APOC3 ) associated with plasma triglyceride (TG) levels ) discovered in 3,202 individuals with low read-depth, whole-genome sequence. We replicate this in 12,831 participants from five additional samples of Northern and Southern European origin (−1.0 s.d. (s.e.=0.173), P-value=7.32 × 10−9). This is consistent with an effect between 0.5 and 1.5 mmol l−1 dependent on population. We show that a single predicted splice donor variant is responsible for association signals and is independent of known common variants. Analyses suggest an independent relationship between rs138326449 and high-density lipoprotein (HDL) levels. This represents one of the first examples of a rare, large effect variant identified from whole-genome sequencing at a population scale.The analysis of rich catalogues of genetic variation from population-based sequencing provides an opportunity to screen for functional effects. Here we report a rare variant in  Population-based genome sequencing provides an increasingly rich resource for the identification of low-frequency, large effect variants associated with clinically important phenotypes. Timpson et al. use UK10K data to identify a variant of the APOC3 gene strongly associated with plasma triglyceride levels. Lipid levels are heritable risk factors for coronary artery disease, and vascular outcomes and their therapeutic manipulation has well-characterized impacts on disease risk2PCSK9 (ref. Rare and highly penetrant variants identified in 24 different human genes have been identified through sequencing studies in families with rare monogenic lipid disorders67910SK9 ref. .http://www.uk10k.org/studies/cohorts.html) uses WGS to study the contribution of low-frequency and rare variation on a broad range of complex quantitative endpoints. Here we applied low read-depth WGS in individuals from two deeply phenotyped British cohorts, TwinsUKAPOC3 gene  strongly associated with plasma TG levels. This represents one of the first examples of a rare, large effect variant identified from WGS at a population based scale.The UK10K Cohorts project =0.99, To search the human genome for low-frequency and rare variants associated with TG levels, we first tested associations with 13,074,236 single-nucleotide variants (SNV) and 1,122,542 biallelic indels (MAF ≥0.1%) called from whole-genome-sequence data . AssociaP-value ≤1 × 10−7) across the UK10K sample and were of interest as they mapped to a region around the APOC3 locus on chromosome 11. Two of the variants found are common, rs964184-C =0.13%, P-value=6.81 × 10−9) and rs66505542-T , in near-complete linkage disequilibrium (r2=0.90 in the UK10K sample) and have been previously associated with TG levels2No variants gave evidence of association at conventional levels of genome-wide significance. However, four variants reached a second more exploratory tier of evidence for discovery in tests of association with TG  in the combined sample of 3,202 TwinsUK and ALSPAC participants with whole-genome sequence data , suggesting that this variant contributes to decreasing TG levels in multiple populations of Northern and Southern European origin, with similar effect sizes and allelic frequency.We first validated whole-genome sequence-derived rs138326449 genotypes using overlapping genotype calls and showed perfect concordance in both TwinsUK and ALSPAC (Methods). We then took forward the variant for replication in five additional cohorts  and with a moderate increase in HDL in 16,062 study participants (0.624 (s.e.=0.143), P-value=1.36 × 10−5; We further tested association of the splice variant with the other three main lipid sub-fractions HDL, LDL and total cholesterol (TC), and with very-low-density lipoprotein (VLDL). The A allele at rs138326449 was associated with decreased VLDL levels in a combined sample of 7,891 participants with available data (−1.312 (s.e.=0.199), Analysis of the residual association between rs138326449 and TG levels conditional on other lipid-sub-fractions showed expected patterns in both children and adults when taking into account lipid-lowering drugs. Adjustment for VLDL removed the association between rs138326449 and TG levels. In the children of the ALSPAC study, there was also evidence of association between rs138326449 and HDL levels after adjustment for either TG or VLDL, which was also seen  in the 1958 British Cohort .APOC3 region. We focused on a 640-kb recombination window containing the novel signal at rs138326449. Association between rs138326449 and TG was assessed conditioning simultaneously on known associated variants best tagging all previously published common variant signals at this locus . Overall, seven windows had evidence for association with TG . The strongest of these was at chr11:116698501–116701500 . Despite specifically testing aggregates of rare variation, either one or a combination of the three independent SNP/SNV variants described above  could account for six out of seven SKAT signals in this region  that could not be accounted for by association of any given individual SNV and that may represent a novel signal driven by multiple rare variants. Regional plots for all major lipid sub-fractions and SKAT results for this region can be found in APOC3 gave greatest evidence for association with APOC3 specifically; however, this region neither yielded results stronger than that shown from non-genic tiling nor were further regions implicated  by using Sequence Kernel Association Testing SKAT in ~3-kbs region . One regplicated .APOC3 region accounted for 2.71% (s.e.=1.39) of phenotypic variance in TG. This is in contrast to estimates of variance explained from the analysis of rs138326449 alone in children and adults not in the original discovery collections, which varied from 0.27 to 0.39%  per minor allele). The rare allele association with plasma TG level is consistent with an effect of between 0.5 and 1.5 mmol l−1 across children and adults dependent on population (FTOrs9939609 variant and adult body mass index (which is ~0.01 s.d. change in body mass index); however, these are still more than 20 times lower than that seen here2Within the cohorts arm of the UK10K study, we have collected low read-depth, whole genome sequence data and used this with a validation and replication panel to describe a rare SNV (MAF ~0.2% in Europeans) strongly associated with plasma TG. The variant rs138326449 accounts for single point and sequence kernel-based association signals at the known Mendelian locus pulation . This is. s.e.=0.3 per minAPOC3 gene is located in a gene cluster together with the APOA1 and APOA4 genes on the long arm of chromosome 11  in the general European population and does not contribute to variance in TG in this study29APOC3 is associated with reduced plasma TG levels and a coincident increase in HDL, in agreement with the inhibiting action of apoCIII on lipoprotein lipase. In the data here from UK10K, the total variance in plasma TGs explained by all genetic variation at this locus (down to and below 1% MAF) is ~2.7%. This is in comparison with this novel, low-frequency, genetic variant that accounts for somewhere between 0.27% and 0.39% of phenotypic variance.The splice donor site reported here lies in a region of chromosome 11 previously shown to contain both common and rare variants affecting plasma TG levels. Restriction fragment length polymorphism variation within the non-coding part of exon 4 at this locus, haplotypic characterization of variation in the region and a single change within exon 3 of APOC3. The rare, TG-decreasing A allele is predicted to disrupt the correct splicing of the first protein-coding exon of APOC3 (containing the Apo-CIII domain (PF05778) and a signal peptide (1–20 aa)), resulting in a marked change of the 5′-splicing site score (from 4.37 (G) to −3.81 (A))The rs138326449 variant affects the essential di-nucleotide 5′-splicing site (GT to AT) of the first protein-coding exon of the protein-coding gene https://www.nhlbi.nih.gov/health-pro/guidelines/current/cholesterol-guidelines/index.htm), TG and TG-rich remnants are probably risk factors in cardiovascular disease3537Recognized within the Adult Treatment Panel III and as part of the definition of the metabolic syndrome  were measured as described previously−1)=TC-LDL Cholesterol–HDL Cholesterol.Non-fasting plasma levels of TC, HDL and TG at age 9 years were measured with enzymatic colorimetric assays (Roche) on a Hitachi Modular P Analyser. HDL, TGs and TC . Over the last 20 years, questionnaire and blood/urine/tissue samples have been collected for over 7,000 subjects, as well as three comprehensive phenotyping assessments. The primary focus of study has been the genetic basis of healthy aging process and complex diseases, including cardiovascular, metabolic, musculoskeletal and ophthalmologic disorders. Alongside the detailed clinical, biochemical, behavioural and socio-economic characterization of the study population, the major strength of TwinsUK is availability of several ‘omics’ technologies for the participants. The database was used to study the genetic and environmental aetiology of age-related complex traits and diseases. The St Thomas’s Hospital Ethics Committee approved the study and all participants gave signed consent to the study.Enzymatic colorimetric assays were used to measure serum levels of TC, HDL and TGs, and were measured using three analysing devices ). The majority of discovery samples were fasted before measurement (96%).Low read-depth WGS (cohorts data set). Low read-depth WGS was performed at both the Wellcome Trust Sanger Institute and the Beijing Genomics Institute (BGI). DNA (1–3 μg) was sheared to 100–1,000 bp using a Covaris E210 or LE220 . Sheared DNA was subjected to Illumina paired-end DNA library preparation. Following size selection (300–500 bp insert size), DNA libraries were sequenced using the Illumina HiSeq platform as paired-end 100 base reads according to the manufacturer’s protocol.Data generated at the Sanger Institute and BGI were aligned to the human reference separately by the respective centres. The BAM files produced from these alignments were submitted to the European Genome-phenome Archive. The Vertebrate Resequencing Group at the Sanger Institute then performed further processing.ftp://ftp.1000genomes.ebi.ac.uk/vol1/ftp/technical/reference/human_g1k_v37.fasta.gz). Reads were aligned using BWA (v0.5.9-r16)Sequencing reads that failed quality control (QC) were removed using the Illumina GA Pipeline, and the rest were aligned to the GRCh37 human reference, specifically the reference used in Phase 1 of the 1000 Genomes Project The observation of spikes in the insertion/deletion ratio in sequencing cycles of a subset of the sequencing runs were linked to the appearance of bubbles in the flow cell during sequencing. To counteract this, the bamcheck utility from the samtools package was used to create a distribution of INDELs per sequencing cycle. Lanes with INDELs predominantly clustered at certain read cycles were marked as problematic (159 samples). In the next step, we checked mapped positions of the affected reads to see whether they overlapped with called INDELs, which they did for 1,694,630 called sites. The genotypes and genotype likelihoods of affected samples were then set to the reference genotype unless there was a support for the INDEL also in a different, unaffected lane from the same sample. In total, 140,163 genotypes were set back to reference and 135,647 sites were excluded by this procedure. Note that this step was carried out on raw, unfiltered calls before Variant Quality Score Recalibration filtering.P<10−6 to remove sites that failed the Hardy–Weinberg equilibrium  and removed sites with evidence for differential frequency  between samples sequenced at BGI and Wellcome Trust Sanger Institute .Variant Quality Score RecalibrationP-value≤0.01 and formed a list that were subsequently filtered out from the genotype set, removing the batch effect due to sequencing centre.Given the presence of structure by genotyping batch, we ran a genome-wide association analysis for the binary variable ‘sequencing centre’ (‘BGI’/‘SANGER’) using a logistic regression model. SNPs  were associated with batch at a conservative threshold of Of the 4,030 samples  that were submitted for sequencing, 3,910 samples  were sequenced and went through the variant calling procedure. Low-quality samples were identified before the genotype refinement by comparing the samples with their GWAS genotypes using ~20,000 sites on chromosome 20  because of one or more of the following causes: (i) non-reference discordance with GWAS SNV data>5% (12 TwinsUK and 1 ALSPAC), (ii) multiple relations to other samples (13 TwinsUK and 1 ALSPAC) or (iii) failed sex check (3 TwinsUK and 0 ALSPAC).To exclude the presence of participants of non-European ancestry in our data set, we merged a pruned data set to the 11 HapMap3 populationsThe final sequence data set that was used for the association analyses comprises 3,621 samples .SHAPEIT2 ref.  was thenFor each of the cohorts, we had additional GWA data available. For ALSPAC, 6,557 samples were measured on Illumina HumanHap550 arrays and passed QC >0.125)). For TwinsUK, 2,575 samples were genotyped on Illumina HumanHap300 or Illumina Human610 arrays. These samples passed QC on relatedness (IBS>0.125), population stratification, heterozygosity, zygosity and sex checks. Samples from the imputed data sets were unrelated to the sequence data sets (IBS>0.125). Variants discovered through WGS of the TwinsUK and ALSPAC cohorts were used for the development and use of a reference panel for imputation within the TwinsUK and ALSPAC GWA data sets. In other collections, these along with variants known from 1000 Genomes were imputed increasing the sample size for single point association analysis to 12,724 subjects. We developed new functionality in IMPUTE2 ref.  that usewww.lgcgenomics.com/; see For ALSPAC, the entire cohort  was genotyped using KASP at KBioscience  from the sample mean for a given trait were excluded from analysis . The filtering of TG data by extremes of phenotype does not have a substantive impact on the numbers of rare variant carriers in this data set . To approximate normality, each data set was inverse normal rank transformed in each group separately, and residuals were further computed by adjustment for age and ageWe assessed associations between 14,196,778 genetic variants  and lipid traits  calculated as described before, using linear regression models assuming additive genetic models. For primary analyses, associations were tested using a genotype dosage-based test implemented in the SNPTESTv4.2 software packageSummary statistics from individual studies were combined using fixed-effect inverse variance meta-analysis implemented in GWAMA v2.1 ref. .http://hapmap.ncbi.nlm.nih.gov/downloads/recombination/2011-01_phaseII_B37/), and this analysis was limited to a 640-kb window of chromosome 11 marked by a recombination fraction <25%. Evidence for previous genetic associations was available from existing studies2APOC3 region in this data set were assessed using GCTAP<1 × 10−3) alongside those previously associated with lipid levels irrespective of association result in this sample58Conditional analyses of genotype and rare variant aggregate association were undertaken within a joint sample from the UK10K cohort WGS data set (ALSPAC and TwinsUK) adjusting for study origin by residualizing transformed TG on an indicator variable for study. Records of region-specific recombination used to derive the recombination interval boundaries were retrieved from  for genes contained within the APOC3 region. Genes for this analysis were defined by GENCODE (v15) within positions 115,820,914 and 117,103,241 on chromosome 11. In more detail, variants within exons and splice variants were tested in windows up to 51 variants per window. If there were >50 exonic and splice variants per gene, then variants were split in two ways: first by combining neighbouring exons so that the number of variants was about evenly split between windows, and second by tiling across the concatenated exons with maximal 51 variants per window but starting halfway the first window that was generated by the first approach.SKATDescription of the replication samples is given in the www.bris.ac.uk/alspac/researchers/data-access/data-dictionary). T.D.S. is holder of an ERC Advanced Principal Investigator award.This publication is the work of the authors and N.J.T., G.D.S. and S.R. will serve as guarantors for the contents of this paper. N.J.T. and G.D.S. work within a MRC unit at the University of Bristol. Please note that the ALSPAC website contains details of all the data that is available through a fully searchable data dictionary .Supplementary Figures 1-7, Supplementary Tables 1-7, Supplementary Methods and Supplementary References


Despite the large amount of variation found in the night (scotopic) vision capabilities of healthy volunteers, little effort has been made to characterize this variation and factors, genetic and non-genetic, that influence it. In the largest population of healthy observers measured for scotopic visual acuity (VA) and contrast sensitivity (CS) to date, we quantified the effect of a range of variables on visual performance. We found that young volunteers with excellent photopic vision exhibit great variation in their scotopic VA and CS, and this variation is reliable from one testing session to the next. We additionally identified that factors such as Circadian preference, iris color, astigmatism, depression, sex and education have no significant impact on scotopic visual function. We confirmed previous work showing that the amount of time spent on the vision test influences performance and that laser eye surgery results in worse scotopic vision. We also showed a significant effect of intelligence and photopic visual performance on scotopic VA and CS, but all of these variables collectively explain <30% of the variation in scotopic vision. The wide variation seen in young healthy volunteers with excellent photopic vision, the high test-retest agreement, and the vast majority of the variation in scotopic vision remaining unexplained by obvious non-genetic factors suggests a strong genetic component. Our preliminary genome-wide association study (GWAS) of 106 participants ruled out any common genetic variants of very large effect and paves the way for future, larger genetic studies of scotopic vision. The visual system operates over a remarkable range of lighting conditions through transduction by two classes of photoreceptor cells, rods and cones . In photVisual acuity (VA) and contrast sensitivity (CS) are important measures of visual function, though the latter—while being a better predictor of traffic incidents –is oftenThe majority of scotopic vision research to date has centered on diseased populations and treatment efficacy from refractive procedures such as laser eye surgery and intraocular lens transplantation . In addiThe contribution of a genetic component to healthy scotopic vision has yet to be explored, even though obvious heritable differences in photopic visual abilities exist , 21. A hIn this study, we first characterize normal scotopic VA and CS performance in a large population of healthy observers following 20 minutes of dark adaptation. We then define the contributions of a wide range of variables to scotopic performance. After controlling for confounding factors, we perform a preliminary genetic analysis using scotopic VA and CS as our outcomes. Our overall goal is to identify genetic variants with an influence on scotopic vision in healthy observers, leading to a better understanding of the complex molecular mechanisms involved in scotopic vision.The Duke University Institutional Review Board approved all procedures, and participants provided written, informed consent (IRB #: Pro00006828).A total of 734 participants attempted the visual tasks as part of a larger battery in the Duke Genetics of Cognition and Other Normal Variation study –31. Due Prior to psychometric and visual testing, participants completed an extensive survey that queried demographics, medical history, and several standardized scales. To identify new variables associated with scotopic vision, we investigated multiple measures for association with this phenotype. Many of these variables, such as years of education and ethnicity, have never before been compared to the scotopic visual performance of a person.A total of 451 participants completed the Beck Depression Inventory-II (BDI) . ParticiThe Circadian Energy Scale (CIRENS) is a two-question chronotype measure based on self-report energy levels throughout the day: once at night and once in the morning. Energy levels are described on a Likert scale: . The difference between the evening score and morning score determines the overall chronotype score, ranging from -4 (most marked morning preference) to +4 (most marked evening preference) . PreviouPrior to visual tasks, participants indicated their current level of tiredness on a Likert scale: [energetic (0) to very tired (4)].Participants self-reported eye health and characteristics. For iris color, participants were divided into one of three groups: a baseline group of participants indicating brown or black irises, a second group with green or hazel irises, and a third group with blue or gray irises. Previous studies have investigated the impact of iris color on certain aspects of ocular function but have not investigated whether it is associated with scotopic vision –38. PartParticipants completed a cognitive battery assessing diverse areas of cognition as previously described . PrincipW (CW = luminance difference divided by the surround luminance); its value is typically around 0.01 for photopic vision and 20 times higher for scotopic vision. [For isolated optotypes on a large background the Weber contrast is more relevant than the Michelson contrast.] This contrast threshold is converted to log contrast sensitivity (logCSWeber = log(1/CW)).VA and CS thresholds were assessed using the Freiburg Visual Acuity and Contrast Test  . FrACT eParticipants first completed both the FrACT VA and CS tasks twice in photopic conditions. The tests were given twice to allow assessment of test-retest agreement and to improve our ability to identify outliers. Participants then dark adapted for 20 minutes and then completed the FrACT VA and CS an additional two times. Participants were required to finish both repeats in five minutes to allow for a standard start time for the next visual task and to standardize the dark adaptation length for all participants; if they did not complete both, then only one measure was used.The same computer was used for both photopic and scotopic tests. The results were obtained with a 17” monitor at a distance of 1.54 m. The diameter of the Landolt C was set to 200 minutes of arc (arcmin) at a constant optotype contrast of 100%. For high reliability, a total of 24 trials for each task were used . The monResults for all visual tasks are monocular and reflect performance in the participant’s right eye (n = 490) unless a participant self-reported worse vision compared to the left eye. The amount of time spent per task was recorded for FrACT in ms and log-transformed for analysis; referred to as ‘task duration’. We did not use a fixation task but attempted to minimize variation in testing strategies by informing participants to not continuously fixate on the stimulus. Auditory feedback was provided to indicate correct or incorrect.2); output was digitally controlled by an M-Audio PCI soundcard [Following FrACT completion that accounted for 25 minutes of dark adaptation, 248 participants completed the absolute detection threshold measurement using a LED-based dark adaptometer built in Dr. Cao’s laboratory. The test size was 4° in visual angle and included a 10° temporal eccentric fixation. The stimuli were 100ms pulse square waves with a dark background (i. e. 0 cd/moundcard . Two indIn addition to the exclusionary criteria (described below) to define a healthy population, 14 participants were also excluded from DA analyses because their threshold was more than 3 SD above the group mean, indicating greatly impaired scotopic vision. Our final DA sample size was 234 participants.To evaluate the reliability of our tasks, 40 participants completed both the FrACT and DA tasks at two separate testing sessions, a mean of 85 days apart (SD = 18). We were also able to evaluate test-retest agreement within the same testing session as most participants completed two photopic and scotopic scores during the initial testing session .The primary outcomes were FrACT phenotypes, which were generated based on the average of two independent thresholds for photopic conditions and for scotopic conditions after twenty minutes of dark adaptation. When only one photopic or scotopic score was recorded (n = 102), this measure was used as the phenotype.For the test-retest agreement, we calculated the 95% limits of agreement .Useable data were generated for 664 participants who were judged by our psychometricians to fully comprehend the task and have no technological or methodological barriers to performance. To define a healthy population of observers, those with photopic VA worse than 20/25 (corresponding to ≥ 0.1 logMAR), who were older than age 40, who did not recognize any optotypes after 20 minutes of dark adaption, or who were more than 3 SD beyond the mean scotopic VA performance were excluded from all analyses.In addition, those with previous laser eye surgery were excluded from genetic analyses because this procedure had significant impact on scotopic performance in our task, concordant with the pertinent literature and potentially due to dry eyes –46.p < 0.01 were performed for the VA and CS scotopic phenotypes, with the phenotype as the outcome and variables listed in p > 0.001) for normal distribution. The scotopic CS population did not pass Shapiro-Wilks (p < 0.001) due to a tail of low performers. Scores were normally distributed when restricting to those who performed no more than 2 SD below the mean (n = 25). This restriction had no significant impact on the predicted stepwise model, so we retained these 25 individuals in the analysis.All non-genetic statistical analyses were performed using STATA 13.1 . StepwisTwo variables, BDI and iris color, were unavailable for some participants and were subsequently excluded from the stepwise analyses to maintain the largest sample size possible. A total of 50 individuals were missing BDI score, and 146 individuals did not have iris color responses. These variables were not found to be significantly associated with the phenotypes in univariate analyses.Variables that were significant in the stepwise model were used as covariates in subsequent genetic analyses and in the multivariate analyses results presented in A genome-wide association study (GWAS) was performed on 106 participants who had Illumina Humanexome chip data available. Of these, most were also genotyped with the Infinium HumanCore GWAS chip (n = 93), and others were genotyped with either the Human610-Quad BeadChip (n = 6) or HumanHap550 (n = 5). Two of these 106 samples did not have GWAS genotypes. Variants from any of these chips were included in the analysis provided they passed QC and met the below inclusion criteria.-7 to reach significance. We also performed a focused analysis on 94 candidate genes annotated as being involved in the phototransduction and retinol metabolism pathways [http://omim.org/).Our single variant analysis restricted to variants genotyped in at least 50% of these participants with a minor allele frequency (MAF) of at least 0.01 A linear regression was used in PLINK  for scotpathways  as well To assess the effects of the low frequency variants genotyped with the exome chip, we used a gene-based collapsing analysis as previously described . Brieflyhttp://igm.cumc.columbia.edu).Power calculations were performed using GWASpower/QT   but explained only 4.1% of the variance ; performance was significantly correlated with scotopic CS (p < 0.001) but explained only 2.5% of the variance  with 30.7% of the final population arriving at this threshold. This ceiling effect was due to the pixel resolution of the computer monitor at the testing distance used. VA performance in photopic conditions was significantly correlated with VA performance in scotopic conditions (variance . Photopivariance .p < 0.001), and scores on one explain 9.8% of the variance in the other.Individual performance on photopic VA and CS was significantly correlated .Scotopic VA performance was significantly correlated with scotopic CS performance (p < 0.0001), accounting for a total of 27.0% of the variance in VA and 26.5% in CS performance. Three additional variables passed inclusion criteria for both models : photopic VA or CS , intelligence , and previous laser eye surgery .Stepwise regression analyses assessed the contribution of a number of demographic variables, eye characteristics, photopic visual performance, task duration, time of day, and intelligence to scotopic VA and CS . Variablp < 1.8x10-7). Our genome-wide association study had 80% power to identify a common variant explaining at least 25% of the variation in this trait, and our gene-based collapsing analysis of low-frequency coding variants had 80% power to identify associations explaining at least 22% of the variation. When restricting to 139 candidate genes annotated as being involved in the phototransduction and retinol metabolism pathways or implicated in Mendelian diseases causing night vision defects, we still found no significantly associated variants. However, we did identify a trend for participants with rare coding variants in RP1 to exhibit worse scotopic CS . This association was driven by three nonsynonymous variants—rs137887415, rs16920621, and rs142318038—each of which had one heterozygous carrier in our dataset.After correcting for multiple tests, we identified no variants or genes with statistically significant associations with these traits . Four data sets are depicted: Visual acuity (left panel) and contrast sensitivity (right panel) at photopic luminance . Result of the first test on the abscissa, second test on the ordinate. Grey 45°-line is the identity line, next to it the ± limits of agreement . Visual acuity in logMAR units have an inverted scale, and contrast sensitivity is in logCS(PDF)Click here for additional data file.S2 FigWeber units, meaning that better performance corresponds to higher scores. Task duration is shown on a log scale and explained <0.1% of the variance in scotopic VA and 3.3% of the variance in scotopic CS.Visual acuity in logMAR units have an inverted scale, and contrast sensitivity is in logCS(PDF)Click here for additional data file.S1 FilePhenotypic and covariate information for the samples included in this study.(XLSX)Click here for additional data file.S2 FileResults from the gene-based collapsing analyses included in this study.(XLSX)Click here for additional data file.S3 FileResults from the GWAS analyses included in this study.(ZIP)Click here for additional data file.


Graphical models are increasingly used in genetic analyses to take into account the complex relationships between genetic and nongenetic factors influencing the phenotypes. We propose a model for determining the network structure of quantitative traits while accounting for the correlated nature of the family-based samples using the kinship coefficient. The Gaussian graphical model of age, systolic blood pressure, diastolic blood pressure, hypertension, blood pressure medication use, and smoking status was derived for three time points using real data. We also explored binary sparse graphical models of single-nucleotide polymorphisms (SNPs), covariates, and quantitative traits for exploratory analysis of the data. We validated the applicability of this method by producing a network graph using 20 causal variants, 21 noncausal variants, and 6 binary and quantitative phenotypes using the simulated data. To improve the model's ability to identify associations between the causal variants and the phenotypes, we intend to conduct follow-up studies investigating how to use the relationships between SNPs and between SNPs and phenotypes when analyzing genome wide association data with multiple phenotypes. Graphical models are popular methods for exploratory data analysis . UnderstY is a n individuals and p quantitative traits . The individuals are correlated because they were sampled from pedigrees. The correlation among the samples attributed to shared genetics and environment can be modeled using the kinship coefficient [Suppose fficient . The kinΩ is the inverse of the covariance of the p quantitative traits, and R is the covariance matrix of the n related samples. Because the individuals from different pedigrees are independent and the correlation exists only within a pedigree, R is a block diagonal matrix, with each block corresponding to a pedigree. Within each pedigree, the correlation coefficients between the samples are specified as twice the value of the kinship coefficient. Using reported heritability values of SBP and DBP and using phenotypic correlation between monozygotic twins [The data can be modeled using matrix normal distribution , which mic twins , we estiΩ over the model's likelihood function. But to identify the conditional independence structure of the p traits, we have to find traits that are conditionally independent given all other traits. This information is contained in the inverse of the correlation matrix Ω between the traits. If i and trait j are conditionally independent, given all other traits. To enforce sparsity in the estimation of Ω, the LASSO penalty can be imposed on the likelihood. The resulting penalized log likelihood is Many methods are available to optimize al model  of depenal model . Becauseal model .The proposed model is not suitable for exploring the association between single-nucleotide polymorphisms (SNPs) and quantitative traits such as SBP and DBP. In the case of continuous traits, conditional independence between nodes is directly estimated using the partial correlation coefficients, which are related to the inverse of the correlation matrix. This interpretation of conditional independence is not possible, however, when the traits are discrete. The assumptions required for Gaussian graphical models are not satisfied for discrete phenotypes such as hypertension and smoking status or for SNP genotypes. Some of the popular approaches for estimating graphical networks of discrete variables are based on the LASSO regression ,8.Y is a n individuals and p quantitative and discrete traits. The p variables, or nodes, of the network contain SNPs, quantitative phenotypes such as age, SBP and DBP, and binary phenotypes such as hypertension, blood pressure medication, and smoking status. Each of the variables  is considered as a response and is regressed on all of the other variables, which are considered as predictors. The predictors associated with the response variable are considered to be in the neighborhood of a particular variable. After computing the neighborhoods for all the variables, an AND operator or an OR operator is used to determine the conditional independence of 2 traits i is in the neighborhood of j AND/OR j is in the neighborhood of i, they are conditionally dependent, given all other variables). The strength of the dependence can be measured by taking the maximum, minimum, or average of the 2 neighborhood measures between Assume Using the Gaussian graphical model for pedigrees and sparse graphical models for discrete and quantitative traits, we analyzed Genetic Analysis Workshop 18 (GAW18) data, which includes genome-wide association data for 400,000 SNPs, along with simulated and real phenotypic information SBP, DBP, hypertension, blood pressure medication use, and smoking status. The real data contained 939 individuals within 20 pedigrees at 4 time points. Missing data were present at all the time points. We excluded individuals with missing data for each of the time points and performed our analyses on the remaining data. For the analysis of data using the Gaussian graphical model for quantitative traits in pedigrees, we analyzed the first 3 time points for the 6 phenotypes in the real data. The fourth time point was excluded from the analysis because most of the data was missing for this time point.For the sparse graphical model with discrete and continuous traits, we concentrated on chromosome 3. We used genome-wide association data for constructing the network. Two hundred replicates of simulated data for the 3 time points were available that were generated using the real pedigree structures. We used a single replicate of the simulated data for the phenotypes. Only the unrelated individuals from the first time point were used for this analysis. In the simulated data, a total of 1457 genetic variants were causal for either SBP or DBP across all the chromosomes. Of these 1457 causal variants, 188 variants were located on chromosome 3. We randomly sampled 20 of these 188 variants on chromosome 3 in our analysis.We derived the graphical models of 6 traits and covariates, accounting for pedigree structure: age, SBP, DBP, hypertension, blood pressure medication use, and smoking status. Because hypertension, blood pressure medication use, and smoking status are discrete phenotypes, we transformed these variables into quantitative phenotypes using a logistic regression framework in which all the other phenotypes were regressed as dependent variables in the logistic model. At each time point, the graph shows the conditional relationships among the phenotypes. For example, in Figure We validated the sparse graphical methodology using the simulated genome-wide association data. Twenty of the 188 causal SNPs on chromosome 3 were randomly sampled. We also analyzed 21 consecutive noncausal SNPs from the same chromosome. The causal and noncausal SNPs analyzed are detailed in Figure We performed LASSO regression using all 47 genetic and nongenetic factors and constructed the graph as described in the methods section. We used the AND operator for the conditional independence of 2 nodes to get a sparser graph. The strength of dependence was measured using the maximum measure of the 2 regression coefficients. Figure We also conducted additional validation of the proposed method where we randomly selected 21 noncausal SNPs from chromosome 3 that were not in LD with any of the causal SNPs or among themselves. All of the other phenotypes and the causal SNPs were as in the previous scenario. As expected, the resulting sparse graphical network (not shown) had no edges among the noncausal variants, and there were no edges connecting the causal variants and noncausal variants. The part of the network corresponding to the phenotypes and the causal SNPs was similar to the previous scenario.Graphical models provide an intuitive and straightforward way to visualize and use complex relationships among data. These models have mainly been used for analyzing case-control data among unrelated individuals. Here we have proposed a straightforward graphical method of accounting for correlation in pedigrees that can be used for decorrelating family data or, in general, for decorrelating correlated samples. If one is analyzing family data and needs to use a methodology that is suitable for case-control data with unrelated individuals, the data must first be decorrelated. In such cases, we can use the correlation structure identified from such graphical models as the variance-covariance matrix for the phenotypes. In this paper, we used c, induced correlation due to the shared environment, to be equal to 0.1. However, we have found that the method is robust to slight departures from the true value of c. The proposed model can be directly incorporated as a hierarchy into a Bayesian hierarchical model for simultaneously analyzing the phenotypes while taking into account the correlation among the family members.p-value) to the sparse graphical network as it is a data-driven structure. In the future, we intend to conduct follow-up studies investigating how to use the relationships between SNPs and between SNPs and phenotypes when analyzing genome-wide association data with multiple phenotypes.We also explored a sparse network model that constructs an intuitive network graph including SNPs and discrete or continuous phenotypes. The network structure with the genetic and nongenetic factors is not perfect . This may be because of the small sample size of the study. This raises an important question of whether we need to increase the number of pedigrees or the number of individuals within a pedigree. It is also important to note that one cannot assign a statistical significance (e.g., The authors declare that they have no competing interests.RT and SS conceived and designed the overall study, and RT conducted statistical analyses. RT and SS drafted the manuscript. Both authors read and approved the final manuscript.


Family based association studies are employed less often than case-control designs in the search for disease-predisposing genes. The optimal statistical genetic approach for complex pedigrees is unclear when evaluating both common and rare variants. We examined the empirical power and type I error rates of 2 common approaches, the measured genotype approach and family-based association testing, through simulations from a set of multigenerational pedigrees. Overall, these results suggest that much larger sample sizes will be required for family-based studies and that power was better using MGA compared to FBAT. Taking into account computational time and potential bias, a 2-step strategy is recommended with FBAT followed by MGA. Phenotypic variation in complex traits is conferred through both common and rare variants. It has been suggested that common variation plays a role at the level of the population, whereas rare variation has stronger effects at the levels of the clan (extended family) and the nuclear family . To dateThe measured genotype approach (MGA) and family-based association testing (FBAT) are 2 broad strategies to examine family-based association in the context of large extended families. MGA from a variance components framework utilizes a mixed model in which familial relationships are accounted for using random effects and genetic variants are incorporated as fixed effects. In contrast, FBAT relies solely on within-family information by constructing a score test that essentially provides a correlation between phenotype and genotype. However, performance of these approaches in the context of variants of varying frequency with modest to moderate effect in extended family data is unclear.Thus, this paper evaluates the performance of MGA and FBAT in the context of large extended families genotyped for both common and rare variants . To accomplish this, we will use chromosome 3 variants from single-nucleotide polymorphism (SNP) genotyping chips, as well as the simulated phenotypes from the Genetic Analysis Workshop 18 (GAW18) data set based on the multigenerational structure of the San Antonio Family Studies (SAFS) .We analyze 20 large pedigrees generated from SAFS that range from 21 to 76 members in size. We used the chromosome 3 data to test for association in the 200 simulation replicates by employing both MGA  and FBATDetails regarding the San Antonio Family Heart Study (SAFHS) and the San Antonio Family Diabetes/Gallbladder Study (SAFDGS), which comprise the SAFS, have been provided elsewhere ,14. PertFirst, we used MGA ,15 as imμ is a grand mean for DBP, β is the SNP effect, and g and e are random genetic (additive polygenic) and residual effects. We assume that g and e are normally distributed with zero mean and variances g) and residual (e) effects. To test a SNP effect, the log likelihood of the model estimating an unconstrained SNP effect is compared to the log likelihood of the model in which the SNP effect is constrained to zero. Assuming that trait values follow a multivariate normal distribution, twice the difference in the log likelihoods of these 2 models is asymptotically distributed as where Second, we used FBAT to test for association. Here we define the FBAT test statistic byjth nonfounder of the ith family after regression on age, age squared, sex, and blood pressure medication use, all at the first exam; jth nonfounder of the ith family . FBAT analysis was performed with PBAT's [http://www.goldenhelix.com).where atistics  for the h PBAT's  hybrid pIn addition to examining FBAT test statistics marginally, we also employed the Van Steen screening approach , which aEach of the 17 SNPs from the simulation model that are causal for DBP To assess false-positive rates, we examined the trait Q1 simulated with no genetic influence. Linkage disequilibrium (LD) was used to prune the chromosome 3 SNPs and create a subsample of 1228 uncorrelated SNPs. These SNPs were used to estimate type I error rates, using both MGA and FBAT to maintain consistency across approaches. The pruning approach has 2 advantages. First, it reduces the computational burden, which was especially problematic in MGA where computation time increases substantially with the degree of pedigree complexity as a result of estimation of the mixed model. Second, it results in an error rate more in line with the number of true comparisons, as Bonferroni correction assumes uncorrelated tests. To calculate a comparable assessment of type I error using the Van Steen screening approach, the proportion of noncausal SNPs declared significant in each replicate was averaged.Of note, the multiple testing correction approach differed between the power and the type I error evaluation. Specifically, the LD pruning step was not performed when examining empirical power. Although it is optimal to use the same procedure to assess error rate and power, the varying pruning step should not bias our results.MAP4 had a considerably large effect on DBP (heritability 2.29%) and a minor allele frequency (MAF) of 2.6%. The other 2 SNPs with marginal power, rs4683602 and rs16851435, are common  but exhibited a much more modest effect (heritability 0.003% and <10−5). After accounting for multiple testing, only rs11711953 had the power to be detected, and then only by using MGA. When using the Van Steen top 10 screening approach (FBAT-VS) the MAP4 SNP was detectable, but not at the rate conferred by MGA.Overall, there was low power to detect causal variants Table . Only 3 Using the Q1 phenotype, we found that both MGA and FBAT methods appropriately controlled for type I error rate using a nominal significance (type I error rate 0.05 for both). After controlling for multiple testing, no false positives were identified with any of the methods.Using a cohort of extended families, we evaluated the performance of 2 family based methods (MGA and FBAT) to identify causal variants of varying allele frequency and effect size. Overall, the approaches exhibited low power with only 3 variants identified more than 20% of the time. Nevertheless, both approaches also exhibited very appropriate family-wise false-positive rates. Taken together, these results suggest that family-based studies require large sample sizes to detect the majority of effects.The variant identified across all approaches (rs11711953), had a MAF of 0.026 and a true effect size of −6.2235 (with heritability of 2.29%). It appears that the ability to detect this variant was driven by the very strong effect size (more than 10× greater than any other variant). The other 2 variants identified were more common, but had relatively small effect sizes. As other common variants had larger effect sizes, there is clearly a complex interplay of factors influencing power to detect effects.Both methods suffered from overall low power. This suggests that substantially larger data sets and methodological extensions incorporating multiple variants such as FBAT-RV  will be One of the major challenges in these analyses was the computational time, especially for the MGA, where genome-wide analyses are infeasible. MGA analysis took approximately 30 seconds per SNP, while the FBAT took one-eighth second per SNP. Ideally, without any constraints on computation time and with sufficient evidence to rule out population stratification, it is best to perform both MGA and FBAT approaches across the genome and focus on regions of overlap, that is, those with most evidence for true association. However, because both time and population substructure are often constraints, when considering between MGA- or FBAT-type analyses, we recommend initially employing an FBAT screening approach with a less-stringent significance threshold because of its speed and robustness to population stratification, and then following up regions of interest with MGA for confirmation to identify variants most likely to be causal.In summary, analysis of the GAW18 simulated phenotypes, DBP and Q1, allowed us to examine the performance of family-based association methods in the context of extended families and variants of varying frequency. Overall, we found that the GAW18 data was underpowered to detect all but one of the variants regardless of the approach used. Approaches to ease the burden of multiple testing are beneficial, and simulations with explicit population stratification are needed to further discern comparisons between these methods.The authors declare that they have no competing interests.DWF, XZ, and LJM designed the overall study. DWF, XZ, and KN conducted the statistical analyses and created tables. DWF, XZ, and LJM drafted the manuscript, which was revised by LD, HH, BGK, ESA, TBM, VP, LK, and KN. All authors discussed the project throughout, read, and approved the final manuscript.


The genetic variants associated with blood pressure identified so far explain only a small proportion of the total heritability of this trait. With recent advances in sequencing technology and statistical methodology, it becomes feasible to study the association between blood pressure and rare genetic variants. Using real baseline phenotype data and imputed dosage data from Genetic Analysis Workshop 18, we performed a candidate gene association analysis. We focused on 8 genes shown to be associated with either systolic or diastolic blood pressure to identify the association with both common and rare genetic variants, and then did a genome-wide rare-variant analysis on blood pressure. We performed association analysis for rare coding and splicing variants within each gene region and all rare variants in each sliding window, using either burden tests or sequence kernel association tests accounting for familial correlation. With a sample size of only 747, we failed to find any novel associated genetic loci. Consequently, we performed analyses on simulated data, with knowledge of the underlying simulating model, to evaluate the type I error rate and power for the methods used in real data analysis. Despite the tremendous success of genome-wide association studies (GWAS) to uncover genetic variants influencing complex traits and diseases, only a fraction of the total heritability of these traits is explained by the loci identified so far. Because GWAS focuses on common variants, a possible source of the missing heritability might be rare variants that were not included in the earlier genotyping platforms. The next logical step is to investigate rare variants, an endeavor that is now possible because of the ever-decreasing cost of sequencing.Whole genome sequencing has the ability to uncover rare variants, but brings its own challenges. Despite a low error rate, the sheer number of base pairs sequenced makes it hard to distinguish very rare mutations from sequencing errors. Moreover, detecting association with rare variants requires very large sample sizes. Several methods to jointly analyze rare variants within a genomic region have been developed, however, and these methods have the potential to pinpoint additional variants contributing to the overall heritability of traits.Blood pressure (BP) and hypertension are prime examples of the limitations of GWAS. Meta-analysis of GWAS from a large number of cohorts has identified multiple genetic loci over the genome that affect systolic blood pressure (SBP), diastolic blood pressure (DBP), hypertension, or a combination of these traits ,2. HowevIn this article, we investigate the association of rare variants in genomic regions that have been previously implicated by GWAS to identify the source of the original GWAS signal and to discover additional genetic loci influencing BP using either burden tests adjusting for familial correlation (famBT) or sequence kernel association tests (SKAT)  for famiWe used imputed single-nucleotide polymorphism (SNP) dosage files from odd-numbered chromosomes provided by the Genetic Analysis Workshop 18 (GAW18) as our genotypes in all analyses. For real data analysis, we took baseline measurements of the covariates and traits for each participant, defined as the first exam with nonmissing values for age, SBP, DBP, current use of hypertension medications (BPmeds), and current smoking (smoke). We removed participants with at least 1 missing value of these variables in all 4 exams. We also excluded participants on antihypertensive medication at the baseline we defined, resulting in a sample size of 747 participants. Table For the BP candidate gene study, we performed both common and rare-variant analysis. Common variants were defined as any variants with minor allele frequency (MAF) >5% in our subset of participants, and rare variants were variants with MAF between 0% and 5%. We performed common variant analysis as single-marker association tests using linear mixed-effect models  to accoun, Y is a vector of the trait of interest; X is an n by p matrix of covariates; α is a vector of the covariate effects; G is an n by q matrix of rare variants with columns jG; j wis the weight for variant j; and this is the combined genotype score:Assuming that the sample size is The model isβ is the effect size for the combined genotype score, γ is the random effect vector for familial correlation, and ε is the normally distributed error. We assume H0 : β = 0 versus H1 : β ≠ 0 in this framework.where β is now a vector of length q. The model isWe use the same notation as above, except that W is a diagonal matrix of weights jw, and we assume β ~N . The genotype effects can be tested as H0 : τ= 0 versus H1 : τ> 0.where For the genome-wide rare-variant analysis on real data, we performed famBT and famSKAT, using both a gene-based coding and splicing variants analysis (GB) and sliding-window analysis (SW). GB was performed for each gene, using only nonsynonymous rare variants and rare variants at the splicing sites. SW was performed for all rare variants in each genomic region of 4000 base pairs (bp) length, with 2000 bp each overlapping with the previous and subsequent windows, regardless of the gene annotation.MAP4 on DBP and rSBP.In addition to real data analysis, we also performed rare-variant analysis on simulated data sets, with knowledge of the underlying simulating model. To be consistent with the real data analyses, we adjusted for sex, age, and smoking in all analyses, even though simulated smoking is not associated with simulated SBP or DBP. Because we did not have missing data in the simulated data sets, we took the first exam as the baseline and excluded individuals taking antihypertensive medication at baseline. Therefore, the sample size varies slightly in different simulation replicates. We used both famBT and famSKAT for GB and SW, but we analyzed only chromosome 3 because of limited computing resources. We evaluated the type I errors of these approaches using quantitative trait Q1, which was a simulated trait not associated with any genetic variants. We also calculated the empirical power for CASZ1, MTHFR, ULK4, PLEKHA7, CSK, CSK-ULK3, PLCD3, ZNF652) that were previously reported to be associated with either SBP or DBP [p values within each gene. However, the gene-based approach for SKAT and burden test for all associated regions did not reach our threshold for statistical significance . The tests did not identify evidence of association using real data. Candidate gene association results for common-variant analysis on 2 traits, rank normalized SBP and DBP, are also presented in Table We chose 8 gene regions  yields a p value of 2.69 × 10−8 [MAP4 microtubule decoration restricts with beta-adrenergic receptor recycling, which might explain beta-adrenergic receptor downregulation in heart failure [9 × 10−8 . MAP4 miMAP4--3_47894286, 3_47913455, 3_47957741, 3_47957996, 3_48040283, and 3_48040284--5 were simulated to be negatively associated with both SBP and DBP. The last SNP, 3_47894286, has perfect linkage disequilibrium (r = 1) with 3_47913455. In such a simulation setting, with SNPs all having the same direction of effect, the burden test should outperform most statistical approaches.It is not surprising that in gene-based analysis, famBT is slightly more powerful than famSKAT because, among the 6 rare coding variants in MAP4 is the gene most significantly associated with both SBP and DBP, some rare regulatory variants were simulated to be positively associated with the traits. As a result, famBT has almost no power to detect the association in this gene region. In contrast, famSKAT performs very well because SKAT allows effects to be in different directions. After adjusting for multiple testing, famSKAT still attains good power even at low α levels.In sliding-window analysis, however, even though The SW method is more computationally intensive than GB because more tests are performed. However, by using SW we can generally test all possible rare variants associated with the trait, no matter where they are located. In many scenarios, intergenic variants, especially those within regulatory regions, also may be associated with quantitative traits. Thus, for rare-variant analysis on real data, unless we have strong a priori knowledge that the associated variants are nonsynonymous, we would recommend running a sliding-window analysis. By using famSKAT, we can perform rare-variant analysis on family data and have much better power than simple burden tests when there are variants with both positive and negative effects.The authors declare that they have no competing interests.HC, HL, and JD designed the overall study; HC, SHC, JH, CL, JNM, and JD conducted statistical analyses and drafted the manuscript; CA, SML, and HL performed data quality control and gene annotation. All authors read and approved the final manuscript.


Drosophila Genetic Reference Panel to perform both a genome wide association analysis and genomic prediction for the fitness-related trait chill coma recovery time. We found substantial total genetic variation for chill coma recovery time, with a genetic architecture that differs between males and females, a small number of molecular variants with large main effects, and evidence for epistasis. Although the top additive variants explained 36% (17%) of the genetic variance among lines in females , the predictive ability using genomic best linear unbiased prediction and a relationship matrix using all common segregating variants was very low for females and zero for males. We hypothesized that the low predictive ability was due to the mismatch between the infinitesimal genetic architecture assumed by the genomic best linear unbiased prediction model and the true genetic architecture of chill coma recovery time. Indeed, we found that the predictive ability of the genomic best linear unbiased prediction model is markedly improved when we combine quantitative trait locus mapping with genomic prediction by only including the top variants associated with main and epistatic effects in the relationship matrix. This trait-associated prediction approach has the advantage that it yields biologically interpretable prediction models.The ability to predict quantitative trait phenotypes from molecular polymorphism data will revolutionize evolutionary biology, medicine and human biology, and animal and plant breeding. Efforts to map quantitative trait loci have yielded novel insights into the biology of quantitative traits, but the combination of individually significant quantitative trait loci typically has low predictive ability. Utilizing all segregating variants can give good predictive ability in plant and animal breeding populations, but gives little insight into trait biology. Here, we used the  The ability to accurately predict phenotypes for quantitative traits from genome wide polymorphism data will revolutionize evolutionary biology, medicine and human biology, as well as breeding of agriculturally important plant and animal species. The most commonly used experimental approach to dissect the genotype-phenotype map has been to identify individual quantitative trait loci (QTLs) by linkage to, or association with, segregating molecular markers in mapping populations . These sA second approach computes genome-based relationship matrices among individuals in a population based on all genotyped segregating variants, and uses this to estimate the fraction of additive genetic variance explained by the variants , 5. WhenWith the advent of next generation sequencing technologies, we can now perform genome wide association mapping and genomic prediction on populations of individuals with complete genome sequences. This scenario differs from GWA and genomic prediction analyses in which only a subset of segregating variants are genotyped in that the causal variants are themselves included in the list of polymorphic variants. If the true genetic architecture of a trait differs from the additive, highly polygenic model typically assumed in genomic prediction  , 14, comDrosophila melanogaster Genetic Reference Panel (DGRP) consists of 205 sequenced inbred lines derived from the Raleigh, NC population [Drosophila and other insects [The pulation , 18. Her insects , 20. We  insects . GenomicP = 1.07 x 10−52 for the between line variance and P = 1.40 x 10−6 for the sex by line interaction variance) for chill coma recovery time  , similarvariance , we consOur previous GWA analysis of chill coma recovery time used single nucleotide polymorphisms (SNPs) for 157 Freeze 1.0 DGRP lines . Sequencin males . The incP < 10−5 threshold, we found 68 variants in or near 44 genes associated with female chill coma recovery time and 68 variants in or near 42 genes associated with male chill coma recovery time , and X_18394766_SNP, a non-synonymous polymorphism in Rad51D) and one SNP in males (2L_4513330_SNP in the first intron of dp) had large effects and were genome-wide significant at a Bonferroni-corrected 5% significance threshold  between predicted genetic values using all common variants and observed phenotypes, for each sex separately. Surprisingly, we found that genomic-based predictive ability was very low (r = 0.08) in females, and in males, the estimate of additive genetic variance from the genomic relationship matrix was zero, leading to r = 0. Similar results  were obtained using all variants, suggesting that the low predictive ability of genomic prediction was not due to the omission of rare variants  to assess the average correlation  and estimated the additive genetic variance using a mixed model. We performed 100 replicate under each scenario. While the additive model occasionally led to low estimates of additive genetic variance, the major gene model did so slightly more frequently and the epistatic model substantially more frequently  and none in males  in these analyses to maximize sample size in the training set. In each of the 176 LOOCV iterations, one line was left out and the remaining 175 lines were used to carry out a GWAS for single variants and pair-wise interactions between variants. We then selected the top trait-associated additive variants and/or epistatic pairs with hreshold . Using ain males . Howeverin males . Using tin males . Finallyin males . In thisincluded , which cDrosophila quantitative traits. Here, we performed GWA analyses for a component of fitness, time to recover from a chill-induced coma. The broad sense heritability of chill coma recovery time in the DGRP is moderately high (~0.40), and sex-specific. In contrast to results from most GWA analyses in human populations, in which the top GWA variants have small effects and collectively explain only a small fraction of the total heritability [The sequenced inbred lines of the DGRP provide a unique opportunity to dissect the genetic architecture of tability , 3, 24, tability .The complicated genetic architecture of chill coma recovery time is not unexpected for a fitness-related trait. Fisher’s fundamental theorem of natural selection predicts reduced levels of additive genetic variation for such traits, with residual genetic variance attributable to additive and dominance variance from deleterious alleles maintained at low frequencies by mutation-selection balance and balancing selection of alleles at intermediate frequencies, and epistatic variance from gene-gene interactions . Our GWAa priori [We computed genetic relationship matrices for all common variants and used GBLUP and cross-validation to assess predictive ability for chill coma recovery time. GBLUP is only one of many methods utilized in genomic prediction –29. Simua priori . Howevera priori , 26–28, a priori , 31, sugWhen genetic architecture was not included in the model, the predictive ability we obtained for chill coma recovery time using GBLUP was very low for females and zero for males, due to estimates of narrow sense heritability that are not significantly different from zero in females and equal to zero in males. We hypothesized that this low predictive ability is due to violations of the additive infinitesimal model assumed by GBLUP that is inconsistent with the true genetic architecture of chill coma recovery time. This idea was supported by our simulation study . Our GWAThe GBLUP model utilizes the average genomic relationship over all loci between individuals , and is the same for all traits measured in the population . FurtherThe genomic prediction was greatly improved when adding SNPs or SNP combinations selected based on GWA analyses in each fold of the LOOCVs. Although this study was underpowered to detect epistatic interactions globally due to its small sample size, this does not mean that there is no epistasis among the top hits in the pair-wise epistasis tests. The improvement of prediction accuracy is likely a result of enriching for true causal variants among the list of variants used to construct the genetic covariance matrix. In addition to improved predictive ability, the trait-associated GBLUP approach affords the opportunity to evaluate the stability of particular additive variants and pairwise interactions by enumerating the number of times each enters the training model in the different folds of the LOOCV  Table. NDrosophila [Drosophila, as well as to quantitative traits in other species? There is substantial evidence for epistatic interactions between QTLs in Drosophila and mice [Arabidopsis [Epistasis is a major feature of the genetic architecture of time to recover from a chill-induced coma in osophila . To whatand mice , 24, 34,and mice , Arabidobidopsis  and yeasbidopsis , 38. Epibidopsis –38 and ebidopsis . TherefoThe implications of pervasive epistasis are profound. Complex networks of genetic interactions provide the genetic basis of canalization (hidden genetic variation) . WidesprWe quantified chill coma recovery by transferring (without anesthesia) three to seven day-old flies to empty vials, and placing them on ice for three hours. We transferred the flies to room temperature, and recorded the time it took for each individual to right itself and stand on its legs . We obtaY = μ + S + L + S×L + R(S×L) + ε to the individual level data. Y is phenotype, μ is the overall mean, S is the fixed effect of sex, L is the random effect of line, R is the random effect of replicate, and ε is the residual. We also fitted reduced models separately for males and females. We estimated the broad sense heritability for the full model as y = Wμ + Zg + Zg’ + e to individual level data in females and males separately. The random effects vector was symbolically separated into two in the model to help model specification despite the same incidence matrix Z with rows of unit vectors where one component is 1 and all others are 0 indicating the respective line the individual was from. Furthermore, W = T; μ is the overall mean; the additive genetic value g ~ N is assumed to be multivariate normal where G is the genomic relationship matrix of the n lines ; the non-additive genetic value g’ ~ N where I is the identity matrix; and the error term e ~ N. All variance components were estimated by REML using the Proc Mixed procedure in SAS software .Y = μ + V + a + ε, where V is the fixed effect of the polymorphic variant and a is a polygenic term whose covariance is specified by the genomic relationship matrix to evaluate the effects of markers for males and females separately. We performed these analyses for 1,865,879 biallelic variants for which the Phred scaled variant quality  was greater than 500, the minor allele frequency was ≥ 0.05, and genotype call rate was ≥ 0.8 among the DGRP lines [We performed genome wide association analysis on line means. Associations between single variants and line means for chill coma recovery time were tested by a mixed effect model accounting for relatedness among lines (including relatedness due to shared inversion karytopes) using FastLMM , 47. We RP lines . We consRP lines  quality RP lines .We used SnpEff  for funcy = Wμ + Zg + e (Model 1) to perform genomic prediction. The ith component of the q-vector y is the phenotypic value of the ith line that is used for prediction , W = T; μ is the overall mean; g ~ N is assumed to be multivariate normal where G is a genomic relationship matrix of the n lines; Z is a (q×n) incidence matrix with rows of unit vectors where one component is 1 and all others are 0, indicating the respective positions of lines used for prediction in the g vector of genetic values of all lines; σG2 is the genetic variance; and e ~ N is the residual term, where σE2 is the residual variance. The BLUP of the vector of genetic values can be obtained by solving the mixed model equationsWe used the underlying statistical model y = Wμ + Z1g1 + Z2g2 + e (Model 2) where g1 ~ N and g2 ~ N are assumed to be independent multivariate normal vectors where G1 and G2 are two different genomic relationship matrices, and Z1 and Z2 are the corresponding incidence matrices. The BLUP for this model can be obtained by solving the mixed model equationsWhen including two different random components, we used the statistical model http://CRAN.R-project.org/package=RandomFields) [We estimated variance components for Model 1 and Model 2 using maximum likelihood (ML) as implemented by the R package”RandomFields”, version 2.0.46, and the “fitvario” function (mFields) .We constructed genomic relationship matrices from the 3,742,106 SNPs and 437,096 indels that were polymorphic in the 176 DGRP lines, as well as for the subset of 1,868,905 common variants (MAF ≥ 0.05). We only used variants with a call rate > 0.8 in the 176 lines. Missing genotypes were assigned the allele frequencies based on the set of 176 lines.G, is defined as M is the (n×s) matrix of genotype vectors for the n lines, with the s variants coded as -1, 1; and the jth column of P is (2(pj—0.5),…,2(pj‒ 0.5))T, where pj is the frequency of the second allele at locus j.The additive genomic relationship matrix, pj(1−pj) , where Mj-th pair  of interacting SNPs we built two relationship matrices, Kj1 and Kj2 (one for each of the two SNPs), according toWe calculated pairwise epistatic genomic relationship matrices by modifying an approach according to Astle and Balding . For thexji is the genotype-vector and pji is the allele frequency of SNPji for i = 1, 2. We then calculated the Hadamard-product of Kj1 and Kj2 to obtain a matrix reflecting the interaction of SNP1 and SNP2. This calculation was repeated for all pairs of interacting SNPs and finally averaged over all Hadamard-products, to obtain one final epistatic relationship matrixK is an unbiased and positive semi-definite estimator for the additive x additive relationship matrix for the respective subset of m SNP pairs.where p matrix , and anan epistatic SNP pairs comprising 2n SNPs there are 4n2 SNP by SNP interactions. Our approach accounts for only the n pairwise interactions, i.e., for a SNP interacting with one other SNP, just this interaction is modeled and the interactions with the SNPs in the other n − 1 epistatic pairs are disregarded. In more general terms it is always possible to split the set of all pairwise SNP interactions in two subsets, where one subset reflects all interactions with a significant effect, and the complementary subset comprises all other pairwise interactions. The approach we used is equivalent to accounting for the significant interactions and ignoring the other interactions.Calculating the epistatic genomic relationship matrix as the Hadamard product of the additive genomic relationship matrices constructed from all SNPs involved in epistatic interactions  accountsWe first used 5-fold cross-validation (CV) –54 to asn lines, the LOOCV consists of n folds. In each fold, n-1 observations are used as the training set and the phenotype of the remaining single line is predicted. This is repeated n times such that each line is predicted once. In each of the 176 folds, a GWA analysis for single variants and/or pair-wise interactions between variants was performed on the 175 lines of the training set only, i.e., all single marker and epistatic GWA analyses were repeated 176 times. The single marker GWA analysis was performed as described above while the epistatic GWA was performed as follows. We first pruned the genotype data for LD using the LD pruning utility in PLINK [r2 > 0.8 within a window of 100 variants, and constrained the analysis to interactions between variants of MAF > 0.15 and at least two lines for each of the four possible genotypes. Raw phenotypic data were adjusted for the effects of inversions and major principal components of the genotypic matrix for common variants by fitting them as fixed effects and taking residuals from the fitted model [Y = μ + VA + VB + VA×VB + ε, using FastEpistasis [p < 10-X in the respective training set to construct genomic relationship matrices and predicted the phenotype of the remaining line based on Model 1 and Model 2, incorporating additive and/or epistatic genomic relationship matrices as described above. We computed the predictive ability as the correlation between the vector of estimated genetic values  and the vector of observed line phenotypes, and varied the P-value threshold X to arrive at an optimal value. The same LOOCV approach was also applied using the bigRR package, which implements a variant ridge regression with variant specific shrinkage parameters [G, and the Hadamard product of G, G#G, with the latter of the two representing genome-wide pair-wise interactions among all variants. All cross-validation procedures and the GBLUP approach were implemented using R software [We also performed a leave-one-out cross-validation (LOOCV) for trait-associated GBLUP. With in PLINK  such thaed model . Finallypistasis . After trameters . For comsoftware .b(m1–p1)(m2–p2), where m1 and m2 were the {-1, 1} coded genotypes, p1 and p2 were the allele frequencies of the two interacting loci, and b was the epistatic effect. To achieve equal variance for each interacting pair, we first calculated the sample variance of (m1–p1)(m2–p2) and determined b accordingly.To investigate whether the genetic architecture of a quantitative trait could account for the difficulty of the additive genomic relationship to explain phenotypic variation, we performed simulations under three distinct genetic architectures: (1) a major gene explaining 37% of the phenotypic variation; (2) 100 loci additively explaining 37% of the phenotypic variation; and (3) 50 pairs of interacting loci explaining a total of 37% of the phenotypic variation. For the major gene and polygenic models, we randomly selected QTL sites from the genome and assigned their allelic effects such that each locus explained an equal amount of variance. For each pair of the randomly chosen QTLs in the epistatic model, the genotypic effect was assigned by the formula S1 FigA, B) and males  separately using a ridge regression approach implemented in the bigRR package [A, females; C, males) and in the presence of genome-wide pair-wise epistatic variance .We performed LOOCV in females Click here for additional data file.S1 Table(A) Raw data. (B) Line means.(XLSX)Click here for additional data file.S2 Table(DOCX)Click here for additional data file.S3 Table(XLSX)Click here for additional data file.S4 Table(DOCX)Click here for additional data file.S5 Table(XLSX)Click here for additional data file.S6 Table(XLSX)Click here for additional data file.


Boar taint is an offensive urine or faecal-like odour, affecting the smell and taste of cooked pork from some mature non-castrated male pigs. Androstenone and skatole in fat are the molecules responsible. In most pig production systems, males, which are not required for breeding, are castrated shortly after birth to reduce the risk of boar taint. There is evidence for genetic variation in the predisposition to boar taint.A genome-wide association study (GWAS) was performed to identify loci with effects on boar taint. Five hundred Danish Landrace boars with high levels of skatole in fat (>0.3 μg/g), were each matched with a litter mate with low levels of skatole and measured for androstenone. DNA from these 1,000 non-castrated boars was genotyped using the Illumina PorcineSNP60 Beadchip. After quality control, tests for SNPs associated with boar taint were performed on 938 phenotyped individuals and 44,648 SNPs. Empirical significance thresholds were set by permutation . For androstenone, a ‘regional heritability approach’ combining information from multiple SNPs was used to estimate the genetic variation attributable to individual autosomes.CYP2E1 gene on chromosome 14 (SSC14), which encodes an enzyme involved in degradation of skatole. Nominal significance was found for effects on skatole associated with 4 other SNPs including a region of SSC6 reported previously. Genome-wide significance was found for an association between SNPs on SSC5 and androstenone levels and nominal significance for associations with SNPs on SSC13 and SSC17. The regional analyses confirmed large effects on SSC5 for androstenone and suggest that SSC5 explains 23% of the genetic variation in androstenone. The autosomal heritability analyses also suggest that there is a large effect associated with androstenone on SSC2, not detected using GWAS.A highly significant association was found between variation in skatole levels and SNPs within the CYP2E1 gene has effects on skatole breakdown in the liver. Autosomal heritability estimates can uncover clusters of smaller genetic effects that individually do not exceed the threshold for GWAS significance.Significant SNP associations were found for skatole on SSC14 and for androstenone on SSC5 in Landrace pigs. The study agrees with evidence that the The online version of this article (doi:10.1186/1471-2164-15-424) contains supplementary material, which is available to authorized users. Boar taint is an offensive urine or faecal-like odour, affecting the smell and taste of some cooked pork. Androstenone and skatole, which are lipophilic compounds that accumulate in the fat of mature non-castrated male pigs, have been identified as the main causes of boar taint . A rangeAndrostenone or 5α-androst-16-en-one is a male steroid produced in the testes at sexual maturity. High concentrations of androstenone are present in the saliva of male pigs where it is converted to a pheromone and is an important olfactory trigger for sexual behaviour in sows . Androsthttp://ec.europa.eu/food/animal/welfare/farm/initiatives_en.htm), forcing the industry to explore other methods to prevent tainted carcasses.The most effective solution, to date, for controlling boar taint, is surgical castration shortly after birth. However, as castration removes natural anabolic androgens that promote lean growth, non-castrates are leaner with 10-30% greater efficiency in feed conversion and superior meat quality. Furthermore, concerns over animal welfare have led to legislative control [Selective breeding based on the identification and exploitation of genetic variation in androstenone and skatole levels could ultimately provide a more sustainable solution . Recent Although highly successful at identifying new trait associated loci and pathways, human genome-wide association studies (GWAS) have failed to capture a large proportion of the genetic variation in complex traits , 22. To The objective of this study was to identify genomic regions with effects on boar taint in Landrace pigs.Results are reported from the two approaches used: (i) single SNP analysis using genome-wide association, and (ii) a regional approach dividing SNPs by chromosome and estimating genetic variation attributable to each autosome.We acquired data for a population of approximately 6,000 commercial Danish Landrace boars. The animals were slaughtered at a mean age of 160 (±13) days. Measures for skatole were taken using an in-line procedure at three Danish abattoirs. Power to detect a QTL can be increased in a finite sample by selecting those individuals that differ most from the phenotypic mean i.e. the extremes of the phenotypic distribution. Here, we took extreme animals plus a within-litter ‘control’ in order to maximize power while controlling for family stratification. This strategy maximises the potential genetic information to be gained from the sample , 26. Thu2pedigree (LM 1) using all 6,000 records for skatole and 1,000 records for androstenone were moderate at 0.39 (s.e. 0.03) and 0.52 (s.e. 0.09) respectively and were similar to those previously reported [The measures for both skatole and androstenone were positively skewed and were log transformed prior to analysis  [http://www.ensembl.org/Sus_scrofa/Info/Index).DNA isolated from muscle samples collected at slaughter were genotyped for 63,153 SNPs using the Illumina PorcineSNP60 beadchip . Analysifa10.2/)  and as ak columns whose rows give the coordinates of the points chosen to represent dissimilarities. k is a user defined parameter based on the expected number of clusters, here k = 3. The 3 columns from the mds matrix were fitted into the linear model as covariates in order to account for the population stratification indicated by the model based clustering.Genome wide association is based on differences in allele frequencies associated with differences in the trait under study. Phenomena such as admixture, selection and population stratification can result in spurious patterns of allele frequencies unrelated to the trait. Population stratification can be assessed by clustering individuals based on measures of relatedness and examining clusters for evidence of systematic bias. Here, model based clustering was performed using the mclust function in R software 2.10.1 . Mclust The differences in study design between the two traits  were reflected in the GWAS by very different estimates of lambda, which is an indicator of bias due to population structure. Lambda was close to 1 for all of the skatole analyses where high and low animals were matched sibs, but greater than 2 for the androstenone analyses. This result indicates that the sampling design for skatole was balanced and therefore, unaffected by potential biases arising from any population stratification. Although lambda indicates some bias for the androstenone analyses, this bias was largely accounted for with the inclusion of the co-ordinates from the multi-dimensional scaling (mds) matrix in the model . Any remaining stratification was successfully corrected for by fitting the genomic relationship matrix. Full details are given in the materials and methods.Single SNP associations were performed using a GRAMMAR  analysisSIRI0000194 SNP at the telomeric end of SSC14 on skatole levels was highly significant (P > 1.4E-09) exceeding the genome-wide threshold .The effect of the d Figure  and expl skatole –34. The H3GA0016037 and ASGA0025097 mapping 4 Mbp apart are highly significant. Figure 2 = 0.68 suggesting that both SNPs are tagging the same causal variant. There were also SNPs with large effects on chromosomes 8, 13 and 17  explaining 4% of the phenotypic variation Table . Two SNPN SNPs spanning a region. This method has been implemented in the GCTA software package and it has been shown that the method can be used to estimate genetic variation for any region of the genome [The linear mixed model 2) can be extended to divide phenotypic variance into estimates of the genetic and environmental variance containing information from genotypes of a group of e genome . We divi can be epoly test comparing a linear mixed model fitting systematic or fixed effects and a GRM based on information from all SNPs with a model incorporating an additional variance component for the genetic variance attributable to all SNPs on a chromosome. This provides a test of whether inclusion of individual autosomes provides a better model of the variance than the overall relationship matrix . Estimates of the autosomal heritabilities for effects on androstenone for LRTpoly are summarised in Table Individual LRT (likelihood ratio tests) for each chromosome for androstenone are detailed in Table poly test for genetic variance was chromosome 5 explaining 6% of the phenotypic variation, reflecting the GWAS results. Under the LRTpoly method, autosomes 2, 3 and 13 each explain 2% of the phenotypic variation, however, the estimates are not significant. When all autosomes are fitted simultaneously . A comparison of significance of autosomal heritability of androstenone using three testing strategies is given in Figure ind in Figure drop suggest that chromosomes 2, 3, 5 and 13 explain a significant proportion of the variance.An alternative testing strategy is to fit all autosomes in a full model and then drop them one at a time for a reduced model (LRTSIRI0000194 SNP on skatole estimated by fitting the genotypes as a covariate in the linear mixed model (3) was 5% of the phenotypic variance of the selected population  CC homozygotes were observed in the ‘high’ skatole group [SIRI0000194) SNP have been reported [CC homozygotes exhibited the highest skatole levels. Although SIRI0000194 lies within a block of high LD .A genome-wide association study (GWAS) was carried out to identify SNPs associated with effects on androstenone and skatole in intact male pigs. The effect of the on Table . The exp882_2412 , is locale group . More rereported . Again txpressed   was associated with SNP marker MARC0040638 located on chromosome 6 within the estradiol 17-beta-dehydrogenase 2 (HSD17B2) gene. The HSD17B2 gene and MARC0040638 SNP were located at SSC6:4,514,200-4,578,665 in an earlier genome assembly (Sscrofa9) but are located on unplaced scaffolds on the present assembly (Sscrofa10.2). The assignment of MARC0040638 SNP to SSC6 is confirmed from radiation hybrid mapping data . Incomplete sequence data from this BAC clone contribute to the current pig genome assembly (Sscrofa10.2) on SSC6 6.876-6.939 Mbp. This SNP did not exceed the FDR or genome-wide threshold, however a region on chromosome 6 spanning this gene was previously found to be significant for skatole in Landrace pigs [MARC0040638 SNP and HSD17B2 gene. In an earlier study, we mapped QTL for skatole, as detected by a (human) sensory panel, by linkage analysis with a low density microsatellite marker map with the closest marker SW1353 mapping to SSC6: 9.872 Mbp (Sscrofa10.2 coordinates) [estradiol 17-beta-dehydrogenase 2 (HSD17B2) is involved in the synthesis of the 17 beta-hydroxysteroids: delta 5-androstene-3 beta, 17 beta-diol, testosterone, 17 beta-estradiol and dihydrotestosterone [HSD17B2 gene is thus important for steroid hormone synthesis and is abundantly expressed in pig liver, ureter and stomach (fundus), [http://biogps.org). Another 17-beta hydroxysteroid dehyrdorgenase gene (HSD17B7) has been examined as a candidate gene for an androstenone QTL on SSC4 [The GWAS for skatole was repeated, fitting the SNP ace pigs . Ramos eace pigs  found sifundus),   and retinol dehydrogenase 16 (RDH16) genes. The RDH gene encodes an enzyme which recognizes 5α-androstan-3α,17β-diol and androsterone as substrates and is expressed in liver, testes and other tissues in humans [RDH16 is abundantly expressed in pig liver, testes and placenta [http://biogps.org). Another 17-beta hydroxysteroid dehyrdrogenase gene (HSD17B6) is located about 0.5 Mbp upstream of the ASGA0025097 SNP. The 4 Mbp region between the two top SNPs is gene-rich and exhibits high levels of LD in the Danish Landrace population studied  was 0.14 and the r2 between them was 0.68. Fitting either SNP results in the loss of the effect indicating that both SNPs are tagging the same causal variant. This region has been found to be significant for androstenone measured in the fat of Duroc pigs, and for estradiol in Landrace pigs [A significant effect on androstenone was found associated with the election . The secn humans . RDH16 iplacenta  . FixY is an n × 1, vector of log phenotype, n is the number of individuals, X is an incidence matrix relating solutions for fixed effects of herd and covariates of age, mds co-ordinates contained in β to individuals, u is an n × 1 vector of genetic effects, Z is an n × n incidence matrix relating individuals to genetic effects, and e is an n × 1 vector of individual residual effects. e is distributed as e ~ N. A is the n × n genetic relationship matrix estimated from pedigree relationships.Where n × n genomic relationship matrix (GRM) of relatedness at a population level between n individuals gives the covariance structure for the phenotype based on the premise that the more related two individuals are, or the greater the amount of the genome they share in common, the greater the expectation of phenotypic similarity. The proportion of alleles two individuals share in common are summed across all markers weighted by allele frequencies in the population in order to obtain an accurate estimate of how related two individuals are either across the entire genome or at a given region. Genomic relationship matrices were estimated using GenABEL [SNP genotypes were used to estimate shared coancestry or identity by state between individuals with rare SNPs weighted more heavily. The  GenABEL  and GCTA GenABEL  softwareUsing the marker information for the 1,000 individuals, heritabilities were estimated by fitting the SNP based genomic relationship matrix from GenABEL in a linear mixed model to estimate polygenic effects from marker information (2). A genotypic correlation was estimated by a bivariate analysis of the two traits fitting the genomic relationship matrix using ASReml 2 software .2g is an N × 1 vector of SNP effects, N is the number of SNPs, W is an n × N incidence matrix relating SNP genotypes to g. G is the n × n genomic relationship matrix estimated from SNP genotypes and Where Single SNP association tests were performed using a GRAMMAR  analysisN SNPs from (3).Where T is test statistic for y is a vector of trait residuals from (2), SNP is a vector of SNP genotypes and e is a vector of random residuals.Where A correction factor or lambda , 45 was p(1-p)α2 where p is the allele frequency for the most common SNP allele and α is the estimated effect. A further check was that this estimate was consistent with the difference in phenotypic variance when fitting, and not fitting, SNP genotype as a covariate in the LMM.As grammar analyses tend to underestimate true SNP effects , genome-a is a random polygenic effect estimated using a SNP-based relationship matrix and e is the random residual.Where y = log trait. Herd is fitted as a fixed effect. SNP genotype, slaughter weight, age, meat percentage and co-ordinates from the multi-dimensional scaling (mds) are fitted as covariates, N SNPs spanning a region. This method has been implemented in the GCTA software package and it has been shown that the method can be used to estimate genetic variation for any region of the genome [The linear mixed model (2) can be extended to divide phenotypic variance into estimates of the genetic and environmental variance containing information from genotypes of a group of e genome . We diviTo avoid confounding of genetic variation of the trait and potential variation due to population stratification, eigenvectors were estimated from the genetic relationship matrix and the first 4 principal components fitted as covariates in the linear mixed model. This is slightly conservative and based on the results of the model based clustering described earlier which showed that the data forms 3 distinct clusters. The fixed effects and covariates of herd, age, meat percentage and slaughter weight were fitted into a linear mixed model together with eighteen variance components - one for each of the eighteen autosomes requiring 18 separate genetic relationship matrices to model the covariance structure and to partition the genetic variance into estimates of autosomal heritability.poly). All SNPs were used in the polygenic effect to ensure that the models were truly nested. This conservative approach ensures that the variance explained by an autosome is not inflated by background polygenic effects.To test the significance of individual autosomes a likelihood ratio test (LRT) comparing a model fitting the individual autosome plus a variance component for all SNPs in the grm  was compared to a model fitting only the polygenic effect (LRTind). Secondly, a model fitting all 18 variance components compared with a model dropping each of the autosomes in turn (LRTdrop).Two further approaches were used. Firstly, comparing a model fitting a variance component estimated from the SNPs on a single autosome with a null model (LRTGCTA solves the linear mixed model (LMM) and obtains estimates of genetic and residual variances by restricted maximum likelihood (REML) using the average information (AI) algorithm.P-value for a test assuming 1df was divided in two.A test statistic was obtained using a standard LRT statistic calculated as twice the difference between the log likelihoods of the full model and the null or reduced model that did not fit a genetic component. The LRT was tested against a chi square distribution. The LRT for one extra variance component is distributed as a mixture of point 0 and 1degrees of freedom (df) . To accoAdditional file 1: Figure S1: Distribution of skatole and androstenone measures and log transformed measures of skatole and androstenone. (PDF 70 KB)Additional file 2: Table S1.: Effect of testing structure on estimates and significance of autosomal heritability. (PDF 52 KB)Autosomal heritability analysis for skatole. (PDF 95 KB)Additional file 3::