WNT4, GALNT3, MEPE, CPED1/WNT16, TNFSF11, RIN3, and PPP6R3/LRP5. Variants in the TOM1L2/SREBF1 locus exert opposing effects TB-LM and TBLH-BMD, and have a stronger association with the former trait. We show that SREBF1 is expressed in murine and human osteoblasts, as well as in human muscle tissue. This is the first bivariate GWAS meta-analysis to demonstrate genetic factors with pleiotropic effects on bone mineral density and lean mass.Bone mineral density is known to be a heritable, polygenic trait whereas genetic variants contributing to lean mass variation remain largely unknown. We estimated the shared SNP heritability and performed a bivariate GWAS meta-analysis of total-body lean mass (TB-LM) and total-body less head bone mineral density (TBLH-BMD) regions in 10,414 children. The estimated SNP heritability is 43% (95% CI: 34–52%) for TBLH-BMD, and 39% (95% CI: 30–48%) for TB-LM, with a shared genetic component of 43% (95% CI: 29–56%). We identify variants with pleiotropic effects in eight loci, including seven established bone mineral density loci:  Bone mineral density and lean skeletal mass are heritable traits. Here, Medina-Gomez and colleagues perform bivariate GWAS analyses of total body lean mass and bone mass density in children, and show genetic loci with pleiotropic effects on both traits. Whenever mechanical stimuli exceed a set point, bone tissue is added at the location, where it is mechanically necessary. As muscle mass is a key determinant of bone mineral density (BMD) variation, it is expected that these traits are phenotypically highly correlated4. Recent studies show that the coupling between these two tissues is much more complex6. It extends beyond the well-established mechanical interaction, already beginning during embryonic development, when osteoblasts and muscle cells share a common mesenchymal precursor6. Indeed, there is constant paracrine crosstalk between bone and muscle throughout life, with both sharing common responses to paracrine and endocrine stimulation7. Under this perspective of coupled development and physiological relationships during growth, it is highly likely that both tissues share genetic determinants exerting pleiotropic effects8.The interaction between skeletal muscle and bone has long been perceived as being mechanical, where bone provides an attachment site for muscles and muscles apply forces to bone. Such muscle-derived forces drive an adaptive response in bone affecting its morphology, structure, and strength as portrayed by the mechanostat theory10, can also be derived from the same whole-body DXA scans. A previous study in postmenopausal women9 showed DXA lean mass has a high correlation (ρ=0.94) with skeletal muscle mass of the whole body measured by magnetic resonance imaging (MRI). Furthermore, another study in peri-pubertal children10 showed a 0.98 correlation between DXA derived lean mass in the mid third femur and skeletal muscle mass of the same region measured with MRI.BMD, measured by dual-energy X-ray absorptiometry (DXA), is commonly used in clinical practice to assess bone health in young populations and to diagnose osteoporosis and determine fracture risk in the older populations. Conveniently, lean mass, which is a good proxy for skeletal muscle mass11, whereas heritability estimates for muscle phenotypes, such as lean mass, grip strength, arm flexion, and leg strength, range between 30 and 65%13. Genome-wide association studies (GWAS) have been successful in revealing the genetic architecture of BMD, identifying >60 different loci robustly associated with the trait at different skeletal sites26. In contrast, very few robust associations have been reported to date by GWAS of lean mass27. Notwithstanding, twin studies have calculated the additive genetic correlation of BMD and lean mass to range from 30 to 45%28.Heritability studies have demonstrated that between 50 and 85% of BMD variation can be explained by genetic factors29. Therefore, this approach is well-suited to study the genetic influence over the bone-muscle unit. We applied a bivariate GWAS approach for total-body lean mass (TB-LM) and BMD less head region (TBLH-BMD) in four pediatric cohorts leading to the identification of variants in eight different loci with statistical evidence for pleiotropic effects on both traits. Seven of these are established BMD loci, while the 17p11.2 locus is reported for the first time as associated with musculoskeletal traits. Our functional follow-up points to SREBF1 as the most likely gene underlying the association signal. SREBP1, the product of SREBF1 is implicated in osteoblast and myoblast differentiation by previous studies. Our findings shed a light into the role of SREBF1 in the crosstalk between bone and muscle during development.GWAS usually evaluate one trait at a time, however, recent methods enable multivariate analyses to be performed. Multivariate methodologies offer several advantages over univariate GWAS including, increased power , reduction of the multiple testing burden and enhanced biological insight (in the case of pleiotropy)ρ) adjusted for sex, age, and fat percent was significant (P < 0.01) and of similar magnitude, Generation R (ρ=0.44), ALSPAC (ρ=0.45), BMD-CS (ρ=0.49), and COPSAC (ρ=0.42). GCTA calculated SNP heritability and genetic correlation for adjusted TBLH-BMD and TB-LM were estimated for the Generation R (N = 3027) and ALSPAC (N = 4820) studies, where there was sufficient statistical power for the analysis  level with TBLH-BMD mapping to four different loci all of which have been previously associated with BMD in adult and/or children22, shown in Fig. WNT16/CPED1 locus, leading SNP rs917727-T ; the 11q13.2 LRP5/PPP6R3 locus, leading SNP rs12272917-C ; the 1p36.12 WNT4 locus, leading SNP rs3765350-G ; and the 2q24.3 GALNT3 locus, leading SNP rs6726821-G . A summary of all genome-wide associated SNPs can be found in Supplementary Data 27. QQ-Plots for these two analyses showed no evidence for early inflation as consequence of bias   with both traits  was the only GWS signal where association was stronger with TB-LM than with TBLH-BMD, mapping to a region not previously associated with neither trait. The region underlying this signal  arising from a low frequency (MAF < 0.05) variant not present in our meta-analysis. In addition, a linkage study reported a significant signal (LOD score = 4.86) mapping to the 15q13 locus and multiple suggestive signals (LOD score < 3) in 7p22, 7q21, 7q32, and 13q1132. In our bivariate GWAS meta-analysis none of these regions (including GLYAT) contained significantly associated SNPs . Additionally, we applied a bivariate analysis to the summary statistics of previously reported univariate GWAS meta-analyses of BMD and lean mass traits in adults27 (Supplementary Data P < 0.07).Although using different phenotypes than the ones used in our analysis, Genetic bivariate strategies in adult populations have approached the bone/lean mass relationship. A GWAS bivariate analysis of bone size and appendicular lean mass in Chinese and European individualsr2 > 0.8) of our GWS SNPs in the 17p11.2 region, of which 78 were present in our meta-analysis (Supplementary Table r2 > 0.8)), was previously reported in the GWAS catalog. The G-allele of this SNP has a protective effect on Parkinson’s disease as reported in a recent meta-analysis comprising ~ 30,000 individuals (P = 5.6×10−8)33. The rs11868035 G-allele was nominally associated with lean mass  and approached GWS in our bivariate meta-analysis (Pbivariate=8×10−7). In the proximity of these variants and in high LD (r2 > 0.8) with rs11654081, we identified two RAI1 common missense variants with no clinical annotation (Supplementary Data Pbivariate=9×10−7) was predicted to be ‘tolerated’ by SIFT and ‘probably damaging’ by PolyPhen-2, while the rs3803763/Gly90Ala variant was not present in our meta-analysis.We identified 163 proxy SNPs . Of these, rs1052299 was predicted to create novel-binding sites for two highly conserved miRNAs affecting bone and muscle38 -miR-133a-5p and miR-138-5p—and is more likely to affect miRNA-mediated regulation of TOM1L2.We identified two miRNAs, miR-6777 and miR-33b, in the associated region. Of these, miR-33b is located in an intronic region of the TOM1L2 and the 3′region of SREBF1; the second one, contains two regions within TOM1L2; and the third one, intronic regions from TOM1L2 and ATPAF2. The topological associated domains (TADs) in the region are in line with these results, evidencing areas of complex chromatin structures  in whole blood, based on expression data from 5311 non-transformed peripheral blood samples and publicly available in the Blood eQTL browser39. Nonetheless, SREBF1 expression, represented by two probes in the data set, showed the highest correlation with rs7501812. The G-allele, associated with higher TBLH-BMD  and lower TB-LM  in our study, decreased the expression of the two SREBF1 probes in whole blood. By interrogating the browser per gene, rather than per SNP, we generally found that alleles from the bivariate GWS SNPs associated with higher TBLH-BMD and lower TB-LM in the region, are associated with decreased expression of SREBF1, TOM1L2, and C17orf39 [GID4] but increased expression of ATPAF2 in whole blood  tests using the Blood eQTL browser data set39, indicated that SREBF1 is the most likely gene driving the associations with TB-LM  and TBLH-BMD  . In contrast, GTEx data in skeletal muscle showed a positive correlation for rs7501812-G with SREBF1 expression (P = 6.2×10−4). Furthermore, rs4925114 (P = 3.5×10−6) and rs11654081 (P = 6.0×10−6) major alleles (LD ~ 0.75 with rs7501812-G), significantly upregulated the expression of SREBF1 in skeletal muscle (FDR<0.05)  differentiation using qPCR data. The expression of SREBF1 relative to GAPDH in two donors, showed that SREBF1 expression peaks at the onset of osteoblast mineralization. In contrast, the expression of TOM1L2 was not detected above background level  that produces SREBP-1, a transcription factor ubiquitously expressed (more strongly in lipogenic tissues) and directly regulating the transcription of over 200 genes involved in the de novo synthesis of fatty acids, triglycerides, and cholesterol41. SREBP-1, in its active form, is important for the mineralization of osteoblastic cultures in vitro, as its overexpression increases the number of mineralized foci42. Contrary to its positive regulatory role in osteoblast differentiation and mineralization, in skeletal muscle SREBP-1 protein indirectly downregulates MYOD1, MYOG, and MEF2C, acting as a key regulator of myogenesis. Similarly, overexpression of SREBP-1 inhibits myoblast-to-myotube differentiation 43, reduces cell size and leads to loss of muscle-specific proteins in differentiated myotubes41.Leveraging mouse calvaria-derived cells, we assessed gene expression during osteoblastogenesis, which revealed that SREBF1 muscle expression, assessed from postmenopausal women thigh muscle biopsies (N = 18) showed significant negative correlation with femoral neck BMD of the donor (P < 0.001) and was borderline significantly associated with TBLH-BMD (P = 0.05) , while TOM1L2 expression levels were positively correlated with this trait (P = 0.02). ATPAF2 and C17orf39 [GID4] did not correlate with any of the scrutinized measurements of the donors. Evaluation of expression profiles from trans-Iliac bone biopsies in a separate group of postmenopausal women (N = 80), revealed no correlation (P < 0.05) with donor phenotypes for either of the genes despite being expressed in bone. To note, SREBP-1 has also been shown to interact with lamin A, implicated in muscular dystrophy44, and this mechanism cannot be ruled out as key in the association of this gene with muscle outcomes. In addition, miR-33b may function in concert with the SREBP-1 host gene product to regulate myogenesis or/and osteogenic differentiation, as it does in controlling lipid homeostasis45.We evaluated expression profiles of the genes showing putative interactions from the ENCODE/Roadmap Epigenomics analysis, for association with bone and muscle phenotypes. RAI1, LRRC48, MIR33B, C17orf39, DRG2, MYO15A, SREBF1, TOM1L2, and ATPAF2. Different lines of evidence, arising from in-silico and in-vivo functional follow-up suggest that TOM1L2 or SREBF1 are the main candidate genes underlying the bivariate association signal; confirming the existence of pleiotropic effects on BMD and lean mass arising from the 17p11.2 region.This work illustrates the enhanced power of bivariate analysis to identify associations not detected by the univariate analysis of correlated traits, and to hint at pleiotropic effects. This bivariate GWAS meta-analysis of bone mineral density and lean mass in children identified eight loci associated with both traits. Seven of the identified loci have been reported as associated with BMD in previous GWAS of adult and paediatric populations. The 17p11.2 is a novel locus, not previously associated with lean mass or BMD, marked by a long stretch of LD harbouring multiple genes in the region including 28. Similar to other complex traits, SNP heritability estimates for TBLH-BMD and TB-LM were lower than heritability estimates derived from family studies47. This could be due to inflation of the family based heritability estimates47 but also a consequence of the amount, and type, of markers being surveyed, limited to the genotyped SNPs and the underlying variants that they tag. Thus, if the selected markers are not sufficiently correlated (LD tagging) with the true genetic variants explaining the trait-variance, SNP heritability would certainly be less than true heritability.SNP heritability estimated both by GCTA and LD score regression confirm that TBLH-BMD and TB-LM are moderately high heritable traits. In addition, we found that more than one-third of the tagged additive genetic effect is shared between TBLH-BMD and TB-LM. These estimates are lower than those derived from previous twin studies one of them estimating the heritability for TBLH-BMD to be between 80 and 90%, between 70 and 88% for TB-LM and with a genetic correlation (shared heritability) of 46%21. However, we did not find any GWS result for TB-LM in our meta-analysis in pediatric populations. This is in line with the scarce number of loci identified with lean mass variation in adults. To date, only five loci have been identified as associated with TB-LM in an study comprising > 80,000 adults27. It is worth noting that lean mass in children has a higher proportion of organ tissue vs. skeletal mass as compared to adults. This could hamper the identification of loci influencing muscle mass and claims for new larger efforts in children. Yet, implementing a bivariate meta-analytical approach we identified eight associated loci, three of which were nominally associated (P < 0.05) with both TBLH-BMD and TB-LM. Therefore, the bivariate analysis is an alternative approach to diminish such power limitations. Our bivariate approach showed particularly high statistical power to identify variants, in which the coded allele exerted an opposite effect on TBLH-BMD as compared to TB-LM. Our meta-analysis did not replicate potential pleiotropic signals previously reported in adults32. Nevertheless, the bivariate analysis of summary statistics of published BMD and lean mass efforts in adults we did, identified variants with strong evidence for pleiotropic effects in the 11q13.2 locus. The lack of replication in the other regions may well be a consequence of differential genetic effects during the life course, e.g., the accumulation of environmental factors attenuating effects in the elderly. Overall, the role of protein products of genes such as LRP5, TNSF11, WNT16, MEPE, and WNT4 -mapping to the identified loci- in bone metabolism is long-established. However, none of the genes in the identified loci has been yet implicated in muscle metabolism and opens up new opportunities to study pleiotropic effects and the interplay between bone and muscle.The univariate approach for TBLH-BMD identified four GWS signals, all of them reported before in BMD GWAS at different skeletal sites48. The authors attributed the association signal to Rai1, as knockout mice for this gene present with retarded growth and display malformations in both the craniofacial and the axial skeleton49. RAI1 is responsible for the Smith-Magenis syndrome in humans, that as in mice presents with craniofacial and skeletal abnormalities50. Nonetheless, the two common missense variants in LD with the bivariate GWS SNPs in our meta-analysis, have not been related to the disease.We further focused on the 17p11.2 locus, which is novel in the spectrum of musculoskeletal traits GWAS, and which is the only associated region where the signal was stronger for TB-LM than for TBLH-BMD. Despite the positive correlation between TB-LM and TBLH-BMD, variants in this region exerted opposite effects on these traits, probably facilitating their discovery in a bivariate analysis powerful setting. Moreover, this region was recently described as a QTL for BMD phenotypes in ratsSREBF1, TOM1L2, and ATPAF2). TADs were retrieved for two oncogenic cell lines rather than for osteoblasts and skeletal muscle cells. However, TADs have been shown to be largely invariant across cell types, and physiological conditions51. Therefore, we believe similar interactions might also be taking place in these cells, suggesting that associated SNPs may be influencing distant genes within the TAD. For example, in agreement with the predicted complex chromatin looping in this locus, the lead SNP of the bivariate association signal (rs7501812), has strong correlation with the expression of various genes in the region as seen in whole blood. However, its correlation with SREBF1 is the strongest one. Also, SMR implicates SREBF1 as the most likely candidate gene underlying the 17p11.2 signal. Yet, TOM1L2 or RAI1 probes were not included in the analysis as they lacked a GWS cis-eQTL. Such stringency in the SMR inclusion criteria results from the basic assumption of the instrument used in the Mendelian randomisation approach, i.e., the SNP should be strongly associated with exposure. Further, the e-QTL effects identified in whole blood may not constitute good proxies for eQTLs exerting tissue specific effects. However, there is no eQTL data available from other tissues (particularly bone and muscle) with comparable statistical power to the one provided by the large-scale eQTL study in whole blood used here.We retrieved additional lines of evidence to disentangle the implication of gene(s) underlying the association signal although results were not conclusive. The regulatory landscape across the long stretch of LD in this region contains complex chromatin interactions that are consistent with the potential involvement of more than one gene . Although for most of these loci the association with TBLH-BMD is the dominant association responsible for their significance, we report in the 17p11.2 locus, a stronger association with TB-LM compared to TBLH-BMD. SREBF1 arises as a compelling candidate to underlie this association, as this transcription factor is known to play an opposite role in osteoblast and myoblast differentiation; in line with the finding derived through our bivariate approach. Nevertheless, the level of evidence gathered does not allow us to discard fully that other genes in the region, particularly TOM1L2, may be implicated in these processes, and additional functional assessments are required. Alternatively, this study demonstrates that a considerable increase in power can be achieved when using a bivariate methodology as compared to testing each component of a multivariate phenotype individually, particularly in those cases where the effect of the associated variant goes in opposite directions in the involved phenotypes. Moreover, we show that this methodology may help discover previously unknown mechanisms of pleiotropic effects of genetic variants.In summary, in this first large-scale bivariate GWAS meta-analysis of TBLH-BMD and TB-LM in pediatric cohorts, we identified eight GWS loci , the Bone Mineral Density in Childhood Study (BMDCS), and the Copenhagen Prospective Studies on Asthma in Childhood (COPSAC) cohort (Supplementary Note). All participants underwent TB-DXA scans; measurements were conducted by well-trained research assistants and daily quality control assurance was performed. Before the scan procedure, participants were asked to take off their shoes, heavy clothes, and metal accessories. As recommended by the International Society for Clinical Densitometry, TBLH-BMD was the measurement used in the analysis by all four studies. TB-LM and total body fat mass (TB-FM) were derived from the same scans. In addition, all individuals included in this study had genome-wide array data imputed to the HapMap Phase II reference panel (build 36 release 22) assessed as best guess genotypes, all markers with MAF < 0.05 were excluded from the analysis. The study was approved by the ALSPAC Law and Ethics committee (ALSPAC), The Ethics Committee for Copenhagen and the Danish Data Protection Agency (COPSAC), the Medical and Ethical Review Committee of the Erasmus University Medical Center (Generation R), and the institutional review boards of Children’s Hospital of Los Angeles , Cincinnati Children’s Hospital Medical Center , Creighton University , Children’s Hospital of Philadelphia , and Columbia University  (BMDCS). Written informed consent was provided by all parents or custodians of the participants. A detailed description of each of these study populations is provided in Supplementary Note.ρ) between these variables was evaluated by a Pearson correlation test in each study independently.TBLH-BMD and TB-LM measurements were adjusted by age, gender, height, fat percent (TB-FM/weight), and study specific covariates . Standardized residuals were then generated. Phenotypic correlation 56 software. The obtained genetic relationship matrix (GRM) was then used as input for the GCTA analysis (Supplementary Methods). Samples were excluded from analysis to guarantee that no pair of individuals exceeded the standard GCTA cut-off coefficient of 0.025 for genetic relatedness  in either of the two studies. In total 431 and 1043 individuals were excluded from ALSPAC and Generation R, respectively. The second approach implemented the LD-score regression methodology57 to the TBLH-BMD and TB-LM meta-analyses results.To characterize the extent to which common genetic variants determine pediatric BMD and lean mass, and the shared genetic etiology of these traits, we applied two recently proposed approaches for estimating SNP heritability and genetic correlation based on genome-wide sharing between distantly related individuals, one implemented in the GCTA software package58. This routine enables the user to obtain, in addition to the effect estimates, the means and standard deviations of both TBLH-BMD and TB-LM standardized residuals stratified by genotype. Meta-analysis of 2,276,811 SNPs, present in at least two of the studies, was performed using an inverse variance weighing. In the meta-analysis approach, we used a newly proposed method for bivariate meta-analysis, which is based on calculating the within-study covariances of the outcome specific estimates59. The algorithm uses a general approach and has been proposed for both, discrete and continuous outcomes, but in the case of continuous outcomes, such as the one encountered here, a direct extension of the method proposed by Wei and Higgins60 is applied. We used the additive model of inheritance  for both outcomes and the method of moments for estimation with the mvmeta command in Stata61. Source code and details are given in the webpage of the Department of Computer Science and Biomedical Informatics of the University of Thessaly, Greece. The overall bivariate association test was obtained by a standard Wald-type statistic (chi-square on 2 d.f.) that tests the null hypothesis that a particular SNP is not associated with either one of the outcomes. Genome-wide significance (GWS) was defined as a P <5×10−8. Manhattan plots were generated in Easystrata62.Analysis of the two quantitative traits was performed in each study individually based on best guess data using the “qt-command” available in PLINKN ~ 38,000))27 and BMD )20 performed in adult and elderly population. We chose to use a recently introduced method that performs bivariate GWAS allowing for mixed directions of effect known as empirical-weighted linear-combined test statistics (eLC), implemented in a C++ eLX package and publicly available. Briefly, eLC directly combines correlated Z test statistics  with a weighted sum of univariate test statistics to empirically maximize the overall association signals and also to account for the phenotypical correlations the traits. In this case, we ran two analyses one incorporating the data from femoral neck BMD and total body lean mass meta-analyses, and another with data from lumbar spine and total body lean mass meta-analyses. Markers with nominal effect in both BMD and lean mass traits and with bivariate P<=3.35×10−4 (0.05/149) were deemed to exert pleiotropic effects in adults.We assessed the evidence of association of the GWS markers identified here  with the lead SNPs in the novel identified susceptibility loci, by bivariate analysis, using HaploReg363 for functional annotation. First, these proxy SNPs were scrutinized in the GWAS catalogue64 to ascertain possible association with other traits. Second, we identified non-synonymous variants that were interrogated for likely downstream functional consequences using SIFT and PolyPhen-2 databases. Third, we interrogated two public databases, i.e., miRNASNPv265 and PolymiRTSv366 for presence of any of the associated SNPs in miRNA genes or miRNA-binding sites within 3′UTRs of target genes. Other information about miRNAs, such as miRNA sequence, host gene, conservation and putative targets, were obtained from miRbase (release 21)67 and TargetScan v7.168 websites. Fourth, variants were also evaluated for overlap with regions of predicted regulatory function generated by the Encyclopedia of DNA Elements (ENCODE) Project and the ROADMAP epigenomic project including: regions of enhancer activity, DNase I hypersensitivity, local histone modifications or proteins bound to these regulatory sites in cell lines of potential interest as HSMM , HSMMtube , SKMC  and when present in the different assays, Osteobl (Osteoblasts). In addition, evidence of chromatin interactions was further explored by Chromatin Interaction Analysis by Paired-End Tag Sequencing (ChIA-PET) data from the ENCODE project in both MCF-7 and K562 oncologic cell lines. The UCSC Genome Browser was used for visualizing ENCODE data tracks indicative of regulatory function. Finally, topologically associated domains (TADs) for K562 cells containing the associated SNPs were retrieved from a recent Hi-C interactions browser51.We identified all variants from the 1000 Genomes Project Phase 1 reference panel in strong linkage disequilibrium (LD) (CEU 39 and the GTEx database (GTEx v6p) based on 1641 samples across 43 tissues from 175 individuals30.The eQTL analysis was completed using recently published eQTL data sets from a meta-analysis of the transcriptional profiles from the peripheral blood cells of 5311 Europeans40 in order to identify the most likely gene in the lead SNP region, which has a complicated LD pattern. In short, the SMR approach uses the genetic variant as the instrumental variable to test for the causative effect of the gene expression (the exposure) on the phenotype of interest. This analysis is divided in two-steps. A transcriptome association analysis, where the effect of gene-expression on the trait of interest is expressed as a function of the GWAS summary statistics and the GWS eQTLs reported by Westra et al.39 in whole blood. Then, a test for heterogeneity in dependent instruments (HEIDI) is applied; in principle, if the same causal underlying variant affects gene expression and the trait then, all SNPs in high LD with the causal variant will present the same SMR calculated effect. Therefore, testing for heterogeneity in the cis-eQTL region will be equivalent to testing if there is only one causal variant40. We used Bonferroni correction to account for multiple testing on 394 probes with GWS evidence for cis-eQTL effects in chromosome 17; this resulted in a significance P threshold=1.27×10−4 (0.05/394). We used a conservative HEIDI threshold P=0.05, to exclude signals with high heterogeneity as used previously for different traits40. Plotting was performed in R including only the most significant probe per gene in the SMR analysis and using the lean mass GWAS results.We used the recently developed SMR approachSREBF1 and LM and BMD, we used three different lines of evidence. The expression of SREBF1 was investigated in murine and human cells differentiating to osteoblasts. In addition, the expression of SREBF1 and its neighbouring genes TOM1L2 and ATPAF2 were examined in muscle biopsies from postmenopausal women. Methods specific to each analysis are described below.Aiming to corroborate the functional or regulatory mechanism underlying the association between All procedures and use of mice for the neonatal osteoblast expression studies were approved by the Jackson Laboratory Animal Care and Use Committee (ACUC), in accordance with NIH guidelines for the care and use of laboratory animals. Pre-osteoblast-like cells were isolated from neonatal calvaria from C57BL/6J mice expressing cyan florescent protein (CFP) and RNA was collected at nine time points post differentiation, every other day for 18 days, starting 2 days after the cells were first exposed to an osteoblast differentiation cocktail. mRNA profiles for triplicate samples for each time point were generated by Next Generation High throughput RNA sequencing (RNAseq), using an Illumina HiSeq 2000.3 cells per cm2) and differentiated into osteoblasts , 100 nM Dexamethasone and 10 mM β-glycerophosphate). As mentioned in the datasheet provided by the company, cells were authenticated by FACS analyses for the presence of surface markers CD105, CD166, CD29, and CD44 and the absence of CD14, CD34 and CD45. In addition, osteogenic, adipogenic and chondrogenic differentiation was shown by alizarin red S staining, oil-red-O staining and collagen II staining, respectively. The human MSCs were tested negative for mycoplasma, both by the company and in-house during the culture experiments described in this manuscript. Total RNA was isolated using Trizol  after 0, 1, 4, 7, 17, and 21 days of differentiation. cDNA synthesis and quantitative polymerase chain reaction (qPCR) was carried out in duplicate in osteogenic differentiating hMSC from two different donors by using the following primers: SREBF1-for AGCCCCACTTCATCAAGGC, SREBF1-rev CAGAGACCAGGGGACTGAGA, TOM1L2-for GGCATTAACAATTGCCAGGCT and TOM1L2-rev CACTTGTGACACCCTCCTCCHuman bone marrow derived mesenchymal stem cells ((hMSC), Lonza Group Ltd., Basel, Switzerland) were seeded in 12-well plates , and all sampling and procedures were according to the Law of Biobanking in Norway. All women who volunteered received a full clinical examination including the DEXA and laboratory analyses, and those who participated after fulfilling the inclusion criteria took pride in contributing to accomplish the aim of the study. Written informed consent was provided by all volunteers.n = 18), from whom anthropometric measurements and DXA data were also available. Expression profiling was performed using an Affymetrix HG U133 2.0 plus array. The Affymetrix Cel files were imported into Partek Genomics Suite , and normalized using the RMA (Robust Multichip Average) algorithm. Gene expression patterns were further adjusted for batch effects and differing synthesis times69. The correlation of gene expression profiles of transcripts in SREBF1 and contiguous genes RAI1, TOM1L2 and ATPF2, and DXA parameters as femoral neck BMD, TBLH-BMD, TB-LM, TB-FM, together with muscular thickness of the vastus lateralis, was assessed using Spearman correlation. All parameters were adjusted for age, height and fat percent, except for TB-FM which was adjusted only for age and height.In muscle, gene expression profiles were generated from human thigh muscle biopsies donated by healthy (T-score >−1) postmenopausal women (n = 84) were collected from postmenopausal women without underlying diseases other than osteoporosis or receiving medication (past or present) possibly affecting bone remodeling or representing secondary causes of osteoporosis70. RNA was purified and analyzed using Affymetrix HG U133 2.0 plus arrays as described elsewhere70. Correlation analysis were carried as described for the human muscle biopsies above, for all variables except muscle thickness which was not available.For the bone study, trans Iliac bone biopsies (http://www.compgen.org/tools/multiple-outcomes)Department of Computer Science and Biomedical Informatics, of the University of Thessaly, Greece [mvmeta command in Stata], , http://www.gefos.org/?q=content/data-release-2017). Expression data from bone biopsies is available at the European Bioinformatics Institute (EMBL-EBI) ArrayExpress repository, ID: E-MEXP-1618. All other data supporting the findings of this study are available from the corresponding author upon request.Summary statistics of all meta-analyses are available at the GEnetic Factors for OSteoporosis Consortium (GEFOS) website (Data set 1Data set 2Data set 3Data set 4Supplementary InformationPeer Review File


N up to 4653 unrelated individuals) and compared them with twin heritabilities from the same sample (N up to 4724 twin pairs) for diverse domains of childhood behaviour problems as rated by parents, teachers, and children themselves at ages 12 and 16. For 37 behaviour problem measures, the average twin heritability was 0.52, whereas the average SNP heritability was just 0.06. In contrast, results for cognitive and anthropometric traits were more typical . Future research should continue to investigate the reasons why SNP heritabilities for childhood behaviour problems are so low compared with twin estimates, and find ways to maximise SNP heritability for genome-wide association studies.For most complex traits, DNA-based heritability (‘SNP heritability’) is roughly half that of twin-based heritability. A previous report from the Twins Early Development Study suggested that this heritability gap is much greater for childhood behaviour problems than for other domains. If true, this finding is important because SNP heritability, not twin heritability, is the ceiling for genome-wide association studies. With twice the sample size as the previous report, we estimated SNP heritabilities ( Such problems often persist: children with behaviour problems in childhood have an increased risk of lifetime psychopathology 24 years later2, and half of all lifetime cases of diagnosed psychopathology have their onset by age 143. Evidence from many twin, family, and adoption studies points to significant genetic influence, with heritability estimates ranging from ~ 40% for anxiety and depression to >60% for autistic-like traits and hyperactivity4. Moreover, longitudinal studies indicate the presence of stable genetic influences on behaviour problems7.Behaviour problems including anxiety, depression, autistic-like traits, hyperactivity and conduct problems are common in childhood, with a cumulative incidence of 12% for one or more disorders14. The lack of genetic associations adds to the evidence that these traits are shaped by many common genetic variants with small effects, which will require very large GWA samples to find. The success of the recent large Attention-Deficit/Hyperactivity Disorder GWA study marks the start of progress in this direction15.Genome-wide association (GWA) studies have sought to identify genetic variants responsible for the heritability of childhood behaviour problems but have so far been overwhelmingly unsuccessfulA quantitative genetic method—genome-wide complex trait analysis (GCTA)—offers insight into how much of this ‘missing heritability’ (the gap between twin heritability and significant associations in GWA studies) could theoretically be identified with GWA analyses. GCTA estimates the extent to which measured single-nucleotide polymorphisms (SNPs) contribute to the heritability of a trait. GCTA estimates of genetic influence, known as SNP heritability, will be lower than twin study-based heritability, partly because GCTA detects only the additive effects of causal variants tagged by common SNPs on current DNA arrays used in GWA research, and not non-additive effects or rare variants. These limitations—additive effects and common SNPs—also apply to GWA research. Thus, the current ceiling for GWA analyses is SNP heritability, not twin heritability.17. These low SNP heritability estimates could be owing to low power (N of 2000–2700). Seven subsequent studies yielded mixed results for several quantitative measures of childhood behaviour problems, although power was also marginal in these studies. Supplementary Table Few studies have estimated SNP heritability for childhood behaviour problems. The first reports, from the Twins Early Development Study (TEDS), found negligible SNP heritability for childhood behaviour problems at age 12 , and a mixture of significant and nonsignificant modest estimates for psychotic experiences at age 16, despite substantial twin heritability estimates from the same sampleThe present study extends our previous reports, estimating twin and SNP heritabilities with a sample size almost twice as large for 37 measures of childhood behaviour problems as assessed by parents, teachers, and children themselves at age 16 as well as age 12, as well as cognitive and anthropometric measures for comparison. We now have twice the power to detect low SNP heritability: 70% rather than 35% power to detect a SNP heritability of 20%. We compare SNP heritability estimates directly to heritability estimates derived from twin data using the same sample and the same measures, to investigate the genetic architecture of childhood behaviour problems, and to quantify the likelihood that further GWA studies will find variants involved in their aetiology.18. GCTA analyses for this study were conducted on a sub-sample of unrelated individuals (only one member of each twin pair) with available genome-wide genotyping and behaviour problem data at ages 12 and 16. For the twin modelling, we added the second twin from each pair for individuals included in GCTA, allowing the two heritability estimates to be compared.The sample is drawn from the Twins Early Development Study (TEDS), a multivariate, longitudinal study of >10,000 twin pairs representative of England and Wales, recruited between 1994 and 1996For GCTA analyses, the numbers of individuals with behaviour problem data range from 3553 to 4432 at age 12, and from 3652 to 3760 at 16. For twin analyses, the numbers of twin pairs with behaviour problem data at age 12 range from 3599 to 4467 and from 3716 to 3823 at 16. See legends of Figures N = 3927, rather than 2486), we have approximately twice the power of the previous TEDS studies. If, as with our cognitive and anthropometric traits, true SNP heritability of behaviour problems is half that of our average twin estimate (52%), we have 90% power (up to 95% with maximum N = 4432) rather than 53% as with the previous sample.As previously mentioned, with our greater sample size , suspected non-European ancestry, heterozygosity, array signal intensity, and relatedness. SNPs were excluded if minor allele frequency was <0.5%, if >1% of genotype data were missing, or if the Hardy Weinberg p-value was lower than 10−5. Non-autosomal markers and indels were removed. Associations between the SNP and the platform, batch, or plate on which samples were genotyped were calculated; SNPs with P-values <10−3 were excluded. A total sample of 6710 individuals remained after quality control—3093 individuals and 525,859 SNPs genotyped on the Affymetrix platform and 3617 individuals and 600,034 SNPs genotyped on the Illumina platform.After initial quality control and genotype calling, the same quality control was conducted on the samples genotyped on the Affymetrix and Illumina platforms separately using PLINK24 before merging genotype data obtained from both arrays.Genotypes from the two platforms were separately imputed using the Haplotype Reference Consortium 5 and Minimac3 1.0.1325, after stringent pruning to remove markers in linkage disequilibrium (r2 > 0.1) and excluding high linkage disequilibrium genomic regions so as to ensure that only genome-wide effects were detected.We performed principal component analysis on a subset of 42,859 common (MAF > 5%) autosomal HapMap3 SNPs26, as confirmed in this study  measures have been described previously. In this study, we examined the same measures as in the previous publications27. Composite scores were calculated as unit-weighted means, given complete data for more than half the measures contributing to the composite . All procedures were executed using R. These data preparation steps are the same as the previous study16 to enable comparison.For all phenotypes, z-standardised residuals were derived for each scale regressed on sex and age . Outliers above or below 3 standard deviations from the mean were excluded and the scale was quantile normalised (with van der Waerden transformation) before analysisThe Moods and Feelings Questionnaire (MFQ) (self- and parent-rated) was used to assess depressive symptoms.The Strengths and Difficulties Questionnaire  included four scales for anxiety, conduct, hyperactivity and peer problems, from which a mean composite was created.The Antisocial Process Screening Device (APSD) (parent-rated) was measures three types of psychopathic traits: callous-unemotional, impulsivity, and narcissism.The Childhood Autism Spectrum Test (CAST) (parent-rated) assessed children’s autistic-like traits overall and for three subscales: communication, social and non-social.Conners’ (parent- and teacher-rated) scales measured ADHD behaviours for the subscales of inattention and hyperactivity-impulsivity.Reading ability was measured using the reading comprehension subtest of the Peabody Individual Achievement Test (PIAT), the Global Online Assessment for Learning (GOAL) Formative Assessment in Literacy for Key Stage 3, an adaptation of the Woodcock-Johnson III Reading Fluency Test (WJRF) and by the Test of Word Reading Efficiency (TOWRE).Mathematics ability was assessed with three scales based on the National Foundation of Educational Research 5–14 Mathematics Series: understanding numbers, non-numerical processes, and computation and knowledge.Language ability scales covered syntax (Listening Grammar subtest of the Test of Adolescent and Adult Language), semantics , and pragmatics (Level 2 of the Making Inferences subtest of the Test of Language Competence).Verbal ability was tested with the Wechsler Intelligence Scale for Children as a Processing Instrument (WISC-III-PI) Multiple Choice Information  and Vocabulary Multiple Choice subtests. Nonverbal reasoning tests were Wechsler Intelligence Scale for Children–III (WISC-III-UK) Picture Completion and Raven’s Standard and Advanced Progressive Matrices. General cognitive ability was indexed as a composite of the four verbal and nonverbal tests.Height and weight were self-reported at age 12.As at age 12, the MFQ (self- and parent-rated) was used to assess depressive symptoms; Conners’ parent-rated questionnaire measured ADHD behaviours for the subscales of inattention, impulsivity, and emotional lability; and the SDQ (self- and parent-rated) measured anxiety, conduct, hyperactivity and peer problems, although parent-rated anxiety was not collected at age 16.In addition, the Autism Quotient scale (self- and parent- report) measured autistic traits, including subscales for attention to detail and social skills.28. The measure consists of five self-report subscales, which are always analysed individually: Paranoia, Hallucinations, Cognitive Disorganisation, Grandiosity, and Hedonia, and a parent-report scale of Negative Symptoms.The Specific Psychotic Experiences Questionnaire (SPEQ) (self-reported) assessed individual psychotic experiencesAchievement was reported as a mean GCSE grade for the core subjects of English, Mathematics, and Science.29. The first step in GCTA is the calculation of genetic similarity for each pair of unrelated individuals across all genotyped or imputed genetic markers. Each pair’s genetic similarity is then used to predict their phenotypic similarity. In the present study, individuals with pairwise identity-by-descent (IBD) of >0.025 were removed, so that chance genetic similarity can be used as a random effect in a linear mixed model. By comparing a matrix of pairwise genomic similarity to a matrix of pairwise phenotypic similarity using a random-effects mixed linear model, the variance of a trait can be decomposed into genetic and residual components, using residual maximum likelihood. GCTA detects only those genetic effects tagged by the common SNPs  that are included in commercially available DNA arrays used in GWA studies; the residual component includes any source of variance that is not explained by additive effects of common SNPs, including non-additive genetic effects, rare variants, environment, gene–environment interaction, and error. In our GCTA analyses, phenotypic data were prepared as detailed above, and we used the first 10 principal components as covariates.Genomic relatedness matrix restricted maximum likelihood (GREML), implemented in the Genome-wide Complex Trait Analysis (GCTA) programme, estimates genetic influence directly using individual-level genome-wide genotypes in large samples of unrelated individuals30. Standard errors were derived from 95% confidence intervals. We used the full ACE model  to enable comparison with GCTA results, even when C was not significant (dropping C can inflate A). We fit ADE models when the DZ correlation was less than half the MZ correlation. This correlation pattern indicates non-additivity because gene-gene interactions are shared by MZ twins but not necessarily by DZ twins, therefore contributing to lower similarity between DZ twins.Twin maximum-likelihood model fitting using full-information matrices was carried out using structural equation modelling software OpenMx in RThe differences between genetic and environmental variance components estimated by twin and GCTA analyses should be noted. ‘A’ in twin models includes additive genetic effects of any DNA sequence differences, and is distinguished from ‘C’ as well as ‘E’. In contrast, the equivalent of ‘A’ from GCTA includes only common SNPs in current DNA arrays, and other genetic variance not explained by these SNPs loads onto the environmental component, which is not further decomposed into shared and non-shared environment, and includes non-additive and rare variant effects. A further difference is that standard errors are larger for SNP heritability than for twin estimates because GCTA is based on comparisons of small pairwise genetic differences (IBD < 0.025) compared to the genetic difference of 100 vs. 50% for MZ and DZ twins.31. First, we carried out analyses in the same way except without performing the quantile normalisation. Second, we compared the original analyses to SNP heritability estimates when outliers were kept in. Third, we kept the analyses the same but winzorised outliers rather than removing them, by creating z-scores and then fixing the outliers at z-score 3.29 (the cut-off for 0.1% of scores).Sensitivity analyses were performed to examine the influence of the van der Waerden quantile normalisation on heritability estimates for the behaviour problem phenotypes, which are highly skewed, and to examine the effects of treating outliers in different ways. It has been suggested that removal of extreme cases decreases SNP heritability because these individuals might be genuine, biologically significant outliersFigure Figure Sensitivity analyses were performed to examine the influence of quantile normalisation and removal of outliers on heritability estimates. Results do not differ with and without transformation, as indicated by overlapping standard errors and similar point estimates (never a difference of >0.04). In additional sensitivity analyses we performed quantile normalisation, but then varied the treatment of outliers. There was no difference in estimates associated with removing, retaining or winzorising outliers  indicates better fit to the data. ADE models were not significantly better fitting for height, weight, cognitive abilities and educational achievement. However, ADE models fit significantly better for seven of the 37 behaviour problem variables, although confidence intervals included zero for four of these  in comparison to twin heritabilities (average 52%). This is particularly striking given that SNP-based estimates were half those of twin-based estimates for anthropomorphic and cognitive measures. Although we found significant SNP heritability for a few behaviour problems, it is more appropriate to emphasise the average SNP heritability estimates for the whole study rather than findings for a few measures that might be due to chance. Our results are strengthened by sensitivity analyses indicating that low SNP heritability estimates are not due to approaches taken to outliers or normalisation of the positively skewed behaviour problem distributions.These results thus confirm and extend our previous report of a particularly large gap between SNP heritability and twin heritability for quantitative measures of behaviour problems in children. As detailed in Supplementary Table 32 and 10% in a sample of 536233. Several studies of adults have estimated SNP heritability for neuroticism, a trait that is associated with negative emotional states and psychiatric disorders34 and has a strong positive genetic correlation with depression (0.83)35. Despite substantial twin heritability (~ 40%)36, SNP heritability of neuroticism is low: 6% 37 10% 38 and 13% in UK Biobank 39. Low SNP heritability has also been reported for Linkage Disequilibrium Score Regression analysis (LDSR), which uses GWA summary statistics to estimate SNP heritability. For example, LDSR estimates of SNP heritabilities for quantitative measures of depression and neuroticism in a sample of >160,000 individuals are just 0.05 and 0.0940.We have limited our discussion thus far to studies of quantitative measures of behaviour problems in children  found SNP heritability of ~ 10% for binary self-report depression items39.Results from case-control studies are more difficult to compare to our results because case-control studies rely on the hypothetical construct of a continuous liability but the analyses are based on a dichotomous trait. SNP heritability of ADHD and ASD is substantial in case-control studies (0.20–0.30)If SNP heritability of childhood behaviour problems is truly as low as 6%, genome-wide association (GWA) studies will struggle to find variants involved in their aetiology, because both GWA and SNP heritability are limited to additive effects of common variants.43. Models that take MAF and linkage disequilibrium into account have obtained higher estimates of SNP heritability44. However, there is little evidence that these factors are more prominent for childhood behaviour problems than other domains, so they seem unlikely to explain the gaping gap between SNP and twin heritability specifically for these traits.One way to improve this situation is to investigate why SNP heritability for behaviour problems in childhood is so low compared to twin estimates. Gene-gene interactions, gene-by-shared-environment interactions, and rare alleles are captured in twin but not SNP heritability estimates, which only establishes how much variance can be explained by additive genetic effects tagged by common SNPs on genotyping arrays. Methods exist to investigate the relationship between genetic architecture and SNP heritability. For example, GREML-MS stratifies SNPs by minor allele frequency (MAF) to allow the data to reveal how much variance is explained by each MAF bin16. However, four of the seven traits for which an ADE model was better-fitting also showed significant SNP heritability . This pattern is the opposite of what might be expected if dominance were deflating SNP heritability, which includes only additive effects. Moreover, if non-additive genetic effects are responsible for the gap between SNP and twin heritability estimates for behaviour problems, it will be difficult for GWA studies to achieve adequate power to detect them45.We found some evidence for non-additive genetic influence on several behaviour problem phenotypes in our sample, particularly for hyperactivity and autism, as we would expect from the literature. In contrast, for the cognitive and anthropometric phenotypes, there was no indication of non-additivity. As suggested in the previous report from TEDS, non-additive genetic effects are potentially important in the aetiology of childhood behaviour problemsA more hopeful possibility lies with novel approaches to the study of childhood behaviour problems that increase SNP heritability. Because we used the same measures to assess SNP heritability and twin heritability, improved measurement of behaviour problems is unlikely to narrow the gap between these heritability estimates. However, anything that increases SNP heritability for behaviour problems could be helpful in relation to future GWA studies.50. Multivariate genetic analyses indicate higher genetic correlations across raters, suggesting that what the raters see in common about children’s behaviour problems captures more of the genetic action, although there are also rater-specific genetic influences51. Thus, one possibility for increasing SNP heritability is to create cross-rater composites that reflect their genetic correlations, for example, creating a higher-order factor comprising only common variance.Three examples of this approach that each involve multivariate genetic analyses could lead to increased SNP heritability by creating genetically sensitive composites of behaviour problems across raters, across time and across traits. With respect to raters, self-report, parent ratings, and teacher ratings of behaviour problems correlate only modestly phenotypically (~0.30)7. This finding suggests that a cross-age composite guided by age-to-age genetic correlations might yield higher SNP heritability than any measure at a single age. Again, the idea is to establish measures with higher heritability—not only to increase reliability and therefore the proportion of genetic relative to environmental (including error) variance. The third example is that multivariate genetic analyses suggest substantial genetic overlap across some behaviour problem domains52. A measure that captures more of the common genetic variance might show higher SNP heritability than most of its constituent sub-scales, as previously demonstrated for the ‘general psychopathology factor’ in childhood53.The second example takes advantage of the fact that longitudinal stability of behaviour problems is moderate and mostly attributable to genetic factorsWe are currently testing these hypotheses in TEDS by conducting multivariate genetic analyses across raters, across age, and across domains. We will create behaviour problem composite measures guided by these multivariate genetic results and test the hypothesis that these composites will yield higher SNP heritabilities.54. Advanced phenotypic modelling should include testing the unidimensionality, measurement invariance, difficulty, and discrimination  of our items and measures56.Another approach for increasing heritability is to use item response theory to construct more reliable behaviour problem phenotypes and decompose phenotypic variance into genetic and environmental componentsIn conclusion, we have confirmed a substantial gap between twin and SNP heritabilities of diverse domains of childhood behaviour problems as rated by parents, teachers, and children themselves at ages 12 and 16 in our sample. Future research should continue to investigate the reasons why SNP heritabilities for childhood behaviour problems are so low compared with twin estimates, and pursue methods that maximise SNP heritability for GWA studies.Supplementary Information


The Bayesian methods eCAVIAR and DAP (Deterministic Approximation of Posteriors) provide only modest improvement in resolution. Given the strong empirical evidence that gene expression is commonly regulated by more than one variant, we conclude that the fine mapping of causal variants needs to be adjusted for multisite influences, as conditional estimates can be highly biased by interference among linked sites, but ultimately experimental verification of individual effects is needed. Presumably similar conclusions apply not just to eQTL mapping, but to multisite influences on fine mapping of most types of quantitative trait.Expression quantitative trait locus (eQTL) detection has emerged as an important tool for unraveling of the relationship between genetic risk factors and disease or clinical phenotypes. Most studies use single marker linear regression to discover primary signals, followed by sequential conditional modeling to detect secondary genetic variants affecting gene expression. However, this approach assumes that functional variants are sparsely distributed and that close linkage between them has little impact on estimation of their precise location and the magnitude of effects. We describe a series of simulation studies designed to evaluate the impact of linkage disequilibrium (LD) on the fine mapping of causal variants with typical eQTL effect sizes. In the presence of multisite regulation, even though between 80 and 90% of modeled eSNPs associate with normally distributed traits, up to 10% of all secondary signals could be statistical artifacts, and at least 5% but up to one-quarter of credible intervals of SNPs within  SORT1 example in humans (Since it is now recognized that many SNP–trait associations identified by genome-wide association studies (GWAS) can be attributed to effects on gene expression, precise estimation of the location and effect sizes of regulatory polymorphisms has become important for understanding the relationship between genetic and phenotypic variation . Many inn humans , but concis-eQTL)  the number of independent peaks detected by stepwise conditional analysis, (ii) the accuracy of localization of true causal variants, and (iii) the effect size estimation of discovered causal variants. We also address the question of what proportion of discovered peaks may be driven by undocumented variants in LD with the genotyped sites, and conclude with a comparison of the performance of two recently developed Bayesian joint localization methods, eCAVIAR and DAP, finding minor improvements in detection of linked causal variants but little impact on fine mapping, particularly in regions of high LD or if sample sizes are small.N = 926) (N = 439)   and Emorhttps://github.com/CNSGenomics/impute-pipe. Briefly, the pipeline was as follows: (1) preimputation quality control and data consistency checks; (2) imputation to the 1000G reference panel with Impute2  to refer to models where a single causal variant is modeled as a fixed effect, and multisite (multivariable) where two or more variants are modeled, also as fixed effects. The term multivariate modeling is used for situations where there are two or more dependent variables, whereas in these models we are assessing the joint effects of two or more causal variables. Some models also incorporate random effects of covariates such as a genetic relationship matrix.p(1−p)β2. Thus, an allele with β = 0.8 is expected to explain 20% of the variance if maf P = 0.2, or 32% of the variance if P = 0.5. The influence of LD was assessed at r2 = 0.1, 0.5, or 0.9, noting that as LD increases, high r2 values are not obtained for combinations of a rare and a common allele. For each combination of parameter values, we generated 1000 randomizations of the environmental variance, and assessed (i) the univariate estimate at each genotype, (ii) the mean conditional estimate of the second SNP, and (iii) the joint effect estimates with both SNPs. From these values, we computed the mean absolute value of the deviation between the observed estimate and the true effect size from the univariate, conditional, and joint (two site) models. The univariate estimates agree extremely well with expectations from the analytical solution described in the Results.The first set of simulations assessed the power and accuracy of two site regressions assuming that the identities of the two causal variants are already known. We modeled the influences of effect size, minor allele frequency (maf), LD, and sample size. Environmental variance was randomly generated as a z-score (mean 0 and SD 1) and genotype effects (β) were added in SD units (sdu) multiplied by ± 0, 1, or 2 according to genotype so as to account for from 2 to 30% of the phenotypic variance, computed as 2r2 > 0.8, and performed stepwise conditional regression, documenting instances of primary and secondary signals at P < 10−5, as plotted in The second simulation study asked whether unimputed variants influence the localization of eSNP signals. Since nonimputed SNPs are not present in the CAGE data, we approximated their identities by randomly sampling from a set of CAGE-imputed SNPs weighted to have the same frequency distribution shown in p is computed as √[VE/2p(1−p)]. Subsequently, each phenotype was simulated as N, where βi is the simulated allelic effect size for a SNP i, and igeno is the dosage of minor allele at the simulated SNP for a given sample. The significance threshold for sequential conditional detection of the variants in a sample of 1839 CAGE individuals was set at P < 10−5, since simulations indicated a <1% false discovery rate for null variants at this level. We evaluated (i) how many of the four SNPs were significant in sequential conditional modeling, (ii) the mean LD between each SNP and the other three SNPs in the model, (iii) the effect size estimates from the conditional and joint multisite models, (iv) the difference between these two estimates as a function of the mean LD, and (v) the rank of the discovered SNP for each peak eSNP and the modeled sites, which were assumed to be the causal variants for some trait.The third set of simulations were performed to evaluate the difference in effect size estimates using the multisite linear regression method for parameter estimation from data representative of the LD structure in the CAGE dataset. For each of 500,000 iterations, four sites were chosen at random from a window extending from 200 kb upstream of the transcription start site to 200 kb downstream of the transcription termination site of a randomly picked gene in the CAGE cohort , and assigned an effect size from a uniform distribution of variance explained (VE) relative to environmental noise ranging between 0.02 and 0.1. The effect size β for an allele with maf r2 > 0.3 with one another.The fourth set of simulations was performed to evaluate the influence of two Bayesian methods for fine mapping that is sensitive to the LD structure at a locus. First, eCAVIAR was used to also assess the accuracy of colocalization of eQTL and GWAS signals. Summary statistics were generated for normally distributed traits where either one, two, or three sites chosen at random from contiguous intervals of 100 SNPs in the full sample of 1835 CAGE genotypes were assigned to explain between 2 and 10% of the variance. Effects were assigned in the same direction for each minor allele. Marginal single site estimates were generated by univariable regression, and then eCAVIAR was used to combine the Posterior Probabilities, which were multiplied together to yield the Combined Likelihood Posterior Probabilities (CLPP) with a significance threshold of 0.001 as recommended , three positive and one negative (3:1), or two positive and two negative (2:2) effects of the minor allele on the trait. For the eQTL detection, once the sequential conditional detection was completed, we determined which of the four causal variants was in high LD (r2 > 0.8) with one of the discovered sites. If a peak was in high LD with more than one causal variant, it was assumed that it tagged the highest effect site.Our basic simulation framework utilizes the current standard mapping approach of sequential conditional analysis, in which the residuals from discovery of each SNP are taken forward as the dependent variable in a new scan for an independent SNP (r2 > 0.8) with one of the simulated causal variants. Across all three scenarios, at least three independent peaks are detected in ∼90% of the simulations, and at least four independent peaks in two-thirds of the simulations. Notably, in the scenarios where all four minor alleles influence expression in the same direction, 10% of the simulations detected five or more independent peaks, at least one of which must be a spurious association, despite a <1% false discovery rate for univariate associations of the same magnitude. The proportion of credible intervals (r2 > 0.8 regions around each discovered variant) that contain the actually simulated site ranges between 85 and 90% in each scenario, again indicating relatively poor localization of the causal variant.r2 > 0.8 credible interval for a discovered peak. Across the sets of 500,000 simulations, at least two variants are detected >85% of the time, but the power to detect all of the multiple eSNPs is a function of the number of sites operating in the same direction. It is highest for the case where the minor alleles for all four variants have effects in the same direction and least where two are in one direction and two in the opposite direction. In the 4:0 scenario, three or more of the four eSNPs are detected three-quarters of the time, whereas this proportion drops toward two-thirds with the simulations for 2:2. No variants are detected in just over 1% of the simulations, and just one variant in ∼6% of them, while 80% of the variants are detected overall. This proportion rises to 90% for the two-variant simulations, illustrating how multisite interactions reduce the discovery of independent eQTL peaks.Similar results are reported in File S1).Another way to consider the power of multisite detection is to ask how much of the variance explained by the four SNPs is captured by the discovered variants. Box and whisker plots in Methods). Randomly assigning causal effects resulted in the appearance of a secondary signal at P < 10−5, conditioned on the causal site, at 0.3% of the loci. This is approximately as expected given 8.3 million imputed SNPs at 22,000 loci, and is also the same as the false discovery rate of primary signals in the absence of any simulated causal locus; that is to say, our random expectation is for 0.3% of transcripts to have a false eQTL discovery at P < 10−5 in the CAGE dataset.The detection of more association peaks than the number of simulated sites implies that some fraction of peaks are false positives that arise due to sampling artifacts in the presence of high LD, whereby an imperfectly tagged site is split into two or more spurious signals. An example is shown in r2 value) of these SNPs is a function of maf, being >0.7 for maf > 0.05, but dropping to <0.5 for maf = 0.01, as seen in However, this ignores the possibility that the causal variants are not present in the imputed genotypes. Of the 9.7 million SNPs, indels, and CNV with maf > 0.01 in the European populations in the 1000G database, 1.9 million are not imputed in our CAGE samples. Since we cannot simulate effects at nonimputed SNPs, we approximated such alleles by randomly simulating a causal variant from the CAGE SNPs with the same frequency distribution, but excluding it from the analysis along with all variants that would tag it at the typical level observed for the nonimputed SNPs of the same maf. We then asked how often the effect is captured by multisite signals, as a function of residual tagging efficiency. We allowed for increased effect sizes with lower maf by simulating effects in the constant range of 2–10% of the variance explained. The proportion of such pseudounimputed SNPs that generate primary signals is reduced with lower maf, due in part to the smaller proportion of variance explained by the less common variants that partially tag them. For common variants, there is almost always a second site in high enough LD to capture most of the causal signal in the absence of genotypes at the causal variant, but rare variants are insufficiently tagged to generate a signal at all, 90% of the time.r2 > 0.5 to the “unobserved” causal variant, false secondary associations are observed ≤40% of the time. At the other end of the spectrum, rare variants (maf ∼0.01) that produce a primary signal at a tagging SNP with 0.1 < r2 < 0.3 also produce a secondary signal but less frequently. The blue curve in In the presence of tagging SNPs with These computations argue that up to 10% of the observed >2300 multisite associations reported by P-value relative to the casual site, RTC = /SNPsN, where a value of one indicates identity, and zero that the two sites are in the same locus but highly unlikely to be capturing the same signal. r2 > 0.8) with a simulated variant in the imputed panel of SNPs.A possibly more important measure of estimation bias is the location of the peak SNP relative to the causal site. The most straightforward measure of colocalization is the Regulatory Trait Concordance (RTC) score . It should be noted that there is not strong concordance between the relative proportion of variance explained by the causal variants and whether they are the primary through quaternary association, since LD has a strong influence on detection power. Although the vast majority of discovered sites are within three or four SNPs of at least one of the four causal variants when they are in high LD with one of them, it cannot be concluded that the order of discovery corresponds to the true order of effect sizes.Localization of tertiary and quaternary signals is affected more strongly, but intriguingly, considering just associations within a priori, joint fitting of the two SNPs in a single regression on transcript abundance always results in more accurate effect size estimates, given a large sample size. The bias in estimation due to LD between pairs of SNPs is a function of the two effect sizes (β1 and β2), the correlation between the SNPs (r), and the ratio of the square root of the product of their allele frequencies: Ê(β1)−β1 = rβ2 √(p1(1−p1)/p2(1−p2))  has the greater deviation between the estimated and true effect size. This makes intuitive sense as the larger effect will generally be the first detected one and absorbs much of the effect of the other SNP, which will typically be underestimated in the conditional analysis, but to a lesser extent. If the deviation is computed simply on the uni-site unconditional values, the opposite result is obtained: the deviation is greatest for the smaller effect site. Third, the misestimation is greatest for lower allele frequencies, which is particularly noteworthy since most eQTL have maf in the range of 0.1–0.3.P value cutoff for detection becomes more stringent, the bias in estimation becomes more severe, since the first picked SNP absorbs the effect of both alleles into the same estimate, leaving the statistical power of the other allele, conditioned upon the first one, close to zero. With a sample size of 2000 and intermediate LD, when both alleles are modeled jointly, power to detect both effects remains high across the plausible parameter space once the effect size exceeds 5% variance explained, and the estimation for the β value is still accurate. Downsampling suggests that in order to estimate effects within 0.1 sdu, for pairs of variants with LD r2 ∼ 0.9, each explaining 10% of the variance (namely having effect sizes of at least 0.5 sdu), a sample size of at least 900 is required.We also considered the power to detect joint effects in the presence of LD. As the Consequently, most small sample eQTL studies will fail to resolve linked sites into two effects. These results indicate how the typical assumption that an eQTL effect is due to a single variant in a set of credible SNPs in high LD is potentially highly biased. Similar conclusions apply to the situation where two SNPs operate in opposite directions, with the additional dilemma that they will not be detected at all and consequently strongly underestimate the regulatory variance at a locus.r2 > 0.8 of an independent causal variant, and evaluated the absolute value of these deviations as a function of the mean LD between the causal variant SNP and the other three causal variants in the model. Even though the sequential conditional and multisite models capture essentially equivalent proportions of the variance tallied across sites, biases in estimation of individual site effects ought to be reduced by the multisite modeling. To quantify this difference, we computed the deviation between the observed and true simulated effect sizes (β in sdu) for each discovered peak located within Several results are noteworthy. First, for causal variants in low LD, as expected, neither model results in appreciable estimate bias, but once the average LD rises above 0.5, effects can be misestimated by more than the effect size. For example, for β = 0.5, the absolute value of the difference between the observed and true effect is typically between 1 and 2 sdu, which depending on the allele frequency may correspond to at least 2% of the total gene expression variance. Second, for large effect alleles, the misestimation is appreciable even at intermediate levels of LD, and it is not unusual for estimates to be off by as much as 4 sdu under either model.Third, overall, the multisite modeling corrects some of the sequential conditional analysis bias. The difference in performance of the two estimation procedures is shown in File S1), and simulations with just two or three causal variants yield similar conclusions . The advantage of joint modeling is reduced in the presence of opposing allelic effects, but still prevalent in the region with high allelic effect size and low LD; and, as noted above, a large proportion of causal sites are not discovered in the 2:2 scenario, so are not included in the estimation.Similar trends are seen for the 3:1 scenario, where one of the minor alleles operated in the opposite direction to the other three, as well as in the 2:2 scenario , showing the results in P < 10−5 discovered on average 1.56 (52%) of the three simulated effects and included 2.29 (76%) of the sites within the credible interval. Consequently, DAP does appear to improve performance, at the cost of a considerably higher computational burden.As expected, DAP fails to detect true causal variants in the presence of high LD. Studies of the genetic regulation of gene expression are making a meaningful contribution to the interpretation of GWAS results, as they provide functional insight into the nature of the causal genes. However, efforts to fine map causal variants are complicated by the limits of statistical resolution as it is not uncommon for tens, if not hundreds, of polymorphisms in a credible set to have similar statistical support . Inclusir2 up to, or even exceeding, 0.9. The problem is that the identities of the variants are generally not known, and there are no established methods for comprehensive screening transcriptome-wide for localization of multi-locus local eQTL effects. Two exhaustive search algorithms, PAINTOR (Theory and simulation both indicate that if two linked sites both influence a trait, including gene expression, then multisite models will uniformly outperform sequential uni-site ones with regard to estimation of the true effect size. When the identities of the variants are known, sample sizes of several thousand individuals are sufficient to jointly estimate their effects with high accuracy even in the presence of high levels of LD with We also estimated the bias in the estimates from conditional analysis, by fitting multi-locus linear models to all of the discovered eSNPs at each locus. This revealed only modest improvements in accuracy for most of the discovered sites, but the modesty is in part an artifact of the discovery bias introduced by the sequential conditional process. Our simulations assuming two to four effective sites per locus across a wide and representative range of LD, show that in a sample size of 2000, in general no more than 85% of the simulated causal sites are tagged by discovered associations that explain typically observed magnitudes of effect and would almost always be detected if a single site explained the variance. Similarly, r2 with the other three SNPs is >0.9, will be misestimated in both the single site and joint models, typically by 1.5 sdu or more. The misestimation is on average the greatest where all of the effects are in the same direction, but is consistently observed also in the presence of associations with alternate signs. Unavoidably, the sequential conditional estimates of eQTL effects are actually highly biased for a considerable proportion of variants. Although this does not impact the total amount of variance explained by the discovered variants, it is likely to greatly impact fine mapping efforts, particularly where two or more effects are collapsed into one site in a credible interval.However, any variants with effect sizes >1 sdu, and whose average While large datasets have very good power for detection of complex regulatory contributions for individual genes, there are a host of technical and statistical reasons why fine mapping of causal variants remains a challenge. There are two immediate strong implications of these results. One is that even though the majority of identified eSNPs are expected to map to credible intervals that include the causal variant (www.g3journal.org/lookup/suppl/doi:10.1534/g3.117.043752/-/DC1.Supplemental material is available online at Click here for additional data file.


P < 0.001) marker-trait associations (MTAs) were detected explaining more than 20% of the phenotypic variation observed under both drought-stressed and non-stressed conditions. Significant (P < 0.001) MTA event(s) were observed for DTH, PHT, SPL, SPS, and KPS; under both stressed and non-stressed conditions, while additional significant (P < 0.05) associations were observed for TKW, DTM and GYLD under non-stressed condition. The MTAs reported in this population could be useful to initiate marker-assisted selection (MAS) and targeted trait introgression of wheat under drought-stressed and non-stressed conditions, and for fine mapping and cloning of the underlying genes and QTL.This study determined the population structure and genome-wide marker-trait association of agronomic traits of wheat for drought-tolerance breeding. Ninety-three diverse bread wheat genotypes were genotyped using the Diversity Arrays Technology sequencing (DArTseq) protocol. The number of days-to-heading (DTH), number of days-to-maturity (DTM), plant height (PHT), spike length (SPL), number of kernels per spike (KPS), thousand kernel weight (TKW) and grain yield (GYLD), assessed under drought-stressed and non-stressed conditions, were considered for the study. Population structure analysis and genome-wide association mapping were undertaken based on 16,383 silico DArTs loci with < 10% missing data. The population evaluated was grouped into nine distinct genetic structures. Inter-chromosomal linkage disequilibrium showed the existence of linkage decay as physical distance increased. A total of 62 significant ( Wheat is an important commodity crop and a vital component of a healthy global diet providing starch, dietary proteins, fiber, fats, vitamin B, zinc, calcium, and iron . Its proGenome-wide association study (GWAS) facilitates understanding of the genetic bases and dissection of complex genes controlling economic traits such as drought tolerance. Genome-wide association analysis rely on marker-trait association (MTA) involving representative markers and genetically diverse populations such as elite breeding lines and improved cultivars. The goal of GWAS is to discern genomic regions that could either be markers, genes or QTL associated with key agro-morphological traits for marker-assisted breeding, gene discovery or gene introgression .per se could serves as a predictor of the resolution at which influential genomic regions can be detected through marker-trait-association analysis. Linkage analysis establishes associations among sets of genes, and provides insights on the effect of genetic drift, selection, mutation, recombination, quantitative trait loci, linked genes, or gene-flow in a given population [Triticum aestivum L., 2n = 6x = 42; AABBDD) has a genome size of ≈ 17 giga base pairs [Understanding the population structure and the magnitude of linkage disequilibrium (LD) present in the prevailing genetic resources are important pre-requisites to deduce the genetic makeup, composition and genomic predictions of traits of interest during selection. Linkage disequilibrium pulation , 12. Idese pairs . A consise pairs , 14–17. amplified fragment length polymorphism (AFLP), single nucleotide polymorphism (SNP) and microarray based Diversity Arrays Technology (DArT) markers [Several DNA-based marker systems have been successfully applied in association mapping of complex traits in different crop species. The most widely used marker systems include simple sequence repeat (SSR),  markers –21. Adva markers , 23. The markers . The Div markers , 27, 28.International Maize and Wheat Improvement Center (CIMMYT) for selection and drought tolerance breeding in South Africa. The population comprising of 87 introductions and six local drought-susceptible released varieties were screened using key agronomic traits and proline analyses under drought-stressed and non-stressed conditions [A diverse population of drought and heat tolerant lines were acquired from the nditions . Evaluat2) estimates were calculated from phenotypic variance (σ2p) and the genotypic variance (σ2g) according to Allard [w, l, s and r were the water regime, site, season and replications respectively.The study used a population of 93 bread wheat genotypes that were purposefully selected for drought tolerance breeding, based on their divergent pedigree and reaction to drought stress. This sample size was in the same range with those successfully used in some previous studies , 28, 30.o Allard  using thGenomic DNA of the 93 genotypes was extracted from fresh leaf tissue of 2 week old seedlings following the plant DNA extraction protocol for DArT . The quahttps://sourceforge.net/p/tassel/tassel4-standalone/ci/master/tree/). Linkage disequilibrium was estimated using the squared allele frequency correlations R2 value from which the number of significant allele pairs (P < 0.01) was determined using 1,000 permutations. Marker trait association analysis, probability values and % of the effect of these markers were calculated following the GLM procedure in TASSEL v4.3.15 according to Bradbury et al. [Population structure was determined using the software STRUCTURE v2.3.4 . The pary et al. .P < 0.05) due to the genotype, site, water regime and their interaction effects for all the studied traits. These and the Pearson’s correlations (r) were reported by Mwadzingeni et al. [2g), hence high heritability values of > 50% (2 < 50%).Analysis of variance indicated significant differences . Further, 5,188 (0.87%) of the pairs were in complete LD (R2 = 1). Marker pairs in LD were observed over long distances, however, a clear and rapid decline in LD with distance was observed. Pearson’s correlation coefficients revealed negative correlation (r = -0.0813 between the linkage disequilibrium (R2) and the physical distance (bp); as well as between the P-value and R2 (r = -0.59), revealing the existence of linkage decay.Linkage disequilibrium analysis revealed the presence of 597,871 loci pairs within a physical distance extending up to 16,356 bp. About 45,835 (7.67%) of loci pairs were in significant LD (P < 0.05) marker-trait associations (MTAs) were observed. P > 0.001) MTAs that could also have influence on respective traits. Only the MTAs that had P values < 0.001 (P < 0.05) marker-trait associations were considered because the three traits are highly complex, often with moderate to low heritability [A total of 334 significant  for the number of days-to-maturity, thousand seed weight and grain yield accounting for 24.03%, 23.94% and 22.57% of the phenotypic variation, respectively. The B genome had most of the significant MTAs observed for this trait. The marker 6B|079.586479380|3949288|3949288 explained the highest proportion of the phenotypic variation (41%) under drought-stressed condition while a marker on chromosome 2B explained the least proportion (28.06%) of the phenotypic variation observed under the non-stressed condition. Under drought-stressed condition, the number of kernels per spike was associated with two markers located on chromosomes 2D and 4A, explaining 30.04% and 30.24% of the observed phenotypic variation, in that order. Eight significant MTAs were detected under non-stressed condition on chromosomes 2D, 6B and 7A explaining 28.06% to 36.46% of the variation of the number of spikelets per spike, respectively. Three MTAs on chromosomes 6B, 7B and 5D were considered significant (0.05 > http://blast.ncbi.nlm.nih.gov/Blast.cgi) and GrainGenes (http://wheat.pw.usda.gov/GG2/blast.shtml) databases indicated that this marker has a sequence alignment that is 97% identical to the TaMFT gene that regulates seed dormancy on chromosome 3A .A pleiotropic locus is associated and affects the expression of more than one phenotypic trait. In this study, several pleiotropic loci were identified including the marker 5A|084.411633690|3534155|3534155 that was associated with DTH, PHT and SPS under non-stressed condition . Days-toa et al. ; http://Out of the 65 significant marker-trait associations observed, 25 trait-specific MTAs were differentiated. Chromosome 2B had four trait specific MTAs of which one was associated with spike length under drought-stress, two with plant height under non-stressed condition and one with spike length under non-stressed condition. Traits that were represented by at least one significant trait-specific marker-trait association under either of the two water conditions were days-to-heading, plant height, spike length, number of spikelet per spike, number of kernels per spike, days-to-maturity, and grain yield .Understanding the genetic bases of complex traits in polyploid crops such as wheat, presents an opportunity for drought tolerance breeding. To complement the growing need for such knowledge, the current study explored the population structure and association of genomic regions with yield and yield related traits in a diverse population of drought tolerant and susceptible wheat genotypes. High heritability estimates as well as significant and positive correlations were observed among the studied traits , Table 1Table 1 cThe population evaluated was grouped into nine distinct genetic structures . This isMost of the unique groupings identified could be explained by existence of at least one common parent in the pedigree of genotypes within each cluster. For instance, in Cluster 1, five of the six genotypes from the heat nursery  shared the common parent PASTOR in their parentage. Also, the genotypes LM50, LM51, LM52 and LM53 found in Cluster 2 could be related due to the sharing of crosses involving HUW234+LR34/PRINIA*2// in their parentage. Similarly, the ancestral genotype SOKOLL which was common in most pedigrees of genotypes in Cluster 4 could be the source parent of some similarities in that grouping. Interestingly, all the genotypes in Cluster 5 were descendants from the parents MILAN, KAUZ and PRINIA. Likewise, seven of the genotypes in Cluster 6 had pedigrees containing ATTILA and the parent PBW343 was common in all pedigrees of genotypes in Cluster 7, which could have contributed to formation of these respective clusters.Existence of marker pairs in LD over long distances and closely linked pairs with non-significant LD observed in the current study has been previously reported in various crop species , 30, 39.P < 0.05) marker-trait associations  observed for all significant markers suggests their possible influence on respective traits. In this light, the observed MTAs for grain yield, days to maturity and thousand seed weight, were considered significant at 0.05 > P > 0.001, since the traits are highly complex with low heritability [The present study identified 334 significant .Several research efforts have been directed at locating genes and QTL influencing various agronomic traits to facilitate MAS in wheat improvement in the face of increased droughts along with other key production constraints –44. The enotypes . Blast sTypically, loci or QTL regions that influence a particular trait under stress also control the trait under non-stressed condition . This coHigh phenotypic trait correlations could be explained in terms of direct or indirect contribution of one trait to another. Looking into the genome, loci controlling such traits could be similar. This is evidenced by the existence of several multi-trait associations where one gene will have pleotropic effects on highly correlated traits. Dholakia et al.  reportedP = 0.05 for grain yield, days to maturity and thousand seed weight, which had low heritability values. The markers identified in this study are useful genomic resources to initiate marker-assisted selection and trait introgression of wheat under drought-stressed and non-stressed conditions, and for fine mapping and cloning of the underlying genes and QTL. Further studies are required to validate the significant markers identified in the present study using a larger population, following the multiple loci mixed model (MLMM) as proposed by Segura et al. [Marker trait association is key to identifying genomic regions that are associated with phenotypic traits of breeding significance. The present study identified a total of 65 highly significant marker trait associations under contrasting water regimes . Only ona et al.  to increS1 Table(DOCX)Click here for additional data file.