Low‐altitude aerial imaging, an approach that can collect large‐scale plant imagery, has grown in popularity recently. Amongst many phenotyping approaches, unmanned aerial vehicles (UAVs) possess unique advantages as a consequence of their mobility, flexibility and affordability. Nevertheless, how to extract biologically relevant information effectively has remained challenging.irMeasurer, an open‐source and expandable platform that combines automated image analysis, machine learning and original algorithms to perform trait analysis using 2D/3D aerial imagery acquired by low‐cost UAVs in rice (Oryza sativa) trials.Here, we present AWe applied the platform to study hundreds of rice landraces and recombinant inbred lines at two sites, from 2019 to 2021. A range of static and dynamic traits were quantified, including crop height, canopy coverage, vegetative indices and their growth rates. After verifying the reliability of AirMeasurer‐derived traits, we identified genetic variants associated with selected growth‐related traits using genome‐wide association study and quantitative trait loci mapping.irMeasurer‐derived traits had led to reliable loci, some matched with published work, and others helped us to explore new candidate genes. Hence, we believe that our work demonstrates valuable advances in aerial phenotyping and automated 2D/3D trait analysis, providing high‐quality phenotypic information to empower genetic mapping for crop improvement.We found that the A 236: 1229–1231Yang & Zhai, .See also the Commentary on this article by  Oryza sativa) is one of the key staple foods, feeding > 50% of the global population  affordability, resources are usually limited and solutions need to be cost‐effective; (3) accuracy and repeatability, analysis results should be consistent and reproducible in other trials; (4) processing cycle, the duration between breeding cycles or multiseason experiments is often brief, requiring data to be processed, analyzed and fed‐back promptly to enable timely decisions  and their applications in crop monitoring resulting from their mobility, throughput and affordability , which are complex as a consequence of changing water levels  and many voluntary plants (e.g. duckweed) compared with dryland crops (Ogawa irMeasurer‐derived traits collected from hundreds of rice landraces and recombinant inbred lines (RILs) in a multiseason case study (2019–2021) to genetic mapping studies (i.e. GWAS and QTL mapping) and identified reliable loci.In order to address some of the challenges, we have developed AirMeasurer, an open‐source platform that automates trait analysis for rice trials using 2D orthomosaics and 3D point clouds acquired by low‐cost UAVs. First, we established tailored protocols for regular flight missions and data pre‐processing. Secondly, varied 2D/3D analysis algorithms were integrated into the platform to quantify static traits such as seedling number, plant height, canopy coverage and vegetative indices, using morphological, spectral and textural signals. Thirdly, we developed an original algorithm to compute dynamic traits based on static traits, including growth rates of the target traits and their rapid growth phases, which were time‐consuming or impossible to score previously. To ensure that our work could reach the broader research community, we created a graphical user interface (GUI) for nonexperts to use. Finally, to validate the platform and its utility in research, we applied the Aet al., japonica, 40 intermedia and 111 indica types; (2) the other studied 191 homozygous RILs in Hainan (the 2020/2021 seasons), derived from the crossing parents Nipponbare (Oryza sativa ssp. Japonica) and 93–11 (Oryza sativa ssp. Indica), two popular varieties  were analyzed manually using the Fiji/ImageJ software  to obtain plot‐level canopy height at eight time points throughout the 2019 season .In‐field ground truth measurements to validate Aexperiment setups – including trial design  and ground control points  and 3D point cloud files (in LAS) from acquired aerial images using the pix4dmapper software  expans Figs , S1e;  algorithm  and a digital elevation model  using the Lindsay,  in whiteols Fig. . We defiols Fig. . The DSMMezirow,  in openCCHM Fig. , upper wCHM Fig. , lower. CHM Fig. , upper, asy MPE, GRID, AirSurf, R/UAS::plotshpcreate and FIELDimageR have been applied to segment plots or plant blocks for species such as soybean, wheat and maize, which were valuable advances for dryland crops. We trialled them in our paddy rice experiments and encountered segmentation issues as a consequence of unclear plot boundaries, changing water levels and overlapped rice plants during ripening  appH90th, i.e. top 10% of the 3D points; see reasoning in Notes H95th as a result of short seedlings  over time  using ase Fig. , left; .The Gaussian‐fitted height curve fxdiff, indicating height change phases. To locate theses KPs, we found the first (f′xdiff) and second (f″xdiff) derivatives on fxdiff; KP1 was detected when f′xdiff = 0 and f″xdiff > 0, whereas KP2 was detected when f′xdiff = 0 and f″xdiff < 0. We named the phase between KP1 and KP2 as rapid height growth phase , denoting the period of rapid stem elongation.Turning points  to locate the day when canopy height was changing at the fastest growth rate , between 0 DAS and the FGRheight day.Within the Because dynamic or longitudinal phenotypes can be more informative in revealing plant–environment interactions  and CCI . We also applied fxheight  using a normalized‐curvature curve fxcuv  between 0 DAS and the Maxheight day also were quantified. To compute fxcuv, we used the equation below:fxiheight, Gaussian‐fitted height curve; i is between 10 and 115 DAS).Then, we applied the above steps to analyze airmeasurer GUI using Python programming language together with a modular design, so that each function or module in the platform could be accessed and modified independently. We used the Tkinter toolkit .A relative lack of open analytic solutions impedes researchers from exploiting newly introduced methods. Hence, we chose to develop the Shipman,  to develet al., erl scripts  was performed . We uploaded a series of testing files to our GitHub repository  for researchers to test and improve Aairmeasurer software consists of an input (red dash rectangle) section and a unified workspace , with coloured shading areas denoting 15th–85th percentile confidence intervals , followed by the japonica group  and the indica group . For the 2020 height measures, three flights  were selected to visualize plot‐based height differences  and machine‐learning modelling Notes , includiirMeasurer‐derived traits were validated by a range of ground truth data. The correlation between the 2019‐AirMeasurer‐derived Maxheight and the 2019‐manual‐scored Maxheight was visualized using 177 landraces  with a high accuracy. We also verified the AirMeasurer‐measured height at eight time points (35–115 DAS) using 1416 plots (177 plots per time point) against plot‐based canopy height that was measured manually from eight 3D point clouds  were used to compare the two traits obtained by manual and AirMeasurer‐based approaches, both resulting in strong correlations: R2 = 0.9497 for CCI  was set as 2.5 and known loci were indicated with red arrows. Two QTLs related to Maxheight were identified , c. 0 kb from Ghd7.1, a gene plays an important role in grain productivity and rice heading , two QTLs were identified  and c. 0.97 mb from Ghd7.1 (LOD = 6.5). The 2021 AGRheight (0 DAS – the FGRheight day) was used and located same QTLs  and c. 30 kb from Ghd7.1 (LOD = 3.2). In particular, we identified four loci with the 2020 RGPheight trait , which regulates rice stem development , which is not associated with any known gene.Besides the static trait, we applied the dynamic trait, ied Fig. : c. 110 TLs Fig. : c. 100 ait Fig. , two strirMeasurer‐derived growth traits such as CCI and ExG. The 2021 AGRCCI (0 DAS – the FGRCCI day) was used to locate two QTLs , overexpressing the gene leads to dwarfing and increased tillers, and thus the canopy size , indicating a locus linking to canopy expansion. Actually, TAC1  had three peaks: 20.05 Mb, 21.05 Mb and 21.55 Mb, respectively , several vegetation‐related QTLs were identified on chromosome 1  = 12, indicated with a blue arrow; Fig. c. 208 kb from sd1. On chromosome 3, the strongest signal (−log10(P) = 6.42) identified was c. 10.7 kb from the OsHox32 gene, which is known for pleiotropic effects on plant architecture and leaf development  = 7.14; Fig. Maxheight scoring  = 6.91–6.63; Fig. Besides QTL mapping, we utilized Aue Table  and a faAGRheight (0 DAS – the Maxheight day) and identified four significant SNPs associated with the trait  = 10.62; c. 208 kb from sd1), the second strongest signal (−log10(P) = 6.98) was c. 11 kb from OsHox32 on chromosome 3, followed by a strong signal (−log10(P) = 6.6) c. 122 kb from NOG1 on chromosome 1, and the last one c. 373 kb from OsGSK2 on chromosome 5. The NOG1 gene increases rice grain production  = 8.29; c. 188 kb) and OsGSK2 (−log10(P) = 8.07; c. 373 kb). To test other height‐related dynamic traits, we used the two‐season AGRheight (0 DAS – the FGRheight day) and obtained similar results  = 8.28–10.28; c. 306.106–372.984 away from OsGSK2). Table Next, we chose the 2019 ait Fig. , left. Blts Fig. , with a irMeasurer‐derived CCI and ExG traits to perform GWAS and found three SNPs (Table AGRCCI trait (0 DAS – the MaxCCI day), two signals were identified  = 7.4) was c. 329 kb from the Pit gene on chromosome 1, a disease resistance gene  = 6.49) was c. 224 kb from the PFPβ gene on chromosome 6, which associates with carbon metabolism during grain‐filling , the strongest signal (−log10(P) = 6.07) was c. 304 kb from the CCP1 gene on chromosome 1  onee 1 Fig. , which fes Notes .et al., In order to exploit available genomic resources to address climate change challenges, selected traits need to be assessed under field conditions across locations and years. Conventional phenotyping requires making many measurements of target traits, which is arduous and difficult to implement at busy periods of the season, resulting in newly developed methods  at different growth stages for paddy rice (in particular the landraces), which was complex to study during the season.The use of Aet al., et al., Inspired by previous work  and greater repetition of trials across locations and years in order to produce reliable estimates of trait heritability and QTLs.Additionally, through integrating dynamic traits into machine‐learning modelling, we predicted a complex trait, heading date, which could lead to new estimates of QTL × Environment interactions. Recent studies  also indicate potentially useful loci.Genetic mapping of dynamic or longitudinal traits can be a powerful tool for developing novel molecular markers that cannot easily be revealed using static measurement, partly because of temporal regulation of gene expression  led to the consistent identification of signals such as the nearby genes  relevant to plant height, architecture and growth regulation, indicating the value of dynamic traits in studying genetically diverse landrace populations. Moreover, using dynamic traits such as AGRheight, Maxcov and FGRExG, we located some previously unknown strong signals, which may be valuable for identifying small effects of individual allelic differences  were just c. 17.14 kb from OsSOC1 gene on chromosome 3 (−log10(P) = 5.79) and c. 30.24 kb from the Hd3a gene on chromosome 6 (−log10(P) = 4.65), both of which are heading‐date related , rainfall, or heavy fog  nature illuminance – image colour and contrast could vary noticeably with changing light conditions, and we mitigated this issue by conducting a field‐level imaging; and (4) aviation regulations – the change in aviation regulations casts uncertainty on aerial phenotyping, requiring regular communications with local civil air traffic control authorities; without official authorization, the payload capacity of a drone was restricted, indicating the advantage and practicality in using small drones for routine phenotyping. In the study, we used an RGB camera for growth‐related spectral analysis as a low‐cost alternative to more costly hyper‐ or multispectral sensors. It is worth noting that visible spectra are limited in early diseases detection and sensing abiotic stress responses as accurate spectral information is key to assess plant responses to certain external stimuli  pix4dmapper software to generate 3D point clouds and 2D orthomosaics. We have tested several open‐source software types  for the same task and encountered technical problems such as prolonged computational time, incorrect geo‐referencing, and mismatched 2D/3D patches. Another problem during the processing was to denoise large‐scale 3D point clouds. We used the SOR for the task, which required 15–20 min to denoise 60+ million 3D points. Hence, algorithms such as local‐outlier and cluster‐based outlier detection  can be observed on different panicles. By flying smaller UAVs (e.g. DJI Mini) at a 4‐m altitude, we could achieve a ground‐sampling‐distance (GSD) of 1–1.5 mm per pixel. Thus, it is feasible to measure anther extrusion using high‐density 3D points acquired by smaller drones, which could find applications in hybrid breeding programmes where the selection of male parents with certain flowering characteristics is crucial.Low‐cost UAVs and dynamic trait analysis also could be applied to examine traits such as grain‐filling, which are challenging to quantify using conventional approaches. By conducting daily flights during ripening, fitted curves could enable the estimation and eventually the prediction of the initiation and duration of this key trait. However, it is expected that the Gaussian function might not be suitable for such growth patterns, and thus other fitting methods shall be explored.irMeasurer to analyze other crops, we have successfully applied the platform to examine wheat trials with limited parametric changes . UAV‐based phenotyping was supported by the National Natural Science Foundation of China (32 070 400 to Ji Zhou). RJ and Ji Zhou were partially funded by the United Kingdom Research and Innovation's (UKRI) Biotechnology and Biological Sciences Research Council (BBSRC) Designing Future Wheat Programme (BB/P016855/1). JC was supported by the BBSRC's National Productivity Investment Fund CASE Award, Norwich Research Park's Biosciences Doctoral Training Partnership (BB/M011216/1 to Ji Zhou). GS and Jie Zhou were supported by the Fundamental Research Funds for the Central Universities in China (JCQY201902), as well as by the Jiangsu Collaborative Innovation Center for Modern Crop Production, and the Natural Science Foundation of the Jiangsu Province (BK20191311 to Ji Zhou). Both Ji Zhou and EC were partially supported by a PhenomUK project grant funded by the UKRI (MR/R025746/1 to Ji Zhou).http://creativecommons.org/licenses/by/4.0) waiver applies to the data and results made available in this paper. The source code, testing data, and other datasets supporting the results presented here are available at https://github.com/The‐Zhou‐Lab/UAV/releases/tag/V2.0.2. Other data and user guides are openly available on request.The source code is distributed under the Creative Commons Attribution 4.0 international license, permitting academic use, distribution, reproduction in any medium, provided you give appropriate credit to the original authors and the source, provide a link to the Creative Commons license, and indicate if changes were made. Unless otherwise stated, the Creative Commons Public Domain Dedication .Table S2 Genome‐wide significant association (GWAS) signals of ExG and CCI using EMMAx.Click here for additional data file.Video S1 GUI of AirMeasurer in operation.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 Click here for additional data file.


Immunoglobulin A (IgA) mediates mucosal responses to food antigens and the intestinal microbiome and is involved in susceptibility to mucosal pathogens, celiac disease, inflammatory bowel disease, and IgA nephropathy. We performed a genome-wide association study of serum IgA levels in 41,263 individuals of diverse ancestries and identified 20 genome-wide significant loci, including 9 known and 11 novel loci. Co-localization analyses with expression QTLs prioritized candidate genes for 14 of 20 significant loci. Most loci encoded genes that produced immune defects and IgA abnormalities when genetically manipulated in mice. We also observed positive genetic correlations of serum IgA levels with IgA nephropathy, type 2 diabetes, and body mass index, and negative correlations with celiac disease, inflammatory bowel disease, and several infections. Mendelian randomization supported elevated serum IgA as a causal factor in IgA nephropathy. African ancestry was consistently associated with higher serum IgA levels and greater frequency of IgA-increasing alleles compared to other ancestries. Our findings provide novel insights into the genetic regulation of IgA levels and its potential role in human disease. Immunoglobulin A protects against infectious disease and contributes to autoimmune and inflammatory disorders. Here, the authors perform a genome-wide association study for serum IgA levels, identifying 20 genome-wide significant loci, providing new insights into the genetic regulation of IgA levels. Most of the IgA production occurs at the mucosal surfaces along the gastrointestinal and respiratory tracts, but a large portion of circulating IgA is contributed by bone-marrow plasma cells3. IgA neutralizes mucosal pathogens4 and enhanced IgA responsiveness has been reported in various respiratory and gastrointestinal infections, including acute SARS-CoV-2 infection7. Increased serum IgA levels are a common phenomenon in patients with IgA nephropathy9, diabetes10 and metabolic syndrome11. The serum concentration of IgA can be influenced by a combination of inherited factors and environmental exposures, including age, sex, and lifestyle factors14. The heritability of serum IgA levels has been estimated in the range 20–60%18. Several GWAS have investigated genetic determinants of serum IgA levels in individuals from either European or East Asian ancestry, and nine significant GWAS loci have been identified to date20. Notably, African and other more diverse populations have not been included in prior studies, and limited data exist on the ancestral differences in IgA levels.Immunoglobulin A (IgA) provides protection against mucosal infections and contributes to the pathogenesis of autoimmune and inflammatory disordersP < 10−6) from the previous IgA level GWAS by deCODE Genetics and Lund University (deCODE-Lund)20. We identified novel genetic determinants of serum IgA levels and, through comprehensive functional annotations, we prioritized candidate causal genes at each of the IgA-associated loci. We then investigated the shared genetic architecture between serum IgA levels and other human traits using several approaches, including genome-wide genetic correlation analysis, co-localization of GWAS loci, Mendelian randomization, and phenome-wide association studies. Our results provide new insights into the genetic regulation of serum IgA levels and its role in genetic susceptibility to several human diseases, including immune, infectious, kidney and cardio-metabolic traits.In this study, we conducted a genetic analysis of serum IgA levels in 41,263 individuals, including 22,229 diverse participants across 16 ancestry-defined cohorts with genome-wide imputed data combined with 19,034 individuals with summary statistics data on significant and suggestive association signals . First, we performed laboratory measurements of serum IgA levels in all 5420 participants of the Multi-Ethnic Study of Atherosclerosis (MESA) with available serum samples, providing us with the largest multi-ancestry cohort with standardized IgA measurements. For group comparisons, we generated standardized residuals of log-transformed serum IgA levels after adjustment for age and sex. Notably, MESA participants of African ancestry had significantly higher mean age and sex-adjusted serum IgA levels compared to all other ancestries Fig. . We next20. Each of the 16 cohorts was genotyped with high-density SNP arrays and imputed using the latest genome sequence reference panels  random effects models. Stepwise conditional analyses of the genome-wide significant loci revealed that the HLA locus harbored at least two independently genome-wide significant variants -score regression method22, we estimated the genome-wide SNP-based heritability of age- and sex-adjusted IgA levels at approximately 7% (95%CI: 2–11%).The results of joint meta-analysis are provided in Fig. RUNX3 locus supported by the European cohorts , and the novel GPATCH2 locus supported predominantly by the African ancestry cohorts . Interestingly, the IgA-increasing alleles at 12 of the 20 genome-wide significant loci (60%) were more frequent in African compared to European ancestry, suggesting an enrichment of IgA-increasing alleles in African genomes. To assess if a similar trend was present for all IgA-increasing alleles, we derived a genome-wide polygenic score (GPS) for serum IgA levels (see Online Methods) and compared its distributions between major ancestral populations of the 1000 Genomes Phase 3 reference dataset. The African (AFR) reference population had the highest average GPS of all other ancestral populations . The observed distributional differences in the GPS by ancestry had a strikingly parallel pattern to the differences in serum IgA levels . Similarly, we detected no significant genome-wide correlation by LD score regression between polarized singleton density scores (tSDS)24 for IgA increasing alleles and their effect sizes .As expected, the effect sizes of independently associated variants were inversely related to their minor allelic frequencies Fig. . Two relons Fig. , with thels Fig. , b. BecaP = 0.002, Supplementary Fig. 25 identified five significantly enriched pathways at Bonferroni-adjusted P < 0.05 , signaling by interleukins (P = 1.1 × 10−5), cytokine-cytokine receptor interactions (P = 5.2 × 10−6), TNFs and their physiological receptors (P = 1.4 × 10−5), and IL-6-type cytokine receptor ligand interactions (P = 5.0 × 10−5). Using data-driven expression-prioritized integration for complex traits (DEPICT) analysis26, we further prioritized 17 tissues and cell types at FDR < 0.05 based on our GWAS results, with the strongest enrichment in bone marrow cells, hematopoietic system, as well as blood and myeloid cells  network analysis of positional candidate genes from the non-HLA loci, we observed greater network connectivity than expected by chance RUNX2, ELL2, TNFSF15, TNFSF4, TRAF3, and FADS2 genes, and three loci had missense proxies, in FBXL19, ELL2 and SH2B3 genes. At the previously reported ELL2 locus, we identified the Thr298Ala missense variant in ELL2 exon 7  in linkage disequilibrium with the lead SNP  and located to the end of the ELL2 domain required for transcriptional elongation29. At the known SH2B3 locus, rs3184504 in tight LD with the top SNP (r2 = 0.94) is a missense variant in the canonical transcript of SH2B3 gene, although this variant is predicted to be benign by PolyPhen2. Given that other top signals map to non-coding regions, we evaluated their potential regulatory function by systematic eQTL co-localization analyses using whole blood30 and 13 primary immune cell types31. The co-localization probability (PP4) exceeded 50% in at least one cell type for 14 of 20 GWAS loci, prioritizing biologically plausible candidate genes at each of these loci  regions . This locus was previously associated with IgG glycosylation32 and IgA levels20, and strongly replicated in our study. Interestingly, we have also picked up a novel locus with smaller effect on chr.6p21.1 encoding RUNX2, a related transcription factor . RUNX transcription factors are essential regulators of diverse developmental and signaling pathways34. Both RUNX2 and RUNX3 physically interact35 and have been linked to retinoic-acid- and TGF-β-induced IgA class switching36. The novel RUNX2 locus co-localized with the eQTL for the RUNX2 gene in whole blood with PP4 = 0.99; the IgA-decreasing allele at the index SNP (rs1200427-G) was associated with higher mRNA levels of RUNX2 , the 3′UTR variant in RUNX2 that has the strongest eQTL effects in blood. A similar phenomenon was previously reported for the RUNX3 locus, where the minor allele of the top SNP (rs188468174-T) was associated with lower IgA levels and increased mRNA abundance of the long isoform of RUNX320. Therefore, our study solidifies the evidence for a genetic control model of RUNX transcription factors, in which increased RUNX expression suppresses IgA class switching, reducing circulating IgA levels.The known P = 1.91 × 10−12) encodes lipopolysaccharide-induced TNF-alpha factor (LITAF), a transcription factor regulating TNF-alpha expression in intestinal macrophages38. Notably, macrophage-specific deficiency of LITAF in mice leads to attenuated TNF and IL-6 response upon LPS stimulation39. Our analyses revealed that this locus co-localized with eQTL for LITAF exclusively in monocytes. Moreover, the top SNP represented a monocyte-specific hQTL (H3K27ac)40, suggesting that it alters LITAF enhancer activity in the monocytic lineage  contains the IL1R1 gene encoding Interleukin 1 Receptor Type 1, an important T-cell receptor involved in cytokine-induced immune and inflammatory responses41. This locus co-localizes with eQTL for MIR4772 in Th1 cells (PP4 = 0.69) with concordant effect on IgA levels, and IL1R1 is predicted to be a target of MIR4772 by miRDB42 and TargetScan43. A mouse knock-out of IL1R1 in Th2 cells had decreased IgA levels44.These four loci exhibited T-cell specific eQTL effects. The new locus on chr.2q11.2  contains TRAF3 encoding TNF Receptor Associated Factor 3, a protein participating in the CD40 signaling, inhibition of non-classical NF-κB signaling, and regulation of class switch recombination in B cells47. Interestingly, this locus co-localizes with eQTL for TRAF3 specifically in T cell lineage, where the IgA-increasing variant is associated with higher TRAF3 expression. In contrast to its inhibitory functions in B-cells, TRAF3 is known to promote many T-cell effector functions through enhancing signaling by the T-cell receptor-CD28 complex49.The new locus on chr.14q32.32  encoding ANKRD55 (closest gene) and IL6ST has previously been associated with IgA levels20, IgG glycosylation50 and increased risk of multiple sclerosis, rheumatoid arthritis, and Crohn’s disease  as well as in whole blood (PP4 = 0.98) with concordant effect of ANKRD55 expression and IgA levels. Moreover, we found that rs6859219 intersects two genetically regulated histone-modification peaks (H3K4me1 and H3K27ac) that are T cell-specific40  contains HDAC7 encoding an important histone deacetylase regulating differentiation and function of T-cells52. This new locus co-localizes with eQTL for HDAC7 specifically in T-cells, where the IgA-increasing allele is associated with higher mRNA levels of HDAC7.The fourth locus on chr.12q13.11  encodes multiple candidate transcripts including OVOL1, RELA, and several others. The co-localization analysis suggested that this locus was shared with IgA nephropathy, with the IgA-increasing allele associated with increased risk of IgA nephropathy, a kidney disease due to IgA deposition in the glomeruli. Previous GWAS also pointed to the protective role of this locus from atopic dermatitis , spleen (P = 1.5 × 10−6) and EBV-transformed lymphocytes (4.7 × 10−9) with the IgA-increasing allele (rs10896045-A) associated with higher expression of OVOL1 gene in all three GTEx tissues.The new locus on chr.11q13.1  encodes POU2AF1 (POU class 2 homeobox associating factor 1), the gene involved in B-cell antigen responses required for the formation of germinal centers54. Mouse knock-out of POU2AF1 leads to increased B-cell apoptosis and decreased IgA production55. The IgA-decreasing allele at this locus has been associated with increased risk of primary biliary cholangitis  contains CTF1 (encoding cardiotrophin-1) involved in multiple immune-related pathways including cytokine signaling in immune system and interleukins, IL-6-type cytokine receptor ligand interactions and signaling  contains CITED2, which encodes a CBP/P300 interacting trans-activator functioning as a molecular switch of TGF-α and TGF-β induced signaling56, and previously implicated in immune homeostasis and tolerance57. The new locus on chr.1q41  encodes GPATCH2, but its role in the immune system regulation is unknown. Lastly, our co-localization analysis of the new locus on chr.7q11.23  suggested several candidate effector genes including SRCRB4D, DTX2 and YWHAG in whole blood, ZP3 and SSC4D in monocytes, and POMZP3 in naïve CD8 and B cells , which co-localized with blood eQTL for ELL2  was associated with lower transcript level of ELL2 (P = 6.76 × 10−24), prioritizing ELL2 (encoding elongation factor for RNA polymerase II 2) as the most likely causal gene at this locus. The known TMEM258/FADS2 locus  co-localized with several candidate genes, including FADS1 and FADS2  and the TMEM258 gene , with the IgA-increasing allele (rs968567-T) associated with higher expression of all three transcripts. Notably, rs968567 falls into a genetically regulated histone-modification peak (H3K4me1) for FADS2 in T cells, monocytes, and neutrophils40 , TNFSF8/TNFSF15 locus on chr.9q33.116 , and a suggestive TNFSF13 locus on chr.17p13.119 . These loci encode powerful TNFSF cytokines with partially overlapping receptors60. TNFSF8/TNFSF15 and TNFSF13 loci are also associated with the risk of IgA nephropathy, while TNFSF4/TNFSF18 locus has previously been associated with the risk of eczema, asthma and narcolepsy, all with concordant effects to IgA levels , 29 linked to abnormal immune tolerance (enrichment P = 6.3 × 10−6) and 42 linked to abnormal response to infection (enrichment P = 1.1 × 10−5) . Among the 120 genes with abnormal IgA phenotypes in mice, six additional genes  surpassed a Bonferroni-corrected threshold (P = 0.05/22169 = 2.26 × 10−06) when tested against our GWAS results; these represent plausible new candidate genes for regulation of IgA production in humans.To further prioritize causal genes at our GWAS loci, we tested for significant gene set overlaps with human orthologs that cause immune-related phenotypes when disrupted in mice. This included gene sets for abnormal IgA levels (120 genes), abnormal immune tolerance 408 genes) and abnormal response to infection 562 genes) from the Mouse Genome Informatics (MGI) database2 genes f genes an61 by systematic lookups and co-localization analyses of genome-wide significant loci. Among all 20 independent loci associated with higher IgA levels, 8 had nominal associations with increased risk of IgA nephropathy (P < 0.05), all with concordant effects , both with concordant effects, and another two loci (HLA and CTF1) with high probability of different causal variants . Moreover, this effect remained highly significant when instrumental variables were expanded to encompass all genome-wide significant non-HLA loci for serum IgA levels . In contrast, we detected no significant causal effects between IgA levels and tonsillectomy (P = 0.26), or tonsillectomy and IgA nephropathy (P = 0.96).To further explore the genetic relationships between these traits, we performed two sample Mendelian Randomization (MR) between the co-localized IgA level-associated loci as an exposure instrument and IgA nephropathy and tonsillectomy as disease outcomes. Using this strategy, we estimated significant causal effects between serum IgA levels and IgA nephropathy  of IgA levels with 52 complex traits and diseases, including 13 immune-mediated disorders, 23 infectious diseases, and 16 cardio-metabolic traits using stratified LD score regression23 , tonsillectomy , type 2 diabetes , and BMI . We observed a negative genetic correlation with Crohn’s disease , celiac disease , and inflammatory bowel disease . The observed negative correlation with traits that involve gut inflammation could be potentially explained by the protective anti-inflammatory effects of mucosal IgA. Among infectious disease GWAS, we observed mainly negative genetic correlations, including with bacterial meningitis  and shingles , despite the fact that most existing GWAS for infections are either underpowered or based only on self-report61. For most, but not all phenotypes, genetic correlations with and without HLA were comparable, as summarized in Supplementary Fig. We assessed genome-wide genetic correlations , hypothyroidism , type 1 diabetes , and psoriasis . Among significant risk associations were disorders of iron metabolism  and hematuria, a common manifestation of IgAN . To assess which of these associations were driven by the HLA region, we next repeated meta-PheWAS after excluding all HLA variants from the GPS  and a risk association with morbid obesity . These associations were direction-consistent with our genome-wide genetic correlation analyses.To detect additional genetic associations, we derived a genome-wide polygenic score (GPS) for serum IgA levels and tested for its phenotypic associations using the meta-PheWAS approach across the UK Biobank and eMERGE-III datasets . In the combined analysis of 556,656 participants, we detected 31 significant phenotypic associations of the GPS Fig. . This inGPS Fig. . In this62 and BMI63. Interestingly, we detected no significant causal effects between serum IgA levels and Celiac disease or BMI. In reverse causality analyses we observed no causal effect of Celiac disease on serum IgA levels, however, there was a highly significant causal effect of BMI on serum IgA levels . In sensitivity analyses, there was evidence of directional pleiotropy (Egger intercept test P = 0.004), but the causal effect of BMI became stronger and more significant when the balanced pleiotropy assumption was relaxed . These results suggest that elevated serum IgA levels in individuals with metabolic syndrome may represent a consequence rather than a cause of obesity.Given these results, we next applied Mendelian randomization approach to resolve potential causal relationships between serum IgA levels, Celiac disease, and BMI. For instrumental variables, we used independent genome-wide significant alleles from this study (excluding the HLA region), and from the largest studies for Celiac diseaseOur study provides multiple insights into the genetic regulation of serum IgA levels and dissects shared genetic effects between IgA levels and several human diseases. Our large multi-ethnic meta-analysis identified 20 genome-wide significant loci, 11 of which were novel. These loci encode genes enriched in immune-related pathways, with 13 candidate genes demonstrating IgA abnormalities when genetically manipulated in mice. The complementary enrichment analyses based on gene expression across multiple tissues/cell types highlighted primary immune cells, mainly T cells, B cells and monocytes/macrophages, as the most likely effector cell types regulating IgA production.24.Previous smaller GWAS for serum IgA levels have been limited to European or East Asian ancestry, while African and other ancestries have not been included in these studies. Our multi-ethnic cohort included four diverse ancestral groups and allowed us to demonstrate that African ancestry was associated with higher serum IgA levels compared to other ancestries. Consistent with this observation, IgA-increasing alleles were more frequent in African ancestry compared to non-African participants, and we demonstrated that the African ancestry populations had the highest GPS compared to all other reference populations. Positive correlation of effect sizes with AFR-EUR differences in allele frequency for the top 1% IgA-increasing alleles is supportive of polygenic adaptation. This correlation was not significant when extended genome-wide, but the power is limited by the sample size of our GWAS, low SNP-based heritability of serum IgA levels, and incomplete knowledge about the true underlying genetic architecture. Although not statistically significant in genome-wide analysis, the negative correlation coefficient between IgA-increasing effects and European tSDS scores is consistent with the hypothesis that recent selection might have favored IgA-lowering alleles in Europeans. However, environmental exposures correlated with African ancestry may also be contributing to the observed population differences in IgA levels. Our study was not designed to identify environmental effects, and the existing methods for detecting multi-locus selection have low power and lack sensitivityLITAF locus co-localize specifically with LITAF eQTL but only in monocytes, pointing to monocyte lineage as the most likely causal cell type for this locus. In contrast, TRAF3, HDAC7, IL1R1 and ANKRD55 loci co-localize with eQTLs specifically in human T cell lineages, prioritizing T cells for functional studies of these loci.Our systematic co-localization analysis of GWAS associations and eQTLs in whole blood and 13 different immune cell types prioritized new biologically plausible candidate genes and target cell types for 14 of the 20 significant loci, providing an extensive resource for follow up studies. We also observed that many eQTL co-localizations were present only in specific immune cell subtypes. As an example, variants within the 64.In the analysis of genetic relationships with other traits, we observed shared polygenic determination between IgA levels and several immune, infections, renal, and cardio-metabolic traits. Our findings support the protective role of IgA in susceptibility to various infectious pathogens as well as inflammatory bowel disease. At the same time, we observed a positive genetic correlation between IgA levels and IgA nephropathy, a common form of kidney disease due to IgA deposition. In particular, Mendelian randomization analysis suggested a causal role for elevated IgA levels in the pathogenesis of IgA nephropathy. Ongoing IgA nephropathy clinical trials targeting pathways that reduce IgA levels will shed light on this hypothesis20 were not publicly available, thus we were limited to the use of SNPs with P < 1.0 × 10−6 from that study in our meta-analysis. This reduced our power for new discovery and produced uneven genomic coverage in the meta-analysis. Consequently, our cis-eQTL co-localization analyses were restricted to non-deCODE-Lund cohorts and were additionally limited by low power of presently available eQTL datasets for primary immune cells. Similarly, genome-wide genetic correlation analyses were driven largely by our non-deCODE-Lund cohorts. These analyses were additionally limited by sample sizes of previously published GWAS, and the fact that infectious traits have been generally understudied by GWAS. Our GPS modeling strategy relied on 1000 Genomes for LD reference, since more diverse panels, such as TopMed, are not publicly available, precluding their use for this purpose. The key limitations of our PheWAS approach included missingness of “real life” EHR data, inadequate ICD coding for some relevant conditions , and the fact that rare diagnoses  are generally poorly captured in population-based biobanks.Our study has several important limitations. First, genome-wide summary statistics from the largest previously published study by deCODE-LundIn summary, we reported 20 genome-wide significant loci associated with serum IgA levels, and we prioritized potential effector genes and cell types for 70% of the loci. We demonstrated that IgA levels are positively correlated with African ancestry. We further characterized shared genetic architecture between serum IgA levels and other complex traits, demonstrating that while IgA-increasing alleles appear to have protective effects against infections, they may represent risk factors for selected auto-immune, kidney, and cardiometabolic diseases. Notably, the risk of IgA nephropathy appears to be causally related to elevated IgA levels with several genome-wide significant loci co-localizing between the two traits.2 Fragment of Goat Anti-Human Serum IgA, α Chain Specific; Jackson Immuno Research, #09-006-011), covered, and let to sit on a shaker for 60 min. The plate was subsequently washed three times with a TBS Wash buffer. Next, 250-μL Blocking Buffer was added to the wells, and the plate was again covered, and set on the shaker for 90 min. After aspirating the wells of the blocking buffer, 100 μL/well of samples and standards were added, all in duplicate. Standards were diluted 1:3 in series beginning with 500 ng/mL. The plate was left to incubate for 120 min, after which samples were aspirated and the plate was washed thoroughly five times. Next, 100 μL of secondary/detection antibody , was added at 62.5 ng/mL to the wells and left to incubate for 60 min. The plate was washed an additional five times. Next, 100 μL of TMB 2 component ELISA substrate  was added to the wells, and left to incubate for 12 min. 100 μL of 2 M H2SO4  was added to each well at the end of the 12 min to stop the reaction, per substrate manufacturer recommendation. The samples were then measured using the BIO-TEKPowerWaveTM XS and KC-Jr. plate reading software. The plate was read at 450 nm, with a 630 nm reference wavelength, per substrate manufacturer recommendation. Samples with an internal CV greater than 10% were re-run. Samples that fall outside of the standard range (500–0.2 ng/mL) were re-run at appropriate dilutions.Serum total IgA levels were measured via a previously optimized and validated ‘sandwich’ ELISA protocol. 96-well plates were coated with an IgA capture antibody (AffiniPure F(ab′)65. The recruitment took place across six clinical centers in the United States. The participants were genotyped using the Affymetrix Human SNP array 6.066. We measured serum IgA levels using standardized ELISA protocol (see above) in N = 5420 participants, and these individuals were included in the GWAS analysis. The standard genotype quality control (QC) filters included per-SNP genotyping rate >95%, per-individual genotyping rate >90%, MAF > 0.01, and HWE test p-value >1 × 10−5 within each ancestry group. We assessed for cryptic relatedness and duplicates, and we excluded one individual from any pairs with estimated pairwise kinship coefficients >0.05. Gender of each individual was imputed based on the analysis of sex chromosome markers and individuals with gender mismatch against records were excluded. The imputation analysis was carried out using Minimac367 after pre-phasing in Eagle V2.368 and using 1000 Genomes (Phase 3) as ref. R2 > 0.8 and MAF > 0.01) were imputed and used in the downstream GWAS analyses. The principal component analysis (PCA)70 was used to assign genetic ancestry to the participants based on co-clustering with the major continental reference populations of the 1000 Genomes Phase 369. After exclusions of ancestry outliers, we identified 5 distinct ancestral clusters . Subsequent GWAS analyses were then performed within each ancestry cluster. For ancestry adjustment, we re-run PCA within each cluster, defined significant PCs by Tracy-Widom test, and included the significant PCs for each cluster as covariates in the association testing.The Multi-Ethnic Study of Atherosclerosis (MESA) is a diverse population-based cohort of participants recruited prospectively for studies of cardiovascular disease73. Briefly, we implemented the mimimac3 imputation model with HRC1.1 as references for each genotyping platform in a separate batch. After imputation, we merged all the 81 imputed batches based on genomic position. Post-imputation marker QC filters included MAF ≥ 0.01 and R2 ≥ 0.8 in ≥75% of 81 imputation batches. We also excluded duplicates and cryptic relatedness in the given cohort determined by the estimated pairwise kinship coefficients >0.05. The genetic ancestry for each individual was assigned based on PCA with reference populations of the 1000 Genomes Phase 369. After exclusions of ancestry outliers, the final dataset with matched serum IgA phenotypes consisted of 6047 individuals across five ancestral groups: N = 4261 European ancestry individuals , N = 476 African ancestry individuals , N = 73 East Asian ancestry individuals , N = 235 Admixed 1 cohort  and N = 1002 Admixed 2 cohort . Like the MESA cohort analysis, we repeated the PCA within each ancestry cohort and defined significant PCs to be used as covariates in association testing.The eMERGE consortium consists of 12 medical centers with electronic health records (EHRs) linked to genome-wide genotype data for 102,138 individuals. The serum IgA level was extracted by a lab value query performed by eight active sites in the eMERGE phase III. The genotyping and imputation of the eMERGE cohort have been previously described in detail74. For this study, we included only individuals who were ascertained as ‘healthy population controls’ for GWAS studies, thus these cohorts were not enriched in any specific disease type. The genotyping was performed using Illumina 550v3 (US cohort), Illumina 370-Duo (French cohort), Illumina MEGA v1.0 (Chinese and Japanese cohorts) and Illumina MEGA v1.1 (German cohort). The standard genotype QC filters included per-SNP genotyping rate >95%, per-individual genotyping rate >90%, MAF >0.01, and HWE test p-value >1 × 10−5 within each cohort. We assessed for cryptic relatedness and duplicates, and we excluded one individual from any pairs with estimated pairwise kinship coefficients >0.05. Gender of each individual was imputed based on the analysis of sex chromosome markers and individuals with gender mismatch against records were excluded. The imputation was performed using MACH 1.0 for pre-phasing and then Minimac3 for imputation based on ancestry-matched reference panels of 1000 Genome Project (Phase 3). We performed PCA of each dataset to exclude outliers and define the number of significant PCs by Tracy-Widom test. The final numbers of individuals and high-quality markers  used in downstream GWAS analyses were as follows: the German cohort of N = 156 healthy individuals ; the French cohort of N = 103 healthy individuals , the Chinese cohort of N = 467 healthy individuals , the Japanese cohort of N = 776 healthy individuals ; and the U.S. cohort of N = 93 healthy individuals .The description of these cohorts including recruitment and measurements of serum IgA levels have been published previously16. In order to improve marker density, we re-imputed this cohort based on association estimates of the genotyped markers from the summary statistics using ImPG V1.075 software and the 1000 Genomes (Phase 3) European reference69. Using ImPG software, we derived the posterior mean of z-scores at untyped SNPs given the z-scores at typed SNPs and the correlation matrix among all pairs of SNPs induced by their linkage disequilibrium (LD) that were estimated using the reference panel. The effect size and standard error for each imputed SNP were then estimated based on its imputed z-score and reference allelic frequency as described previously76. A total of 6,907,390 variants were imputed with high quality (R2 > 0.8 and MAF > 0.01) and included in our downstream analysis.We obtained summary statistics for GWAS for total IgA levels for 9,617 participants; the ascertainment, genotyping, and analysis of this cohorts has been published previously20. The association summary statistics for IgA levels including effect size, P-value and minor allele frequencies for 4699 variants with P < 1 × 10−6 were provided by the authors in their publication20. In order to incorporate this dataset using fixed-effects meta-analysis, the unbiased standard error for each variant was derived using the following equation77:The analysis of this cohort composed of 16,883 participants from Iceland and 2151 individuals from southern Sweden has been published previouslyP < 10−6) from the previous GWAS by deCODE Genetics and Lund University 20. Multi-ancestry cohorts were classified into ancestry-specific strata based on global principal component analysis. In each sub-cohort, serum IgA levels were log-transformed and expressed as standard-normalized residuals from regression of log-transformed IgA levels against age and sex. We performed genome-wide association testing in each cohort for the markers that were imputed at high quality  and by testing using Cochrane’s heterogeneity test in PLINK80. The quantile-quantile plot of the final meta-analysis showed no global departures from the expected null distribution, with the genomic inflation factor estimated at 1.016  with 1000 Genomes Phase 369 (European populations only) for LD reference. All other cohorts had genome-wide genotype data available, and we used primary genotype data to perform conditioning before meta-analysis.To detect independent variant associations at each genome-wide significant locus, we performed stepwise conditional analysis within each cohort, followed by meta-analysis of the conditioned summary statistics. For the deCODE-Lund and Swedish cohorts, only summary statistics were available, thus we used the approximate conditioning using conditional & joint association analysis method  with the lead SNP, then extending the genomic region 250 kb upstream and downstream of the first and last proxy SNP based on genomic position. Each region was then annotated using Ensembl human gene annotations. Gene sets were created for all genome-wide significant regions but excluding the HLA region. For tissue/cell type enrichment, we used DEPICT (Data-driven Expression-Prioritized Integration for Complex Traits) to test for tissues and cell-types in which genes from the associated regions were highly expressed as previously described26. Next, for each tissue, empirical enrichment p-values were computed by repeatedly sampling random sets of loci from the entire genome to estimate the null distribution for the enrichment statistic. For pathway enrichment analysis, we used established pathways from the databases including Molecular Signatures Database (MSigDB C2), KEGG, BioCyC, REACTOME, Pathway Interaction Database. Statistical significance for enrichment was set at FDR q-value <0.05.The region of each GWAS locus was defined by first selecting all proxy SNPs in LD  with the top SNP, including all known coding, splicing, and 3′UTR and 5′UTR variants. To prioritize the candidate genes at each of the GWAS loci, we next performed colocalization analyses based on our meta-analysis statistics and gene expression QTLs in whole blood quantified from 31,684 individuals30, as well as 13 human immune cell types from the Database of Immune Cell eQTLs (DICE) project31. After harmonization of effect alleles, we identified all co-localized eQTLs mapping to the region of the index SNP ± 400 kb using Coloc Package in R83. Co-localization with PP4 greater than 0.7 was considered as strong evidence in support of shared causal SNPs, while PP4>0.5 was considered as suggestive. To test for histone QTL effects in immune cells, we interrogated all GWAS index SNPs and their proxies (r2 > 0.5) against histone QTL of three major human immune cell types  based on the analysis of ~200 individuals40.We first defined each GWAS locus by ±400 kb of the genome-wide significant index SNP. We annotated all transcripts within these intervals using the latest assembly of the human genome (hg19) to create sets of positional candidate genes for each locus. Using ANNOVAR software84, a curated and computationally derived regulatory network of 420,000 interactions . The significance of intersections between these gene sets and the list of positional candidate genes from GWAS was determined using a hypergeometric test.We evaluated genes that when genetically manipulated cause abnormal immune phenotypes in mice based on the comprehensive MGI phenotype ontology database. The following mouse phenotypes were evaluated: abnormal IgA levels (MP:0020171); abnormal immune tolerance (MP:0005000); and abnormal response to infection (MP:0005025). The human orthologs of these genes were obtained with the Human–Mouse Disease Connection web tool  and resided within the genomic regions of association with IgA level. For each SNP associations, we manually verified the direction of effect for a reference allele based on original GWAS publications. Next, each selected SNP from the catalogue was queried against our GWAS results to extract the odds ratios and P-values for associations with IgA levels. The directionality of allelic effects was assessed to identify pleiotropic alleles with concordant or opposed effects. We calculated a maximum r2 between SNPs associated with each cataloged trait and the independent SNPs from our study based on 1000 Genomes Project Phase 3 data87. We defined shared susceptibility alleles if r2 between the top SNPs exceeded 0.5. We constructed a susceptibility overlap map that connected each of the IgA GWAS loci to the previously associated GWAS traits and highlights associations with SNPs in high LD with the top signals. The map was visualized with Cytoscape v.3.6 software88. Moreover, given a large number of overlapping GWAS loci between IgA levels, IgAN, and tonsillectomy, we performed systematic locus colocalization analyses based on regional summary statistics using Coloc software83.To systematically cross-annotate our loci against all previously published GWAS findings, we downloaded the GWAS catalog89 which estimates posterior mean causal effect sizes from GWAS summary statistics by assuming a prior for the genetic architecture and LD information from a reference panel. We used GWAS summary statistics for IgA levels assuming 1% fraction of causal variants and for LD reference we used 1000 Genomes (Phase 3), all populations except South Asian ancestry that was not represented in our cohorts69. To test for population differences in the GPS distributions, we applied the score to the 1000 Genomes participants and performed pairwise t-test comparisons between the reference populations. To test for potential polygenic adaptation, we performed rank correlation analysis of LDPred posterior mean causal effect sizes versus AFR-EUR allelic frequency difference calculated based on 1000 Genomes reference populations; given our LDPred priors, we used only the top 1% of GPS variants with the highest weights for this analysis (the weights for the remaining variants were close to zero as expected). Next, we extended this analysis genome-wide by testing for genetic correlation between AFR-EUR allelic frequency difference and GWAS effect sizes for IgA-increasing alleles using LD score regression23. In this analysis, we replace GWAS Z-scores for one of the two traits with a Z-score-normalized IgA-increasing allele frequency difference. Lastly, we used singleton density score (SDS) method based on contemporary European genome sequences that infers recent (~2000–3000 years) changes in derived allele frequencies24. We derived trait-SDS (tSDS) scores from the raw European SDS scores by polarizing them to IgA-increasing alleles . The tSDS scores were standardized to mean 0 and variance 1 within each 1% allelic frequency bin and tested for genome-wide genetic correlation with IgA-increasing effects using LD score regression23. These analyses were performed with R version 3.4 (CRAN).For the purpose of testing cumulative effects of genetic determinants of serum IgA levels, we derived a genome-wide polygenic score (GPS) for IgA levels using LDPred algorithmN = 460,364 participants) and Electronic Medical Records and Genomics-III . The eMERGE-III genotype data was processed in the same way as for GWAS described above, but for ancestry adjustments we performed principal component analysis of the entire eMERGE-III cohort using FlashPCA90 on a set of 48,509 common (MAF>0.01) and independent variants (pruned in PLINK with–indep-pairwise 500 50 0.05 command). The first 3 PCs were included as a covariate in PheWAS. The UKBB is a large prospective population-based cohort that enrolled individuals ages 40–69 for genetic studies91. All 488,377 UKBB participants underwent genotyping with Affymetrix’s UK Biobank Axiom and UK BiLEVE Axiom arrays with genotype imputation using a 1000 Genomes reference panel with IMPUTE4 software94. We applied similar QC filters to eMERGE-III, retaining 9,233,643 common  correcting for 1523 independent phecodes tested.We performed a meta-PheWAS analysis for the GPS across the UK Biobank  were used as instruments in MR studies. We excluded HLA alleles from all instruments, since these alleles are likely to exhibit strong pleiotropic associations with a wide range of immune-related outcomes. For primary hypothesis testing, we used inverse variance weighted MR model under the assumption of balanced pleiotropy by meta-analyzing SNP specific Wald estimates using multiplicative random effects. The random effects model was chosen to account for any potential heterogeneity. Given a total of eight bi-directional MR tests performed between IgA levels and IgAN, tonsillectomy, Celiac disease, and BMI, we used a Bonferroni-corrected significance threshold alpha = 0.05/8 = 0.00625. We additionally tested for the presence of directed horizontal pleiotropy using Egger test for non-zero intercept. Additional sensitivity analyses were performed by testing for outlier effects, and relaxing the assumption of balanced horizontal pleiotropy by using median-based estimator, mode-based estimator, and Egger regression methods. The testing was conducted using the TwoSampleMR package96.Two sample mendelian randomization (MR) analyses were performed using genetic variants as instruments to test the causal effects between an exposure and an outcome. Only SNPs independently associated with the exposure at a genome-wide significance , IRB-AAAQ9205 , IRB-AAAC9458 , and IRB-AAAS3500 .Further information on research design is available in the Supplementary InformationPeer Review FileDescription of Additional Supplementary FilesSupplementary Data 1Supplementary Data 2Reporting Summary


Twin and family studies show a strong contribution of genetic factors to physical activity (PA) assessed by either self-report or accelerometers. PA heritability is around 43% across the lifespan. Genome-wide association studies have implied biological pathways related to exercise ability and enjoyment. A polygenic score based on genetic variants influencing PA could help improve the success of intervention programs. Family and twin studies show that for different types of physical activity, and across device-based or self-report assessment, meta-analyses showed broad sense heritability to be around 26% for females and 35% for males in childhood, to increase to around 42% in both sexes during adolescence, and to remain around 45% throughout adulthood.Genetic correlations derived from multivariate twin studies and genome-wide association studies (GWAS) results suggest that the biology of exercise (train) ability and exercise enjoyment are partly underlying the heritability of physical activity.GWAS on physical activity have identified several replicated genetic variants and allow the computation of polygenic scores (PGS) for physical activity.These PGS can be used to study the causal effects of physical activity on health, to test the interaction between genetics and physical activity interventions, and to tailor physical activity interventions to individual genotypes.Despite the long-standing recognition that physical inactivity is a major burden on our health care systems ,2, the aWhy is PA so hard to change despite our many intervention efforts? Strong forces seem to intercede between the intention to be more physically active and the actual enactment, a discordance alluded to as the intention-behavior gap . These fIn this review, I aim to close the gap between these two perspectives on the theme where the divide in the focus of research on the determinants of PA seems to be the strongest: the contribution of genetic variation to differences between individuals in their regular PA habits.Figure Figure http://links.lww.com/ESSR/A60. Briefly, total physical activity (TPA) can be classified into light , moderate , and vigorous  activity based on fixed energy expenditure cutoffs, with the MPA and VPA often grouped together as a single moderate to vigorous physical activity (MVPA) category. When using “context” as the classifying principle, PA can be subdivided in spontaneous activity , occupational activity , transportation , and all leisure time physical activity (LTPA). LTPA prominently incorporates voluntary sports and exercise activities, but it also contains play for children and, for adults, hobbies like gardening, do-it-yourself home repair, or dancing. Typically, LTPA assessment is focused on moderate to vigorous activities, with a large chunk related to voluntary sports and exercise activities. Again, demonstrating substantial heritability for total daily PA or any of its subclasses shown in Figure In genetic epidemiology, just as in any other area of science, findings depend strongly on the exact definition of PA or, in genetics parlance, the PA “phenotype.” A detailed discussion on the methods used to quantify PA used in the exercise genetics field is provided in Supplemental Digital Content 1, In later sections, I first review and meta-analyze the current evidence for a role of genetics in the individual differences in PA phenotypes from studies comparing the PA of nontwin siblings, of parents and offspring, and of dizygotic (DZ) versus monozygotic (MZ) twins. Subclasses of PA phenotypes used are TPA, MVPA, LTPA, and voluntary exercise behavior (VEB), because these are dominant in the extant literature. Next, I review the genetic variants identified by whole-genome approaches and the biological pathways implicated by functional annotation of these variants. Finally, I address how genetics can assist us in addressing two key issues: 1) testing hypotheses on biopsychological determinants of individual differences in PA phenotypes, and 2) individual tailoring of intervention programs, for example, by using polygenetic scores for PA phenotypes.Engagement in regular PA “runs in the family,” meaning that the chance of one family member being physically active increases the chance of all other family members to be, or to become, physically active. Familial resemblance of PA can be investigated by computing correlations among relatives such as siblings or parents and their offspring Table . Signifihttp://links.lww.com/ESSR/A60, for detailed information and references of the family studies). Figure There is now a substantive literature addressing the family-based intra- and intergenerational resemblance in TPA, MVPA, LTPA, and VEB in nuclear families (parents with multiple offspring) or larger multigeneration pedigrees. This gist of this literature is captured by Figure r = 0.05 for device-based TPA or MVPA to r = 0.19 for self-reported LTPA/VEB. For TPA/MVPA, higher mother-offspring than father-offspring correlations are found, most notably for device-based MVPA/TPA in two samples with a relatively young mean offspring age. The pattern of parent-offspring correlations from the (extended) family studies in Figure Parent-offspring correlations on between 5098 and 137,695 pairs range from r = 0.19 for self-reported TPA/MVPA, r = 0.25 for device-based TPA/MVPA, and r = 0.33 for LTPA/VEB. Direct comparison of sibling correlations to the parent-offspring correlations shows that intragenerational family resemblance is about a factor two larger than intergenerational family resemblance, even though the amount of additive genetic sharing between parents and offspring and sibling pairs is the same (on average 50%). This suggests that the environment shared by the siblings but not the parents has a clear contribution to their PA resemblance, although age-specific expression of genetic factors and nonadditive genetic effects also can contribute to the lower intergenerational resemblances.Average full sibling correlations across the 4342 to 33,605 pairs in the studies depicted in Figure To estimate the specific contribution of genetic effects to familial resemblance, nuclear and extended family studies need to make specific assumptions about the shared environmental effects on PA, or they have to add information that allows the separation of genetic and shared environmental effects. Quite often shared environmental influences were simply assumed to be negligible ,23. In ohttp://links.lww.com/ESSR/A60, I computed a random effect estimate for heritability across all studies using a variance-weighted meta-analysis  is comparable for MZ and DZ twins. The important difference between MZ and DZ twins is that the former share identical genotypes, whereas the latter share on average only half of the genotypes segregating in that family. Consequently, MZ twins share 100% and DZ twins on average 50% of their additive genetic variance, and MZ twins share 100% and DZ twins on average ~25% of their nonadditive genetic variance.If the resemblance in a PA phenotype within MZ pairs is larger than that in DZ pairs, which can be tested by comparing the MZ (rMZ) and DZ (rDZ) twin correlations, this suggests that additive genetic factors influence PA. If the MZ resemblance is more than double as large, it suggests the additional influence of nonadditive genetic factors on PA. If, however, the resemblance in the PA phenotype in DZ twins is more than half as large as it is in MZ twins, this points to the common environment as an additional cause of twin resemblance. Furthermore, the extent to which MZ twins do not resemble each other is a direct estimator of the contribution of unique environmental factors (E). These include all person-specific experiences like differential jobs or lifestyles, accidents, or other life events, and in childhood, differential treatment by the parents, going to different schools, and having nonshared friends and peers, but also somatic mutations and the stochastic part of epigenetic changes. Measurement error will also be subsumed by the unique environmental factor.A simple set of rules of thumb can be used to estimate the contributions to the total variance in PA of the A, C, D, and E variance components Table .Because just two covariances and the overall variance are available in a classical twin study, only three of the four A, C, D, and E factors can be tested simultaneously. The rules of thumb are therefore divided into two different scenarios. One where the MZ correlation is not larger than twice the DZ correlation, suggesting that nonadditivity (D) can be ignored, and one where the MZ correlation is substantially higher than twice the DZ correlation, suggesting that genetic nonadditivity is in play.Applying the rules of thumb in Table http://links.lww.com/ESSR/A60, for detailed information on the twin studies and references). Within each age group, plots are again ordered by assessment strategy and by type of PA, grouping overall (TPA/MVPA) and leisure time–based activities (LTPA/VEB).Figure In childhood, MZ and DZ correlations are both high, and the MZ is substantially less than twice as large as the DZ correlation, particularly for LTPA/VEB. In adolescence and adulthood, a pattern of decreasing twin correlations can be seen, but the decrease is much stronger for the DZ than the MZ twins. By far the largest amount of data is obtained by self-report, but across decent sample sizes, data also are available for device-based TPA/MVPA. Applying the rules of thumb as in Table Differences between male and female same-sex twin correlations indicate quantitative sex differences, but these are relatively modest at all ages except for higher device-based female DZ correlations. Throughout the lifespan, resemblance in PA of DZ opposite sex (DOS) pairs is much lower than that in DZ same sex pairs, showing that either different genetic factors influence the PA of boys and girls or that they are exposed to sex-specific environmental influences. These cannot be modeled at the same time, but most studies, at least on VEB, have favored models with the low DOS correlations explained by different shared environmental factors for girls and boys in childhood ,33, but http://links.lww.com/ESSR/A60, maintaining the three age categories as before. For device-based TPA/MVPA, no sex differences in broad sense heritability estimates were found. In children younger than 12, the mean estimate for the contribution of genetic variance to total variance in device-based TPA/MVPA was 19% . Too few studies estimated the heritability of device-based TPA/MVPA in adolescents or self-reported TPA/MVPA in children and adolescents, but in twins older than 18, the heritability estimate for device-based TPA/MVPA was 54% , and for self-reported TPA/MVPA, it was 37% . As in the family studies, therefore, heritability across device-based MVPA and TPA was clearly higher than for self-reported MVPA/TPA phenotypes.An inverse variance–weighted meta-analysis on the estimates for A and C was performed on the twin studies in Supplemental Digital Content 1, In keeping with previous reports, small but significant quantitative sex differences in heritability estimates for self-reported LTPA/VEB were found, most prominently at younger ages –39, and In parallel to the changing influence of genetic factors, we find a symmetrical change in the role of the shared environment in childhood and adolescence. For both males and females, the mean estimate for the common environmental variance in device-based TPA/MVPA was 55%  in children younger than 12, but reduced to a nonsignificant 2% in adulthood. Likewise, the common environmental variance in self-reported TPA/MVPA (3%) was not significant in adulthood.Estimates of common environmental variance in self-reported LTPA/VEB were 51%  in male children and 62%  in female children. Common environmental influences strongly waned during adolescence, averaging 23%  in male adolescents and 28%  in female adolescents. In adulthood, little evidence for remaining effects of having shared an early environment remains for LTPA and VEB. Indeed, results from the largest study on VEB using an extended twin pedigree design  suggester = 0.87) and DZ (r = 0.85) twin correlations were reported for PA defined as 150 min of MVPA per week. This study was so outlying that it was not used in the meta-analyses above. Further studies are needed to see if this intriguing deviant finding reflects the specific single-question phenotyping used, the relatively poor performance of the zygosity-determining questions compared with DNA testing, or a much stronger structuring environment for family members in China — most other studies being from Europe and the United States.A few notable exceptions to the overall trends of no or low C in adulthood deserve mention ,40–43. TThe studies reviewed by meta-analyses span a total of 70,200 members in family studies and 83,694 complete twin pairs that contributed data at one or more ages and for one or more PA phenotypes. Results unanimously support a strong genetic contribution to PA, and this holds independent of design (family or twin), PA phenotype examined , or method used . This conclusion is fully congruent with earlier narrative and systematic reviews –48 that Even so, the heritability of PA is not “fixed,” and there is a large heterogeneity in estimates within and across studies. The heterogeneity in heritability estimates for PA phenotypes is often stipulated in reviews by statements like “estimates of heritability vary widely, from X% to Y%,” where X and Y take on intimidating large ranges like “9% to 92%” , or “27%Figure e.g., total daily or confined to leisure time) and assessment by self-report or devices. The change in heritability across the lifespan can be caused by age-related changes in the shared and unique environment, in part caused by people gravitating toward environments that suit their genetic propensity. The age-related change in heritability can also reflect an increasing suppression of the genetic propensity for PA by physiological aging and related disabilities. Finally, the same genetic variants may contribute differently to PA at different ages, or different genetic variants may be expressed at different ages. This change in genetic architecture may be partly related to the substantial changes in the amount but also in the intensity and type of PA that occurs across the lifespan . These variants had high appeal because they have functional effects on the efficacy of neurotransmission in the mesolimbic reward system, and the corresponding genes were nominated by research on spontaneous wheel running in rodents  as the correct way forward . Additional studies in Korea, the United States, and Japan also largely failed to detect significant associations after the required stringent correction for the multiple testing burden (The first genome-wide association (GWA) study on PA that we conducted in 2009  tested fg burden ,69. Succg burden .et al. , and four of these had significant reverse effects on sedentary behaviors. Bivariate association using sitting time as an additional phenotype increased the MVPA loci to 19.After applying corrections for work-related PA and an indicator of socioeconomic status, Klimentidis et al.  found asThe above demonstrates a main truism in the GWAS field, namely, that a scale of hundreds of thousands of participants is indeed needed to identify genetic variants in highly polygenetic phenotypes. A second truism is nicely illustrated by analysis in the UKB: increased reliability and heritability of the PA phenotype can somewhat alleviate this need for large sample sizes. Most genome-wide significant loci for PA traits so far have been found using accelerometer-derived PA measures in UKB participants, even if the subset of UKB participants that has such data is only a quarter of the full set of participants with survey-based PA measures. To date, there have been three GWA studies based on accelerometry-derived activity phenotypes in UKB ,66,68. Tet al.  also influenced MVPA.Klimentidis et al.  extracte . Many of these methods focus on prioritizing the most likely genes responsible for the association to the phenotype. These methods use gene-based association tests, identify effects of the significant SNPs on gene expression in phenotype-relevant tissues and cell types, or test for enrichment of the associated SNPs for chromatin-based annotations like promotor sites or DNase I hypersensitivity sites or contact with enhancers .Table The prioritized genes near significant (and suggestive) loci have been used in follow-up analysis to detect their enrichment in specific biological pathways. For PA, several pathways have been nominated, with a few standing out for their recurrence. The most often mentioned biological pathway leading to variation in PA involves the brain, with a clear emphasis on limbic structures and more specifically structures associated with dopaminergic processing of reward signals in structures like the nucleus accumbens. A second pathway relates to skeletal muscle biology, a third to the endocrine systems with the adrenal gland most mentioned, and a fourth to blood cell physiology and immune-related mechanisms. Experimental work, for example, in animal models, on these pathways and their nominating genes is required to confirm or refute their true role in PA.2SNP) estimates will typically only be about one third of twin-based heritability estimates, because tagging SNPs on commonly used assays capture only part of the genomic variation, causing some genetic effects  to be missing in h2SNP compared with twin-based heritability estimates  computes the genetic relatedness matrix across all SNPs for all possible pairs of participants and regresses this relatedness on the phenotypic resemblance of the pair . A seconstimates .2SNP for PA traits, three of which used self-reported PA , also referred to as a polygenic risk score (PRS) when used in the context of disease phenotypes  are almost always made freely available. This means that in any other cohort or intervention study where participants have supplied DNA, one can compute the genetic propensity for PA in these participants based on public downloadable GWAS results. This was done, for example, in two large Finnish cohorts. Participants' PGS that were based on the UKB summary statistics for both accelerometer-based and self-report MVPA successfully predicted MVPA in the independent cohorts, although the explained variance was low . For botThe robust and repeated demonstration of contribution of heritable factors to all PA phenotypes requires that models of the determinants of PA, which are now focused on the behavioral, social, and environmental pathways, incorporate the biological pathways underlying this heritability. Combining the bottom-up gene finding results above with the theory-driven nominations by the consensus paper in Medicine & Science in Sports & Exercise by the GenBioPAC consortium , two bioThe current evidence from the field of genetics in support of these pathways comes mostly from detecting a significant overlap between the genetic factors that influence key phenotypes in the biological pathways (“intermediate” phenotypes) and the PA phenotype of interest. Presence of a significant genetic correlation between PA and intermediate phenotypes like aerobic fitness or the acute psychological response to exercise is compatible with the idea that they are part of the biological pathways leading from genetic variation to individual differences in PA. In contrast, the absence of such a significant genetic correlation — in sufficiently powered studies — directly falsifies a causal role of the biological pathway.There are a variety of ways to detect a genetic correlation between hypothesized determinants and actual PA phenotypes. First, using multivariate extensions of twin or extended family designs, the correlation between the latent genetic factors influencing PA and the intermediate phenotype can be computed from the variance-covariance structure ,85,86. STo be able to engage in regular PA, in particular in the moderate to vigorous class, requires the ability to do so. The importance of exercise ability automatically nominates genetic variants that reduce movement/exercise capability by causing congenital defects in the cardiovascular and respiratory systems ,90, senset al.  are known to be heritable ,94. Thiset al.  have exte damage , and gene damage ,98.rG) between PA and endurance capacity (V˙O2max) as high as 0.43 .Support for a genetic overlap between exercise ability and PA phenotypes comes from bivariate modeling in twin studies that assessed physical fitness phenotypes and daily PA levels. These confirm that PA ability and PA behavior are genetically overlapping, with genetic correlations , but neither this variant nor nearby variants in LD with it were associated with PA phenotypes in the meta-analysis of Wang et al. (ACTN3 and MVPA found was due to a previously unidentified missense variant (rs2229456) that was shown to lower maximal force production during contraction, thus providing protection from exercise-induced muscle damage. Hence, ACNT3 does not play a role in PA through its effect on exercise ability but seems to act almost entirely through its effect on injury sensitivity.Interestingly, the latter g et al. . Insteadet al. (rG < 0.40). They also observed a genetic correlation between extraversion and VEB at follow-up (rG = 0.24). Two studies  with the weekly METminutes spent on VEB. For the other side of the spectrum, “embarrassment” during VEB, substantial heritability (27% to 59%) was also shown, and embarrassment was negatively genetically correlated (−0.30 < rG < −0.40) with the weekly METhours spent on VEB.More positive affective responses to acute bouts of PA have been systematically found to predict higher levels of participation in regular PA ,105 as det al. ,109 esti studies ,106 repo  and accelerometry-derived (rG = 0.26–0.49) PA measures in the UKB. Despite the PGS for PA liking being based on much older UKB participants, its computation in an independent younger sample allowed significant prediction, not just of PA liking but also cross-prediction of regular VEB. Moreover, four of the loci significantly influencing liking of PA  were previously found to be associated with the actual level of engagement in PA  PA, ideally at an above-average level compared with peers  and withThe overwhelming evidence from twin and family studies — corroborated by GWAS and SNP-based heritability — that genetics make a major contribution to individual differences in PA behaviors may lead to feelings of dismay in the interventionist. If immutable genetic factors explain 50% of the variance, is our room to intervene restricted to “just” the remaining 50% environmental variance? This idea, that our ability to intervene on a phenotype may be compromised if there is a large genetic component, is widespread but mistaken. As was shown in Figure The above notwithstanding, it would be inappropriate to not acknowledge that, based on their genotypes, it may be harder to engage some people in PA behaviors than others. Just as in pharmacogenetics where the prescriptions of type and dose of medicine are made dependent on the genomic make-up of the individual, balancing drug efficacy and the risk for adverse events, some individuals may require different types of interventions or be guided to different types of PA. Such a personalized approach based on genotyping is an extra tool to help increase the population levels of PA, not a replacement. Proven approaches like goal setting, social support, reinforcement through self-reward, and structured problem solving remain of unabated importance, as is alerting the public to the hazards of inactivity through repeated campaigns, obligatory physical education at school, commitment of resources to safe and affordable opportunities for exercise and active transportation , and the training of informed PA professionals and creation of social networks that reinforce PA behaviors.None of these proven approaches are to be abandoned, but we need to seek ways to incorporate the new genetic knowledge in these approaches to improve their success. How to achieve this? There are broadly three current strategies to use genetic information in health care: 1) use genetic risk scores to focus our resources for intervention on those who are likely to need the intervention the most, 2) give feedback on where people fall on the genetic risk scale to increase their motivation to engage in the intervention to avert disease outcomes, and 3) tailor our interventions to better fit the person's genetic risk profile.By using the PGS for PA, we could identify vulnerable individuals who are genetically predisposed to low PA and therefore may benefit more from early detection, enhanced monitoring, and more frequent guidance. This idea of focusing our limited resources for intervention and monitoring on those who may need them the most has been advanced in the field of “personalized medicine.” Whereas early GWAS findings made only modest contributions to typical metrics of clinical utility like Number of Patients Reclassified, area-under-the-curve statistic, sensitivity and specificity, and the C-index, recent increases in the scale of GWAS consortia are rapidly changing this. At least for breast cancer, type 2 diabetes, and coronary artery disease (CAD), there are PRS available with sufficient predictive power for clinical implementation . For exaOf course, the genetic risks for CAD and breast cancer remain “unseen” until the disease becomes manifest, whereas we do not need DNA to detect who is physically active and who is not. The clear advantage of PGS over the assessment of ongoing health behaviors is that a PGS can provide an estimate of the risk trajectory across a lifetime, rather than the prediction window of a few years covered by a single snapshot of the current PA level. This means that in terms of forecasting who is at risk of becoming low physically active as an adult, a PGS can guide focused interventions in childhood and early adolescence, when the genetic propensity is not yet as visible in behavior as in late adolescence and adulthood.e.g., more suntan use when confronted with high melanoma risk). Even more sobering, a recent study that tested whether PA assessed by accelerometers was increased after clinical and genetic risk disclosure did not detect any changes in PA behavior  health behavior coaching, it may have a more positive impact. This was illustrated by the GeneRISK study in Finland that evaluated the attitudes of 7342 middle-aged individuals upon receiving personal genome-enhanced information on 10-year CAD risk, and prospectively assessed the impact on the participants' health behavior . AltogetWhen we intervene on PA, we often advertise regular exercise as something that will “make you feel good, improve your cognition, buffer your stress reactivity, reduce your weight.” This generic message completely fails to consider that such benefits will not be experienced by, for example, those who struggle above their preferred intensity level to keep up with the group/expectations, those whose hypothalamus ruthlessly corrects for the increased energy expenditure, and those who lack the outgoing personality or the athletic abilities that often determine one's “rank order” in organized sports activities at school, work, and sports clubs to enjoy such activities. In short, when we advertise the benefits of PA, we assume they apply to all, whereas abundant evidence suggests that both mental and physical health effects of regular PA show large individual differences that are at least partly genetically determined ,121.A better grasp of an individual's innate propensity for PA as well as a better grasp on what benefits and risks regular PA will bring to a specific individual can help tailor programs to more closely fit that individual and hence improve recruitment and retention of the individual into regular PA habits. It could be particularly beneficial to decompose the PGS for PA into PGS for specific types of PA, for example, LTPA, MVPA, LPA, or even sedentary behaviors, as differential odds to engage in these types of behaviors may require differentiated intervention. Another strategy would be to focus on the potential pathways by creating a separate PGS for risk scores for aversive psychological responding to exercise, low exercise ability, or high sensitivity to injury.Those who are expected based on their PGS to respond with “feeling good” to moderate to vigorous exercise need a different advice than those who do not have the neurobiology to enjoy exercise at high-intensity levels. For those with low PGS for PA liking, obtaining increased adoption and adherence to regular PA might be as simple as reducing exercise intensity and presenting a different or larger selection of PA activities. For those with low expected benefits in terms of weight loss, increases in aerobic performance/muscle strength, or stress reduction, a cognitive  restructuring of expected mental and physical benefits based on genotype predictions may be useful, particularly in the initial phases of the PA intervention program. More generally, a PGS pointing to low exercise ability/trainability could be used to shape the intervention such that it reduces direct comparison and competition, for example, by advising solitary over team activities, or inclusive team-based activities over competitive ones.A PGS could also be used to identify specific risks for injury and in turn lead to adaptation of the content and build-up of exercises in training programs . ApplicaAt this point, the examples are mere speculations. The exact strategy to optimize intervention based on genotype first requires a furthering of our current understanding of genetic differences in the propensity to engage in PA. Given the differences in genetic factors expressed across the lifespan, the potential use of the various PGS described above will likely be age dependent. Children may experience rather different enjoyment “gains” when they adopt a physically active lifestyle (enjoyment) than they do in adolescence (being good at it) or in adulthood . In essence, what the genetically tailored interventions at each age should optimally look like, and if they work at all, remains largely to be discovered.A large gap in our knowledge is caused by the near absence of PA intervention studies explicitly looking at gene-by-intervention interaction effects. Whereas most intervention studies are too small for a meaningful candidate gene approach, a PGS could explain 1%–5% of the variance in PA intervention responses. Adjusting for the PGS could help increase the power to detect the effects of PA interventions, and the PGS further allows stratified analyses in subsets of individuals with low, moderate, to high genetic propensity to engage in PA. This can be used to explicitly test if, and which, health benefits are dependent on having a more or less favorable genotype for PA.To enable gene-by-intervention interaction testing, the only three additions that interventionist need to make to their study protocols are the explicit informed consent for biomaterial collection, a secure and qualified biobank facility, and an extra U.S.$35 per participant for the genome-wide array with bioinformatics. These are nontrivial extra efforts, but entirely doable. Paired to the increasing resource of freely available summary statistics of GWAS consortia, they would provide an unprecedented opportunity to move the field of exercise genomics forward. Such studies could show how the heterogeneity in the effects of interventions on PA adoption and the heterogeneity in the effects of PA on the health outcomes is predicted by genotypes.