Myostatin (MSTN) gene, similar to those causing a double-muscling phenotype observed in several mammalian species. The MSTN mutation was in Hardy-Weinberg equilibrium in the population at birth, but significantly distorted amongst animals still in the herd at 110 kg, due to an absence of homozygous mutant genotypes. In heterozygous form, the MSTN mutation was associated with a major increase in muscle depth and decrease in fat depth, suggesting that the deleterious allele was maintained at moderate frequency due to heterozygous advantage . Knockout of the porcine MSTN by gene editing has previously been linked to problems of low piglet survival and lameness. This MSTN mutation is an example of putative balancing selection in livestock, providing a plausible explanation for the lack of disrupting MSTN mutations in pigs despite many generations of selection for lean growth.Balancing selection provides a plausible explanation for the maintenance of deleterious alleles at moderate frequency in livestock, including lethal recessives exhibiting heterozygous advantage in carriers. In the current study, a leg weakness syndrome causing mortality of piglets in a commercial line showed monogenic recessive inheritance, and a region on chromosome 15 associated with the syndrome was identified by homozygosity mapping. Whole genome resequencing of cases and controls identified a mutation causing a premature stop codon within exon 3 of the porcine  Lameness is an important problem in livestock production for both animal welfare and economic reasons. A severe piglet lameness syndrome was observed in a commercial pig population. The incidence of the condition was low (6.3%), but was higher in affected families (~25%), which suggested a genetic basis and a recessive mode of inheritance. We discovered a region on Chromosome 15 where cases shared the same alleles that were different to healthy piglets. In this region, we discovered a mutation that causes a premature stop codon in the myostatin gene. Myostatin causes ‘double-muscle’ phenotype in several mammalian species. Piglets with two copies of this mutant allele suffer the lameness syndrome and do not survive post 40 kg live weight. However, those that carry a single copy have higher muscle depth and lower fat depth compared to wild type. We suggest that despite the negative consequences of the mutant allele in homozygous form, the mutation was maintained in the herd due to positive selection for this allele in heterozygous form. This is an interesting example of so-called ‘balancing selection’ and may explain why naturally occurring myostatin mutations have not previously been reported in pigs despite centuries of selection for lean growth. SignLandrace , which mLandrace . Taken tmannose receptor C type 2 gene (MRC2) responsible for crooked tail syndrome and also associated with increased muscle mass in Belgian Blue [ryanodine receptor 1 (RYR1) gene [Deleterious alleles can be maintained at relatively high frequency in commercial livestock populations due to heterozygous advantage for traits under selection . Exampleian Blue , and a lian Blue . In pigsR1) gene , 14 is lR1) gene . More reR1) gene .Sus scrofa chromosome (SSC) 15 associated with the trait. A mutation causing a premature stop codon in exon 3 of the Myostatin (MSTN) gene  region likely to contain the underlying causative mutation, and became the focus of further analyses to discover and characterise this mutation. The SNP genotype used in the homozygosity mapping at SSC15 is given in Homozygosity mapping was used to map the underlying recessive variant, and the longest shared homozygous segment was a region of ~ 8.3 Mbp on SSC15. This unique region, contained 55 informative single nucleotide polymorphisms (SNPs) on the Illumina PorcineSNP60 SNP chip . These 5MSTN locus that results in the replacement of a codon for glutamic acid with a stop codon in exon 3 at position 274   . The mutp.E274*) . FunctioMSTN c.820G>T mutation showed no statistically significant deviation from Hardy Weinberg equilibrium (HWE) in the 486 piglets sampled at birth . Random mating of the dioecious population would be expected to result in a value of α that is slightly negative [2 = 12.2, P > 0.001) and at 110 kg . This was due to the loss of homozygous mutant piglets, with all but one dying (or being euthanized) shortly after birth, and the remaining piglet being euthanized due to poor health before it reached 110 kg. For detailed information, see q and a are due to the selective loss of homozygotes, and that any other mortality or culling was at random with respect to MSTN genotype.The MSTN c.820G>T mutation with performance traits was assessed on 384 pigs which had completed a commercial performance test. Given the loss of the homozygous mutant animals the effect of the MSTN c.820G>T mutation was only estimated by the difference between the heterozygotes and the wild type pigs. The genotype means and differences are shown in The association of the porcine 2 vs 11.5 μm2, P < 0.001), suggesting comparative myofibre hypertrophy. However, there was no difference in myofibre number between the two animals, which does not suggest hyperplasia  and a heterozygous piglet revealed had significantly larger myofibre sizes in the homozygote  superfamily, which is highly conserved across species, and is typically expressed in developing and mature skeletal muscle as a key regulator of muscle growth [MSTN gene has been a gene of interest to animal breeders for over twenty years since the discovery of loss-of-function mutations in the cattle MSTN gene, which cause muscle hypertrophy leading to double muscling phenotypes [MSTN [MSTN variant causing double muscling results is due to replacement of Glutamic Acid with a stop codon (the same change observed in the current study) in the equivalent position in the protein as observed in the current study [MSTN mutation described herein is likely to cause the increase in muscle depth and decrease in fat depth associated with carrier animals. A histological analysis of muscle showed larger myofibre sizes in a homozygous TT (putative double MSTN knockout) than a heterozygous piglet suggesting comparative myofibre hypertrophy. A similar phenotype was observed in MSTN gene knockout piglets from a genome editing study [MSTN knockout piglets [e growth . The MSTenotypes , 27, 28.nt study . This obng study . However piglets , where aMSTN-inactivating mutations have been retained due to selection for increased lean growth associated with meat production, particularly for double-muscled cattle. However, despite many generations of selection for lean growth in pigs no such mutations have been reported previously. Associations between polymorphisms within the porcine MSTN gene and production traits have been shown in small-scale studies [MSTN, two of these SNPs overlap with the region on homozygosity indicative of an IBD region in the current study. Therefore, it is plausible that this association may be due to linkage disequilibrium (LD) with the MSTN variant, or that other variants impacting performance traits exist on SSC15. The selection index applied in this line explicitly benefited animals with positive muscle depth and negative fat depth estimated breeding values, with analysis of the index showing that heterozygotes had a slight but significant advantage (p<0.05). Therefore, it is likely that the MSTN c.820G>T mutation was maintained at moderate frequency in this line despite its deleterious impact on piglet mortality due to heterozygous advantage for muscle and fat traits. Similar examples of balancing selection have observed to explain the maintenance of deleterious alleles at moderate frequency in commercial cattle [MSTN via gene editing to increase lean growth in pigs, and provide a plausible explanation of why MSTN loss-of-function mutations have not been previously reported in pigs despite decades of selection for lean growth.In livestock breeding schemes,  studies –32. Furt studies . While tl cattle , 12 and l cattle  populatihttps://assurance.redtractor.org.uk/) where sick or injured livestock that do not respond to treatment are promptly and humanely euthanized by a trained and competent stockperson.All samples were collected on a commercial nucleus farm as part of standard husbandry and management procedures in the nucleus herd, which complied with conventional UK red tractor farm assurance standards  was searched for previous reports of leg weakness in pigs but the syndrome observed here did not appear. These problems frequently resulted in death from either starvation or being crushed by the sow. Where the outcome resulted in poor welfare the piglets were euthanized in accordance with the Ethics Statement. Additional farrowing data were collected on the females in the commercial cohort including numbers born alive, dead or mummified, parity and year of birth. Body weights, ultrasound muscle and fat depths were obtained for individuals in the survey cohort that were retained in the herd until commercial slaughter age (when average weight is 110 kg). The ultrasonic measurements taken were the average depth of the m. longissimus dorsi and overlaying subcutaneous fat layer across the last four ribs.The leg weakness in the phenotyped animals was visually classified as normal or affected . The leg defect is characterised by the piglet not being able to straighten its legs to stand, this being most apparent for the front legs, and being slow to suckle. Detailed post mortems were conducted on two affected individuals but the results were ineffective in providing additional diagnostic aids. The Online Mendelian Inheritance in Animals database , sex (1 df), parity (7 df), numbers in litter born dead (1 df) or alive (1 df), with design matrix X; u, additive polygenic effects, assumed to be distributed MVN with design matrix Z1; v, random litter effects assumed to be distributed MVN, with design matrix Z2; w, maternal environment effects across litters assumed to be distributed MVN with design matrix Z3; and e residuals assumed to be distributed MVN. Variations in this model were fitted replacing the individual polygenic effects with sire and/or dam effects, assumed to be distributed MVN and MVN respectively. All models were fitted using the ASREML software [2p = σ2u + σ2v+ σ2w + σ2e. Where sire and dam models were used, σ2u was replaced by σ2s + σ2d. Heritability (h2) was calculated as σ2u/σ2p or 2(σ2s + σ2d)/σ2p depending on the model. The proportion of variance explained by the litter and maternal environmental effects were estimated as σ2v/σ2p and σ2w/σ2p, respectively. Heritabilities on the observed scale (0/1) was transformed to an underlying liability scale following Dempster and Lerner [Initial studies to establish the genetic basis of the leg weakness syndrome were undertaken by fitting linear mixed models to the commercial cohort. The binary record of the syndrome was modelled on the observed 0 / 1 scale and the full model fitted was:software . Likelihsoftware . For thed Lerner  using thInspection of the data suggested that the syndrome may be due to a single gene with the predisposing deleterious allele showing a recessive mode of inheritance, and this hypothesis was tested using chi square tests and segregation analyses. An initial test of a monogenic recessive mode of inheritance was carried out by pooling all affected litters, estimating the probability of being affected conditional on being born in an affected litter, and using chi-squared to test the null hypothesis that the probability of being affected was 0.25. A weakness of this approach is that some litters by chance will have no affected offspring, so a more complex segregation model was fitted. This model included all known phenotypes and pedigree data, and assumed a monogenic inheritance with environmental variation fitted by Gibbs sampling .Ten affected animals from different litters and 10 unaffected full-sib controls from the commercial cohort were genotyped using the Illumina PorcineSNP60 SNP chip . Only thThe genomes of the ten cases used for homozygosity mapping and six separate dams with affected offspring, assumed heterozygotes, were whole genome shotgun sequenced on an Illumina HiSeq 2500 platform as 125 bp paired-end reads. The dams were individually sequenced with a 10x genome coverage. The piglets were barcoded and individually sequenced at 3x coverage to achieve 30x coverage for the pool. The full sequencing output resulted in ~1.3 billion paired-end reads with an average of 48 million paired-end reads/sample for the piglets and 157 million paired-end reads/sample for the dams. Quality filtering and removal of residual adaptor sequences was conducted on read pairs using Trimmomatic v.0.32 . Only rehttp://broadinstitute.github.io/picard). Variant calling was performed using the Genome Analysis Toolkit (GATK) HaplotypeCaller after read recalibration [Whole genome resequencing was followed by alignment to the Sscrofa11.1 assembly; using the Burrows-Wheeler Aligner with default parameters . The aveibration . The parSus scrofa control sequences obtained from a public database [Candidate loci were identified from the sequences of the 16 animals from this study plus 22 additional database , comprisHomozygous for the same allele in all the affected pigletsHeterozygous in parents of affected piglets (i.e. putative carriers)Homozygous for the alternative allele (i.e. the one not observed in the affected offspring) in the control (unaffected) animalsIt is worth noting that the limited sequencing depth means that both alleles will not be detected for all bases in all individuals. This limitation is particularly relevant to the reliable detection of heterozygous SNPs.MSTN stop codon in large numbers of animals. The survey cohort of 486 piglets sampled at birth were genotyped by LGC. Of these, 265 remained as candidates for the final selection at 110 kg with a complete record of their performance test, together with another 119 pigs phenotyped at slaughter age . In both the 486 piglets and the subsequent subsets surviving to 40 and 110 kg, the frequency of the MSTN mutation (q) was calculated by counting, and the departure from Hardy Weinberg equilibrium the genotypes was estimated as α = 1-Hobs/Hexp [obs is the observed heterozygosity and Hexp is the expected heterozygosity calculated as 2q(1-q). The significance of departure from true random mating genotype frequencies (α = 0) was tested using a chi-squared test.A ‘kompetitive allele specific PCR’ (KASP) assay was designed by LGC Genomics  to enable genotyping of the mutation in the obs/Hexp  where HoMSTN c.820G>T locus and variation in performance traits in commercial testing conditions were examined in the survey cohort (n = 384). The performance test was started at a target weights of 40 kg, at an average age of 85 days, and continued for 54 (s.d. 12) days. The performance tests were performed over two distinct periods. The traits available were live weights and ages at the start and the end of the test, ultrasonic muscle and fat depths measured at the end of the test, days from birth to 40 kg and days from 40 to 110 kg. Univariate mixed models were fitted to these data in ASReml-R4 using the following model:y is the vector of phenotypes; μ, a fitted mean, and 1, a vector of 1’s; β, a vector of fixed nuisance effects with design matrix X1; b, a scalar fixed effect for the effect of SNP genotype with design matrix X2, this has only 1 df due to the absence of homozygotes completing the test; u, additive polygenic effects assumed to be distributed MVN, with design matrix Z; and e, residuals assumed to be distributed MVN. For all traits, the sex of the piglet (1 df), parity of dam (4 df) and period of testing (1 df) were fitted as nuisance factors, together with cubic smoothing splines for the start date of the test fitted separately within each period [Associations between the https://fiji.sc/), and for each image blood vessels were manually removed. The green channel was chosen, and the threshold adjusted to identify myofibres. This was followed by two binary functions (‘fill holes’ and ‘watershed’ consecutively), and the resulting mask was measured using the particle analyser, filtering particles below 5μm2 in size. The myofibre size was compared between the two samples using a Mann-Whitney test to assess hypertrophy. For assessment of hyperplasia, the total number of myofibres was expressed as the number of myofibres per 100 μm2.Samples of biceps femoris were taken from one myostatin homozygous knockout piglet (TT) and one heterozygous piglet (TG), and fixed in 10% neutral buffered formalin. After 24h of fixation, the tissues were processed to paraffin embedded blocks, and 4 μm sections were taken and stained with haematoxylin & eosin. One Tiff image was taken from each animal in an area where myofibres had been sectioned transversally using a Zeiss Axiocam 105 colour camera using the same light settings, and measurements were calibrated using a microscope graticle. For image analysis, images were loaded in FIJI , featuring histologically normal myofibres for both genotypes, and the results of the morphometric measurement on the same area of the sections . Panel B, contains a dotplot chart of the comparative measurements for each myofibre taken from an identical area for each animal. The white dot indicates the median value for each group. The homozygous myofibres were significantly larger that the heterozygous (P<0.001), with a median of 13.4μm2 and 11.5μm2 respectively. This is suggestive of significant hypertrophy of myofibres in the homozygous group. Conversely, the density of myofibres is comparable in both groups (i.e. 2.2myofibres/100μm2 for both groups), suggesting no hyperplasia is present in the homozygous group. Stain: Haematoxylin and Eosin, scale bar = 50μm .(TIF)Click here for additional data file.S1 TableAll models have litter variance (σ2v) fitted.(DOCX)Click here for additional data file.S2 Table(DOCX)Click here for additional data file.S3 TableAlso included is the longest segment of 55 SNPs identified in the homozygosity mapping analysis.(XLSX)Click here for additional data file.S4 TableVariants are considered consistent if they are: 1) homozygous for the same allele in all the affected piglets ; 2) heterozygous in parents of affected piglets ; 3) homozygous for the alternative allele (i.e. the one not observed in the affected offspring) in the control (unaffected) animals.(XLSX)Click here for additional data file.


This paper summarizes the contributions from the Genome-wide Association Study group (GWAS group) of the GAW20. The GWAS group contributions focused on topics such as association tests, phenotype imputation, and application of empirical kinships. The goals of the GWAS group contributions were varied. A real or a simulated data set based on the Genetics of Lipid Lowering Drugs and Diet Network (GOLDN) study was employed by different methods. Different outcomes and covariates were considered, and quality control procedures varied throughout the contributions.The consideration of heritability and family structure played a major role in some contributions. The inclusion of family information and adaptive weights based on data were found to improve power in genome-wide association studies. It was proven that gene-level approaches are more powerful than single-marker analysis. Other contributions focused on the comparison between pedigree-based kinship and empirical kinship matrices, and investigated similar results in heritability estimation, association mapping, and genomic prediction. A new approach for linkage mapping of triglyceride levels was able to identify a novel linkage signal.This summary paper reports on promising statistical approaches and findings of the members of the GWAS group applied on real and simulated data which encompass the current topics of epigenetic and pharmacogenomics. To date, more than 1000 GWAS have identified more than 4000 significant loci as associated with 500 human diseases and traits , 3. PoteTo increase power and interpretability of GWAS, researchers of the Genome-wide Association Study Group of GAW20 have focused on topics such as gene-level association studies for main  or gene–Here, we provide a summary of the current literature with respect to methods in GWAS and ways to increase the power of these tests. We then provide contributions from the Genome-wide Association Study Group of GAW20 in the methods and results sections and conclude with recommendations and open problems in the discussion section.GWAS is considered as the standard approach to detecting common genetic variants associated with complex traits. It is now popular to extend the most popular single-nucleotide polymorphism (SNP)-level analysis to gene-level analysis by aggregating multiple SNPs in a gene, pathway, network, or any region in the genome, such as a haplotype block . SNP-setp values of single-marker tests [Several multimarker methods have been proposed based on dimension-reduction techniques, such as principal component analysis , partialer tests –23. Howeer tests , 25, sucer tests , 27, theer tests , the sumer tests , variabler tests , and haper tests . They prIncreasing evidence shows that pleiotropy, the effect of 1 variant on multiple traits, is a widespread phenomenon in complex diseases . FurtherThe GAW20 data are derived from the Genetics of Lipid Lowering and Diet Network (GOLDN) study, which aims to identify genetic markers of lipid response to fenofibrate treatment. The real data consists of high-density lipid cholesterol (HDL-C) and triglyceride (TG) levels measured before visits 1 and 2) and after (visits 3 and 4) treatment with fenofibrate in 822 pedigree-based European Americans from 2 different centers in the United States . In GAW20, genome-wide methylation, as well as genome-wide SNP data, from the GOLDN project was made available. Furthermore, simulated post-treatment and pre-treatment TG, methylation levels and SNP genotype data are provided based on the real GOLDN data set. Tables  and 2 anThe 9 contributions from the GWAS group of GAW20 extend upon the current literature and reflect varied goals, including the creation of new statistic tests, development of phenotype imputation methods, and application of the empirical kinship matrices. Table To perform a gene-level association test to detect genes significantly associated with a single trait using the GAW20 data while effectively controlling for the false-positive rate, Park et al.  extendedZhou et al.  proposedExisting methods to detect the main effect of rare variants cannot be readily applied for testing the gene environment interaction effect of rare variants, as those methods either have unstable results or inflated Type I error rates when the main effect exists. To overcome these difficulties, Gao et al.  developep value can be obtained using the Davies method [Advances in high-throughput technologies provide comprehensive assessment of biomarkers, which enable us to systematically study the role of different types of omic data  in human diseases. The collection of multilevel omic data from these studies provides us a great opportunity to integrate information from different levels of omic data into association analysis. Although omic-based association analysis holds great promise for discovering novel disease-associated biomarkers, there is a lack of appropriate statistical tools to analyze multilevel omic data , 42. Thes method .The true genetic sizes and the direction of associations are usually unknown (a priori) and therefore one would not know which approach is the best for the study. Ray et al.  proposedPhenIMP [The aim of GWAS is the identification of particular SNPs associated with an outcome of interest, such as the TG or HDL levels in the GAW20 data set. To identify associated SNPs with small and large effect sizes, the power  of GWAS should be sufficiently high. Missing phenotype data, owing to cost, loss of follow-up or inaccessibility of data, might lead to a decrease of statistical power and consequently to the loss of true SNP associations. A well-known approach to deal with missing data in GWAS is imputation based on available phenotypic data, for instance with methods such as PhenIMP . Chen etPhenIMP  modifiedPhenIMP  derived PhenIMP .“Kinship” typically refers to the degree of genetic relatedness or coefficient of relationship between individual members of a pedigree. There are at least 2 ways to model kinship of a pedigree: (a) pedigree-based kinship uses specified pedigree relationships, and (b) empirical kinship estimates familial relationships using genomic data. Pedigree-based kinship estimation may be inaccurate or incomplete. Compared to the pedigree-based kinship estimation, the uncertainty surrounding pedigree relationships is reduced with empirical kinship estimates  The potePhenotypes are influenced by environmental and genetic factors. When it is predicted with genome-wide markers alone, this is called genomic prediction. The resulting empirical genetic value is interpreted as the individual’s phenotype with environmental effects removed. One promising approach for genomic prediction is the genomic–best linear unbiased prediction method (G-BLUP), which uses kinship estimates. With the pedigree and empirical kinships as respective input to G-BLUP, Porto et al.  applied APOA5 and ZNF259) near rs964184, while none of the other gene-level tests nor the standard test on each individual SNP detected any significant associations in a genome-wide scan.Park et al.  applied Zhou et al.  used a 2Simulation studies of Gao et al.  showed tMYO3B was significant when the methylation information was considered in the analysis .The joint conditional autoregressive model of Shen et al.  was applDeng et al.  applied Chen et al.  evaluateBlackburn et al.  estimatePorto et al.  studied Peralta et al.  (collaboA central goal of human genetics is to identify genetic risk factors for common, complex diseases such as schizophrenia and Type II diabetes. GWAS that measures and analyzes DNA sequence variations from across the human genome is a valuable effort to identify genetic risk factors for diseases that are common in the population. The ultimate goal of GWAS is to use genetic risk factors to make predictions about who is at risk and to identify the biological underpinnings of disease susceptibility for developing new prevention and treatment strategies.Contributions from the GWAS group of GAW20 provided various statistical approaches which are beneficial in GWAS. Figure p values than the existing methods when applied to the GAW20 data. Therefore, when there are multiple traits available, we suggest jointly analyzing multiple traits, which can increase both the power and the interpretability of the findings.In genetic association studies of complex diseases, multiple related traits are usually measured. For example, correlated TG and HDL are provided in the GAW20 data, hypertension is evaluated using systolic and diastolic blood pressures, metabolic syndrome is based on observing 3 of 5 criteria , and theThe genetic variants discovered by GWAS account for only a small portion of the heritability of complex traits , 56. OneTo increase the power of a gene-set–based test, many weighting strategies have been proposed . Gao et GWAS have discovered hundreds of common genetic variants associated with multifactorial diseases. These variants can be added to classical clinical and environmental risk factors for the improvement of risk-prediction assessment. However, for most common diseases, the addition of genetic variants to traditional risk factors has produced only modest improvements , 65. TheIn summary, the contributions from the GWAS group of the GAW20 provide useful tools for genetic association studies regarding to single variant single-trait analysis , gene-ba


Pain constitutes a major component of the global burden of diseases. Recent studies suggest a strong genetic contribution to pain susceptibility and severity. Whereas most of the available evidence relies on candidate gene association or linkage studies, research on the genetic basis of pain sensitivity using genome-wide association studies (GWAS) is still in its infancy. This protocol describes a proposed GWAS on genetic contributions to baseline pain sensitivity and nociceptive sensitisation in a sample of unrelated healthy individuals of mixed Latin American ancestry.A GWAS on genetic contributions to pain sensitivity in the naïve state and following nociceptive sensitisation will be conducted in unrelated healthy individuals of mixed ancestry. Mechanical and thermal pain sensitivity will be evaluated with a battery of quantitative sensory tests evaluating pain thresholds. In addition, variation in mechanical and thermal sensitisation following topical application of mustard oil to the skin will be evaluated.This study received ethical approval from the University College London research ethics committee (3352/001) and from the bioethics committee of the Odontology Faculty at the University of Antioquia (CONCEPTO 01–2013). Findings will be disseminated to commissioners, clinicians and service users via papers and presentations at international conferences. We propose a genome-wide association study of both baseline pain sensitivity as well as nociceptive sensitisation.The study will be conducted in an admixed population in Colombia, with variable proportions of European, Native American and sub-Saharan African ancestry.Phenotypic data will be collected by a single trained examiner, thus producing high-quality measures.Because we are focusing on highly reproducible pain phenotypes obtained at a single centre, our initial sample size is limited to 1500–2000 participants. This will allow us to identify only genetic variants that have intermediate and large  effect sizes.Pain constitutes a major component of the global burden of diseases, with lower back and neck pain representing the single leading cause for years lived with disability followed closely by migraine and other musculoskeletal disorders.11A powerful technology for identifying genetic determinants of human complex phenotypes is genome-wide association studies (GWAS). However, GWAS analyses of pain remain limited, mainly due to the high numbers of individuals required to enable adequate power, and the complexities of accurately phenotyping traits that ultimately represent a personal subjective perception.An important drawback of most genetic studies in the pain field so far is that they have been performed mostly in populations of European ancestry, thus, they have explored only a small fraction of human phenotypic and genetic diversity.This protocol proposes a GWAS on genetic contributions to baseline pain sensitivity and nociceptive sensitisation in a sample of unrelated healthy individuals of mixed European/Native American/African ancestry. We will evaluate baseline cutaneous pain thresholds as well as the variation in sensitisation following mustard oil (AITC) application, a controlled model of tissue injury. We hypothesise that we will identify SNPs associated with experimental pain stimuli in the naïve and sensitised state. Elucidating the genetic basis of pain variation has the potential to reveal targets for future analgesic development. This can be translated into improved pain management potentially tailored to an individual’s pain risk or resilience, including sensitivity differences between different human populations.This GWAS follows the Strengthening the Reporting of Genetic Association Studies guidelines.Healthy participants aged 18–40 will be recruited in Medellin, Colombia via public noticeboards at local Universities, distribution of flyers and through the local print media. In addition, we are inviting previous participants from the Consortium for the Analysis of the Diversity and Evolution of Latin America (CANDELA)Recruiting healthy young participants has advantages in GWAS studies. They are less likely to have undetected illnesses or other problems that may influence their biological pathway of pain sensitivity. Young people will also have less overall accumulated exposure or risks from environmental  factors which may affect their pain sensitivity. Such factors increase the overall variability of participants’ pain perception response and reduce the power of detecting genetic causes. Since most traits are affected by a combination of genetic and environmental factors, many studies including CANDELA tend to use young participants for genetic variant discovery.27Participants will be excluded if they have chronic pain or any chronic medical condition . Participants currently taking analgesics, anti-inflammatories, opioids, antihistamines, antidepressants or antiepileptic drugs will be excluded. Women who are pregnant or in their menstrual phase (self-report) will be excluded from the study. Participants will be advised to not smoke or consume coffee within 1 hour of testing, and to avoid psychoactive substances or alcohol within 8 hours prior to testing. Further exclusion criteria include current or past self-inflicted injuries, as well as dermatomal, traumatic or infectious conditions affecting the arm, and a history of severe allergic reactions to any kind of medication, materials, food or insect bites. Participants with moderate to severe anxiety  laboratory at the Universidad de Antioquia, Medellín. Following informed consent, age and self-reported gender will be recorded and participants will answer questions regarding their self-reported ancestry (see online 3410.1136/bmjopen-2018-025530.supp1Supplementary data2 using a 255 mN von Frey hair. This will be repeated five times and the ratio will be established as the mean rating of the trains of stimuli divided by the mean rating of the single stimuli. Vibration detection thresholds (VDT) will be determined by recording the mean of 3 disappearance thresholds with a Rydel-Seiffer tuning fork. Pressure pain thresholds (PPT) will be recorded in triplicate with a manual algometer  and their mean used for analysis.We will determine sensory function in the naïve state and following nociceptive sensitisation. Baseline sensory function will be evaluated using specific static and dynamic QST. These include cold detection threshold and warm detection thresholds, thermal sensory limen and heat pain thresholds (HPT) using a ThermoTester . Recording of thermal thresholds will strictly follow published QST guidelines.The side to be tested will be randomised and patients will first be familiarised with the sensory tests on the forearm on the control side, before performing the actual measurements on the test arm. All tests will be performed halfway over the volar side of the forearm except for VDT (ulnar styloid) and PPT (thenar muscles).2 area on the volar forearm and held in place with a Tegaderm (3M) for 10 min.After the baseline sensory measures, an acetate template will be used to mark a star with eight spokes each containing eight points at 1 cm increments on the volar forearm . The skiAfter mapping of the flare, the area of brush-evoked hypersensitivity will be determined with a brush  by applying 1 cm long strokes at each of the points on the eight spokes, starting from the outside and moving towards the sensitised centre. The area of punctuate hypersensitivity will be determined with a 98.1 mN filament  following the same procedure.Following mustard oil sensitisation, the MPT and HPT will be repeated using the same methods as described above. All postsensitisation tests will be performed within 5 min of mustard oil removal.To determine intratester reliability, we repeated the sensory function protocol performed by the same investigator in n=12 healthy volunteers on two different occasions within 2–6 weeks. Intraclass correlation coefficients (3.1) revealed good to excellent agreement for all sensory testing variables .3838Each participant will donate blood or saliva  for DNA extraction. DNA samples will be genotyped on the Illumina HumanOmniExpress chip containing ~700 000 markers. In volunteers who already participated in the CANDELA GWAS,Whole-genome genotype data from the Illumina array will undergo quality control10.1136/bmjopen-2018-025530.supp2Supplementary dataet al.https://github.com/kaustubhad/gwas-power.The power for GWAS of experimental pain phenotypes for varying sample and effect sizes was estimated following the formulae described in Visscher −8, while the threshold for a suggestive significant association is commonly 10−5. Formulae to calculate power in GWAS with genome-wide and suggestive significance thresholds are presented in online In whole-genome SNP-based GWAS studies, the association analysis is usually conducted with a multivariate linear regression model, where the trait values are regressed onto an SNP genotype (with additive coding) and other covariates which commonly include age, gender, BMI and genetic principal components (PCs). The p value threshold10.1136/bmjopen-2018-025530.supp3Supplementary data−8. Sample size (n) varies from 100 to 5000, while the proportion of trait variance explained by the marker  varies from 0.01% to 6%. As sample size increases, power increases quickly for a range of trait variance values to reach 100%.−5. As expected, power is higher at similar sample and effect sizes for this less stringent threshold.Simplified power estimates are shown as power curves in et al,The range of trait variance has been taken from Doehring Power of a GWAS depends on the allele frequency of the SNPs through their effect on the test statistic. While the majority of GWAS studies are conducted in European-origin individuals, including the experimental pain study used to determine sample size here,The CANDELA project includes genotpyes of ~2000 patients from Medellin. We anticipate to contact and phenotype 50%–75% of these participants as well as contacting an additional 500 participants to bring the initial sample to 1500–2000.The cleaned genetic data will first be merged with reference samples worldwide, such as the 1000 Genomes Project,The genetic data allow estimation of the narrow-sense heritability of any quantitative trait, which is the fraction of trait variance that is explained by the genetic data. Estimates of heritability, obtained using the software GCTA,To facilitate better identification of associated loci, the genotype data will be imputed to approximately 10 million loci using the 1000 Genomes phase 3 imputation reference panelGWAS studies will be conducted in Plink252 58The number of genetic PCs to be included in the regression depends on the sample composition, such as variation in ancestry and presence/absence of genetic outliers. It would be determined by inspecting the proportion of variance explained by each PC (displayed on a scree plot) and by checking PC scatter plots.In addition to being used as exclusion criteria, anxiety and depression scores could be used as covariates in GWAS. The exact set of covariates to be used will be determined based on initial diagnostic analyses such as correlation analysis.−8 for genome-wide significance and 10−5 for suggestive significance.42These single-locus association results, obtained as p values, will be visualised via the Manhattan plot. Commonly used p value thresholds for selecting associated loci are 5×10An extension of this additive multivariate linear regression model, still within the single-trait single-locus setting, called the mixed linear model analysis which better controls for any cryptic relatedness or population substructure, will also be performed in GCTA.54There are several extensions of the single-trait single-locus association studies that increase power for detecting associated loci: combining several related traits that may share a biological basis, using multivariate Wald tests as implemented in MultiPhenIt might not be possible to record some traits in some individuals, even though the completeness of the first 100 samples suggests that missingness will be low. The single-trait methods used in traditional GWAS analyses automatically exclude individuals from the analysis of a trait who have missing values for that trait. The same applies to individuals having missing genotypes for any particular SNP. However, genotyping success rate using the Illumina HumanOmniExpress chip in the CANDELA cohort is very high (>99.8%), so the number of excluded individuals in any analysis would be very low overall.Some multivariate analyses such as PCs when applied on the set of phenotypes require having recorded values of all phenotypes for an individual. Instead of using the subset of individuals who have the complete set of phenotypes recorded, which would incur some loss in sample size, the missing phenotype data for each individual will be imputed following standard statistical procedures as implemented in the R package ‘mice’.58This GWAS including a well-defined cohort of healthy participants will provide important insights into the genetic aspects underlying experimental pain sensitivity in the naïve and sensitised state. This may allow further exploration of potential biological mechanisms underlying pain sensitivity. Future studies will be required to extrapolate these findings to patient populations with chronic pain.No patient involvement is performed during this study.Findings will be disseminated to commissioners, clinicians and service users via papers and presentations at international conferences such as the biennial World Congress of International Association for the Study of Pain. We will also post our findings to the publicly available painnetworks.org database.


Cracticus tibicen has a continent-wide distribution featuring several distinct plumage morphs, differing primarily in colour of back feathers. Black or white-backed morphs occur in eastern Australia, with intermediate morphs common in a narrow hybrid zone where the two morphs meet. This study investigated heritability of back colour phenotypes in a hybrid zone population  based on long-term observational data and DNA samples collected over an 18 year period (1993–2010). High extra-pair paternity (~ 36% offspring), necessitated verification of parent–offspring relationships by parentage analysis. A total of 538 birds (221 parents and 317 offspring) from 36 territories were analysed. Back colour was a continuous trait scored on a five-morph scale in the field (0–4). High and consistent estimates of back colour heritability (h2) were obtained via weighted mid-parent regression (h2 = 0.94) and by animal models . Single-parent heritability estimates indicated neither maternal nor paternal non-genetic effects  played a large role in determining offspring back colour, and environmental effects of territory group and cohort contributed little to trait heritability. Distinctive back colouration of the Australian magpie behaves as a quantitative trait that is likely polygenic, although mechanisms responsible for maintaining these geographically structured morphs and the hybrid zone where they meet are unknown.Colour polymorphisms have evolutionary significance for the generation and maintenance of species diversity. Demonstrating heritability of polymorphic traits can be challenging for wild populations of long-lived species because accurate information is required on trait expression and familial relationships. The Australian magpie  Many species exhibit some form of morphological polymorphism, and colour polymorphic species in particular offer biologists tractable systems to study a variety of evolutionary processes , a medium sized passerine with a lifespan of 25 years, that lives in permanent territorial groups  can lead to significant underestimations of the heritability of a trait of interest; and when these EPP/EGP are high , heritability estimates can be off by up to 40%  and white-backed (WB) sub-species intergrade into a full range of intermediate back-colour forms, a large population outside the town of Seymour in Victoria has been the subject of a long-term study. At this study site, a mix of BB, WB and intermediate plumage forms coexist and interbreed , and examine whether paternal, maternal or shared environmental effects significantly influence the narrow-sense heritability of back colour.The Seymour study site consisted of a north-south transect of approximately 14 km along a road in which magpie territories either side of the road have been the subject of long-term monitoring. Over the 24 years of this longitudinal study, more than 140 territories have been surveyed and observed, with well over 2000 birds banded and DNA-sampled. Field methods presented here vary little from those outlined in Hughes et al. . These tColour banding enabled subsequent confident identification of individual birds at a distance, using telescopes and binoculars. Back colours were recorded at time of banding, as well as each time an individual’s back-colour was confidently observed during subsequent monitoring of territory occupancy. The assignment of back colour was based on a five-morph (0–4) scale reflecting the relative proportion of dark anterior vs. light posterior coloured sections on the back , during the period spanning 1993 to 2010.Territories were selected based on several factors including: completeness of watching data  and whether all adult birds present were banded and bled. Back-colour observations for the 538 individual birds were calculated as the mean of all recorded sightings of a bird, helping to make back colour a somewhat continuous trait again. The number of back-colour observations per individual varied between 1 and 42 (mean = 6.89), and fledglings dominated the lower end of that spectrum, as many dispersed from their natal territory or died within the first year of being banded.C. tibicen and previously used for parentage analyses in magpie populations were utilised  of a phenotype  following Lynch and Walsh  for all parent combinations above. All parent/mid-offspring heritability estimates were weighted with an ICC (intra-class correlation coefficient), using iterative reweighting procedures until nd Walsh .A restricted subset of data, composed only of families in which paternity and maternity confidence levels (C.L.) were above 90%, and parent-fledgling trio or pairs did not mismatch at any loci, was also examined using the same set of regressions in order to assess confidence in, and congruence with the larger dataset. Results of these additional analyses are omitted here but available in Supplementary File Table A mixed-effects model , was also used to analyse heritability of back-colour. Animal models cope well with unbalanced designs and missing data, a common feature of long-term studies of natural breeding populations, and can utilise all of the complex familial relationships common in the pedigrees of natural breeding populations, as well as enable inclusion and estimation of potential sources of environmental variance  within the model. Animal models are also relatively robust to bias from inbreeding or assortative mating . Models were run for 100 million iterations  to reduce autocorrelation and were evaluated for convergence, good sampling and adequate effective sample size (ESS). Variance component ESS in all models ranged from 32,000 to 33,000. The posterior distribution of the heritability and its upper and lower 95% credible intervals were calculated, along with analogous variance components attributable to the random effects. Deviance information criterion (DIC) was used to compare model fit and complexity and determine if the inclusion of either or both random effects was justified (Hadfield The R package MCMCglmm (Version 2.22.1) Hadfield , implemeHadfield .The number of microsatellite alleles ranged from 4 to 24 per locus, while heterozygosity varied between loci, from 0.40 to 0.877 . The distribution of the back colour pairings reflect frequencies of back colours observed in the sampled population, with dark backed individuals occurring at a higher frequency than white backs at this location within the hybrid zone across a 24 year observation period  between female and male back colours of these mating pairs was not statistically significant  produced mostly light backed offspring, and two ‘dark’ backed parents (morph score 3–4) produced mostly dark backed offspring Fig. . HoweverHeritability estimates presented below reflect parentage assignment at a confidence level of ≥90%, and with ≤1 loci mismatch  is included in Supplementary File Table p ≤ 0.001)  Table . Regressh2 = 0.94, indicating a high level of heritability of magpie back colour variation  is considered the most robust regression-based measure of narrow-sense heritability out of all the types of kin relationships calculated in this study  (Table h2 = 0.84–0.92) . Model 2 was found to be most parsimonious; the addition of the random factor of year of birth substantially improved model fit for magpie back colour, with the lowest DIC value by a large margin. Therefore, to avoid biased parameter estimation, model 2 was chosen to estimate heritability of this trait  Table  agreed w2) Table . Fittingh2) estimates ranged between 0.3 and 0.8 (µ = 0.43), while fitness traits ranged between 0.05 and 0.3 (µ = 0.18) . Therefore, the high heritability estimates for magpie back colour variation found in this study (weighted mid-parent regression: h2 estimate (h2 = 0.92) than a model with no random effects (h2 = 0.91), indicating that not including the cohort term very slightly increased the error term of the model rather than artificially inflating the heritability estimate. If we accept the model with the lowest DIC, (Model 2: back colour and year of birth), the estimated h2 value of 0.92 (95% CI 0.8–0.99) is indeed highly congruent with h2 estimates obtained using weighted regressions of mid-parent on mid-offspring of 0.94.Maternal and paternal effects seem unlikely to have significantly influenced heritability estimates, as both maternal–offspring regressions and paternal–offspring back colour regressions estimated lower heritability values than mid-parent–offspring back colour regressions Table . The incOur analyses indicate a very-high level of back colour heritability in this population of Australian magpies—on par with melanin-based trait heritability estimates for other bird species. Several other points are consistent with this finding. First, magpie back colour has been observed to be stable at the level of individuals, both spatially and temporally, and does not materially change across seasons or moults. Second, melanin-based traits have often been shown to follow Mendelian laws of inheritance across several bird species, indicating these traits are under strong genetic control, rather than environmental or body condition-dependant Roulin . This maAlthough this study does not reveal information on the polygenic architecture of this trait, Hughes and Mather  suggesteThe range of offspring back colours produced by different crosses of parental phenotypes in this study Fig.  do show The low environmental contribution to magpie back colour variation implied by the high heritability estimates could be different in different populations or timeframes. This is because heritability can vary within and between populations, as well as temporally, and a pattern of higher heritability of morphological traits in ‘better’ or more stable environments may be a result of decreased environmental variance in such environments , play a large role in determining offspring back colour, and animal models indicate the environmental effects of patch quality and cohort also contribute very little to the heritability of this trait in magpies. Patterns of inheritance in familial groups suggest there may be some effect of dominance of black backed alleles, and that at least two genes are likely to play a role in determining magpie back colour. The high heritability of back colour variation found in this study also indicates that whole-genome sequencing may be profitable for further elucidation of the genetic basis of plumage colour variation in this species and others (e.g., Bourgeois et al. Data available from the Dryad Digital Repository: https://doi.org/10.5061/dryad.fc384rkSupplementary File 1Supplementary Tables and Figures


Ovis aries chromosomes (OAR). The identified SNPsaccounted for 18.76 % of the phenotypic variance for PCV, 21.24 % forlnFEC, and 34.38 % for FAMACHA scores. Analyses pointed out the importanceof OAR2 for PCV, OAR3 for FAMACHA scores, and OAR6 for lnFEC. The 125 kbregions surrounding the identified SNPs overlapped with seven previouslyreported quantitative trait loci (QTLs) for the traits analyzed in the current work. The onlychromosome harboring markers associated with the three traits studied wasOAR2. In agreement with the literature, two different chromosomal areas onOAR2 can play a major role in the traits studied. Gene-annotation enrichmentanalysis allowed us to identify a total of 34 potential candidate genes for PCV(6 genes), lnFEC (4), and FAMACHA scores (24). Annotation analysis allowed us toidentify one functional term cluster with a significant enrichment score(1.302). The cluster included five genes  involved inimmunity-related and cell-proliferation processes. Furthermore, thisresearch suggests that the MBL2 gene can underlie a previously reported QTL forimmunoglobulin A levels on OAR22 and confirms the importance of genesinvolved in growth and size (such as the ADAMTS17 gene on OAR18) for GIN resistancetraits. Since association studies for the ascertainment of the genetic basisof GIN resistance may be affected by genotype–environment interactions,obtaining information from local sheep populations managed in harshenvironments contributes to the identification of novel genomic areas offunctional importance for GIN resistance for that trait.A total of 184 Djallonké lambs from Burkina Faso with phenotypesfor packed-cell volume (PCV), log-transformed fecal egg count (lnFEC), andFAffa MAlan CHArt (FAMACHA©) eye scores were typed with the OvineSNP50 BeadChip of Illumina to contribute to the knowledge of the genetic basis ofgastrointestinal (GIN) parasite resistance in sheep. Association analysisidentified a total of 22 single-nucleotide polymorphisms (SNPs) related with PCV (6 SNPs), lnFEC (7), and FAMACHAscores (9) distributed among 14  It is of particular importance totropical production systems in which the costs of disease have beenestimated as 35 % to 50 % of turnover in market-oriented terms . Furthermore, there is an increasing interest to limit the use ofanthelmintic medicine to avoid the emergence of negative consequences for humanhealth such as the emergence of resistant strains of parasites and thepresence of drug residues in animal products .Among different alternatives, the implementation of selection schemes aimingto increase resistance to GIN infections has been proposed . However, recording indirect indicators of GIN resistance, immune response to infection  levels), or the impact of infection  is difficult, frequently invasive, and dependent on theexistence of an efficient performance recording system. Furthermore, mostheritability estimates reported for traits related to GIN resistance variedfrom moderate to low . Therefore, selection for such a complex phenotype iscostly and time consuming. These requirements are not frequently met in thesmall-ruminant industry, particularly in low-income smallholder systemswhich are predominant in developing countries .Molecular markers could be used to enhance the genetic response to selectionfor GIN resistance. However, the identification of candidate genes for GINresistance has remained elusive. Quantitative trait loci (QTL) studies,using microsatellite markers, identified multiple significantly associatedloci, scattered throughout the ovine genome, with little regions of overlapamong the results reported . The availability of medium-density single-nucleotide polymorphism (SNP)chips has allowed us to refine the information obtained from previous QTLstudies . However, gathering information from different sheeppopulations is still needed to reach a deeper understanding of the geneticarchitecture of GIN resistance. Different breeds may vary in ability to liveand produce in environments highly contaminated with GIN larvae . Furthermore, genotype–environmentinteractions may be expected if animals are managed in environments thatdiffer in terms of the extent of parasite challenge or the quality ofavailable nutrition . Therefore, information about GINresistance from a broad array of sheep populations is still a pending issue.Recently, a field trial designed to ascertain the factors affectinggastrointestinal parasite resistance in Djallonké (West African Dwarf)lambs of Burkina Faso was performed . Thisinformation was partially used to estimate genetic parameters and estimatedbreeding values (EBVs) for GIN resistance traits using pedigree-free animalmodels . Within this context, a genome-wideassociation study (GWAS) was performed in the Djallonké sheep ofBurkina Faso to contribute to the knowledge of the genetic basis of GINresistance in sheep.22.1Performance data were obtained from a trial designed to assess environmentalfactors affecting gastrointestinal parasite resistance in Djallonkésheep of Burkina Faso. Morphological and genetic description of this sheeppopulation can be found in Traoré et al. (2008) and Álvarez et al. .Data were obtained from (a) a field trial, involving 434 lambs, carried out in the surroundings of Mangodara (Comoé province)located in the southern Sudan to Guinea savannah region of Burkina Faso duringthe rainy season 2014  and from (b) 19 lambssampled in Dédougou (Mouhoun province), southwestern Burkina Faso, andsubmitted simultaneously to the same protocol at the facilities of theKamboinsé station of the INERA  nearOuagadougou . Climateand sheep management in southern Burkina Faso was previously describedin Álvarez et al. (2009) and Traoré et al. (2017). Briefly, thehumid southern sudan to Guinea savannah region of Burkina Faso covers fromlatitude 9As reported by Traoré et al. (2017), lambs were dewormed with levamisol,following the manufacturer's recommendations, at a dose of 7.5 mg kghttp://snp.toulouse.inra.fr/~alegarra/, last access: 15 May 2015). Briefly, modelsfitted for analyses  included the following fixedeffects: (a) contemporary group (10 levels) formed by the individuals of thesame sex and the same age (in months) assessed in either the field trial(Mangodara) or the station trial (Kamboinsé), (b) days after deworming(two levels: days 28 and 35), and (c) body weight at the moment of assessmentas a linear covariate. Models also used the following random effects: (a) permanentenvironment associated to the individual and (b) the additivegenetic effect. FAMACHA scores were treated assuming a threshold model. EBVsestimated for PCV, lnFEC, and FAMACHA scores using univariate models werefurther used as phenotypes for association analyses.Up to 271 individuals (252 sampled in Mangodara) yielded blood samples useful for DNA extraction using standardprocedures . As described in Álvarez et al. (2018), 29 microsatellites were typed on all samples  to infer an artificialpedigree of 10 discrete generations using the software MOLCOAN. The algorithmimplemented in MOLCOAN maximizes the correlation between the coancestrymolecular matrix, given the data, and the genealogical coancestry matrixbuilt from the artificial pedigree. Performance data and the artificialpedigree were used to estimate genetic parameters and EBVs for PCV, lnFEC,and FAMACHA scores under a Bayesian approach using the program TM. The use of “yield deviations”  estimated by adjusting performance for major environmental factorsaffecting phenotypes is a widely used alternative. However, the adjustmentof performance for the main environmental effects in our data was difficult:although the householder of the individuals was recorded, the identificationof the communal grazing unit  to which the animals belonged was not known; actual pedigrees were notknown and major effects such the membership to a given litter of the familyeffect  could not be included in the model to be fitted;and, finally, the actual age was not known but approximated by examiningdentition. In such a scenario, we opted to use the EBVs estimated inÁlvarez et al. (2018). Note that the EBVs used are basically thephenotypes corrected for the influence of a molecular relatedness matrixconstructed using microsatellites.2.2http://www.illumina.com, last access: 12 March 2019). The software GenomeStudio  was used to generate standard .ped and .map files. Sample andmarker-based quality control measures were performed using the softwarePLINK v 1.07 . A GenCall score cutoff of 0.15 andan average sample call rate of 99 % were considered. All unmapped SNPs, thosemapping to sexual chromosomes, SNPs with a genotyping rate lower than 90 %,or those below a minor allele frequency (MAF) threshold of 0.05 were removed. Toavoid departures from Hardy–Weinberg proportions due to genotyping errors,SNPs that did not pass Hardy–Weinberg test for Only 184 DNA samples  from both the Mangodara(166) and the Dédougou (18) trials had enough quality to be analyzedusing the OvineSNP50 BeadChip following standard protocols and Arlequin 3.5  were used to compute parameters characterizing geneticdiversity of dataset , which calculates maximumlikelihood estimates of individual ancestries based on data provided bymultiple loci. Analyses were conducted for 2.3http://CRAN.R-project.org/, last access: 15 May 2019). The pLARmEB method integrates aleast angle regression with empirical Bayes method to perform multi-locusgenome-wide association analysis (GWAS) under polygenic background controlusing an algorithm of model transformation that whitens the relationshipmatrix of the polygenic matrix SNPs associated with EBVs estimated for PCV, lnFEC, and FAMACHA scores wereidentified using the polygenic-background-control-based least angleregression plus empirical Bayes method implemented in the mrMLM software package  of R using the BioMarttool . All the identified genes were processed usingthe functional annotation tool implemented in DAVID Bioinformatics Resources6.8  to determine enriched functional terms. Anenrichment score of 1.3, which is equivalent to the Fisher exact testCandidate genes were considered if their boundaries fell within 125 kb upstreamor downstream of the significant SNPs . Protein-codinggenes found within the candidate regions were retrieved from the EnsemblGenes 91 database, based on the Oar v3.1 ovine reference genome. TheintersectBed function of the BedTools software  wasused to overlap these QTLs with the identified candidate regions.The ovine QTLs previously mapped on the ovine Genome AssemblyOar_v3.1 were downloaded from the sheep QTL database(33.1Mean (and SD) of the EBVs estimated for PCV, lnFEC, and FAMACHA scores were1.430 (1.126), 0.278 (0.378), and 0.189 (0.408), respectively. Theheritabilities estimated using the same models fitted for estimating theEBVs used was 3.2Ovis arieschromosomes (OAR). SNPs associated with the three traits analyzed were onlyfound on OAR2. SNPs associated with PCV and lnFEC were found on OAR8, whileSNPs associated with PCV and FAMACHA scores were found on OAR18. Theidentified SNPs accounted for 18.76 % of the phenotypic variance for PCV(varying from 0.02 % to 7.37 %), 21.24 % for lnFEC (from 0.07 % to8.10 %), and 34.38 % (from 0.93 % to 6.48 %) for FAMACHA score.The pLARmEB algorithm identified a total of 22 SNPs (Table 1) associatedwith PCV (6), lnFEC (7), and FAMACHA scores (9) distributed among 14 Gene-annotation enrichment analysis allowed us to identify a total of 34potential candidate genes in the 125 kb upstream and downstream regionssurrounding the SNPs associated with performance for PCV (6 genes), lnFEC (4genes) and FAMACHA scores . Twelve out of 22 regionsassociated to the identified SNPs  did not span candidate genes based on the Oar v3.1 ovinereference genome (Table 1). This set included the two SNP regions associatedwith PCV and lnFEC on OAR8.TRIB3, CDK4, CSNK2A1, MARK1, andSPATA5) involved in immunity-related and cell-proliferationprocesses (Table S2).A full description of the 34 potential candidate genes is given in Table S2.Functional annotation informed that these genes were associated withdifferent biological functions, including spermatogenesis and transmembraneand kinase activities (Table S2). Annotation analysis carried out on these34 genes identified three functional term clusters (Table S3). However, only oneof them had an enrichment score higher than 1.3 (Table 2). The cluster includedfive genes  overlapped with the regions surrounding SNPsOAR2_117867801.1 and OAR2_64824262.1,associated with PCV and FAMACHA scores, respectively (Table 2); (b) on OAR8one QTL for FEC  overlapped with SNPOAR8_8982479.1 (lnFEC) and two QTLs for Trichostrongylus adult and larvacount reported by Crawford et al. (2006) overlapped the regions surroundingthat SNP and marker OAR8_16568165.1 (PCV); (c) on OAR13 a QTLfor FEC  overlapped with the region surrounding SNPs09612.1 (FAMACHA scores); (d) on OAR18 a QTL for hematocrit  overlapped with the region on which SNP OAR18_5508052_X.1 (FAMACHA scores) was located; and finally (e) aQTL for immunoglobulin A level reported by Atlija et al. (2016) on OAR22overlapped with the region surrounding marker OAR22_6293170.1(FAMACHA scores).A total of 102 QTLs previously reported on 10 different chromosomes indomestic sheep intersected with the 125 kb regions surrounding 19 out of the22 SNPs associated with PCV, lnFEC, and FAMACHA EBVs in Djallonké sheep(Table S4). Only seven of these QTLs were directly related with the traitsanalyzed in the current work: (a) a QTL for The other QTLs listed in Table S4 were associated with various traits,namely related to weight and growth , fleece, carcass , meat ,and dairy traits .4Although most of the analyzed individuals were sampled from a single localpopulation, the available dataset gathered a noticeable diversity .Djallonké sheep is a basically unselected population and, therefore,wide differences among individuals' performance are expected. Usingmicrosatellites, Álvarez et al. (2018) also identified some geneticstructuring in the current population. These authors considered structuringa consequence of different unexpected founder events. In Burkina Faso,management of Djallonké sheep flocks is carried out under verytraditional conditions  with no supervised matings.In any case, Álvarez et al. (2018) explained that individuals classifiedinto different genetic clusters were randomly distributed among thecomparison groups used in the model fitted for the estimation of the EBVs.Therefore, the EBVs used here as phenotypes for association analyses are notlikely to be biased. Overall, the variability included in the currentdataset can be considered useful for the intended purposes.4.1IFNG) locus  and within or adjacent to themajor histocompatibility complex (MHC) region  . GWASrefined the information provided by QTL analyses: the putative importance ofOAR3 and OAR20 regions on GIN resistance was confirmed but some regions onOAR1 and OAR6 tended to appear consistently across studies . In addition, Sweeney et al. (2016) suggested that chromosomalregions of OAR14, probably linked to the toll-like receptor IRF3 gene , could be important for resistance to GIN infection.Microsatellite-based QTL approaches identified a very wide number ofchromosome regions with small to moderate effects associated with GINresistance in sheep . In any case, analyses established a consensus about the importance ofchromosomal regions surrounding the interferon gamma  for GIN resistance in sheep. Causes ofsuch a lack of consistent results among studies are (a) the age of individualsused, with key pathways preventing primary parasite infections (in lambs)probably being different from those involved in subsequent infections inadult sheep ; (b) GIN type challenge, withsome marker regions probably being exclusive to particular parasite species; (c) environmental  differences, which may interact with differences in geneticbackground among the sheep populations studied ; and (d) thequantitative nature of host resistance, probably determined by multiplegenes with varied effects rather than by a limited number of major genes. Furthermore, a non-negligible practical concern isthat GIN resistance studies assume that all animals have been exposed duringthe same time to the same level of infective larvae in the pasture. Whilethis assumption is almost impossible to fulfill in a grazing environment,data coming from field trials, such as that implemented in Djallonkésheep , can minimize someenvironmental factors affecting results.The current work did not identify significant SNPs associated to GINresistance neither on OAR14 nor on OAR20. Although information on theimportance of OAR14 is less apparent, many studies have reported associationbetween OAR20 and for GIN resistance traits . However, exceptions are not rare: several QTL and GWASstudies have not yielded associations between such performance and the areassurrounding the MHC region . Furthermore, no clearselection signatures have been reported in this region of OAR20 usingmedium-density SNP Chips . The highly polymorphic natureof the MHC region is likely to make it difficult to identify SNPs useful forselection for GIN resistance .In any case, the current research relatively agrees with the importance ofOAR3 and OAR6 for GIN resistance traits.INFG locus (Table 1). This scenario is consistent withexpectations suggesting that several OAR3 areas harbor genes with a role inGIN resistance traits .Only performance for FAMACHA scores was associated with markers located onOAR3. The two SNPs identified  were located in distant chromosomalareas with marker OAR3_161498140.1 located in the relativevicinity of the Finally, the region surrounding marker s16493.1 on OAR6 (Table 1) had noannotated genes and no information on possible functional pathwaysunderlying the control of lnFEC was obtained. In any case, marker s16493.1is located in the vicinity of segment N of OAR6 on which several genes(mainly belonging to the toll-like receptor and chemokine C-X-C motif ligandfamilies) considered putative candidates to act on GIN resistance havebeen identified .Furthermore, the current research suggests that OAR2 may play a major rolein GIN resistance traits. Our results are highly consistent with theprevious identification of two different SNP clusters on OAR2 influencingaverage FEC in Red Maasai 4.2There is scientific consensus on the quantitative nature of GIN resistance. Literature is clear in consideringthat the genetic basis of GIN resistance traits is not only related to genesinvolved in immune response and acquired immunity but also to genes involvedin the gastrointestinal mucus production, parasite expulsion, and hemostasisregulation . In this scenario, the identification of candidate genes with majoreffects on performance is unlikely and literature gives evidence on multiplepotential candidate genes affecting GIN resistance.TRIB3 gene, on OAR13, encodes a pseudokinasewhich is upregulated in macrophages and suppresses cytokine expression . The CDK4 gene, on OAR3, encodes a protein belonging to theserine/threonine kinase family acting on cell proliferation during the cellcycle G1 phase . Encoding a protein belonging to thesame kinase family, the CSNK2A1 gene on OAR13, is involved in cell cycle controland apoptosis . The MARK1 gene, on OAR12, encodesa kinase involved in the regulation of cell shape and polarity duringdifferentiation, chromosome partitioning at mitosis via the phosphorylationof microtubule-associated proteins . Finally, theSPATA5 gene, on OAR17, encodes an ATPase enzyme involved in cellular developmentprocesses and the maintenance of mitochondrial integrity and function.The functional cluster for GIN resistance identified in the current research(Table 2), including genes located on OARs 3, 12, 13, and 17, is consistentwith expectations. In a meta-analysis, Sayre and Harris (2011) identified upto 14 functional pathways which were common to QTL and gene expressionstudies for GIN resistance in domestic sheep. Among them, the upregulatedpathways were related to immune functions and responsiveness to externalsignals, while the downregulated pathways were related to cell activity,immune function, and disease. The ADAMTS17 (OAR18_5508052_X.1) and theMBL2 genes (OAR22_6293170.1), respectively. The MBL2 gene encodes aserum lectin involved in innate host defense . Thiswould confirm the function of the QTL for immunoglobulin A levels reportedby Atlija et al. (2016) on OAR22. In turn, the ADAMTS17 gene encodes ametalloprotease with a role in extracellular matrix degradation and involvedin stature in humans  with no clearrelationship with the QTL reported for hematocrit by Silva et al. (2012). Inthis respect, it is worth mentioning that most QTLs overlapping the regionssurrounding the SNPs associated in Djallonké sheep with GIN resistancetraits were mainly involved in growth and size (see Table S4). This is notsurprising according to the literature: a significant number of QTLsidentified for GIN resistance traits seems to be also related to otherperformance traits . It is well known that,phenotypically, live weight and growth gain are highly correlated with GINresistance in lambs . Although EBVsused as phenotypes in the current research were estimated including liveweight as a covariate in the model fitted for additive genetic analyses, an influence of the genes involved in growth andsize on GIN resistance are still likely to exist, at least for OAR18. Whether a QTL associated with bothGIN resistance and performance traits results from pleiotropy or theexistence of various loci in close linkage remains unascertained.It is worth discussing the consistency of our findings with previous QTLinformation available in the literature. Most SNPs overlapping sevenpreviously reported QTLs for GIN resistance traits did not span candidategenes in their surrounding areas. However, two markers associated withFAMACHA scores overlapped with previously reported QTLs for hematocrit and immunoglobulin A levels neighboring the 5Ovis aries chromosomes. Some of theseareas harbored candidate genes involved in immunity-related and cellproliferation processes  of functional importance for the traits analyzed.Furthermore, new insights into the importance of both OAR2 and candidate genesinvolved in growth have been obtained. This research confirms the importanceof obtaining information from local sheep populations managed in harshenvironments to gather information on genomic areas of functional importancefor GIN resistance.In this research 22 novel genomic areas of putative importance for GINresistance have been identified in 14 different 10.5194/aab-62-313-2019-supplementThe supplement related to this article is available online at: https://doi.org/10.5194/aab-62-313-2019-supplement.


The availability of both pedigree and genomic sources of information for animal breeding and genetics has created new challenges in understanding how they can be best used and interpreted. This study estimated genetic variance components based on genomic information and compared these to the variance components estimated from pedigree alone in a population generated to estimate non-additive genetic variance. Furthermore, the study examined the impact of the assumptions of Hardy–Weinberg equilibrium (HWE) on estimates of genetic variance components. For the first time, the magnitude of inbreeding depression for important commercial traits in Nile tilapia was estimated by using genomic data.The study estimated the non-additive genetic variance in a Nile tilapia population of full-sib families and, when present, it was almost entirely represented by additive-by-additive epistatic variance, although in pedigree studies this non-additive variance is commonly assumed to arise from dominance. For body depth (BD) and body weight at harvest (BWH), the proportion of additive-by-additive epistatic to phenotypic variance was estimated to be 0.15 and 0.17 using genomic data (P < 0.05). In addition, with genomic data, the maternal variance (P < 0.05) for BD, BWH, body length (BL) and fillet weight (FW) explained approximately 10% of the phenotypic variances, which was comparable to pedigree-based estimates. The study also showed the detrimental effects of inbreeding on commercial traits of tilapia, which was estimated to reduce trait values by 1.1, 0.9, 0.4 and 0.3% per 1% increase in the individual homozygosity for FW, BWH, BD and BL, respectively. The presence of inbreeding depression but lack of dominance variance was consistent with an infinitesimal dominance model for the traits.The benefit of including non-additive genetic effects for genetic evaluations in tilapia breeding schemes is not evident from these findings, but the observed inbreeding depression points to a role for reciprocal recurrent selection. Commercially, this conclusion will depend on the scheme’s operational costs and resources. The creation of maternal lines in Tilapia breeding schemes may be a possibility if the variation associated with maternal effects is heritable. This work is a part of a wider study on the non-additive genetic effects in Nile tilapia and their potential use in tilapia breeding programs. A previous study  used thePedigree-based selection methods have been gradually supplemented with genomic information in various livestock species , and in The genomic best linear unbiased prediction (GBLUP) model for a trait builds a matrix of relationships between all individuals based on genomic data, and uses it to partition the phenotypic variance into components, and hence to predict breeding values. Assumptions made in methods for constructing the relationship matrix have a direct effect on the accuracy of the results. There are a variety of methods for constructing relationship matrices using genomics, which typically differ in the scaling parameters –8 used, Inbreeding depression is a natural phenomenon that is widely assumed to be deleterious for traits of commercial importance and, thus, can have major practical implications –17. Its e latter , 19. ThuOn a wider scale, our aim was to assess the impact of genomic methods on dissecting maternal, additive and non-additive genetic variances in important commercial traits of Nile tilapia. The insights gained into their genetic architecture will provide important information for the future design and operation of breeding programs in an important commercial species.The population used in this study and the experimental design were previously described . In shorOf these 2524 fish, 1882 individuals were genotyped with the Onil50 SNP-array , and were used for further analysis. Additional file m had an effect for each maternal family.ASReml-4  was usedThe distributional assumptions for the random effects were multivariate normal, with means zero andollowing –26. The The phenotypic variance was calculated as The relationship matrices mentclasspt{minimamentclasspt{minimaVanRaden .Let As noted above and shown in Additional file Calculatingandin the HWE approach. Both the contrasts and the scaling differ between the HWE and NOIA approaches to calculate In the HWE approach, the relationship matrix for mentclasspt{minimant}k = 1 . If the Calculatingandin the NOIA approach. The NOIA approach removes the assumption of HWE by using dominance values of The matrix Note that, in general without HWE, The relationship matrix for epistatic effects Scatterplots for different relationship matrices are presented in Additional file The software used to calculate the matrices  does not0: 1: 0 for one, two, and three components were:Likelihood ratio tests (LRT) were used to measure the goodness-of-fit of each model and compared to the LRT of the full model ADME. Critical values for type I error rates of 0.05, 0.01 and 0.001 were corrected for boundary effects when testing the null hypothesis Hollowing . These wThe six traits analyzed could be differentiated into three distinct groups based on their likelihood ratio tests for the various models Table : BD and Detrimental effects of genomic homozygosity were evident for all six traits, although the magnitude differed between traits. BWH and FW were more affected by inbreeding than the other traits, with a 0.01 fractional decrease in the trait per 0.01 increase in the individual homozygosity . Inclusion of maternal environmental effects reduced estimates of the additive genetic variance compared to estimates from the A model, which implies that some of the variance associated with dams was attributed to additive genetic effects in the A model. Including a maternal effect (AME model) also reduced estimates of the additive-by-additive epistatic variance compared to the AE model. As above, these reductions were minimal for BT and FY. The NOIA and HWE approaches resulted in similar estimates of variance components for all models. Thus, in the following, reported estimates are based on the NOIA approach , unlessModel-dependent differences in estimates of additive variance were also reflected in the estimates of narrow sense heritability. For BT and FY, for which the best-fit model was the A model, heritabilities were the least dependent on the model used. For the other traits, the differences in estimates of heritability between models were as large as 50% for some traits. For the best-fit models, the estimates of heritability were low to moderate, ranging from 0.08 ± 0.03 for BL to 0.19 ± 0.04 for FY .A major finding of this study is that the use of genomic relationship matrixes found non-additive genetic variance to be almost entirely represented by additive-by-additive epistasis. In pedigree-based analyses, the primary source of non-additive variance is commonly assumed to be dominance , 31, 32,ariances . Under aariances , 34. ThuA), while for genomic-based analyses using Van Raden matrices [The findings from this study add a new dimension to our previous paper . The avamatrices  the estimentclasspt{minimaWhen models include non-additive genetic components, there are additional reasons why estimates of variance components can differ between models using different relationship matrices (e.g. pedigree and genomic models). In the tilapia population studied here, the estimate of additive variance is qualitatively different when the source of non-additive variation is assumed to be dominance instead of additive epistasis (see Additional file Genomic models were more robust to misspecification in partitioning the variance among the components of the genetic and environmental models, which is another potential cause of differences between genomic and pedigree-based models. The greater robustness of the genomic model was clearly observed when the A model was fitted to traits for which the genetic architecture was found to be more complex (results are in Additional file GBLUP uses a genomic relationship matrix, VanRaden , assume ly Table . Such qumentclasspt{minimaThe NOIA and HWE approaches are statistical models in that they partition the variance observed in a population and use the resulting estimates of variance components to estimate breeding values and dominance deviations . As suchAbsence of dominance variance does not imply absence of inbreeding depression when the genetic architecture of a trait approaches the infinitesimal model, and we found evidence for inbreeding depression in precisely the four traits for which the A model was rejected. To the best of our knowledge, these estimates of inbreeding depression are the first reported for commercial traits in Nile tilapia. Most of the previous quantifications of inbreeding depression were based on pedigree information in other aquaculture species, e.g. –47, and In a polygenic model, allelic additive effects (ch as in , since fIn the long term, additive-by-additive epistatic variance is expected to be exploited indirectly because it is converted to additive genetic variance due to random drift and selection. As a result, additive-by-additive epistatic variance affects medium and long-term selection response indirectly . This arNevertheless, for some traits, substantial additive-by-additive epistasis was observed although epistatic variance is expected to be much smaller than the additive genetic variance in elite commercial populations , 50. ThiOur findings show that the non-additive genetic variance in the Nile tilapia population was almost entirely additive-by-additive epistatic variance, when using genomic relationship matrices, while these non-additive effects were found to be associated with dominance when using pedigree-based relationship matrixes. Presence of inbreeding depression and lack of dominance variance were consistent with an infinitesimal dominance model for the studied traits. Finally, creating maternal lines in Tilapia breeding schemes may be desirable if the observed maternal variance is heritable.Additional file 1: Table S1. Number of animals genotyped in different full-sib families. Table S2. Descriptive statistics for the six traits. Table S3. Mean values of the genomic relationship matrices constructed with NOIA and HWE approaches. Table S4. Transformation of the variances on a similar scale based on the relationship matrices.Additional file 2: Figure S1. Scatterplots, histograms and the correlations of the 6 traits studied. Figure S2. Scatterplots for different additive and dominance relationships. Figure S3. Scatterplots for different additive, dominance and epistasis relationships using NOIA and HWE approaches. Figure S4. Scatterplot and correlation of the additive and dominance relationships using imputed and without imputed genotypes. Figure S5. Density plots, scatterplot and LOESS regression between the selected and non-selected individuals.Additional file 3. Equivalence of contrasts used to construct Ha for HWE and NOIA genomic relationship matrices. The file presents the proof that the difference between Additional file 4. Assumptions on the nature of non-additive genetic variance and the impact on estimates of additive genetic variance. The file presents the numerical proof that the estimate of additive genetic variance is reduced when the non-additive variation is assumed to be additive-by-additive epistasis rather than dominance, and this reduction is of the order of 3/8 epistatic variance.Additional file 5. Impact of inbreeding depression on models. The file contains the tables with the models without individual homozygosity as the covariate to account for the impact of the inbreeding depression in the models.


Genetic epistasis is an often-overlooked area in the study of the genomics of complex traits. Genome-wide association studies are a useful tool for revealing potential causal genetic variants, but in this context, epistasis is generally ignored. Data complexity and interpretation issues make it difficult to process and interpret epistasis. As the number of interaction grows exponentially with the number of variants, computational limitation is a bottleneck. Gene Network based strategies have been successful in integrating biological data and identifying relevant hub genes and pathways related to complex traits. In this study, epistatic interactions and network-based analysis are combined in the Weighted Interaction SNP hub (WISH) method and implemented in an efficient and easy to use R package.The WISH R package (WISH-R) was developed to calculate epistatic interactions on a genome-wide level based on genomic data. It is easy to use and install, and works on regular genomic data. The package filters data based on linkage disequilibrium and calculates epistatic interaction coefficients between SNP pairs based on a parallelized efficient linear model and generalized linear model implementations. Normalized epistatic coefficients are analyzed in a network framework, alleviating multiple testing issues and integrating biological signal to identify modules and pathways related to complex traits. Functions for visualizing results and testing runtimes are also provided.The WISH-R package is an efficient implementation for analyzing genome-wide epistasis for complex diseases and traits. It includes methods and strategies for analyzing epistasis from initial data filtering until final data interpretation. WISH offers a new way to analyze genomic data by combining epistasis and network based analysis in one method and provides options for visualizations. This alleviates many of the existing hurdles in the analysis of genomic interactions.The online version of this article (10.1186/s12859-018-2291-2) contains supplementary material, which is available to authorized users. High throughput genotyping data have been used extensively in many contexts to explain phenotypic variation of complex traits in a wide range of Genome Wide Association Studies (GWAS). GWAS can however, only partially explain observed phenotypic variation , and phe11 computations and a large memory consumption, both generally intractable. Therefore, it is important to have strategies to properly filter and reduce input data dimensionality. In general, when analyzing a specific trait it is assumed that most variants are not causal or associated with the trait. Furthermore, many variants will be in high linkage disequilibrium (LD) when using modern high-density genotyping arrays, meaning that their resulting interactions will be highly correlated. Thus, it is not only necessary to filter the input space due to computational issues, but also meaningful from an analysis perspective.Calculation of epistasis is a computational challenge, even on modern computing facilities. To calculate first order epistatic interactions, that is, interaction between pairs of genotypes, of N loci, it is necessary to do minimum Beyond computational issues, interpretation of epistatic interactions can also be difficult. As the number of tests increases to the square of the input, multiple testing correction will be very stringent, making it difficult to rely on individual interactions. From a biological perspective, it would be useful to look at groups of genes and pathways instead of focusing on single variants. One way of integrating and combining signal from multiple sources is to use network-based strategies. Using networks-based methods is a useful and successful approach in identifying pathways and genes related to complex traits , 11. A wWe have developed an efficient and easy to use R package based on the WISH method and added several features including LD based data dimensionality reduction. Using input genotypes and a phenotype the WISH R package filters the data, calculates genome-wide epistatic interactions and generates biologically meaningful networks.The WISH R package is based on the WISH method . The inp2 measure of LD [2 values between all variant pairs is above a selected threshold. When the blocks are identified, the variant with the highest average r2 in the block is selected as a representative for the block.Once a suitable set of variants has been selected it is possible to further filter the data by using LD. Variants in high LD are redundant and will lead to the same nearly identical models being estimated several times. In the context of WISH-R we are not interested in a probabilistic measure of LD, but in the observed LD in a given dataset. If an allele is co-occurring with another allele in a data set they will yield similar epistatic interactions regardless of allele frequencies and sample sizes and we therefore use the rre of LD . In pracThe main computational challenge is the calculation of epistatic interactions. Therefore, we have several tools to optimize the calculations of the models. The model used for calculating the epistasis is a heterogeneity model , 17:\docy represents a phenotype of interest, μ is the intercept, β1 and β2 are the SNP main effects, ϵ is a noise term and most importantly β3 represents the epsitasis of the two loci. To represent the genotypes snpj and snpi we code genotype data as 2 , 1 (hetrozygote) and 0 . The selection of the values for the genotype affects the model hypothesis. Here there is an assumption of multiplicative interaction between minor alleles in the two sites. We also test for the opposite but mathematically identical model by reversing the minor and major homozygote labels in one of the loci. This test is in case the interaction is between minor and major alleles. There is one more parametrization available in the package, which is 2 , 1.5 (heterozygote) and 1 . This parametrization tests interaction on the gradient of one allele pair set to the other allele pair, which means that all four alleles are involved in the interaction. This is more powerful description but also more difficult to fit as it requires all four alleles to be related to changes in the phenotype for an optimal fit. In the package there is also a generalized linear model (GLM) implemented so that case-control studies (where case-control are coded in binary form as 1–0) can be analyzed. The GLM version is about twice as slow as the non-binary version, as it fits an underlying liability threshold models. The basic linear model uses implementations linked to underlying C++ code, ensuring fast computations of epistatic interactions. The algorithm is fully parallelized. A test setting is included to test runtimes based on input data and the number of threads used.Here β3in the model) to correlations by rescaling them from − 1 to 1. This is done by treating the negative and positive β3 separately to insure that values close to zero correspond to a correlation of zero. The resulting similarity matrix is then used to calculate the topological overlap measure (TOM) [The original idea of WGCNA was based on using correlations in expression data to find interconnected gene. From there it is a natural extension to genomic interactions in networks, by converting the epistatic estimates (the re (TOM) . The nexre (TOM) .Visualizing high dimensional data from epistasis in an informative and meaningful way can be a challenge. In the WISH R package, we have implemented several functions for visualizing and summarizing epistatic interactions. The first method is a pseudo Manhattan plot, based on calculating the sum of -log likelihoods for each variant across all tested interactions. See Fig. When dealing with epistasis it is important to have efficient algorithms. We tested the performance of this part of the package using randomly simulated phenotypes and genotypes. In Fig. SNPassoc [EPIBLASTER [10 possible pairs. Their implementation does not include the epistasis modelling, requiring more work to get the epistasis results., FastEpistasis [In general, it is difficult to compare methods that calculate epistasis as different models and definitions of epistasis are used. SNPassoc  can calcIBLASTER  reports pistasis  has a sipistasis  also focpistasis  offers vpistasis  are ableEpistasis in an important component of genetic variation and may have causal effects in certain diseases or complex trait manifestation in humans, animals, plants and other organisms. However, analysis of epistasis on genome-wide scale is an overlooked subject with several challenges, mainly interpretation and data dimensionality issues. We have previously proposed the WISH method for calculating epistasis and applying the results in a network framework, thus offering solutions for some of the main issues in the analysis of epistasis. Here we have implemented WISH-R, an efficient R package for calculating linear interaction between genomic variants from standard genotype data and generating modules of groups of interacting variants. WISH-R is easy to install and use, and provides tools for analyzing epistasis in complex traits and diseases based on whole genomic data from data filtering to final interpretation.Project name: WISH-R package.Project homepage:https://github.com/QSG-group/wishOperating system: Platform Independent.Programming Language: R.Other requirements: R 3.0 or >.License: GPL-3.Restrictions to use by non-academics: license needed.Additional File 1:Figure S1. Example visualization of the package function pairwise.chr.map function displaying the strength of epistatic interaction between regions on two chromosomes. (DOCX 38 kb)Additional File 2:Figure S2. Visualization of the runtime scaling of the method based on changes in sample size. (DOCX 27 kb)