Drosophila melanogaster Genetic Reference Panel (DGRP) is a community resource of 205 sequenced inbred lines, derived to improve our understanding of the effects of naturally occurring genetic variation on molecular and organismal phenotypes. We used an integrated genotyping strategy to identify 4,853,802 single nucleotide polymorphisms (SNPs) and 1,296,080 non-SNP variants. Our molecular population genomic analyses show higher deletion than insertion mutation rates and stronger purifying selection on deletions. Weaker selection on insertions than deletions is consistent with our observed distribution of genome size determined by flow cytometry, which is skewed toward larger genomes. Insertion/deletion and single nucleotide polymorphisms are positively correlated with each other and with local recombination, suggesting that their nonrandom distributions are due to hitchhiking and background selection. Our cytogenetic analysis identified 16 polymorphic inversions in the DGRP. Common inverted and standard karyotypes are genetically divergent and account for most of the variation in relatedness among the DGRP lines. Intriguingly, variation in genome size and many quantitative traits are significantly associated with inversions. Approximately 50% of the DGRP lines are infected with Wolbachia, and four lines have germline insertions of Wolbachia sequences, but effects of Wolbachia infection on quantitative traits are rarely significant. The DGRP complements ongoing efforts to functionally annotate the Drosophila genome. Indeed, 15% of all D. melanogaster genes segregate for potentially damaged proteins in the DGRP, and genome-wide analyses of quantitative traits identify novel candidate genes. The DGRP lines, sequence data, genotypes, quality scores, phenotypes, and analysis and visualization tools are publicly available.The  Drosophila melanogaster have revealed basic principles and mechanisms underlying fundamental genetic concepts of linkage and recombination and were instrumental in identifying canonical and evolutionarily conserved cell signaling pathways. Most D. melanogaster genes are evolutionarily conserved, leading to fly models for understanding common human diseases and behavioral disorders, dipteran disease vectors, and insects impacting agriculture, medicine, and forensics. Despite nearly a century of research on D. melanogaster, however, a large fraction of its coding and noncoding sequence has no known function , a publicly available population of 205 sequenced inbred lines. Previously, we cataloged SNPs segregating in 168 DGRP lines (DGRP Freeze 1.0)   and non-eze 1.0) . Here, wD. melanogaster genome using BWA (Novocraft.com), recalibrated base quality scores, and locally realigned reads. The average mapped sequence coverage was 27× per line .We obtained Illumina sequences for 48 DGRP lines that were not sequenced previously or for which only 454 sequence data were available, as well as for six DGRP lines with low Freeze 1.0 coverage . We aligned sequence reads to the sing BWA  and NovoThere are many algorithms for detecting SNP and non-SNP variants from short-read sequence data . IdentifOn average, the methods called over 580,000 SNPs, and 130,000 small (<100 bp) and 1400 large (≥100 bp) non-SNP variants per line; however, there was heterogeneity in the number of variants called by each method and the overall concordance among methods . TherefoWe used three strategies to validate genotype calls. First, we targeted 384 small (1–18 bp) indels affecting coding regions and 384 randomly chosen larger (30–313 bp) indels for validation by Sanger sequencing in five DGRP lines. A total of 315 small and 384 large indels were successfully assayed with Sanger technology for at least three lines. Of the 1463 small indel/line and 1876 large indel/line combinations with both Sanger and Illumina calls, 1458 (99.66%) and 1872 (99.79%), respectively, were concordant .Second, we performed high-density tiling microarray-based validation experiments using published data for six DGRP lines  to assesThird, we used the 454 sequence data from 38 lines , suggesting that our integrated variant calling approach eliminated false SNPs near indels. Using a model tailored to the experimental design , we geneThe DGRP lines were derived by 20 generations of full-sib inbreeding and have an expected inbreeding coefficient of F = 0.986 . TherefoWe assessed the number of segregating sites for each line by chromosome  and found substantial variation in the number of segregating sites between and within chromosomes. Approximately 96% of the lines had 2% or fewer segregating X-linked variants, while on average 84% of the lines had 2% or fewer segregating autosomal variants . TherefoD. melanogaster populations harbor polymorphic inversions  had very low numbers of segregating sites. In(3L)Y is only present as a single heterozygote in the sample and could be of recent origin and hence not genetically differentiated from the standard karyotype. However, other chromosomes heterozygous for In(3R)Mo and In(2L)t had large numbers of segregating sites . Possibly, these inversions do not have a single origin, and the old and new inverted sequences are segregating in the DGRP; or they could have undergone an even number of recombination events as heterokaryotypes, recovering a standard nucleotide configuration. Nevertheless, there is nearly a perfect correlation between large numbers of segregating sites and inversion heterozygosity (Fisher’s exact test P = 1.91 × 10−81).We identified 16 different segregating autosomal inversions . A possiWolbachia pipientis is a maternally transmitted endosymbiotic bacterium that infects ∼20% of all insects . Wolbachia sequences have been inserted into eukaryotic genomes . However, PCR-based analyses revealed that only four DGRP lines contained the Wolbachia insertions . The insertions were incorrectly called in the remaining lines infected with Wolbachia because Wolbachia sequence reads were present for these lines, and the genotyping algorithm assigned them to the location to which they uniquely mapped in the four lines. This artifact did not occur for any other large insertions, all of which were either unique, as expected for a new D. melanogaster sequence present in DGRP lines but not the reference strain, or were homologous to other D. melanogaster sequences, as expected from insertions arising from transposable elements (TEs), local tandem duplications, and nonhomologous recombination. insects . Wolbachsmission . D. melanfection . Wolbach viruses , but the genomes . TherefoF204, 811 = 2.61, P < 0.0001), ranging from 169.7 to 192.8 Mb . Genome size differences were verified by the presence of double peaks in copreparations from lines with different average genome size NS, In(3L)P, and In(3R)K and heterozygous for In(3L)Y had larger average genome sizes than the corresponding standard homozygous karyotypes, whereas lines homozygous or heterozygous for all other inversions had smaller average genome sizes than the standard karyotypes. We regressed genome size on the total number of “smaller” inversions and found a significant negative effect  . Although inversions account for only 4% of the variation in genome size, the magnitude of the effect is substantial at 0.5 Mb per inverted region.Lines homozygous for ρ) for estimates of SNP nucleotide polymorphisms (π) . Since population genomic inferences from analyses of SNP variation remain the same, we primarily focus here on indel variation.Previously, we performed a population genomic analysis of SNPs in the DGRP Freeze 1.0 . The SNPisms (π)  among 10Drosophila simulans and determined the ancestral and derived status of 210,268 biallelic indels. We found that 86% of “deletions” and 74% of “insertions” inferred from the reference genome were true deletions and insertions according to the polarized estimates.We defined insertions and deletions in our variant calling algorithm with respect to the reference sequence. For population genetic inferences, we polarized insertion/deletion status evolutionarily with respect to n = 145,015; 69%) outnumber insertions  by 2.2:1 . This estimate is among the highest estimates of the deletion:insertion ratio for D. melanogaster but is consistent with previous estimates that indicate a bias toward higher deletion than insertion rates (P = 0) and 74% fewer insertions (P = 0) on the X chromosome than on the major autosomal chromosomal arms , consistent with stronger selection against indels on the X chromosome. The observed bias toward deletions is not an artifact of the greater difficulty of calling large insertions than deletions. We called approximately equal numbers of insertions and deletions except for the largest variants, where we called more deletions than insertions relative to the reference , deletions are, on average, larger than insertions . However, the longest indels are insertions, most of which correspond to r genome . Most lar genome  and in hr genome .The minor allele frequency (MAF) spectra  show an excess of low MAF indels compared to SNPs for all functional classes. Given that lower MAF variants are likely enriched for variants under purifying selection, these data are consistent with deleterious fitness effects of indels . InsertiThese results suggest that natural selection acts differently on insertions and deletions, with stronger purifying selection on deletions . This isπ) in the DGRP was reduced near centromeres and telomeres and was positively associated with local recombination rate (for recombination rates < 2 cM/Mb) . There is a strong positive correlation between indel and nucleotide diversity for all chromosome arms  . The patTo test the hypothesis that indels are mutagenic, we plotted the number of SNPs ± 100 bp from indels with MAF between 0.4 and 0.5, for different SNP minor allele counts. Intermediate-frequency SNPs are clustered near intermediate-frequency indels . Assuminρ = 0.037 genome-wide, P = 0.205) excludes mutations associated with recombination as the cause of the correlation between π and local recombination.Evolutionary models of hitchhiking and background selection predict a positive correlation between recombination and polymorphism for all variants . We replWe determined enrichment or depletion of variants for five chromatin types  of individual segregating variants, identifying 6637 potentially damaging variants that affect splice donor or acceptor sites, cause frame-shift mutations, loss of start or stop codons, or lead to premature stop codons. Collectively, they affect 3868 genes in at least one DGRP line. The allele frequency distribution of these potentially damaging variants is shifted to the lower end of the frequency spectrum relative to those of less damaging variants , as expected if they have deleterious fitness effects.Next, we identified closely linked cosegregating variants that might ameliorate these potentially damaging variants . We founDrosophila protein coding genes) . On average, each of these affected genes is damaged in ∼13 of the 205 DGRP lines, and each line contains ∼136 potentially damaged genes t and In(3R)Mo from all other lines (In(2L)t from those with In(3R)Mo  analysis reveals clusters of related lines that carry major inversions. The first two PCs separate lines carrying both er lines , while tIn(3R)Mo . The PC In(3R)Mo . Lines wWe also computed genomic relationships separately for each chromosome arm . The chromosome-wide relationships among the lines are specific to each arm and are different from the genome-wide pattern . The genomic heterogeneity of relatedness among chromosomal arms suggests that population structure other than the known inversions is likely minimal; otherwise, inter-chromosomal correlation of relatedness would arise.r2 parameterization (We assessed pairwise linkage disequilibrium (LD) between polymorphic variants using the rization . Averagerization , consistrization . There irization . In gener2 > 0.95) with it locally (within 1 kb) and genome-wide. There are consistently very few (mean = 1.43) variants in high local LD with the focal variant. However, the number of long-range variants in high LD with focal variants depends on the minor allele count of the focal variant and can be in the thousands for very low frequency variants  studies using the DGRP. However, long-range LD could significantly impair our ability to identify QTLs. For each of 1000 randomly sampled variants with a specified number of minor alleles in the population, we counted variants that are in strong LD . In(3R)K is associated with starvation resistance in females and acute oxidative stress resistance in males; In(2L)t, In(2R)NS, and In(3R)Mo are associated, often strongly and in a sex-specific manner, with sleep traits; and In(2L)t and In(3R)Mo are associated with olfactory behavior in both sexes . The DGRP lines vary significantly in genome size, which could also affect variation in quantitative trait phenotypes. However, correlations of quantitative traits with genome size were small for all traits and not significant in any analysis.The range and magnitude of effects of ill coma , resistaill coma  and chroill coma  oxidativill coma , and olfill coma . The effWolbachia infection status. Association tests can be performed for individual variants or by gene. The former can identify putative causal alleles but is restricted to the 1,920,276 variants with minor allele frequencies ≥0.05 to avoid spurious associations due to LD caused by limited sample size , and genome size .Prior to performing GWA analyses using the DGRP, we must adjust the phenotypic data to account for cryptic genetic relatedness, effects of inversions  and ple size . Gene-baple size , and canple size . In eithWolbachia and inversions and account for relatedness is illustrated by quantile-quantile plots  from single variant GWA analysis of starvation resistance in females, which is associated with In(3R)K . Unadjusted data show substantial systematic inflation of test statistics, while adjusting for Wolbachia and inversions and accounting for relatedness using a mixed model significantly alleviate the inflation. The top associations for the individual and gene-based tests for all three traits are only partially overlapping, highlighting the complementary nature of these tests. Only a few variants/genes reached conservative Bonferroni-adjusted significant thresholds, and all suggest novel candidate genes affecting the traits. Examples include a SNP in genghis khan , associated with female starvation resistance, and SNPs in pointed  and CG32521 (a gene of unknown function), associated with genome size. myotubularin (mtm), which is involved in chromosome segregation and the mitotic cell cycle  indels, which comprise >98% of all variants. We performed a cytogenetic analysis of large segregating inversions, genotyped all lines for the presence of the maternally transmitted Wolbachia endosymbiont, and estimated genome size by flow cytometry. These data provide a comprehensive characterization of natural variation in genome architecture in this powerful genetic model organism that can be used to gain insights about natural selection and the evolution of genome size, and enhance the functional annotation of the D. melanogaster genome. We also describe improved statistical methodology for genome-wide association mapping of quantitative traits in a scenario where all variants are known and the rapid decay in LD with physical distance enables high-resolution mapping.Here, we present a molecular polymorphism map for 205 sequenced inbred Drosophila  Gene Disruption Project . Molecular population genomic analyses using the DGRP highlight genomic regions under purifying selection, complementing modENCODE functional motifs. GWA analyses of quantitative traits in the DGRP provide new functional annotation of the D. melanogaster genome by identifying novel candidate genes associated with these traits. These genes typically have well-described effects on other traits, play key roles in early developmental processes, or are computationally defined genes with no known function, but have never been associated with the focal trait. Subsequently, the full power of Drosophila genetics can be applied to validating marker-trait associations: mutations, RNAi constructs, and outbred QTL mapping populations  hosts an updated pipeline for single marker GWA analysis which accounts for effects of Wolbachia infection and major inversions as well as cryptic relatedness among the DGRP lines; a new genome browser track for visualizing individual line genotypes and functional annotations for any specified genomic region; and all published phenotypes. These data will be useful for testing new analytical methods as well as for teaching general principles of population and quantitative genetics.The DGRP lines, sequence data, genotypes, quality scores, and phenotypes are publicly available. The DGRP website  and purified the samples by phenol-chloroform extraction. We constructed high molecular weight double-strand genomic DNA samples into Illumina paired-end libraries according to the manufacturer’s protocol (Illumina)  and sequenced shotgun DNA libraries on the Illumina HiSeq 2000 or GAII platforms, according to the manufacturer’s specifications.http://flybase.org) with BWA (v0.5.9-r16) (Novocraft.com) using default parameters. We used GATK (v1.0.5506)    and Novo.0.5506)  software.0.5506) . We excl.0.5506)  and JGIL.0.5506)  to the B.0.5506)  and Prin.0.5506)  to the B.0.5506) , Pindel .0.5506) , CNVnato.0.5506) , and Gen.0.5506)  as descrphred scale quality scores were at least 500 and genotypes whose sequencing depths were at least one and genotype quality scores at least 20. The final VCF genotype file (http://dgrp2.gnets.ncsu.edu) containing 4,438,427 variants gives the number of supporting and opposing reads for each variant in each line, genotypes with the maximum posterior probability, and the corresponding quality scores . The resulting 6,149,882 nonredundant variants were then assigned variant and genotype quality scores using JGIL . We retay scores .Drosophila 1.0R tiling arrays for six DGRP lines  and the reference strain to validate deletions >25 bp . Finally, we used 454 sequence (Roche) data from 38 DGRP lines  indels affecting coding regions and 384 larger (30–313 bp) randomly chosen indels on five DGRP lines . Second, we used previously published data  on genomRP lines  to validWe assessed inversion genotypes by cytogenetic analysis of polytene salivary gland chromosomes of third instar larvae by staining with lactic-acetic orcein. We identified inversions by comparison to the standard map of Wolbachia pipientis . We purified PCR products for lines positive for Wolbachia insertions using the Zymo Clean and Concentrator kit (Zymo Research Corporation) and subjected them to Sanger sequencing using the ABI 3730XL platform.We used PCR to determine the infection status of each line with respect to the endosymbiont, Drosophila virilis (1C = 328 Mb) as an internal standard, as described in Y = μ + G + ε, where Y is the standard deviation of genome size within a line and G is the number of segregating inversions  within lines.We estimated genome sizes for 1016 individual females  using flow cytometry with D. melanogaster reference genome  using the 5.49 version annotations of the e genome . We discsimulans  as an ouπindel to describe indel polymorphism, a measure analogous to nucleotide diversity (π), which does not take into account indel size. We used an analogous measure to estimate divergence (k)  in 100-kb nonoverlapping windows. We also estimated the minor allele frequency (MAF) distribution for indels and the derived allele frequency (DAF) distributions for both deletions and insertions. We used the nonparametric Spearman rank correlation coefficient (ρ) to test for covariation among the diversity estimates. We used the recent high resolution recombination map of D. melanogaster (We used ence (k) . We esti Browser . We estinogaster  to correWe annotated the functional consequences of variants on annotated genes (FlyBase R5.49)  using SnG among all DGRP lines using biallelic common variants (MAF > 0.05) with a call rate >80%. This computation was performed using the r2 was smaller than 0.2. We excluded variants within 2 Mb of the major inversions  from this analysis. We tested the significance of the top eigenvalues using the Tracy-Widom statistic implemented in EIGENSTRAT.We calculated the realized genome-wide relationship matrix Wolbachia infection and major inversions  based on mean phenotypic values of each line. We then used the adjusted line means to fit a linear mixed model in the form of y=Xb+Zu+ e, where y is the adjusted phenotypic values, X is the design matrix for the fixed SNP effect b, Z is the incidence matrix for the random polygenic effect u, and e is the residual. The vector of polygenic effects u has a covariance matrix in the form of Aσ2, where σ2 is the polygenic variance component. We fitted this linear mixed model using the FastLMM program (v1.09)  .We performed a burden test and a nonburden sequence kernel association test (SKAT) to assess the cumulative effect of all variants within one kilobase of each annotated gene. The weighted burden test weights the contribution of each variant in a gene by the reciprocal of the standard deviation of its estimated minor allele frequency and uses the weighted averages to estimate a score statistic . The SKAWolbachia infection status, major inversions, and the 11 principal components explaining the most genetic variation in the DGRP . Wolbachia infection status was fit with a logit link function in a likewise manner, excluding the fixed effect of Wolbachia infection status. We performed gene-based tests for all variants, and for common (MAF ≥ 0.05) and rare (MAF < 0.05) variants separately.We performed both the weighted burden test and SKAT using the SKAT package  in R v3.Drosophila Stock Center (http://flystocks.bio.indiana.edu/Browse/DGRP.php) . Raw sequence data have been submitted to the NCBI Sequence Read Archive  under accession numbers listed in Supplemental Data File S1, and to the Baylor College of Medicine Human Genome Sequencing Center (https://www.hgsc.bcm.edu/arthropods/drosophila-genetic-reference-panel). The genotypes, quality scores, phenotypes, and web-based analysis tools are available from the DGRP website (http://dgrp2.gnets.ncsu.edu).The DGRP lines are available from the Bloomington


CFTR), medium-chain-acyl-Co-A-dehydrogenase deficiency (ACADM), phenylketonuria (PAH) and alpha 1-antitrypsin deficiency (SERPINA1).Several recessive Mendelian disorders are common in Europeans, including cystic fibrosis , forced vital capacity (FVC)) for SERPINA1 (protease inhibitor (PI) Z allele, rs28929474) showed enhanced FEV1 and FVC (0.13 z-score increase in FEV1 (p=1.7×10−5) and 0.16 z-score increase in FVC (p=5.2×10−8)) in PI-MZ individuals. Height adjustment  revealed strong positive height associations of the Z allele (1.50 cm increase in height (p=3.6×10−10)).Findings were mostly negative but lung function in HMGA2. However, height only partially attenuates the SERPINA1-FEV1 or FVC association (around 50%) and vice versa. Height SNP variants have recently been shown to be positively selected collectively in North versus South Europeans, while the Z allele high frequency is localised to North Europe. Although PI-ZZ is clinically disadvantageous to lung function, PI-MZ increases both height and respiratory function; potentially a balanced polymorphism. Partial blockade of PI could conceivably form part of a future poly-therapeutic approach in very short children. The notion that elastase inhibition should benefit patients with chronic obstructive pulmonary disease may also merit re-evaluation. PI is already a therapeutic target: our findings invite a reconsideration of the optimum level in respiratory care and novel pathway potential for development of agents for the management of growth disorders.The PI-MZ rare (2%) SNP effect is nearly four times greater than the ‘top’ common height SNP in  ACADM) to ∼10% (protease inhibitor (PI)-MS)). Unlike in homozygote carriers, no clinical features are evident in heterozygotes although biochemical phenotype may be detectable ). The AAT deficiency phenotype is continuous across the six genotypes of the S and Z alleles . However, only individuals of ZZ genotype are notable clinically; the condition results in early-onset lung emphysema with a penetrance of 60% for ZZ individuals.Heterozygote carriers for recessive Mendelian (monogenic) disorders such as cystic fibrosis MIM: 219700), medium-chain-acyl-Co-A-dehydrogenase deficiency MIM: 201450), phenylketonuria (MIM: 261600) and alpha 1-antitrypsin (AAT) deficiency MIM: 613490) are relatively common in the UK population  allele is PI-M and the most common pathogenic allele is PI-Z. Mendelian disease alleles such as PI-Z may be prevalent in a population through new mutation and chance, with insufficient time for fitness and selection to take effect, or through balancing selection where heterozygote advantage outweighs homozygote disadvantage. A textbook example of the latter is sickle cell anaemia where resistance to malaria confers a heterozygote advantage.CFTR, SERPINA1) and cognitive function . We tested heterozygotes against equivalent later life traits accordingly, with an additional analysis of physical capability  collaborationSERPINA1 from HaplotterA well-known signature of recent selection in humans is the very fast increase in frequency of the favoured allele (or haplotype) in a population.A list of acronyms used in this article is shown in Individuals included in this analysis belonged to the HALCyon collaboration.18We selected the most common causal mutation to genotype for medium-chain acyl Co-A dehydrogenase deficiency  and considering mutations with highest frequency in UK populations.Mutation selection was more complex for phenylketonuria because several hundred causal mutations have been identified to date. We selected rs5030861 (IVS12+1 G>A), rs5030858 (R408W) and rs75193786 [T to C mutation] (I65T) after consulting a review of PKU mutations in Europehttp://www.lgcgenomics.com/), with the exception of rs17580 and rs28929474 in ELSA and WHII for which genotype data were already available. We inferred rs17580 and rs28929474 genotypes in the NSHD using PI classes from isoelectric focusing.Genotyping was performed by LGC Genomics  were transformed to z-scores by subtracting the mean and dividing by the SD of the measure within cohorts using all data available. All outcomes were further harmonised across cohorts before z-scoring, as detailed in online supplementary appendix S3.Chronic obstructive pulmonary disease (COPD) status was determined using the Global Lungs Initiative ERS Task Force 2012 regression equations, which derive the lower limit of normal  values for forced expiratory volume in 1 second (FEV1) and FEV1/forced vital capacity (FVC) ratio given an individual's age, sex and height.PAH mutations were combined so that a non-carrier was homozygous for all three SNPs and a carrier was heterozygous for at least one SNP. In the analysis of PI status, separate analyses were conducted for PI-MS, PI-MZ, PI-SS, PI-SZ and PI-ZZ versus PI-MM (with PI-MM coded as 0).Carrier status was defined as a binary variable in all analyses and was coded as [0] non-carrier and [1] carrier. The three Several of the outcomes were transformed prior to z-scoring to improve the normality of the residual distributions. Four choice reaction time in CaPS was inverse transformed, search speed was natural log transformed (NSHD and ELSA) and Mill Hill was squared in WHII.Analyses of FVC were repeated with a square-root transformation and of FEV1/FVC ratio with a cube transformation. Analyses of weight and body mass index (BMI) were repeated with a natural log transformation, although these anthropometric outcomes were not z-scored.Prior to analysis, individuals of non-European ancestry (self-reported or detected from genome-wide data) and related individuals were removed from the data set.All analyses were conducted using Stata v.13.1The analysis of lung function by AAT PI status tested for a linear association between binary PI status  and (1) FEV1, (2) FVC and (3) FEV1/FVC ratio. Analyses were repeated in current, ex and never smokers and in individuals classified as having COPD. Associations in all individuals were repeated with adjustment for (1) height and height-squared and (2) height, height-squared and height-cubed. Associations in COPD cases were also repeated with simultaneous adjustment for height, height-squared and smoking status. The analysis of physical capability by AAT PI status tested for association of binary PI-status with continuous or binary outcome, adjusted for age and sex.2). Associations with height were repeated with simultaneous adjustment for FEV1 and FVC.To explore the change in effect of PI status on lung function following height adjustment, we tested for association of PI status with height (cm), weight (kg) and BMI  or deltaF508 carrier status with COPD case status.PAH and ACADM tested for an association of mutation carrier status with continuous or binary outcome, adjusted for age and sex.The analysis of physical and cognitive capability outcomes for To produce estimates by cohort, linear regression was implemented for continuous outcomes and logistic regression for binary outcomes.A one-step meta-analysis approach using the IPD from all eight cohorts was used to derive estimates of effect sizes across all studies. This approach was adopted rather than the two-step method because the mutations are rare and thus the exposure of interest (carrier status) was often a rare event in the cohorts. One-step meta-analyses are based on the exact likelihood for the data, do not assume a normal distribution of effect estimates and do not assume that the SE of the effect estimate is exact; they are thus more appropriate in this instance.mixed outcome binary_genetic_exposure i.study study#c.age study#sex || study: binary_genetic_exposure, noconstant residuals).To implement a one-step RE meta-analysis for continuous outcomes, we used the following command in StataThis mixed model tests for an RE of carrier status by cohort. The fixed portion of the model includes adjustment within cohorts for age and sex, and an intercept by cohort. Residuals are modelled to have study-specific distributions. A random intercept is not assumed.meqrlogit outcome binary_genetic_exposure i.study study#c.age study#sex || study: binary_genetic_exposure, noconstant.To implement a one-step RE meta-analysis for binary outcomes, we used the following command in StataThis similarly tests for a random carrier effect by cohort, with covariate adjustment within cohorts in the fixed part of the model.ij is the normally distributed residual term with mean 0 and cohort specific variance and u5j is the random carrier effect by cohort with mean 0 and variance estimated by the model. The corresponding mathematical modelThe corresponding mathematical model for the continuous outcomes, with β coefficients for FEs and u coefficients for REs, as per the nomenclature in the Stata Reference ManualIn practice, we generally found that the estimated variance of the random component of the carrier status effect  was negligibly small. An FE model was, therefore, more appropriate. The results presented in the main tables also include an FE model using linear regression for continuous outcomes and logistic regression for binary outcomes, pooling all of the data across cohorts, and including a dummy variable for study. In all FE models, the covariates were again included as factor variables to adjust for effects by cohort . For completeness, all tables provide the RE and the FE estimates in addition to the estimated variance of the random carrier effect for interpretation. While the variance of the RE is informative as to whether the genotypic effect was the same across cohorts, it should also be noted that the RE model for continuous outcomes assumed heteroscedastic residuals (by cohort) while the FE model used a simplification of homoscedastic residuals. In a two-step framework, heteroscedastic residuals are modelled because associations are implemented within studies before meta-analysis of the effect estimates. Our main results were robust to either implementation. For the binary outcomes of COPD status and ability to balance, we make the simplifying assumption of independent and identically distributed residuals across cohorts.The within-cohort estimates are provided for completeness, but these often analyse a rather small number of heterozygotes . The meta-analysed estimates are the most reliable as these pool the data to maximise the sample size of the carriers. Online supplementary table S2, which details sample size for the meta-analyses by outcome, should be taken into account when interpreting the coefficients.In total, 9912 ALSPAC children were genotyped using the Illumina HumanHap550 quad genome-wide SNP genotyping platform by Sample Logistics and Genotyping Facilities at the Wellcome Trust Sanger Institute and LabCorp supported by 23andMe. Complete data for linkage disequilibrium (LD) analysis were available for 7583 unrelated individuals.et al.28EHH was analysed as previously described.SERPINA1 gene and surrounding genomic region (of 1 Mb either side). To this end, Haplotter considers data available for ∼800 000 common SNPs and 309 unrelated individuals from three populations. This web tool displays the results of selection from HapMap data by computing iHS, Fay and Wu's H, Tajima's D and Fst.13We used the Haplotter programSERPINA1 (see online supplementary tables S3–S35), CFTR (see online supplementary tables S36–S40), ACADM (see online supplementary tables S41–S43) and PAH (see online supplementary tables S44–S46).For completeness, we show both RE and FE analyses. All analyses are given as online supporting information in the order: CFTR, ACADM or PAH. There was limited evidence for any carrier effect of K304E or the three PAH mutations combined. There was weak evidence for a negative effect of deltaF508 heterozygosity on height-adjusted FVC (see online supplementary table S37). The individual cohort and meta-analysed effect estimates for CFTR, ACADM and PAH are provided in online supplementary tables S37–S46. Overall for SERPINA1, there was no compelling evidence of an association between PI status and physical capability (see online supplementary table S24). However, there was consistent evidence across the cohorts for a respiratory difference of PI-MZ individuals compared with MM individuals (−5) and a 0.16 SD increase in FVC (p=5.2×10−8) using IPD data from all eight cohorts. Taking the study SDs and multiplying by these coefficients, this corresponds to a difference of approximately 81–108 mL (FEV1) and 115–170 mL (FVC). There was no association with FEV1/FVC ratio (see online supplementary table S6). Our analysis of the possible effect of smoking is shown in The genotype frequencies are provided in online supplementary tables S3–S5, S36, S41 and S44. There were no mutant homozygote calls for ividuals . No effeConsidering the well-known correlation of lung function with height,−10, FE analysis) but was not observed for PI-MS. MZ individuals averaged approximately 1.5 cm taller than MM individuals. The FE and RE meta-analyses were repeated in individuals <55 years of age. The coefficient was reduced slightly , but contained the CI including all eight cohorts. We, therefore, concluded that the PI-MZ effect on height represents a growth not age-related shrinkage effect. There is also some hint (see online supplementary table S27) that mean height may increase across genotypes MM;MZ;SZ;ZZ. The association of PI-MZ versus MM and height was additionally simultaneously adjusted for FVC and FEV, which attenuated but did not remove the association (see online supplementary table S29). We note that both the respiratory and height associations occur in geographically confined cohorts. The by-cohort analyses show no evidence for a geographically stratified effect. The PI-MZ age-adjusted and sex-adjusted associations with height, FEV1 z-score and FVC z-score did not appear to be driven by population stratification when we ran models adjusted for principal components in four of the studies , although sample size was markedly attenuated. A homogeneity analysis (χ2 contingency test) to test whether genotype frequencies of AAT deficiency PI status differ among cohorts did not reveal significant heterogeneity (p=0.310). Nominal but minor differences between observed and expected genotype frequencies were observed for HAS (contributions to χ2>3.84), but these are related to low numbers and may be explained as type I error. We concluded that the PI-Z allele may have pleiotropic effects on height and respiratory function . We observed no association for BMI and an effect estimate for weight that was consistent with what is predicted given the observational correlation between height and weight.A previous population-based study showed a lower FEV1 in PI-MZ compared with PI-MM in individuals with clinically defined COPD, adjusted for age, sex, height and smoking status.Online supplementary table S2 shows that overall we conducted in the region of 182 one-step meta-analyses across all outcomes and genetic variants. Many of these tests were not independent due to the outcomes  or the genetic exposures  or due to subgroup analyses  or rerunning adjusted models. However, even with a Bonferroni adjustment based on this number (p=0.00027), our main results still produce comparatively small p values. The results of further sensitivity analyses are described in online supplementary appendix S4.EHH results involving rs28929474 and rs17580 show small decay of EHH, from 1 to 0.6, after 90 kb from 5′ (see online supplementary figure S1). This relatively small reduction is observed for two rare haplotypes  each of them including the rare allele of each SNP. The decay of EHH is more pronounced to the 3′ end, with EHH for both rare haplotypes being reduced to 0.5 at a distance of 30 kb. These results were qualitatively unchanged with the addition of neighbouring SNPs to the core region.st show no evidence of selection driving alleles at intermediate or high frequency in and around the SERPINA1 gene (see online supplementary figure S2).Results observed from Haplotter for common SNPs analysed by iHS, Fay and Wu's H, Tajima's D and FRecombination data from the ALSPAC sample combined with pairwise LD between SNPs around rs28929474 suggested an allele age of between 100 and 250 generations (see online supplementary figure S3). In contrast, using Z allele frequency this estimate was 1758 generations.HMGA2 for height. However, PI-MZ is not represented on GWAS chips, so the largest height meta-analyses of up to 250 000 individualshttp://www.UK10K.org) would not have analysed enough individuals to robustly detect the effect even if calls and imputation on low read depth were efficient. While analyses of the possible contribution of common SNPs to height suggest that they could explain the large part of this highly heritable trait,The PI-MZ rare (2%) SNP height effect is about fourfold greater than that for the top common SNP in Our main results of interest (MZ carrier effect on lung function and height) were obtained from a large number of carriers (>600) at the meta-analysis level. Neither association explains the other, although there is partial phenotypic correlation. The enhancement (rather than reduction) of FEV1 and FVC by PI-Z allele heterozygosity was unexpected and is in apparent contrast with the suggestion of greater incidence of respiratory infections in PI-MZ childrenSERPINA1 from HaplotterMicrosatellite dating of the Z allele suggests appearance 107–135 generations ago, with high prevalence in North Europe.AAT is a therapeutic agent and target in relation to its respiratory importance.


Prior searches for genetic variants (GVs) implicated in initiation of cannabis use have been limited to common single nucleotide polymorphisms (SNPs) typed in HapMap samples. Denser SNPs are now available with the completion of the 1000 Genomes and the Genome of the Netherlands projects. More densely distributed SNPs are expected to track the causal variants better. Therefore we extend the search for variants implicated in early stages of cannabis use to previously untagged common and low-frequency variants. We run heritability, SNP and gene-based analyses of initiation and age at onset. This is the first genome-wide study of age at onset to date. Using GCTA and a sample of distantly related individuals from the Netherlands Twin Register, we estimated that the currently measured (and tagged) SNPs collectively explain 25 % of the variance in initiation . Chromosomes 4 and 18, previously linked with cannabis use and other addiction phenotypes, account for the largest amount of variance in initiation . No individual SNP- or gene-based test reached genomewide significance in the initiation or age at onset analyses. Our study detected association signal in the currently measured SNPs. A comparison with prior SNP-heritability estimates suggests that at least part of the signal is likely coming from previously untyped common and low frequency variants. Our results do not rule out the contribution of rare variants of larger effect—a plausible source of the difference between the twin-based heritability estimate and that from GCTA. The causal variants are likely of very small effect  and are uniformly distributed over the genome in proportion to chromosomes’ length. Similar to other complex traits and diseases, detecting such small effects is to be expected in sufficiently large samples.The online version of this article (doi:10.1007/s10519-015-9723-9) contains supplementary material, which is available to authorized users. Cannabis is among the drugs with the highest frequency of (ab)use. About 1 in 5 Europeans aged 15–64 reported to have experimented with cannabis. In the United States the prevalence in ages 16–34 was estimated at 51.6 % . Regular cannabis use has been associated with health problems, including mood and anxiety disorders  have an important role in the initiation of cannabis use . Yet, age at onset is a complex trait  SNPs without identifying individual SNPs. Second, we conduct SNP-based association analyses of initiation and age at onset. Our primary focus is on identifying genes tagged by the SNPs, relevant to our traits. Therefore, next, we incorporate these SNP-based results in two gene-based analyses. These analyses are exploratory, i.e., conducted genomewide.All analyses are performed in a sample of Dutch families from the Netherlands Twin Register (NTR). To maximize statistical power, imputation of genotypes in the NTR sample was based on two alternative reference panels: the 1000G Phase 1 project reference panel  in the NTR surveys 1993, 1995, 2000, and by an open-ended question  in survey 2009. These surveys were sent to all adult twin families and were returned by 23 597 individuals. In addition, data collection in adolescent twins and sibs which took place since 1987 in age-specific surveys (around age 14 and age 16), included a multiple choice question (“Have you ever used soft drugs such as hashish or cannabis?”) assessing frequency of use  in the whole life, in the last 12 months and in the last 4 weeks. This question was completed by 16 556 participants. The phenotypic data obtained from subjects who reported at more than one time point were checked for consistency, and unreliable measures were discarded. Due to inconsistencies, 284 self-reported measures were dropped. Next, the measurements were collapsed into a dichotomous phenotype . Furthermore, we included in the analysis only family members for whom both phenotypes and genotypes were available, i.e., N = 6744 participants. Of these, 5387 individuals reported never to have used cannabis, whereas the remaining 1357 individuals had initiated cannabis use. The age at the time of the last survey ranged from 10.5 to 94 years . The participants were clustered within 3479 families varying in size from 1 to 9 family members . More than half of the sample (60.9 %) consisted of females.A subset of the genotyped NTR sample (N = 5148) had declared never to have used cannabis, or declared an age at onset older than 10 years of age in survey 2009 . Among them, 852 (16.6 %) had initiated cannabis use, whereas 4296 observations had not initiated at the time of data collection . The participants were clustered within 2992 families of sizes varying from 1 to 8 members. Females represented 62.3 % of the sample and the age ranged between 16 and 99 years .−4). Both the imputation quality and Hardy–Weinberg equilibrium  were assessed in the phenotyped sample using SNPTEST . The GoNL- and the 1000G-based imputed datasets contained ~6 million well imputed SNPs . The association and survival analyses were carried-out by varying the reference panel used for imputation, while including the same phenotyped sample . The analyses included no monozygotic twin pairs, because genotypic data were available for only 1 twin of a pair in the GoNL dataset.Genotyping was performed based on buccal or blood DNA samples collected in different research projects  and the relevant covariates included in the genomewide SNP-based analysis (see below) were also used in the GCTA analysis. Furthermore, one of a pair of closely genetically related individuals  was dropped, which left for the analysis 3616 distantly related individuals. We specified the prevalence as equal to 22 %, value chosen in line with the prevalence of cannabis use estimated in Europeans . Heritability of age at onset was not estimated as GCTA cannot handle survival data. We also investigated the relationship between chromosome length and the amount of variance explained in the trait. Consistent with the model of a polygenic trait, we expect—on average—the longer chromosomes to explain a larger amount of the variance. We tested this in a linear regression (one-tailed test) where we regressed the estimated proportion of variance explained by each chromosome on the chromosome length.We used the Genome-wide Complex Trait Analysis (GCTA) software  procedure with an exchangeable working correlation matrix and a sandwich correction to correct the standard errors for misspecification of the background model . Relative to the logistic model, the survival model is expected to show superior power especially for locating low frequency causal GVs , 3 principal components to correct for Dutch population substructure . We included this approach as it utilizes all available information on the age of initiation among those who have initiated. It is expected to show superior power relative to an analysis of the “ever-never” dichotomy or an analysis restricted to those who initiated  implemented in the Knowledge Based Mining System for Genome-wide Genetic Studies software .Gene-based tests of association with initiation and age at onset were carried out by using the Results indicate that 25 % [standard error (SE) = 0.088] of the variance on the observed scale in initiation is explained by the SNPs. This amount of variance explained collectively by the SNPs is significantly greater than zero . The chromosome-by-chromosome heritability analysis indicated that the largest amount of variance in the trait is explained by chromosome 4  = 7.93, P = 0.002). Chromosome 18 accounted for about 3.6 % (SE = 0.01) of the variance on the observed scale in initiation (LRT(1) = 4.99, P = 0.012).t test(20) = 1.731, P < 0.05). On average longer chromosomes explain a larger percent of variance . We found that chromosome length is significantly associated with proportion of variance explained . The region harboring this SNP is displayed in Supplemental Figure S3 , the non-coding RNA–microRNA 643  and the Zinc Finger Protein 766 gene , all located on chromosome 19.None of these genes had an association P-value below our chosen genomewide level of significance of α = 4.3 × 10−8; MAF < 5 %). The region around the top SNP associated with initiation—rs142324060 on chromosome 5 is displayed in Supplemental Figure S6. The Supplemental Table S3 contains the top SNPs associated with age at onset (P < 1 × 10−5).We conducted two genomewide survival analyses of age at onset in a sample comprising 5148 participants. Similar to the previous analysis, the association signals attained with the genotypes imputed based on the GoNL reference panel were used as input for the gene-based analysis, as these signals were stronger relative to those observed in the 1000G imputed sample . As we observed a slight inflation, we corrected the SNP-based P-values (genomic control λ = 1.1171) to prevent potential false positives. Supplemental Figure S5 contains the lambda corrected quantile–quantile plots. The SNP with the strongest association signal was the low-frequency rs142324060 (lambda-corrected P = 7.6 × 10Table −7. The genes showing the strongest association with our phenotype were Gem (nuclear organelle) associated protein 5 (GEMIN5) on chromosome 5 (P = 4.7 × 10−4) and the uncharacterized LOC101927911 on chromosome 17 (P = 4.7 × 10−4), followed by the Metallothionein 4 (MT4) on chromosome 16 (P = 5.2 × 10−4). The SNP with the strongest association signal—the rs142324060 (lambda-corrected P = 7.6 × 10−8) was not assigned to a gene in the GATES analysis.In our exploratory gene-based analysis none of the genes reached the genomewide significance threshold of α = 4.3 × 10The aim of the study was to explore the contribution of GVs to initiation of cannabis use and age at onset. Using GCTA and a sample of distantly related individuals from the NTR, we estimated that the genomewide SNPs collectively explain 25 %  of the variance in initiation. Although lower than the twin-based heritability estimate , the same region being also reported by Uhl et al. to be associated with illicit drug abuse , followed by chromosome 18 . Regions on both chromosome 4 and 18 have been reported to play a role in cannabis use and other addiction phenotypes. For instance, regions on chromosome 4 harboring the GABRA cluster of genes were identified in a linkage study by Agrawal et al.  that showed the most significant association P-values in the meta-analysis sample. Although we compare summary results for the same phenotype (cannabis initiation) such an analysis is similar in scope to a search for significant pleiotropic effects (genetic overlap): we aimed to single out sets of SNPs showing concordant effects in the two samples beyond what is expected by chance. Concordance of effects was assessed by exact binomial tests. We observed no significant excess of SNPs with concordant effects in the two datasets. It is possible that the effects of the causal variants are too small to be accurately captured by the two samples. It is also likely that the causal GVs were imperfectly tagged by the selected SNPs , and this further decreased the estimation precision in both samples.We further explored how our results compare with previously published ones. Using the SNP effect concordance method Nyholt  and the −7). However, our results have pinpointed several possible candidate genomic regions, likely to have a bearing on the early stage of cannabis use. To name a few, the ZNF181 and the ZNF766 genes, both located on chromosome 19, yielded the strongest association signal in the gene-based analysis of initiation . According to the GO annotations, ZNF181 and ZNF766 are functional genes belonging to the zinc finger family of genes, being involved in nucleic acid binding and metal ion binding. The most strongly associated genes with age at onset were the protein coding genes GEMIN5 (P = 4.7 × 10−4) on chromosome 5 and MT4 on chromosome 16 (P = 5.2 × 10−4). GEMIN5 plays a role in protein binding and snRNA binding, whereas MT4 is involved in copper ion and zinc ion binding. The role these genes play in initiation and age at onset has yet to be clarified, as none have been previously reported to be associated with cannabis use or other addiction phenotypes.None of the tested genes achieved genomewide significance (P < ~4.3 × 10http://cameliaminica.nl/research.php). Clearly, further research on the genetic basis of age at onset would be of interest as the trait may serve as a proxy for both heavy use and experimentation with other drugs.To our knowledge this is the first genomewide survival analysis of age at onset of cannabis use to date. The survival modeling approach appears to be appropriate and computationally tractable given the detailed genotypic data currently available . The causal variants are likely distributed over much of the genome, in proportion to the chromosomes’ length. Our results do not rule out the contribution of rare variants of larger effect imperfectly tracked by the measured SNPs—a plausible source of the difference between the twin-based heritability estimate and that from GCTA. Powerful analytic strategies and very large samples combined with considering the contribution of rare variants (MAF < 1 %) will allow one to further understand the causes of individual differences in the liability to initiate cannabis use.Supplementary material 1 (DOCX 1311 kb)


Genetic mapping studies in the mouse and other model organisms are used to search for genes underlying complex phenotypes. Traditional genetic mapping studies that employ single-generation crosses have poor mapping resolution and limit discovery to loci that are polymorphic between the two parental strains. Multiparent outbreeding populations address these shortcomings by increasing the density of recombination events and introducing allelic variants from multiple founder strains. However, multiparent crosses present new analytical challenges and require specialized software to take full advantage of these benefits. Each animal in an outbreeding population is genetically unique and must be genotyped using a high-density marker set; regression models for mapping must accommodate multiple founder alleles, and complex breeding designs give rise to polygenic covariance among related animals that must be accounted for in mapping analysis. The Diversity Outbred (DO) mice combine the genetic diversity of eight founder strains in a multigenerational breeding design that has been maintained for >16 generations. The large population size and randomized mating ensure the long-term genetic stability of this population. We present a complete analytical pipeline for genetic mapping in DO mice, including algorithms for probabilistic reconstruction of founder haplotypes from genotyping array intensity data, and mapping methods that accommodate multiple founder haplotypes and account for relatedness among animals. Power analysis suggests that studies with as few as 200 DO mice can detect loci with large effects, but loci that account for <5% of trait variance may require a sample size of up to 1000 animals. The methods described here are implemented in the freely available R package DOQTL. QUANTITATIVE trait locus (QTL) mapping is an approach widely used for detecting and localizing genetic variants responsible for phenotypic variation. Traditionally, QTL intervals are defined by the statistical association of phenotypes with genetic markers in backcross or intercross progeny derived by crossing two inbred strains . This exHeterogeneous stocks (HS) derived from multiple inbred founder strains and maintained as outbreeding populations for many generations can increase mapping precision and introduce additional genetic diversity . The DO QTL mapping analysis in HS populations presents a number of analytical challenges. The genome of each animal is a unique mosaic of founder haplotypes. Accumulation of meiotic recombination events over many generations of breeding results in small haplotype blocks that provide high mapping resolution but also require high-density genotyping (>50K SNPs) to reconstruct the haplotypes. We demonstrate the necessity of high-density genotyping to obtain accurate haplotype reconstructions and show that accuracy is improved by directly utilizing probe intensity data from genotyping arrays rather than genotype calls. We produce probabilistic estimates of founder haplotypes and these provide a basis for genetic mapping and genome-wide SNP imputation. QTL mapping regression models can be formulated in a variety of ways using founder haplotypes or SNP allele calls and we implement three different regression models and discuss their relative merits. Unlike simple cross designs, HS mice are not all related to the same degree and covariance among related individuals necessitates the use of a mixed-model linear regression with a kinship term . MethodsWe have implemented specialized QTL mapping software (DOQTL) for use with DO mice and other HS mapping populations. DOQTL implements a complete analytical pipeline for haplotype reconstruction and QTL mapping in the DO. It includes alternative modes of analysis for some steps in the pipeline. The modular design of the software will enable development and evaluation of new methods and extension to other HS populations with relatively few modifications.We isolated DNA using the Promega  Maxwell 16 and Tissue DNA purification kit according to the manufacturer’s instructions. GeneSeek  carried out genotyping assays on a total of 4542 DO mice. Genotype calls—A, B, H, or N—were generated using Illumina’s BeadStudio algorithm. Here A represents homozygosity for the reference allele, B is homozygous for the alternative allele, H is a heterozygous genotype, and N is “no call.”We genotyped 3029 of the 4542 DO mice from outbreeding generations G4 through G9 using the Mouse Universal Genotyping Array (MUGA) . The MegaMUGA assays 77,818 SNPs across the mouse genome with a mean spacing of 33 kb. We used a set of 57,977 SNP assays that distinguish among the genotypes of the eight founder strains of the DO and their heterozygous F1 offspring.We obtained MUGA and MegaMUGA data for the eight founder strains and most of their F1 hybrid progeny. We adjusted the founder and DO sample intensities for batch differences by quantile normalization across samples at each marker and calculated the intensity quantiles at each marker for the DO samples and the founders/F1’s separately. We then normalized the intensities of the founder and F1 samples to match the quantiles of the DO samples.X and Y intensity values on combined data from founder, F1 and DO DNA samples  to reconstruct the diplotypes from the genotype array data by generating a probabilistic estimate of the diplotype state (s) at each marker locus (j) in each DO animal (i). We denote these diplotype probabilities as ij(s)p.Each chromosome pair of a DO animal is composed of a unique mosaic of founder haplotypes. We refer to the pair of founder haplotypes at a given locus as the founder transition model and an emission model. The transition model is a Markov chain with 36 states that defines the probability of transitioning from one diplotype state to another between adjacent marker loci by meiotic recombination. We calculated transition probabilities between markers based on genetic map positions of markers  using the founder and F1 samples as reference points. Initial parameter estimates for each diplotype state were obtained from these clusters . The E-step is computationally intensive but it can be applied to one individual and one chromosome at a time. The M-step uses the diplotype probabilities to compute new estimates of the (emission) model parameters. The M-step can be computed one marker at a time; it combines information across individuals. These steps are iterated until a convergence criterion is satisfied. We stopped iterations when the log-likelihood of the model changed by <1/1000 of the initial log-likelihood in successive iterations. Best results are obtained with large collections of individuals, at least several hundred.The E-step uses current estimates of model parameters to execute a forward–backward HMM algorithm and generates diplotype probabilities marginal reconstruction of DO haplotypes can be obtained by assigning each locus to the diplotype state that has maximum probability. The diplotypes are phased to obtain a pair of haplotypes by minimizing the number of recombination events. Phasing is not critical for QTL mapping but we use phased chromosomes for graphical representation and to count recombination events. Our phasing algorithm could result in undercounting of recombination events but it is simple and appears to work well. We place transitions at the midpoint between flanking marker loci. SNP genotypes across the entire genome can be imputed from the marginal reconstruction by reference to the founder genome sequences to obtain a diploid genome sequence for each DO animal. Probabilistic imputation of SNP genotypes can also be obtained as discussed below.A full model, the additive haplotype model, and the additive SNP model. The full and additive haplotype models can be computed at the marker loci or, if desired, at points between marker loci using interpolation to approximate the diplotype probabilities. The additive SNP model is computed at imputed SNP loci.We computed genome scans by regression on diplotype probabilities . We fit j asiy is the phenotype for animal i, ikx is an indicator for covariate k and animal i, ka is the effect of covariate k, ij(s)p is the diplotype probability at a marker locus j for animal i generated by the HMM, iγ is an adjustment for kinship, and iε is the residual error. The model includes 36 regression coefficients, sβ, one for each diplotype state s. The full model is general and allows for arbitrary patterns of additive and dominance effects among the founder alleles. However, the likelihood-ratio test with 35 degrees of freedom is expected to perform poorly unless sample sizes are very large.The full model is an unconstrained regression on the 36 diplotype states. We express the full model at marker locus allelic dosage of founder h at each marker at marker locus j ash(s)N is the number of alleles coming from founder h in diplotype s. For example, cN(CC) = 2, cN(BC) = 1, and cN(BB) = 0.The additive model assumes that the effects of founder alleles combine additively; there are no dominance effects in this model. To implement the additive model, we compute the iy, ikx, ka, iγ, and iε are the same as in Equation 1, and ijd(h) is the allelic dosage of founder h at marker locus j from Equation 2. The model includes eight coefficients hβ representing the additive genetic effects of each founder haplotype. Thus the likelihood-ratio test has 7 degrees of freedom. We expect a significant gain in power and more precise estimation of the regression coefficients compared to the full model. However, we risk missing dominance interactions among founder haplotypes.We express the regression equation asj(s)G is the number of occurrences of the reference allele at marker locus j for an animal with allelic dosage of founder h at that locus. An advantage of mapping at the two-state SNP level is the potential to introduce dominance effects with one additional degree of freedom. We focus on the SNP-based additive model, which is widely used in human association mapping d is the allelic dosage of founder h for sample i at marker locus j (Equation 2). We have implemented genome scans using the leave-one-chromosome-out method  . MatrixEA Bonferroni correction can be used to obtain genome-wide thresholds. For MUGA the 0.05 Bonferroni corrected threshold is −log(p) > 5.2, and for MegaMUGA it is −log(p) > 6.0. The equivalent LOD thresholds are dependent on the sample size and choice of regression model. Bonferroni corrections are often too conservative and it is desirable to use a stringent but less conservative threshold based on permutation analysis . HoweverSupport intervals for QTL localization were determined using a 95% Bayesian credible interval . The LODWe randomly sampled genomes from a set of marginal reconstructions of 1129 DO animals at outbreeding generation G8. We simulated phenotypes using sample sizes of 200, 400, 600, 800, and 1000 animals, with MAF of 1, 2, 3, and 4 and QTL effect sizes of 0.125, 0.25, 0.375, 0.5, 0.625, 0.75, and 1.0 in standardized units. The MAF refers to the number of founders that contribute the causative allele. Each combination of these parameters was simulated 1000 times. Phenotypes were simulated as the sum of a QTL effect plus a polygenic background component and an independent normal error component each with unit variance. Thus the polygenic and independent error variance components contribute equally to the total background variance. For each simulation, the QTL location was selected at random on the autosomal genome, minor allele status was assigned to the appropriate number of founder strains, and the genotype was determined by the diplotype of the marginal reconstruction. The QTL effect size was added to one homozygous class and subtracted from the other. We ran genome scans using the additive haplotype model and declared detection of the target QTL if the LOD score exceeded a genome-wide adjusted p ≤ 0.05 threshold. LOD thresholds were determined once for each set of simulations at a given sample size based on the maximal LOD score obtained on each of 1000 genome scans of phenotypes drawn independently from a normal  distribution. We tabulated power as the detection of significant QTL within 5 Mb of the simulated QTL location. Note that while the type I error rate is controlled genome-wide, power is calculated at a fixed locus and thus can fall below the nominal 0.05 level.n  and drew n DO genomes at random from our G8 animals. We simulated a null phenotype by sampling n values from a Gaussian distribution. We mapped the phenotype using an additive haplotype model with a kinship adjustment. We repeated this process 1000 times for each sample size and report the proportion of times in which a QTL was detected on any autosome at the p ≤ 0.05 significance thresholds. Significance thresholds for the X chromosome require special consideration  and fed NIH-31 6% fat mouse diet ad libitum. Acidified (pH 2.8–3.2) or chlorinated  water was provided in water bottles. Mice were maintained on a 12 hr light/12 hr dark cycle. At ages ranging from 8 to 10 weeks, 50 ml of blood was collected via retro-orbital bleed and stored at 2–8° in K2EDTA. Neutrophil counts and other whole blood parameters were measured within 24 hr of collection on the Drew Scientific HemaVet 850 FS analyzer . Samples were processed in 15 batches of ∼50 animals each and some failed samples were removed; 5 mm of tail were collected for genotyping. These mice were a subset of the 3029 mice genotyped on the MUGA array. All procedures were reviewed and approved by the Jackson Laboratory’s Institutional Animal Care and Use committee (Protocol JW10001/JW202).DO mice  from outbreeding generations G4 and G5 were obtained from the Jackson Laboratory. Mice were housed in polycarbonate cages (50 in.h2 (The narrow sense heritability of neutrophil counts in the 742 DO mice was estimated by fitting a mixed model with sex and phenotyping batch as fixed effects and the additive genetic matrix as a random effect . The rathttp://do.jax.org/. The DOQTL R package is available from Bioconductor (http://bioconductor.org/) . Discordant diplotype states occurred most often in regions where one or more of the founder strains are identical by state and as a result, two or more diplotype states have very similar probabilities.We used marginal haplotype reconstructions to compute the proportion of each founder summed across markers for each DO animal . The medThe average number of autosomal recombination events in DO mice is expected to increase linearly at a rate of 23.9 events per generation . We compMaterials and Methods control the type I error at or below the desired threshold . In particular, the type I error at α = 0.05 is properly controlled. The computational time required to fit a linear mixed-model genome scan repeated for each permuted data sample can be substantial. Thus our ability to obtain valid thresholds with a simple linear model fit has practical importance.Null model simulations established that type I error rates are correctly controlled for permutation-based thresholds . We simuTable S1). The percentaage variance explained provides an effective summary of the combined effects of these factors on power  and find, as expected, that increasing MAF, effect size, or the sample size increases power . Support intervals can range (on average) from 5 Mb for effects that explain 5% of the variance down to <1 Mb for effects that explain 30% of the variance in mapping population of 1000 mice.Larger sample sizes and larger effect sizes also provide narrower QTL intervals . Males  had higher median neutrophil counts than females . The trait has an estimated heritability of 0.132.Materials and Methods. We fit the full model (Equation 1) by regressing the log of neutrophil counts on the 36 diplotype probabilities at each marker and found that no LOD peaks exceeded the significance threshold (adjusted p ≤ 0.05)  . We fit  ≤ 0.05) . We fit  ≤ 0.05) . In the  ≤ 0.05) . The fouGm25384 and chemokine receptor 4 (Cxcr4). Gm25384 is a noncoding RNA with no polymorphisms in the DO. Cxcr4 has three SNPs in the coding region of its transcript, two synonymous SNPs , and one nonsynonymous SNP . The minor allele at each of these three SNPs distinguishes strains C57BL/6J, CAST/EiJ, and PWK/PhJ from the remaining founders, consistent with the estimated effects from the additive haplotype model in Cxcr4 has two alternative transcripts and the nonsynonymous SNP is in an exon shared by both isoforms. One of the synonymous variants (no rs#) at 128.589098 Mb occurs in one of the isoforms. The other synonymous variant (rs8256193) occurs in both isoforms. In addition, the founder strains C57BL/6J and PWK/PhJ share a segment of common ancestry (130.414213–130.698555 Mb) from the Mus mus musculus progenitor (http://csbio.unc.edu/ccv/)  or linked but independent variants (One advantage of linkage mapping in multiparent populations such as the DO is that we obtain estimates of the founder allele effects at each locus. The pattern of allele effects and sequencing data from the eight founders were critical in narrowing the neutrophil QTL to a single gene. For some QTL the allele effects split into two clearly delineated groups, DO mice, after multiple generations of outcrossing, will have complex kinship relationships, including siblings, cousins, and extended multigenerational relationships of unknown degree. These relationships must be accounted for in the mapping analysis or increases in false positives may be observed (We recommend using a larger number of animals for a DO mapping study than is typical for mapping studies in the mouse. In our experience loci of major effect have been precisely mapped with <200 animals. It would be prudent to plan on large studies when using the DO to investigate complex traits. The DO provides substantial advantages, as illustrated in the above QTL mapping example.http://do.jax.org. This site will serve as a repository for DO related data and we encourage groups using the DO to contribute their published data.The DOQTL software implements the methods described in this article from reconstruction of haplotypes through QTL mapping and graphical summaries and reports. With a few modifications, DOQTL can be used to analyze other multiparent populations (


A 4:1 male to female sex bias has consistently been observed in autism spectrum disorder (ASD). Epidemiological and genetic studies suggest a female protective effect (FPE) may account for part of this bias; however, the mechanism of such protection is unknown. Quantitative assessment of ASD symptoms using the Social Responsiveness Scale (SRS) shows a bimodal distribution unique to females in multiplex families. This leads to the hypothesis that a single, common genetic locus on chromosome X might mediate the FPE and produce the ASD sex bias. Such a locus would represent a major therapeutic target and is likely to have been missed by conventional genome-wide association study (GWAS) analysis.versus unaffected females, considering three tiers of single nucleotide polymorphisms (SNPs) as follows: 1) regions of chromosome X that escape X-inactivation, 2) all of chromosome X, and 3) genome-wide.To explore this possibility, we performed an association study in affected No evidence of a SNP meeting the criteria for a single FPE locus was observed, despite the analysis being well powered to detect this effect.The results do not support the hypothesis that the FPE is mediated by a single genetic locus; however, this does not exclude the possibility of multiple genetic loci playing a role in the FPE.The online version of this article (doi:10.1186/s13229-015-0014-3) contains supplementary material, which is available to authorized users. Autism spectrum disorder (ASD) is characterized by impairments in reciprocal social behavior, deficits in language development, and repetitive behavior or restricted interests. ASD is highly heritable , and proThe presence of a biological mechanism that reduces the incidence of ASD in a risk-exposed population raises the possibility of artificially inducing this protection as a therapeutic or preventative measure for ASD. Hence, we sought to investigate the molecular nature of the FPE. While the FPE is clearly discordant between sexes, other general sexual dimorphisms could confound discovery of FPE-specific mechanisms. One approach is to try to identify a subset of females in whom the FPE is absent, for example, females with ASD.Epidemiological evidence suggests that a substantial portion of ASD risk is mediated by genetic risk factors acting in an additive manner . FamilieThe Social Responsiveness Scale (SRS) is a quantitative measure of ASD behaviors in affected and unaffected individuals . TreatinWe considered whether this bimodal distribution could reflect the categorical presence (low score) or absence (high score) of a protective effect in females. If the FPE was, itself, mediated by multiple protective factors, we would still expect a normal distribution of SRS scores Figure E with thTo estimate whether a genome-wide association study (GWAS) would detect such a protective factor, we performed a power analysis. We expect the protective factor to be enriched in female controls compared with female cases, but to have no effect in male subjects, therefore the presence of males adds ‘noise’ to a GWAS analysis Figure A. Under de novo risk  and the National Institutes of Health database of Genotypes and Phenotypes, dbGaP [http://www.ncbi.nlm.nih.gov/gap/].The datasets supporting the results of this article are available in the repositories: Simons Foundation Autism Research Initiative, SFARI [


Arabidopsis thaliana from Sweden under two growth temperatures (10°C and 16°C), and observed massive G x E variation. To identify the genetic polymorphisms underlying this variation, we conducted genome-wide scans using both SNPs and local variance components. The SNP-based scan identified several variants that had common effects in both environments, but found no trace of G x E effects, whereas the scan using local variance components found both. Furthermore, the G x E effects appears to be concentrated in a small fraction of the genome (0.5%). Our conclusion is that G x E effects in this study are mostly due to large numbers of allele or haplotypes at a small number of loci, many of which correspond to previously identified flowering time genes.Understanding how genetic variation interacts with the environment is essential for understanding adaptation. In particular, the life cycle of plants is tightly coordinated with local environmental signals through complex interactions with the genetic variation (G x E). The mechanistic basis for G x E is almost completely unknown. We collected flowering time data for 173 natural inbred lines of  Arabidopsis thaliana, we meausured flowering time under two temperature regimes, and scanned the genome for polymorphisms responsible for variation in this trait. Although most of the variation is due to G x E, genome-wide scans using SNPs only revealed direct genetic effects (G), and failed to reveal any significant G x E associations. In contrast, scanning the genome using local windows of polymorphism suggested that almost all the observed variation can be explained by 2% of the genome. Previously identified flowering time genes are strongly overrepresented in these regions, and our results are compatible with a model under which G x E is mainly due to many alleles at a relatively small number of loci.Many traits are influenced by genetic variation in interaction with the environment, so called G x E variation. In agriculture, for example, different varieties are optimal in different environments. In evolution, G x E is also crucial for local adaptation. Identifying the genes underlying G x E has proven extremely challenging, however. Using a collection of inbred lines of the model plant  The transition from vegetative to reproductive growth is a key developmental step in the life cycle of higher plants, and its timing is tightly regulated by both genes and environment, often in an interactive manner, so that the effect of genetic variants depends on the environment , 2. Suchet al. [Analytically, G x E can be described in terms of “reaction norms” as genetic variation in the phenotypic response to the environment . The pheet al.  proposedet al. , 5, 7.Attempts to map G x E variation, whether using classical linkage mapping or GWAS , 5, 8–10A. thaliana lines from Sweden [A. thaliana [Here we present a GWAS for flowering time at two temperatures  were extremely high (over 90%) at both temperatures , demonstrating strong genetic effects, in agreement with published results  effect test” that compares the full model against the genetic model [p-values . Three ic model . In agrep-values .FLOWERING LOCUS C (FLC)  , which hSNP x E effect test, suggesting that the polymorphism has the same effect in both temperatures. The peak is quite broad (FIONA1 (FIO1), is related to the circadian clock, and the null mutant shows early flowering [p-value = 2.97E-08; FIO1, which would result in a frameshift, however, this exon appears not to be present in mRNA-seq data from leaves [A. thaliana. A stop codon is found 26-amino acids upstream of the insertion in the closely related Arabidopsis lyrata and Capsella rubella. The putative frameshift polymorphism is due to eight vs nine GA repeats, and is in strong linkage disequilibrium with several non-synonymous polymorphisms, which are slightly less strongly associated with flowering time . Given that the number of a priori candidates (genes known to be involved in flowering time) is on the order of a percent of total genes  to 10−5  among these candidates is less than 20% [a priori candidates were identified using this approach ; SHORT VEGETATIVE PHASE (SVP), which mediates ambient temperature signaling by regulating FLOWERING LOCUS T (FT) [VERNALIZATION INSENSITIVE 3 (VIN3), which is involved in the epigenetic silencing of FLC during vernalization, but has hitherto not been identified in natural populations [FLC, VIN3). These SNPs are excellent candidates for being causal, and it seems likely that we simply lack the power to pick them up in a genome-wide scan. What the FDR is among the approximately 10 peaks that do not correspond to a priori candidates but are significant using the same threshold is not known  , and hasS T (FT) ; and VERulations . Some ofot known .a priori candidates were identified  and compare that to the variance component that is due to the rest of the genome . We refelocal x E with the null model, and; an “interaction  effect test” that compares the full model with the local model. For each test, log-likelihood ratios were calculated , a significant enrichment of a priori candidates exist for likelihood ratios of 5 or higher, for which FDR is less than 20% , especially for genes with reasonably strong association with flowering, while Grelation . Becausethan 20% . Notablyentation . Thus thFLC and VIN3 showed high common local effect as well as common SNP effect, while FPA, an FLC suppressor in the autonomous pathway [local x E locus. Furthermore, CENTER CITY (CCT) was significant in using the full local test. CCT, also known as CRYPTIC PRECOCIOUS (CRP), is a flowering regulator that acts as a promoter of FT and a suppressor of FLC [FRIGIDA (FRI) and has previously been detected in GWAS [A total of four flowering time genes showed significant peaks at the log-likelihood threshold of 5 . FLC and pathway , showed r of FLC , 30. It  in GWAS .local  delays the transition to flowering [local x E only was found at the top of chromosome 1  and includes eight genes, none of which are known to be involved in flowering.local  overlap lowering . The mosp = 0.001; a priori candidates explain almost 40% of the variation, a clearly significant overrepresentation . Flowering time is clearly neither. As shown in a priori candidates using less stringent thresholds, there is no trace of G x E associations. The same was true using various summaries of the traits, like the slope of the reaction norm. In contrast, there is ample evidence for polymorphisms that do not interact with the environment (include two that reach genome-wide significance), although this type of variation is only 28% of the phenotypic variation.The main purpose of this study was to investigate the genetic architecture of G x E variation using a population and experimental setting where such variation was massive. Roughly 66% of the variation for flowering time among lines across environments in this study is due to G x E , yet a sThe much-discussed “missing heritability” problem in human genetics refers to the fact that individually identifiable (mappable) SNPs do not explain the genetic variation . AlthougIn the present study, we have no “missing heritability” for common genetic variation, since the SNPs we identified account for almost all of this , a strong up-regulator of FLC [FRI is not known to be segregating in the Swedish population, it is clearly possibly that FRI alleles could lead to confounding at CCT [a priori candidates, the peak in FIO1 is clearly significant at the genome-wide level, and the association is currently being confirmed experimentally.A secondary purpose of this project was to investigate the genetics of flowering time variation in a local population sample from Sweden. From an than 20% . FLC, inresponse . Althougresponse , we founC and FT . Brieflyd period . CCT and pathway , 30, 45.t of FLC . It shour of FLC –49 knownr of FLC , 51). Alg at CCT . In addiFLC and SVP, none of the genes identified here have previously been shown to be important in natural variation. This demonstrates the advantages of using a local sample for GWAS when working on a trait important in local adaptation, and is in agreement with the G x E results above. Given that allelic heterogeneity can have a major effect on the power of GWAS even within Sweden, it should come as no surprise that flowering time is recalcitrant to GWAS in global samples [With the exception of  samples .173 Swedish lines and Col-0 were used for experiments . These lSeeds were sown on soil and stratified for three days at 4°C in the dark. They were then transferred into a single pot after germination. All plants were grown in MTPS144 Conviron walk-in growth chambers  set to long-day conditions (16 h photoperiod) under 10°C or 16°C constant temperatures. Periods from germination to presence of first buds were recorded as flowering time for multi-individuals for each line. Measurements were taken twice a week, until 190 days from germination.H2) was calculated using all individuals as VG/VP, where VP is the total phenotypic variance and VG is the genetic variance (estimated from the between-line phenotypic variance).The broad-sense heritability (Y is a vector of n × p phenotypic means (one mean for each of n lines in p environments), μ10 and μ16 are temperature specific mean values, x is the vector of genotypes to be tested (SNPs or indels), A is a trait design matrix (environment), B is the effect size estimate corresponding to A, R is a genomic relatedness (sample-sample covariance matrix) estimated from SNPs, C is the trait-trait covariance matrix, Q is a trait-trait noise covariance matrix, and δ are scaling factors. SNPs and indels were analyzed separately in the model and R calculated with only SNPs was used for both analyses. Three different tests using likelihood ratio test were carried out [A = Ip tested against a null model x = 0. This test identifies “any effect” including environment persistent and specific marker (SNPs or indel) effects between two environments.The full model with SNP x E) as environment specific marker effects, the full model was tested against a genetic model as A = 1p1,.To identify “interaction effect”  analysis was also conducted using LIMIX function as well as the tests in MTMM. In both MTMM and MLR, Bonferroni-corrected 5% significance thresholds were used. Rare alleles  were not included in final results and Bonferroni corrections.Ulocal and Uglobal are random effects corresponding to local and global relatedness, respectively, and ψ is noise. Ulocal and Uglobal can each be decomposed into an environment-persistent and an environment-specific variant component:Rlocal and Rglobal are sample-sample covariance matrices that estimate genetic relatedness (kinship) based on local and global SNPs, respectively. The local region of a gene was defined as the gene body plus 15 kbp from the 5’ and 3’ UTR, respectively, and global was defined as the rest of the genome. The parameters σ1,2 are the noise covariance parameters. To evaluate the “full local” (including environment persistent and specific effect), “common local” (environment persistent effect) and “Glocal x E” (environment specific effect) effects, three different tests were carried out and log likelihood-ratio was calculated:Ulocal).A “full local effect” was tested by comparison of a full model, including local and global effects and their interactions with E, with a null model that does not include any local effect  effect” was tested by comparison of the full model with the local model.An “interaction , environmental and G x E effects on flowering time variations in p-value within 15 kbp from a gene was assigned for significance of the gene. First, genes in each flowering and non-flowering time gene lists were ranked according to significance of these genes from smallest to largest, and the ranks were scaled by number of genes in the list. We assumed (as a null hypothesis) that a distribution of significances of genes in both lists are same, and genes that have a same rank after scaling will have same significance. To help interpretation of the plots, 95% confidence interval was calculated . For this, we conducted random sampling (1000 times) that maintained the chromosomal order of all observations but shuffled the relative positions of the two variables . Randomet al. [a priori candidates. We estimated the enrichment as X/Y and the FDR asY is the fraction of non a priori genes Click here for additional data file.S2 FigTop row, MTMM; bottom row, MLR. Colored lines show results after removing 30 kbp regions containing a peak more significant than the value listed.(PDF)Click here for additional data file.S3 Fig(PDF)Click here for additional data file.S4 FigSNP x E effects.From top to bottom, results for full SNP test, common SNP effects, and G(PDF)Click here for additional data file.S5 Figa priori flowering time genes and green points indicate the other genes.Blue points indicate (PDF)Click here for additional data file.S6 Figa priori list (bottom). Orange lines show actual estimated values in Full local variance component and the log-likelihood ratios for all local (top) and (PDF)Click here for additional data file.S7 FigBoxes indicate genes that are involved in the network . Arrows indicate positive effects (upregulation) and T arrows indicate negative effects (suppression). Black arrows show experimentally confirmed regulations in previous studies. Dot arrows show hypothesized associations.(PDF)Click here for additional data file.S1 Table(PDF)Click here for additional data file.S2 Table(CSV)Click here for additional data file.S3 Table(PDF)Click here for additional data file.S4 Table(PDF)Click here for additional data file.S5 Table(PDF)Click here for additional data file.


FH2613 with BW and BC, marker FH2097withBL and BC, marker ZUBECA6 with BW and BC, whereas marker REN75M10 was associated with BL and TL. The strongest LD (r2 ranged from 0.15 to 0.33) was estimated between nine loci with significant effect on economically important traits .Polymorphism of 30 canine-derived microsatellites was studied in a group of 200 red foxes kept on 2 Polish farms. 22 out of 30 microsatellites were selected to study association between marker genotypes and body weight (BW), body length (BL), body circumference (BC), tail length (TL), ear height (EH), length of the right front limb (FRLL), length of the right rear limb (RRLL), length of the right front foot (FRFL) and length of the right rear foot (RRFL). A total of 112 alleles and 243 genotypes were found at 22 autosomal microsatellite loci. Three monomorphic loci deemed as uninformative were excluded from the study. The association between marker genotypes and the studied traits was analysed using general linear model (GLM) procedure and least squares means (LSM). Linkage disequilibrium (LD) was estimated to assess non-random association between microsatellite loci. Out of 19 microsatellites studied four markers showed no association with the studied traits, three markers had a significant effect on one trait, and another three markers had significant effect on two traits. Among ten microsatellites with significant effect on four economically important traits  four were associated with two characters: marker  Vulpes vulpes) had a common ancestor with the domestic dog (Canis lupus familiaris) about 12–15 million years ago , which combines traditional genetics and molecular biology would become a valuable tool in selecting foxes for traits of interest, such as body size, fur quality, fertility, tame behaviour or disease resistance. The information from the DNA testing, combined with the observed performance records for foxes could improve the accuracy of selection and increase the possibility of identifying individuals carrying desirable traits at an earlier stage of breeding.Canidae, including an assembled genome sequence  kept on two farms  located in a region of the Leszno city (West Poland) were studied. To ensure unrelatedness of foxes the pedigree of each animal was carefully checked. Only unrelated animals originating from different litters (one fox from a litter produced by unrelated parents) were sampled for the study. The analysed traits were: body weight (BW), body length , body circumference , tail length (TL), ear height (EH), length of the right front limb (FRLL), length of the right rear limb (RRLL), length of the right front foot (FRFL) and length of the right rear foot (RRFL). The traits measurements were taken at the end of the farm season (December) after technological slaughter of animals.post mortem using a razor blade. The DNA was isolated in accordance to ARK Genomic self-protocol.Tissue samples for DNA extraction were taken from tongues of the studied animals. Thin slices (approximately 1–2 mm) of tongues were taken Thirty microsatellite markers described and localized in the dog genome were chosen for the study Table . The mulijklyis the studied phenotypeμis the overall meaniFis the ith farm effectjSis the jth sex effectkGis the kth microsatellite marker genotype effectijkleijklth phenotype, N.is the random error associated with yThe association between microsatellite genotypes and the studied traits was analysed using general linear model (GLM) procedure and the following linear model:The least squares means (LSM) for the studied traits in relation to microsatellite genotype were computed using ‘lsmeans’ procedure implemented in the R package. LSM were estimated only for genotypes with the frequency higher than five. Multiple comparisons of LSM were performed with the Bonferroni-Dunn test. The studied microsatellite loci were tested separately (one at a time) and the significant differences between microsatellite genotypes for each locus were indicated.2) computed using MIDAS  was estimated. The squared coefficient of correlation  were observed in all studied silver foxes, while markers FH3970 and FH3771 were shared by the lowest number of foxes . A total of 112 alleles and 243 genotypes were found at 22 autosomal microsatellite loci. The number of alleles found in the studied loci ranged from one  to 12 (locus ZUBECA6) with an average of 5.09 per locus. The three monomorphic loci  deemed as uninformative were excluded from further study.Table FH3824 (0.758) followed by loci REN75M10, REN75M10 and REN258F18 . The lowest heterozygosity was found at loci REN64E19 and FH2613 . An average heterozygosity per locus was estimated at 0.485. Monomorphic loci as mentioned above were not considered for further study.The highest heterozygosity Table  was founet al. 2000), researchers started to search for the linkage of the canine-derived microsatellites in the red fox and arctic fox genomes  to 150 . Out of 19 microsatellites studied four markers  showed no association with the studied traits, three markers had a significant effect on one trait , and another three markers had significant effect on two traits . A significant effect on the highest number of traits  was estimated for marker FH2097, whereas markers REN75M10 and FH3970 were significantly associated with four traits .Least squares means and their standard errors of the silver fox morphometrics and body weight for different genotypes of ten microsatellites with significant effect on four economically important traits  are presented in Table To our knowledge association between the polymorphism of canine-derived microsatellites and traits under selection pressure in red fox farming has not yet been studied. In the fox breeding the most important traits are those of economic importance. These are usually litter size, pelt size and pelt quality characters. According to Peura et al.  who carrFH2613, FH3771, REN64E19, REN88H03 and ZUBECA6), while the remaining three traits are significantly affected by three markers: FH2097, REN135K06 and REN75M10 are associated with BL; FH2097, FH2613 and ZUBECA6 are associated with BC; FH3970, REN30J23 and REN75M10 are associated with TL. Out of the ten microsatellites with significant effect on the four economically important traits, four were associated with two characters: marker FH2613 was associated with BW and BC, marker FH2097 was associated with BL and BC, marker ZUBECA6 was associated with BW and BC, whereas marker REN75M10 was associated with BL and TL. Thus, in further studies on implementation of MAS in red fox breeding efforts should be focused on the set of ten microsatellite loci significantly associated with traits of breeders’ interest.The analysis of association between the studied microsatellites and four economically important traits Table  indicateP < 0.01) with body weight.In recent years most of the association studies carried out in red foxes have mainly focused on searching for genetic markers related to body weight. Skorczyk et al. , who conMC4R), in relation to body weight of the red fox was studied by Nowacka-Woszuk et al.  and the arctic fox (Vulpes lagopus)  and the fat mass and obesity associated gene (FTO), in four species belonging to the family Canidae  were studied by Grzes et al. (FTO gene (−28T>C in the 5′-flanking region) and one in the INSIG2 (10175C>T in intron 2), were used for the association studies in the red fox. The evidence was observed for their association with body weight  and weight of raw skin . These associations indicate that both genes are potential candidates for growth or adipose tissue accumulation in the red fox.The polymorphisms of two candidate genes, the insulin induced gene 2 . The strongest LD was found between ZUBECA6 and FH3771 (r2 = 0.33), followed by ZUBECA6 and FH3970 (r2 = 0.25), FH3771 and FH3790 (r2 = 0.25), FH3824 and REN75M10 (r2 = 0.20). The FH2097 microsatellite was strongly linked with three loci: REN210I14 (r2 = 0.16), REN258F18 (r2 = 0.16) and REN64E19 (r2 = 0.15). The pattern of LD found between the studied microsatellites may indicate that many decades of artificial selection towards genetic improvement of economically important traits in the red fox, favoured those marker loci which were tightly associated with preferred phenotypes. This strong footprint of selection in regions of the genome that are important for controlling important traits, opens up opportunities to successfully utilize LD-markers in MAS, which may markedly improve genetic progress in red fox farming.Figure A similar conclusion was drawn by Boyko et al.  who geneHTR1B), body weight (e.g. GHR), formation of hair structure and fur coat (e.g. KRT17).A closer look at the canine chromosomal regions where the studied microsatellite markers are located reveals a number of genes positioned in close distance to the markers  can be considered less important in selective breeding of foxes. These traits do not directly and significantly influence efficiency of fur production, thus the genetic improvement of these traits is not included in the breeding goal in fur farming (Wierzbicki et al. The present study confirms that canine-derived microsatellites can be successfully applied in red fox breeding. MAS using a set of selected LD-markers with significant effects on economically important traits may increase genetic progress, and thus profitability of fur farming. Further studies, including the candidate gene approach are needed to fully exploit possibilities of MAS in shaping the genetic structure of the farm red fox populations.