The frequency of the SNP associated with increased SCC was 21.7% and the Socs2 genotype explained 12% of the variance of the trait. The point mutation induces the p.R96C substitution in the SH2 functional domain of SOCS2 i.e. the binding site of the protein to various ligands, as well-established for the growth hormone receptor GHR. Using surface plasmon resonance we showed that the p.R96C point mutation completely abrogates SOCS2 binding affinity for the phosphopeptide of GHR. Additionally, the size, weight and milk production in p.R96C homozygote sheep, were significantly increased by 24%, 18%, and 4.4%, respectively, when compared to wild type sheep, supporting the view that the point mutation causes a loss of SOCS2 functional activity. Altogether these results provide strong evidence for a causal mutation controlling SCC in sheep and highlight the major role of SOCS2 as a tradeoff between the host’s inflammatory response to mammary infections, and body growth and milk production, which are all mediated by the JAK/STAT signaling pathway.Mastitis is an infectious disease mainly caused by bacteria invading the mammary gland. Genetic control of susceptibility to mastitis has been widely evidenced in dairy ruminants, but the genetic basis and underlying mechanisms are still largely unknown. We describe the discovery, fine mapping and functional characterization of a genetic variant associated with elevated milk leukocytes count, or SCC, as a proxy for mastitis. After implementing genome-wide association studies, we identified a major QTL associated with SCC on ovine chromosome 3. Fine mapping of the region, using full sequencing with 12X coverage in three animals, provided one strong candidate SNP that mapped to the coding sequence of a highly conserved gene,  Mastitis is an inflammation of the mammary gland mainly caused by invading bacteria. Ruminants show natural variability in their predisposition to mastitis, and therefore provide unique models for study of the genetics and physiology of host response to bacterial infection. A genome-wide association study was conducted in a dairy sheep population for milk somatic cell counts as a proxy for mastitis. Fine mapping, using whole genome sequencing, led to the identification of a mutation in the Suppressor of Cytokine Signaling 2 gene (socs2). This mutation was shown to cause a loss of functional activity of the SOCS2 protein, which suggested impairment of feedback control of the JAK/STAT signaling pathways in susceptible animals. Additionally, size, weight and milk production were increased in animals carrying the susceptible variant suggesting a pleiotropic effect of the gene on production versus health traits. Results gave strong evidence of the role of SOCS2 in the host’s inflammation of the udder and provided new insights into the key mechanisms underlying the genetic control of mastitis. Mastitis is an inflammation of the mammary gland mainly caused by bacteria, which develop in the gland cistern after penetration through the teat canal. Mastitis is the main infectious disease of dairy ruminants, with respect to industry and public concern, economic impact, zoonotic potential and animal welfare ,2. This How mammals defend against a microbial intra-mammary infection is still poorly understood. Animal models, especially ruminants, have provided useful information about the mechanisms underlying immunity. Mammary defense depends on the early recognition of invading pathogens by sentinels, such as the dendritic cells and macrophages, and also mammary epithelial cells . Early aThe existence of a genetic basis for mastitis resistance has been well documented in dairy ruminants ,7,8,9. IThe availability of genome sequencing data has opened up new fields of investigation that can be applied to domestic ruminant species with already-sequenced genomes ,16. IndeSocs2 gene was validated by implementing i) genotyping and analysis of the mutation in the discovery population, ii) functional analysis of the isoforms of the SOCS2 protein and, iii) phenotyping of animals carrying contrasted genotypes at the Socs2 gene. Our results provide new insights into the key mechanisms underlying the genetic control of the host’s response to intra mammary infections.We mapped the genetic loci determining resistance/susceptibility to SCC as a proxy for mastitis by performing a large-scale QTL analysis using an outbred population of dairy sheep. Following discovery of such loci using a 50K SNP chip, we used fine mapping and whole genome sequencing for molecular dissection of a major region identified on ovine chromosome 3. A possible candidate mutation in the QTLs associated with SCC were mapped by performing a genome scan (26 autosomes) in 1009 dairy sheep distributed in 33 half-sib families. The SCC trait pertaining to mastitis susceptibility was the lactation average of somatic cell score (LSCS) as this trait is highly correlated with intra mammary infections. The phenotype was the daughter yield deviation provided by the routine genetic evaluations. These represent the average performance of the daughters of a sire, corrected for the environmental effects and the genetic value of the mates.All animals were genotyped with the 50K OvineSNP50Beadchip . Haplotype-based linkage and association analyses were used to detect QTLs on 22 chromosomes at a 5% chromosome-wide threshold . Five reftp://ftp.ensembl.org/pub/current_gtf/ovis_aries/) (The OAR3-QTL was subjected to further fine mapping using whole genome sequencing in a trio of rams (average read-depth of 12X). This trio included a segregating sire carrying the most susceptible haplotype Q (Qq), and two homozygous sons for alternative haplotype alleles (QQ and qq), whose progenies were extremely divergent for the SCC phenotype. Out of the total of 1543 SNPs found in a region of 0.5 Mb, 207 SNPs were retained that were heterozygous (0/1 genotype) for the sire and homozygous, with different genotypes (0/0 or 1/1) for the sons and annotated with the current gtf file for sheep genome (_aries/) . The dis_aries/) . The pre2 M-1 S-1, Kd = 1.6 X 10−3 S-1, Kt = 3.06 X 107 RU M-1 S-1 and KD = 2.0 μM. KD were close to those previously published for rhuSOCS2, i.e. 2 vs 1.6 μM  to normalize the data distribution and averaged per lactation to compute the analyzed trait LSCS as described in [The phenotypic measurement was the milk somatic cell count (SCC) measured in lactating ewes and available from the national data base, . Blood samples were collected as part of the official national preservation collection and the coded samples stored at LABOGENA. Data were cleaned using in-house pipelines automated for the whole dairy sheep population genotyping data. In brief, any individual with a call rate below 98% or showing pedigree inconsistency had been previously discarded (less than 3%). SNP quality control included the following inclusion criteria: call rate above 97%, minor allele frequency above 1% and Hardy-Weinberg P-value above 10−6. After edits, a total of 41,501 autosomal SNPs, distributed on ovine chromosomes OAR1 to OAR26 were included for further analyses. The marker order and positions were based on the Ovine Assembly v3.1 (http://www.livestockgenomics.csiro.au/sheep/oar3.1.php).All 1009 rams were genotyped using the Illumina Ovine SNP50 BeadChip assay. DNA extraction from blood samples and genotyping were performed at the Laboratoire d’Analyses Génétiques pour les Espèces Animales, Jouy en Josas, France . For LA, interval mapping [n, where n is the number of chromosomes, i.e., 26 in sheep [For discovery association, both linkage analyses (LA) and genome-wide association study (GWAS) were applied to the data using the QTLMap software . 3 μg of each DNA were fragmented on a Covaris M220 focused-ultrasonicator and used to generate the libraries on a Tecan EVO200 automate. Quality controls were assessed with the High Sensitivity DNA chip (PN 5067–4626) on a 2100 Bioanalyzer (Agilent), and libraries were quantified using the Kappa Library Quantification kit (PN KK4824) on an ABI 7900 HT (Life Technologies). Each library was paired-end sequenced (2x100 bp) on a single lane of an Illumina HiSeq2000 flowcell using the Illumina TruSeq SBS Kit v3 (FC-401-3001).http://www.ensembl.org/Ovis_aries/Info/Index) with BWA software, version 0.7.0-r313 [http://picard.sourceforge.net, version 1.91), GATK (version 2.4.9) base quality score recalibration [FASTQ sequences were aligned to the sheep genome v3 . The primers used for this genotyping test are listed in Genotyping of the Socs2 heterozygous sheep were isolated from EDTA blood samples using centrifugation over Ficoll gradient, and cultured overnight in complete RPMI medium with 10% fetal calf serum. For cDNA preparation, a two-step purification of RNA was used with phenol-chloroform extraction followed by purification on silica columns . RNA integrity number (RIN) was determined using an Agilent 2100 bioanalyzer, and was above 6.5. cDNA was synthesized on 1 μg RNA with Superscript III reverse transcriptase (Invitrogen) following the manufacturer’s recommendations.Peripheral blood mononuclear cells (PBMC) from a Socs2-5’ and OAR_Socs2-3. PCR products were cloned in pCR2.1 plasmid using Strataclone PCR (Stratagene) and Sanger sequencing was performed with universal primers.WT and p.R96C SOCS2 cDNA were cloned by PCR using primers OAR_E. coli strain, recombinant proteins were purified using His-tag and dialyzed overnight against a large volume of Tris-HCl 50 mM NaCl 150 mM pH 9 for refolding. Protein concentration was then determined using the Bradford reaction and purity of the recombinant proteins was monitored by SDS/PAGE and Coomassie blue staining.Sequences of each variant were transferred into prokaryotic vectors downstream of the Thioredoxin and 6xHis-tag sequences. Upon expression in BL21 All binding and kinetics studies based on surface plasmon resonance (SPR) technology were performed on a BIAcore T200 optical biosensor instrument . Immobilization of biotinylated phosphopeptides (Proteogenix) was performed on a streptavidin-coated (SA) sensorchip in HBS-EP buffer  . All immobilization steps were performed at a flow rate of 2 μl/min with final peptide concentrations of 2.5 μg/ml and 25 ng/ml for 350 RU or 75 RU immobilized peptides, respectively. A channel (Fc2) was used for immobilization of GHR peptide and a channel with an immobilized scramble peptide (Fc1) was used as a reference surface for non-specific binding measurements. Binding analyses were performed with solubilized purified SOCS2-WT and SOCS2-p.R96C proteins at different concentrations over the immobilized peptide surface at 25°C for 2 minutes at a flow rate of 30 μl/min.A single-cycle kinetics (SCK) analysis to determine association, dissociation and affinity constants  was carried out by injecting different protein concentrations (100 nM–8.1 μM). Binding parameters were obtained by fitting the overlaid sensorgrams with the 1:1 Langmuir binding model of the BIAevaluation software version 1.0.Socs2 genotype on milk production traits. A single SNP test of association by analysis of variance (ANOVA) was performed using the mixed procedure of the statistical analysis system (SAS) programs (9.1) for the 468 genotyped animals. The dependent variables were DYD for LSCS and production traits  available from the national genetic evaluation procedure. The three possible Socs2 genotypes were fitted as a fixed explanatory variable and a significance threshold of p<0.05 was selected. The varcomp procedure of SAS was used to fit the genotype effect as random and estimate the proportion of variance explained by the genotype. The sire was included as a random effect in both mixed and varcomp models.For the 468 rams from the discovery population that were genotyped with the KASPar test, data from the national data base CTIG were used to quantify the effect of the Socs2 genotypes were bred in the INRA experimental unit of La Fage . The 18 sheep were triplets of Socs2-C/C,-C/T and -T/T genotype, each triplet being sired from a common ram, except one (C/T and T/T from common sire and a non-related C/C match). The sheep were born in 2010, 2011 and 2012. Body weight was measured to the nearest 0.1 Kg at 12 time points during a 3 year period, from birth to third lambing. Number of records per ewe was 11 on average and varied from 7 to 12. Repeated body weight data within animal were analyzed with a linear model using the mixed procedure of SAS . Fixed effects were Socs2-genotype within time point, year of birth and triplet match, whereas the animal was treated as a random effect. Body size was measured in 17 out of the 18 sheep once in October 2013 to obtain fourteen measurement points of height, width and bone lengths as illustrated in A group of 18 female sheep with the three Commercial rams did not belong to any experimental design but were sampled by veterinarians and/or under veterinarian supervision for routine veterinary care and DNA collection. For the experimental animals , breeding conditions were similar to commercial sheep flocks. Blood collection and measurements followed procedures approved by the Regional Ethics Committee on Animal Experimentation, Languedoc-Roussillon (France), under the Agreement 752056/00.S1 TableQTL detection was based on both linkage (LA) and haplotype-based association (GWAS) analyses using the 50K OvineSNP50Beadchip . The 26 ovine autosomes were analyzed. The 95% confidence intervals of the QTL locations were estimated by logarithm of odds drop-off method. The QTL effect (average substitution effect) is expressed in deviation units (SD) for the trait. Significance thresholds: 5% (*) and 1% (**) chromosome wide (*) and 5% genome wide (***).(DOCX)Click here for additional data file.S2 TableSNP were obtained in a trio of rams  after quality and genotype filtering. For each variation, the position on sheep genome v3.0, the dbSNP ssnumber or dbVar reference, the reference base, the alternate sequence, quality index as determined by GATK, GT:AD:GQ:PL values for susceptible son, resistant son and father and annotation as determined by SNPEff are indicated. GT:AD:GQ:PL are GATK SNP calling results and indicate respectively the genotype , Allelic Depth for the reference and the alternate alleles, the genotype quality and the List of Phred-scaled genotype likelihoods for the 0/0, 0/1 and 1/1 genotypes.(ODS)Click here for additional data file.S3 Tablehttp://www.ensembl.org/Ovis_aries/Info/Index.For each primer, the DNA sequence (5’3’), strand, Location (bp) and Comments are indicated. Location of primers are based on the OARv3.1 assembly available on (DOCX)Click here for additional data file.S1 FigM1: Thoracic circumference (at elbow), M2: Thoracic circumference (at hypochondria), M3: Height at wither, M4: Height at sacrum, M5: Height at hock, M6: Height at elbow, M7: Breast width, M8: Width between elbows, M9: Width between ischium, M10: Width between hips, M11: Body length (from base of neck to base of tail), M12: Humerus length, M13: Femur length, M14: Tibia length.(TIF)Click here for additional data file.S1 File(XLSM)Click here for additional data file.


PCA revealed 13 components explaining >75% of the total variance. Nine components yielded statistically significant h2 values ranging from 0.22 to 0.54 (P<0.05). The most heritable component was loaded with 7 phenotypic measures reflecting metabolic and renal dysfunction. A GWAS of this composite phenotype revealed statistically significant associations for 3 adjacent SNPs on chromosome 1p22.2 (P<1x10-8). These SNPs form a 42kb haplotype block and explain 11% of the genetic variance for this renal function phenotype. Replication analysis of the tagging SNP (rs1396315) in an independent US cohort supports the association (P = 0.000011). Blood transcript analysis showed 35 genes were associated with rs1396315 (P<0.05). Gene set enrichment analysis of these genes revealed the most enriched pathway was purine metabolism (P = 0.0015). Overall, our findings provide convincing evidence for a major pleiotropic effect locus on chromosome 1p22.2 influencing risk of renal dysfunction via purine metabolism pathways in the Norfolk Island population. Further studies are now warranted to interrogate the functional relevance of this locus in terms of renal pathology and cardiovascular disease risk.Multiphenotype genome-wide association studies (GWAS) may reveal pleiotropic genes, which would remain undetected using single phenotype analyses. Analysis of large pedigrees offers the added advantage of more accurately assessing trait heritability, which can help prioritise genetically influenced phenotypes for GWAS analysis. In this study we performed a principal component analysis (PCA), heritability  in an independent US population. The findings presented here are of particular interest as they implicate pleiotropic effect loci and newly associated biological pathways underlying cardiovascular disease risk. Cardiovascular diseases (CVD) are highly prevalent conditions and are the leading cause of morbidity and mortality in many developed nations . In geneAPOC1, BRAP and PLCG1 that may confer pleiotropic effects on composite traits associated with CVD [It is well known that risk factor traits for CVD tend to be correlated in populations suggesting the presence of underlying genetic variants that affect multiple different phenotypes, i.e. exert pleiotropic effects. GWAS analysis aimed at multiple CVD phenotypes simultaneously may reveal such genetic loci, which could remain undetected using univariate (single) phenotype analysis methods. For example, a large-scale population-based GWAS of >25,000 Americans revealed previously unrecognised variants within with CVD . These rGWAS analysis of isolated populations have also been successfully utilised to map genes for CVD traits –6. FeatuThe Norfolk Island Health Study (NIHS) is a long running investigation of the genetics of CVD traits in the isolated population of Norfolk Island—situated off the Australian East Coast . The Nor2) analysis to estimate genetic variance of resultant components. This was followed by pedigree-based GWAS of heritable components using SNPs to identify major pleiotropic effect loci for CVD risk phenotypes in this population.This study focused on a well-characterised core pedigree from the Norfolk Island genetic isolate . This coP<0.05). After correction for multiple testing was applied only Component 3 and Component 9 remained significant (P<0.0038). Component 3 was the most heritable (h2 = 0.55) and was loaded with 7 measures related to CVD—% body fat, waist-to-hip ratio, systolic and diastolic blood pressure, creatinine, blood urea nitrogen, and uric acid. The measures most strongly correlated with Component 3 were blood urea nitrogen (0.76), creatinine (0.74) and uric acid (0.61) which are all kidney function markers suggesting that the Component 3 phenotype may represent renal dysfunction. The relationship between all extracted components is displayed in -8).An ‘unsupervised’ PCA of 37 CVD related traits was conducted (trait summaries listed in -7). -11). Analysis of the individual component traits revealed that this peak was entirely associated with total and direct bilirubin, and maps to the well-known bilirubin metabolising gene—UGT1A1 [Following the PCA, we performed GWAS analysis for the 9 nominally significantly (p<0.05) heritable components shown in e—UGT1A1 –18, Fig -9 for rs1396315), as well as a supportive peak below the threshold of study-wide significance. GWAS of each of the 7 traits loaded into Component 3 individually did not show any significant peak at this locus  analysis of the SNPs spanning the Component 3 peak on chr 1p22.2 illustrated that rs1396315 tagged the associated haplotype and could be used as the reference SNP for further analysis . The allZNF326, being ~325kb upstream  an outgroup of the Norfolk Island cohort (n = 375) who are genetically unrelated (independent) to the core pedigree and for whom comparable genotype and phenotype data was available , and b) Due to the difficulties of finding an independent replication cohort with the exact same set of 37 traits with which to replicate the PCA and generate Component 3 scores we had to explore other options for replication. We decided to use a linear modelling approach to attempt to predict individual Component 3 scores using only the 7 most heavily loaded traits for that component from the discovery phase. The linear model as generated in the original Norfolk Island discovery cohort is :2 0.92, p < 2.16x10-16). The model (fit1) was used to predict Component 3 scores in the n = 375 unrelated Norfolk Island outgroup using the 7 traits of interest. The regression equation required slight modification for the US replication cohort because uric acid measures were missing. To do this we tested the effects of dropping uric acid on the overall linear model and observed a minimal reduction in predictive value , suggesting that the updated model should still yield accurate Component 3 score prediction using only 6 trait measures. Predicted Component 3 scores were then incorporated into their respective replication data sets for association analyses to be performed.The values from this model showed a very strong correlation with the original Component 3 scores and provided excellent predictive value  associations with the rs1396315 and available gene expression data. We interrogated 1712 genetically heritable expression transcripts previously identified in the Norfolk Island population and measured in the same individuals [PDE6D, GART, and NME2. This finding is interesting as previous work in Norfolk Island reported on a set of eQTL associated genes implicated with CVD-risk traits which also resided with the purine metabolism pathway [Due to the lack of functional genes in close proximity to the identified region on chromosome 1p22.2 we explored the potential of Many heritable CVD risk traits tend to be correlated suggesting the presence of underlying genes acting pleiotropically to influence multiple traits. GWASs aimed at composite CVD traits may reveal such genes, which would remain undetected using conventional single phenotype analyses. Analysis of large pedigrees from isolated populations can offer advantages for mapping disease genes including the ability to estimate heritability for disease prioritisation and the possible presence of larger effect loci. Here we conducted a study to search for pleiotropic effect loci associated with CVD risk in the well characterized genetic isolate of Norfolk Island. We performed PCA and heritability testing and identified a heritable component trait strongly loaded with indices of metabolism and kidney function. We subsequently performed pedigree-based GWAS to identify a risk haplotype on chromosome 1p22.2 that explains 11% of the variance in this composite trait. This represents a very large effect exerted by a single locus and may reflect the fundamental importance of pleiotropic genes on this phenotype in this genetic isolate. Because the composite phenotype was not able to be standardised for comparison in external populations and given the unique nature of the Norfolk Island pedigree we could not perform conventional replication analysis, instead using a proxy prediction based approach. Replication analysis in an independent outgroup from Norfolk Island supported the association of rs1396315 and Component 3 (P = 0.048). We also investigated trait/rs1396315 association in a completely independent US cohort and observed significant association between the predicted Component 3 score and rs1396315 (P = 0.000011). Additionally, the clinical relevance of the Component 3 score was explored by comparison with the well-known Framingham CVD risk score  which shet al., reported association of SNPs in this region to metabolic syndrome (including obesity) in 2554 Indian men [et al., reported association to liver enzyme indices in a population of 133,653 Europeans and Asians [et al., reported association of chromosome 1p22.2 locus to coronary artery disease in 984 whites [A number of GWAS studies have reported association of loci at chromosome 1p22.2 with CVD risk traits. Zabaneth dian men . Beck et4 whites . Additio4 whites  and live4 whites . Interes4 whites .ZNF326 (zinc finger protein) and BARHL2 (BarH-like homeobox 2)  . We are trans-eQTLs explored in our analyses, it is interesting to note that there are several, such as FOXA1, that are directly associated with metabolism in tissues such as the liver. FOXA1 is of particular interest as its family members have been shown to play an important role in the differentiation of the pancreas and liver and regulate metabolism in mice [Following this line of thought, we investigated available annotation, including that from several pertinent UCSC tracks: evofold RNA loop prediction; ENCODE transcription factor binding sites from ChIP-SEQ, and tissue specific RRBS methylation information from liver and kidney (both relevant tissue types for the identified phenotype). This feature based annotation revealed important structures and control regions within, or in close proximity to, the association peak. This includes tissue specific epigenetic markers from kidney and liver, as well as several transcription factor binding sites . While n in mice . Additiotrans-acting mechanisms we conducted an association analysis between the top ranking Component 3 SNPs and heritable gene expression transcripts measured in the same individuals [NME1 and PAPPS1 in the purine pathway in the Norfolk Island cohort [GART, NME2 and PDE6D were found to be associated with rs1396315. Whereas NME1 encodes for the A isoform, NME2 encodes for the B isoform of nucleoside diphosphate kinase (NDPK) which maintains the balance of nucleosides and high energy nucleotides (ATP/GTP) via the reversible transfer of phosphate. Interestingly, GART plays a central role in the de novo synthesis of nucleotides where it catalyses three of the ten steps involved in the conversion of phosphoribosyl pyrophosphate into inosine monophosphate [GART and NME2 would have the potential to have fundamentally important effects on nucleotide metabolism and homeostasis.To investigate potential ividuals . Pathwayd cohort . In the hosphate . GeneticPDE6D was also identified in relation to the purine pathway. PDE6D encodes phospohdiesterase (PDE) 6δ subunit of the enzyme PDE6 that specifically hydrolyzes cyclic GMP [GART also plays a role in ocular development [Modifications in the purine metabolic pathway are linked with a number of diseases including kidney-related diseases such as hyperuricaemia and gout . Combineclic GMP . PDE6 isclic GMP . These fclic GMP . Intereselopment .Overall, our findings provide convincing evidence for a significant pleiotropic effect locus at chromosome 1p22.2 associated with a composite trait representing metabolic and/or renal dysfunction. This study illustrates the value of the phenomics approach in large pedigrees and genetic isolates for identifying underlying genetic regulators of inter-correlated disease traits that would go undetected using conventional single phenotype approaches. Further studies are now warranted to better understand the importance of the associations in the broader population and to more comprehensively interrogate the functional relevance of this locus in terms of renal pathology and CVD risk more generally.et al., 2008 [et al., 2010 [The Norfolk Island Health Study (NIHS) is well established ,14,22,38l., 2008 , and hasl., 2010 . The updThe replication cohort in Atlanta is the Center for Health Discovery and Well Being (CHDWB) cohort of the Emory-Georgia Tech Predictive Health Institute . Whole gBaseline statistics for the core-pedigree (discovery cohort) were calculated for 37 CVD-related measures including all the biochemical measures as well as body size and composition traits . Phenoty2 (heritability). Both the individual and component traits were screened for covariant effects of age|sex interactions. All traits which SOLAR calculated as having a high kurtosis were log transformed before calculating heritability estimates.The genetic analysis program SOLAR  was util−5. After quality control, 590,603 SNPs were used for association analyses. Genotype data was then exported from PLINK and imported into the CRAN package GenABEL [EDTA anticoagulated venous blood samples were collected from all participants. Genomic DNA was extracted from blood buffy coats using standard phenol-chloroform procedures (Qiagen). Genome-wide genotyping was carried out using the Illumina Human610-Quad v1.0 beadchip. Raw data from Illumina idat files was SNP genotyped in R using the CRLMM package . Genotyp GenABEL  for anal2). The polygenic model takes into account the fact that potentially thousands of genetic variants contribute to a trait phenotype. The function implemented in GenABEL maximises the likelihood of the data under the polygenic model with covariates and reports twice negative maximum likelihood estimates and the inverse of the variance-covariance matrix at the point of maximum likelihood. GenABEL also estimates residuals of the trait and the inverse of the variance-covariance matrix for further use in association analysis with the mmscore function. The polygenic model within GenABEL implements variance components defined to account for linked major gene effects, background polygenic effects, and environmental effects. Age and sex were included as covariates, as well as genetic structure which was assessed by principal components analysis using the KING [Meff adjustment (P = 1.84x10−7). Haplotype/LD testing was conducted in Haploview 4.2 [A pedigree based GWAS analysis of the 9 heritable components was batched using custom R scripts and the package GenABEL, using the polygenic model with age and sex interactions, as well as genetic structure . Specifthe KING  program.the KING . The stuview 4.2 . Exploraview 4.2  within RIdeally replication would be conducted on the exact same set of 37 clinical traits, and involve generating component scores using the original coefficients. However, it is extremely difficult to obtain a separate population with the exact same phenotype information, making this option not possible. We ultimately have performed a linear model approach, using the 7 most highly loaded Component 3 traits to predict the score in our replication cohorts, the unrelated Norfolk Island outgroup (n = 375), and the US CHDWB replication cohort (n = 738). Details of the modelling are given in results, but overall the model was constructed from the discovery cohort  using the lm (linear models) base R function:Predictions were then made using the predict function in base R as follows:Association testing of Component 3 (predicted) and rs1396315 in the replication cohorts was performed using linear modelling, with the predicted component score as the outcome and the genotype as the independent variable. The genotype was coded in an additive fashion: 0 (CC); 1 (CT), 2 (TT).Meff adjusted threshold (1.84x10-7) were extracted and used as a basis for an eQTL-centric association analysis. This analysis involved Component 3 phenotype being run in a GWAS-based approach in GenABEL [-2.The generation and list of eQTLs in the Norfolk Island cohort has previously described in detail . Blood f GenABEL  using thS1 Table(PDF)Click here for additional data file.S2 Table(PDF)Click here for additional data file.S3 Table(PDF)Click here for additional data file.S4 Table(PDF)Click here for additional data file.S1 FigMeff adjusted threshold (p<1.84x10-7).The red dotted line indicated the study-wide (PDF)Click here for additional data file.S2 Fig(PDF)Click here for additional data file.S3 Fig(PDF)Click here for additional data file.


Musa sp.) is a vegetatively propagated, low fertility, potentially hybrid and polyploid crop. These qualities make the breeding and targeted genetic improvement of this crop a difficult and long process. The Genome-Wide Association Study (GWAS) approach is becoming widely used in crop plants and has proven efficient to detecting candidate genes for traits of interest, especially in cereals. GWAS has not been applied yet to a vegetatively propagated crop. However, successful GWAS in banana would considerably help unravel the genomic basis of traits of interest and therefore speed up this crop improvement. We present here a dedicated panel of 105 accessions of banana, freely available upon request, and their corresponding GBS data. A set of 5,544 highly reliable markers revealed high levels of admixture in most accessions, except for a subset of 33 individuals from Papua. A GWAS on the seedless phenotype was then successfully applied to the panel. By applying the Mixed Linear Model corrected for both kinship and structure as implemented in TASSEL, we detected 13 candidate genomic regions in which we found a number of genes potentially linked with the seedless phenotype . An additional GWAS performed on the unstructured Papuan subset composed of 33 accessions confirmed six of these regions as candidate. Out of both sets of analyses, one strong candidate gene for female sterility, a putative orthologous gene to Histidine Kinase CKI1, was identified. The results presented here confirmed the feasibility and potential of GWAS when applied to small sets of banana accessions, at least for traits underpinned by a few loci. As phenotyping in banana is extremely space and time-consuming, this latest finding is of particular importance in the context of banana improvement.Banana ( Musa spp.), including plantain, is a starchy crop cultivated in more than 130 countries. In 2012, the total production of banana in the world reached nearly 140 million tonnes, out of which close to 14% was deemed for export ,6],,[6],5],,5],M. acuminata banksii originating in Papua, the main domestication syndrome trait for this crop is the production of seedless and fleshy fruits  ,,,58]. Fo. Fo58]. Given the LD pattern of the panel, the genome coverage of the selected SNPs is not optimal and we may have missed markers truly associated to our phenotype. In addition, and given the likely occurrence of long-distance LD, the possibility of detecting regions distantly associated with causal SNPs cannot be ignored even though we did not identify such cases. We aimed to present the panel and show its potential to successfully perform GWAS using easy-to-handle analysis tools, notably TASSEL, one of the most used software to perform association studies in crop plants. Given that the panel i) was suspected to exhibit complex and unbalanced levels of genomic structure , ii) is A. thaliana successfully detected known loci linked to the different traits studied ,[51,[51Arabibidopsis . It was bidopsis . In suchbidopsis .Finally, the association studies were performed on a general phenotype that we named seedlessness. Seedlessness, induced by parthenocarpy combined with sterility, is the main trait associated with the domestication syndrome in banana. However, it is more than likely that domestication resulted in the concomitant selection of other traits, such as for example the propensity for vegetative propagation, which may have been unintentionally considered in this association study. The resolution of the genomic basis of banana domestication will thus require further investigation, including both genomics and phenomics approaches. However, we may have performed here a substantial first step in that direction. In addition, the absence of seeds in edible bananas and the need to incorporate new traits in the cultivated genepools result in the wide use of wild accessions as female parents in the crosses performed by breeders. This practice results usually in various levels of seed production within the progeny obtained. If validated, the potential role of CKl1 in female sterility would be of major importance for marker-assisted selection.The main goal of this study was to present a set of banana accessions allowing performing GWAS on this vegetatively propagated, weakly fertile, potentially hybrid and polyploid crop species. We used this panel to perform a GWAS on a qualitative trait as a proof of concept for feasibility in banana. The set of accessions presented here appeared successful in detecting, through GWAS, interesting candidate genes for a phenotype that is of prime importance for banana breeding, i.e. the capacity to set unseeded fruits without the need of prior pollination. We assume thus that GWAS in banana is not only a feasible but also a promising approach, at least for traits underpinned by few loci, conditional to stringency in the various steps of the association process.M. acuminata clones available, but not only. A number of constraints weigh on banana phenotyping, which is far more difficult to perform than in cereals. Even though early mass screening for disease resistance are being developed under greenhouse conditions [http://www.crop-diversity.org/mgis/content/gwas-panel-gbs) with links to all SNP datasets that are publically available and visualizable. Even though GWAS for complex traits underlined by genes with moderate or small effects has not been tested yet with this panel, it is likely that these elements will allow a broader use of GWAS in banana and will speed-up the process of discovering genes coding for traits of interest at the level of the banana community [The selection of a small number of accessions for this GWAS panel was largely due to the relatively small number of different diploid nditions , banana ommunity .Finally, Brachi et al  proposedS1 FigM. acuminata genetic background genotyped with 498 DArT markers. b. NJ tree based on the dissimilarity matrix calculated following Sokal and Michener [M. acuminata genetic background genotyped with 498 DArT markers.a.Frequency histogram of the pairwise genetic distances calculated following the IAM for 224 diploid accessions with pure Michener  index fo(PDF)Click here for additional data file.S2 Fighttp://sniplay.cirad.fr) [Graph were generated using the SNP density tool provided by SNIplay (irad.fr) .(PDF)Click here for additional data file.S3 Fig(PDF)Click here for additional data file.S1 Table(PDF)Click here for additional data file.S2 Tablehttp://www.greenphyl.org/cgi-bin/seq2families.cgi) [Classification of the gene families was performed using seq2families provided by GreenPhyl (ies.cgi) .(XLSX)Click here for additional data file.


SETX statistically significant by a genome-wide gene-based threshold . The function of SETX in hypertension may be worthy of further investigation.Advances in next-generation sequencing technology have made it possible to comprehensively interrogate the entire spectrum of genomic variations including rare variants. They may help capture the remaining genetic heritability which has not been fully explained by previous genome-wide association studies. Here we performed a gene-based genome-wide scan to identify hypertension susceptibility loci in analysis of a whole genome sequencing cohort of 103 unrelated individuals. We found that collapsing singletons may boost signals for associating rare variants and identified  Assuming the "common disease-common variant" hypothesis, genome-wide association studies (GWAS) have successfully identified hundreds of common variants that contribute to human traits and diseases . These cRecent advances in next-generation sequencing (NGS) technology have made it technically and economically feasible to capture the full spectrum of genomic variation. NGS provides a powerful tool for systematic exploration of common and rare variants in the entire genome, even in large population-scale studies . HoweverThe Genetic Analysis Workshop 18 (GAW18) provided a whole genome sequencing data set for a hypertension cohort of 483 individuals. These samples were sequenced by Complete Genomics with approximately 60× coverage, and odd-numbered autosomes data were made available for analysis. After quality control, 464 individuals and 24 million single-nucleotide polymorphisms (SNPs) remained. Of those SNPs, more than 51% had MAFs <1%, which was the focus of our analysis in this article. The longitudinal hypertension phenotypes were provided for up to 4 time points. Because our analysis was focused on binary traits, we treated individuals diagnosed with hypertension in any of the 4 times as cases. We extracted 103 genetically unrelated individuals with both phenotype data and sequencing data. We found 39 unaffected controls and 64 cases affected by hypertension.The variants were stored in VCF files. We preprocessed them as follows. To end up with rare variants, we filtered out SNPs that were present in dbSNP132 or MAFs >1%. We also filtered out SNPs with a genotype missing rate >5%. The remaining missing genotypes were resampled from nonmissing individuals.Next, we grouped variants into sets based on RefSeq gene annotations , requiri2 Ttest. For these 3 tests, we used permutation to evaluate association significance. Because permutation was computationally expensive, we utilized a 2-step strategy in searching and testing candidate loci. Specifically, we first used 1000 permutations, from which we can identify candidates with estimated p value <0.001. Then for these candidates we conducted 106 permutations so as to know if any loci were significant at a genome-wide gene-based threshold  using a Bonferroni assumption.We employed 3 recently published rare association tests, qMSAT , C-alphahttp://www.r-project.org). The qMSAT, C-alpha, and CMC identified 10, 6, and 7 genes with an estimated p value <0.001, respectively . Although it contains 26 exons, the total length of coding regions is only 8,034, suggesting that SETX includes large intronic regions. To pinpoint causal regions, we divided the 380 variants of SETX into 3 groups based on its functional annotations. Specifically, we applied ANNOVAR  , which was consistent with the results from 3 rare variant association tests. Second, we checked each rare variant and singleton individually by performing the same test. It turned out that none of them were significant, when the minimum p value was merely 0.14. This demonstrated that the significance of SETX was very unlikely a result of technical artifact, such as systematic sequencing error or imputation bias, because the new feature was a combination of hundreds of singletons. It also highlighted that collapsing singletons may increase power when studying association of rare variants using a relatively small sample size. Third, we took a closer examination of allele frequencies of the 380 rare variants located in SETX. Of the rare variants, 92 could be found in the 1000 Genomes Project . We found their allele frequencies in general population were extremely low (mean frequency = 0.0004 for 92 rare variants), indicating that these variants were so rare that they may collectively have a composite effect of OR = 8.8 while missed in previous studies.We then sought to elucidate why and where the signal came from. To this end, several in-depth analyses for SETX were performed. First, the Fisher's Exact test was conducted on the super feature we created by collapsing singletons. We found that, by collapsing all the 135 singletons on SETX, it achieved a very significant  CI = 3.1, 27.43   and an aWe performed 3 rare-variant association tests for the analysis of a whole genome sequencing data set to identify susceptibility genes in hypertension. We grouped and collapsed rare variants in a gene-based manner for 2 reasons: (a) the deleteriousness of variants could come from protein-coding sequence changes or noncoding intronic regions that contain regulatory elements. (b) Based on the previous simulation study , the powThe analysis can be extended and improved in several ways. First, it was shown in qMSAT  that incThe authors declare that they have no competing interests.WW conducted statistical analyses; WW and ZW drafted the manuscript. All authors read and approved the final manuscript.


Drosophila have identified conserved signaling pathways controlling size; however, how these pathways control phenotypic diversity is unclear. We performed GWA of size traits using the Drosophila Genetic Reference Panel of inbred, sequenced lines. We find that the top associated variants differ between traits and sexes; do not map to canonical growth pathway genes, but can be linked to these by epistasis analysis; and are enriched for genes and putative enhancers. Performing GWA on well-studied developmental traits under controlled conditions expands our understanding of developmental processes underlying phenotypic diversity.Organismal size depends on the interplay between genetic and environmental factors. Genome-wide association (GWA) analyses in humans have implied many genes in the control of height but suffer from the inability to control the environment. Genetic analyses in  Drosophila have elucidated conserved signaling pathways and environmental factors that together control organismal size. In humans, hundreds of genes are associated with height variation, but these associations have not been performed in a controlled environment. As a result we are still lacking an understanding of the mechanisms creating size variability within a species. Here, under carefully controlled environmental conditions, we identify naturally occurring genetic variants that are associated with size diversity in Drosophila. We identify a cluster of associations close to the kek1 locus, a well-characterized growth regulator, but otherwise find that most variants are located in or close to genes that do not belong to the conserved pathways but may interact with these in a biological network. We validate 33 novel growth regulatory genes that participate in diverse cellular processes, most notably cellular metabolism and cell polarity. This study is the first genome-wide association analysis of natural variants underlying size in Drosophila and our results complement the knowledge we have accumulated on this trait from mutational studies of single genes.Genetic studies in  Drosophila have revealed core molecular mechanisms governing growth control and have shed light on the role of humoral factors and the environment on adult size [How animals control and coordinate growth among tissues is a fundamental question in developmental biology. A detailed mechanistic but global understanding of the processes taking place during normal physiological development is furthermore relevant for understanding pathological growth in cancers. Classical genetic studies in ult size –4. Two mult size –7. Howevult size –10, and ult size , 12. TheDrosophila melanogaster show abundant genetic variation for size, with heritabilities approaching 50% [D. melanogaster since there are parallel clines in body size and correlated traits clines across different continents [3R and 2R are, respectively, associated with body size and wing area [3R loci seem to be located within the polymorphic c; interestingly, the majority of the hromosomal inversion In(3R)Payne [In(3R)Payne include hsr-omega, the microsatellite loci DMU25686 and AC008193, and genes in the Insulin signaling pathway  [In(2L)t is associated with a body size cline across several continents; genes in the IIS/TOR pathway  are located in the inversion region and Pi3K21B and Idgfs 1–3 are located immediately proximal to it [smp-30 (Dca) and InR have been causally associated with body weight [Artificial selection experiments have revealed that naturally occurring populations of hing 50% . Usuallyhing 50% . Selectihing 50% . Body sintinents , 17. Locing area , 17; int3R)Payne , 19 Cand1, Akt1) , 21. Simy weight , 23. Recy weight , indicatArabidopsis [Drosophila [Drosophila, but GWA studies of height have revealed that many loci with small effect contribute to size variation in human populations [The advent of next-generation sequencing technology has enabled the rapid and relatively cheap acquisition of complete genome sequences, and thereby the generation of very dense genotype information that enables genome-wide association (GWA) mapping with a much higher resolution than previously possible. GWA studies aim to link variation in quantitative traits to underlying genetic loci in populations of unrelated individuals genome-wide , 26. GWAbidopsis , 30, Droosophila –33 and mosophila  as well osophila , 36 and osophila –41, wherulations ,10,42,43ulations , 44. Altulations , 45–47.Drosophila Genetic Reference Panel (DGRP) [Drosophila widely applicable. The DGRP lines harbor the substantial natural genetic variation present in the original wild population and show copious phenotypic variation for all traits assayed to date [In contrast to human studies, GWA studies in model organisms benefit from the feasibility of functional validation, more stringent environmental control and, when using inbred strains, the possibility of measuring many genetically identical individuals to obtain an accurate estimate of the phenotype for a given trait and genotype. All three factors can substantially improve the power of a GWA analysis. The establishment of the inbred, sequenced lines of the l (DGRP) , 49 has  to date , 50, 51.Drosophila. We find pervasive trait and sex-specificity of top variants, validate a substantial number of novel growth regulators, and extend our knowledge of the genetic control of size beyond existing growth regulatory networks.Here, we used the DGRP to perform single- and two-locus associations for size-related developmental traits in We cultured 143 DGRP lines under conditions we had previously shown to reduce environmental influences on size , Fig 1A CS = a*Xb (where X = IOD or TL) varied substantially between lines, from near independence (b = 0) to hyperallometry  (rMF) and phenotypic correlation , and interocular distance , respectively . IOD shotry b>1) . We obsetry b>1)  Tables, relation . 15% of relation . Though Drosophila, we performed single marker GWA analyses for 1,319,937 SNPs for a wing disc derived (CS) and an eye disc derived (IOD) size measure using Fast-LMM [In(2L)t and In(3R)Mo, were correlated with both CS and IOD and we addressed their effect on size by modeling their presence in the homozygous state (IC and IODIC. In(3R)P, which is known to be correlated with Drosophila size [To identify common loci contributing to size variation in Fast-LMM . This asus state , yieldinp-value threshold of 3.8x10-08. However, the significance of these six SNPs dropped below the genome-wide level when we applied GWAS on rank-normalized IOD, which was probably due to an outlier line (>4SD) in the minor allele class of all six SNPs. Overall, the p-values between normalized and non-normalized GWAS showed good correlation and the locus clearly segregates with size, as 75% of the major allele class lines had a smaller IOD than the lines of the minor allele class (2L (2L: 12’805’398–12’806’812), 12-13kb upstream of the gene encoding the EGFR pathway regulator kek1  and several blocks of higher LD are formed across it t (2L: 2’225’744–13’154’180) [CR43818, was located in the 20kb region upstream of kek1, and the region was spanned by the intron of CG9932, a poorly characterized gene that interacts genetically with Bx and Chi during wing development [In(2L)t.Only for one trait  did we observed significantly associated SNPs when applying a stringent Bonferroni corrected le class .The six tor kek1 . Three mIOD Figs  and S3.  it Figs  and S3, elopment . Clearlyp-values below 10−05  of SNPs from the GWA analysis with uncorrected trait values remained candidates in the GWA analysis with corrected phenotypes. Somewhat surprisingly, despite the significant genetic correlation between CS and IOD, no candidate SNPs were shared between these phenotypes . In bothp<0.001, hypergeometric test) (IC (FDR corrected p = 0.08) [ex and wts.Nominally associated variants predominantly mapped to intergenic regions, but were nevertheless enriched in gene regions (ic test) , demonst = 0.08) . Given tp-value for each gene considering all SNPs within a gene while correcting for gene length and linkage disequilibrium between SNPs. None of the genes reached genome-wide significance (p<3.75x10-06)  across known growth genes might be significant. To this end we determined gene-based statistics using the sum of chi-squares VEGAS method , which c5x10-06) . The oveIC and rCS in both sexes) for functional validation by tissue-specific RNAi. A total of 64% to 79% of tested genes had significant effects on wing area  and Wilcoxon test p-values showed different distributions between candidate and random knockdowns  of candidates identified by each of our six wing size GWAS , commonly known growth genes from the IIS/TOR, EGFR and Hippo pathways, and growth regulators identified in screens by our group. This list is not comprehensive but should serve as a rough framework for the most relevant growth pathways. Overall, 15 interactions reached Bonferroni-corrected significance (p<7.9x10-13), but we observed none of our GWA candidates among the significant epistasis partners. Generally, more interactions reached genome wide significance in males than in females. The most significant interaction  occurred between mask, a positive regulator of JAK/STAT signaling [tutl, a JAK/STAT target gene during optic lobe development [nkd, a downstream target of Dpp [Ptp99A , which has been shown to interact with InR and the Ras signaling pathway [mask and tutl, and PtP99A and nkd shared more DroID interactors than 95% of all possible pairs of the 32 genes involved in the significant interactions. Due to their already known growth-related functions we consider the interactions between these genes as prime candidates for future functional validation.To place novel genes within the network of known growth pathways, we next performed tests for two-locus associations  to CSIC,cal SNPs . This geignaling  and tutlelopment  Fig 5)..IC, IODIt of Dpp , 61, and pathway , 63. Fur pathway , 65 maskp<10−09. Counting only those interactions where the interacting SNP lay in or within 1kb of a gene (p<0.001) [p-value of 0.46 and 0.22, respectively). We next asked whether the candidate gene sets identified by normal GWAS and the epistasis approach were nevertheless biologically related to each other. To this end we used the STRING database [p<<0.001). Analyzing pairwise interactions may thus help to place genes into pre-established networks.To investigate whether our GWA candidates or genes from the ‘previously known’ catalog would be enriched further down the list, we lowered the stringency for reporting interactions to a discovery threshold of p<0.001) , which sdatabase , which rp<0.01, hypergeometric test) of SNPs lying in regions with H3K4Me1 or H3K27Ac, epigenetic signatures of active enhancers  of the Hippo pathway regulator ex narrowly missed the reporting threshold . However, its genomic location suggests this variant could affect a novel regulatory region for this gene. The region surrounding it was annotated with the enhancer methylation signatures H3K4Me1 and H3K27Ac and had assigned state 4 of the 9 state chromatin model suggestive of a strong enhancer [D. melanogaster ex gene showed high similarity to ~3kb regions slightly more upstream of expanded orthologs in the genomes of D. sechellia, D. yakuba and D. erecta  or via noncoding RNAs, which have been shown to regulate many biological functions including cellular processes underlying growth [With the exception of the osophila ; and, sug growth –75.There are few nonsense and missense SNPs among our candidates ; these variants are prime contenders for effects on protein function. However, confirming such effects requires testing the SNP in an isogenic background. Knockdown of most candidate genes resulted in a small change in median wing size (-19.4% to 10.1%), indicating a redundant or mildly growth enhancing or suppressing role in this tissue, which may explain why they were not discovered by classical mutagenesis screens. However, larger effects might be observed upon ubiquitous knockout, knockdown or overexpression.eIF2D (ligatin) and SNF4agamma [Lar with InR. Lar can phosphorylate InR [Epistasis analysis revealed 15 loci showing Bonferroni-significant interactions with SNPs in previously known growth genes, demonstrating the usefulness of this approach for extending existing biological networks. In addition to the interactions described in the main text, we note there is an interesting interaction between F4agamma . Furtherlate InR , so polylate InR , undersckek1 is the only locus where a larger genome region shows association. The minor allele frequency of this haplotype was between 4.7% and 6.2% (present in 7–10 lines). We explain the large effect of this locus by the fact that most lines with the minor alleles of the significant SNPs have an IOD that exceeds the 75th percentile of the IOD distribution in lines with the major allele. Obviously the effect may be overestimated due to relatively few lines having the minor allele, and due to effects of the genetic background. Nevertheless, the region contains some prime contenders for an effect on size: the SNPs lie in a region that could serve as a regulatory element for kek1 or the poorly characterized CG9932, which has been implied in wing disc development in another study [In(2L)t, which itself shows association to size, and the observed effect could be due to this inversion. None of the lines with the minor allele haplotype had any copy of the inversion, which agrees with the observation that In(2L)t homozygous flies are relatively smaller than lines without the arrangement. On the other hand, only two lines in our dataset were homozygous for In(2L)t (DGRP_350 and DGRP_358), leaving many of the lines with the major allele with none or only one copy (nine lines) of the inversion, and we corrected for its presence, so we would not expect this to cause the association.The region upstream of er study . Further) and 15 were associated with nutritional variation in Drosophila [et al. [et al. used a population from California, which likely has a different allele composition from our North Carolina population. A minor factor may be the rather coarse and general size measure used by Turner et al. Sieving selected for generally bigger flies, with no distinction between flies with bigger wings, heads, thoraxes, or legs that obstructed passage through the sieve. As growth of individual body parts is controlled by both systemic and organ intrinsic factors [InR or smp-30 [smp-30 allele was identified in a population of different geographic origin and could thus simply not be present in the DGRP lines. The InR allele is an indel, whereas we only analyzed single nucleotide polymorphisms in this study. Although both genes likely contain further polymorphisms that are present in the DGRP, their effects may be far smaller and thus not detectable given the restricted size of the DGRP. In terms of chromosomal loci, we do identify SNPs on 3R, several of them located in the region spanned by In(3R)P  [In(3R)P correlating with size, However, the inversion was present in too few lines of our dataset to reliably estimate its effect in a model.Apart from expected processes like signaling, transcription, translation and morphogenesis, we validated genes involved in transmembrane transport, planar cell polarity (PCP), metabolism and immunity. A total of 24 single marker or two way interaction candidate genes from our GWA analyses were discovered to be enhancers or suppressors of major growth pathways in another study  S11 Tab and 15 wosophila , supportr smp-30 , 24. The569,732) . FurtherDrosophila. The yeast ortholog of Mid1 , is a stretch activated Ca2+ channel with a role in the polarized growth of mating projections [2+. The human ortholog of another candidate, the transmembrane channel Trpm , showed association with anthropometric traits during puberty, indicating a role during the postnatal growth phase. The mucin Muc68Ca, identified in the top 20 of the gene-based association to rCSF, showed one of the largest knockdown effects . Mucins form a protective layer around vital organs, and the expression pattern of Muc68Ca in the larval midgut concurs with a putative effect on growth via the control of intestinal integrity [Most of the loci we validated have not been previously linked with growth in jections , 80). Asntegrity .Lar , aPKC, the Fz target Kermit [kermit was a candidate interactor of EGFR, which acts in a combinatorial manner with Fz signaling in PCP [A dual role in PCP, the establishment of cell polarity within a plane in an epithelium, and growth control has been shown for many genes, which regulate these two processes via distinct but coordinated downstream cascades , 83. Lart Kermit  and the t Kermit . Interesg in PCP , providiCep89, a gene involved in mitochondrial metabolism and growth in Drosophila and humans [Cep89 and other validated candidates with putative roles in metabolism, e.g. CG3011, CG6084 and Fbp2, whose human ortholog has been linked to growth defects and cancer [e.g. Metabolic genes are prime candidates for improving our understanding of growth, which depends on the amount of energy and precursors available for biosynthesis, and thus to metabolic coordination. The recent findings that the growth and PCP regulator Fat can couple growth and metabolism and mitochondrial proteins can causally affect growth pathway activity  underscod humans  was assoOf the top 100 SNPs for each trait only 25% - 43% are shared between the sexes, a surprisingly small overlap given the high genetic and phenotypic correlations between sexes. In some cases the SNPs still lie in the same gene, implying that this gene differentially affects size in both sexes, but the responsible SNP is different. In other cases, we only detect associated SNPs for a gene in one sex. Here, the gene may affect size in both sexes but genetic variation in this gene affects size differentially in only one sex. As we have a low powered study this is only a hint and these results need to be further analyzed in a bigger population or by allele replacements using e.g. the CRISPR/Cas9 system to be corroborated. Unfortunately we cannot conclude anything about sex-specificity of our variants from the knockdown results. A knockdown is a very different perturbation from the effects of, for example, a regulatory variant. The knockdowns are performed in a different background than the one the association was discovered in, and they have much larger effect on the levels of a gene than a regulatory variant. So even though RNAi on a gene might show effects in males and females it does not exclude that different alleles of a SNP in this gene only affect wing size differentially in one sex.dsx had the lowest p-value in the female relative wing size GWAS but had a smaller effect size in males. Dsx is a transcription factor with sex-specific isoforms, and has many targets with sex- and tissue specific effects [Interestingly, an intronic SNP in the sex determination gene  effects . We alsoet al. observed males with more elongated wings but also smaller males had the highest mating successes [Considering that males use their wings to produce a courtship song that is instrumental for mating success, it may well be possible that selection pressure is different for male and female wing size or wing size in relation to other body parts. Indeed, the selection response for wing length seems to be more constrained in males . In our uccesses . These sDrosophila, where many genes and pathways affecting growth have been documented by mutational analyses. However, such screens are far from saturation and do not scale well to investigating effects of combinations of mutations. Here we took advantage of naturally occurring, multifactorial perturbations genome-wide to identify novel genes affecting growth and to place them in genetic interaction networks. Rather than deepening our understanding of growth control, the identification of ever more growth regulators raises new questions about how all these loci interact to govern growth. The challenge for the future will be to shift our focus from studying genes in isolation towards investigating them in the context of developmental networks, and to assess the effects of network perturbations on intermediate molecular phenotypes of transcript, protein and metabolite levels.Growth control has been well studied, particularly in Fly food was prepared according to the following recipe: 100 g fresh yeast, 55 g cornmeal, 10 g wheat flour, 75 g sugar, 8 g bacto-agar and 1 liter tap water. Experiments were performed with 149 of the DGRP lines. RNAi lines used are listed in 1, five males and five females were put together in duplicate vials and discarded after seven days of egg laying. From the F2, thirty males and thirty females were mated in a laying cage with an apple juice agar plate plus a yeast drop as food source and allowed to acclimatize for 24 hours. A fresh plate of apple juice agar plus yeast drop was then applied and flies were left to lay eggs for another 24 hours. From this plate, F3 L1 larvae were picked with forceps and distributed into three replicate vials, with 40 larvae per vial. The food surface in the vials was scratched and 100μl of ddH2O added prior to larvae transfer. The adult F3 flies were pooled from the three vials and frozen at -20°C approximately 1–2 days after eclosion. The whole experiment was performed in a dedicated incubator  with a 12-hour day-night cycle, constant humidity of 65–68% and constant temperature of 25.5°C +/- 1°C. Vials were shuffled every two days during the first and second round of mating but left at a fixed position in the incubator for the duration of the development of the F3 generation.Lines were set up in duplicate vials, with five males and five females per vial. After seven days, the parental flies were removed. From the F1 matings, different food batches had to be used due to different developmental timing of the lines. F2 matings were set up using the same batch of apple agar plates and yeast for all lines. F3 larvae were distributed on four different food batches and the batch number was recorded for each line.For the parental generation, lines were all set up on the same day on the same food batch. For the FThe control experiment  was perffemales, CSmales, IODmales) and 24 for IODfemales; exact numbers are given in Depending on the number of flies available, between five and twenty-five flies per sex and line were measured for the dataset . PCA was performed on data of individual flies using the package FactoMineR. Allometric coefficients (b) were determined for each line and sex from the model log(y) = log(a) + b * log(x), where y = CS and x = IOD or TL, using the lm function in the stats package. 95% confidence intervals for the parameter b were computed using the confint function in the stats package. The total phenotypic variance in the control experiment was partitioned using the mixed model Y = S + L + SxL + R(L) + ε, where S is the fixed effect of sex, L is the random effect of line (genotype), SxL is the random effect of line by sex interaction, R is the random effect of replicate and ε is the within line variance. The brackets represent that replicate is nested within line. The total phenotypic variance in the dataset was partitioned using the mixed model Y = S + L(F) + SxL(F) + F + ε, where S is the fixed effect of sex, L is the random effect of line (genotype), SxL is the random effect of line by sex interaction, F is the random effect of food batch and ε is the within line variance. The random effects of line and line by sex are nested within food batch, as each line was raised only on one of the four food batches. Models of this form were fitted using the lmer function in the lme4 package in R. We also ran reduced models separately for males and females. The rand function in the lmerTest package was used to assess significance of the random effects terms in the dataset.All analyses were performed in R Studio using the R statistical language version 2.15 (σ2P) was calculated as σ2i / σ2P where σ2i represents any of σ2L, σ2LxS, σ2F,σ2R, σ2E, and σ2P = σ2L + σ2LxS + σ2C + σ2E. σ2C stands for σ2R in the control dataset and for σ2F in the analysis of the GWAS dataset. σ2L = variance due to genotype, σ2LxS = variance due to genotype by sex interactions, σ2F = variance due to food, σ2R = variance due to replicate and σ2E = residual (intra-line) variance. The broad sense heritability for each trait was estimated asRelative contributions of the variance components to total phenotypic variance (H2 = σ2G / σ2P = (σ2L + σ2LxS) / (σ2L + σ2LxS + σ2C + σ2E). The cross-sex genetic correlation was calculated as rMF = σ2L/ (σLFσLM) where σ2L is the variance among lines from the analysis pooled across sexes, and, σLF and σLM are, respectively, the square roots of the among line variance from the reduced models of females and males. Similarly, cross-trait genetic correlations were calculated as rAB = σ2G(AB)/(σGAσGB) where σ2G(AB) is the genetic covariance between traits A and B, and σGA and σGB are the square roots of the genetic variance for traits A and B, respectively. The phenotypic correlation between sexes was determined using the cor function with method = “spearman” in R.In(2L)t and In(3R)Mo were associated with IOD and to a lesser extent CS. We modeled these covariates using a mixed model. The food batch was modeled by a random effect and the rearrangements were coded as  depending on whether both, one or no inversion was present in the homozygous state. We did not observe correlation between Wolbachia infection status and any trait and thus did not include this as a covariate in the model. Specifically, the models used were: CSraw = α + X1β1 + X2β2 + Fu + ε, where X1 refers to the sex covariate, X2 refers to the inversion covariate, ε~Νn with n being the number of lines, u~Nk with k being the number of food batches and Fu an -indicator matrix, associating each line to its respective food batch. The GWA analyses were performed using the estimated residual of this model (CS = ε).We found a large effect of food batch on CS, and inversions CSraw = α + IOD + X1β1 + Fu + ε, where X1 and Fu refer again to the sex-effect and the food batch effect. We did not model the inversions because the residuals of this model were not correlated with the inversions. The residuals ε from this regression were used as relative size phenotypes. All phenotypes were rank normalized before GWAS.To find loci that specifically affected variation in wing size unrelated to the overall body size variation we constructed an additional phenotype (rCS) that had the effect of IOD on CS removed via regression: http://dgrp2.gnets.ncsu.edu). Only SNPs that were missing in a maximum of ten lines and occurred in at least ten lines  were used. GWA was performed using FaST-LMM [manhattan function in the R package qqman [n SNPs (p<10−05) and calculated the correlation between genotypes at these loci across all DGRP lines used in the GWA analyses. We used the FaST-LMM SNP p-values to apply the sum of chi-squares VEGAS method [http://popdrowser.uab.cat/gb2/gbrowse/dgrp/), but we included also SNPs lying within 1,000 bp up- or downstream of these margins. The correlation matrix was calculated from the genotypes themselves.We performed GWA analyses using male and female line means. Genotypes for 143 of the 149 lines were obtained from the DGRP Freeze 2 website  promoter. The VDRC line containing a UAS-RNAi construct against the CG1315  gene served as a negative control for the knockdowns. We decided to use this line as reference because it was in the same background as most of our tester lines, an essential factor to consider when assessing genes that presumably only have a small effect on size upon knockdown. The CG1315 knockdown had never shown an effect in any setting and it allowed us to evaluate unspecific effects of RNAi knockdown on wing size. Prior to the experiment, driver lines were bred under controlled density to eliminate cross-generational effects of crowding on size. Wings were phenotyped as described above and wing area used as a phenotypic readout. Change in median wing area relative to the control was tested with a Wilcoxon rank sum test (function wilcoxon.test in R) for each line and for separate sexes. If possible, 25 flies per cross and sex were phenotyped for statistical analysis, however sometimes the number of progeny was lower. The number of phenotyped flies per cross and sex is given in fisher.test function in R to determine if the proportion of validated genes was different among candidates and random lines, and the wilcoxon.test function to test for a difference in median p-value between candidates and random lines. The comparison between candidate and random knockdowns was done for females exclusively as only this sex was measured for the random lines. Only genes not previously implied in wing development or growth control were included in the analysis, which excluded chinmo, aPKC, tws and Ilp8 from the candidates and EloA and spz5 from the random list.SNPs with an association rom VDRC . For oneDrosophila against all DGRP SNPs with missingness <11 and present in at least 10% of the lines. We compiled a list of SNPs within and 1 kb up- or down-stream of genes that were previously known to play a role in growth control  or wing development  and used these as focal SNPs  yielding set K: K = {k: SNPk is an epistasis hit}. We then generated random samples.We explored epistatic interactions between SNPs lying within and 1 kb around genes that were previously found to be involved in growth or wing development in cal SNPs . All pheg Circos . To calcj, do:For random sample K, add a random integer rj between 0 and n-1. Define new index as the modulo n: kij= mod, which yields Kj= {kij; j = 1,.., m}. Given the shifted positions Kj, we look up the SNP positions PKj. For a given gene list, we record the number xj of gene regions that overlap a position in PKj. Let x be number of gene regions overlapping an epistasis hit. Our p-value estimate is then Papprox≈ 1/m ∑1{xj ≥ x}.For all elements in Drosophila genome from the study of Young et al. [phyper) in R).We determined the number of SNPs from each GWA candidate list and the overall number of SNPs that located within modENCODE  elementsg et al.  and searexpanded locus (2L: 421227..431227) from FlyBase [Drosophila species [expanded gene in the same orientation in the genome. We performed multiple sequence alignment using the discontiguous megablast option on NCBI BLAST [We downloaded the sequence of the region 10 kb upstream of the annotated transcription start site of the  FlyBase , as well species , which cBI BLAST .phyper in R). We determined Drosophila orthologs of gene annotations of all associated SNPs in Wood et al. , resulting in 374 ortholog pairs supported by at least 3 prediction tools, and searched for overlap of these orthologs with the 62 of our GWAS candidate genes that had a human ortholog supported by at least 3 prediction tools, which resulted in 12 matches. Of those, only five matches were supported by three or more prediction tools (score > = 3) and we used only those for enrichment calculation. As background we used the total number of Drosophila-Human ortholog relationships  [We combined candidate genes from GWA analyses of all phenotypes and searched for orthologs in humans using DIOPT-DIST . Enrichm 28,605) .S1 Figa) Genetic correlation between morphometric traits in males. The two modules of higher correlation observed in females are still visible (bright yellow in the upper left and lower right corners) but the overall clustering is more influenced by the more inaccurately measured smaller veins and areas. b) Cumulative variance explained in male data by increasing number of principal components. As in the female dataset, the first two PCs explain almost 75% of the variance in the data. c) Factor map for the variables. PCs 1 and 2 split the data into two groups. d) Correlation between PCs and traits. PC1 reflects a general size component and PC2 is highly correlated with head/thorax traits, effectively splitting the data in two groups.(PDF)Click here for additional data file.S2 Figb for the relationship between CS and IOD in females (a), in males (b) and between CS and TL in females (c) and males(d). e) Boxplot and individual datapoints of the data in a-d. Red = females and black = males. 95% confidence intervals for b (In(2L)t nor In(3R)Mo present, 1 (green) = homozygous for either In(2L)t or In(3R)Mo, 2 (blue) = homozygous for both In(2L)t and In(3R)Mo. Datapoints are individual flies.Histograms of the estimates for the allometric coefficient ls for b  are very(PDF)Click here for additional data file.S3 Figkek1. SNPs marked by a star pass Bonferroni correction. Grey = major allele, white = minor allele. b) Lines with the minor haplotype are distributed across all four foodbatches. Black dots = major allele, blue dots = minor allele. The IOD distribution for each foodbatch is plotted for females for the most significant SNP. The distribution is the same for all other SNPs of the cluster as all minor alleles form a haplotype. c) Correlation between p-values from GWAS with normalized IOD (y-axis) and non-normalized iod (x-axis) in females. Axes are on the–log10 scale. d) Several blocks of higher LD are visible in the region 20kb upstream of kek1. Blue = no correlation, orange = complete correlation.a) The minor allele haplotype of the genome-wide significant cluster is associated with an increased IOD in females. Boxplots of female IOD by genotype at the nine SNPs annotated to (PDF)Click here for additional data file.S4 Figp-values are–log10 transformed (y-axis) and plotted against the–log10 transformed theoretically expected p-values under the assumption of no association . Centroid size (a), inversion corrected centroid size (b), interocular distance (c), inversion corrected interocular distance (d) and relative centroid size (e).Observed association (PDF)Click here for additional data file.S5 Figp-values are–log10 transformed (y-axis) and plotted against the–log10 transformed theoretically expected p-values under the assumption of no association . Centroid size (a), inversion corrected centroid size (b), interocular distance (c), inversion corrected interocular distance (d) and relative centroid size (e).Observed association (PDF)Click here for additional data file.S6 Fig2L, 2R, 3L, 3R, X) and black dividers separate chromosomes. Within one chromosome arm SNPs are ordered according to their position on that chromosome with each tile representing one SNP. The color code is depicted on the right: orange = complete correlation (1) and blue = no correlation (0). Centroid size (a), inversion corrected centroid size (b), interocular distance (c), inversion corrected interocular distance (d) and relative centroid size (e).The SNPs are ordered according to chromosome arm ((PDF)Click here for additional data file.S7 Fig2L, 2R, 3L, 3R, X) and black dividers separate chromosomes. Within one chromosome arm SNPs are ordered according to their position on that chromosome with each tile representing one SNP. The color code is depicted on the right: orange = complete correlation (1) and blue = no correlation (0). Centroid size (a), inversion corrected centroid size (b), interocular distance (c), inversion corrected interocular distance (d) and relative centroid size (e).The SNPs are ordered according to chromosome arm ((PDF)Click here for additional data file.S8 Figp-values in females (x-axis) are plotted against their respective p-values in males (y-axis). The Spearman rank correlation is given for each trait and the red lines denote the significance cutoff. a = CS, b = CSIC, c = IOD, d = IODIC, e = rCS.SNP (PDF)Click here for additional data file.S9 FigCG1315 RNAi upon wing-specific knockdown of the validated candidate genes in males. Only the lines yielding a significant wing size change  are depicted. Median, 25th and 75th percentile for each are given in Percent change in median wing area compared to (PDF)Click here for additional data file.S10 Figp-value densities of the candidate (black) and combined control (red) data sets. The two p-value distributions differ by a location shift that is not zero (i.e. are not the same); specifically, the–log10 transformed control p-value distribution (red) is shifted towards the left of the–log10 transformed candidate p-value distribution (black) (one sided Wilcoxon rank sum test p = 0.02). b: The distribution of candidate effect sizes (percent change in wing size upon knockdown) is shifted towards positive effect sizes (white boxes), whereas the control knockdown effect size distribution (red) is more centered on 0. The two exceptions at -28% (CG17646) and -42% (CG3704) are lines whose wings not only show a size reduction but also considerable morphological defects (c). N = 43 candidates (white), N = 22 control (red); only data from females was used for these analyses.a: -log10 transformed (PDF)Click here for additional data file.S11 Fig(PDF)Click here for additional data file.S1 TableN = 2000, 25 flies/sex of 10 replicates of four DGRP lines. Allometry: Allometric coefficients (b) calculated from the equation log(CS) = log(a) + b*log(trait) and their 95% confidence interval (CI) are given for the allometric relationships between CS and IOD and CS and TL for each line and sex. Though the CI varies substantially for some lines due to few data points used for fitting the models, the upper CI boundary is close to 0 for some  for the CS-IOD relationship). QGA Dataset: Quantitative genetic analysis of CS and IOD in the dataset consisting of N = 6978 flies from 149 DGRP lines. pSex = significance of fixed effect of sex, pLine = significance of random effect of Line, pSex x Line = significance of random effect of Line by sex interaction, pReplicate = significance of random effect of replicate, pFood = significance of random effect of foodbatch. Estimated parameters are variance due to genotype (VG), genotype by sex interaction (VGxS), food (VF), replicate (VR) and intra-line variance (VE), as well as the cross-sex genetic (rMF) and phenotypic correlation between sexes. H2 = broad-sense heritability and VP = total phenotypic variance. Phenotypic variation: Smallest and largest trait values and the percent difference are given per sex for each phenotype. Population means (Mean) and standard deviations (STD) are used to calculate the coefficient of variation (CV).Control experiment: Only 2% of total population variance in CS and IOD was due to flies coming from replicate vials, a negligible fraction compared to the 78% and 71% attributable to differences in genotype. This indicates that the standardized culture protocol sufficiently deals with confounding effects on size phenotypes. (XLSX)Click here for additional data file.S2 TableRaw data for all traits and line means (Mean), standard deviations (STD) and number of phenotyped flies (N) listed by sex for centroid size (CS) and interocular distance (IOD).(XLS)Click here for additional data file.S3 Tablep<10−05) from GWAS for Centroid size (CS), inversion modeled centroid size (CSIC), interocular distance (IOD), inversion modeled interocular distance (IODIC) and relative centroid size (rCS) in both sexes.Nominally significant SNPs ((XLS)Click here for additional data file.S4 Tablep<10−05) identified in each of the GWAS and percent overlap between sexes. SNPs F = number of SNPs nominally significant in females, SNPs M = number of SNPs nominally significant in males, Common MF = number of SNPs nominally significant in both sexes, % M in F = percent nominally significant male SNPs also nominally significant in females, % F in M = percent nominally significant female SNPs also nominally significant in males. Between sex overlap: SNP and gene level overlap between sexes for different thresholds. Percent overlap of SNPs (exact associated positions), annotated SNPs , and genes (only includes SNPs that locate within or 1kb around a gene). A consistent difference in associated loci in terms of the exact SNP that is associated (top block) is detectable between sexes, though the loci apparently are located more or less in a similar region (middle block). For SNPs in or close to genes, the differences between sexes are more pronounced: of the top 100 associated genes about 50% overlap, while the other 50% are private to one sex but this percentage increases with inclusion of more genes . Between phenotype overlap: Proportion of nominally significant SNPs that are shared between phenotypes.GWAS SNPs: Number of nominally significant SNPs ((XLS)Click here for additional data file.S5 Tablep-values for SNPs localizing to different genomic regions. LincRNA: LincRNA loci that overlap significant SNPs and their expression during different developmental stages. LincRNA data from Young et al. [Enrichment: Enrichment g et al. .(XLS)Click here for additional data file.S6 Tablep<0.001).Last column indicates the percent change in median wing size of genes tested by RNAi, and asterisks (***) indicate significant change ((XLS)Click here for additional data file.S7 Tablechinmo, aPKC, tws and Ilp8 as positive controls, but did not include them in the calculation of these percentages. Fisher’s exact test: We performed a two-sided Fisher’s exact test to determine if the proportions of validated genes was different between candidates and random genes. The results are shown for different Wilcoxon test p-value validation thresholds. Only not previously known candidates and random lines were used The lines are ordered according to decreasing significance of the change in median wing area upon knockdown (Wilcoxon rank sum test). Crosses in columns pigmentation, bristles, veins indicate a slightly abnormal corresponding phenotype. N = number of individuals tested, Median = median wing area, Q25 and Q75 = first and third quartile of wing area distribution. MAF 3 or MAF 5 in brackets means a SNP in or near this gene was found among the top associated genes in a GWAS for wing size with a lower MAF cut-off (SNPs present in min. 3% or 5% of lines). This gene was tested for wing size since a SNP in it showed association to body size with the used MAF cutoff and thus a corresponding RNAi line was available. Overview: Overview validation results. Number of SNPs that were tested and the number and percentage that were validated for each trait. We tested the known genes (XLS)Click here for additional data file.S8 Tablep<10−09) for female absolute inversion corrected wing size (CSFIC), male absolute inversion corrected wing size (CSMIC), female absolute inversion corrected body size (IODFIC), male absolute inversion corrected body size (IODMIC), female relative wing size (rCSF) and male relative wing size (rCSM). X is the interactor locus and Y the focal (= previously known) locus.Focal genes: Genes previously implied in growth regulation or wing development that were used as focal genes for epistasis. Top interactions ((XLS)Click here for additional data file.S9 TableExpanded orthologs: Genomic location of ex orthologs in 12 Drosophila species [D. melanogaster (+). MSA details: Details of MSA of the 10kb region upstream of D. melanogaster ex gene. Sequence = genomic region in each species used in the MSA, Identity = percent identical nucleotides, Aligned Length = length of alignment, Query Cover = percent of the query sequence (D. melanogaster sequence) aligned to the sequence in the respective species, E-value = significance of alignment , Score = strength of alignment. species . Name of(XLS)Click here for additional data file.S10 Table(XLS)Click here for additional data file.S11 Tableet al. [et al. [Candidates found as suppressors or enhancers of major growth pathways by Schertel et al. . Candida [et al. .(XLS)Click here for additional data file.