Genetic variants regulating RNA splicing and transcript usage have been implicated in both common and rare diseases. Although transcript usage quantitative trait loci (tuQTLs) have been mapped across multiple cell types and contexts, it is challenging to distinguish between the main molecular mechanisms controlling transcript usage: promoter choice, splicing and 3ʹ end choice. Here, we analysed RNA-seq data from human macrophages exposed to three inflammatory and one metabolic stimulus. In addition to conventional gene-level and transcript-level analyses, we also directly quantified promoter usage, splicing and 3ʹ end usage. We found that promoters, splicing and 3ʹ ends were predominantly controlled by independent genetic variants enriched in distinct genomic features. Promoter usage QTLs were also 50% more likely to be context-specific than other tuQTLs and constituted 25% of the transcript-level colocalisations with complex traits. Thus, promoter usage might be an underappreciated molecular mechanism mediating complex trait associations in a context-specific manner. Genes contain all instructions needed to build an organism in form of DNA. Humans share around 99.5% of DNA, but it is the remaining 0.5% that contain the small genetic variations that make us unique. Subtle differences in genes can, for example, influence the color of our hair or eyes.To build gene products, such as proteins, DNA first needs to be transcribed into RNA. Some genetic variants can affect how a gene is transcribed into an RNA molecule, for example by making it be transcribed too much or too little, which can lead to diseases. These variants can also influence where the transcription begins through a process called promoter usage. This can lead to shorter or longer RNAs, which can have different biological impacts.With current research methods it is difficult to detect changes in the latter kind of alteration. As a result, it is harder to distinguish these from other types of changes. Now, Alasoo et al. wanted to find out what proportion of genetic variants that alter traits influence promoter usage, compared to other changes. To do so, a new computational method was developed to directly measure how genetic variants influence different parts of the RNA, such as promoters, middle sections and ends. The method was then applied to datasets of human immune cells. The experiments revealed that genetic variants often influence promoter usage. Many of the effects could only be found when cells are exposed to external stimuli, such as bacteria.The results highlight that to discover genes responsible for human traits and disease we need to consider all the possible ways genetic differences between individuals could alter the gene products. Large published datasets could be reanalyzed using this method to identify new genes that could be implicated in human health and disease, potentially leading to new treatment options in future. Genome-wide association studies (GWAS) have discovered thousands of genetic loci associated with complex traits and diseases. However, identifying candidate causal genes and molecular mechanisms at these loci remains challenging. Complex trait-associated variants are enriched in regulatory elements and are therefore thought to act via regulation of gene expression levels, often in a cell type- and context-specific manner . HoweverIn addition to splicing analysis, RNA-seq data can also be used to quantify promoter and 3ʹ end usage. The simplest approach would be to first quantify the expression of all annotated transcripts using one of the many quantification algorithms   exposed to three inflammatory stimuli  . We alsoSalmonella infection, and IFNɣ stimulation followed by Salmonella infection) and one metabolic stimulus (24 hr acLDL stimulation). While the gene expression analysis of the IFNɣ+Salmonella dataset from 84 individuals has previously been described -derived macrophages exposed to three inflammatory stimuli  fuescribed   . LeafcutRF5 gene  while otRF5 gene  or positRF5 gene . Alternace versa .2 >0.8) with each other . Consistent with previous reports that tuQTLs are largely independent from eQTLs , independent of which quantification method was used  from different methods were in high linkage disequilibrium (LD)  compared to 10.2% of the internal exon and 3ʹ end usage QTLs . These overlaps could correspond to both distal regulatory elements affecting promoter usage or direct changes in local promoter accessibility. To focus on local promoter accessibility, we further required the center of the accessible region to be no farther than 1000 bp from the closest promoter of the gene, leaving 46/786 (5.8%) promoter usage QTLs with a putative coordinated effect on promoter accessibility. One such example affecting promoter usage and promoter accessibility of the HDLBP gene is highlighted in Motivated by the enrichment of promoter usage QTLs in open chromatin regions , we analTo assess the relevance of different QTLs for interpreting complex trait associations, we performed statistical colocalisation analysis with GWAS summary statistics for 33 immune-mediated and metabolic traits and diseases . We found that 47 of 138 colocalised QTLs influenced total gene expression level  PP3+PP4. SimilarCD33 and Alzheimer’s disease .Next, we explored how the genetic effects of eQTLs and tuQTLs varied in response to stimuli. To define response QTLs, we started with QTLs detected (FDR < 10%) in each of the four simulated conditions  and used an interaction test to identify cases where the QTL effect size was significantly different between the simulated and corresponding naive condition (FDR < 10%). To exclude small but significant differences in effect size, we used a linear mixed model to identify QTLs where the interaction term explained more than 50% of the total genetic variance in the data . Although the fraction of QTLs that were response QTLs varied greatly between conditions  and corrCD40 gene that colocalises with a GWAS signal for rheumatoid arthritis . Samples were initially collected under ethics for iPSC derivation , which require managed data access for all genetically identifying data. Later samples were collected under a revised consent  under which all data, except from the Y chromosome from males, can be made openly available. The ethics approval was obtained from East of England - Cambridge East Research Ethics Committee. The iPSC lines used in this study are commercially available via the European Collection of Authenticated Cell Cultures. No new primary human samples were collected for this study.Human induced pluripotent stem cells (iPSCs) lines from 123 healthy donors   were obtSalmonella study have been described previously  per well of a six-well plate. Lysates from two wells were immediately pooled and stored at −80°C. RNA was extracted using a RNA Mini Kit (Qiagen) following the manufacturer’s instructions and eluted in 35 µl nuclease-free water. RNA concentration was measured using NanoDrop, and RNA integrity was measured on Agilent 2100 Bioanalyzer using a RNA 6000 Nano Total RNA Kit.All RNA-seq libraries from the acLDL study were constructed manually using poly-A selection and the Illumina TruSeq stranded library preparation kit. The TruSeq libraries were quantified using Bioanalyzer and manually pooled for sequencing. The samples were sequenced on Illumina HiSeq 2000 using V4 chemistry and multiplexed at six samples/lane. The control and acLDL stimulated RNA samples from a single donor were always sequenced in the same experimental batch. Sample metadata is presented in We used four alternative strategies to quantify transcription from RNA-seq data: (i) gene-level read count quantified with featureCounts , (ii) fuSalmonella and acLDL dataset, we used only genes with mean expression in at least one of the conditions greater than one transcripts per million (TPM)  (We used featureCounts v1.5.0  to counton (TPM)  in all don (cqn)  R packagon (cqn) .We downloaded the FASTA files with messenger RNA (mRNA) and non-coding RNA sequences from the Ensembl website (version 87). We concatenated the two files and used salmon v0.8.2  with ‘--https://github.com/kauralasoo/txrevise). This step was necessary, because Ensembl contains a large number of truncated transcripts (marked with cds_start_NF or cds_end_NF tags)  , but thiWe developed the txrevise R package to pre-process transcript annotations prior to quantification. First, we extended all truncated protein coding transcripts using exons from the longest annotated transcript of the gene that was part of the GENCODE Basic gene set . We alsoNext, we focused on splitting full-length transcripts into alternative promoters, internal exons and 3ʹ ends. However, some genes contained either non-overlapping transcripts or very short transcripts that complicated this process. Thus, for each gene we first identified a group of transcripts that shared the largest number of exons with each other. We then used the shared exons as a scaffold to construct three types of independent transcriptional events: alternative promoters, internal exons and 3ʹ ends (group 1) . We alsoFinally, we used Leafcutter  to quantSalmonella study  and the acLDL study . The same VCF files were used for all downstream analyses and were imported into R using the SNPRelate R package , where we used the ±200 kb cis window around each lead QTL variant. However, the colocalisation analysis was still based on the lead QTL variants identified in the ±100 kb window. Thus, even if a stronger QTL lead variant was detected in the ±500 kb window, this was not used for any downstream analysis.Secondly, we used the nominal pass to calculate nominal association p-values in a ± 500 kb 2 >0.8) with each other. We then repeated this analysis for all pairs of quantification methods. Note that this measure is not necessarily symmetric between the quantification methods and also depends on the statistical power of each method. Since Leafcutter had lower statistical power than other methods on our dataset, it also replicated smaller fraction of QTLs detected by the other methods. In contrast, ~50% of the Leafcutter QTLs were replicated by txrevise and full-length transcript usage  . After chttps://doi.org/10.5281/zenodo.1170560).We downloaded the coordinates of accessible chromatin regions in macrophages across four conditions  from our previous study . Specifihttps://www.encodeproject.org). Each protein was measured in two biological replicates, resulting in 186 sets of peaks. We only used data from the K562 myelogenous leukemia cell line. We further used Supplementary Table 1 from  in each of the four stimulated conditions  and used an interaction test to identify cases where the QTL effect size was significantly different between one of the stimulated and corresponding naive condition (FDR < 10%). We performed this test separately for each of the four stimulated condition . Furthermore, to take advantage of our profiling of gene expression in overlapping set of donors in the stimulated and naive conditions, we also included the cell line as a random effect and fitted a linear mixed model using the lme4  package.H0: phenotype ~genotype + condition + (1|donor)H1: phenotype ~genotype + condition+condition:genotype + (1|donor)where (1|donor) denotes the donor-specific random effect. We obtained the p-value of rejecting the null hypothesis and used the p.adjust function to identify phenotype and lead variant pairs that were significant at 10% Benjamini-Hochberg FDR.For some QTLs, we noticed that although the interaction test p-value was significant, the difference in the effect size between the two conditions was very small. To identify response QTLs with large effect size differences between naive and stimulated conditions, we turned to variance component analysis. Specifically, for the same phenotype and lead variant pairs tested above, we also fitted the following linear mixed model:phenotype ~ (1|genotype) + (1|condition) + (1|condition:genotype)where genotype, condition and the interaction between the two were all fitted as random effects. We then quantified the variance explained by each of the three components using the VarCorr function form the lme4 package. Finally, we calculated the variance explained by the interactions term relative to the total genetic variance:2relative = σ2interaction /(σ2interaction+σ2genotype)σ2relative > 0.5 from the variance component analysis. Although fitting genotype as a random effect in this way is suboptimal because it ignores the expected linear relationship between the alternative allele dosage and phenotype, we empirically found that filtering both on the p-value of the interaction test as well as σ2relative was effective at identifying QTLs with large effect size differences between conditions.We defined response QTLs as those with FDR < 10% from the interaction test and σWe obtained full summary statistics for ten immune-mediated disorders: inflammatory bowel disease (IBD) including ulcerative colitis (UC) and Crohn’s disease (CD) , Alzheim−5. We used the following prior probabilities: p1 = 10−4, p2 = 10−4 and p12 = 10−5. We then applied a set of filtering steps to identify a stringent set of eQTLs and tuQTLs that colocalised with GWAS hits. Similarly to a previous study .The Snakemake  files usSalmonella study is available from ENA (PRJEB18997) and EGA (EGAS00001002236). The imputed genotype data for HipSci cell lines is available from ENA (PRJEB11749) and EGA (EGAD00010000773). Processed data and QTL summary statistics are available from Zenodo: https://zenodo.org/communities/macrophage-tuqtls/.RNA-seq data from the acLDL stimulation study is available from ENA (PRJEB20734) and EGA (EGAS00001000876). RNA-seq data from the IFNɣ+ In the interests of transparency, eLife includes the editorial decision letter and accompanying author responses. A lightly edited version of the letter sent to the authors after peer review is shown, indicating the most substantive concerns; minor comments are not usually included.eLife. Your article has been reviewed by three peer reviewers, and the evaluation has been overseen by Stephen Parker as the Reviewing Editor and Mark McCarthy as the Senior Editor. The following individual involved in the review of your submission has agreed to reveal their identity: Roger Pique-Regi (Reviewer #2).Thank you for submitting your article "Genetic effects on promoter usage are highly context-specific and contribute to complex traits" for consideration by The reviewers have discussed the reviews with one another and the Reviewing Editor has drafted this decision to help you prepare a revised submission.Summary:Most transcript usage QTL (tuQTL) studies do not differentiate between events leading to differential isoform usage, such as alternative promoter, alternative 3ʹ end, or alternative splicing that are a likely consequence of different molecular mechanisms. Alasoo and colleagues describe the development of such a tool – txrevise, which processes (Ensembl) transcript annotations to build an annotation of independent promoters, internal exons and 3' ends for use with an external tool such as Salmon for transcript quantification. They additionally map genetic associations for total gene expression, full length transcript usage and exon-exon junction using established methods and compare the approaches and results to those from txrevise. The authors show that promoter usage QTLs are generally more context specific and colocalize with genetic signals for complex traits. The approach is sound, and the methods/observations will be helpful to the field. The paper is well-written and we think will be appealing to a broad audience. However, there are several points that should be addressed, which we outline below.Essential revisions:1) There were two QTL scans done, where the initial scan happened over a smaller window. Was there any instance where the larger +/- 500 kb scan resulted in a stronger signal compared to the initial +/- 100 kb scan? If so, how were these treated?2) More details about assignment of groups 1 and 2 during the txrevise process would be helpful. Surely there are examples that are not as simple as the one depicted in Figure 1—figure supplement 3. How are those more complicated cases treated? Another way of phrasing this: how do you decide when to choose more exons vs. fewer transcripts? Clarification of this approach will be helpful.3) The word "group" may have different meanings in different sections of the txrevise methods. For example, in the last paragraph of the subsection “Quantifying transcriptional events with txrevise”, "group" describes the two different approaches to creating common scaffolds. At the end of the same section of the Materials and methods, it's not clear here what "group" means. Does it mean as above (the two different scaffold approaches) or one of the three different categories ? Clarity here will be helpful for other labs that want to use this approach.4) Not clear how multiple testing correction happens with txrevise – are you using the --grp-best flag across all the separate bits  of a gene model, or only within the three different partitions? And what about across the two groups that are created? When mapping multiple QTLs, it is not clear if the authors map them all simultaneously or if they use a conditional on the lead QTL strategy. The authors should clarify this.5) In Figure 2B, authors performed a replication analysis of QTLs. This appears to be only based on the LD between the lead QTL variants for different comparisons. However, the direction of effect size is ignored. This should also be included in all the replication analyses.6) Given that promoter shifts are an important component of context specific tuQTL and are also enriched for complex traits, the authors could use their ATAC-seq data to further illustrate the mechanism and perhaps validate. Are changes on promoter usage dependent on the promoters being open? Do these types of tuQTL also have a QTL on ATAC-seq on the promoters? Even if the outcome is negative, which might indicate a more complex relationship between promoter usage and chromatin accessibility, this would further strengthen the manuscript.7) The coupling part was uniformly perceived as weaker compared to the rest of the work and could be removed as it is a bit orthogonal to the main focus, especially considering it is not tied to any main figure. Essential revisions:1) There were two QTL scans done, where the initial scan happened over a smaller window. Was there any instance where the larger +/- 500 kb scan resulted in a stronger signal compared to the initial +/- 100 kb scan? If so, how were these treated?We have now modified the section “Association testing” in Materials and methods to clarify that only the lead variants identified in the initial +/- 100 kb scan were used for all downstream analyses. Since genes often have multiple independent eQTLs, we think it is likely that a larger scan in a +/- 500kb window will sometimes identify stronger signals. However, we did not quantify this, because the summary statistics from the larger window were only used for colocalisation analysis based on the lead variants identified in the smaller +/- 100kb. We did this to ensure that all of the variants in the +/- 200kb region around the lead QTL variant were included in the colocalisation analysis, because truncated summary statistics can lead to overestimation of the posterior probability of colocalisation.2) More details about assignment of groups 1 and 2 during the txrevise process would be helpful. Surely there are examples that are not as simple as the one depicted in Figure 1—figure supplement 3. How are those more complicated cases treated? Another way of phrasing this: how do you decide when to choose more exons vs. fewer transcripts? Clarification of this approach will be helpful.We agree that, in general, it is not easy to automatically decide whether to prioritise more shared exons between transcripts or larger number of transcripts; indeed, txrevise does not solve this particular problem. We have now modified the legend for Figure 1—figure supplement 3 to clarify that for each gene, txrevise always identifies two groups of transcripts and then performs all of the analyses across the two groups.3) The word "group" may have different meanings in different sections of the txrevise methods. For example, in the last paragraph of the subsection “Quantifying transcriptional events with txrevise”, "group" describes the two different approaches to creating common scaffolds. At the end of the same section of the Materials and methods, it's not clear here what "group" means. Does it mean as above (the two different scaffold approaches) or one of the three different categories ? Clarity here will be helpful for other labs that want to use this approach.We thank the reviewer for pointing out these inconsistencies. We have now edited the text in the Materials and methods section  and in the caption for Figure 1—figure supplement 3. In particular, the term “group” now always refers to the subset of transcripts used to identify the scaffold for event constructions. For the three different categories  we now consistently use the term “types of transcriptional events”.4) Not clear how multiple testing correction happens with txrevise – are you using the --grp-best flag across all the separate bits  of a gene model, or only within the three different partitions? And what about across the two groups that are created? When mapping multiple QTLs, it is not clear if the authors map them all simultaneously or if they use a conditional on the lead QTL strategy.We have now modified the “Association testing” subsection in the Materials and methods to clarify that the permutations were performed across the two subsets of transcripts used for event construction, thus reporting at most three tuQTLs per gene, i.e. one for each event type . After identifying permutation p-values with QTLtools, we also use Benjamini-Hochberg correction to account for the number of independent features that we have tested. Thus, even though we test 3x more features with txrevise, we account for that by using the Benjamini-Hochberg correction. We have also clarified that we mapped multiple QTLs only in the txrevise and Leafcutter analyses, because with those approaches we tested multiple event types  or multiple intron clusters per gene. We did not use conditional analysis.5) In Figure 2B, authors performed a replication analysis of QTLs. This appears to be only based on the LD between the lead QTL variants for different comparisons. However, the direction of effect size is ignored. This should also be included in all the replication analyses.The replication analysis is only presented to highlight the fact that different quantification approaches detect complementary sets of genetic associations. Including the effect size direction in our analysis can only reduce the number of QTLs that replicate between methods and not increase it. Therefore, this would not change the conclusions of our analysis.Nonetheless, we agree that including effect direction in the replication analysis would be beneficial in principle. However, defining the direction of effect in such a way that is consistent between different quantification methods is challenging. For eQTLs, a reasonable definition would be the effect size of the alternative allele on the total read count originating from the gene. However, this becomes much more challenging for full-length transcript usage QTLs, because any variant that is associated with the increased usage of one transcript is by definition also associated with decreased usage of some other transcripts of the gene . Thus, it is not clear which transcript should be used to define the direction of the effect. Similarly, it is challenging to compare the direction of effect sizes between Leafcutter tuQTLs and full-length tuQTLs, because it is not clear how to map Leafcutter splicing events to specific Ensembl transcripts, especially if Leafcutter analysis includes novel splice junctions not present in Ensembl. We now explicitly acknowledge this limitation of our analysis in the Materials and methods section (subsection “QTL replication between quantification methods”).6) Given that promoter shifts are an important component of context specific tuQTL and are also enriched for complex traits, the authors could use their ATAC-seq data to further illustrate the mechanism and perhaps validate. Are changes on promoter usage dependent on the promoters being open? Do these types of tuQTL also have a QTL on ATAC-seq on the promoters? Even if the outcome is negative, which might indicate a more complex relationship between promoter usage and chromatin accessibility, this would further strengthen the manuscript.2 > 0.9 between the lead variants) . To illustrate how this can yield mechanistic insight into promoter usage QTLs, we now also discuss one such example in more detail, as shown in Figure 2—figure supplement 7. We further describe this analysis in the Discussion, noting that although the overlap that we detected is small, this is likely influenced by low power in our chromatin accessibility dataset that contained only 41 individuals .We thank the reviewers for this excellent suggestion. We have now performed additional analysis assessing the overlap between promoter usage QTLs that we detect in our current study and chromatin accessibility QTLs that we have reported previously . These analyses revealed that 6% of the promoter usage QTLs are linked to putative coordinated changes in promoter accessibility (r7) The coupling part was uniformly perceived as weaker compared to the rest of the work and could be removed as it is a bit orthogonal to the main focus, especially considering it is not tied to any main figure.We thank the reviewers for the suggestion. We initially added this section to justify the need for event-level analysis. However, we agree that it is orthogonal to the main focus of the paper and we have now removed this section as well as Supplementary Figure 8 from the manuscript. We have also removed the related section from the Discussion.


Genome-wide association studies (GWAS) have now been conducted for hundreds of phenotypes of relevance to human health. Many such GWAS involve multiple closely-related phenotypes collected on the same samples. However, the vast majority of these GWAS have been analyzed using simple univariate analyses, which consider one phenotype at a time. This is despite the fact that, at least in simulation experiments, multivariate analyses have been shown to be more powerful at detecting associations. Here, we conduct multivariate association analyses on 13 different publicly-available GWAS datasets that involve multiple closely-related phenotypes. These data include large studies of anthropometric traits (GIANT), plasma lipid traits , and red blood cell traits (HaemgenRBC). Our analyses identify many new associations , many of which replicate when follow-up samples are available. Overall, our results demonstrate that multivariate analyses can help make more effective use of data from both existing and future GWAS. Genome-wide association studies (GWAS) have become a common and powerful tool for identifying significant correlations between markers of genetic variation and physical traits of interest. Often these studies are conducted by comparing genetic variation against single traits one at a time (‘univariate’); however, it has previously been shown that it is possible to increase your power to detect significant associations by comparing genetic variation against multiple traits simultaneously (‘multivariate’). Despite this apparent increase in power though, researchers still rarely conduct multivariate GWAS, even when studies have multiple traits readily available. Here, we reanalyze 13 previously published GWAS using a multivariate method and find >400 additional associations. Our method makes use of univariate GWAS summary statistics and is available as a software package, thus making it accessible to other researchers interested in conducting the same analyses. We also show, using studies that have multiple releases, that our new associations have high rates of replication. Overall, we argue multivariate approaches in GWAS should no longer be overlooked and how, often, there is low-hanging fruit in the form of new associations by running these methods on data already collected. Genome wide association studies (GWAS) have been widely used to identify genetic factors—particularly single nucleotide polymorphisms (SNPs) and copy number variations (CNVs)—associated with human disease risk and other phenotypes of interest , 2. IndeThe vast majority of these many genetic association analyses consider only one phenotype at a time . This is despite the fact that measurements on multiple phenotypes are often available, and joint association analysis of multiple phenotypes  can substantially increase power –8. ThereNonetheless, we believe that multivariate association analysis has an important role to play in making the most of costly and time-consuming GWAS studies. One way forward is to conduct multivariate analyses of previously-published GWAS, checking for additional associations that may have been missed by the initial univariate association analyses. This is greatly facilitated by the fact that many GWAS now make summary data from single-SNP tests freely available –13, and Here we demonstrate the potential benefits of reanalyzing published GWAS using multivariate methods. Specifically we apply multivariate methods from  to reanabmass . The software requires as input univariate GWAS summary statistics, for the same set of SNPs, on d related phenotypes.  Then, ypes see .d phenotypes, there are 3d possible assignments of phenotypes to these 3 categories, and each assignment corresponds to a different “model” γ. For example, one model corresponds to the “null” that all phenotypes are Unassociated; another model corresponds to the model that all phenotypes are Directly associated; another model corresponds to just the first phenotype being Directly associated, etc. The goal of the association analysis is to determine which of these models is consistent with the data and, in particular, to assess overall evidence against the null model.For γ, relative to the null model, is summarized by a Bayes Factor (BFγ). Large values of BFγ indicate strong evidence for model γ compared against the null. One advantage of Bayes Factors over p-values is that the Bayes Factors from different models can be easily compared and combined. For example, the overall evidence against the null is given by the (weighted) average of these BFs:wγ are chosen to reflect the relative plausibility of each model γ. In bmass we implemented the Empirical Bayes approach from Please let us know if you have any questions while making these revisions.Yours sincerely,Michael P. EpsteinAssociate EditorPLOS GeneticsHua TangSection Editor: Natural VariationPLOS GeneticsReviewer's Responses to QuestionsComments to the Authors:Please note here if the review is uploaded as an attachment.Reviewer #1: Turchin & Stephens show that many more associations can be identified with their Bayesian multivariate analysis. This paper advocates an important message that statistical power can easily improved from univariate to multivariate analysis.Overall the paper is well written and results are striking. One interesting question is that whether the performance gain is from the bayesian framework  or from multivariate analysis, a simple way to address that is to apply another multivariate method and compare its resultswith that from bmass.Reviewer #2: Overview:This manuscript takes the much needed step to show consortiums what they miss by not considering multivariate analysis of related phenotypes. The authors re-analyzed several publicly available datasets to compare findings from multivariate analysis of multiple phenotypes against those from single-trait analyses. Further, the authors empirically validate their additional findings from multivariate analysis by replicating those findings in larger, more recent datasets (e.g. in studies with multiple releases where later releases have larger sample sizes). The authors additionally emphasize that multivariate analysis is different from multiple single-trait analyses.I have a few discussion-type comments that will likely help other researchers adopt multivariate analyses in GWAS  and a few minor comments for the authors to consider.*Major comments*1. The authors give a couple reasons for why multivariate analyses are not adopted more widely. One reason that I have encountered from consortiums is that two traits may have widely different sample sizes, and no one wants to “sacrifice” higher sample size of one trait by trying to jointly analyze the two traits. This idea is probably stemming from the fact that multivariate analysis based on individual-level data requires the two traits to be measured on the same set of individuals. So, I think it will be useful to emphasize that these days, multivariate analysis can be done using methods based on GWAS summary statistics that do not have such a requirement. However, it also comes with additional questions that needs discussion:(a) if the consortiums have individual-level phenotype-genotype data where each trait has widely varying sample sizes, should the analysts first obtain single-trait single-SNP GWAS summary statistics and then proceed with multivariate analysis (using methods based on summary data)? Is this 2-step process an efficient approach? What would the authors recommend?(b) are the multivariate methods based on summary data robust across wide differences in sample sizes? Or can the authors give a thumb-rule  on the ratio of sample sizes of the traits beyond which summary-data-based-multivariate-methods may not be robust and may not prove more effective than univariate analysis?In my opinion, another hurdle in the adoption of multivariate analysis in consortiums: usually the multivariate analyses focus on genetic variants with MAF 1% or more (due to concerns about type I error inflation) while univariate analyses can conveniently go to lower MAFs without as much worry about inflation. Consequently, univariate analyses may end up detecting significantly associated genetic regions that are not even considered for multivariate analyses in the first place (due to more stringent MAF screening). Apparently then multivariate analysis is not giving anything over and above the findings from univariate analysis - the end result is the publication of univariate analysis results only. How would the authors sell multivariate methods to consortiums in such a context where univariate analysis appears more effective?2. Since the authors are advocating multivariate analyses (which I completely agree with), I think readers will find it useful if the authors can provide a general multivariate analysis pipeline that consortiums can broadly follow. For instance, a general univariate analysis pipeline involves (1) linear or logistic regression of each trait, (2) then clumping of significant variants into unique loci, (3) then performing conditional analyses to determine which signals are independent from signals that are known from past studies, (4) then trying to find biological relevance of independent new signals for the corresponding trait, etc. On the other hand, for multivariate analysis, to avoid the problem of sacrificing higher sample size of one trait (as pointed out in the previous comment), (1) the first step will still be univariate analysis, (2) then multivariate analysis using say BFav approach based on the summary stats from first step (is BFav only applicable to continuous traits?), (3) then clumping of unique loci, (4) then performing conditional analyses? How to do conditional analyses with BFav approach? Which SNP-trait associations from past studies should be considered for conditional analyses? For the 5th step, how should one go about determining biological relevance of a multivariate signal? What next steps can be taken?3. The authors defined “new multivariate association” as significant “if its BFav exceeds that of any previous univariate association’s BFav in the same study . The rationale is that the evidence for these multivariate associations exceeds the evidence for univariate associations that are generally accepted as likely to be real.” - Is this, in some sense, equivalent to (in the frequentist world) defining significance by looking at the multivariate p-value; if it is smaller than the minimum of the univariate p-values, then we have a new multivariate association? If so, are we going to call this situation new multivariate association: p_Trait1 = 0.01, p_Trait2=0.005 and p_multivariate=0.001? If yes, the definition does not seem right because in GWAS, we look at more stringent significance thresholds. Can the authors explain in more detail?4. Figure 2: Some of the new multivariate SNPs are below the 45-degree line. If I am interpreting it correctly, these SNPs have larger negative log-transformed p-values  in the earlier release than the later release (later release with larger sample size). When can this happen? Are these variants low-frequency?*Minor Comments*1. Since the authors recommend re-analyzing published GWAS using multivariate methods that use single-SNP summary data, I think it is worth emphasizing that consortiums ensure detailed summary data files are made publicly available. For instance, I have often come across publicly available summary data files that are almost useless because they have missing MAF or risk allele frequency, or missing info on the risk allele or the reference allele, or missing info on the Z statistics (or beta estimates) making direction of effect indeterminable.2. “in total, 84 of 94 new associations have smaller univariate p-values in the later release, and indeed the majority of these reach univariate GWAS significance in the later release.” - Do the authors mean exactly the same variants have smaller univariate p-values in later release compared to the first release?3. “at a univariate threshold of 5e-7 only 66% of the univariate significant SNPs are also multivariate significant across these three studies.” - Was 5e-7 threshold used for determining multivariate significance as well? What’s the equivalent BFav value (since the multivariate approach used the BFav Bayesian approach)?4. Can the authors explain the following choices?(a) Looks like the author screened our variants with MAF < 1% for all datasets but one. The MAF threshold for GEFOS2015 is 0.5%.(b) “We used a minimum N threshold of 50,000”.(c) The authors used “a univariate significant GWAS p-value threshold of 5e-8” for most studies but not all .5. “There may be many reasons why such variants went unreported, but one reason may be physical proximity to a variant with a stronger signal.” - Is it possible that such variants were not reported because they were no longer significant in subsequent conditional analyses?6. Supplementary Figure 3 plots effect sizes for each trait. Does the BFav approach provide effect size estimates as well?Reviewer #3: This is a nicely written paper applying a multivariate method to 13 datasets to search for new associations. The paper presents a large number of new findings, with the ultimate goal of encouraging increased use of the multivariate method.General comments:The performance of this multivariate approach is illustrated by looking at very closely related phenotypes, such as blood cell subtypes and anthropometric measures. Would this method still yield substantial power gains when investigating joint associations for less similar traits (i.e.: BMI and different cancer types)? Is the power of this approach related to the degree of correlation (genetic and/or phenotypic) between the traits?The authors note that “we declared a multivariate association as significant if its BFav exceeds that of any previous univariate association’s BFav in the same study.” Thus, the multivariate is deemed significant if it has a Bayes factor larger than any one univariate result. Is this criterion too liberal, and might it result in false positives for the multivariate approach?It is unclear why examining associations “that would be declared significant if the univariate significance threshold were relaxed” and comparing these to multivariate association results illustrates that multivariate analysis is not the same as multiple univariate analyses. I agree that these are not the same thing, but wouldn’t it make sense to look at whether a given SNP is associated with traits 1-3 in multiple univariate analyses and then see if it is also identified in the multivariate analysis of the same traits?The “Reanalysis also identifies new univariate associations” part is a little vague. Does this mean that there were significant SNPs in the original summary stats files that were simply not reported? Or are these new SNPs that were identified in the multivariate analysis, but were only associated with one trait?Showing replication is very important to support the use of the multivariate approach. When using a later release of the same study/consortium to replicate associations, were the older studies excluded from the new release? What is the degree of overlap between the two, and can this be considered independent replication? If the initial study is included in the latter release, wouldn’t one expect the univariate p-value to decrease based on the larger sample size regardless of it being a true signal?When evaluating the independence of new multivariate associations, it seems that pruning was based on distance (SNPs at least 0.5 Mb apart), but was LD was explicitly considered based on an r2 cut-off?Why were different significance thresholds used for different studies? HaemgenRBC 2012: p<1�10-8, HaemgenRBC 2016: p<8.319�10-9, GEFOS2015: p<1.2�10-8In the Reanalysis section the authors state “Indeed the very different multivariate patterns of effect size suggest that not only are these associations non-redundant but likely involve different biological mechanisms as well.” Is there evidence to support this statement? Do we really think that different effect sizes mean different mechanisms?**********Have all data underlying the figures and results presented in the manuscript been provided?PLOS Geneticsdata availability policy, and numerical data that underlies graphs or summary statistics should be provided in spreadsheet form as supporting information.Large-scale datasets should be made available via a public repository as described in the Reviewer #1: YesReviewer #2: YesReviewer #3: Yes**********what does this mean?). 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With regards to the authors’ “response to reviewers”, it would have been less time intensive to review if the authors highlighted the text they added in the manuscript. Or, at least mention the relevant page numbers wherever “We have now included […] in the manuscript” is mentioned in their response. I’d request the authors to consider giving this info and/or an annotated manuscript in any subsequent review.*Minor Comments*1. The authors added “More generally, although not necessarily essential for our analyses here, it is always helpful to include additional key meta-data .” However, earlier the authors mentioned in section 6.1 that they dropped variants from GWAS datasets that had missing MAF or for which reference/alt alleles did not match across phenotypes. So, I am not sure why the authors say meta-data such as “reference allele, or effect allele, the minor allele frequency” are “not necessarily essential” for their analyses. It may not be necessary for implementing their R program bmass but these info seem essential for subsequent interpretation .2. In the response, the authors mention “In particular our current framework does not immediately have options for including covariates in the multivariate analysis, so correcting for confounders such as population structure should be done on the first set of univariate analyses. We have added specific mention of this to the Discussion.” In the Discussion, I found this text: “in many cases summary data remain much easier to obtain and work with; there are big practical advantages as well to modular pipelines that first compute summary data and then use these as inputs to subsequent (more sophisticated) analyses. For example, the multivariate analyses we present here are simplified by assuming that the summary data were computed while adequately adjusting for population stratification.” I may have missed other relevant text but the above quoted text from Discussion does not seem to emphasize/convey the message that any necessary covariate adjustment *has* to be done before obtaining the single-trait GWAS summary statistics, and cannot be done at the multivariate analysis step. So, if one has access to GWAS summary data from a study that did not adjust for population stratification, one cannot expect it to be taken care of by the multivariate analysis.3. In the response, the authors mention “Under the scenario where a consortium finds that running a multivariate analysis does not indicate any new SNPs as being genome-wide significant, then we would not criticize them for choosing to focus on univariate analyses. We have added a discussion of this to the manuscript.” I have probably missed but I couldn’t find this discussion in the manuscript.4. It is not clear if the BFav approach is only applicable to continuous traits . If so, it is worth emphasizing.Reviewer #3: No additional comments.**********Have all data underlying the figures and results presented in the manuscript been provided?PLOS Geneticsdata availability policy, and numerical data that underlies graphs or summary statistics should be provided in spreadsheet form as supporting information.Large-scale datasets should be made available via a public repository as described in the Reviewer #1: YesReviewer #2: YesReviewer #3: Yes**********what does this mean?). 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Developing high yielding rice varieties that are tolerant to drought stress is crucial for the sustainable livelihood of rice farmers in rainfed rice cropping ecosystems. Genomic selection (GS) promises to be an effective breeding option for these complex traits. We evaluated the effectiveness of two rather new options in the implementation of GS: trait and environment-specific marker selection and the use of multi-environment prediction models. A reference population of 280 rainfed lowland accessions endowed with 215k SNP markers data was phenotyped under a favorable and two managed drought environments. Trait-specific SNP subsets (28k) were selected for each trait under each environment, using results of GWAS performed with the complete genotype dataset. Performances of single-environment and multi-environment genomic prediction models were compared using kernel regression based methods (GBLUP and RKHS) under two cross validation scenarios: availability (CV2) or not (CV1) of phenotypic data for the validation set, in one of the environments. Trait-specific marker selection strategy achieved predictive ability (PA) of genomic prediction up to 22% higher than markers selected on the bases of neutral linkage disequilibrium (LD). Tolerance to drought stress was up to 32% better predicted by multi-environment models  under CV2 strategy. Under the less favorable CV1 strategy, the multi-environment models achieved similar PA than the single-environment predictions. We also showed that reasonable PA could be obtained with as few as 3,000 SNP markers, even in a population of low LD extent, provided marker selection is based on pairwise LD. The implications of these findings for breeding for drought tolerance are discussed. The most resource sparing option would be accurate phenotyping of the reference population in a favorable environment and under a managed drought, while the candidate population would be phenotyped only under one of those environments. Drought stress is a major constraint for rice production in rainfed rice growing ecosystems that represent approximately 45% of rice growing areas . It is eGenomic selection (GS) has recently emerged as an alternative option to conventional marker-assisted selection targeting mapped QTLs –17. By sOne major feature of all the above-mentioned genomic prediction studies is the use of phenotypic data from only one environment, usually non-stressed. The main reason for such focalization was the lack of appropriate statistical framework that model G×E interactions for the purpose of genomic prediction. A number of such frameworks have recently been proposed. First, the single-trait single-environment genomic best linear unbiased prediction (GBLUP) model was extended to multi-environment context . Then a et al.  and the three possible 2x1 combinations, . In each case, the PA was assessed with two different cross-validation schemes. The first method (CV1), which resembled what was done in the single environment, used 80% of the observations as a training set and the remaining 20% as the validation set and assumed that phenotypic observations for the two or three environments were available for the individuals serving as training set, and that no phenotypic data were available for the individuals in the validation set. CV1 corresponds to the situation in which the phenotypes of newly generated individuals have to be predicted based only on their genotypic information . The secUnder both single and multi-environment models, one hundred replicates were computed for all random partitioning in the training and validation sets. The PA of each partition was calculated as the Pearson correlation coefficient between predictions and phenotypes in the validation set. The resulting estimates of PA were averaged and the associated standard error was calculated. For the multi-environment models, the correlation was calculated within each environment. For each trait  and each statistical method , the same partitions were used to compute the PA. The number of replicates under GWAS-derived marker selection method was limited to 20.The correlation coefficient data of all prediction experiments were transformed into a Z-statistic using the equation: Z = 0.5{ln[1 + r]−ln[1 − r]} and analyzed as a dependent variable in an analysis of variance. After estimation of confidence limits and means for Z, these were transformed back to r variable. In each case, ANOVA was performed to partition the variance of PA into different sources, with all effects declared as fixed.Box-plots of the adjusted means of the three phenotypic traits under the three environments are presented in Correlation between the adjusted means under non-drought (E1) and drought environments (E2 and E3) was above 0.50 and highly significant for DTF and PH, close to zero for GY . CorrelaH² was rather high even for GY. H² ranged from 0.75 to 0.86 for DTF, from 0.68 to 0.91 for GY, and from 0.81 to 0.96 for PH. Given the differences in the experimental design in the three environments, combined analysis of data was performed using accessions’ adjusted mean from each environment. The genotype effect was highly significant for the three traits. This was also the case for the residual variance that included the phenotypic variance due to G×E interactions.Partitioning of the observed phenotypic variation into different sources of variation via the mixed model analysis is shown in Within the 215k genotypic dataset, the average marker density along the 12 chromosomes was one marker every 1.95 kb. However, the SNP loci were unevenly distributed . The 28kThe distribution of MAF was skewed toward low frequencies in both 215k and 28k datasets. The MAF was below 10% for 50% and 37% of loci in 215k and 28k datasets, respectively.The average LD was rather low, 0.123 and 0.063 in the 215k and 28k datasets, respectively. The decay of LD along physical distance is presented in the . For betindica group and the fifth assembled accessions from aus group. The distance-based analysis of diversity also confirmed the assignment of the accessions to indica and aus groups as well as the presence of subgroups within the indica group , performed similarly. Whatever the environment, the size of the population, and the trait, the differences in PA between the two methods did not exceed 2%.Comparisons of the PA of the multi-environment genomic predictions for the three traits under the two cross validation strategies, three drought environments and two prediction methods are summarized in Whatever the trait or the environment, the multi-environment models combined with the CV2 strategy provided the highest average PA of 0.630, and outperformed single environment models, with an average gain of 22% for DTF, of 6% for GY and 15% for PH. Under CV2, the multi-environment models using interaction between the non-stressed environment E1 and one of the drought environments E2 or E3, to predict the phenotypic performance in E2 or E3 (i.e. E2(E1) or E3(E1)), resulted in similar PA to single environment models. Conversely, the multi-environment model E2(E3) and E3(E2) resulted in PA gains of 22% and 13% respectively compared to single environment model. The multi-environment models predicting phenotypic performances in one of the drought environments, using data from the non-stressed environment and the remaining drought environment (i.e E2(E1+E3) and E3(E1+E2)), resulted in PA gains of 24% over the corresponding single environment model.Performances of the multi-environment prediction models combined with the CV1 strategy (average PA of 0.408) did not significantly differ from their single environment model counterpart, whatever the trait and the environment and the prediction method considered. Multi-environment models predicting performances in drought stressed environments from E2(E1+E3) and E3(E1+E2) or from E2(E1) and E3(E1) did not result in significantly different PA either.Among the multi-environment prediction models, RKHS, with average PA of 0.600 performed systematically slightly better than the GBLUP, average PA of 0.566.The drought stress applied in this study was so severe that 25% of the accessions did not reach the flowering stage. However, the stress corresponded to the drought severity that rice crops are often facing in the drought-prone rainfed lowland ecosystem of South Asia . Despiteindica group, the proportion of aus accessions increased in the population of 204 accessions from 12% to 17%.The promise of GS to improve response to selection in populations that are under artificial selection is often presented as depending on the availability of high-density genotypic data to maximize the number of QTLs in LD with at least one marker –17, and Compared to LD-derived marker selection, GWAS-derived marker selection led, on average, to 22% higher PA in cross validation experiments within the population of 280 accessions. The gains in PA were smaller  but still statistically significant, in cross validation experiments within the population of 204 accessions. Similar results were reported in a simulation-based comparison of GEBV obtained with the GBLUP method using relationship matrices derived from trait-specific markers or from non-trait-specific markers . The difThe PA of genomic predictions obtained with the multi-environment models were, on average, similar to their single environment counterparts under the CV1 cross-validation strategy, and significantly higher under the CV2 cross-validation strategy. Gains in the PA of the multi-environment models combined with CV2 were the highest in the two drought affected environments, E2 and E3. The two multi-environment models tested  led to significantly different predictive abilities. Under GBLUP, the overall average gain in PA was 7% and ranged from– 4% for GY to 15% for PH. These gains were significantly lower than the 30% gain reported in  comparinImproving resilience to drought during floral development and anthesis is an important target for major cereal crops including rice , 66. It In the present study, we showed that (i) reasonably high PA of genomic prediction can be obtained with as few as 3,000 SNP markers, even in a population of limited LD extent, provided the markers are selected on the basis of LD. (ii) Trait-specific markers selection resulted in higher PA of genomic prediction than markers selected on the basis of neutral LD, especially when the size of the training population is large enough to allow the accurate estimation of the effect of the markers. (iii) Tolerance to drought stress was better predicted by multi-environment models  that accounted for G×E interaction , than their single-environment model counterparts, when associated with the CV2 prediction strategy. (iv) Finally, yet importantly, even under the less favorable CV1 prediction strategy, multi-environment models achieved similar PA to their single environment counterparts.The final finding suggests the feasibility of a genomic breeding scheme aiming at simultaneous improvement of yield potential and drought tolerance. It requires a training population carefully phenotyped under both favorable environmental conditions and managed drought, while in the first step of selection, the candidate population would be phenotyped only under favorable environments. The selected candidate would be phenotyped under managed drought to ascertain their GEBV and to update the multi-environment prediction model for the next breeding cycle –70. The S1 Table(XLSX)Click here for additional data file.S2 Table(XLSX)Click here for additional data file.S3 Table(A) Predictive ability (PA) of genomic prediction obtained in cross validation experiments within the population of 280 rice accessions using GBLUP and RKHS prediction methods and 15 genotypic matrices combining three levels of minor allele frequency (MAF) and five levels of Linkage disequilibrium (LD). Mean predictive ability (PA) and standard deviation (STD) of 100 cross-validation replicates are presented. (B) Analysis of factors that influence the variation in predictive ability.(XLSX)Click here for additional data file.S4 TableComparison of Linkage disequilibrium (LD) and GWAS based marker selection.(XLSX)Click here for additional data file.S5 Table(XLSX)Click here for additional data file.S6 Table(XLSX)Click here for additional data file.S1 Fig(TIF)Click here for additional data file.S2 FigThe curve represents the average r² according to pairwise distance between markers among the 12 chromosomes and the bars represent the associated standard deviation.(TIF)Click here for additional data file.S3 Figaus group. Other colors: different subgroups of indica as established in the 3K rice genome project [Green: accessions belonging to  project .(TIF)Click here for additional data file.


Variation in natural selection across heterogeneous landscapes often produces (a) among‐population differences in phenotypic traits, (b) trait‐by‐environment associations, and (c) higher fitness of local populations. Using a broad literature review of common garden studies published between 1941 and 2017, we documented the commonness of these three signatures in plants native to North America's Great Basin, an area of extensive restoration and revegetation efforts, and asked which traits and environmental variables were involved. We also asked, independent of geographic distance, whether populations from more similar environments had more similar traits. From 327 experiments testing 121 taxa in 170 studies, we found 95.1% of 305 experiments reported among‐population differences, and 81.4% of 161 experiments reported trait‐by‐environment associations. Locals showed greater survival in 67% of 24 reciprocal experiments that reported survival, and higher fitness in 90% of 10 reciprocal experiments that reported reproductive output. A meta‐analysis on a subset of studies found that variation in eight commonly measured traits was associated with mean annual precipitation and mean annual temperature at the source location, with notably strong relationships for flowering phenology, leaf size, and survival, among others. Although the Great Basin is sometimes perceived as a region of homogeneous ecosystems, our results demonstrate widespread habitat‐related population differentiation and local adaptation. Locally sourced plants likely harbor adaptations at rates and magnitudes that are immediately relevant to restoration success, and our results suggest that certain key traits and environmental variables should be prioritized in future assessments of plants in this region. This natural complexity has significant impacts on individual survival and reproduction, and thus plant evolution  within grasses, forbs, shrubs, and deciduous trees native to the Great Basin, asking how common these signatures are, as well as which phenotypic traits and environmental variables were most commonly associated with these signatures. We also present results by taxonomic group, lifeform, lifespan, distribution, and mating system. This first objective encompassed all possible studies, including those that did not provide sufficient details for formal meta‐analysis, which allowed us to incorporate the broadest range of studies, including older studies that provided minimal quantitative detail. Our second objective was to examine links between the magnitude of trait and environmental divergence  among populations across multiple taxa, for the subset of experiments amenable to this approach, asking whether populations from more similar environments were more similar in phenotypic traits. We also used meta‐analysis to ask which traits and environmental variables showed the strongest patterns of association.We expected to find widespread evidence of local adaptation and its signatures in the plants of the Great Basin, and we hypothesize that phenological and size‐based traits, which show phenotypic variation in response to climate variation in both plants and animals Measured the trait(s) of the populations in at least one common environment .We began by using the search engines Google Scholar and Web of Science to search for combinations of key terms , only experiments in which outdoor reciprocal transplants or common gardens were performed using a local population in at least one garden were considered Appendix . AdditioOur dataset, which had uneven numbers of experiments representing each species, contained the possibility of bias associated with highly studied taxa influencing patterns more than less‐studied taxa. To ask how this affected overall results, we compared tallies of all scores without correcting for multiple experiments per species to tallies using an average score for each species for each signature. To generate these average scores for signature 1 and 2, we totaled all “Yes” and “Authors claim Yes” scores for each species and divided by the total number of scores  for that species. For signature 3, all forms of “Yes”  were totaled into a Y and divided by the total number of scores. Then, we averaged these per species scores to re‐calculate overall effects in which each species was represented only once, and compared the results of the different averaging methods for each signature.2.3As a complement to the survey of author‐reported results described above, we conducted a further, quantitative analysis of trait and climate values. Specifically, to examine associations between the differences in trait values and the differences in environmental and geographic distance among population origins, we utilized experiments from which population‐specific trait data and geographic coordinates could be extracted or obtained through author contact. Data from laboratory and greenhouse experiments were not considered for this extraction. First, we identified the most commonly measured traits across studies, which were then manually extracted from text, tables, or graphical data Appendix . Next, wR2 values of 0.2 or less were excluded from this particular analysis, and the median slope across experiments was retained as an estimate of the trait‐by‐environment relationship. The arbitrary cutoff (R2 = 0.2) for this step was used simply as a way to focus on and report effect sizes from some of the stronger biological relationships that could be of particular interest to managers, restoration practitioners, and evolutionary ecologists. Due to limited sample sizes for factors such as lifeform, mating system, geographic distribution, we did not include these factors in any of the quantitative analyses, but present lifeform  information for each trait response as additional results in the Appendix To reduce the likelihood of spurious correlations or false negative results, we limited this dataset to traits measured in at least 5 populations in at least 20 common garden locations , resulting in 81 locations (from 56 experiments) that measured at least one of eight frequently measured phenotypic traits Table  in the s33.1Our literature search revealed 170 published studies that measured trait responses from more than one population in at least one common environment, resulting in 327 separate experiments involving 121 taxa of 104 species of grasses, shrubs, forbs, and deciduous trees , and, similarly, forbs and shrubs had more population differentiation than grasses . There were no significant differences in signature 1 among plants with different geographic distributions, life span, or breeding systems.Of the 305 experiments appropriate for addressing among‐population trait variation (signature 1), 290 (95.1%) experiments reported finding variation among populations in at least one phenotypic trait, with 230 75.4%) of these 290 reporting significant variation, and 60 (19.6%) claiming such variation in the absence of any supporting statistics  that had differences between populations in over 75% of experiments (with or without supporting statistics) were floral structure, vigor, emergence, plant size, number of leaves, plant structure, shoot biomass, leaf structure, and number of inflorescences .Of the 161 experiments appropriate for testing trait‐by‐environment associations (signature 2), 131 81.4%) reported associations for at least one comparison, with 81 (50.3%) supported by statistical tests and 50 (31.1%) supported by claims in the absence of statistics  generated 39 scores (some experiments measured multiple fitness traits), with 27 scores (69.2%) reporting signature 3 for at least one fitness trait in at least one of the tested gardens during at least one sampling date, and the remaining 12 scores (30.8%) not reporting signature 3 at any point Figure c. Thirty3.5Artemisia tridentata), with a median of 1 (IQR = 1–4). The most highly represented species were Artemisia tridentata (25 experiments), Elymus elymoides (24), Ericameria nauseosa (17), Achnatherum hymenoides (17), Krascheninnikovia lanata (13), Pascopyrum smithii (11), Atriplex canescens (9), Leymus cinereus (9), and Poa secunda, (8). Results in which scores were averaged for each species (see Methods) were similar to uncorrected results: Signature 1 was 4% higher when corrected (98% vs. 94%), signature 2 was 1% lower when corrected (79% vs. 80%), and signature 3 was 8% higher when corrected (78% vs. 70%). Thus, uncorrected calculations were used throughout our study.The number of experiments per species in our dataset ranged from 1 (52 species) to 25 (Only 19 experiments (5.8%) used an experimental design that could control for maternal effects . An additional 30 experiments (9.2%) were unclear on this point, and the remaining 278 (85%) experimented directly on populations differing in maternal environment. The incidence of population differences (signature 1) was 100% in the 16 experiments that moderated maternal effects, 95% for the 259 that did not make an attempt, and 97% for the 30 which were unclear. Too few of the experiments that attempted to control for maternal effects were appropriate for measuring signature 2 (4 experiments) and signature 3 (1 experiment) to compare incidences of these signatures.3.6Overall, we found positive relationships between the magnitude of differences among populations in all eight phenotypic traits and the magnitude of differences between MAT and MAP at the collection locations Figure . The str4Our results represent the most extensive review of intraspecific variation and local adaptation for plants native to the floristic Great Basin, a region comprised of largely continuous but increasingly imperiled arid and semi‐arid plant communities  found differences between populations (signature 1) in the majority of traits measured in a common environment, which indicates that different traits are variable among populations, at both small and large geographic scales. Additionally, a clear majority (81.4%) of experiments found trait‐by‐collection environment associations (signature 2), suggesting that intraspecific variation is frequently an adaptive outcome of natural selection in heterogeneous environments (Linhart & Grant, There are many processes that can reduce or prevent the development of local adaptation, such as the lack of divergent selection between sites, high gene flow, rapid or extreme environmental change, high phenotypic plasticity, and/or low genetic diversity (Blows & Hoffmann, Some traits and environmental characteristics stood out as particularly important indicators of local adaptation and its signatures across the studied taxa. For example, in our quantitative comparison of divergence in traits and environments, flowering phenology was strongly affected by MAT, with a median change of 3.5 days in flowering time per degree change in MAT of collection origin. Flowering phenology, along with germination phenology, were also in the top tier of frequently measured traits that showed significant correlations with environmental variables, consistent with other studies that have shown reproductive (Bucharova, Michalski, et al., As in any review and analysis of published papers, there are elements of our design that were difficult to control. For example, consistent with other reviews (Gibson, Espeland, Wagner, & Nelson, Current approaches to seed sourcing in restoration and conservation include genetic (Williams, Nevill, & Krauss, 5Reestablishing and maintaining native plant communities in arid regions has proven challenging (Svejcar, Boyd, Davies, Hamerlynck, & Svejcar, None declared.EAL, OWB, FFK, EKE, RF, TNK, and JBSC conceived and designed the study; OWB conducted the literature search; OWB, ACA, FFK, JO, RCJ, and JBSC categorized, compiled, and extracted data; OWB, EAL, FFK, ACA, and MLF analyzed data; OWB, EAL, and ACA drafted the manuscript; all authors critically revised the manuscript for important intellectual content and approved of the version to be published.We would like to dedicate this paper to the memory of our co‐author Dr. Erin K. Espeland, friend and collaborator to all of us, who worked on this manuscript. Erin's light and life will never be forgotten by those who knew her, and we want to recognize her creative contributions to the field of plant ecology, including this effort. Erin is dearly missed. Click here for additional data file.


The objectives were to compare variance components, genetic parameters, prediction accuracies, and genomic-polygenic estimated breeding value (EBV) rankings for milk yield (MY) and fat yield (FY) in the Thai multibreed dairy population using five single nucleotide polymorphism (SNP) sets from GeneSeek GGP80K chip.The dataset contained monthly MY and FY of 8,361 first-lactation cows from 810 farms. Variance components, genetic parameters, and EBV for five SNP sets from the GeneSeek GGP80K chip were obtained using a 2-trait single-step average-information restricted maximum likelihood procedure. The SNP sets were the complete SNP set , top 75% set (SNP75), top 50% set (SNP50), top 25% set (SNP25), and top 5% set (SNP5). The 2-trait models included herd-year-season, heterozygosity and age at first calving as fixed effects, and animal additive genetic and residual as random effects.The estimates of additive genetic variances for MY and FY from SNP subsets were mostly higher than those of the complete set. The SNP25 MY and FY heritability estimates (0.276 and 0.183) were higher than those from SNP75 (0.265 and 0.168), SNP50 (0.275 and 0.179), SNP5 (0.231 and 0.169), and SNP100 (0.251and 0.159). The SNP25 EBV accuracies for MY and FY (39.76% and 33.82%) were higher than for SNP75 (35.01% and 32.60%), SNP50 (39.64% and 33.38%), SNP5 (38.61% and 29.70%), and SNP100 (34.43% and 31.61%). All rank correlations between SNP100 and SNP subsets were above 0.98 for both traits, except for SNP100 and SNP5 .The high SNP25 estimates of genetic variances, heritabilities, EBV accuracies, and rank correlations between SNP100 and SNP25 for MY and FY indicated that genotyping animals with SNP25 dedicated chip would be a suitable to maintain genotyping costs low while speeding up genetic progress for MY and FY in the Thai dairy population. Genomic selection refers to selection of animals based on genomic estimated breeding values (GEBV) ,2. CurrePrices for high-density genotyping chips are still too high for widespread use in the multibreed dairy cattle population in Thailand. The current option in the Thai dairy population of genotyping sires and highly represented dams in the pedigree with high-density chips and the rest of the cow population with low-density chips may need to remain in place until a suitable cheaper and effective option is found. One such option would be to design low-density chips with small numbers of single nucleotide polymorphism (SNP) that account for a reasonable percentage of the genetic variation for the traits of interest. Use of these low-density assays containing selected SNPs could potentially be as effective as high-density chips for a fraction of the price ,6. DeterCattle used in this research were from the Thai multibreed dairy population, a Holstein-upgraded population composed of purebred and crossbred cows, sires, and dams. Breeds represented in Thai dairy cattle are Holstein, Brahman, Jersey, Red Dane, Red Sindhi, Sahiwal, and Thai Native . The larPenicum maximum), Ruzi grass (Brachiaria ruziziensis), Napier grass (Pennisetum purpureum), and Para grass (Brachiaria mutica). Other sources of fiber were rice straw and agricultural byproducts .The majority of Thailand is hot and humid most of the year. Thailand’s climate is highly influenced by the seasonal monsoon weather characterized by wind pattern changes and heavy precipitation. Seasons are classified as winter (November to February), summer (March to June), and rainy (July to October). Yearly means across regions and seasons range from 17°C to 36°C for temperature, 1,200 to 1,600 mm/yr for rainfall, and 73% to 80% for relative humidity . Cows weBlood or semen samples were collected from 2,661 animals . Genomic DNA was extracted from semen samples using a GenElute Mammalian Genomic DNA Miniprep Kit , whereas a MasterPure DNA Purification Kit  was used for blood samples. Concentration and purity of DNA per sample was measured using a Thermo Fisher NanoDrop 2000 spectrophotometer . The minimum acceptable DNA concentration was 15 ng/μL with an absorbance ratio of 1.8 at 260/280 nm.The DNA samples were dried using a Freeze-dry machine for 12 hours Dried DNA samples of 50 μL were airmailed to GeneSeek  for genotyping with GeneSeek Genomic Profiler BeadChips (GGP). Budgetary restrictions determined the type of genotyping chip used for each DNA sample. The GeneSeek Genomic Profiler 80K chip (GGP80K) was used to genotype sires and highly represented cows in the pedigree (n = 139), and lower density chips were used to genotype the remaining cows , and Varocedure  using a Animal EBV for MY and FY for the five SNP sets  were computed using the estimates of variance components at convergence for each set of genotypes. The EBV accuracies for each trait were obtained as the correlation between EBV and true breeding values computed using the formula: 2 (SNP5) to 178,510.00±26,620.00 kg2 (SNP50) for MY and from 201.54±60.91 kg2 (SNP100) to 231.49±57.71 kg2 (SNP25) for FY, and additive genetic covariances ranged from 4,041.40±813.25 kg×kg (SNP5) to 4,758.10±984.95 kg×kg (SNP25). Estimates of environmental variances ranged from 467,000.00±22,641.00 kg2 (SNP25) to 488,910.00±19,276.00 kg2 (SNP5) for MY and from 1,033.30 ±57.18 kg2 (SNP25) to 1,065.90±60.37 kg2 (SNP100) for FY, and environmental covariances ranged from 14,780.00±945.30 kg×kg (SNP25) to 15,590.00±1,046.00 kg×kg (SNP100). Estimates of phenotypic variances ranged from 635,580.00± 13,307.00 kg2 (SNP5) to 650,520.00±13,794.00 kg2 (SNP100) for MY and from 1,257.00±33.25 kg2 (SNP5) to 1,267.20± 33.61 kg2 (SNP75) for FY, and phenotypic covariances ranged from 19,286.00±566.53 kg×kg (SNP5) to 19,678±580.44 kg×kg (SNP75).Estimates of variances and covariances for MY and FY using genomic-polygenic models with five sets of SNP are shown in Estimates of additive genetic variances and covariances for MY and FY were higher for models with SNP75 , SNP50 , SNP25  than those estimated using SNP100. However, the model with SNP5 yielded lower estimates of additive genetic variance for MY (−10.34%) and covariance between MY and FY (−1.15%), and higher estimate of additive genetic variance for FY (5.37%) than the model with SNP100. Conversely, environmental variances and covariances for MY and FY were lower for the model with SNP75 , SNP50 , SNP25  than those estimated using SNP100. The model with SNP5 produced a higher environmental variance for MY (0.40%), but a lower environmental variance for FY (−1.92%) and covariance between MY and FY (−2.19%) than the corresponding estimates from the model with SNP100. Lastly, estimates of phenotypic variances and covariances for MY and FY from models with SNP75, SNP50, and SNP25 were nearly identical (differences were near zero or below one percent) to the corresponding values from model with SNP100, whereas the corresponding differences for the model with SNP5 were all negative and mostly higher than one percent .The slightly higher additive genetic variances and covari ances but lower environmental variances and covariances for MY and FY obtained with SNP75, SNP50, and SNP25 than with the complete SNP set indicated that these SNP subsets may have been able to more accurately accounted for MY and FY additive variability in this population than the complete SNP set. This may have occurred because the SNP markers in these subsets were on the average more closely associated with quantitative trait locus (QTL) affecting MY and FY than the complete set of SNP .Heritabilities and additive genetic, environmental, and phenotypic correlations between MY and FY are presented in Heritability estimates for MY and FY across the five SNP sets were similar to values estimated in previous studies in the Thai dairy population using various SNP sets . HeritaEstimates of additive genetic, environmental and pheno typic correlations between MY and FY across the five sets of SNP were virtually identical . CorrelaThe comparable or slightly higher additive genetic vari ances and heritabilities for MY and FY from SNP25, SNP50, and SNP75 than from SNP100 indicated that selecting a subset of SNP genotypes with the approach used here would be a reasonable alternative to increase the effectiveness of genomic-polygenic evaluation and selection in the Thai dairy population. However, reducing SNP genotypes to 3,826 SNP (SNP5) or 5% of the SNP in the GGP80K chip would yield lower variance component and heritability estimates than with the SNP25, SNP50, and SNP75 subsets, or with the complete SNP set. Further, the highest genetic variance component and heritability estimates for MY and FY obtained with SNP25 indicated that higher EBV prediction accuracies and selection responses for these traits would be achieved using a genomic-polygenic model with SNP25 than with SNP100, SNP75, SNP50, and SNP5.The accuracies genomic-polygenic EBV for MY and FY with the five sets of SNP genotypes  are shown in The higher mean EBV accuracies obtained with four GG P80K subsets than with the complete SNP set supported the findings from previous research in dairy ,27–29 anPairwise Spearman rank correlations between MY and FY EBV from of the complete SNP set and each of the four SNP subsets are shown in Estimates of additive genetic variances, heritabilities, and EBV accuracies for MY and FY from genomic-polygenic models with SNP subsets were higher than those with complete SNP set. Genomic-polygenic evaluation with SNP25 had the highest estimates of additive genetic variances, heritabilities, and EBV accuracies for MY and FY. Further, genomic-polygenic EBV obtained using SNP subsets and complete SNP set had high rank correlations. Thus, utilization of the SNP25 set would be a suitable alternative to reduce genotyping costs and increase selection response for MY and FY in this dairy population.


A growing number of studies clearly demonstrate a substantial association between chronic obstructive pulmonary disease (COPD) and cardiovascular diseases (CVD), although little is known about the shared genetics that contribute to this association.case = 12,550, Ncontrol = 46,368) from the International COPD Genetics Consortium and four primary cardiac traits: resting heart rate (RHR) , high blood pressure (HBP) , coronary artery disease (CAD), and stroke  from UK Biobank, CARDIoGRAMplusC4D Consortium, and International Stroke Genetics Consortium data.We conducted a large-scale cross-trait genome-wide association study to investigate genetic overlap between COPD (NP = 0.008).RHR and HBP had modest genetic correlation, and CAD had borderline evidence with COPD at a genome-wide level. We found evidence of local genetic correlation with particular regions of the genome. Cross-trait meta-analysis of COPD identified 21 loci jointly associated with RHR, 22 loci with HBP, and 3 loci with CAD. Functional analysis revealed that shared genes were enriched in smoking-related pathways and in cardiovascular, nervous, and immune system tissues. An examination of smoking-related genetic variants identified SNPs located in 15q25.1 region associated with cigarettes per day, with effects on RHR and CAD. A Mendelian randomization analysis showed a significant positive causal effect of COPD on RHR  contains supplementary material, which is available to authorized users. Chronic obstructive pulmonary disease (COPD) is a chronic inflammatory disease of the lungs that is the fourth leading cause of death in the world, accounting for more than 3 million deaths each year . There iWe and colleagues recently identified shared genetic architecture between COPD and lung function/pulmonary fibrosis , asthma Therefore, we investigated the genetic correlation between COPD and cardiac traits and attempted to describe the specific shared genetic loci and biological pathways between traits. We conducted a large-scale, genome-wide association study (GWAS) cross-trait analysis of COPD from the International COPD Genetics Consortium (ICGC) and 4 cardiac traits from UK Biobank, CARDIoGRAMplusC4D Consortium, and International Stroke Genetics Consortium (ISGC) data, including RHR, high blood pressure (HBP), CAD , and strcase/control = 60,801/123,504) from CARDIoGRAMplusC4D Consortium [case/control = 40,585/406,111) from ISGC [We included 4 major data sources—ICGC, UK Biobank, CARDIoGRAMplusC4D Consortium, and ISGC—in the overall study design Fig. . Previounsortium , and strrom ISGC . CAD diarom ISGC . The ISGWe performed GWAS analysis on RHR and HBP using a linear mixed model (LMM) method  based onWe conducted post-GWAS genetic correlation analysis with LDSC, which estimates genetic correlation between true causal effects of two traits (genetic correlation estimate Rg ranging from − 1 to 1) . CardiacIn addition, we performed genetic correlation analysis between COPD and ischemic stroke subtypes, and metabolic traits .To characterize genetic overlap at the level of functional categories, we estimated genetic correlation between COPD and cardiac traits in 11 annotation categories using LDSC. These annotations included transcribed regions, transcription factor binding sites, super-enhancers, introns, DNaseI digital genomic footprinting (DGF) regions, DNaseI hypersensitivity sites (DHSs), fetalDHSs, and histone marks h3k9ac, h3k4me1, h3k4me3, and h3k27ac . For eacP < 0.05/1703) according to the original method description [To identify local genetic correlations between COPD and cardiac traits, we performed ρ-HESS to estimate local genetic correlation between a pair of traits at each LD-independent region in the genome . Approxicription .After assessing genetic correlations among all traits, we applied 2 cross-trait GWAS meta-analysis methods to combine binary or continuous traits . We usedP < 1 × 10− 5, r2 > 0.2, and < 500 kb away from a peak). The variant with the lowest p-value was defined as the sentinel variant. Putative genes for each variant were considered to be those within the clump. We used Variant Effect Predictor based on Ensembl/GENCODE basic transcripts database for detailed variant annotation [We applied PLINK  clumpingnotation .To identify the 99% credible set of variants within each 500-kb sentinel variant, we identified a credible set of causal variants at each shared locus that met cross-trait meta-analysis criteria using the Bayesian likelihood fine-mapping algorithm . The algTo gain biological insights for shared genes, we used the WebGestalt tool  to assesP-values using false discovery rate (FDR) Benjamini-Hochberg procedure (FDR < 0.05).To identify shared COPD and cardiac trait gene expression associations in specific tissues, we conducted TWAS using the FUSION software package based on 43 GTEx (version 6) tissue expression weights . MultiplTo evaluate the potential effect of smoking-related genetic variants between COPD and cardiac traits, we retrieved 129 genome-wide significant SNPs for cigarette per day (CPD) from the Tobacco and Genetics Consortium (TAG) . We alsoP-value less than 5 × 10− 8. Prior to running MR-PRESSO, we removed strand-ambiguous SNPs and SNPs in the MHC region (chr6:25-34 M).Finally, we performed MR analysis using Mendelian Randomization Pleiotropy RESidual Sum and Outlier (MR-PRESSO)  in ordergc) from LDSC for RHR and HBP were 1.8405 [LDSC intercept: 1.1256, standard error (SE): 0.0502; Additional file The phenotype–genotype association test was carried out on ~ 460,000 samples and ~ 5.26 million SNPs from UK Biobank data after quality control. The genomic inflation factor (λP = 0.0434) and HBP   and h3k9ac  .In partitioned LDSC analysis, we used 11 functional annotations to evaluate genetic correlations between COPD and cardiac traits by specific functional category. The highest magnitude of significant genetic correlation between COPD and HBP was in introns  had strong local genetic correlations (P = 7.42 × 10− 7)  and 5q32  regions . After pruning, we found 21 loci significantly associated with COPD and RHR (Table (Pmeta = 1.92 × 10− 19), located at the HHIP locus. The second most significant locus  was located at the CASZ1 locus [ASSET and CPASSOC were applied for genome-wide meta-analysis to identify genetic loci associated with COPD and cardiac traits  [(Pmeta = 9.69 × 10− 19).In addition, we found 22 loci significantly associated with both COPD and HBP (Table  10− 47) , NotablyHHIP, we also observed two more overlapping significant loci in meta-analyses of COPD/RHR and COPD/HBP. The first locus was EEFSEC  [BMP8A  [Pmeta = 3.17 × 10− 12) is a transcript of long non-coding RNA gene RP11-563P16.1. The second locus represented by rs8108474 (Pmeta = 1.49 × 10− 8) was mapped to DMPK [Pmeta = 3.80 × 10− 8), was mapped to BCAR1 [In addition to rs7655625 in OPD/HBP) , 41. TheOPD/HBP) , 43. Fin to DMPK . The thito BCAR1 . DetaileWe identified a credible set of causal SNPs using Bayesian fine-mapping at each shared loci meeting significance criteria in the COPD–cardiac traits meta-analysis. The credible set of variants at each locus were 99% likely to contain the causal variant. A list of credible sets of SNPs for each locus is provided in Additional file MACF1, SYNPO2L, RIN3, TNS1, and MLN) for COPD/RHR  for COPD/HBP  for COPD/CAD    for expression of cross-trait-associated genes for COPD and RHR traits, the top of which was brain amygdala Fig. . In addiTo identify associations between COPD and cardiac traits with gene expression in specific tissues, we conducted TWAS analysis in 44 GTEx tissues. A total of 231 gene–tissue pairs were significantly associated with COPD, in addition to 8504 gene–tissue pairs with RHR, 8272 gene–tissue pairs with HBP, and 805 gene–tissue pairs with CAD. Most associations were found in heart, vascular system, and lung tissues. Notably, 18 COPD-associated gene–tissue pairs were shared with RHR, 16 pairs were shared with HBP, and 2 pairs were shared with CAD  with CPD genetic effect, were also identified to be associated with RHR and CAD traits. These SNPs also had a moderate effect in COPD with M-values more than 0.5 , but not on HBP  or CAD  . Table S6. Local genetic coveriance analysis between COPD and HBP (only P < 0.01 shown in this table). Table S7. Local genetic covariance analysis between COPD and CAD. Table S8. Genome-wide significant loci by cross-trait meta-analysis at sentinel SNPs. Table S9. Genome-wide significant loci by cross-trait meta-analysis at sentinel SNPs. Table S10. Genome-wide significant loci by cross-trait meta-analysis at sentinel SNPs. Table S11. Detailed annotation of cross-trait meta-analysis genome-wide significant SNPs. Table S12. Fine-mapping credible set analysis for 21 top loci. Table S13. Fine-mapping credible set analysis for 22 top loci. Table S14. Fine-mapping credible set analysis for 3 top loci. Table S15. Missense variants in 99% credible set. Table S16. Missense variants in 99% credible-set. Table S17. Missense variantsin 99% credible-set. Table S18. GO biological process pathway analysis for COPD and RHR. Table S19. GO biological process pathway analysis for COPD and HBP. Table S20. GO biological process pathway analysis for COPD and CAD. Table S21. Significant overlap transcriptome-wide association analysis results. Table S22. Characterization of trait-specific association for the smoking related. Table S23. Mendelian randomization analysis between COPD and cardiac traits. (XLSX 240 kb)Additional file 2:Online Data Supplemental Text. (DOCX 129 kb)Additional file 3:Figure S1. QQ plot of resting heart rate. Figure S2. QQ plot of high blood pressure. Figure S3. Genetic Correlation between COPD and Cardiac Traits by Functional Category. (PDF 360 kb)