointed to traits reflecting CS for each population (type C traits). At our study site, growth was mainly limited by very low/freezing temperature during the dehardening phase in spring and cold acclimation phase in autumn, when trees are probably the most vulnerable  was stronger for populations from southernmost provenances and significantly increased with MATp  and late spring freeze (CSBAI–Freeze.Junt) was greater for warm provenances  following lower than average temperatures (e.g. −2°C) during autumn 2002 and spring 2003 (type B traits). The relative growth reduction following this event was significantly more pronounced for populations from warmer provenances  in the common garden revealed existing genetic variation for tolerance to freeze and drought. The positive influence of a warm autumn during the year preceding ring formation  Fig. b, CSBAI–(t) Fig. b and CSB(t) Fig. . Provenaet al.  that incorporates both the phenotypic trait under study (i.e. growth or wood density) and the response to climate in association genetic studies. We identified important climatic constraints and the critical times of the year when those constraints affect growth, information that cannot be derived from classic measurements such as height and DBH. We believe that this novel approach will provide a valuable contribution to the study of differences in climate sensitivity between provenances in common garden experiments, and more generally to examine forest adaptation to environmental changes.J.M.H., S.N., C.D., I.D., P.L., N.I. and M.P.G. planned and designed the research; J.M.H., S.N., M.P.G. and C.D. performed the experiments and analyzed the data; J.M.H., S.N., C.D., I.D., P.L., N.I. and M.P.G. wrote the manuscript. J.M.H. and S.M. contributed equally to this work.New Phytologist Central Office.Please note: Wiley Blackwell are not responsible for the content or functionality of any Supporting Information supplied by the authors. Any queries  should be directed to the Fig. S1 Monthly mean temperature, drought code, minimum temperature and precipitation in 2002 and 2003 at Valcartier, compared to interannual variability during the 1993–2014 period.Fig. S2 Spring mean temperature and August drought code in 2003, and number of days with minimum temperature below zero in October 2002 at Valcartier, compared to interannual variability in standardized residual BAI scores.Fig. S3 Correlation analysis for climatic sensitivity traits basal area increment and monthly temperature.Fig. S4 Correlation analysis for climatic sensitivity traits basal area increment and monthly drought code.Fig. S5 Genetic structure assessed through a discriminant analysis of principal components (DAPC).Fig. S6 Correlation analysis for climatic sensitivity traits basal area increment and monthly number of days below 0°C.Fig. S7 Correlation analysis for climatic sensitivity traits basal area increment and monthly precipitation.Fig. S8 Comparison of growth–climate relationships at the provenance and at the individual levels.Fig. S9 Correlation analysis for climatic sensitivity traits ring average density and monthly drought code.Fig. S10 Correlation analysis for climatic sensitivity traits ring density and monthly mean temperature.Table S1 Summary of provenance location, mean annual temperature of provenance, mean annual precipitation and number of sampled trees per provenanceTable S2 Spearman rank correlations between monthly climate variablesTable S5 Comparisons between the number of SNPs detected in GPAs with tassel between candidate and noncandidate SNPsClick here for additional data file.Table S3 Detailed results of the genotype–phenotype association (GPA) analysis with tasselClick here for additional data file.Table S4 Detailed results of the genotype–phenotype association (GPA) analysis with tassel when excluding the two populations from the southern groupClick here for additional data file.


By a female meta-analysis, we identify additional three significant loci. We also find significant genetic correlations of AIS with body mass index and uric acid. The cell-type specificity analyses show the significant heritability enrichment for AIS in multiple cell-type groups, suggesting the heterogeneity of etiology and pathogenesis of AIS. Our findings provide insights into etiology and pathogenesis of AIS.Adolescent idiopathic scoliosis (AIS) is the most common pediatric spinal deformity. Several AIS susceptibility loci have been identified; however, they could explain only a small proportion of AIS heritability. To identify additional AIS susceptibility loci, we conduct a meta-analysis of the three genome-wide association studies consisting of 79,211 Japanese individuals. We identify 20 loci significantly associated with AIS, including 14 previously not reported loci. These loci explain 4.6% of the phenotypic variance of AIS. We find 21  Adolescent idiopathic scoliosis (AIS) is a common pediatric disease leading to spinal deformities. Here, the authors report GWAS followed by genome-wide meta-analysis in up to 79,211 Japanese individuals, identifying 20 genetic loci for AIS, 14 of which were previously unreported, and perform in vitro validation for rs1978060. AIS is a common disease affecting ~2.5% of adolescents in Japan2 and millions of children worldwide are affected with a prevalence of 2–3%4. AIS is regarded as a multifactorial disease affected by genetic and environmental factor5. The importance of genetic factors in the etiology and pathogenesis of AIS has been demonstrated by many twin, family and population studies7.AIS is a complex spinal deformity which is defined as a lateral spinal curvature with a Cobb angle of >10 degrees13. We previously conducted two GWASs (GWAS1 and GWAS2) in Japanese populations and identified three loci significantly associated with AIS susceptibility 10. A subsequent meta-analysis using multi-ethnic cohorts confirmed their robust associations14. A total of seven significantly associated loci have been identified in the meta-analysis; however, the proportion of the AIS heritability explained by the seven loci is estimated to be only ~3%. Therefore, it is indispensable to identify additional susceptibility loci for understanding the etiology and pathogenesis of AIS.Genome-wide association study (GWAS) is one of the most effective methods to identify genetic factors of complex traits, including common diseases. To date, several GWASs have been conducted for AIScis-expression quantitative trait loci (eQTL)-associated genes in 7 out of 14 previously unreported susceptibility loci. In vitro functional analyses suggest that one of these eQTLs, rs1978060, regulates the expression of TBX1, and the difference in FOXA2 binding causes difference in cis-acting transcriptional regulatory function between alleles. These findings provide insights into etiology and pathogenesis of AIS.In the present study, we perform a large Japanese GWAS followed by a meta-analysis of three GWASs. We replicate the association of 6 previous reported susceptibility loci and identify 14 previously unreported susceptibility loci. By a sex-stratified analysis, we further identify three significant loci for female AIS. The integrative analyses indicate the significant genetic correlations of AIS with body mass index (BMI) and uric acid (UA), and show the significant heritability enrichment for AIS in multiple cell-type groups. In addition, we find 21 λGC = 1.16) showed an inflation in the GWAS; however, the linkage disequilibrium (LD) score regression analysis indicated that the inflation was mostly from polygenicity (85.5%) and biases have a small contribution (estimated mean χ2 = 1.29 and LD score intercept = 1.04). Compared to the previously reported GWAS, the bias was not large15; we therefore did not apply the GC correction. As a result of the meta-analysis, we identified 20 significant AIS loci including 14 previously unreported loci (Table P = 2.01 × 10−82) as with our previous GWASs10. Including this locus, significant associations of the six previously reported loci 14 were replicated in the present meta-analysis (Table https://github.com/bulik/ldsc) with the use of common variants in hapmap3 data is 42%. The lead variants of the 20 significant loci explained 4.6% of the phenotypic variance. We performed a stepwise conditional analysis to detect multiple independent signals within the 20 significant loci. We found five additional signals that reached the locus-wide significance (P < 5.0 × 10−6) . In addition, we reanalyzed the previous GWASs (GWAS1 and GWAS2) by updating the reference panel (Methods). For each of the three GWASs, imputation analysis was performed separately. Subsequently, the meta-analysis combining the three GWASs was performed  constructed by the Roadmap Epigenomics Project17. First, to obtain an overview of AIS-related cell types, we assessed heritability enrichment of 10 major cell-type groups that are constructed reflecting system- or organ-level structures from the 220 individual cell-type-specific annotations18. The most significant enrichment was observed in the cardiovascular cell group (P = 1.07 × 10−5) (Supplementary Table P < 5.0 × 10−3)  (Supplementary Data P = 4.82 × 10−6) and significant enrichment was also observed in H3K4me1 in other fetal organs such as lung, trunk muscle and leg muscle. Significant enrichment was also observed in H3K4me1 in stomach smooth muscle and H3K4me3 in penile foreskin fibroblast primary. These results suggest that the etiology and pathogenesis of AIS are heterogeneous and multifactorial.AIS is a complex disease and several hypotheses including neuromuscular, biomechanical, genetic, developmental and growth-related have been proposed to explain the etiology and pathogenesis of AIS19 . Sixty-two pathways showed the nominal significance (P < 0.05); however, no specific pathway significantly associated with AIS (P < 4.6 × 10−5) was detected .To gain biological insights, we conducted a pathway analysis with the result of the meta-analysis using PASCAL AIS P < .6 × 10−523 and the number of male case samples was limited, we conducted a female-specific meta-analysis of the GWASs . As a result, 15 loci showed the genome-wide significance , 3DSNP , RegulomeDB (http://regulomedb.org/), etc.) and explored the biological role of these variants. For each locus, we searched all variants in high LD (r2 > 0.8 in East Asians (EAS) of 1KGP3) with the most associated variant (lead variant) and found some candidate variants with regulatory function . As demonstrated by the cell-type-specific analysis, AIS is a multifactorial heterogeneous disease and multiple tissues may be associated with AIS. Moreover, because the tissues used in the GTEx project are limited and there are no data on AIS-related tissues such as intervertebral disc, cartilage and bone, we searched data on all tissues currently available. We observed significant cis-eQTLs at 7 of the 14 previously unreported loci, and the expression levels of 21 genes in the 7 cis-eQTL loci were associated with some variants that were in high LD (r2 > 0.8 in EAS) with the lead variants (Supplementary Table r2 > 0.8 in EAS) with the lead variants  and velocardiofacial syndrome (OMIM #192430), and scoliosis is one of the clinical features, which is highly prevalent (47–49%) in association with these syndromes28. There is substantial evidence that Tbx1 haploinsufficiency is responsible for the physical features of these syndromes, and it has also been reported that Tbx1 knockout mice show vertebral anomalies30. In addition, other T-box gene family member, TBX6 is known to cause congenital scoliosis31. We therefore selected the TBX1 locus for further functional analysis. To prioritize candidate variants at the locus, we used a simple scoring system. The candidate variants were scored when they possess the following functional information: (i) promoter histone mark, (ii) enhancer histone mark, (iii) DNase protein binding and (iv) motif change. Based on the system, we selected rs1978060 as a most likely causal variant at this locus. rs1978060 was also a promising candidate causal SNP in other scoring systems such as 3DSNP and RegulomeDB  to have a higher binding score in the non-risk allele and binding to this genomic region has also been demonstrated by chromatin immunoprecipitation assays. Furthermore, an electrophoretic mobility shift assay using FOXA2 antibody showed a super-shift in the presence of the antibody  identified in the present study will also be replicated in a multi-ethnic meta-analysis. From this point of view, rs73235136, a common lead variant (MAF > 0.05) at a female-specific susceptibility locus, also has a large difference in MAF between JPN and EUR, but the association is expected to be replicated. On the other hand, the low-frequency variants (rs141903557 and rs188915802), which have relatively large effects, are rare or monomorphic in EUR .In the cis-eQTL-associated genes other than TBX1, there are several interesting candidate genes. One of them is DSE at chromosome 6q22.1. DSE encodes dermatan sulfate epimerase, an enzyme that is necessary for dermatan sulfate biosynthesis. Bi-allelic loss-of-function mutations in DSE are reported to cause Ehlers–Danlos syndrome (EDS) musculocontractural type 2 (OMIM #615539), which is characterized by progressive multi-system fragility-related manifestations. The patients of this type of EDS present spinal deformity including scoliosis and kyphoscoliosis36. Consistent with this, cis-eQTL analysis shows that risk allele of rs482012 is associated with decreased expression of DSE 2D3) in osteocytes44. Vitamin D is essential to maintain bone and mineral metabolism. Its status is greatly influenced by gender45 and its deficiency is well known to be associated with osteoporosis and autoimmune disease which show significant sexual dimorphism46. Thus, BOC might be specifically associated with female AIS through the vitamin D metabolism.Sex-stratified analyses identified three susceptibility loci that are significant only in the female meta-analysis. Among the loci, we found an evidence of sex-heterogeneity at the TBX1 was identified as one of the AIS susceptibility genes within the loci. Further studies are necessary to validate the association of these susceptibility loci in other ethnic groups. Identifying susceptibility SNPs within the loci and elucidating the function of candidate susceptibility would lead to understanding the etiology and pathogenesis of AIS. Although the mechanism behind sexual dimorphism in AIS remains unknown, the female-specific susceptibility locus we identified will be a key clue to understanding sexual dimorphism in AIS.In this study, 14 previously unreported AIS susceptibility loci were identified in Japanese, and 10. All subjects were Japanese and were diagnosed with AIS between the ages of 10 and 18 years by expert scoliosis surgeons47. Congenital, juvenile, adult-onset scoliosis and scoliosis secondary to some other disorders were excluded. The control subjects were randomly selected from the subjects registered in the BioBank Japan project48 (https://biobankjp.org/). For the quality control of GWAS samples, we removed samples with a call rate <0.98. We removed related individuals with PI_HAT > 0.25. PI_HAT is an index of relatedness between two individuals based on identity by descent implemented in PLINK49 (https://www.cog-genomics.org/plink2). This filtering of relatedness was applied in each GWAS and combined data of the three GWAS separately and resulted in 102 cases and 806 controls excluded in total. To identify population stratification, we conducted principal component analysis (PCA) for genotype using FastPCA50. We excluded outliers from the East Asian cluster (distance from the mean of the cluster should be within 3 SD). There was no overlap of individuals in the three GWASs. Informed consents were obtained from all participants and from the parents of subjects who were minor. The ethical committees at all collaborating institutions and RIKEN approved the study.In GWAS3, as in GWAS1 and GWAS2, the case subjects with a Cobb angle of 10° or greater measured on standing spinal posteroanterior radiographs were recruited from collaborating hospitalsP value ≤ 1.0 × 10−5. We also excluded the variants whose allele frequencies had differences of >0.16 between the GWAS dataset and the Asian data in reference panel. For each of the three GWASs, we pre-phased the genotypes using EAGLE and imputed dosages with the 1000 Genomes Project Phase 3 reference panel  with 1037 Japanese in-house reference panel using minimac3. For X chromosome, pre-phasing was performed in males and females together, and imputation was performed separately for males and females. Dosages of variants in X chromosomes for males were assigned between 0 and 2. Since variants in the pseudo-autosomal region (PAR) were not contained in the reference panel, we did not analyze PAR. We used variants with an imputation quality score Rsq ≥ 0.3 and MAF ≥ 0.005 for the subsequent association study. The quality control and imputation analysis for three GWASs were processed separately. The number of SNPs through analysis steps is illustrated in Supplementary Table GWAS3 subjects were genotyped by using the Illumina Human OmniExpress Genotyping BeadChip or a combination of Illumina HumanOmniExpress and HumanExome BeadChips. For quality control of variants, we applied the standard QC measures and excluded those with (i) SNP call rate < 0.99, (ii) MAF < 0.05 and (iii) Hardy–Weinberg equilibrium Q-test, P < 0.0001). This filtering excluded 484 variants in autosomes and 24 variants in X chromosomes. Regional association plots were produced by Locuszoom (http://locuszoom.org). Adjacent genome-wide significant (P < 5.0 × 10−8) variants were grouped in one locus if they were located within 1 Mb apart from each other. To identify multiple independent signals within the 20 significant loci, we performed a stepwise conditional meta-analysis. We first performed conditional analyses of GWAS1-3 independently, and then combined the results using a fixed-effects model with the inverse-variance method. We repeated this process until the index variants fell below the locus-wide significance threshold of 5.0 × 10−6, based on the approximate average number of multiple tests in each locus. Association studies were conducted by plink2 or mach2dat software. We estimated the AIS heritability using the LDSC software. The variance explained by SNPs was calculated based on a liability threshold model by assuming prevalence of AIS as 2.5%. In this model, we assume that subjects have a continuous risk score and that subjects whose score exceed a certain threshold develop AIS.Association analyses of autosomes of GWAS1-3 were performed independently in a logistic regression model with top 10 principal components as covariates. The three GWAS were meta-analyzed with inverse-variance method under fixed effect model. For X chromosome, an association analysis was conducted separately for male and female at each GWAS, and meta-analyzed the association results. We filtered variants showing strong heterogeneity .We separately extracted imputed genotypes of male and female subjects from the GWAS data and applied the same quality control criteria in each GWAS of males and females. We empirically estimated difference in effect sizes in males and females. We randomly generated 100,000 true effect sizes in males and females based on correlation coefficients and standard errors in sex-specific GWAS and compared effect sizes between males and females to compute https://www.r-project.org/) to detect a signal with P value of 5 × 10−8 assuming disease prevalence of 3%. Calculation was done for entire dataset, males and females  with the 14 AIS lead variants. LD was calculated using LDlink51 (https://ldlink.nci.nih.gov), a web-based application. We searched for the overlaps between these associated variants and promoter and enhancer marks using HaploReg v4.1 setting the source for epigenetic annotation as ChromHMM core 15-state model. We also searched for the overlaps between the associated variants and lead cis-eQTL variants in GTEx (release v7). We considered only the cis-eQTLs with FDR < 0.05, and listed the variants showing the most significant association for each gene in Supplementary Tables To characterize associated variants, we used HaploReg v4.1, 3DSNP and RegulomeDB to gain functional annotation of variants in LD  and the variants within the major histocompatibility complex (MHC) region (chromosome 6: 25–34 Mb) were excluded from the regression analysis. We defined significant heritability enrichments as those with P < 0.05 after Bonferroni correction.To assess the heritability enrichment in cell types for AIS, we performed stratified LD score regression by combining data from specific annotations of 10 cell-type groups and 220 cell types and four activating histone marks  from the Roadmap Epigenomics project19. PASCAL computes gene scores (max or sum scores) by aggregating SNP P values from a GWAS meta-analysis and calculates pathway scores by combining the scores of genes belonging to the same pathways. We used sum statistics and predefined pathway libraries from KEGG, REACTOME and BIOCARTA with default parameters.To investigate biological pathways associated with AIS, we performed PASCAL15 using the East Asian LD score and summary statistics of the current GWAS meta-analysis. We excluded SNPs found in the MHC region (chromosome 6: 25–34 Mb) from the analysis because of its complex LD structure. We defined significant genetic correlations as those with FDR < 0.05, calculated via the Benjamini–Hochberg method to correct multiple testing.To estimate genetic correlations across the 67 traits (61 quantitative traits and 6 diseases), we conducted bivariate LD score regressionTBX1 promoter  with oligonucleotides (sense: 5′-TCGATGTCTAATGTACRCACCAGCTCGGA-3′and antisense: 5′-TCGATCCGAGCTGGTGYGTACATTAGACA-3′) containing either risk or non-risk alleles of rs1978060 into the multicloning site of a promoterless pGL4.10[luc2] vector (Promega). We also cloned a cDNA of FOXA2 (FOXA2-F: 5′-GTTAAGCTTGCCACCATGCACTCGGCTTCCAGTAT-3′ and FOXA2-R: 5′-ACTGGATCCAGAGGAGTTCATAATGGGCC-3′) into the multicloning site of p3xFLAG-CMV-14 vector (Sigma-Aldrich) for protein expression in mammalian cell lines. We sequenced the inserts of all constructs. The phRL-SV40 vector (Promega) was used as an internal control to normalize the variation in transfection efficiency. MCF-7 and OUMS-27 cells are generally used for luciferase assay. MCF-7 cells  were cultured at 37 °C under 5% CO2 in Dulbecco’s modified Eagle’s medium (DMEM) supplemented with 10% fetal bovine serum (FBS), 0.01 mg ml−1 human recombinant insulin, 50 U ml−1 penicillin and 50 μg ml−1 streptomycin. OUMS-27 cells (JCRB Cell Bank) were cultured at 37 °C under 5% CO2 in DMEM supplemented with 10% FBS and 50 μg ml−1 kanamycin. All cell lines were authenticated by STR analysis and confirmed to be mycoplasma negative. MCF-7 and OUMS-27 cells were seeded in 24-well plates at a density of 5 × 104 and 1 × 105 cells per well, respectively. After 24 h, cells were transfected using TransIT-LT1 (Mirus Bio) according the manufacturer’s instructions. The luciferase activities were measured using the PicaGene Dual Sea Pansy Luminescence kit (Toyo Ink).We constructed luciferase reporter vectors by cloning We prepared probes for the risk (G) and non-risk (A) alleles of rs1978060 by annealing 25-bp complementary oligonucleotides (sense: 5′-TGTCTAATGTACRCACCAGCTCGGA-3′ and antisense: 5′-TCCGAGCTGGTGYGTACATTAGACA-3′) and labeling with digoxigenin (DIG)-11-ddUTP (Roche). The nuclear extracts were prepared from MCF-7 and FOXA2-overexpressing OUMS-27 cells. DNA–protein binding reactions were performed using a DIG gel shift kit according to the manufacturer’s instructions (Roche). For competition assays, nuclear extracts were pre-incubated with excess unlabeled probes prior to adding DIG-labeled probes. For a super-shift assay, 2 μg of FOXA2 antibody  was added to the reaction mixture and incubated for 20 min at room temperature. DNA-protein complexes were resolved on 6% DNA retardation gels (Thermo Fisher Scientific), and the signal was detected using a chemiluminescent detection system according to the manufacturer’s instructions (Roche). Uncropped gel images can be found in the Source Data file.Further information on research design is available in the Supplementary informationsSupplementary Data 1Supplementary Data 2Supplementary Data 3Supplementary Data 4Supplementary Data 5Supplementary Data 6Supplementary Data 7Reporting SummaryDescription of Additional Supplementary FilesPeer Review FileSource data


Ensifer meliloti, an agriculturally and ecologically important bacterium because it fixes nitrogen when in symbiosis with leguminous plants. We identified candidate alleles and gene presence-absence variants underlying variation in symbiosis traits, antibiotic resistance, and use of various carbon sources; some of these candidates are in genes previously known to affect these traits whereas others were in genes that have not been well characterized. Our results point to the potential power of association analyses in bacteria, but also to the need to carefully evaluate the potential for false associations.Genome-wide association analyses are a powerful approach for identifying gene function. These analyses are becoming commonplace in studies of humans, domesticated animals, and crop plants but have rarely been conducted in bacteria. We applied association analyses to 20 traits measured in  de novo mutations for characterizing gene function. Although bacteria should be amenable to GWAS, few GWAS have been conducted on bacteria, and the extent to which nonindependence among genomic variants  and the genetic architecture of phenotypic traits will affect GWAS performance is unclear. We apply association analyses to identify candidate genes underlying variation in 20 biochemical, growth, and symbiotic phenotypes among 153 strains of Ensifer meliloti. For 11 traits, we find genotype-phenotype associations that are stronger than expected by chance, with the candidates in relatively small linkage groups, indicating that LD does not preclude resolving association candidates to relatively small genomic regions. The significant candidates show an enrichment for nucleotide polymorphisms (SNPs) over gene presence-absence variation (PAV), and for five traits, candidates are enriched in large linkage groups, a possible signature of epistasis. Many of the variants most strongly associated with symbiosis phenotypes were in genes previously identified as being involved in nitrogen fixation or nodulation. For other traits, apparently strong associations were not stronger than the range of associations detected in permuted data. In sum, our data show that GWAS in bacteria may be a powerful tool for characterizing genetic architecture and identifying genes responsible for phenotypic variation. However, careful evaluation of candidates is necessary to avoid false signals of association.Genome-wide association studies (GWAS) can identify genetic variants responsible for naturally occurring and quantitative phenotypic variation. Association studies therefore provide a powerful complement to approaches that rely on IMPORTANCE Genome-wide association analyses are a powerful approach for identifying gene function. These analyses are becoming commonplace in studies of humans, domesticated animals, and crop plants but have rarely been conducted in bacteria. We applied association analyses to 20 traits measured in Ensifer meliloti, an agriculturally and ecologically important bacterium because it fixes nitrogen when in symbiosis with leguminous plants. We identified candidate alleles and gene presence-absence variants underlying variation in symbiosis traits, antibiotic resistance, and use of various carbon sources; some of these candidates are in genes previously known to affect these traits whereas others were in genes that have not been well characterized. Our results point to the potential power of association analyses in bacteria, but also to the need to carefully evaluate the potential for false associations. Association analyses, or genome-wide association studies (GWAS), provide a complementary approach for identifying gene function by using statistical approaches to associate naturally occurring allelic variation with phenotypic variation , may be problematic. Recombination in prokaryotes typically operates by gene conversion or double recombination (reviewed in reference Ensifer meliloti (formerly Sinorhizobium meliloti) is an ecologically and agriculturally important species that has been extensively studied. Much of the work on Ensifer has been motivated by its role as a facultative symbiont of legumes, primarily Medicago species. As a symbiont, Ensifer converts atmospheric N2 into a plant-usable form, thereby providing plants with an essential nutrient and contributing to plant growth and productivity  and gene presence absence variants (PAVs) and phenotyped for 20 diverse symbiotic, metabolic, growth, and environmental tolerance traits. Because the performance of association mapping depends on the population genomic characteristics of the sample, we first characterized genomic diversity and genome-wide LD. We then used association analyses to characterize the number, minor allele frequency, and effect sizes of variants contributing to phenotypic variation, using permutations to identify those variants that had greater contribution than expected by chance.The primary objectives of this work were to evaluate the performance of phenotype-genotype mapping in bacteria and to advance our understanding of the genetic basis of phenotypic variation in Ensifer meliloti strains to a mean depth of 19.5×, and then aligned reads to E. meliloti strain USDA1106 to identify genomic variants. Across the three main replicons of the E. meliloti genome , we identified 439,288 segregating sites with <20% missing data, 96.2% of which were biallelic, and 66,283 annotated genes that varied in whether they were present or absent (PAVs) . Of these variants, 123,955  had a minor allele frequency (MAF) >5%. Consistent with previous characterizations of genomic diversity in Ensifer  than either pSymB  or pSymA . Similar patterns were obtained when including only those variants with MAF > 0.05 . Most  of the common (MAF > 0.05) PAVs were not present in the reference genome, and so we did not attempt to determine their genomic location. Of the PAVs that were present in the reference, the replicon with the greatest proportion of variable genes was megaplasmid pSymA , followed by pSymB , and the chromosome .We used the Illumina platform to sequence 153 r2 ≥ 0.95, approximately 20% of the SNPs  and 80% of the PAVs  were not grouped with any other variants. However, the majority of variants were strongly linked to one or more other variants. The median size of these groups was three variants, and most groups contained only SNPs  or only PAVs (632 groups) (Variants in strong linkage disequilibrium (LD) are nonindependent and thus statistically indistinguishable in association analyses  , indicatr2 between pairs of SNPs was 0.24 on the chromosome, 0.05 on pSymB, and 0.12 on pSymA  Number of LD groups containing PAVs or SNPs, and number of LD groups containing SNPs found on each replicon, also at r2 = 0.80. (C) Distributions of number of variants per LD group for LD groups at r2 = 0.95 containing only SNPs, only PAVs, or only SNPs on one of the replicons. Download FIG S1, PDF file, 1.3 MB.(A) Distribution of number of variants in each LD group at Copyright © 2018 Epstein et al.2018Epstein et al.Creative Commons Attribution 4.0 International license.This content is distributed under the terms of the Association analyses are more likely to identify genes responsible for variation of Mendelian traits than for traits with continuous variation determined by genetic variation at many loci with small effects . Given t10.1128/mSphere.00386-18.3FIG S2FIG S2, PDF file, 0.3 MB.Distributions for all 20 phenotypic traits. The five focal traits are in red, and the 11 traits with PVE > 0 are in bold. Download Copyright © 2018 Epstein et al.2018Epstein et al.Creative Commons Attribution 4.0 International license.This content is distributed under the terms of the To verify that the estimates of PVE exceed what is expected by chance, we created 100 permuted data sets in which we randomly assigned phenotypic values to genotypes and reran the PVE analyses, resulting in an empirical null distribution of PVE estimates. For nearly all traits, genetic variation explained <25% of the variation in the permuted phenotypes . HoweverThe PVE analyses estimate the proportion of phenotypic variation that has a genetic basis but do not identify the causal variants. To identify causal variants, we conducted single-variant association analyses in which a measure of pairwise strain relatedness, the K-matrix, is used as a covariate and the effect of relatedness is statistically removed before testing each variant. Before analyzing the empirical data, we generated null expectations, i.e., empirical false discovery expectations, for the strength of variant-phenotype associations for each trait using the same data permutations we used to evaluate the performance of the LMM and BSLMM. Permutation tests are computationally intensive but produce null distributions that match the properties  of each data set . The perBy comparing variants identified for each trait to the empirical null expectations, we determined which associations were stronger than expected by chance. For example, for A17 plant biomass (reflective of the symbiotic benefit each strain provides the host), the cumulative effects of the 10 most strongly associated variants explain more variation than is expected by chance ; however10.1128/mSphere.00386-18.4FIG S3FIG S3, PDF file, 0.9 MB.PVE explained by top variants from GWA after model selection. As in Copyright © 2018 Epstein et al.2018Epstein et al.Creative Commons Attribution 4.0 International license.This content is distributed under the terms of the 10.1128/mSphere.00386-18.5FIG S4FIG S4, PDF file, 0.6 MB.PVE by LMM  and by the BSLMM. The gray lines indicate the lower 95% of random permutations. Note that random permutations were only conducted for BSLMM on the five focal traits because of computational limitations. The five focal traits are in red, and the nine traits with PVE by either the K-matrix or BSLMM >95% of the random permutations are in bold. Download Copyright © 2018 Epstein et al.2018Epstein et al.Creative Commons Attribution 4.0 International license.This content is distributed under the terms of the P ≤ 0.01) relative to the empirical null distribution, and for five other traits the LD groups were slightly to moderately enriched for SNPs (P < 0.20)  of the candidates to the MAF of the variants most strongly associated with the permuted data. This analysis revealed that candidates most strongly associated with variation in A17 biomass, and, to a lesser extent, annual mean temperature, were highly enriched for intermediate frequency variants . We cond < 0.20) . These r10.1128/mSphere.00386-18.6FIG S5FIG S5, PDF file, 1.3 MB.Size and composition  of top 10 LD groups from the testing of the real phenotype data (column 1). (Column 2) The distribution of the mean proportion of SNPs in the top 10 LD groups from 100 GWA runs with randomly permuted phenotypes . The vertical line indicates the mean proportion of SNPs in the real data, and the percentage of random runs with a value greater than the real data is annotated to the right of the line. The genome-wide mean proportion of SNPs is 0.73. (Columns 3 and 4) The distribution of mean LD group size and mean MAF, respectively, for the permutations compared to the real data. Download Copyright © 2018 Epstein et al.2018Epstein et al.Creative Commons Attribution 4.0 International license.This content is distributed under the terms of the de novo mutation, for understanding gene function and genomic diversity. The association analyses we conducted on the model rhizobium Ensifer meliloti help to evaluate the potential success of these analyses in bacterial lineages and identified candidate genes that may underlie phenotypic variation in important ecological and agronomic traits.Genome-wide association analyses provide a potentially powerful approach for identifying the functional role of naturally occurring allelic variation in bacterial populations , 14. As Escherichia coli, 176 Klebsiella pneumoniae, and 992 Staphylococcus aureus strains and Porter et al.  is not extensive. Our sample exhibits extensive nucleotide and gene presence-absence variation (PAV), with more than 100,000 common (MAF > 0.05) SNPs and more than 10,000 genes with common PAVs, and a pangenome that contains nearly 67,000 genes. The number of SNPs used for association mapping here is comparable to other bacterial association studies—Earle et al.  identifir et al.  identifie et al.  found onnd hosts , 38.r2 of ∼0.06, and r2 ≤ 0.12 between variants on each of the megaplasmids  and were found as part of LD groups that are larger  than expected by chance . CandidaInterestingly, only one of the candidates we identified was found on the chromosome , despitenifA, which regulates the expression of nitrogenase genes  approach to identify genes potentially responsible for variation in the ability of rhizobia to extract benefits from the host under competitive conditions , and an aldehyde dehydrogenase  SNPs segregating in our sample, although none of these changed amino acid residues that are known to affect streptomycin resistance in E. coli . Similarly, we found six common PAVs annotated as aminoglycoside resistance genes, but these also were not strongly associated with aminoglycoside resistance , and all strains carried at least three genes annotated as aminoglycoside resistance genes. That we did not detect strong associations with previously identified resistance genes underscores the need to view genome annotations with caution and suggests that there may be unidentified, naturally occurring mechanisms of aminoglycoside resistance. It is also possible that some of the genes that do show strong associations are statistical false positives. For example, it is surprising that variation in nifH, a gene that encodes part of the nitrogenase enzyme, is found to be associated with antibiotic resistance rather than nodule number or plant biomass. The genes we identified should be viewed as candidates for further functional characterization. They may not contain the causative alleles but rather be in LD with causative alleles that did not meet our criteria for testing.In contrast with the host benefit traits, our association analysis did not identify any genes with functions related to mechanisms previously implicated in resistance to the three aminoglycoside antibiotics we studied. These mechanisms include alteration of the ribosomal target through mutations in rogenase . Associa E. coli , and thea priori candidates. Nevertheless, it is striking that for three of these phenotypes nitrogen fixation or metabolism genes  are associated with phenotypic variation. GWAS can also be used to find genetic variants associated with the climate of origin . For instance, Yoder et al.  using Medicago sp. trap plants. From each strain DNA was extracted using a MoBio UltraClean microbial DNA isolation kit (12224), used to prepare dual-indexed Nextera XT libraries, and sequenced using 300-bp paired-end runs on an Illumina MiSeq. Strains were sequenced to a depth of 9.5 to 36.9 reads per base  after trimming and alignment. Reads were trimmed with Sickle . In particular, we removed SNPs with quality scores <20 and all indels, split sites with more than two alleles into multiple biallelic entries (bcftools norm), converted heterozygous calls  into missing data, and removed sites with missing genotype calls in more than 20% of the strains. Because FreeBayes sometimes treats adjacent SNPs as a single multinucleotide polymorphism (MNP), we retained each MNP as a single variant. For simplicity we refer to both MNPs and SNPs as SNPs. Only the ∼110,000 SNPs with minor allele frequency (MAF) ≥ 5% were included in association analyses. We characterized the extent of nucleotide variation using two standard measures, Watterson's theta (θW)—an estimate of the number of variants per bp  , bcftools per bp —and θπ—as per bp . These ds per bp  on all sde novo assemblies of each strain. The assemblies were constructed using SPAdes (v3.6.2) (We identified presence-absence variants (PAVs) using (v3.6.2)  using re(v3.6.2)  and anno(v3.6.2) . After o(v3.6.2) , 73 withhttps://doi.org/10.5061/dryad.tn6652t/1).We conducted association analyses on 20 traits selected from 180 traits on which we had initially collected phenotypic data. The 180 traits included 95 quantitative Biolog plate traits ; 78 “binary” antibiotic resistance, stress tolerance, enzymatic activity, and toxin tolerance traits; growth rate in liquid TY medium; two climatic variables describing where the strains were collected; and four symbiosis traits. A full description of phenotyping methods is provided in the supplemental methods , and ful10.1128/mSphere.00386-18.1TEXT S1Text S1, PDF file, 0.1 MB.Supplemental methods describing phenotype measurement methods in detail. Download Copyright © 2018 Epstein et al.2018Epstein et al.Creative Commons Attribution 4.0 International license.This content is distributed under the terms of the l-fucose, 2-aminoethanol, N-acetylglucosamine, putrescine, and formic acid as carbon sources). We then added the four symbiosis traits (nodule number and plant biomass in two plant genotypes), two climate variables , approximately 50 binary traits for which fewer than five strains were found at each phenotype value, and 50 traits for which <10% of the phenotypic variance was attributed to genetic differences among strains (PVE), as determined by a Bayesian sparse linear mixed model . Precipitation-related variables are shown in blue, and temperature-related variables are shown in red. Annual mean temperature (AMT) and annual precipitation (AP) were chosen as the representative variables for further analysis. Variable abbreviations: AMT, annual mean temperature (temp.); MDR, mean diurnal range; IT, isothermality; TS, temp. seasonality; TWaM, max. temp. warmest month; TCM, min. temp. coldest month; TAR, temp. annual range; TWeQ, mean temp. wettest quarter; TDQ, mean temp. driest quarter; TWaQ, mean temp. warmest quarter; TCQ, mean temp. coldest quarter; AP, annual precipitation (precip.); PWeM, precip. wettest month; PDM, precip. driest month; PS, precip. seasonality; PWeQ, precip. wettest quarter; PDQ, precip. driest quarter; PWaQ, precip. warmest quarter; PCQ, precip. coldest quarter. Download Copyright © 2018 Epstein et al.2018Epstein et al.Creative Commons Attribution 4.0 International license.This content is distributed under the terms of the 10.1128/mSphere.00386-18.9TABLE S1Table S1, PDF file, 0.1 MB.Descriptions of the 20 phenotypes selected for association analyses. The phenotypes are listed by category. For binary traits, instead of a mean and standard deviation, the number of resistant strains (strains that grew) and the number of susceptible strains (strains that did not grow) are reported (resistant/susceptible in the Mean column). Download Copyright © 2018 Epstein et al.2018Epstein et al.Creative Commons Attribution 4.0 International license.This content is distributed under the terms of the https://doi.org/10.5061/dryad.tn6652t/1). We formed groups with two LD thresholds, r2 ≥ 0.95 and r2 ≥ 0.80.To identify variants in strong LD, we (i) sorted all variants by MAF, (ii) used the variant with highest MAF and fewest ungenotyped individuals as a “seed” for an LD group, and (iii) grouped all variants in LD with the seed. Steps 2 and 3 were repeated, using the next ungrouped variant as a seed. To reduce computational time and eliminate unnecessary statistical tests, we performed association analyses (below) on only the seed variant from each group, but then annotated groups based on all variants in the group .We tested for an association between variants and phenotypic variation using a linear mixed model (LMM) implemented with the -lmm option in GEMMA (v0.94.1) . We inclThere are several statistical biases that should be considered when interpreting results from association analysis . One biaWe estimated the proportion of phenotypic variance explained by genetic variants (PVE) using three methods. First, to estimate the PVE explained by top variants, we performed linear regression with forward model selection —i.e., the amount of phenotypic variance that can be explained by among-strain relatedness. This estimate is analogous to heritability, with a value of one indicating that all phenotypic variation can be explained by the patterns of relatedness among strains. Third, we used a Bayesian sparse linear mixed model (BSLMM) implemented in GEMMA to estimate PVE. BSLMM fits a model with a few variants with larger effects in addition to the background effects captured by relatedness. The analysis was run with the default options, 2.5-million-step burn-in, and 6 million steps (continuous phenotypes), 25 million steps (most binary phenotypes), or 80 million steps (Cd tolerance) after the burn-in. A preliminary analysis indicated that 6 million steps was sufficient for continuous phenotypes, but binary phenotypes needed more steps to converge. The estimates were recorded every 500 steps, and we combined the results of 5 independent chains for each phenotype. The distributions of the hyperparameters indicated that nearly all chains converged.SRR6055493 to SRR6055666 (https://www.ncbi.nlm.nih.gov/sra) under BioProject PRJNA401434. Computer code, phenotype data, and full results files have been deposited in Dryad (https://doi.org/10.5061/dryad.tn6652t/1).Sequence data were deposited in the NCBI Sequence Read Archive (SRA) under accession numbers 10.1128/mSphere.00386-18.8FIG S7P values from likelihood ratio tests in linear mixed model GWA. The five focal traits have red titles, and the eleven traits with PVE greater than expected by chance have bolded titles. The P value distributions for four of these 11 traits showed no evidence for inflation, five traits  showed some mild inflation, and resistance to spectinomycin and streptomycin both showed substantial inflation; that is, the distribution of P values was strongly skewed toward lower (more significant) values. Download FIG S7, PDF file, 0.9 MB.QQ plots for the Copyright © 2018 Epstein et al.2018Epstein et al.Creative Commons Attribution 4.0 International license.This content is distributed under the terms of the