Hip dysplasia and osteoarthritis continue to be prevalent problems in veterinary and human medicine. Canine hip dysplasia is particularly problematic as it massively affects several large-sized breeds and can cause a severe impairment of the quality of life. In Finland, the complex condition is categorized to five classes from normal to severe dysplasia, but the categorization includes several sub-traits: congruity of the joint, Norberg angle, subluxation degree of the joint, shape and depth of the acetabulum, and osteoarthritis. Hip dysplasia and osteoarthritis have been proposed to have separate genetic etiologies.NOG), a bone and joint developmental gene on chromosome 9, and nanos C2HC-type zinc finger 1 (NANOS1), a regulator of matrix metalloproteinase 14 (MMP14) on chromosome 28. Osteoarthritis mapped to a long intergenic region on chromosome 1, between genes encoding for NADPH oxidase 3 (NOX3), an intriguing candidate for articular cartilage degradation, and AT-rich interactive domain 1B (ARID1B) that has been previously linked to joint laxity.Using Fédération Cynologique Internationale -standardized ventrodorsal radiographs, German shepherds were rigorously phenotyped for osteoarthritis, and for joint incongruity by Norberg angle and femoral head center position in relation to dorsal acetabular edge. The affected dogs were categorized into mild, moderate and severe dysplastic phenotypes using official hip scores. Three different genome-wide significant loci were uncovered. The strongest candidate genes for hip joint incongruity were noggin (Our findings highlight the complexity of canine hip dysplasia phenotypes. In particular, the results of this study point to the potential involvement of specific and partially distinct loci and genes or pathways in the development of incongruity, mild dysplasia, moderate-to-severe dysplasia and osteoarthritis of canine hip joints. Further studies should unravel the unique and common mechanisms for the various sub-traits. Canine hip dysplasia (CHD) is a common multifactorial hereditary disorder that has perplexed dog owners, breeders as well as veterinarians and researchers for decades. A standardized system for CHD grading has been developed in the countries that belong to the Fédération Cynologique Internationale (FCI). The FCI score is divided into five categories alphabetically: A to E, where A is normal and E is severe CHD. In Finland, the FCI score is defined separately for both hip joints, hence the format is given as: left hip score / right hip score. The FCI score is determined from different ‘sub-traits’ of the hip: congruency of the joint, Norberg angle (NoA), subluxation degree of the joint, shape and depth of the acetabulum, and whether there are any visible signs of osteoarthritis (OA) in the joint or not. FCI has derived the grading rules, from which the Finnish Kennel Club (FKC) has defined their guidelines for radiographing and scoring hip dysplasia . The aboAs the FCI or any other combinatory score does not accurately correlate with the various CHD sub-traits, these have to be studied separately. NoA and femoral head center position in relation to dorsal acetabular edge (FHCDAE) reflect the incongruity of the hip joint, which impacts the development of CHD . Hip joiThe current consensus is that CHD is polygenic, and genetic contribution to the phenotype can vary from small to moderate –14. VariTo better understand the genetic etiology of CHD related phenotypes, we have carried out here a successful genome-wide association study (GWAS) in a cohort of over 750 well-phenotyped German Shepherds to map loci for CHD and related sub-traits. We report three loci with genome-wide significance and two suggestive loci for different traits with physiologically relevant candidate genes.P = 0.028, Additional file Incongruity of the hip joint contributes to CHD. Therefore, we carried out two different association analyses on incongruity related traits, FHCDAE and NoA, which were assessed by two different veterinarians in our group. Both traits were measured for right and left hip, but we used only the worst measure in the analysis. NoA showed significant inter-observer variation in a linear regression model  of Pearson’s correlation coefficient between pairs of SNPs     and nanos C2HC-type zinc finger 1 (NANOS1)   were compared with all cases regardless of the severity of OA (N = 163), revealed a genome-wide significant locus on chromosome 1 . The dogs exhibited either no radiographic evidence of OA (controls) or had mild, moderate or severe signs of OA (cases). A case-control association analysis, where all controls   and AT-rich interaction domain 1B (ARID1B)  was observed between these six SNPs, even though the region they covered was over 1.1 Mb long  and BICF2S2305568 within the first intron of Dynein light chain Tctex-type 1 (DYNLT1).The two genome-wide significant SNPs, as well as four out of the six SNPs showing suggestive association with OA on this chromosome, located between NADPH oxidase 3 . In the second analysis, the same controls were used but cases had a bilateral FCI score of D or worse (Ncases = 166). In the third analysis we compared mild CHD dogs  with dogs that had moderate-to-severe (at least an FCI score D or worse for either hip) CHD . The summary of the results of these three comparisons are shown in Table To identify loci for CHD according to the FCI hip scores, we carried out three sets of case-control association analyses. In the first case-control analysis, the controls had a bilateral FCI hip score A and cases B/C, C/B or bilateral FCI score C or worse  and TIGRP2P126345 located ~ 8 kb downstream from the same gene. These two SNPs are in high LD  estimates from the polygenic mixed model for the different traits varied from 36 to 64% . The latter is an aggregate phenotype and visible signs of OA (or the lack of them) are part of its evaluation. Therefore, it is not surprising to observe overlapping results.The locus on chromosome 1 lies in a long intergenic region between 1B Table  Neither NOX3 is a member of NADPH oxidases and an interesting candidate for articular cartilage degradation. NADPH oxidase participates in the generation of hydrogen peroxide, which is used by myeloperoxidase as a substrate to produce a highly reactive hypochlorous acid, and in some circumstances chlorine gas [NOX3, but BICF2S23248027  is located in the intron between NOX3 exons 9 and 10 , another candidate gene for CHD [(TIAM2) further upstream ; CSS is caused by ARID1B variants and 66% of the CSS patients exhibit joint laxity [rine gas , 30. Therine gas , 30. The0 Tables . Moreove tissues , thus, ta STRING –35. We ha STRING  that the for CHD . The promodeling . Interest laxity , 39.Previous studies have suggested seven different loci for OA, none of them overlapping our loci. A multi-breed study by Zhou et al. (2010)  suggesteNOG associated with the incongruity trait FHCDAE  was ~ 24 times as strong as what we observed for the FCI hip score [NOG to FHCDAE remains elusive but may offer some leads to reduced joint congruity. Decreased noggin activity could possibly strengthen the acetabular bone via bone morphogenic protein (BMP) signaling and help the repair of microfractures and other damage caused by mechanical wear in growing dogs. Interestingly, delayed ossification of the femoral head has been associated with CHD in later life [NOG is a crucial gene for many developmental processes, such as neural tube fusion, joint formation and skeletal development [NOG mutations cause some congenital disorders with abnormal joints [Nog leads to a state where the mice lack most of the joints in the limbs [Nog results in osteopenia, bone fractures and decreased bone formation, when the function of osteoblasts becomes defective [Nog in rats with small interfering RNA leads to down-regulation of Nog and increases both BMP-mediated differentiation of osteoblasts and the mineralization process of extracellular matrix.A locus in chromosome 9 near E Tables  and 5. TE Tables . We prevhepherds . The assip score . The putter life , 42. NOGelopment , 44. In l joints , and knohe limbs . On the efective . A recenefective  showed tCACUL1, a cell-cycle associated gene [NANOS1 that upregulates MMP14 a.k.a. membrane type 1-matrix metalloproteinase (MT1-MMP) thus promoting epithelial tumor cell invasion [NANOS1 – MMP14 interplay needs to be targeted in tissues relevant to CHD.The third locus with genome-wide significance involved also FHCDAE and resided on chromosome 28 Tables  and 5. Tted gene , and NANinvasion . MT1-MMPinvasion , 52 and invasion . The posIntriguingly, chromosome 28 has been previously associated with NoA in two studies of which one included also German Shepherds , 54. AltLHX1, AATF (both on chromosome 9) and SLC7A1 (chromosome 25)  in our previous study [AATF is located close to LHX1 but its role in CHD remains uncertain. Both LHX1 and AATF have been associated with the levels of macrophage inflammatory protein 1b (MIP-1b) [, CCL4) is located on chromosome 9, ~ 795 kb away from TIGRP2P126345 and ~ 803 kb from AATF  , 58. MIP(MIP-1b) . The canb , 58. MConsidering the clinical complexity of CHD, it is not surprising that we have successfully mapped several loci, which contain candidate genes that are involved in different biological pathways. Identification of these pathways is an important step in understanding the pathophysiology of CHD. Some of the genes in these networks may have no direct function on the disorder but have a circuitous effect through other genes . As demoBesides sample size, accurate and reliable phenotyping is another essential factor when studying complex traits. This is particularly important when the trait comprises of many interconnected sub-traits that explain only small parts of the total variation. As long as the assessment of CHD relies on the FCI scoring, it is crucial to have standardized high-quality radiographs and a minimal number of people assessing them to reduce inter-observer bias . More reNOX3 and ARID1B on chromosome 1, NOG on chromosome 9, and NANOS1 on chromosome 28. Future studies will focus on ascertaining their role in CHD by resequencing the candidate region for putative risk variants.In conclusion, we have performed a successful association study with a large cohort of accurately and robustly phenotyped German Shepherds and describe three loci with genome-wide significance and two suggestive loci for CHD-related traits. The candidate genes include We acquired the data for our study from the Finnish Kennel Club. Before quality control we had a total of 775 samples of German Shepherds and of these 356 were controls, 322 were cases with both hip joints scored C or worse and 97 were of intermediate phenotypes with at least one hip joint scored as B. Majority of the dogs had either the same FCI score bilaterally or had maximum one score grade difference between the right and the left hip; three dogs had more than one grade difference . The average age at radiographing was 1.55 years ranging from 1.01 to 5.83 years with a standard deviation of 0.63 years. 435 of the dogs were female and 340 were male. We collected at least one blood sample from all the dogs with ethylenediaminetetraacetic acid (EDTA) as an anticoagulant.The FCI-standardized ventrodorsal extended hip radiographs were taken by different veterinarians, but the hip scoring was done by two specialized veterinarians at the FKC. Therefore, inter-observer bias was reduced in this data set . All of Two veterinarians in our group carefully evaluated the acquired radiographs for more specific hip phenotypes. These phenotypes were: findings suggestive on osteoarthritis (in four categories from 0 = no signs to 3 = severe signs), NoA (in degrees), and FHCDAE (in millimeters). The phenotyping process was carried out as follows: One veterinarian  assessed all the radiographs for the study cohort that was used in our previous study . HoweverP = 0.028, Additional file N = 498), some mild changes affiliated with OA , moderate changes , or severe osteoarthritis . However, radiographs are relatively insensitive in detecting early osteoarthritic changes [NoA varied between 70 and 108 degrees in our cohort Table ; the sma changes . TherefoThe original EDTA preserved blood samples for this study are stored at the Dog DNA bank at the University of Helsinki. The DNA was extracted from these samples with a Chemagic Magnetic Separation Module I with a standard protocol by Chemagen . Thereafter the DNA samples were genotyped at Geneseek  with a high density 173 K canine SNP array from Illumina . Genotyping of the samples was done in multiple batches.We used information from a genomic relationship matrix built from the SNP data to divide our highly stratified German Shepherd population into three subpopulations  0.00001 (from controls only). After these quality controls and data merging a total of 100,435 SNPs and 775 samples were transferred from PLINK to R. The final QC was done in R with GenABEL [We used PLINK  to merge GenABEL , and the GenABEL . After tWe performed a GWAS by using polygenic mixed models in GenABEL, with functions “polygenic” and “mmscore” (FASTA: Score test for association in related people) . The app2 values for the top SNPs were estimated in R with “r2fast” function [The rfunction  from GenP-values in this study.Bonferroni correction can be seen as a too stringent method to correct for multiple testing as it expects independency between the tests, which is untrue in many association studies because of LD between markers . This isAdditional file 1. Summaries of glm and lm modelsAdditional file 2. List of genes within +/− 1 Mb from the associated lociAdditional file 3. Pairwise LD (r2) for the top SNPs by chromosomeAdditional file 4. Allele frequencies for the significant SNPs from the case-control analysesAdditional file 5. Heritability (h2) estimates from the polygenic model (GenABEL) for all of the traitsAdditional file 6 Results from a STRING database search for the candidate gene NOX3Additional file 7. Classification plot of the population structure from R-package “mclust”. The population is divided into three subpopulations, where the green triangles represent individual dogs of one subpopulation and the red squares and the blue dots represent the individual dogs of the two other subpopulationsAdditional file 8. Q-Q plots of the various association analyses corresponding to Tables 4


Elite athlete status is a partially heritable trait, as are many of the underpinning physiological, anthropometrical, and psychological traits that contribute to elite performance. In recent years, our understanding of the specific genetic variants that contribute to these traits has grown, such that there is considerable interest in attempting to utilise genetic information as a tool to predict future elite athlete status. In this review, we explore the extent of the genetic influence on the making of a sporting champion and we describe issues which, at present, hamper the utility of genetic testing in identifying future elite performers. We build on this by exploring what further knowledge is required to enhance this process, including a reflection on the potential learnings from the use of genetics as a disease prediction tool. Finally, we discuss ways in which genetic information may hold utility within elite sport in the future, including guiding nutritional and training recommendations, and assisting in the prevention of injury. Whilst genetic testing has the potential to assist in the identification of future talented performers, genetic tests should be combined with other tools to obtain an accurate identification of those athletes predisposed to succeed in sport. The use of total genotype scores, composed of a high number of performance-enhancing polymorphisms, will likely be one of the best strategies in the utilisation of genetic information to identify talent in sport.  ACTN3, R577X (rs1815739), has been shown to modify the attainment of elite speed–power athlete status [ACTN3, α-actinin-3, which is expressed exclusively in fast twitch muscle fibres. As a result, XX genotypes tend to have lower proportions of fast-twitch muscle fibres [ACTN3 has subsequently been labelled a “gene for speed” [ACTN3, however, is not the only gene associated with elite athlete status, with a recent review reporting that at least 155 genetic markers are potentially linked to elite athlete status, at least in some cohorts or ethnicities [It is widely recognised that some individuals are naturally talented in specific physical traits that are, in turn, related to sports performance . In somee status ,5. Here,e fibres  and, becR577X rs115739, hae fibres . Conseque fibres ,11,12, Ar speed” ,14,15. Anicities . Whilst many of the currently established SNPs associated with elite athlete status are associated with physiological traits such as aerobic capacity, muscle strength or speed, there is the potential that other SNPs may exert a less direct—but no less important—effect on the attainment of elite sports performance. For example, both height  and bodyGiven the wide-ranging and potential influence of genetic variations on the attainment of elite athlete status, there is considerable interest in collecting genetic information to identify athletes with the potential to achieve elite status. Indeed, the number of companies that offer direct-to-consumer (DTC) genetic testing to predict sports performance/injury risk associated with sport has rocketed in recent years . MoreoveACTN3 rs1815739 [ACE) insertion (I)/deletion (D) polymorphism (rs1799752) [PPARGC1A or simply PGC1-1α) rs8192678 [TFAM) rs1937, have yet to be satisfactorily replicated [As previously mentioned, over 155 genetic markers have been tentatively linked with elite athlete status , althoug1799752) , and pers8192678 ,26 have plicated . Of the plicated , 31 haveplicated . Accordiplicated . ACTN3 rs1815739, whilst the R allele is associated with elite athlete status in speed–power sports events [ACTN3 genotype frequencies between these athletes and their non-athletic peers, with 97% of non-athletes possessing at least one R allele [ACTN3 genotype is not particularly useful from a talent identification perspective. Furthermore, there are exceptions to the belief that an R allele is required for elite speed–power performance. In their study of elite sprinters, Papadimitriou and colleagues [ACTN3 R allele does not preclude attainment of elite status in speed–power events. Additionally, performance-enhancing polymorphisms may still have a low prevalence in elite athlete populations. For example, a SNP (rs7181866) in nuclear respiratory factor 2 (NRF2) gene has been associated with elite athlete status, with a significantly higher proportion of the AG genotype compared to the AA genotype found in elite endurance athletes when compared to controls [Another issue is that the currently available markers appear to offer poor specificity and sensitivity as talent identification tools. Returning to s events . In a stR allele . Therefolleagues  reportedlleagues  reportedSuch findings demonstrate the difficulties of a single gene approach for talent identification, highlighting that such an approach is no longer viable. In light of these findings, researchers have turned to Total Genotype Scores (TGS), whereby a number of elite athlete-associated SNPs are combined into a single polygenic score. Ruiz and colleagues ,32 utiliAs a summary of the above discussion, it seems clear that the provision of elite athlete status is a highly complex, polygenic trait, and that we know very few of the genetic variations that contribute to such a trait. As a result, it appears a fundamental requirement that, if genetic testing is to be utilised for talent identification purposes, a far greater number of polymorphisms associated with elite athlete status needs to be uncovered and then combined into a complex TGS model. p < 5 × 10−8), and the (often) very low effect sizes of any SNP, GWAS studies require very large subject numbers and might fail to identify some variations with a small effect. This is problematic when it comes to research on elite athletes, who are, by definition, rare. This problem was encountered in a GWAS carried out in multiple cohorts of elite endurance athletes by Rankinen and colleagues [n = 315) and controls, along with a cohort of elite Japanese runners (n = 60) and controls, for the discovery phase of the GWAS. Following this discovery phase, in which no SNP met genome-wide significance, forty-two suggestive SNPs were taken through to a replication phase involving endurance athletes and controls from seven other countries, and from different ethnicities. Again, no genome-wide significant SNPs were found, although upon meta-analyses, a single SNP, rs558129 in polypeptide N-Acetylgalactosaminyltransferase 6 (GALNTL6) gene, was significant, with the T allele less frequent in athletes than in controls. As such, the authors summarised that there appeared to be no common SNP associated with elite endurance athlete status across this cohort, although they acknowledged their low sample size as a major limiting factor. This limitation is difficult to overcome and represents a significant roadblock in the search for genetic variants associated with elite performance. One potential method is to conduct a GWAS-approach analysis in a cohort of interest, and then attempt to replicate the suggestive findings  of this analysis in additional, hold-out cohorts. Whilst novel, this approach has only been used in few sport genetics studies [The evolution of Genome-Wide Association Study (GWAS) methodology potentially offers an opportunity to expand the number of genetic variants currently associated with elite athlete status, or to other traits associated with sporting success, such as the likelihood of suffering performance-limiting injuries . Whilst lleagues . The aut studies ,39, despEPOR) gene. This variation is linked to a disease called erythrocystosis-1, where affected people have increased red blood cell production, and hence greater oxygen carrying capacity [MNA) gene—related to muscular dystrophy—that was found in Canadian sprint hurdler Priscilla Lopes-Schliep [MSTN) has been reported, in which the carrier was described as “extraordinarily muscular” [A further avenue for exploration is that of rare gene variants that may predispose to elite performance. Generally, research focuses on common variants present in >1% of the population  . Howevercapacity . This oucapacity . Prelimi-Schliep . Finallyuscular” . Such a uscular” , not leauscular” . ACTN3 R allele), or those that, whilst linked to elite athlete status, are still present at relatively low frequencies in elite cohorts . An area of potential previous confusion is the number of performance-enhancing polymorphisms an individual may require before they are capable, from a genetic standpoint, of elite performance. Both Williams and Folland  and HughIn addition to genetic variations that might predispose, at least partially, to elite performance in a given sports, favourable epigenetic modifications that predispose to a natural talent trait (direct effect), and/or enhanced response to physical training and reduced risk of injury  would ap < 5 × 10−8. However, the lowering of this threshold has been shown to lead to explanation of a greater proportion of heritability [p < 5 × 10−8), a number of SNPs had suggestive significance, and so may hold predictive ability as part of a TGS. As such, it appears that, in order to successfully predict future elite athlete status, models involving multiple genetic variants with low effect sizes are likely required, although this approach has recently been criticised from a disease-prediction viewpoint [One area where the use of genetics to make informed predictions of a future event has been well explored is that of disease risk. Whilst some diseases, such as cystic fibrosis, are monogenic, most diseases are complex and polygenic in nature . Similartability . Recentltability  utilisedtability , such asiewpoint . Howeveriewpoint —a numberThere are serious and well-placed concerns about the use of genetic information, either alone or in combination with existing measures, for talent identification within sport . It is gIf and when genetic testing is used to predict future elite athlete status, there will be many false positives and false negatives . WhilstIt has previously been argued that, instead of using genetic information for talent identification in the traditional sense, this information could be used to match individuals to the type of training to which they are most suited, and from which they will elicit the greatest adaptations ,63,64. ACOMT) gene, rs4680, has been linked with competition performance in swimmers [Whilst traditional research has focused on the physiological drivers of elite athlete status (reviewed in detail by Ahmetov and colleagues ), there swimmers  and withswimmers . Catechoswimmers ; variatiswimmers . Emerginswimmers . As a reswimmers . That saAlthough there is a well-established effect of genetics on the attainment of elite athlete status, based on the available evidence, it is clear that the current use of genetic tests for the prediction of future elite athlete status is ineffectual, a finding that echoes recent consensus statements ,71. Desp


Physiological components influenced the flowering time and grain morphology, and morphology and grain morphology influenced yield. In summary, we show the Bayesian network coupled with factor analysis can provide an effective approach to understand the interdependence patterns among phenotypes and to predict the potential influence of external interventions or selection related to target traits in the interrelated complex traits systems.With the advent of high-throughput phenotyping platforms, plant breeders have a means to assess many traits for large breeding populations. However, understanding the genetic interdependencies among high-dimensional traits in a statistically robust manner remains a major challenge. Since multiple phenotypes likely share mutual relationships, elucidating the interdependencies among economically important traits can better inform breeding decisions and accelerate the genetic improvement of plants. The objective of this study was to leverage confirmatory factor analysis and graphical modeling to elucidate the genetic interdependencies among a diverse agronomic traits in rice. We used a Bayesian network to depict conditional dependencies among phenotypes, which can not be obtained by standard multi-trait analysis. We utilized Bayesian confirmatory factor analysis which hypothesized that 48 observed phenotypes resulted from six latent variables including grain morphology, morphology, flowering time, physiology, yield, and morphological salt response. This was followed by studying the genetics of each latent variable, which is also known as factor, using single nucleotide polymorphisms. Bayesian network structures involving the genomic component of six latent variables were established by fitting four algorithms ( Since multiple phenotypes may exhibit mutual relationships, knowledge of the interdependence among agronomically important traits can improve the efficacy of selection and rate of genetic improvement in systems with complex traits.A primary objective in plant breeding is the develop high yielding varieties with specific grain qualities, resilience to pests and abiotic stresses, and superior adaption to the target environment. As a result, plant breeders devote considerable resources to extensive phenotypic evaluation of germplasm and select on multiple traits. These traits are often correlated at a genetic level through common genetic effects  of ph (DAG) . In the With the advent high-throughput phenotyping (HTP) platforms, plant breeders have been provided with a suite of tools for phenotypic evaluation of large populations . The central idea of FA approaches is to reduce the dimensions of multivariate data sets by constructing unobserved, latent factors, or modules, from correlated phenotypes . The bioe.g., yield, plant morphology, phenology, and stress resilience). First, we constructed latent variables, using prior biological knowledge obtained from the literature. Then we connected the observed high-dimensional phenotypes with these to establish latent variables via Bayesian confirmatory factor analysis (BCFA) to reduce the dimensions of the dataset. Further, factor scores computed from BCFA were considered new phenotypes for a Bayesian multivariate analysis to separate breeding values from noise. This was followed by adjustment of breeding values via Cholesky decomposition to eliminate the dependencies introduced by genomic relationships. Finally, the adjusted breeding values were considered inputs to assess the network structure between latent variables by conducting a Gaussian BN analysis. This study is the first, to our knowledge, in rice to characterize various phenotypes with graphical modeling such as BCFA and BN.This study aimed to leverage CFA and graphical modeling to elucidate the genetic interdependencies among traits typically recorded in breeding programs . The data used from The rice dataset comprised A CFA under the Bayesian framework was performed to model 48 phenotypes. The number of factors and the pattern of phenotype-factor relationships need to be specified in BCFA prior to model fitting. We constructed six latent variables . Posterior mean estimates of genomic correlation between latent variables and predicted breeding values  with nodes (V) connected by one or more edges (E) conveying the probabilistic relationships and the random vector K random variables. The joint probability distribution can be factorized asA BN depicts the joint probabilistic distribution of random variables through their conditional independencies BN=i.e., add, delete, or reverse the edges of initial graph). The hybrid algorithm includes two steps, restrict and maximize, which harness both constraint-based and score-based algorithms to construct a reliable network. In this study, the two score-based (Hill Climbing and Tabu) and two hybrid algorithms  were used to perform structure learning. A flow diagram to illustrate the concept of constraint-based Bayesian netwrok structure learning algorithm is shown in The adjusted breeding values hese two . The conhese two , which uWe quantified the strength of edges and uncertainty regarding the direction of networks, using 500 bootstrapping replicates with a size equal to the number of accessions and performed structure learning for each replicate in accordance with http://www.ricediversity.org/). Phenotypic data used herein are available in https://doi.org/10.25387/g3.7970642.Genotypic data regarding the rice accessions can be downloaded from the rice diversity panel website , and seed morphological traits. The second study consisted of 14 phenotypes were recorded in a greenhouse environment on plants in the active tillering stage  under control and saline , morphological responses to salinity , and the ionic components of salinity stress , 0.920; height to newest ligule in control (Hlc), 0.899; height to the tip of first fully expanded leaf in salt (Hfs), 0.907; and height to tip of first fully expanded leaf in control (Hfc), 0.925;) suggesting that this latent factor is an overall representation of plant size. Yield showed large positive contributions to the observed phenotypes primary panicle branch number (Ppn) and seed number per panicle (Snpp) , suggesting that larger values for yield indicate a higher degree of branching and seed number. Observed phenotypes describing seed size  and brown rice volume (Bvl) ) were most strongly associated with grain morphology. The latent factor ionic components of salt stress showed strong positive contributions to two observed phenotypes that quantify the ionic components of salt stress  and Flowering time in Arkansas in 2007 (Fla7)  conditioned on its direct causes, 2) the probability distribution of variables is supported by a DAG, where the d-separation in DAG provides all dependencies in the probability distribution, and 3) no additional variables influence the variables within the network. Although it may be difficult to meet these assumptions in the observed data, a BN is equipped with suggesting potential causal relationships among latent variables, which can assist in exploring data, making breeding decisions, and improving management strategies in breeding programs  contributed strongly to salt indices for shoot biomass, root biomass, and two indices for plant height . Thus, me.g., those that are more tolerant) tend to also harbor alleles for poor yield  tend to also harbor alleles for a tolerant growth response to salt stress. However, no studies have yet reported an association between alleles for grain morphology and morphological salt response. Therefore, it remains to be addressed whether this relationship is due to LD or pleitropy.Genomic correlation analysis among the six latent variables showed meaningful correlations among several pairs. These genetic correlations can either be caused by linkage or pleiotropy. The former is likely to prevail in species with high LD, which is the case in rice where LD ranges from 100 to 200kb . For insor yield . The ricng lines . MoreoveGenetic correlations observed between other latent variables may suggest a pleiotropic effect among loci. For instance, a negative relationship was observed between morphological salt response and ionic components of salt stress, indicating that accessions harboring alleles associated with superior morphological salt response also tend to harbor alleles for reduced ion content under salt stress . The relThe genomic relationships among latent variables including morphology, yield, and grain morphology may have resulted from the selection of alleles associated with good agronomic characteristics. A positive relationship was observed between yield and grain morphology, suggesting that alleles that positively contribute to productive panicles also may contribute to large, round grains. Furthermore, the negative genomic correlation observed between morphology and yield indicates that alleles negatively influencing total plant biomass also have a positive contribution to traits for productive panicles. This genomic relationship may reflect the genetics of harvest index, which is defined as the ratio of grain yield to total biomass. Over the past 50 years, rice breeders have selected high harvest index, resulting in plants with short compact morphology and many highly productive panicles . However, caution is required to interpret this network as a causal effect, as the causal BN requires more assumptions, which are usually difficult to meet in observational data  in BN may be driven by LD between alleles associated with grain morphology and alleles for salt tolerance rather than pleitropy. Thus, given the current data set, genetic effects for grain morphology may still be conditionally dependant on ionic components of salt stress and the BN may be true, even if there is no direct overlap in the genetic mechanisms for the two traits.i.e., the path from morphology to flowering time was supported 60% (Hill Climbing), 51% (Tabu), and 52% , while the reverse direction was supported 51% (Max-Min Hill Climbing) upon bootstrapping. In addition, the two score-based algorithms captured edges between morphological salt response and flowering time with 70% and 76% bootstrapping evidence. However, this connection was not detected in the two hybrid algorithms. In general, inferring the direction of edges was harder than inferring the presence or absence of undirected edges. Finally, the whole structures of BN were evaluated in terms of the BIC score and BGe. Ranking of the networks was consistent across BIC and BGe and the two score-based algorithms produced networks with greater goodness-of-fit than the two hybrid algorithms. The optimal network was produced by the Tabu algorithm. This is consistent with the previous study reporting that the score-based algorithm produced a better fit of networks in data on maize (We found that there are some uncertain edges among BN in e.g., In conclusion, the present results show the utility of CFA and network analysis to characterize various phenotypes in rice. We showed that the joint use of BCFA and BN can be applied to predict the potential influence of external interventions or selection associated with target traits such as yield in the high-dimensional interrelated complex traits system. We contend that the approaches used herein provide greater insights than pairwise-association measures of multiple phenotypes and can be used to analyze the massive amount of diverse image-based phenomics dataset being generated by the automated plant phenomics platforms e.g., . With a


We identify loci for lipid species that are shown to predict CVD e.g., SPTLC3 for CER(d18:1/24:1). We show that lipoprotein lipase (LPL) may more efficiently hydrolyze medium length triacylglycerides (TAGs) than others. Polyunsaturated lipids have highest heritability and genetic correlations, suggesting considerable genetic regulation at fatty acids levels. We find low genetic correlations between traditional lipids and lipid species. Our results show that lipidomic profiles capture information beyond traditional lipids and identify genetic variants modifying lipid levels and risk of CVD.Understanding genetic architecture of plasma lipidome could provide better insights into lipid metabolism and its link to cardiovascular diseases (CVDs). Here, we perform genome-wide association analyses of 141 lipid species , followed by phenome-wide scans with 25 CVD related phenotypes . We identify 35 lipid-species-associated loci  lipid species which highlights shared genetic loci with CVD and that traditional lipids have low genetic correlation with other lipids.Cardiovascular diseases (CVD) are associated with plasma lipid levels. Here, Tabassum  Plasma lipids, the well-established heritable risk factors for CVDs2, are routinely monitored to assess CVD risk. Standard lipid profiling measures traditional lipids , but does not capture the functionally and chemically diverse molecular components—the lipid species3. These molecular lipid species may independently and specifically affect different manifestations of CVD, such as ischaemic heart disease and stroke. Lipid species including cholesterol esters (CEs), lysophosphatidylcholines (LPCs), phosphatidylcholines (PCs), phosphatidylethanolamines (PEs), ceramides (CERs), sphingomyelins (SMs) and triacylglycerols (TAGs) potentially improve CVD risk assessment over traditional lipids9.Cardiovascular diseases (CVDs) encompass many pathological conditions of impaired heart function, vascular structure and circulatory system. CVDs are the leading cause of mortality and morbidity worldwide11. For the majority of the lipid loci, however, their effects on detailed lipidome beyond traditional lipids are unknown. Only a few studies have reported genetic associations for lipid species either through studies on subsets of the lipidome13 or GWASs on metabolome20.Understanding of the genetic architecture and genetic regulation of these lipid species could help guide tool development for CVD risk prediction and treatment. Genetic studies of traditional lipids have identified over 250 genomic loci and improved our understanding of CVD pathophysiologyIn light of the limited information about the genetics of lipidomic profiles and their relationship with CVDs, we carried out a GWAS of lipidomic profiles of 2181 individuals using ~9.3 million genetic markers followed by PheWAS including 25 CVD-related phenotypes in up to 511,700 individuals , whereas phosphatidylinositols (PIs) showed the least heritability . Sphingolipids had higher heritability than glycerolipids ranging from 0.24 to 0.41  had stronger genetic correlations with each other than with other lipid species  . HDL-C and LDL-C levels had low genetic and phenotypic correlations with most of the lipid species . This re-analysis provided the similar results as the primary analysis  Fig. . Howeveries Fig. . Consisties Fig. . To ruleP < 1.5 × 10−9 accounting for 34 principal components that explain 90% of the variance in lipidome)  that are not previously reported for any lipid measure or related metabolite  (P = 1.9 × 10−10). ROCK1 encodes for a serine/threonine kinase that plays key role in glucose metabolism23. In line with our observation of higher heritability for lipids with C20:4, C20:5 or C22:6 acyl chains, we detected associations for 15 out of 21 lipids with these acyl chains.Next, we performed the genome-wide association analyses for 141 lipid species with ~9.3 million genetic markers. We identified 2817 associations between 518 variants located within 11 genomic loci (1MB blocks) and 42 lipid species from 10 lipid classes at study-wide significance (e) Table 3. TheseFADS2, SYNE2, LIPC, CERS4 and MBOAT7 with the same lipid species20. The previously reported associations at the known loci identified in previous metabolomics GWASs are provided in Supplementary Data http://snipa.org) using block annotation and PhenoScanner v2 (http://www.phenoscanner.medschl.cam.ac.uk/), and were manually curated to include associations from literature search. In addition, we also identified new locus–lipid species associations at previously reported lipid loci including new associations of variants at ABCG5/8 with CE  (P = 3.9 × 10−10), MBOAT7 with PI  (P = 3.0 × 10−12) and GLTPD2 with SM  (P = 3.4 × 10−22)  (Supplementary Data P < 3.7 × 10−4 (accounting for 134 comparisons) in our study  with 141 lipid species. Of these known variants, 76 variants from 12 loci showed association with 98 different lipid species with −9>P < 5.0 × 10−8). Among these additional loci, 13 loci were located in genomic regions not previously reported for any lipid measure or related metabolite, and 8 loci were located near known loci for lipids but were independent of any previously reported variant  were associated with at least one of the CVD outcomes (FDR < 5%) , GALNT16 with angina (P = 9.3 × 10−4), MBOAT7 with venous thromboembolism (P = 1.3 × 10−3), GLTPD2 with atherosclerosis (P = 5.3 × 10−4) and SPTLC3 with intracerebral haemorrhage (P = 1.0 × 10−3)  and aterial embolism and thrombosis (P = 2.5 × 10−4). BLK (rs1478898-A) was also found to be associated with decreased risk of obesity  and type 2 diabetes .As many of the lipid species have previously been shown to predict CVD risk, we determined if the variants associated with lipid species affect individuals’ susceptibility to CVD-related phenotypes in FinnGen and UK Biobank cohorts. We identified 25 CVD-related phenotypes from the clinical outcomes derived from health registry data in the FinnGen and UK Biobanks (Supplementary Table 9. We found that the CER (d18:1/24:1) decreasing variant SPTLC3 rs364585-G was associated with decreased risk of intracerebral haemorrhage, while CER (d18:1/24:0) increasing variant ZNF385D rs13070110-C was nominally associated with increased risk of intracerebral haemorrhage. Furthermore, consistent with the observation that elevated plasma SMs levels are atherogenic24, we identified association of GLTPD2 rs79202680-T (associated with reduced levels of SMs) with reduced risk of atherosclerosis.Several studies have suggested a role for sphingolipids, including CERs and SMs, in the pathogenesis of CVDs. CER (d18:1/24:0) and CER (d18:1/24:1) have been reported to be associated with the increased risk of CVD eventsFADS) gene cluster that has been consistently reported to be associated with omega-3 and omega-6 fatty acids levels with inverse effects on different PUFAs. Its mechanism, however, has not been fully deciphered. Here, we found that the FADS2 rs28456-G was associated with increased levels of lipids with a C20:3 acyl chain and decreased levels of lipids with C20:4, C20:5 and C22:6 acyl chains  . LPL codes for lipoprotein lipase that is the master lipolytic factor of TAGs in TAG-enriched chylomicrons and VLDL particles. We found that LPL rs11570891-T was associated with reduced levels of medium length TAGs (C50–C56), with strongest associations with TAG . This suggested that LPL enzyme might have different efficiency in hydrolysis of TAGs of different length. We explored this possibility by evaluating (1) the effect of LPL rs11570891-T on LPL enzymatic activity and (2) the relationship between LPL activity and plasma levels of TAGs of different length, using post-heparin LPL measured in the EUFAM cohort. We found that LPL rs11570891-T (an eQTL increasing LPL expression) was associated with increased LPL activity, which in turn was associated with TAG species with stronger effect on medium length TAGs than other TAGs  associated with long, polyunsaturated lipids at genome-wide significance. Of these, FADS2, APOA5, LPL and MBOAT7 variants were also associated with risk of CVDs . CER species, particularly CER (d18:1/24:0) and CER (d18:1/24:1) are recently reported to be associated with the increased risk of CVD9. We identified two variants near SPTLC3 and ZNF385D that modulate the plasma levels of CER (d18:1/24:1) and CER (d18:1/24:0), respectively, and risk for intracerebral haemorrhage. This information could also guide future studies to establish the causal relationship between lipid species and CVD.Further, we mapped genetic variants for lipid species from several lipid classes, including CERs, CEs, TAGs, SMs and PCs, that are shown to predict CVD riskFADS2 and LPL on lipid metabolism and CVD risks. We show how the inverse effects of FADS2 rs28456-G on the expression of two desaturases (FADS2 and FADS1) could explain its opposite effects on lipids with different PUFAs. The delta-6 desaturation by FADS2 generates gamma-linolenic acid and stearidonic acid that by elongation yield dihomo-gamma-linolenic acid and eicosatetraenoic acid  and aterial embolism and thrombosis.The detailed lipidomic profile also provided clues towards understanding the mechanisms of effects of well-established lipid loci like LPL variant increases the LPL activity resulting in decreased levels of medium length TAGs. The association of the LPL variant with reduced susceptibility to CVD and type 2 diabetes could be mediated through the decrease in medium length TAGs . We show that an PTPRN2 and rs10790495-G near MIR100HG showed distinct association patterns with TAGs, with strongest association with long polyunsaturated TAGs. PTPRN2 codes for protein tyrosine phosphatase receptor N2 with a possible role in pancreatic insulin secretion and development of diabetes mellitus32, while MIR100HG rs10790495 is an eQTL for the heat-shock protein HSPA8 that has a role in cell proliferation33. However, it is not known if PTPRN2 and MIR100HG or HSPA8 have any role in lipid metabolism.Similarly, the patterns of assocations of newly mapped loci also suggested their involvement in the regulation of lipid metabolism. For example, rs10281741-G near 34. Similarly, our study demonstrates that molecular lipid species have stronger statistical power compared with traditional lipids at known lipid loci using the same sample size. However, in contrast to Petersen et al., we found that many of the lipid species, including LPCs and PCs that have previously been associated with incident coronary heart disease risk6, have low phenotypic and genotypic correlations with traditional lipids. We also show that the known lipid variants for traditional lipids explain less of the variance of lipid species than traditional lipids. Altogether, as expected these results suggest that lipidomic profiles could provide novel information that could not be captured by traditional lipids and lipoprotein measurements.Finally, we show that lipidomic profiles capture information beyond traditional lipids and provide an opportunity to identify additional genetic variants influencing lipid metabolism and disease risk. Previously, Petersen et al. showed that lipoprotein subfractions correlate with traditional lipids and strengthen genetic associations at known lipid loci and that these loci explain more of the variance of lipoprotein subfractions than of serum lipids35. The UK Biobank cohort is reported to have a “healthy volunteer” effect36, which may affect the PheWAS results, however, given the large sample size, this is unlikely to have a substantial effect on genetic association analyses. Furthermore, lipidomic profiles were measured in whole plasma, which does not provide information at the level of individual lipoprotein subclasses and limits our ability to gain detailed mechanistic insights. We also excluded poorly detected lipid species to ensure high data quality that narrowed the spectrum of lipidomic profiles. Further advances in lipidomics platforms might help to capture more comprehensive and complete lipidomic profiles, including the position of fatty acyl chains in the glycerol backbone of TAGs and glycerophospholipids and detection of sphingosine-1-P species and several other species, that would allow to overcome these limitations.Our study had some potential limitations. Though our study represents one of the largest genetic screen of lipidomic variation, larger cohorts are needed to achieve its full understanding. Blood samples for the EUFAM cohort were drawn after an overnight fast whereas the FINRISK cohort samples had varied fasting duration. This, however, does not seem to have substantial effect on the results and their interpretation as shown in Supplementary Data In conclusion, our study demonstrates that lipidomics enables deeper insights into the genetic regulation of lipid metabolism than clinically used lipid measures, which in turn might help guide future biomarker and drug target discovery and disease prevention. study cohort is comprised of the Finnish familial combined hyperlipidemia families37. The families in EUFAM study were identified via probands admitted to Finnish university hospitals with a diagnosis of premature coronary heart disease. The probands had premature coronary heart disease and high levels of the total cholesterol, triglycerides, or both (≥ 90th Finnish age-specific and sex-specific population percentile), or low HDL-C levels (≤ 10th percentile). Invitation was extended to all the family members and spouses of the probands if at least one first-degree relative of the proband had high levels of the total cholesterol, triglycerides, or both. Venous blood samples were obtained from all participants after overnight fasting. Triglycerides and total cholesterol were measured by enzymatic methods using an automated Cobas Mira analyser 38. HDL-C was quantified by phosphotungstic acid/magnesium chloride precipitation procedures, and LDL-C was calculated using the Friedewald formula39.The study included participants from the following cohorts: EUFAM, FINRISK, FinnGen and UK Biobank. The EUFAM 40. Collections from the 1992, 1997, 2002, 2007 and 2012 surveys are stored in the National Institute for Health and Welfare /THL) Biobank. Lipidomic profiling was performed for 1142 participants that were randomly selected from the FINRISK 2012 survey . For the Finnish Institute of Health and Welfare (THL) driven FinnGen preparatory project , all patients and control subjects had provided informed consent for biobank research, based on the Finnish Biobank Act. Alternatively, older cohorts were based on study specific consents and later transferred to the THL Biobank after approval by Valvira, the National Supervisory Authority for Welfare and Health. Recruitment protocols followed the biobank protocols approved by Valvira. The Ethical Review Board of the Hospital District of Helsinki and Uusimaa approved the FinnGen study protocol Nr HUS/990/2017. The FinnGen preparatory project is approved by THL, approval numbers THL/2031/6.02.00/2017, amendments THL/341/6.02.00/2018, THL/2222/6.02.00/2018 and THL/283/6.02.00/2019. All DNA samples and data in this study were pseudonymized.47. The data were analysed using in-house developed lipid identification software based on LipidXplorer49. Post processing and normalisation of data were performed using an in-house developed data management system. Only lipids with signal-to-noise ratio >5 and amounts at least fivefold higher than in the corresponding blank samples were considered for further analyses. Reproducibility of the assay was assessed by the inclusion of reference plasma samples (eight reference samples for EUFAM and three reference samples for FINRISK) per 96-well plate. Median coefficient of variation was <10% across all batches. The data were corrected for batch and drift effects. Lipid species detected in <80% of the samples in any of the batches and samples (N = 64) with low lipid contents were excluded. Among the lipid species which passed quality control, a total of 141 lipid species from 13 lipid classes . Samples were analysed by direct infusion in a QExactive mass spectrometer (Thermo Scientific) equipped with a TriVersa NanoMate ion source (Advion Biosciences)N = 83) that were not captured previously by other platforms. The list of the lipid species detected by different platforms and overlaps across the platforms are provided in the Supplementary Data It is to be noted that Lipotype platform used in the study detected many additional lipid species (P < 1 × 10−6) were excluded. Imputation was performed using IMPUTE250, which used two population-specific reference panels of 2690 high-coverage whole-genome and 5093 high-coverage whole-exome sequence data. Variants with imputation info score <0.70 were filtered out. After QC on lipidomic profiles and imputed variants, all subsequent analyses included 2045 individuals and ~9.3 million variants with MAF >0.005 that were available in both cohorts.Genotyping for both EUFAM and FINRISK cohorts was performed using the HumanCoreExome BeadChip . The genotype calls were generated together with other available data sets using zCall at the Institute for Molecular Medicine Finland (FIMM). Genotype data underwent stringent quality control (QC) before imputation that included exclusion of samples with low call rate (<95%), sex discrepancies, excess heterozygosity and non-European ancestry. Variants with low call rate (<95%) and deviation from Hardy–Weinberg Equilibrium (HWE P < 1e-6) and low minor allele count (MAC < 3) were removed. Pre-phasing of genotyped data was performed with Eagle 2.3.5  with the default parameters, except the number of conditioning haplotypes was set to 20,000. Imputation was carried out by using the population-specific SISu v3 imputation reference panel with Beagle 4.1  as described in the following protocol: [dx.doi.org/10.17504/protocols.io.nmndc5e]. SISu v3 imputation reference panel was developed using the high-coverage (25–30x) whole-genome sequencing data generated at the Broad Institute of MIT and Harvard and at the McDonnell Genome Institute at Washington University; and jointly processed at the Broad Institute. Variant callset was produced with GATK HaplotypeCaller algorithm by following GATK best-practices for variant calling. Genotype-, sample- and variant-wise QC was applied in an iterative manner by using the Hail framework v0.1 [https://github.com/hail-is/hail]. The resulting high-quality WGS data for 3775 individuals were phased with Eagle 2.3.5 as described above. Post-imputation quality control involved excluding variants with INFO score < 0.7.FinnGen samples were genotyped with Illumina and Affymetrix arrays . Genotype calls were made with GenCall and zCall algorithms for Illumina and AxiomGT1 algorithm for Affymetrix chip genotyping data. Genotyping data produced with previous chip platforms were lifted over to build version 38 (GRCh38/hg38) following the protocol described here: dx.doi.org/10.17504/protocols.io.nqtddwn. Samples with sex discrepancies, high genotype missingness (> 5%), excess heterozygosity (+-4SD) and non-Finnish ancestry were removed. Variants with high missingness (> 2%), deviation from HWE  of genetic population structure, lipid medication, hormone replacement therapy, thyroid condition and type 2 diabetes were used as phenotypes. SNP-based heritability estimates were calculated using the variance component analysis using a genetic relationship matrix (GRM) as implemented in biMMN = 172).The genetic correlation between each pair of lipid species and between each lipid species and traditional lipids was determined using the generated GRM with bivariate linear mixed model as implemented in biMM. The correlations based on the plasma levels (termed as phenotypic correlations) between all the pairs of the lipid species and traditional lipids were calculated using Pearson’s correlation coefficient. The heatmaps and hierarchical clustering based on genetic and phenotypic correlations were generated using heatmap.2 in R. As lipid-lowering medications could affect the plasma levels of lipid species, all analyses were adjusted for the usage of lipid-lowering medications, and separate analyses were also performed after excluding individuals using lipid-lowering medications  were also performed for the identified variants to determine the independent effect on lipid species.We performed univariate association tests for 141 individual lipid species, 12 total lipid classes and 4 traditional lipid measures , in all batches to control for possible batch effects and combined the summary statistics by meta-analysis. The association analyses for the EUFAM cohort were performed using linear mixed models, including the above-mentioned covariates as fixed effects and kinship matrix as random effect as implemented in MMMλ values if >1.0 before meta-analyses. Genomic inflation factor (λ) ranged from 0.98 to 1.19 across the batches whereas the final λ values for meta-analysis ranged from 0.998 to 1.045  that explain over 90% of the variance in lipidomic profiles. Only the associations consistent in effect direction in all three batches were considered significant. Variants were designated as new if not located within 1 Mb of any previously reported variants for lipids ; and as independent signal in known locus if located within 1 Mb but r2 < 0.20 with the previous lead variants and confirmed by conditional analysis. Variants with the strongest association in the identified lipid species loci was identified as the lead variants, and were annotated to the nearest gene for the new loci.Test statistics were adjusted for 57. Associations between selected binary phenotypes and 35 lead variants in UK Biobank were obtained from Zhou et al. that were tested using logistic mixed model in SAIGE with a saddle point approximation and adjusting for first four principal components, age and sex (https://www.leelabsg.org/resources)58. Data for four phenotypes were not available from Zhou et al. and hence were obtained from http://www.nealelab.is/uk-biobank/. Associations of quantitative traits were tested using linear regression models with the same covariates as mentioned above, both for Finnish and UK Biobank cohorts. Meta-analyses of both cohorts were performed using the inverse variance weighted method for fixed effects model in METAL. The P-values obtained from the meta-analyses of the two cohorts are reported for PheWAS associations. All the PheWAS associations with false discovery rate (FDR) <5% evaluated using the Benjamini–Hochberg method and consistent direction of effects were considered significant.We identified 25 CVD-related outcomes from the derived phenotypes in the FinnGen and UK Biobanks  were available in our QC passed imputed genotype data  after 15 min of heparin load was measured for 630 individuals in the EUFAM cohort using the ELISA method developed by Antikainen et al.Further information on research design is available in the Supplementary InformationPeer Review FileReporting SummaryDescription of Additional Supplementary FilesSupplementary Data 1Supplementary Data 2Supplementary Data 3Supplementary Data 4Supplementary Data 5Supplementary Data 6Supplementary Data 7Supplementary Data 8Supplementary Data 9Supplementary Data 10Supplementary Data 11Supplementary Data 12Supplementary Data 13


The increased prevalence of non-communicable chronic diseases (NCDs) is reflected in the rising economic burden of health conditions. Observational studies conducted in health economics research are detecting associations of NCDs or related risk factors with economic measures like health insurance, economic inequalities, accessibility of jobs, education, annual income, health expenditure, etc. The inferences of such relationships do not prove causation and are limited to associations which are many times influenced by confounding factors and reverse causation. Mendelian randomization (MR) approach is a useful method for exploring causal relations between modifiable risk factors and measures of health economics. The application of MR in economic assessment of health conditions has been started and is producing fruitful results. The world is facing epidemiological transition that has been attributed the rise of non-communicable chronic diseases (NCDs) and shifted the goal-post from fatal communicable diseases . United Among the South Asian countries, India and Pakistan, had received the bulk of development assistance for health (DAH) which is a financial (or in-kind) resource transferred from development agencies  to low- and middle-income countries (LMICs) primarily for maintaining or improving health , 5.Over 60% of deaths in India are due to NCDs . NCDs maHealth economists are interested in studying the role of socio-economic patterning of health inequalities related to NCDs and their risk factors . A studyThe interpretation of association between two factors in an observational study is limited to the plausible relationship and it does not prove causality . EpidemiMR utilizes genes as an instrument for modifiable exposures in order to test their causal relationship with health outcomes or risk factors . The ranThe random distribution of known and unknown confounders in MR approach is natural in nature which is achieved by genotypes that are known to be associated with specific health condition. Like intention-to-treat in RCTs where study participants are analyzed irrespective of their compliance with the intervention, MR investigates people irrespective of their genotypes that may lead to differences in outcomes . TherefoThe potential genetic variant for IV analysis must comply with three main assumptions of MR , 26: 1)1) the geMR assumptions lose credibility to some extent when exposures are polygenic traits i.e., multiple genetic variants may be responsible for the exposure. The multiple genetic variants used for polygenetic traits may lead to pleiotropy i.e., a single gene having multiple expressions, by being involved in multiple pathways , 28. ThiOne of the important step of the MR is to rely on a single genetic instrument which is used as a proxy for the modifiable exposure. This can be partially addressed by using multiple variants and using weighted allelic score as an instrument . It incrThe identification of genetic instrument variable (IV) to be used as proxies for exposures in MR analyses should satisfy the underlying assumptions of an IV method.https://www.ebi.ac.uk/gwas/) for identifying genetic variants associated with exposure of interest. “PhenoScanner” is another online resource developed by University of Cambridge, which can be used for genotypic-phenotypic associations  from the original GWAS articles. This list further guides the second round of literature search for identifying the primarily identified variants that have been replicated and validated in other populations to prepare a list of well-established genetic variants. Researcher should give more weightage to those genetic variants which are well replicated and validated in the target population of interest, having strong association with the exposure, and have relatively large effect sizes (explaining the maximum variation in the exposure). In case of unavailability of reliable instruments, one can also use multiple SNPs to generate a “risk score” (a polygenic score) which can be used as an instrument in MR studies instead of using one single SNP.Relevance means the genetic variant(s) that can be used as an instrument that robustly predicts the exposure. In order to identify genetic variants associated with the exposure, we need to conduct a comprehensive literature search (using PubMeD and MEDLINE) for articles based on genome-wide association studies (GWASs) directly related to the exposure. Nowadays, there is an online GWAS catalog of published literature available  . TherefoR package  available at “MR-Base” website which is also a database of genotypic-phenotypic associations based on GWAS studies  leads to exchangeable subgroups, independent of all observed and unobserved confounders, having similar distribution of confounders like RCTs. However, this assumption cannot be tested empirically. Nevertheless, the genetic variant to be used as instrument should not be directly associated with any of the possible confounders affecting the relationship between exposure and outcome of interest. Moreover, one can assess the biological mechanisms given in  that canThe genetic instrument should be associated with the outcome only through the route of exposure, and not directly. The conditional assumption of exclusion restriction can be evaluated through the knowledge of biological pathway of the genetic instrument used . Since tThe estimation of healthcare cost expenditures of medical conditions are important for planning public health programmes. Many times confounding factors, like socio-economic status, natural histories of diseases, ascertainment bias and measurement error make the economic assessment of health outcomes difficult in observational studies . MoreoveScholder et al.  examinedHMGA2 (rs1042725), ZBTB38 (rs6440003), GDF5 (rs6060373), LOC387103 (rs4549631), EFEMP1 (rs3791675), SCMH1 (rs6686842), ADAMTSL3 (rs10906982), DYM (rs8099594), and C6orf106 (rs2814993). Firstly, authors tested the validity of genetic variants to be used as instruments for height by testing all the MR assumptions. The genetic instruments were found to be uncorrelated with a large set of family background variables which my confound the relationship between height and outcomes of interest. Authors also ran two falsification checks. They first examined the effect of height on maternal education for which they did not assumed any effect. Thereafter, the effect of height on body weight was examined for which they assumed a strong effect. After confirming the validity of instruments they examined the causal relationship between height and set of cognitive, behavioral and mental health outcomes.Authors used a set of nine genetic variants associated with height among Europeans: The IV results showed that there is causal relationship between taller height and cognitive function among girls with respect to national exam taken at age of 14 years  and IQ test at the age of 8 years . They did not find any evidence of height effecting scholastic self-esteem, self worthself-worth, or depression. Authors suggested that height presents disadvantage rather than advantage as it was fund to increase hyperactive behaviour among girls and emotional and peer problems among boys.Highly useful in strengthening the evidence from purely observational to causality which can effectively paves a path for RCTs.MR also useful in detecting the direction of causality of association between the exposure and outcome.Use of genetic variants as proxy for exposure helps in the random allocation of confounders between exposed and unexposed that also eliminates selection bias. Moreover, the genetic data can be robustly generated without errors at affordable cost that tend to decline more in future.Methodology for conducting MR analysis is well established due to the continuous efforts made by the researchers, and it has the potential to grow further in future.Identification of genetic instruments from available databases is useful and manageable even for researchers, having no background in genetics, with little training.The inferences based on modeling driven MR approach are heavily dependent upon the credibility of the assumptions underlying MR.The execution of MR is contingent upon the identification of genetic proxy for the exposure of our interest.Large sample sizes are required for MR analysis, due to small effect sizes of genetic variants to meet the optimum statistical power, which is difficult to obtain in low and middle income countries.Genetic instruments of MR are selected from hypothesis free genome-wide association studies which lacks in-depth understanding of the mechanisms of associations between genetic variants and traits/diseases.in utero; the results were ambiguous due to confounding of socio-economic position. Therefore, MR approach was applied by using ADH1B gene as an instrument variable for maternal alcohol exposure and found long term negative effects on child's educational achievements  with lower annual household income (~ £2940)  are associated with high SES in developed countries , 59. The~ £2940) .Physical traits and economic success are inextricably entwined through body size, beauty and height –61. A loFurther, education is one of the three components of a regular instrument for assessing socio-economic position. In the above study, protective causal effects of education on obesity in Finnish population using MR design was reported . It was MR has high potential in research related to health economics and health insurance consequences, and will be very useful in making informed decisions with respect to policy coverage on latest medical technologies. The utility of MR is even higher in countries with limited resources for health care financing and moving toward universal health coverage as an important public health policy.GW, VG, and MS conceptualized the idea of the manuscript. VG and GW completed the literature search. GW and VG wrote the manuscript. MS critically reviewed and improved the manuscript.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


As) in rice grains, in a large proportion of the rice growing areas, is a critical issue. This study explores the feasibility of conventional (QTL-based) marker-assisted selection and genomic selection to improve the ability of rice to prevent As uptake and accumulation in the edible grains. A japonica diversity panel (RP) of 228 accessions phenotyped for As concentration in the flag leaf (FL-As) and in the dehulled grain (CG-As), and genotyped at 22,370 SNP loci, was used to map QTLs by association analysis (GWAS) and to train genomic prediction models. Similar phenotypic and genotypic data from 95 advanced breeding lines (VP) with japonica genetic backgrounds, was used to validate related QTLs mapped in the RP through GWAS and to evaluate the predictive ability of across populations (RP-VP) genomic estimate of breeding value (GEBV) for As exclusion. Several QTLs for FL-As and CG-As with a low-medium individual effect were detected in the RP, of which some colocalized with known QTLs and candidate genes. However, less than 10% of those QTLs could be validated in the VP without loosening colocalization parameters. Conversely, the average predictive ability of across populations GEBV was rather high, 0.43 for FL-As and 0.48 for CG-As, ensuring genetic gains per time unit close to phenotypic selection. The implications of the limited robustness of the GWAS results and the rather high predictive ability of genomic prediction are discussed for breeding rice for significantly low arsenic uptake and accumulation in the edible grains.The high concentration of arsenic ( As) in the paddy fields or in the irrigation water  has been reported in more than 70 countries in Asia, America and Europe  [As is arsenite [Mechanisms of rice plant response to soil and salt . Howeversporters . Overexpsporters . At the As(III)] , 15. TheAs(III)] , 17, or As(III)] , and/or As(III)] . Within arsenite . Arsenitarsenite . Transpoarsenite , 22 and arsenite , 24.As accumulation has been reported in rice, under overexposure to As in hydroponic cultivation and in field experiments −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 variables.To analyze sources of variation in the predictive ability of genomic predictions, the correlation , the predictive ability of the prediction experiment was calculated as the correlation between the predicted and the observed phenotypes of the 95 lines.The predictive ability of genomic prediction across populations was evaluated under three scenarios of composition of the training set. Under the first scenario (S1), the training set included all 228 accessions of RP. Under S2, the training set was composed of 100 accessions of the RP with the lowest average pairwise Euclidian distances with the 95 lines of the VP. Under S3, 100 accessions of the training set were selected among the 228 accessions of RP, using the CDmean method of optimization of the training set . In this package . For eacAs concentrations of about 10 mg kg-1 dry soil weight. During the same period, the monthly survey of the irrigation water revealed variable As contents (0.014 to 0.034 mg l-1) with an average of 0.021 mg l-1. Similar soil and water As contents were observed in 2015 and 2016  measured in 2014 among the 300 accessions of RP are presented in As ranged from 1.393 (Bexibell accession) to 15.509 mg kg-1 (Auzgusta accessions) and averaged 5.881 mg kg-1 of dry weight. Variation in CG-As ranged from 0.147 (Suweon accession) to 0.656 mg kg-1 (Octet accession) and averaged 0.335 mg kg-1 . This rather loose relationship between FL-As and CG-As corroborates the significant accession effect observed for the Ratio trait.The distributions of the three traits , while the range of variation in CG-As was slightly narrower . However, these differences in the range of variation did not change the relative ranking of the 50 accessions observed in 2014. Indeed, the Spearman coefficient of rank correlation between performances of the 50 RP accessions in 2014 and 2015 was r = 0.724 (p < 0.0001) for FL-As, r = 0.681 (p < 0.0001) for CG-As, and r = 0.587 (p < 0.0001) for Ratio. The determination coefficient between FL-As and CG-As of the 50 accessions in 2015 was higher  than the one observed in 2014 for the 300 accessions.In 2015, the range of variation in FL-As among the 95 accessions in the VP ranged from 3.241 to 37.760 mg kg-1 and averaged 14.651 mg kg-1 . The computed Ratio varied between 0.179 and 0.636 and averaged 0.336.Variation in FL- mg kg-1 . VariatiThe RP and VP genotyping experiment yielded 39,497 and 67,658 SNP loci, respectively, among which 22,370 were common to the two populations. This working dataset is presented in 2 was 0.671 and 0.733 in RP and VP, respectively. In the RP, the r2 value dropped to half its initial level at around 450 kb, reached 0.2 at 1.25 Mb, and 0.1 at 2.10 Mb. In the VP, r2 reached the 0.2 threshold only at pairwise distances of around 1.70 Mb, and the 0.1 threshold at distances above 3,0 Mb. No major difference in LD decay was observed between the 12 chromosomes. Given these extents of average LDs, marker density and distribution along the chromosome should not be a major limiting factor for the next steps of the study, i.e. detection of significant associations and genomic prediction. Both RP and VP populations showed similar MAF patterns for the 22,370 common SNP loci. RP and VP had the same minor allele in 95.4% of the common loci. In both populations, the MAF distribution was slightly skewed toward low frequencies, the average MAF was close to 22.2%, and the proportion of loci with MAF < 10% was close to 75%. Likewise, the Spearman correlation between the MAF of the 21,343 loci with identical minor alleles in the two populations was r = 0.849 (p <0.011).The decay of LD over physical distance in the two populations is presented in japonica (65% of accessions) and tropical japonica (35% of accessions) sub-groups  evaluated in 2014. The former subgroup had the highest As contents  was low and did not exceed 12%. Among the 41 SNPs significantly associated with FL-As, 11 corresponding to three independent loci had marker R2 > 10%. The highest marker R2 observed among the 23 SNPs significantly associated with CG-As, was 8%. Among the 82 SNPs significantly associated with Ratio, nine corresponding to six independent loci had marker R2 > 10%.Results of association analysis for FL-As, 75 for CG-As and 8 for Ratio. These associations represented 8, 30 and 5 independent loci. These loci were composed of 1–22 not always adjacent SNPs, with p-values ranging between 1e-05 and 1e-09. Similar to what was observed in association analysis in the RP, in the VP significant SNP loci for the three traits did not colocalize. The MAF of the significant SNP ranged from 2.6% to 46.8% and averaged 28.0% for FL-As, 9.0% for CG-As and 9.1% for Ratio. The significant SNPs contributed much more, on average, to trait total variance than the ones observed in the RP. The mean marker R2 was 18% for SNPs associated with FL-As, 24% for SNPs associated with CG-As and 16% for SNPs associated with Ratio.Results of association analysis for the three traits in the VP are presented in As. The application of a margin of tolerance of 1.7 Mb between a significant locus in the RP and its counterpart in the VP (corresponding to the average distances for LD of 0.2 in the VP) increased slightly the number of colocalizations: four additional colocalizations for CG-As and one for Ratio. The number of such colocalizations increased markedly  when the threshold of significance of association in the two populations was lowered to a p-value < 1e-04 , transporters , abiotic stress responsive genes , and transcription factors .Out of a total of 146 SNPs significantly associated with one of the three ed sites . The proAs stress  surrounding each significant SNP in the RP and in the VP, led to the identification of at least one gene with the product involved in plant response to abiotic stresses or reported in the literature as responsive to s stress . The latizations .As, 0.535 for CG-As and 0.333 for Ratio. Based on ANOVA results, differences in predictive ability between the three traits were highly significant (P < 0.0001). Conversely, differences in predictive ability between the three prediction methods were not significant. This was also the case for interactions between prediction methods and phenotypic traits.The nine cross-validation experiments involving the three prediction methods and the three phenotypic traits yielded predictive ability ranging from 0.326 to 0.543 . The ave2 = 1), the cross-validation experiment was also implemented with the full set of SNPs available , under GBLUP. Results showed negligible effects on predictive ability: r = 0.449 versus 0.450 with the incidence matrix of 16,902 for FL-As, r = 0.535 versus 0.531 for CG-As, and r = 0.326 versus 0.328 for Ratio.In order to evaluate the effect of exclusion of highly redundant SNP  of the 95 lines of VP was on average 0.426 for FL-or Ratio . The thror Ratio .As, CG-As and Ratio, respectively (As (0.491), and much lower than under S1 for FL-As (0.341) and for Ratio (0.073).The predictive ability of GEBV were much lower under S2, with averages of 0.266, 0.411 and -0.016 for FL-ectively . Under Sjaponica subspecies, adapted to cultivation in Mediterranean Europe, to restrict As accumulation in the edible grains, and (ii) the potential of the two major options of marker-assisted selection for the improvement of the trait, i.e. QTL-based selection and genomic estimate of breeding value (GEBV)-based selection.The aim of this work was to explore (i) the phenotypic diversity of the rice As accumulation in the flag leaf and in the cargo grain was evaluated in field experiments with uncontrolled intensity of exposure to As. However, a rather stable soil As concentration, of about 10 mg kg-1, was observed across the crop cycles, and in the three consecutive years of field experiments. This concentration corresponded to the class of rather high As contents reported for paddy fields in countries including Bangladesh [As (0.147 to 0.656 mg kg-1) among the accessions of RP was similar to the range observed by [O. sativa species (http://www.ricediversity.org/), evaluated during multilocation trials in Bangladesh, China and USA. Relationship between FL-As and CG-As was rather loose suggesting there are differences between accessions in the ability to limit As transfer from the leaves to the grains. To our knowledge, the existence of such genetic diversity for the CG-As/FL-As ratio has not been reported yet in the literature. Correlation between the FL-As and CG-As observed in 2014 and 2015 for the 50 RP accessions of RP that were evaluated in the two consecutive years was rather high. This confirmed the robustness of our findings concerning the extent of genetic diversity for FL-As and CG-As and on the relationship between the two traits. Interestingly, the extent of FL-As and CG-As in the VP was as large as that observed in the RP, despite its much smaller size.Phenotypic diversity for ngladesh , China [ngladesh  and the ngladesh . The ranerved by , 32 in aAs accumulation in the grains, association analysis was performed in the RP to detect QTLs. A large number of QTLs was detected for each of the three traits considered. Some of these QTLs colocalized with already reported QTLs [japonica), similar relationship between population structure and variability of the target trait (the temperate japonica having the highest As contents) and similar MAF distribution. Thus, the limited colocalisation between the QTL detected in RP and in VP should be attributed mainly to the rather small size of the VP.In order to explore the potential of marker-QTL association-based breeding for aptitude to restrict ted QTLs –28, candted QTLs , 67, or ted QTLs , 37. To ted QTLs , 69. GWAted QTLs . Our valjaponica, our GWAS results might have been subject to an abnormal rate of false negatives due to a confounding phenomenon [japonica accessions of RP. These analyses detected, at best, 50% of the QTLs detected with the entire RP, without markedly increasing the P-value for each association  it is unlikely that a single GWAS makes it possible to establish robust and precise genotype–phenotype associations, especially for complex traits, (ii) validation of GWAS results by a second independent GWAS experiment is a complex process with uncertain results, (iii) new validation options, such as development of b-parental mapping population or the genome editing approach , need toAs accumulation in the grains, three prediction methods were tested. Although these methods represented contrasted hypotheses regarding the effects of markers and/or the genetic determinism of target trait (see M&M section), they did not show significantly different predictive ability for FL-As and CG-As. This is in agreement with the large number of QTLs of rather small effect detected in our GWAS experiment and suggest a mainly additive genetic determinism of FL-As and CG-As. Similar limited differences in predictive ability between prediction methods, for complex traits, were reported in rice [To explore the potential of genomic prediction options for marker-assisted breeding for the ability to restrict  in rice , 52 and  in rice , 74. Giv in rice .As and CG-As in the cross-validation experiments was similar to the levels reported in the literature for traits of equivalent heritability in rice [The level of predictive ability for FL- in rice  and othe in rice –75. PredAcross population genomic prediction with models trained with RP data led to slightly lower predictive ability than the predictive ability observed in the cross-validation experiments. Similar decreases in predictive ability were reported in rice , barley As in the rice grains in a large proportion of rice growing areas has recently resulted in steady efforts to understand the molecular mechanisms involved in plant response to overexposure to As [As tolerance is a complex trait involving large number of loci with limited individual effect on the trait.The critical importance of reducing the presence of re to As , 24 and re to As , 18, 21.re to As , transcrre to As , 67 and re to As  suggest The number of candidate loci makes marker-assisted pyramiding of the favorable alleles unpractical. Moreover, uncertainty concerning the exact genomic position of some of the loci makes the outcome of marker-assisted pyramiding unpredictable. Indeed, as discussed above, GWAS results raise robustness issues and this also seems to be the case for transcriptome analyses .As and CG-As were reasonably accurate in both intra-population  and across-population (RP/VP) prediction experiments. Translation of those predictive abilities into average phenotypic performances of VP lines selected based on their GEBV, by model trained with the RP, is even more encouraging. Indeed, the average FL-As and CG-As of the best 10 VP lines selected on the basis of phenotypic data were 41% and 65% of the average FL-As and CG-As of all 95 lines of VP. The average FL-As and CG-As of the best 10 VP lines selected on the base of GEBV were 55% and 85% of the average FL-As and CG-As the all 95 lines of VP Click here for additional data file.S2 Table(XLSX)Click here for additional data file.S3 Table(ZIP)Click here for additional data file.S4 Table(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.S7 Table(XLSX)Click here for additional data file.S8 Table(XLSX)Click here for additional data file.S9 Table(XLSX)Click here for additional data file.S10 Table(XLSX)Click here for additional data file.S1 Fig(TIF)Click here for additional data file.S2 Fig(TIF)Click here for additional data file.