NSE = 656) was screened for candidate genes and the non-Swedish (NS) EUROSPAN cohorts  were used for replication. In total, 3 SNPs were associated in the Swedish sample and were replicated in the non-Swedish cohorts. While SNP rs1532624 was a replication of the previously published association between CETP and HDL cholesterol, the other two were novel findings. For the latter SNPs, the p-value for association was substantially improved by inclusion of environmental covariates: SNP rs5400  in the SLC2A2 (Glucose transporter type 2) and rs2000999  in the HP gene (Haptoglobin-related protein precursor). Both showed evidence of association with total cholesterol. These results demonstrate that inclusion of important environmental factors in the analysis model can reveal new genetic susceptibility loci.Genome-wide association studies (GWAS) have identified 38 larger genetic regions affecting classical blood lipid levels without adjusting for important environmental influences. We modeled diet and physical activity in a GWAS in order to identify novel loci affecting total cholesterol, LDL cholesterol, HDL cholesterol, and triglyceride levels. The Swedish (SE) EUROSPAN cohort ( LC2A2 (Glucose transporter type 2) and the HP (Haptoglobin-related protein precursor) gene whose effects on total cholesterol have not been reported yet. The results show that inclusion of important environmental factors in the analysis model can reveal new insights into genetic determinants of clinical parameters relevant for metabolic and cardiovascular disease.In this article we report a genome-wide association study on cholesterol levels in the human blood. We used a Swedish cohort to select genetic polymorphisms that showed the strongest association with cholesterol levels adjusted for diet and physical activity. We replicated several genetic loci in other European cohorts. This approach extends present genome-wide association studies on lipid levels, which did not take these lifestyle factors into account, to improve statistical results and discover novel genes. In our analysis, we could identify two genetic loci in the S Genome-wide association studies (GWAS) have identified more than 38 larger genetic regions which influence blood levels of total cholesterol (TC), low-density lipoprotein cholesterol (LDL-C), high-density lipoprotein cholesterol (HDL-C) and triglycerides (TG) NSNP = 311,388) were available p-values when the model was adjusted for environmental covariates. We then used the other, non-Swedish EUROSPAN cohorts for replication of our strongest associations in a candidate gene association study (CGAS).In order to explore the usefulness of including both environmental and genetic factors in the analysis model, we used lipid measurements from the EUROSPAN study, comprising 3,938 individuals for whom genome-wide SNP data , but homogeneity in other environmental aspects such as climate p-values in the diet- and activity-adjusted model (corresponding to about 150 SNPs per lipid). For total cholesterol, 88 of these were located in a gene and 14 in genes that have been associated with energy metabolism (http://www.ncbi.nlm.nih.gov/omim/). For LDL-C, 65 SNPs were located in a gene, of which 8 were functionally relevant. Several of the SNPs for LDL-C were identical with those affecting total cholesterol, as expected from the high correlation (r = 0.91) between both phenotypes. For HDL-C, SNP rs2292883, located in the MLPH gene (Melanophilin), showed a genome-wide significant p-value (p = 1.06×10−07). 69 SNPs for HDL-C were located in a gene and 14 of those genes were reported as having a metabolic effect. Finally, for triglycerides, 63 SNPs were located in a gene, but only 4 SNPs in genes with a functional annotation of interest  between the unadjusted and the adjusted model. For TC, 83 (53%) SNPs were found in both top SNP lists. Those lists contained 102 (64%) identical SNPs for LDL-C and 103 (65%) for HDL-C. The analyses resulted in the same 74 (47%) top SNPs for TG levels  model and the unadjusted (restricted) model resulting in an up to 27-fold p-value decrease  was applied using similar lifestyle covariates  in the HP gene (Haptoglobin-related protein Precursor) on TC level and the effect of rs1532624  in CETP (Cholesteryl ester transfer protein) on HDL-C. In the Swedish cohort (SE), the unadjusted genetic effect of rs2000999 in the HP gene is equivalent to a moderately large difference in average TC level of 20.21 mg/dl between the homozyguous genotypes −MeanSE,unadj(TC|G/G) = 243.16−222.95, Effect SizeSE,unadj = 0.41, Effect SizeSE,adj = 0.44)(Effect Size (ES) = (MA/A−B/BM)/SDpooled). Equivalent effects were observed in the Scottish replication sample −MSC,unadj(TC|G/G) = 235.36 mg/dl−222.54 mg/dl = 12.82 mg/dl, ESSC,unadj = 0.29, ESSC,adj = 0.52). SNP rs1532624 in the CETP gene is associated with a large, unadjusted difference in HDL-C level of 9.99 mg/dl −MSE,unadj(HDL-C|C/C) = 68.14 mg/dl−58.15 mg/dl, ESSE,unadj = 0.73, ESSE,adj = 0.48) in the discovery cohort and similar effects regarding direction and size in the replication cohort −MSC,unadj(HDL-C|C/C) = 69.79 mg/dl−60.75 mg/dl = 9.04 mg/dl; ESSC, unadj = 0.59, ESSC, adj = 0.57).A food- and activity-adjusted candidate gene association study of the final 39 candidate SNPs in the Scottish (SC) sample  and aggregated the results in a meta-analysis (NSp = 4.68×10−02) in SLC2A2 on TC. We again found that rs2000999  in HP influences TC levels and rs1532624  in CETP (Cholesteryl ester transfer protein) affects HDL-C levels. The unadjusted genetic effect of rs5400 is equivalent to a moderately large difference in mean TC level of 27.11 mg/dl between homozyguous genotypes −MSE,unadj(TC|G/G) = 249.30 mg/dl−222.19 mg/dl, ESSE,unadj = 0.57, ESSE,adj = 0.66) in the Swedish Cohort and a small total effect in all non-Swedish samples −MNS,unadj(TC|G/G) = 236.69 mg/dl−223.34 mg/dl = 13.35 mg/dl, ESNS,unadj = 0.30).We also performed an unadjusted candidate gene analysis of the 39 candidate SNPs in all non-Swedish (NS) EUROSPAN cohorts . The genome-wide significant SNP rs2292883 in the Melanophilin (MLPH) gene found in the Swedish cohort was not confirmed.No other associations, including LDL cholesterol or triglycerides levels, were replicated  and is predominantly expressed in the liver. Mice deficient in GLUT-2 are hyperglycemic and have elevated plasma levels of glucagon and free fatty acids SLC2A2 on type 2 diabetes. These studies suggest that these lifestyle factors could have masked genetic effects in previous, unadjusted GWAS. This is emphasized by the strong increase in statistical significance of the SLC2A2 polymorphisms after adjusting for diet and physical activity, indicating that the examined lifestyle factors modified the effect of this gene. Our supplemental results show that physical activity markedly moderated the genetic effect on total cholesterol.HP gene encodes the Haptoglobin-related Protein Precursor (Hp), which binds hemoglobin (Hb) to form a stable Hp-Hb complex and, thereby, prevents Hb-induced oxidative tissue damage. Asleh et al. p-value for association between the HP SNP rs2000999 and total serum cholesterol concentration decreased in the model adjusted for diet and physical activity, suggesting that the genetic effect is moderated by diet and physical activity. Our supporting material points out the moderating role of physical activity in particular.The CETP and HDL-C levels. The CETP protein catalyzes the transfer of insoluble cholesteryl esters among lipoprotein particles. Variation in CETP is known to affect the susceptibility to atherosclerosis and other cardiovascular diseases p-value of this SNP. Our supporting results indicate that the genetic effect is mediated by diet or by physical activity in a similar way.We also observed a highly significant association between rs1532624 in APOE, are not represented in our candidate SNP list. Therefore, our approach is not comprehensive and may have failed to identify other relevant lifestyle-sensitive genetic variants. Nonetheless, we decided to apply this approach to make the best out of the available lifestyle data. Second, our study provides only limited information on the role of individual lifestyle factors for a genetic variant. However, in this study we aimed at amplifying genetic effects by adjusting for a maximum amount of environmental variance in a single model and, therefore, we neglected some of these aspects here. Third, we did not model genetic covariates in known lipid-relevant genes which may also moderate the effect of other genetic predictors. This is due to the focus of this paper on gene-environment relationships.This study also has some limitations. First, we are aware that our candidate gene association approach covers only a very small fraction of all genomic loci, which is one of the potential reasons why some classical lipid-influencing genes, such as In summary, we have demonstrated that modeling environmental factors, in particular major food categories and physical activity, can improve statistical power and lead to the discovery of novel susceptibility loci. Such models also provide an understanding of the complex interplay of genetic and environmental factors affecting human quantitative traits. Inclusion of environmental covariates represents a much needed next step in the quest to model the complete environmental and genetic architecture of complex traits.All EUROSPAN studies were approved by the appropriate research ethics committees according to the Declaration of Helsinki http://www.eurospan.org). All studies include a comprehensive collection of data on family structure, lifestyle, blood samples for clinical chemistry, RNA and DNA analyses, medical history, and current health status. All participants gave their written informed consent The examined subjects stem from five different population-representative, pedigree-based cohorts from the EUROSPAN consortium  represents a cross-sectional study conducted in the community of Karesuando in the subartic region of the County of Norrbotten, Sweden, in 2006 The Orkney Complex Disease Study (ORCADES) is a longitudinal study in the isolated Scottish archipelago of Orkney N = 719) were used for the presented analysis. The sample comprised 334 men and 385 women aged between 18 and 100 years. The inclusion of diet and activity covariates in the analytical model and according missing values reduced the effective sample size by less than 5%.The The VIS study is a cross-sectional study in the villages of Vis and Komiza on the Dalmatian island of Vis, Croatia, and was conducted between 2003 and 2004 Microisolates in South Tyrol Study (MICROS) is a cross-sectional study carried out in the villages of Stelvio, Vallelunga, and Martello, Venosta valley, South Tyrol, Italy, from 2001 to 2003 The Erasmus Rucphen Family Study (ERF) is a longitudinal study on a population living in the Rucphen region, the Netherlands, in the 19th century The pHWE<1×10−6) or with a minor allele frequency of less than 1% were excluded from the analysis.DNA samples were genotyped according to the manufacturer's instructions on Illumina Infinium HumanHap300v2 or HumanCNV370v1 SNP bead microarrays. Both arrays have 311,388 SNP markers in common that are distributed across the human genome. Analysis of the raw data was done in the BeadStudio software with the recommended parameters for the Infinium assay and using the genotype cluster files provided by Illumina. Individuals with a call rate below 95% and SNPs with a call rate below 98%, deviating from Hard-Weinberg equilibrium , low-density lipoprotein cholesterol (LDL-C), high-density lipoprotein cholesterol (HDL-C), and triglycerides (TG) were quantified by enzymatic photometric assays using an ADVIA1650 clinical chemistry analyzer  at the Institute for Clinical Chemistry and Laboratory Medicine, Regensburg University Medical Center, Germany.N = 94) versus non-reindeer herders (N = 505). We observed highly significant, large effect sizes in men  and women  in the expected direction corresponding with an approximately three times higher consumption of absolute overall game intake in reindeer herders compared to others. A similar approach was used for the measurement and analysis of dietary data collected with a food frequency questionnaire in the Scottish cohort  h cohort .In the NSPHS cohort, we used two self-report scales to measure overall physical activity at work and at leisure. The Work Activity Scale  addresses typical occupational physical activities: sitting, standing, walking, lifting, and general indicators of physical activity, i. e. sweating and tiredness after work. The Leisure Activity Scale  asks for various typical freetime activities such walking, cycling, other sporting activities, and sweating as a general indicator of physical activity. Participants reported the frequency of each activity on a 5-point rating scale . Both scales showed satisfying internal consistency with Cronbach's α(WAS) = 0.73 and Cronbach's α(LAS) = 0.70. A similar approach was used for the measurement and analysis of data on physical activity collected with a self-report questionnaire in the Scottish cohort .r =   for game meat, r =   for non-game meat, r =   for fish, r =   for physical activity at work, and r =   for physical activity at leisure  and other  leisure . We finaMLR) test. We inferred a significant better model fit of the full model if the difference of the χ2 value between both models had an equal or lower probability than p = 0.05  on a χ2 distribution with k degrees of freedom. The degrees of freedom k are equal to the difference of the number of parameters in each model. The difference of χ2 values between both models is calculated according to the following formula with MLE indicating the maximum likelihood estimates per model: χ2(rest−full) = −2 (log10(MLErest)−log10(MLEfull)). The comparison of the goodness of fit between the unadjusted and the diet- and activity-adjusted full model, using a MLR test, showed a statistically significant improvement for all four lipid traits . All included polygenic, anthropometric and lifestyle factors  explained 64.07% (58.02%) of the variation of TC, 59.47% (56.47%) of the variation of LDL-C, 83.73% (82.59%) of the variance of HDL-C and 58.68% (41.80%) of the variation of TG levels. Dietary measures accounted for 22% (TC), 40% (LDL-C), 74% (HDL-C), and 7% (TG), respectively, of the variance explained by lifestyle factors with physical activity being responsible for the rest. GWAS results for models adjusted for sex, age, and diet only , rs1532624 , and rs5400   .polygenic of the R/GenABEL package. Third, genome-wide association analysis was performed using a score test, a family-based association test mmscore function of R/GenABEL. It uses the residuals and the variance-covariance matrix from the polygenic model and additional the SNP fixed effect coded under an additive model . Fourth, genome-wide significance of a genetic loci was based on a local type I error of α = 0.05/311 388 SNPs = 1.6×10−7 according to a Bonferroni adjustment.First, deviations from normality for all quantitative traits  were corrected by inverse-normal transformation without adjusting for covariates. Second, linear mixed effects models were fitted for the transformed outcomes  using the above mentioned covariates in the Swedish EUROSPAN sample and corresponding measures in the Scottish EUROSPAN sample . The anaThe same statistical approach was used for association analysis of candidate loci with a local type I error of α = 0.05. No Bonferroni adjustment was applied to protect against α inflation since this method would be biased for the following reasons. The applied selection procedure for candidate loci makes the assumption of a global null hypothesis highly unlikely. Additionally, the phenotypes and some of the genotypes are highly correlated decreasing the number of independent tests. Instead all confirmatory tests are reported to allow the reader to evaluate the overall significance of the findings λ coefficients of lifestyle-adjusted genome-wide analysis varied in a low range between 1.00 and 1.04 in the Swedish cohort  GenABEL (V1.4.2) biomarRt (V1.16.0) Ensembl (http://www.ensembl.org) and Online Mendelian Inheritance in Men (http://www.ncbi.nlm.nih.gov/omim/).We performed all analysis with the statistical analysis system Figure S1−7.Manhattan plots of genome-wide effects on total cholesterol, LDL cholesterol, HDL cholesterol, and triglyceride levels in the Swedish discovery cohort. Results for two GWAS analysis models are presented. The unadjusted model (dark blue and light blue circles) included only sex and age as covariates. The adjusted model (red and orange squares) additionally contained dietary measures  as predictors. The dashed line indicates the local Bonferroni-adjusted α error = 1.6×10(0.31 MB DOC)Click here for additional data file.Figure S2−7.Manhattan plots of genome-wide effects on total cholesterol, LDL cholesterol, HDL cholesterol, and triglyceride levels in the Swedish discovery cohort. Results for two GWAS analysis models are presented. The unadjusted model (dark blue and light blue circles) included only sex and age as covariates. The adjusted model (red and orange squares) additionally contained physical activity measures  as predictors. The dashed line indicates the local Bonferroni-adjusted α error = 1.6×10(0.31 MB DOC)Click here for additional data file.Figure S3QQ-Plots for the unadjusted GWAS on total cholesterol, LDL cholesterol, HDL cholesterol, and triglyceride levels in the Swedish discovery cohort. The analysis model was only adjusted for sex and age, but not for diet and activity measures .(0.12 MB DOC)Click here for additional data file.Figure S4QQ-Plots for the adjusted GWAS on total cholesterol, LDL cholesterol, HDL cholesterol, and triglyceride levels in the Swedish discovery cohort. The analysis model was adjusted for sex, age, diet and activity measures .(0.12 MB DOC)Click here for additional data file.Table S1GWAS results for all top candidate SNPs (0.05%) in the Swedish (SE) discovery cohort, the Scottish (SC), and all non-Swedish (NS) replication cohorts.(0.41 MB XLS)Click here for additional data file.Table S2Comparison of the diet- and activity-adjusted analysis model in the Swedish and the Scottish cohort.(0.04 MB DOC)Click here for additional data file.Table S3p-values of the inverse-normal transformed lipid, dietary, and physical activity measures in the Swedish cohort.Pearson correlations, determination coefficients (explained variance), and (0.03 MB XLS)Click here for additional data file.Table S4GWAS results for all top SNPs (0.05%) in the Swedish (SE) discovery cohort, and for all candidate SNPs in the Scottish (SC), and in the non-Swedish (NS) replication cohorts including only individuals without lipid-lowering treatment.(0.34 MB XLS)Click here for additional data file.


HLA region interacting with sex. Haplotype-based analyses were performed using a haplotype-sharing-based Mantel statistic and a test for haplotype-trait association in a general linear model framework. A step-down minP algorithm was applied to derive adjusted p-values and to allow for power comparisons. These methods were also applied to the GAW15 real data set for PTPN22.Accounting for interactions with environmental factors in association studies may improve the power to detect genetic effects and may help identifying important environmental effect modifiers. The power of unphased genotype-versus haplotype-based methods in regions with high linkage disequilibrium (LD), as measured by D', for analyzing gene × environment (gene × sex) interactions was compared using the Genetic Analysis Workshop 15 (GAW15) simulated data on rheumatoid arthritis with prior knowledge of the answers. Stepwise and regular conditional logistic regression (CLR) was performed using a matched case-control sample for a For markers in strong LD, stepwise CLR performed poorly because of the correlation/collinearity between the predictors in the model. The power was high for detecting genetic main effects using simple CLR models and haplotype-based methods and for detecting joint effects using CLR and Mantel statistics. Only the haplotype-trait association test had high power to detect the gene × sex interaction.PTPN22 region with markers characterized by strong LD, all methods indicated a significant genotype × sex interaction in a sample of about 1000 subjects. The previously reported R620W single-nucleotide polymorphism was identified using logistic regression, but the haplotype-based methods did not provide any precise location information.In the  The inclusion of gene × environment interaction terms in association analyses may improve the power to detect genetic effects and may contribute to the identification of important environmental effect modifiers . CurrentPTPN22 using markers in strong LD (in terms of D'). The data are a subset of the published data on rheumatoid arthritis (RA) [PTPN22 gene [In addition, we explored the performance of these methods in the GAW15 real data set for tis (RA) . RA is atis (RA)  and withN22 gene ,8,9, wheN22 gene ,10.HLA region from the high-density scan on chromosome 6 with 18,000 SNPs, the disease loci DR and C have the same physical position and are in high LD. The DR locus has been simulated to have a strong effect on the risk of RA independently of sex, while locus C was generated to interact with sex, such that it increases risk only in women. We investigated these loci and the 10 flanking SNPs on either side, spanning a region of about 300 kb. The alleles of the multiallelic DR locus were recoded as biallelic based on the answers, i.e., the risk allele vs. all other alleles combined.From the GAW15 simulated data modeling RA, we selected 100 replicate samples to represent a matched case-control study. For each replicate, the first affected offspring within an affected sibling pair of the first 500 families  was chosen as case. Out of 2000 unrelated unaffected controls, 500 were matched to the cases by age and sex. The proportion of females:males in the case-control pairs was 2:1. In the simulated PTPN22, we used all 1001 individuals from the NARAC (North American Rheumatoid Arthritis Consortium) sample for whom genotypes were available, these were 665 unrelated RA cases and 336 unrelated unaffected controls  both the main effect of each marker individually and their interaction with sex; 4) SCLR (stepwise CLR with backwards model selection based on the Akaike information criterion) with the full model including all main and first-order interaction effects of the 22 markers simultaneously. The SCLR approach was proposed specifically to distinguish between causal variants and those merely in LD [We investigated four different sets of CLR models: 1) the main effect of each marker individually in 22 separate regression models; 2) only the joint effect of each genotyped marker individually with sex as an interaction term. This approach was considered in order to compare CLR and the Mantel statistics for joint effects, PTPN22 because cases and controls were not matched. Here, gene × environment (current smoking) and gene × sex interactions were investigated. All CLR and LR analyses were performed using the computer program R (version 2.3.1), using the general linear model (glm) procedure and the step function for stepwise model selection.We employed analogous unconditional LR models for the real data on x denotes a putative disease locus, i and j are haplotypes, Lij(x) is measured as the number of intervals surrounding x flanked by markers identical by state (haplotype sharing). The sum is over all pairwise comparisons of haplotypes. si and sj, and is defined as the mean-corrected product μy denotes the sample mean, and si and s j. To analyze the joint effect of a marker locus and an environmental factor, a measure of exposure similarity si and sj. M1 is a test for joint effects and does not distinguish between genetic main effects and interaction effects.We applied the approach of Mantel's statistics for space-time clustering to correlate genetic and phenotypic similarity : M0(x):=We applied a test for haplotype-trait association in a general linear model framework, using maximum likelihood estimates for main and interaction effects of haplotypes and non-genetic factors, allowing for haplotype phase to be ambiguous ,11. Two PTPN22 data set, we used a model which included the main effects of the haplotypes, sex, and smoking, as well as the first-order haplotype-sex or haplotype-smoking interaction terms.For the p-values by a step-down minP algorithm [The numbers of tests and degrees of freedom differed between the statistical methods and models. Thus, we permuted the case-control status while keeping together genotypes and sex-as well as smoking status in the analysis of the real data-for each individual, and calculated adjusted lgorithm .As depicted in Table The haplotype-sharing-based Mantel statistics had 100% power for all markers both for the genetic main effect and for the joint effect Table  even whePTPN22 region, LR identified significant effects at R620W (p = 0.04) and rs1217413 (0.05), considering main effects only and when considering also interaction effects with sex   both with sex and with smoking was found . Six common haplotypes with frequencies ≥ 5% were estimated in PTPN22  with the disease loci and non-matching allele frequencies [r2-values between these markers and the disease loci. Therefore, in the simulated scenario, it was not possible to localize the functional variant within this region, e.g., there was no general tendency for the true causal locus to have the smallest p-value or largest effect size estimate. The simulated genetic main effect was also much stronger than the interaction effect, so that there was no improvement adding a gene × sex interaction term in the model. A much larger sample size would have been necessary for detecting the interaction effect.Current technology permits genotyping of dense marker sets across the whole genome. Once associated regions have been identified, efforts will be made to confirm these results in independent samples by a hypothesis-based candidate gene approach in which control of the type I error rate will be of great importance. Accounting for gene × environment interaction given moderate genetic main effects may improve the power to detect or confirm an association with a gene, a genetic region, or causal variant(s). Given this scenario, we investigated the performance of some methods proposed for detecting genetic main and gene × environmental interaction effects. We found that stepwise LR or CLR performed poorly given strong LD (as measured by D') between markers-a phenomenon called (multi-)collinearity that results in overfiting of the model and weak robustness . It is wquencies . This doM1), however, is more powerful than a similar LR modeling of interaction effects (data not shown).There was only a limited number of haplotypes estimated from the 22 simulated markers due to the strong LD (as measured by D') between them. Both haplotype-based methods were able to detect association in the investigated region but they failed to identify the disease variant(s). The findings regarding joint effects suggest that analysis for joint effects may be an option for detecting genetic effects in some situations. For smaller sample sizes, the Mantel statistics for joint effects  declare that they have no competing interests.


The Cochran-Armitage trend test (CATT) is powerful in detecting association between a susceptible marker and a disease. This test, however, may suffer from a substantial loss of power when the underlying genetic model is unknown and incorrectly specified. Thus, it is useful to derive tests obtaining the plausible power against all common genetic models. For this purpose, the genetic model selection (GMS) and genetic model exclusion (GME) methods were proposed recently. Simulation results showed that GMS and GME can obtain the plausible power against three common genetic models while the overall type I error is well controlled.χ2 with 2 degrees of freedom (df) test in matched case-control association designs through simulation studies.Although GMS and GME are powerful statistically, they could be seriously affected by known confounding factors such as gender, age and race. Therefore, in this paper, via comparing the difference of Hardy-Weinberg disequilibrium coefficients between the cases and the controls within each sub-population, we propose the stratified genetic model selection (SGMS) and exclusion (SGME) methods which could eliminate the effect of confounding factors by adopting a matching framework. Our goal in this paper is to investigate the robustness of the proposed statistics and compare them with other commonly used efficiency robust tests such as MAX3 and χ2 with 2 df test may be used. To illustrate the robust procedures, the proposed tests are applied to a real matched pair case-control etiologic study of sarcoidosis.Simulation results showed that if the mean genetic effect of the heterozygous genotype is between those of the two homozygous genotypes, then the proposed tests and MAX3 are preferred. Otherwise,  Besides, if a linear trend among the rows can be assumed, a more powerful test which utilizes the score test for a logistic regression can be obtained. This score test is known as the Cochran-Armitage trend test (CATT)2. In the unmatched study, denote the HWD coefficients in the case group and the control group as Δp = Pr(DD|case) - [Pr(DD|case)+Pr(Dd|case)/2]2 and Δq = Pr(DD|control) - [Pr(DD|control) + Pr(Dd|control)/2]2, Zheng and Ng − and strongly suggested an ADD model. Thus, for SGMS we select ADD and for SGME we exclude REC and DOM. Using formulas (6) and (8) we obtained the p-values as 0.0398 for ZSGMS and 0.0310 for ZSGME, both suggesting a marginally significant association. According to our simulation, ZSGME is the most powerful robust test under the ADD model. We also obtained the minimum correlation of the optimal tests ρ* = 0.603, which indicates that ZSGME is a better choice than ZMAX3 according to our previous discussion for Table Then we applied g et al. . Thereafχ2 with 2 df test. Simulations were carried out to examine the robustness of all these tests. The tests were also used to analyze a real pair matched data set of sarcoidosis. Simulation results indicate that when the genetic model is unknown, a mis-specification of the genetic model may result in a substantial loss of power for the MTTs. In this situation, robust tests are preferred. Further comparisons among the robust tests were also conducted. According to our simulation, when the genetic effects are ordered in accordance with their genotypes, MAX3, SGMS and SGME are preferred. On the other hand, if the less plausible genetic models such as the over-dominant and under-recessive models cannot be excluded, then χ2 with 2 df test is a good choice.In this paper, we extended the GMS  and GME We adopted the matching framework in the stage of recruiting samples so our study is a pre-matched case-control association study. In practice, even in the unmatched case-control design matching is still an important tool to eliminate the effect of latent confounding factors such as the population stratification and cryptical relatedness. For example, Guan et al.  recentlySimulation results and real data analysis show that SGMS and SGME can keep a correct Type I error rate for stratified data while have good efficiency robustness against genetic model uncertainty. Besides, the proposed formulas in this paper can easily be used to calculate the corresponding p-values. Thus, SGMS and SGME are useful for genetic data analysis of matched case-control design.ZY carried out the project and wrote the draft of the manuscript. FWK proposed the idea and revised the manuscript. Both authors read and approved the manuscript.x ρbetween ZSMRT and ZMTT(x). Define U(x) as the numerator of ZMTT, under the null hypothesis,First we derive the correlation Following Zheng and Ng , we can n → ∞, When lp, we obtain the estimate x ρ.Substitute xρ,0.5 between ZMTT(x) and ZMTT(0.5) . Under the null hypothesis,Next we report the correlation Since pil , we obtain the estimate ρx, 0.5 .Substitute


FOXP2, CNTNAP2, ATP2C2, CMIP, and lysosomal enzymes, may advance our understanding of the etiology of speech and language disorders and enable us to better understand the relationships between the different forms of impairment across the spectrum.Developmental speech and language disorders cover a wide range of childhood conditions with overlapping but heterogeneous phenotypes and underlying etiologies. This characteristic heterogeneity hinders accurate diagnosis, can complicate treatment strategies, and causes difficulties in the identification of causal factors. Nonetheless, over the last decade, genetic variants have been identified that may predispose certain individuals to different aspects of speech and language difficulties. In this review, we summarize advances in the genetic investigation of stuttering, speech-sound disorder (SSD), specific language impairment (SLI), and developmental verbal dyspraxia (DVD). We discuss how the identification and study of specific genes and pathways, including  Developmental disorders of communication represent one of the most common reasons for pediatric referrals  and accoAlthough many of these speech and language disorders are differentiated at a clinical level, they are highly comorbid with each other. Many children do not fall neatly into a single diagnostic cluster, and others may change between categories as their language develops. Approximately 15% of children with persistent speech disorders also have a language disorder and approximately 5% of children with SLI also experience speech difficulties . Over 60It is well documented that genetic factors contribute to susceptibility to speech and language impairments. Speech and language deficits are heritable and show strong familial aggregation e.g., . MoreoveIn this review, we provide an overview of recent developments in the genetic study of spoken developmental speech and language disorders. We do not describe the study of developmental dyslexia as excellent reviews of this area have recently been published .For some genetic disorders, it is possible to select candidate genes on the basis of their function alone. However, for speech and language disorders, in which the underlying biological mechanism is unclear, the identification of susceptibility genes usually starts with an unbiased screening approach. This step allows the identification of a candidate region and thus acts to reduce the number of possible contributory genes to a manageable size prior to a more in-depth investigation. These screening approaches usually take the form of genome-wide linkage or association studies .In a linkage study, one investigates families with members affected by the disorder under study. By genotyping polymorphic genetic markers spread across the genome, it is possible to generate measures of genetic identity between sibling pairs. Linkage studies look for regions of the human genome in which there is a correlation between the level of genetic identity and the level of phenotypic similarity for any given sib pair. Perhaps the most simple example of linkage analysis is provided by the study of single-gene disorders in large families. In such circumstances, one is able to identify specific chromosome segments that cosegregate with disease status between affected relative pairs. Nonetheless, as discussed below, the study of such pedigrees in complex disorders is the exception rather than the rule. Instead, the majority of linkage studies for complex disorders investigate large numbers of smaller nuclear families and compare genetic identity between sibling pairs within family units. Such analyses can be performed under the assumption of a defined genetic model  or by using a model-free method . Linkage analyses provide a robust and powerful method of detecting contributing genetic factors but afford a low level of resolution. Thus, although linkage studies can be completed with relatively small sample sizes, they often lead to the identification of large genetic intervals, which necessitate follow-up higher-resolution (targeted association) investigations to allow the identification of a specific candidate gene. Linkage studies are usually completed at a genome-wide level  but may target specific chromosome regions (targeted linkage) if the researcher has an a priori reason to do so.Because linkage studies simply investigate the level of genetic sharing (joint inheritance), they do not allow the identification of specific contributory genetic variants. For this crucial step, an association study is required. In contrast to linkage, an association study is based upon the principle that contributory genetic variants will be more common in affected (cases) than unaffected (controls) individuals. In the simplest example, if a given genetic variant was necessary and sufficient for the onset of the disorder then we would expect this variant to be present in 100% of cases but never in controls. In complex disorders, the effect is not expected to be so stark but the rationale still holds. Association studies provide a better resolution of genetic information than linkage studies but are not as powerful and necessitate the genotyping of a high number of genetic markers in extremely large samples. These factors bring their own complications in terms of the logistics of data generation and multiple testing issues. Nonetheless, it is now possible to routinely generate genotypes for 2.5 million genetic variants (single-nucleotide polymorphisms [SNPs]) in thousands of case and control individuals enabling a so-called genome-wide association (GWA) study. The fact that association models were developed for case-control cohorts means that they often do not provide a convenient method for the fine mapping of linkage peaks in family-based samples. However, it is possible to apply such methods to genetic data collected from families by using parents or unaffected siblings as controls  or by apIt should be noted that although association studies are theoretically capable of identifying specific causal variants with relatively low effect sizes, in practice the genetic variants identified by association studies are not necessarily functional. Instead, they tend to be proxies that mark the approximate position of the etiologic genetic variant within a small segment of DNA. Thus, while association studies provide a strong starting point for functional studies, even high-density SNP association screens will require follow-up investigations to enable proof of causality .FOXP2. A summary of loci that have been implicated in these various speech and language disorders, as well as particular genes that have been identified, is provided in Linkage and association investigations of speech and language disorders followed on from similar investigations of the related disorder developmental dyslexia in the 1990s . Over thFOXP2 gene (OMIM 605317) that was present in all affected family members but not in unaffected individuals , molecular studies of gene function and expression, and population and evolutionary studies  and that its disruption has exceptionally severe consequences for the development of speech. Many detailed reviews of FOXP2 have been previously published and we refer the reader to these for a more in-depth discussion of this gene   with reduced performance across a number of linguistic measures  in a cohort of language-impaired families  have also been implicated in a range of neurodevelopmental disorders including autism  . Recent  604569) . This stfamilies . The mecg autism , Gilles g autism , schizopg autism , epilepsg autism , ADHD  according to specific motifs within the DNA binding domain (FOXP subfamily includes four genes (FOXP1-4) with diverse functions. The proteins encoded by these genes are found to bind to each other to form active heterodimer DNA binding molecules . The primary clinical presentation was described as blepharophimosis (drooping eyelids) and arthrogryposis (contractures of hands and feet). However, the child also presented with developmental delays and speech and language deficits, which the authors hypothesized might be caused by the disruption of FOXP1  with moderate learning disabilities, global developmental delays, and severe speech and language disorders. MRI and electroencephalography of the patients did not reveal any gross structural brain abnormalities, and a similar chromosome deletion was again observed in a control individual who was not reported to have learning difficulties.On the basis of g domain . The FOXolecules . In partolecules . In 2009of FOXP1 . A subse proband . However proband . More re proband . This chXP1 gene . Althougsability . This inFOXP1 sequence. Furthermore, FOXP1 has also been associated with the skin disorder vitiligo , which is characterized by the substitution or emission of speech sounds leading to intelligible speech. Speech-sound errors are a common feature of the language acquisition process but in children with SSD persist beyond the appropriate developmental time point. For example, it is estimated that while approximately 16% of 3-year-old children use inappropriate speech-sound formations, these errors only persist in 4% of children at 6 years of age (606896) and phonological memory and phonological decoding traits in SSD families (608995)    and is commonly deleted in Prader-Willi Syndrome  . By defi607134) (606711) (606712)  and the CMIP gene . Variation in both of these genes was predominantly associated with performance on a task of phonological short-term memory, supporting the importance of memory processes in language acquisition  in a sample of low-language performers selected from a population cohort. Thus, although this is perhaps the most thoroughly investigated locus described in this review, these results still require external replication, and a functional variant or mechanism has yet to be identified. Little is known regarding the functions of the CMIP and ATP2C2 proteins in the brain but both represent reasonable candidates. ATP2C2 encodes a calcium ATPase responsible for the regulation of cellular calcium and manganese levels. Calcium is an important intracellular messenger and is involved in many neuronal processes including short-term memory (CMIP was identified through a screen to identify large proteins expressed in the brain (300017). Other studies have found that it also interacts with the RelA (a subunit of the transcription factor NF-kappaB)  , 16 (SLI(606711) , and 19 (606712) . These s(606712) . The SLI(606712) . Signifiuisition . Regressm memory . Manganem memory , and manm memory . CMIP wahe brain  and encohe brain . Cytoskehe brain . The CMIhe brain , mutatio-kappaB)  and PI3 -kappaB) , indicat2 target , and no er ADHD,  indicatiFOXP2 region of chromosome 7 in language-impaired families (FOXP2), and 15 (DYX1), with various measures of linguistic ability. Association analyses of a denser panel of variants across FOXP2 and KIAA0319 revealed a marginal level of association (minP = 0.01) between KIAA0319 and measures of speech, language, and reading and between FOXP2 and reading, language, and articulation   encodes subunits of the N-acetylglucosamine-1-phosphotransferase enzyme, which plays a role in lysosomal targeting processes. An additional subunit of this enzyme is encoded by the GNPTG gene (OMIM 607838) on chromosome 16p and a downstream enzyme, N-acetylglucosamine-1-phosphodiester alpha-N-acetylglucosaminidase is encoded by the NAGPA gene on chromosome 16p (OMIM 607985). Sequencing of these related transcripts revealed additional coding mutations that were not observed in control individuals   . Sequencividuals  The GNPTividuals . Across screened . This st 252605) . These dFOXP2 precipitated a whole field of research that continues to advance our understanding of the foundations of speech and language. It is likely that in the future, the investigation of the pathways that involve and intersect with FOXP2 will identify many more candidate genes and mechanisms underlying speech and language disorders. The investigation of SSD has indicated that this disorder may share some genetic basis with developmental dyslexia. The study of SLI has enabled the identification of two candidate genes on chromosome 16 (ATP2C2 and CMIP), and stuttering research has identified the lysosomal enzyme pathway  as another candidate mechanism.In summary, the last decade has seen an explosion in our understanding of the genetic basis of speech and language disorders. The identification of Although the recent progress in this field is promising, it should be noted that, in comparison to other developmental disorders with a genetic contribution, speech and language disorders are relatively understudied. Many of the studies described above involved relatively small samples and have yet to be replicated in independent cohorts. As genetic technologies develop, the generation of larger data sets becomes progressively easier, allowing the identification of genetic variants with smaller effect sizes. Nonetheless, the application of these technologies demands the existence of large sample sets with consistent ascertainment and assessment standards. Even then, as we have seen above, and as evidenced by many other GWA studies, the interpretation of findings is far from straightforward. GWA studies in other complex disorders have enabled a revolution in candidate gene identification, but it is estimated that these loci still only account for a small proportion of the observed familial clustering. The source of this so-called “missing heritability” is still a matter of debate but has been suggested to arise from the presence of common genetic variation with minimal effect sizes, rare variants, structural deviations, and genetic modifications and interactions that are not well characterized by currently available GWA technology or algorithms .FOXP2 story demonstrates the continuing validity of the genetic approach and exemplifies how such data can be used to complement and inform larger genome-wide and biological pathway investigations.Thanks to advances in technology, however, alternative methodologies are now available to complement the GWA. It is now feasible to directly sequence entire genomes for increasingly large sample sizes, and this will be important in the identification of rare mutations . It is aThrough this review, we hope not only to have summarized the findings of genetic investigations of speech and language disorders but also to have indicated the challenges faced during the genetic investigation of such disorders. As genetic throughput expands, the true challenge is not the generation of data but the interpretation of the findings and the proof of causality. It will be interesting to see whether the recent findings pertaining to speech and language disorders can be replicated and how these avenues of investigation will eventually combine with those generated from larger high-throughput studies. Ultimately, it is hoped that the research summarized in this paper will allow a better understanding of the causes of speech and language disorders and the complex relationships between these impairments, thereby facilitating better diagnostic and treatment schedules for affected individuals.


Large-scale genetic association studies can test hundreds of thousands of genetic markers for association with a trait. Since the genetic markers may be correlated, a Bonferroni correction is typically too stringent a correction for multiple testing. Permutation testing is a standard statistical technique for determining statistical significance when performing multiple correlated tests for genetic association. However, permutation testing for large-scale genetic association studies is computationally demanding and calls for optimized algorithms and software. PRESTO is a new software package for genetic association studies that performs fast computation of multiple-testing adjusted P-values via permutation of the trait.PRESTO is an order of magnitude faster than other existing permutation testing software, and can analyze a large genome-wide association study  in approximately one hour of computing time. PRESTO has several unique features that are useful in a wide range of studies: it reports empirical null distributions for the top-ranked statistics (i.e. order statistics), it performs user-specified combinations of allelic and genotypic tests, it performs stratified analysis when sampled individuals are from multiple populations and each individual's population of origin is specified, and it determines significance levels for one and two-stage genotyping designs. PRESTO is designed for case-control studies, but can also be applied to trio data (parents and affected offspring) if transmitted parental alleles are coded as case alleles and untransmitted parental alleles are coded as control alleles..PRESTO is a platform-independent software package that performs fast and flexible permutation testing for genetic association studies. The PRESTO executable file, Java source code, example data, and documentation are freely available at  Permutation testing can be applied to both case-control studies and trio studies (parents and affected offspring). In permutation testing, the case/control status of the individuals (for case-control studies) or the transmitted/untransmitted status of the parental chromosomes (for trio studies) are randomly permuted. The maximum test statistic, maximized over all tests for all markers, is calculated for the original affection/transmission status and for each permuted affection/transmission status. If ent is k+/(P+1) [1Permutation testing is computationally demanding for large-scale genetic association studies and requires an optimized software implementation. The PRESTO software package provides fast permutation testing for genome-wide association studies with thousands or millions of markers genotyped on thousands of samples. In addition to using permutation of the trait status to determine statistical significance of user-specified allelic and genotypic tests, PRESTO has three additional useful features: it can compute empirical distributions of order statistics so that the significance of sophisticated multi-marker statistics such as truncated products can be determined ,3, it caPRESTO is designed to be flexible and user-friendly. Input files have a simple format with rows corresponding to markers and columns corresponding to individuals (two columns per diploid genotype). This format is well-suited to large-scale genetic studies where there are typically many more markers (rows) than individuals (columns). Genetic marker data can be split up over multiple input files (e.g. one file per chromosome). There are no restrictions on how alleles or missing data are coded, and any sequence of non-white space characters can be used. Multi-allelic markers are permitted and are analyzed by creating a diallelic marker for each allele  and testing each diallelic marker for association with the trait status. If the cases and controls are sampled from a stratified population and the strata are specified, PRESTO will automatically perform stratified allelic and genotypic tests ,6 and wiPRESTO can also compute significance levels of combined single locus and multi-locus analysis by representing clusters of haplotypes as diallelic markers as described in Browning and Browning . By defaN/(N-1) where N is the sample size. The CMH test is used to test for recessive, dominant, or overdominant effects. Although the CMH test can be used to test for allelic effects, the Mantel trend test is the preferred allelic test since it is robust to departure from Hardy-Weinberg equilibrium.PRESTO performs a Cochran-Mantel-Haenszel (CMH) test with continuity correction and a Mantel trend test ,6. The Cj-th order statistic is the j-th largest test statistic. For each marker, the test statistic is the largest allelic or genotypic test statistic for that marker. The empirical distributions of the top-ranked order statistics enable one to calculate an empirical P-value for any order statistic or any function of order statistics. An example of a function of order statistics is the j-th rank truncated product statistic, which is defined as the product of the nominal P-values of the j largest test statistics [For each permutation of the trait status, PRESTO can store and report the top-ranked order statistics. The atistics . Rank trPRESTO can also calculate significance levels for two-stage genotyping designs from the first-stage genotype data using the technique described by Dudbridge . In a twk-th binary indicator gives the affection status of the k-th chromosome in the input file. Each genetic marker is read once and is tested against all stored permutations of the trait status, so that only one marker is stored in memory at a time.PRESTO employs several techniques to optimize permutation testing on large-scale data sets. The permutations of the trait status are computed once and are stored. Each permutation of the trait status is represented as an array of Boolean (1 bit) variables in which the N genotypes, and 90% of these are major allele homozygotes, then contingency tables for each permutation of the trait status are constructed by examining the permuted trait status of N/10 individuals instead of N individuals. This optimization is expected to be increasingly effective as denser marker sets are developed since markers with low minor allele frequency are much more numerous than markers with high minor allele frequency. An analogous optimization is used to obtain allele contingency table counts.For each permutation of the trait status and each diallelic marker, a 2 × 3 contingency table is created where the rows are the cases and controls and the columns are the three possible genotypes. PRESTO obtains the 2 × 3 contingency table counts without having to check the permuted trait status and genotype for all individuals. Instead, PRESTO stores the indices of individuals with missing genotypes, heterozygote genotypes, and minor (least common) allele homozygote genotypes. The indices of individuals with major allele homozygote genotypes do not need to be stored because the case and control major allele homozygote genotype counts can be calculated from the case and control sample sizes, the case and control missing genotype counts, and the case and control heterozygote and minor allele homozygote genotype counts. For example, if there are PRESTO produces three output files: a log file, a P-value file, and a null distribution file. The log file summarizes the analysis and reports the command line parameters, the running time, and a list of all markers with a multiple-testing adjusted P-value less than 0.2.t0 when tested for association with the unpermuted trait status, and if for k out of P permutations of the trait status there exists at least one marker with a maximum test statistic ≥ t0, then the multiple-testing adjusted P-value for the marker is (k+1)/(P+1) [The P-value file gives the chi-square test statistics for each allelic and genotypic test performed for each marker, and the permutation P-value for the maximum test statistic for each marker . If a marker has a maximum test statistic ker is k+/(P+1) [11)/(P+1)  for filtP permutations and the K largest test statistics are saved, then the null P-value file is a P × K white-space delimited matrix whose rows correspond to permutations and whose columns are the empirical distributions of the top-ranked order statistics. The entry in row i and column j is the j-th largest test statistic for the i-th permutation. Thus the j-th column gives the empirical distribution of the j-th largest test statistic.The null distribution file gives the largest test statistics for each permutation of the trait status. If there are Table Running times for PRESTO 1.0.1 and PLINK 1.0  were comPRESTO's running time is linear in the number of samples, linear in the number of markers and linear in the number of permutations. Generally, 1000 permutations are sufficient to determine experiment-wide significance. PRESTO can also be run in parallel as described in the documentation.P permutations, N individuals, and the distributions of the top K order statistics are reported, then 2NP/8 bytes are allocated to store the permutations of the trait status and 8KP bytes are allocated to store the top-ranked order statistics (8 bytes per floating point number). In practice, 500 Mb of memory should suffice for values of P ≤ 10,000, K ≤ 2000, and N ≤ 20,000.Since only one marker is stored in memory at a time and since the trait status for each individual is stored using 2 bits, PRESTO's memory requirements are modest. If there are Permutation testing with 1000 permutations of a large case-control genome-wide association study with 5000 individuals genotyped for 500,000 markers can be performed using PRESTO in approximately one hour of computing time  [-8, 1.5 × 10-8, and 6.2 × 10-9 respectively. If additional permutations are desired, 104 or 105 permutations are easily performed on a large genome-wide data set like the WTCCC data set in Table There has been some debate regarding the number of permutations required. When performing Permutation testing is particularly appealing because of its simplicity. Recently, several more complex alternatives to permutation testing have been proposed -14. ThesSome methods for computing adjusted P-values exploit the fact that for many common statistical tests, the correlated tests have an asymptotic multivariate normal distribution under the null hypothesis of no trait-marker correlation. Seaman and Müller-Myhsok have proposed estimating the asymptotic distribution and sampling directly from it , and ConThere are some limitations with these approaches that estimate the asymptotic multivariate normal distribution of the test statistics. These methods do not estimate significance levels for two-stage genotyping designs. A more severe restriction is that these methods are typically limited to several hundred correlated tests. Seaman and Müller-Myhsok and Conneely and Boehnke suggest that the number of samples should be at least 10 times the number of tests performed in order to accurately estimate the asymptotic multivariate normal distribution ,14. So tOther alternatives to permutation testing are based on importance sampling. Kimmel and Shamir  have proThese importance sampling methods lack some of the features that are found in PRESTO. The methods do not calculate significance for two-stage genotyping designs, and they do not calculate adjusted P-values for general order statistics. In the extension to stratified data, the association test statistic used in Kimmel et al  will havMethods for computing multiple-testing adjusted P-values that are based on asymptotic multivariate normal distributions or importance sampling, are more complex than permutation testing, and require the asymptotic approximations to be accurate. In addition, when testing a single binary trait, these alternative methods provide little or no decrease in computational time relative to permutation testing with PRESTO, unless one is performing more than 1000 permutations.PRESTO is a flexible, platform-independent software package that determines multiple-testing adjusted statistical significance for large-scale genetic association studies by using permutation of the trait status. PRESTO is faster than existing permutation testing software and can analyze a large genome-wide association study  in approximately one hour of computing time. PRESTO can be used with stratified data from multiple populations and with two-stage genotyping designs. PRESTO can also report empirical null distributions for the top-ranked statistics (i.e. order statistics) so that statistical significance can be determined for any test statistic calculated in terms of order statistics.Project name: PRESTO• Project home page: • Operating system(s): Platform independent• Programming language: Java• Other requirements: standard edition (SE) Java Runtime Environment (JRE) 5.0 (or higher)• License: freely available for academic and commercial use.•


We identified ten iQTL that display much more complex and diverse effect patterns than previously assumed, including four loci with effects similar to the callipyge mutation found in sheep. Three loci display a new phenotypic pattern that we refer to as bipolar dominance, where the two heterozygotes are different from each other while the two homozygotes are identical to each other. Our study furthermore detected a paternally expressed iQTL on Chromosome 7 in a region containing a known imprinting cluster with many paternally expressed genes. Surprisingly, the effects of the iQTL were mostly restricted to traits expressed after weaning. Our results imply that the quantitative effects of an imprinted allele at a locus depend both on its parent of origin and the allele it is paired with. Our findings also show that the imprinting pattern of a locus can be variable over ontogenetic time and, in contrast to current views, may often be stronger at later stages in life.Parent-of-origin–dependent gene expression resulting from genomic imprinting plays an important role in modulating complex traits ranging from developmental processes to cognitive abilities and associated disorders. However, while gene-targeting techniques have allowed for the identification of imprinted loci, very little is known about the contribution of imprinting to quantitative variation in complex traits. Most studies, furthermore, assume a simple pattern of imprinting, resulting in either paternal or maternal gene expression; yet, more complex patterns of effects also exist. As a result, the distribution and number of different imprinting patterns across the genome remain largely unexplored. We address these unresolved issues using a genome-wide scan for imprinted quantitative trait loci  or their father . This “parent-of-origin–dependent” pattern of gene expression is known as genomic imprinting and has been shown to play an important role in modulating a variety of traits ranging from developmental processes to cognitive abilities and associated disorders. While various molecular techniques have allowed for the identification of many imprinted genes, very little is known about the contribution of imprinting to variation seen among individuals in continuously varying traits such as body size. Here we address this issue by using a genome-wide analysis aimed at finding regions of the genome that show an effect of imprinting on body weight and growth in mice. We identified ten loci that displayed complex and diverse patterns of effect, including four loci with effects similar to the unusual callipyge mutation found in sheep and three that displayed a new phenotypic pattern that we refer to as bipolar dominance. Surprisingly, most imprinting effects were strongest during the post-weaning period, and many showed shifts in the pattern of imprinting over ontogenetic time. Genomic imprinting refers to the phenomenon of monoallelic gene expression that depends on the parent-of-origin of alleles, where either the maternally or paternally inherited copy is expressed while the other copy is silenced Most of our current knowledge about the number, distribution and effects of imprinted genes comes from studies using gene-targeting techniques focusing on regions of the genome with chromosomal aberrations Furthermore, while both large and small additive and dominance effects have been successfully mapped for a wide variety of traits iQTL) is likely to yield a more comprehensive understanding of the complex patterns and likely evolutionary origins of imprinting.Using a three-generational intercross between two inbred strains originally selected for divergent adult body size , we scanned the genome for loci showing significant parent-of-origin effects on body size and growth traits. We present a hypothetical model for the functional origin of these complex effects and demonstrate that further dissection of imprinted quantitative trait loci  that was significant at the chromosome level, but the overall test for the locus was not significant. However, because of the strong parent-of-origin effect at the locus, it was included as a suggestive iQTL. Significance tests using the fit of the various possible forms of imprinting (see below) suggest that 8 of the 10 loci were significant at the genome-wide threshold, with the other two being significant at the chromosome level, lending additional support to our findings that these loci represent true iQTL. Post-hoc analyses tested whether the parent-of-origin effect appeared in heterozygous offspring of heterozygous mothers and confirmed that the parent-of-origin dependent effects of these ten loci were indeed caused by genomic imprinting and not maternal genetic effects. These analyses revealed that the parent-of-origin dependent effects of five other loci were caused by maternal genetic effects, and consequently, these loci are discussed elsewhere In a genome-wide scan for patterns . Five ofiQTL are generally pleiotropic, with effects on weight at different stages in development. The effects of four of these loci show a change in the imprinting pattern over time  being more than 50cM away from the distal (Wti3.2) and the two are thus regarded as independent from each other. In addition, two-QTL mapping analysis (see Wti3.3).Genomic imprinting was found to affect all weekly weights and growth traits. The effects of the ver time . The dasysis see  revealedi to the additive and dominance genotypic values (a and d) as well as their sign. Six loci showed paternal expression at some point during development  showed maternal expression. Three loci showed a hitherto undescribed pattern which we refer to as bipolar dominance , but by week 7 the pattern had shifted to maternal expression. Likewise, Wit2.1 changed from polar underdominance to paternal expression from week 1 to week 3.Across all loci, we discovered five different patterns of imprinting: paternal expression, maternal expression, polar over- and underdominance imprinting and bipolar dominance. n week 4 , but then week 4 . The chan week 4 , with tha/i or d/i; see a/i ratios larger than 1. In many cases of paternal expression, the additive effect is more than twice the imprinting effect; this is illustrated in Wti7.1 on week 6 weight, where the additive effect is just over twice the imprinting effect.Many of the loci showed patterns consistent with partial imprinting, where the difference between the two homozygotes is larger than the difference between the two heterozygotes. This can be seen in the relationships between the additive or dominance genotypic values to the imprinting genotypic value  together accounted for between 1 and 15.8% of the phenotypic variance in age-specific weights and weight gain, with the imprinting effects alone accounting for between 0.8 and 5.7% of the phenotypic variance. The overall effects of individual loci  explained between a 0.34 and 6.5% of the phenotypic variance (with an average of 1.6%), with the imprinting effects alone accounting for between 0.2 and 1.7% of the phenotypic variance. Surprisingly, the strongest effect of genomic imprinting on weight occurred between weeks 6 and 10 (The effects of the 6 and 10 .Wti2.1) showing effects limited to the pre-weaning period. The QTL with imprinting effects over the longest period is located on proximal chromosome 3 (Wti3.1) with a significant effect on most weights from week 1–9.Turning to the duration of imprinting effects and their onset we found that most imprinted loci showed significant effects over long periods during development, with only a single locus . These parent-of-origin-dependent allelic effects may also be akin to dominance in that the effect of an imprinted allele not only depends on its parent-of-origin, but also on the allele it is paired with at a locus. Finally, the results of this study demonstrate that imprinting effects can vary over time both in their patterns , a second model for the maternally expressed Igf2r gene utilizes cis-mediated silencing of maternally expressed genes by non-coding paternally expressed RNA (e.g. Igf2r). Finally, at microimprinted domains oocyte-derived methylation in the promoter region of protein-coding genes is assumed to be the key mechanism. In addition to the ‘traditional’ imprinting patterns we found four loci with polar dominance effects causing a pattern equivalent to that described for the callipyge (CLPG) locus in sheep and pig homologues DLK1-GTL2CLPG locus that results in the expression of a number of core group genes in cis in addition to an interaction in trans between reciprocally imprinted genes The processes underlying the diversity of imprinting patterns found in our study are likely due to different mechanisms 1 and 2 is comprised of two variable sites, A and B, that are in close linkage, with gene A being paternally expressed while B is maternally expressed. The effect of the paternally derived QTL copy will be determined by variation at site A while the effect of the maternally inherited copy will be determined by variation at site B. In this scenario, allele 1 of the QTL may have a positive effect on a trait when paternally inherited but a negative effect when maternally inherited whereas allele 2 may show the opposite pattern. When the same allele (1 or 2) is inherited from both the father and the mother the effects cancel out, yielding no difference between the two homozygotes. However, if two different alleles are inherited from the parents then the joint effects of the paternal and maternal copy do not cancel and, as a result, produce a pattern of bipolar dominance , such that the alternative alleles are composed of variants at both the maternally and the paternally expressed loci. This scenario is illustrated in ominance . As withwww.geneimprint.com) may in part reflect a research bias toward regions of the genome with chromosomal aberrations and loci with large phenotypic effects, especially in the light of recent research showing that as many as 600 genes are predicted to be imprinted iQTL found in our study with those of currently known imprinted genes (www.geneimprint.com), we find that most of our loci are likely to be novel. No currently known imprinted genes are located on chromosomes 1, or 3, where we detected a total of four iQTL. There are known imprinted genes on chromosomes 2 and 5, but they all lie well outside of the confidence intervals iQTL locations . Chromosome 12 has a number of imprinted genes that are located close to but outside of either end of the confidence interval, with Mirn337 more than 20Mb proximal and several genes  more than 10Mb distal to the confidence interval. The iQTL with the strongest effect (Wti7.1) is located on Chromosome 7, which contains nearly half of the currently confirmed imprinted genes in mice (www.geneimprint.com). The confidence interval for the location of this locus includes 17 known imprinted genes , including for example, Peg3, Peg4 (Snrpn), Peg6 (Ndn) and Peg12. More than half of these (10 of 17) and 10 of 14 loci with a known imprinting pattern are characterized by paternal expression, which matches the pattern we identified for the iQTL in this region. On chromosome 9, the imprinted gene Rasgrf1 falls within the confidence region of Wti9.1. Rasgrf1 was the first imprinted gene found to affect postnatal growth only Rasgrf1 is congruent with the effect on postweaning growth found for Wti9.1, we found a bipolar pattern for growth and polar overdominance for weekly weights affected by this locus in contrast to paternal expression reported for Rasgrf1. However, we note it is unclear whether a bipolar pattern would emerge for Rasgrf1 for the post-weaning growth period of 3–10 weeks. Further studies both on the growth trajectories of Rasgrf1 mutants and on fine-mapping our identified iQTL are required to determine whether Rasgrf1 could be a candidate gene. Finally, the confidence interval for the location of Wti14.1 includes a single known imprinted gene, Htr2a, which is known to be maternally expressed in mice, in contrast to the pattern of paternal expression seen for the iQTL, suggesting it is an unlikely candidate gene.The currently known number of imprinted loci in mice  has effects that are restricted entirely to the preweaning period. Many more loci show effects that do not appear until week 5 or later and many of these extend to mature adult weight at 9 or 10 weeks. Additive and dominance QTL mapping in this population of mice has shown that different sets of QTL affect variation in growth and body weight between 1 and 3 weeks of age and between 4 and 10 weeks Somewhat surprisingly, we found only two loci that show imprinting effects during the pre-weaning period from 1 to 3 weeks and only one of these loci . Moreover, by not restricting our analysis to traits expressed early in life we demonstrate that imprinting effects can appear and often be stronger later in life , and may also change their pattern of effect during growth and development. More generally, our investigation provides a framework for classifying the diversity of patterns that imprinted loci may show . Further2 and F3 generation of an intercross between the inbred mouse strains Large (LG/J) and Small (SM/J) 1 population consisted of 52 individuals, which were randomly mated to produce 510 F2 animals, representing the parental generation in our study. These F2 animals, again, were randomly mated to produce 200 full-sibling families of the F3 generation with a total of 1632 individuals. Males were removed from the cages when females were visibly pregnant. Half litters were reciprocally cross-fostered at random between pairs of females that gave birth on the same day. In total, offspring in 158 families were cross-fostered in this way. Pups were weaned at 21 days of age and randomly housed with three or four other same sex individuals. Further details of the husbandry are given in We used the FPups were weighed weekly starting at one week of age through week 10 using a digital scale with an accuracy of 0.1g. Growth was calculated as the difference between weekly weights such that, for example, the growth from week 1 to week 3 is the difference between week 3 weight and week 1 weight. The traits analysed in this study are weekly individual bodyweights corrected for sex and litter size beginning with weight at week 1 and ending with weight at week 10. Growth traits were obtained for preweaning growth from week 1 to 3 and for the postweaning growth from week 3 to 10.2 and F3 individuals using Qiagen DNeasy tissue kits. After standardizing DNA concentration, the samples were scored for 384 SNPs using the Golden Gate Assay by Illumina, San Diego, USA. These markers were previously found to be polymorphic between LG/J and SM/J as part of the Oxford/CTC genotyping collaboration (http://www.well.ox.ac.uk/mouse/INBREDS/). After further testing, 15 loci were found not to have been reliably scored and were excluded from the analysis. Sixteen loci were scored on the X chromosome and are not included in this analysis because the genome structure and the statistical model for the X are complex and unresolved. This leaves 353 loci across the 19 autosomes for analyses. A genetic map of these markers based on Haldane's centiMorgans (cM) was produced using R/QTL www.ensembl.org). The average map distance between markers in the F2 generation is 4 cM. Markers are evenly placed throughout the genome except for regions in which LG/J and SM/J have been found to be monomorphic DNA was extracted from livers of the F2 animals and a set of ordered haplotypes  for the F3 animals. The ordered genotypes of the F3 allowed us to distinguish between the four possible genotypes at a given locus, LL, SL, LS or SS  where the first allele refers to the paternally derived allele and the second to the maternally derived allele.The combined genotypes of parents and offspring were used to reconstruct haplotypes for all animals with the program PedPhase LL, LS, SL and SS) were assigned additive, dominance and imprinting (parent-of-origin) genotypic index values following Mantey et al. r is the reference point for the model (the mid-point between homozygotes), a is the additive genotypic value , d is the dominance genotypic value (the difference between the mean of the heterozygotes and the mid-point of the homozygote means), and i is the parent-of-origin or imprinting genotypic value  The four ordered genotypes at the marker loci  to detect loci showing significant parent-of-origin-dependent effects (i.e. significant i effects). A mixed general linear model was used to estimate the overall significance of a locus as well as the significance of the additive, dominance, and imprinting effects. To test the overall significance of a locus, a model with ordered genotype class as a fixed effect and family as a random effect was fitted using restricted maximum likelihood (REML) as implemented in the Mixed Procedure of SAS . The significance of the individual genetic effects was determined using a mixed model with the a, d and i index values as fixed regression effects and family as a random effect (fitted again using REML in the Mixed Procedure of SAS). Family was included as a random effect to account for variation among families not attributable to the effects of the locus in question. A power analysis combined with a simulation to determine significance thresholds (see below) showed that the inclusion of family greatly improved power while also removing any bias in significance tests introduced by family structure.These index values in equation (1) were used to build a model to scan the genome in the Fa), dominance (d) and imprinting (i) effects. These probability values were then transformed to a logarithmic probability ratio (LPR) in order to make them comparable to the LOD scores typically seen in QTL analyses (LPR = −log10(probability)). Significance thresholds were determined using a Bonferroni correction, which was calculated using the effective number of markers method α = 0.05) of 3.41. Chen & Storey p<0.05) to determine whether the locus also had additive, dominance or imprinting effects, or affected more than one trait. Confidence intervals for the positions of iQTL were determined using a one LOD drop  following Lander & Botstein The mixed model with the fixed genetic effects and random family effect was used to scan the genome to produce a probability distribution for the overall effect of the locus as well as the additive  using the expectation:2R. The proportion of variance explained would, therefore, be Vg/Vp (Vp being the phenotypic variance). To obtain the variance explained by the parent-of-origin effect alone we calculated To determine the relative proportion of variance explained by the loci overall and by genomic imprinting effects, we calculated the approximate variance contributed by a locus  = +1 if there is paternal expression and (a/i) = −1 for maternal expression, and also that d/i = 0. A locus was said to show partial maternal or paternal expression when the (a/i) ratio was closer to zero but both a and i were still statistically significant or when the ratio was much larger than one. To detect maternal expression, the LL and SL genotypic values were contrasted with the LS and SS genotypic values , while the opposite contrast was used to detect paternal expression.With complete or partial monoallelic expression , we expect that the two genotypes sharing the same expressed allele should have the same average phenotype. Complete silencing of one allele implies that  and dominance (d) values of zero, thus d/i = 0 and a/i = 0. This pattern cannot be attributed to simple silencing since such a process would necessarily result in a difference between the homozygotes. Possible mechanisms producing this pattern are discussed in the main text. To detect bipolar dominance, the SL genotypic value was contrasted with the LS genotypic value.A locus with a significant callipyge locus in sheep d) that is equal to the imprinting value (i) and with an additive effect (a) of zero, thus d/i = ±1 and a/i = 0. To detect polar dominance, either the LS or SL genotypic values were contrasted with the values of the other three genotypes.A third possibility is a more general case of the pattern previously called polar overdominance We note that actual genotypic values may deviate from the definitions given above and patterns with genotypic value ratios approximating −0.5 or 0.5 cannot be unequivocally categorized.2 and F3 populations maintaining the observed distribution of family size to evaluate power of alternative mapping approaches  versus just the imprinting effect (i) and the possibility of inflation of significance thresholds caused by family structure). F2 animals' genotypes were produced by combining recombinant gametes from their F1 parents. We used the recombination rates observed in the F2 population. F2 animals were randomly paired and gametes produced for each offspring, again using the same recombination rates and the observed distribution of family sizes. A thousand random independent loci, one from each of a thousand iterations of the simulation, were used for power analyses. These family-structured genotypes were then paired with the observed phenotypes. Thus, each simulated locus had the same distribution of genetic correlation between individuals as the actual population. The distribution of phenotypes within and between families was also maintained, fixing heritability and genetic correlation. However, in the simulated population there is no relationship between any specific locus and the distribution of phenotypes, unless such a relationship was simulated.We simulated the production of the FVg .QTL were simulated by altering the phenotypes of individuals based on their genotype at a locus in a way that simulated two patterns of imprinting, parental imprinting and bipolar dominance, and two patterns of QTL effects that did not include imprinting, pure additivity and overdominance effects. QTL effects were simulated following the definition of the genotypic values given in equation (1) in a way that did not alter the trait mean. QTL were simulated to account for ½, 1, 2 and 5% of the phenotypic variance according to the relationship shown in eq. (3) for Because our goal is to present an analysis of imprinting effects and not a general analysis of statistical approaches, we only briefly present the relevant details and results here in the The null model with no QTL effects shows that the threshold for a model not including the random effect of family  has an inflated significance threshold due to the presence of the family structure , where the 5% significance threshold is 0.0007, rather than 0.05 as expected. In contrast, the model that includes the random effect of family in the mixed model has a 5% significance threshold of 0.048. This shows that the inclusion of the random effect of family, by removing the among family variance, removes any bias in significance tests caused by family structure. Because we have identified loci using a test of the overall effect of a locus prior to determining whether the locus shows imprinting, our expected rate of false positives is actually much lower than the 5% expected based on a 5% significance threshold for the locus. This is because most loci showing a significant overall effect do not show an imprinting effect. Indeed, when the fixed effect of family is included in the model, we find that, as expected, only a fraction of the false positives show imprinting, indicating that the actual rate of false positives for loci showing an imprinting effect is just over 1%. When the model does not include the random family effect, we find that the false positive rate of loci showing imprinting is just over 2%.Vp), power is higher when the test is based solely on the imprinting effect, but the proportion showing the correct pattern is generally similar to the test based on the overall effect of the locus. The increased power is due to the fact that the bipolar pattern only contributes to the imprinting term, and therefore, a test based on the overall effect of the locus is less sensitive than one based solely on the imprinting effect. For this reason, we have included loci that show a strong imprinting effect even when the overall effect of a locus is not significant . When the locus shows significant parental imprinting, the contrasts generally identify the correct pattern of imprinting, ranging from 83% correct at the genome-level threshold when the locus accounts for just ½% of Vp to 99% correct when it accounts for 5% of Vp. In the rare cases where the contrasts identify the wrong pattern of imprinting, the incorrect patterns are evenly distributed between polar and bipolar dominance, but the locus almost never shows the alternative form of parental expression . When the locus shows bipolar dominance, power is generally higher than the case for parental expression and the contrasts also generally identify the correct pattern of effect of the locus. However, unlike parental expression, when the contrasts identify the incorrect pattern of imprinting, the locus shows polar dominance the majority of the time (⅔ to ¾ of the incorrect patterns are some form of polar dominance). This suggests that cases of polar and bipolar dominance may be somewhat confounded, but bipolar dominance very rarely appears as parental expression.iQTL) provides strong support for the diversity of patterns of effect we describe for the detected iQTL.When the locus was simulated to show additive or dominance effects, imprinting effects were significant less than 5% of the time , indicating that non-imprinted patterns of genetic effect generally do not lead to the incorrect identification of a locus as showing imprinting. This result strongly suggests that a detected significant imprinting effect represents a case of true imprinting at a locus since the rate of false positives is lower than the rate expected under the null model. Furthermore, the fact that the model is very successful at identifying the correct pattern of effect of a locus . The effect of the QTL is listed for all traits where the imprinting effect was significant. These are given as the additive (a), dominance (d) and imprinting (i) genotypic values, their standard errors (SE) and significance (p) value. These are followed by the LPR for the overall effect of a locus and the significance threshold at the chromosome level for the chromosome containing the QTL. Under the heading ‘Characterizing the pattern of imprinting’ are the ratio of the additive to the imprinting genotypic values (a/i), the dominance to imprinting ratio (d/i), the standardized imprinting values (i/SD), the standard deviation of the trait (SD), the R 2 (rsq%) value of the imprinting effect alone and R 2 for the overall variance explained by a locus, the best fit pattern along with the LPR value associated with the contrast for that pattern. These are followed by the genotypic values of the four ordered genotypes LL, LS, SL, SS and their standard errors (SE), which were estimated by the mixed model fitted by REML .Additional information about the significant (0.08 MB XLS)Click here for additional data file.Table S22 map position in cM [Map Pos (cM)] and their physical position based on mouse genome build 36 (ensembl.org) [Phy. Pos. (bp)].Listed are the 353 SNP markers used in the study. The table includes the chromosome (Chr), marker name (Marker), F(0.06 MB XLS)Click here for additional data file.Table S3iQTL showing either parental or bipolar expression. The ‘iQTL’ column lists the pattern of effect simulated for a locus, ‘Family effect’ indicates whether the random effect of family was included in the model and ‘%Vp’ list the percent of phenotypic variance accounted for by the locus. These are followed by three pairs of columns that give the percent power and percent correct assignment of the real QTL effect pattern using a locus, chromosome and genome level significance test.Power analysis of the mixed model to detect (0.10 MB DOC)Click here for additional data file.