HLA-DRB1 show very strong association with rheumatoid arthritis. Because some previous functional studies have reported association of gene KCNB1 to rheumatoid arthritis, we evaluate whether the gene KCNB1 contributes to the genetics of rheumatoid arthritis in this data set. Fifteen single-nucleotide polymorphisms from this gene were chosen. The association of KCNB1 gene to rheumatoid arthritis seems to be moderate.With the rapid development of large-scale high-throughput genotyping technology, genome-wide association studies have become a popular approach to mapping genes underlying common human disorders. Some genes are discovered, but many more have not been. Because these genes were not initially identified, it is reasonable to assume that their main effect is weak. We propose a method to accommodate such a situation. It is applied to the Genetic Analysis Workshop 16 Problem 1 case-control data in which shared-epitope alleles of  Conventional methods such as linear regression and logistic regression are widely used in genetic association tests in the presence of gene-gene or gene-environment interaction. In recent years several new methods have been developed. Based on Tukey's non-additivity model, Chatterjee et al.  proposedHLA-DRB1 using its shared-epitope alleles and KCNB1. The relationship between HLA-DRB1 and increased risk for rheumatoid arthritis has been studied for 20 years, and several high risk alleles of HLA-DRB1 are treated as shared-epitope alleles , the asymptotic quantiles of statistic based on this test seem to be rather different from those of the interaction-only test and the both-effects test. We set M to 0.4 in this analysis because the value of M should reflect the moderate association of KCNB1 and it should not be too small. Because the distribution of the likelihood-ratio statistic of this proposed test is unknown for case-control study, and in order to compare p-values, the estimation of the p-values need to be based on the same procedure. Ten thousand permutations are used to estimate the p-values in three tests for each SNP.An appropriate value for gression , when M KCNB1 jointly. The principal components were obtained via the statistical package R (version 2.6.2) based on the correlation coefficient matrix.We also use principal-component analysis to test multiple SNPs in HLA-DRB1 and KCNB1, a p-value < 10-100 for HLA-DRB1 and the range of p-values for KCNB1 from 10-4 to 10-7 were set as thresholds for significance.The result of a genome-wide association scan is consistent with previous studies , with thKCNB1  is 110,667 bp long; mRNA is 3756 bp long and encodes a protein of 858 amino acids. Position information comes from Homo sapiens chromosome 20 genomic contig, NT_011362.9. There are 36 SNPs available in KCNB1 for this dataset, rs1051295 (A/G) is located in 3'UTR and all others are located in introns. The most significant 15 SNPs from this gene in the initial scan were selected in subsequent analysis. The first principal component accounts for about 80% of variation for 15 SNPs. The proposed method was applied to these 15 SNPs and the first component , and both effects test (H1 : α2 ≠ 0 or α3 ≠ 0). In Table p-values show that the interaction-effect-only test and the proposed test are more significant than the both-effects test, and the overall strengths of these two tests are similar. p-Values of six SNPs based on the proposed test are smaller than those of interaction-only test; p-values of four SNPs based on interaction-only test are smaller than those of the proposed test; p-values of two SNPs are the same. Because of limitation of the number of permutations, p-values of three SNPs and the first principal component cannot be compared .HLA-DRB1 gene and rheumatoid arthritis is very strong. Except for some known genes, a genome-wide scan found no other genes that show obvious association. One possibility is that other genes have a weak main effect, making them hard to detect. We propose a method for case-control study that allows the main effect of the second gene to be weak relative to its interaction effect with the first gene. Using this model, we studied the association between KCNB1 and rheumatoid arthritis.The association between KCNB1 may mainly be manifested through its interaction with the HLA-DRB1 gene.The results of three tests used in this study support our assumption that the effect of M = 0) and the proposed test (M = 0.4) perform better than the both-effects test; for six SNPs, the proposed test even performs better than interaction-effect-only test.The interaction-effect only test (M value reflects the strength of main effect in the proposed model. M values of 0.1 and 0.4 represent small effect and moderate effect, respectively. Considering the moderate main effect of KCNB1, we set M value to be 0.4. The M value should be determined by researchers, every gene has different M value. A wrongly chosen M value will reduce the power in the test, so we suggest that a rough range of M values should be determined by some prior information, and then a number of interations be used to get the appropriate M value. Computing burden is a big concern in case-control study. When the number of permutations is larger than 10,000, comparisons cannot be made, so a more efficient algorithm for permutation needs to be developed. This method is more applicable in linear regression because the asymptotic distribution of the likelihood-ratio test statistic has been derived [The  derived .KCNB1 gene. Based on our analysis, the association strength between KCNB1 and rheumatoid arthritis seems to be moderate in the GAW16 Problem 1 data.Previous studies have reported association of rheumatoid arthritis to KCNB1 gene and rheumatoid arthritis. Based on results, the strength of the association is moderate. This association needs further confirmation.We used two methods, including one developed by us, to investigate the association between GAW16: Genetic Analysis Workshop 16; SNP: Single-nucleotide polymorphism.The authors declare that they have no competing interests.XX participated in the design of the study and performed the statistical analysis. KW conceived of the study, participated in its design and coordination, and helped to draft the manuscript. YZ participated in the design of the study. All authors read and approved the final manuscript.


European Americans are often treated as a homogeneous group, but in fact form a structured population due to historical immigration of diverse source populations. Discerning the ancestry of European Americans genotyped in association studies is important in order to prevent false-positive or false-negative associations due to population stratification and to identify genetic variants whose contribution to disease risk differs across European ancestries. Here, we investigate empirical patterns of population structure in European Americans, analyzing 4,198 samples from four genome-wide association studies to show that components roughly corresponding to northwest European, southeast European, and Ashkenazi Jewish ancestry are the main sources of European American population structure. Building on this insight, we constructed a panel of 300 validated markers that are highly informative for distinguishing these ancestries. We demonstrate that this panel of markers can be used to correct for stratification in association studies that do not generate dense genotype data. Genetic association studies analyze both phenotypes (such as disease status) and genotypes (at sites of DNA variation) of a given set of individuals. The goal of association studies is to identify DNA variants that affect disease risk or other traits of interest. However, association studies can be confounded by differences in ancestry. For example, misleading results can arise if individuals selected as disease cases have different ancestry, on average, than healthy controls. Although geographic ancestry explains only a small fraction of human genetic variation, there exist genetic variants that are much more frequent in populations with particular ancestries, and such variants would falsely appear to be related to disease. In an effort to avoid these spurious results, association studies often restrict their focus to a single continental group. European Americans are one such group that is commonly studied in the United States. Here, we analyze multiple large European American datasets to show that important differences in ancestry exist even within European Americans, and that components roughly corresponding to northwest European, southeast European, and Ashkenazi Jewish ancestry are the major, consistent sources of variation. We provide an approach that is able to account for these ancestry differences in association studies even if only a small number of genes is studied. European Americans are the most populous single ethnic group in the United States according to U.S. census categories, and are often sampled in genetic association studies. European Americans are usually treated as a single population , and the use of labels such as “white” or “Caucasian” can propagate the illusion of genetic homogeneity. However, European Americans in fact form a structured population, due to historical immigration from diverse source populations. This can lead to population stratification—allele frequency differences between cases and controls due to systematic ancestry differences—and to ancestry-specific disease risks –5.Previous studies have carefully analyzed the population structure of Europe –8, but hAlthough genome-wide association studies that generate dense genotype data are becoming increasingly practical, targeted association studies—such as candidate gene studies or replication studies following up genome-wide scans—will continue to play a major role in human genetics. These studies typically analyze a much smaller number of markers than genome-wide scans, making it far more difficult to infer ancestry in order to correct for stratification and identify ancestry-specific risk loci. To address this, a possible strategy is to infer ancestry by genotyping a small panel of ancestry-informative markers , and thiTo investigate whether we could identify consistent patterns of European American population structure, we analyzed four European American datasets involving a total of 4,198 samples. These samples were genotyped on the Affymetrix GeneChip 500K or Illumina HumanHap300 marker sets in the context of genome-wide association studies for multiple sclerosis (MS), bipolar disorder (BD), Parkinson's disease (PD) and inflammatory bowel disease (IBD) see . For eacWe were able to characterize the main ancestry components in the IBD dataset, because a subset of these individuals self-reported their ancestry as northwest European, southeast European or Ashkenazi Jewish see . We useE. We no. A histoTo determine whether the visually similar patterns observed in these four datasets each represent the same underlying components of ancestry, we constructed a combined dataset of MS, BD, PD and IBD samples using markers present in all datasets. The top two principal components of the combined dataset, displayed in To simplify the assessment of ancestries represented in each dataset, we discretely assigned each sample to cluster 1 (mostly northwest European), cluster 2 (mostly southeast European), or cluster 3 (which contains the great majority of self-reported Ashkenazi Jewish samples) based on proximities to the center of each cluster in FST statistics between clusters 1 (mostly NW), 2 (mostly SE) and 3 (mostly AJ), restricting our analysis to individuals unambiguously located in the center of each cluster  = 0.005, FST = 0.004 and FST = 0.009. The additivity of these variances (0.005 + 0.004 = 0.009) would be consistent with the drift distinguishing clusters 1 and 2 having occurred independently of the drift distinguishing clusters 2 and 3, as might be expected under a hypothesis of drift specific to Ashkenazi Jews due to founder effects .,11.x/(1+kers see B. Becausg HapMap  samples −6) in 2,189 samples between height and a candidate marker in the lactase (LCT) gene; this association would be statistically significant even after correcting for the hundreds of markers typically genotyped in a targeted association study . We concluded based on several lines of evidence that the association was due to stratification—in particular, both LCT genotype and height track with northwest versus southeast European ancestry. We focused our attention on a subset of 368 samples and observed that after genotyping 178 additional markers on these samples, stratification could not be detected or corrected using standard methods [To empirically test how effectively the panel of 300 markers corrects for stratification in real case-control studies, we genotyped the panel in 368 European American samples discordant for height, in which we recently demonstrated stratification . In thatLCT locus to infer ancestry and correct for stratification . Furthermore, the top two axes of variation explain 22% of the variance of the candidate LCT marker , indicating that the association of the candidate marker to height is affected by stratification. Indeed, the observed association is no longer significant after correcting for stratification . This is a consequence of the fact that grandparental origin explains only 3.2% of the variance in height and 17% of the variance of the candidate marker, both substantially less than is explained by ancestry inferred from the panel of 300 markers. These results provide further evidence that genetically inferred ancestry can provide useful information above and beyond self-reported ancestry [It is important to point out that the panel of 300 markers provides a better correction for stratification than self-reported ancestry, even for a study in which the ancestry information is more extensive than is typically available. Although the association between the ancestry .We wondered whether using only the 100 markers chosen to be informative for NW vs. SE ancestry would be sufficient to correct for stratification in the height data. The top axis of variation inferred from these markers explains 19% of the variance of the candidate marker, but only 3.6% of the variance in height. Because this axis captures most of the variation attributable to ancestry at the candidate marker, stratification correction is almost as effective as before . However, this axis is not fully effective in capturing variation attributable to ancestry in height, because it does not separate clusters 2 and 3 – we observed that samples in cluster 2 are strongly biased towards shorter height but samples in cluster 3 show no bias in height in this dataset (data not shown). Thus, although the 100 NW vs. SE markers may be sufficient to correct for stratification in some instances, associations in European American sample sets between other candidate loci and height could be affected by stratification unless the full panel of 300 markers is used. More generally, the complete panel of 300 markers should enable effective correction for stratification in most targeted association studies involving European Americans.We have analyzed four different genome-wide datasets involving European American samples, and demonstrated that the same two major axes of variation are consistently present in each dataset. The first major axis roughly corresponds to a geographic axis of northwest-southeast European ancestry, with Ashkenazi Jewish samples tending to cluster with southeastern European ancestry; the second major axis largely distinguishes Ashkenazi Jewish ancestry from southeastern European ancestry. We identified and validated a small panel of 300 informative markers that can reliably discern these axes, permitting correction for the major axes of ancestry variation in European Americans even when genome-wide data is not available. We note that while we have corrected for stratification using our EIGENSTRAT method, the panel of markers is not specific to this method, and the STRAT method  or otherOur success in building a panel of markers informative for within-Europe ancestry relied on multiple complementary strategies for ascertaining markers. All strategies were successful in identifying informative markers. We particularly emphasize the success of applying principal components analysis to genome-wide data from European American samples and selecting markers highly differentiated along top axes of variation. This strategy was the source of most of our markers, and will become even more effective as datasets with larger numbers of samples become available, enabling further improvements to the panel and ascertainment of markers to address stratification in other populations.The panel of 300 markers informative for within-Europe ancestry is practical for genotyping in a small-scale study, and permits correction for population stratification in European Americans at a very small fraction of the cost of a genome-wide scan. We envision three applications:1. The panel can be used to evaluate study design prior to a genome-wide association study. By randomly choosing a few hundred prospective cases and controls and genotyping them on this panel, one can statistically determine whether or not cases and controls are well matched for ancestry in the overall study. If they are poorly matched, then properly matched cases and controls for the study can be ascertained by genotyping all cases and all controls using this panel see .2. The panel can be genotyped in a targeted association study, such as a candidate gene study or a replication study following up a genome-wide association study, in which variants are targeted in large numbers of samples that have not been densely genotyped. The data from markers in the panel can be used to correct for stratification using methods such as EIGENSTRAT , to ensu3. The panel can be used to remove genetic outliers and assess genotyping quality of samples in a targeted association study. Although the panel was not ascertained for evaluating continental ancestry, it is sufficiently informative to identify samples with different continental ancestry . It can Though we have focused here on the importance of inferring ancestry in association studies, the panel of markers may prove useful in a broad range of medical and forensic applications.The MS dataset consists of 1,018 European American parents of individuals with MS that were genotyped at Affymetrix GeneChip 500K markers as part of a trio-design genome-wide scan for multiple sclerosis; most of the individuals (>85%) were sampled from San Francisco. The BD dataset consists of 1,727 European American controls that were genotyped at Affymetrix GeneChip 500K markers as part of a genome-wide scan for bipolar disorder; 1,229 individuals were sampled from throughout the U.S. and 498 were sampled from Pittsburgh. The PD dataset consists of 541 European Americans (270 cases and 271 controls) that were genotyped at Illumina HumanHap300 markers as part of a genome-wide scan for Parkinson's disease ; individAlthough the discrete approximation does not fully capture the continuous northwest-southeast cline described by the data, to simplify our analysis we assigned samples to three discrete clusters so as to minimize distances to centers of clusters, defined as  for cluster 1,  for cluster 2 and  for cluster 3 .In association analyses involving the MS and BD datasets, we excluded markers that had >1% missing genotypes, or failed Hardy-Weinberg equilibrium , or had a low minor allele frequency (<5%), in either the MS or BD datasets. Roughly 200,000 of the Affymetrix 500K markers remained after imposing these strict constraints. We also repeated our computations with less stringent data quality filters .Genome-wide genotype data used to ascertain markers included the MS, BD, PD and IBD datasets described above, plus three additional European American datasets: a previously described dataset of 488 samples with rheumatoid arthritis (RA) genotyped at Affymetrix GeneChip 100K markers , a datasFST, with a correction for sample size. (iii) 112 markers from regions of high excessp [excessp were identified as windows of consecutive markers with average excessp values above 0.4, 0.5 or 0.6, comparing allele frequencies in the CEU sample with the pooled YRI+HCB+JPT sample. Within each of the longest such windows, a marker was selected with the highest FST and a higher derived allele frequency in CEU than in the other populations. (iv) 30 markers that were both in the top 1% of the genome for iHH  as reported in [LCT gene and MATP, OCA2, TYRP1, SLC24A5 and MYO5A pigmentation genes [Markers were ascertained using multiple methods: (i) 185 markers highly differentiated along the top axis of variation in genome-wide datasets. Differentation was defined as the correlation between genotype and coefficient along the top axis of variation, with a correction for sample size. (ii) 300 markers highly differentiated between individuals discretely assigned to cluster 2 (SE) or cluster 3 (AJ) in genome-wide datasets. Differentiation was measured using  pexcess  data. Reorted in , and alsorted in , and alsorted in ). (vi) 3orted in . (viii) on genes ,33,7. Mahttp://www.sequenom.com/seq_genotyping.html). We designed primers using the Sequenom software, MassARRAY Assay Design. We restricted subsequent analysis to markers that genotyped successfully in >85% of samples and had 1 or fewer discrepancies between replicate samples. We excluded markers with Hardy-Weinberg P-values less than 0.01 in more than one ethnic group.The sample collection was assembled on two plates. The first plate included 60 samples from Sweden , 60 UK sThe samples were collected by Genomics Collaborative/SeraCare, as described previously . GenotypFST and FST for each marker passing quality control filters. Due to the limited representation of clusters 2 and 3 on the first plate and to minimize differential bias and differences in quality control filters between plates, only the second plate of European samples was used. We first selected 100 markers with the highest FST and subsequently selected 200 markers with the highest FST, and required that each marker be located at least 1Mb from each previously selected marker. The number of markers from each ascertainment source that were included in the final panel of 300 markers is reported in Defining clusters 1, 2 and 3 as described see , we compFST values for the panel of 300 markers are biased upward since they were computed using the same samples that we used to select the 300 markers. We computed unbiased estimates of the value of FST for these markers by dividing the samples into four quartiles, with the same distribution of ancestries in each quartile. For each quartile, we selected 300 markers as described above using only samples from the remaining quartiles, then used samples from that quartile to compute unbiased FST values, and averaged the results across quartiles. FST computations were performed using the EIGENSOFT software, which fully accounts for differences in sample size [ple size .We applied the EIGENSTRAT program  with defhttp://genepath.med.harvard.edu/~reich/EUROSNP.htm (panel of 300 markers).Figure S1Samples are labeled based on cluster assignments inferred from combined dataset (see text).(1.7 MB PDF)Click here for additional data file.Figure S2(96 KB PDF)Click here for additional data file.Table S1(35 KB DOC)Click here for additional data file.Table S2(31 KB DOC)Click here for additional data file.Table S3(32 KB DOC)Click here for additional data file.Text S1(43 KB DOC)Click here for additional data file.


Genome-wide homozygosity estimation from genomic data is becoming an increasingly interesting research topic. The aim of this study was to compare different methods for estimating individual homozygosity-by-descent based on the information from human genome-wide scans rather than genealogies. We considered the four most commonly used methods and investigated their applicability to single-nucleotide polymorphism (SNP) data in both a simulation study and by using the human genotyped data. A total of 986 inhabitants from the isolated Island of Vis, Croatia  were included in this study. All individuals were genotyped with the Illumina HumanHap300 array with 317,503 SNP markers.Simulation data suggested that multi-point FEstim is the method most strongly correlated to true homozygosity-by-descent. Correlation coefficients between the homozygosity-by-descent estimates were high but only for inbred individuals, with nearly absolute correlation between single-point measures.Deciding who is really inbred is a methodological challenge where multi-point approaches can be very helpful once the set of SNP markers is filtered to remove linkage disequilibrium. The use of several different methodological approaches and hence different homozygosity measures can help to distinguish between homozygosity-by-state and homozygosity-by-descent in studies investigating the effects of genomic autozygosity on human health. A number of studies in plants and animals have suggested that an increased level of genome-wide homozygosity is expected to have negative effects on health, fitness and survival in a wide range of environmental conditions -3. TheseIn contrast, studies on the effects of homozygosity levels on human biology and health are quite rare. One of the first accounts of the beneficial effects of heterozygosity was proposed by Penrose, who suggested that increased heterozygosity may have beneficial effects on a large number of human traits . SubsequSeveral different methods for the estimation of individual genome-wide homozygosity have been developed in both animal and human genetics. The most basic measure  is multilocus heterozygosity, defined as the proportion of heterozygous loci in all genotyped loci of an individual . InternaThe aim of this study was to compare and validate the results obtained by four different methods to estimate individual homozygosity-by-descent (HBD) using human genome-wide SNP scan data from a Croatian isolated population.This study was based on data obtained from a large genetic epidemiology project that is being carried out in the isolated islands of Croatia. The initial goals of this project were to describe and understand human variation by investigating isolated communities ,24, and A total of 986 inhabitants of the Croatian Island of Vis were included in this study. The population on the island of Vis has been well characterized in terms of demographic and population genetic events , and thea-priori expectations of expected genome-wide homozygosity levels. This was based on a combination of information from genealogical and demographic sources , based on the self-reported information and parish records. No inbreeding loops suggestive of a parental relationship of first-cousin F = 0.0625) or closer were seen in the genealogical data, confirming the strong influence of the local Catholic Church on the avoidance of inbreeding . Despite5 or closes Table . The higes Table .DNA was obtained from blood samples provided by all examinees, which were frozen on the site and then sent to the lab for DNA extraction. Extraction was performed using Nucleon kits , at the Institute for Anthropological Research in Zagreb, Croatia. A total of 986 individuals were genotyped using the Illumina HumanHap300 (v1) array, with a total of 317,503 SNP markers.http://pngu.mgh.harvard.edu/purcell/plink/].We excluded 63 individuals because of genotyping rate lower than 95%. We then removed 864 markers based on departure from Hardy-Weinberg equilibrium , 17,856 markers due to low call rate (<95%) and 10,552 markers due to low minor allele frequency (MAF < 0.05). Additionally, only the autosomal markers for which a genetic location was available from Illumina were included. This left a total of 274,577 SNPs and 923 samples. The quality control procedure was performed with PLINK, version 1.01 .For comparison purposes, we also computed a single-point version of FEstim (referred to as FEstimSPT). At each marker locus, it uses the same modelling as FEstim but ignores marker dependencies (i.e. the hidden Markov structure). FEstim version 1.2 was used for computations [available upon request: celine.bellenguez@inserm.fr]. MASEL has been applied in the framework of linkage analysis but the issues in terms of LD are the same here. We considered two different LD thresholds: r2 ≤ 0.1 (M0.1) which selected a set of 49,987 SNPs  and r2 ≤ 0.05 (M0.05) which selected 16,339 SNPs (~6%). LD was estimated from HapMap CEU data.The multi-point approach, FEstim assumes linkage equilibrium and may provide inflated inbreeding coefficient estimates if this assumption is violated . Haplotyhttp://www.stat.washington.edu/thompson/Genepi/pangaea.shtml]). We used the marker map from a 10K Affymetrix chip with linkage disequilibrium removed, leaving a total of 4,849 SNPs in the analysis dataset. A total of 10,000 replicates were performed. Because the data were simulated, we could determine which loci were homozygous-by-descent (HBD) and which were not. This allowed us to compute the true proportion of loci that were HBD and hence the true inbreeding coefficient of an individual. For comparison purposes, the negative values reported by FPLINK and FADC were set to zero.In order to investigate the correlation between some of these methods, we simulated genotypes for the offspring of first cousins, second cousins and third cousins by gene-dropping on the genealogy , with the threshold for statistical significance set at P < 0.05.PLINK, FADC and FEstimSPT) were comparable to one another, but always lower than that of FEstim  yielded the highest correlation coefficients of true homozygosity-by-descent with FEstim; correlation coefficients of true HBD for the single-point methods  were not substantially affected by changes in marker selection, while both MLH and FEstim were. Notably, the average value of FEstim calculated with the less restrictive MASEL selection threshold (M0.1) was twice greater than the more restrictive selection, illustrating the inflation in inbreeding estimates in the presence of LD using multi-point approaches. As for the MLH estimates, their values increased with marker selection sets since MASEL always selects the most informative markers (highest heterozygosity). There were no substantial differences in the standard errors or in the ranges between the same methods, when used with different thresholds for LD.On the Vis island dataset, the mean heterozygosity for the entire sample and full marker set was 0.354, suggesting that 35.4% of genotyped SNP markers in full marker count were heterozygous and 64.6% homozygous  are highly correlated to one another, and also highly correlated to MLH . The multi-point method FEstim, which takes neighbouring marker information into account, was the only method that managed to clearly differentiate between groups of various degrees of endogamy. This is in agreement with the fact that multi-point approaches should provide more HBD information, as suggested by the simulation study. In the presence of linkage disequilibrium, one has to use some care in applying multi-point methods, by either removing markers in LD (as we did with MASEL) or by including LD in the data modelling. The results of this study suggest that even a small amount of LD may affect the results of multipoint homozygosity methods, as seen in the difference between FEstim measures that were based on the MASEL 0.1 and 0.05 cut-offs. The correlation between very restrictive and less restrictive marker selection suggested that inbreeding estimates in more inbred individuals will be similar, while in less inbred individuals the presence of LD will tend to overinflate inbreeding estimates  information only and is therefore of limited use in inbreeding and admixture studies as has been previously reported ,6,14. ThResults from some animal studies have suggested that molecular-marker based estimates may not be the optimal way of measuring genome-wide heterozygosity , as thesOne of the problems that may arise in individual genome-wide homozygosity estimation with methods that give more weight to rare alleles, is the introduction of foreign individuals into an isolated population, sometimes referred to as "sample contamination" . This isADC and FEstim values . Other groups had decreasing homozygosity values, but notably FEstim managed to distinguish between groups V and VI, indicating that only this method is capable of detecting cryptic inbreeding .Demographic history and population genetic structure may have a strong effect on individual genome-wide homozygosity values in a population. We observed a gradual decrease in average genome-wide homozygosity, which was in line with expectations based on demographic history and decreasing levels of endogamy, as previously reported . The mosThe shortcomings of this study include the low sample size for some groups . Although humans generally experience lower inbreeding coefficients than many plant and animal species, it is very interesting to explore patterns of cryptic, more ancient inbreeding in humans, which may have strong effect on some human traits . AnotherPLINK and FACD) do not measure much more than the simple proportion of heterozygous loci (multilocus heterozygosity), but they do have the advantage of not being sensitive to the presence of linkage disequilibrium. Multi-point FEstim is the best approach tested here for inbreeding estimation from genetic markers (i.e. the closest to the true HBD information) when there is no LD present. It remains unclear which is the best method when there are dense markers with high LD. The next substantial advancement in the study of genome-wide homozygosity levels is likely to be based on fully sequenced human genomes, providing an even more precise estimate of individual genome-wide homozygosity and its distribution across the genome.This study provides the most comprehensive comparison of different genome-wide homozygosity measures to date. Our findings suggest that the most commonly used single-point methods (FOP, ALL, HC, AW and IR conceived the study; CH and VV performed laboratory procedures and data cleaning; IR, OP, IK and VS performed the sampling and organization of field work; OP and ALL drafted the manuscript; ALL, CB and OP wrote the scripts for the homozygosity estimation; UG, JFW, RM and AW contributed to the idea and provided conceptual ideas for the initial draft broadening and adding the crucial study elements; OP and ALL performed the statistical analysis. All authors read and approved the final version of manuscript.


Overall, Bayesian latent class analyses provided no obvious improvement over maximum-likelihood estimation. However, our results may not be able to be generalized due to the large effect simulated in the human leukocyte antigen-DR locus.Although rheumatoid arthritis, a chronic and inflammatory disease affecting numerous adults, has a complex genetic component involving the human leukocyte antigen region, additional genomic regions most likely affects susceptibility. Whole-genome scans may assist in identifying these additional candidate regions, but a large number of false-positives are likely to occur using traditional statistical methods. Therefore, novel statistical approaches are needed. Here, we used a single replicate from the Genetic Analysis Workshop 15 simulated data to assess for marker-disease associations in 1500 rheumatoid arthritis cases and 2000 controls on chromosome 6. The statistical methods included a maximum-likelihood estimation approach and a novel Bayesian latent class analysis. The Bayesian analysis "borrows strength" from multiple loci to estimate association parameters and can incorporate differences across loci in the prior probability of association. Because of this, we hypothesized that the Bayesian analysis might be better able to detect true associations while minimizing false positives. The Bayesian posterior means for the log alleleic odds ratios were less variable than the maximum likelihood estimates, but the posterior probabilities were not as good as the simple  Rheumatoid arthritis (RA) is the most common cause of inflammatory polyarthritis in adults . This chAdvancements in genotyping technology have facilitated the ability to generate large amounts of genetic data. The large numbers of single-nucleotide polymorphisms (SNPs) genotyped by whole-genome scans may overwhelm conventional statistical approaches such as maximum-likelihood estimation (MLE). A few novel statistical approaches reducing the dimensionality of large data sets and detecting the structural relationship between variables have been described elsewhere -5. SimplWe randomly selected one RA case from the affected-sibling pair (ASP) in the first replicate of the GAW15 simulated data for Problem 3. After selecting all of the controls, our final data set included 1500 cases and 2000 controls. In order to ensure a significant finding we reviewed the answers to the simulated data prior to our analyses. Since the strongest signal for RA was simulated to the HLA region on chromosome 6, we limited our analyses to the dense genotyping for chromosome six. In total 17,820 SNPs were simulated on chromosome 6, yielding an average inter-marking spacing of 9586 base pairs. This corresponds to the density one would expect from a genome-wide 300,000 K SNP set.i, we modeled the 2 × 2 allele-by-disease status table using a hypergeometric likelihood with OR = exp[βi]  . The priβij is ~N and Xi is binomial or trinomial  with probabilitieswhere X > 0) and non-associated  classes. First, we naively assume all non-null loci are derived from the same distribution (J = 1). Second, we assume some markers are positively associated with the outcome, i.e., OR > 1, and others are inversely associated with the outcome, i.e., OR < 1 (J = 2).We considered two ways to separate the markers into associated  and σj ~ Inv-χ2. The hyperparameters we used were,A vast majority of the disease-marker associations will be null, so we used conjugate priors to update is issue . The conJ = 1for J = 2and for π = . For example, when J = 1, π ~Dirichlet . To account for differences in prior probability of association, we also varied Dirichlet hyperparameters across regions. We selected three candidate regions with identical, high prior probability of association after reviewing the answers for the simulated data and performing a literature search on RA. The HLA region, where the causal SNP was simulated, and two upstream regions from the literature search were up-weighted. The two upstream regions contained several genes, including WISP3 and VIP, with a potential biologic role in RA [π ~Dirichlet in non-candidate regions and π ~Dirichlet in candidate regions. In the interest of time and to reduce the computational burden we chose every fifth SNP from the dense data on chromosome 6, for a total of 3564 markers. Of these, 62 were in the HLA region and 22 in each of the two upstream regions. In principle the latent class analysis has no limit as to the number of SNPs that can be analyzed, and given optimized code, more computing resources and more time, an analysis of 300,000–500,000 markers from a genome-wide scan is feasible. Each analysis presented here took approximately 12 hours on three nodes (each with two 3.2 GHz CPUs), so a scan with 300,000 markers would take less than five days on a 30-node cluster.We put Dirichlet priors on chlet 1, 99. To acle in RA . We fixeμj, σj, and πj could be updated by directly sampling from their conditional posterior distributions. The parameters β and X were simultaneously updated using the Metropolis-Hastings algorithm.We used three parallel Gibbs sampling chains with 3000 iterations each in order to fit the model. The parameters β) estimates from MLE are more dispersed than the Bayes estimates. In Figure J = 1 and J = 2 (black). The top panels represent the model without weights and the bottom panel represents the model with weights. The probabilities for a true-positive result are nearly evenly dispersed across the markers for the Bayes estimates, whereas the MLE probabilities peak near the HLA region and decrease substantially elsewhere  and Bayes (black) models in Figure β values are given for four regions and a combined region. Although the estimates were nearly equal for the non-candidate region, the average β values for the HLA region differed slightly. The Bayesian models produced nearly equal values (-0.305 with priors and -0.329 without priors), whereas the MLE average was greater (-0.575). For candidate region 2, the estimates were nearly equal across the models, but the average β values differed for candidate region 1. The average β from the MLE model was greater in candidate region 1.The average estimated log allelic odds ratios varied slightly across the MLE and Bayes models Table . The avep-values from maximum-likelihood based tests. We applied this latent class analysis to the GAW15 simulated chromosome 6 data, but found that the latent class models provided no obvious improvement over MLE. However, our overall results may not be generalizable due to the large simulated effect in the HLA-DR locus.We developed and implemented a Bayesian latent class analysis because we hypothesized that by "borrowing strength" across multiple loci and incorporating prior probabilities of association, such an analysis might be more sensitive and specific than β is less variable than the MLE. This suggests that if researchers are choosing markers to follow up on the basis of estimated effect size, some sort of smoothing procedure could be useful. MLEs for rare SNPs may be very unstable. Smoothing MLEs to a group mean will account for differences in information across SNPs and hence could reduce the false-positive report probability [As can be seen in Figures bability .X > 0) from the latent class analyses was not as good as simple p-values distinguishing a signal from a non-signal. Negative consequences, although minor, occur when up-weighting regions that are not true candidates , is much too large for a majority of the loci. Setting f(β) equal to point mass at 0 may be too stringent. One potential solution is to allow for some noise to distinguish signals near, but not directly at, zero from true effects. Additionally, it appears the priors are overwhelmed by the large amount of data (loci). Because a majority of the loci are likely null, the prior sample size  could be increased. Other drawbacks to our implementation include the facts that the ratio of priors comparing candidate regions to non-candidate regions is fixed, and only loci in regions sharing the same prior contribute to estimating the posterior odds of association in those regions. A hierarchical Bayesian approach may be more appropriate because it estimates both baseline and relative odds of association from the data, rather than fixing them [There are clearly drawbacks to the latent class approach as we have implemented it. The posterior probability of belonging to a non-null class, Pr(ing them . By not The author(s) declare that they have no competing interests.


T2 test is best for normally distributed data, but it can lose considerable power when normality is not met. To achieve satisfactory power in most cases, without compromising the overall significance level, we propose to adopt a two-stage adaptive analysis strategy – several statistics are compared on a portion of the samples at the first stage and the most powerful statistic is then used for the remaining samples. We evaluated this procedure by mapping the quantitative trait locus of IgM with the simulated data in Genetic Analysis Workshop 15 Problem 3. The results show that the gain in power of the two-stage adaptive analysis procedure could be considerable when the initial choice of test statistic is wrong, whereas the loss is relatively small in the case that the optimal test chosen initially is correct.Genetic association studies offer an opportunity to find genetic variants underlying complex human diseases. Various tests have been developed to improve their power. However, none of these tests is uniformly best and it is usually unclear at the outset what test is best for a specific dataset. For example, Hotelling's  T2 test for QTL linkage disequilibrium (LD) mapping, which is uniformly the best test for normally distributed data. However, if the assumption of a normal distribution is not met, T2 may lose considerable power. When the trait distribution is unclear, some nonparametric tests may be preferred because they are only slightly less powerful than T2 when the trait is normally distributed, but much more powerful than T2 in some cases of non-normality. In general, it is unclear what test is the best when the trait distribution is unknown. Some investigators only report the most significant result from several statistics, but the type I error rate cannot be properly controlled when this is done. It is also not wise in this situation to use an approach such as the Bonfferoni method to control the type I error rate because the various tests are usually highly correlated and therefore the result will be overly conservative.Association studies currently offer an exciting approach to mapping complex quantitative trait loci (QTLs). Wallace et al.  recentlyTo achieve satisfactory power in most cases, without compromising the overall significance level, we consider adopting a two-stage adaptive analysis strategy: several statistics are compared on a portion of the samples in the first stage and the statistic that is found to be most powerful is then used for the remaining samples. Previously, two-stage strategies have been adopted in genetic association studies to reduce the cost of genotyping ,3 or thep-values. For the procedure of combining these p-values to be valid, however, we need to specify before the analysis which statistic will be used to obtain the p-value (p1) from the exploratory stage in the combination. The p-value from the second stage (p2) is calculated based on the most powerful statistic found at the first stage. Under the null hypothesis, each p value is, at least asymptotically, distributed uniformly on U. The final decision then depends on a combining function f. The most common such function may be Fisher's combination test [The procedure examines the power of various statistics using a portion of the data in an exploratory first stage and then applies this most powerful test to the rest of the data in the second stage. The statistics from the two stages are combined to make full use of the information. This approach of combining the results of the two stages is equivalent to a more general method of combining ion test , which iχ2 with 4 degrees of freedom. Another example is the weighted inverse normal method,where under the null hypothesis Fisher's statistic will be distributed as a wi < 1 and w12 + w22 = 1. This statistic will be distributed as a standard normal distribution.where Φ is the cumulative distribution function of a standard normal distribution, 0 <p2, we have to estimate the power of the various statistics at the exploratory first stage. Traditional power calculation methods require the trait distribution to be known, which is not the case here. A bootstrap method of using the data from the exploratory stage can be adopted to approximate the power [p-values by employing a permutation procedure to generate the null distribution of the statistic that is used for a test. Here, on the other hand, we want to estimate the power of a statistic, and for this we need the distribution of the statistics under the alternative hypothesis; a permutation procedure cannot be directly applied for this purpose. Let the trait values of individuals with genotype g be denoted xg, where g = 0, 1, 2 for an additive SNP marker and g = 0, 1 for a recessive/dominant marker. For this example, we assume a dominant model for the rarer allele. We denote the sample size for each genotype ng. We assume the distribution of trait values for different genotypes have similar shape, but the locations of the distributions are shifted by dg. The hypothesis to detect association between a marker and the trait is then defined as H0:d = 0. The power function of the statistic T for d = δ at the significance level α is then given by P The method of Collings and Hamilton [P by a nonparametric bootstrap procedure is as follows:To obtain he power ,7. The bHamilton  to approg, a random sample of n2 <n0, trait values is drawn with replacement. The sampled trait values are denoted Xgb = . A simulation sample of trait values, Ygb, is then obtained by adding Xgb to , where 0 is a row vector of n0 elements each of which is 0 and δ is row vector of n1 + n2 elements, each of which is δ. The corresponding genotype groups are set to be Gb = .1. For each genotype group Ygb and Gb, and the corresponding p-values (pgb) are recorded.2. Different statistics are calculated on the simulated sample values B times. The estimated power function of 3. Steps 1 and 2 are repeated 4. Finally, we estimate the power of the different statistics using the weighted average estimates of the different genotype groups, given by π1), different methods of combining tests  and two statistics . These T2 turns out to be severe. The two-stage analysis obtains substantial gain in power by choosing the right statistic for the second stage from "learning" at the exploratory stage. This analysis shows that using 30% of the samples at the first stage gives a good prediction of the better analytic method to use in terms of power. The results also show that the difference between the two methods of combining p-values is small.From Table T2 has been proved to be a powerful statistic, even with sample selection. However, the advantage of T2 depends on the trait distribution. On the other hand, although a nonparametric statistic is not the most powerful one when normality of the trait holds, it usually works well. So it is reasonable to consider combining the p-value of a nonparametric statistic from the exploratory stage with the p-value of the most powerful statistic for the second stage.Two-stage designs have been applied to large-scale genetic association studies to substantially reduce genotyping cost while maintaining power. In addition to the knowledge of which markers are promising, we can obtain information about the distribution of the phenotype based on the data from the exploratory stage. This knowledge is useful for the choice of a statistic to use at the second stage and can therefore lead to a considerable gain in power. In our analysis, we evaluated this idea by considering just two statistics. Hotelling's The idea of a two-stage analysis can be further generalized in genetic association studies. Because LD patterns vary greatly, it is often unclear whether a single-marker analysis or a multiple-marker analysis or a haplotype-based analysis is most powerful for a specific data set. Further work on developing a data-driven adaptive procedure to choose the type of analysis to perform on the second stage data would be potentially useful.The adaptive two-stage procedure can lead to considerable gain in power by guiding the choice of a test based on the knowledge learned from an exploratory stage. At the same time, the type I error rate can be well controlled.The author(s) declare that they have no competing interests.


Transmissible Spongiform Encephalopathies (TSEs) are a group of progressive fatal neurodegenerative disorders, triggered by abnormal folding of the endogenous prion protein molecule. The encoding gene is a major biological factor influencing the length of the asymptomatic period after infection. It remains unclear the extent to which the variation between quantitative trait loci (QTLs) reported in mouse models is due to methodological differences between approaches or genuine differences between traits. With this in mind, our approach to identifying genetic factors has sought to extend the linkage mapping approach traditionally applied, to a series of additional traits, while minimising methodological variability between them. Our approach allows estimations of heritability to be derived, as well as predictions to be made about possible existence of genetic overlap between the various traits.Our data indicate a surprising degree of heritability (up to 60%). Correlations between traits are also identified. A series of QTLs on chromosomes 1, 2, 3, 4, 6, 11 and 18 accompany our heritability estimates. However, only a locus on chromosome 11 has a general effect across all 4 models explored.We have achieved some success in detecting novel and pre-existing QTLs associated with incubation time. However, aside from the general effects described, the model-specific nature of the broader host genetic architecture has also been brought into clearer focus. This suggests that genetic overlap can only partially account for the general heritability of incubation time when factors, such as the nature of the TSE agent and the route of administration are considered. This point is highly relevant to vCJD  where the route of primary importance is oral, while the QTLs being sought derive exclusively from studies of the ic route. Our results highlight the limitations of a single-model approach to QTL-mapping of TSEs. Prnp locus has long been established as the major genetic determinant of Transmissible Spongiform Encephalopathy (TSE) incubation time, however its effects do not fully explain the variance associated with this trait, thus other critical factors must also play an important role in the aetiology of these disorders PrnpThe The contrasting genetic approaches of linkage and association are complementary and normally explore competing hypotheses regarding the underlying genetic architecture of disease progression. They provide a means by which to interrogate the genome for both common variants of small effect and rarer variants of intermediate/large effects. Current evidence suggests that the two models are not mutually exclusive and may coalesce Prnp while, the variability between the loci reported gives some indication of the scale on which influential variants may exist. However still this leaves little objective rationale for distinguishing the more empirically relevant loci from those whose effects are confined to only certain genetic backgrounds.Methodological variability precludes most meaningful comparisons between previous linkage efforts, as studies differ by either the chosen genetic background, prion source, or the host adaptation status of the agent ic) transmission, which involves the introduction of prion material directly to the brain. This paradigm may model infection arising from exposure to larger peripheral doses of infectivity, as the relatively long period of replication in the spleen that would normally be seen in peripheral administration is bypassed in this instance, suggestive of direct neuroinvasion . The question of whether ip transmissions have a unique genetic basis or one that may be shared across routes and between agents has to date not been addressed. A comparative approach to defining genetic commonality between traits may therefore be useful for classifying QTLs according to their empirical relevance to TSE transmissions.In all, 5 previous linkage studies have implicated QTLs on 12 different chromosomes s7) prion allele. This effectively eliminates genetic variation at this locus from the study. The recombinant inbred (RI) method exploits the fact that the individuals within each line are genetically identical, hence trait values are based on group means as opposed to individual measurements. This makes QTL detection feasible even for traits with low heritability, due to a compensatory gain in the accuracy of phenotype measurement.The BXD set of recombinant inbred mice were the genetic cross chosen for this study. TSE incubation QTLs have not been explored in this particular background, therefore it is of interest to determine whether QTLs identified in previous studies could also be detected here. The BXDs derive from an F2 intercross of C57BL/6 and DBA/2J progenitors Additionally, the integrative ability of the RI approach can be exploited in order to produce genetic correlations between traits which allow both estimations of genetic overlap and heritability of traits. With this in mind the goals of the work were as follows:To determine the total contribution made by genetic variants of small effect (QTLs) through estimations of trait heritability.To estimate possible overlap between the genetic architectures regulating different transmissions by genetic correlation.de facto experimental model for vCJD.To identify new QTLs for different treatment conditions, including the transmission of natural BSE, the To provide validation of any previously identified QTL candidates.Raw phenotype data for the BXD lines and the progenitor strains C57BL/6 and DBA/2J are presented in ic, (ip). Internal reliability was assessed from BXD incubation period data using the Split-Half method The lowest overall variances are seen in Me7 ic,   to 0.60 for Bovine Spongiform Encephalopathy given ic (BSE ic). This suggests between 30 to 60% of phenotypic variance may be due to variation in genes. The upper limit of 0.6 is somewhat surprising given that direct effects from Prnp (the major genetic locus for the disease) have been excluded by experimental design. The extent to which variance between mouse strains (on which these estimates are based), may be impacted on by factors pertaining to litter or maternal rearing differences is beyond the scope of this study. Such interference may occur by way of various epigenetic processes. These may include the chemical modification of DNA (genomic imprinting) or interference of messenger RNA. The reader should refer to The heritabilities shown in exceed the major locus effect of Prnp. However neither the existence nor involvement of trans-acting regulators of Prnp,  can be ruled out based on our data.Broadly speaking, the range of heritabilities shown in Genetic correlations were performed in order to investigate the possible involvement of QTLs across multiple transmission models.The correlations in ip or the ic route reaches 0.52, p<0.05 (Me7 ic versus BSE ic) and peaks at 0.67, p<0.05 (for BSE ip versus Me7 ip).Some comparisons reach statistical significance. For example, the correlation between traits exclusively involving either the The latter correlation in particular suggests that a partially conserved genetic infrastructure regulates transmission through the ip route and seemingly provides evidence of a dynamic effect across a range of agents, which encompass differences in host-adaptation.ic and BSE ip  contrasts with the low level of correlation found for Me7 ic and Me7 ip (r = 0.38). The case for genuine disparity between the levels of genetic conservation across routes for natural and host-adapted isolates would benefit from further replication of these findings. Curiously, a greater degree of overlap (r = 0.6) is observed between Me7 ic, and BSE ip (p<0.05) than exists between the two traits involving Me7. This is notable as it suggests that tight conservation of the supporting architecture can potentially transcend seemingly random differences in route and agent. However it is also surprising, given that the relatively high correlation is found to be unique when the reciprocal comparison (BSE ic vs Me7 ip) is made (r = 0.29).A strong correlation between BSE ic and BSE ip. Moreover, due to the absence of a cloned BSE isolate (such as 301C) from this study, we have been unable to discern whether the BSE correlation effect is a pre or post species barrier phenomenon.Unfortunately the study design employed is not well-suited for discerning what (if anything) is so special about the combination of Me7 Despite the high level of statistical significance achieved the level of genetic overlap estimated for BSE traits is far from perfect. The large number of QTLs likely to be operating on this complex trait dictates that even smaller deviations from a perfect correlation could be significant from a biological perspective.Loci meeting nominal significance criteria were interrogated for superimposed effects from other background loci . Results for the composite interval mapping (CIM) and simple interval mapping (IM) are given in The number of QTLs found exceeds theoretical expectation as, only one false positive is anticipated per genome-wide scan at the suggestive level ic; Me7 ic (chromosome 4)BSE ip; Me7 ic (chromosome 6)BSE ip; BSE ip (chromosome 6)Me7 Though the possibility of genetic heterogeneity within QTLs is not excluded, the data suggest correspondence between these results and the empirical scheme of genetic overlap outlined by ip) is actually derived from the lowest IM score shown in this table (ie. 8.1). All loci remained significant after Bonferroni multiple testing correction was applied to CIM scores.The refining effect of CIM, when applied to simple IM data is also demonstrated in The strongest QTL candidates from the initial BXD mapping data were selected for confirmatory follow-up in an F2 population. The F2 sample were derived from an identical genetic background to that of the BXDs. Transmissions of TSE material were carried out using the same combinations of agent and route described for BXDs see . Incubatr between BXD and F2 trait means is 0.813 (P = 0.049). Additionally, trait means follow identical rank order to those obtained in BXDs.Data in The level of correlation found suggests that for TSE incubation periods, the comparatively lower phenotyping accuracy of F2s can be overcome with a sufficiently large F2 cohort. Such a strategy may be attractive for avoiding problems associated with breeding and maintaining a large RI mouse colony.ic and Me7 ip transmissions at the P<0.01 level. BXD traits values are derived from strain means thus, given the high reliability scores associated with these values (data not shown), it is more likely that individual discrepancies are the result of greater phenotyping error among F2s.Despite the overall similarity between BXD and F2 mouse panels, individual discrepancies are noted: The panels differ for Me7 ic is the only trait for which QTLs are not detected, this reflects the low phenotypic variance observed in F2s. In the case of F2 interval mapping the effects of composite mapping on linkage peaks is much less dramatic than is observed in the BXDs. CIM should ideally be performed after ensuring that marker coverage is sufficient to provide an unbiased estimation of background effects. However this study has only tested those markers chosen to flank BXD-mapped QTLs.QTLs reaching minimum suggestive (p = 0.63) significance (composite model) are presented. In all, nine QTLs reach our significance criteria, . As in tQTLs on chromosomes 1, 2 and 10 are linked with more than one trait. These loci demonstrate a corresponding level of allelic heterogeneity. This, along with the observation of genetic correlations , supportic and ip indicated in ip transmissions that extends to natural (BSE) and host-adapted (Me7) isolates. This is contrasted against QTLs on chromosomes 4 and 6, which show specific influences on BSE ic.The prevailing BXD account of genetic overlap between Our primary criteria for confirming (ie. replication) is contingent on synchrony between BXD and F2 loci in terms of both statistical significance and allelic direction.ic was replicated. Failures are mostly due to an inadequate significance level being attained.ic models, but falls short of our criteria, due to allelic discrepancies. As discussed above, this too may be a symptom of including a small and biased pool of markers in the background-effects model. In order to circumvent the effect of such constraints to QTL detection, the empirical importance of QTLs effect across BXD and F2s was also explored by meta-analysis.Loci such as chromosome 4 make for difficult interpretation of the data, as the locus is clearly implicated by both BXD and F2 BSE p values. The random effects model places greater emphasis on the consistency of effect sizes between studies. Full meta-analysis results are shown in r) are provided, along with standard errors and significance. Data are initially combined between BXDs and F2s (p 8.0×10−4 (suggestive) and p 2.6×10−5 (significant).Meta-analysis is used in the context of this study to estimate the overall influence on incubation period of candidate regions under different experimental treatments. Our methodology, an implementation of the Random Effects model  and F2s  before a and F2s . One-tai−4) significance implies relatively low genetic heterogeneity between effects found for BXDs and F2s. This further corroborates the phenotypic similarity suggested by The high frequency of loci reaching suggestive (p = 8.0×10−5). These are:The application of a random effects meta-analytical method to BXD/F2 transmission data has resulted in ten traits meeting minimum criteria for linkage. Of these, three reach genomewide significance . This locus also achieves the highest overall significance, at 10−8. The ten hits are distributed across six intervals and apply to three out of four traits analysed  is found for Me7 ic, a trait from which the lowest phenotypic variations were derived. The possibility exists that statistical significances associated with the trait Me7 ic merely reflect this low variation across BXDs and F2s given the emphasis of random effects methods on effect homogeneity. However, we fail to find evidence that the group of uncombined effect sizes found for this trait differs significantly from other traits analysed, Ptwo-tailed = 0.927 (T-test).The strongest overall effect is found at chromosome 4 . Additional significant effects are observed for ic and Me7 experiments (P = 2×10−7 and 7×10−7 respectively). Effects for BSE reach suggestive (P = 1×10−4). ip transmissions show only a trend towards significance (P = 0.002). This is due to the larger standard errors associated with these estimated effects. All are consistent in direction, with reductions in disease progression (which corresponds with lengthening incubation time) mediated by DBA alleles.The rate of linkage at any level of significance diminishes as traits are combined , thus wer2  acquired TSE (2narrow senseh range  = 0.3 to 0.6). As in Prnp and suggest a cumulatively modest contribution of non-Prnp effects. We demonstrate that they may nevertheless outweigh those of the only known major locus, in response to varying the parameters explored by this study.The heritability of age at onset and death for the human r 0.813, p = 0.049). In this instance, the use of the expanded BXD set now available was not possible, but may well have resolved such curiosities as a recurrent but unconfirmed locus on chromosome 10 for BSE ic (BXD), Me7 ip (F2), and BSE ip (F2). Such patterns may not occur randomly and may actually reflect additional complexity at these loci. Evidence that the distribution of BXD and F2 QTLs in our data is non-random derives from the fact that:Our QTL data, only partially justifies our findings in relation to heritability, thus we acknowledge and draw further attention to the many possible sources of bias on which such estimates may rely A combination of stringently employed mapping and meta-analytical methodologies have yielded many pre-existing QTLs known to be involved in controlling prion incubation times.a priori expectations based on extrapolated BXD data (P2Yates corrected χ<<0.0001) and argues that the methodological procedures in the BXD analysis offer sufficient protection against Type 1 error in this study. This implies enrichment for genuine QTLs in BXD and F2 data as well as further complexity in the mechanism underlying these QTLs. That previously reported QTLs were detected by this study goes some way to reaffirming the validity of a novel suggestive (p<8×10−4) locus on chromosome 1 (35–62 cM) and a significant (p<2.6×10−5) locus on chromosome 18 (4–17 cM).There are a high number of F2 QTLs in relation to the much smaller hypothesis region tested . The F2 number is significantly higher than ic that reached the genomewide threshold for suggestive significance (p = 0.63). That this locus did not survive the subsequent random effects meta-analysis procedure merely emphasises the heterogeneity between the BXD and F2 estimates and does not contradict the initial evidence suggesting linkage at this region. The chromosome 2 QTL lies approximately 50 centiMorgans (cM) upstream of Prnp (located at 75 cM). Independence of the chromosome 2 locus from Prnp is assumed. Among the loci identified for ic transmissions are a number of familiar linkage regions,  previously linked to this route F2 mapping was, by itself, able to reproduce a locus on chromosome 2 specific to BSE We are the first study to attempt to quantify the genetic basis of aetiological overlap governing different routes and agents. The overall implication is that any lingering uncertainty about the identity of risk variants in vCJD may be best tackled using mapping strategies that specifically target the more relevant oral route. Reassuringly, Prnp) and that of its paralogue Doppel (Prnd), although sequence symmetry between progenitor strains at this locus rules out effects from within either gene. We are the third group to independently report the existence of QTLs for prion disease on chromosome 11. Previous studies have already established this chromosome as a good linkage candidate. Crucially however, our meta-analysis data attaches empirical significance to this locus. This suggests wider involvement across a variety of other transmission types not characterised previously. Candidate-based explorations of the region have so far failed to reveal the origins of such effects Our data represent the third independent report of a distinct TSE locus on chromosome 2 . The linic route Lloyd et al Previous studies show a bias towards C57-derived genetic backgrounds. Of the QTLs shown in STMN2 and RARB. In addition, previously uncharacterised variants have also been discovered within PRNPRARB and THRB. The other genome-wide significant finding in this study highlighted a locus for orally acquired TSEs (vCJD and Kuru), using meta-analysis. Underlying genetic heterogeneity found between TSE categories in this study mirrors our own findings. We assume this to reflect that factors such as agent, dose and route also have an important role in aetiological determinism and its corresponding genetic framework.A recent GWA study of human TSEs draws attention to novel common variants associated with clinical phenotypes, implicating genes such as The low recurrence rate of familial vCJD precludes studies of human linkage, in which rare disease loci may be detected by virtue of their increased occurrence among affected relatives. This and the low global incidence of vCJD, means increasingly-available next-generation sequencing methods cannot yet be fully exploited to define the possible contribution of rare variants to the TSEs. Recent progress made, across psychiatry in particular, has helped to demonstrate the pathogenic potential of this variant class ic transmissions, to a general effect spanning across ic/ip routes and natural/host adapted prion agents. The generality of this locus may make it a prime candidate for involvement in the general pathogenesis of TSEs, including vCJD. Our demonstration of the substantial selectively with which host genetics may act across different transmission models suggests that not all QTLs identified using the historically favoured ic model will feature as strongly for orally acquired TSEs. The enduring aetiological relevance of this route to vCJD contrasts with the apparent decline in the number of cases resulting from blood transfusions oral TSE transmissions should be the next priority for mapping studies of this type.Our findings concur with previous reports linking QTLs on chromosomes 2, 4, 6 with TSE incubation time. We also report a novel QTL on chromosome 18. Additionally our results enable us to extrapolate from a previous linkage region on chromosome 11 linked with Animals were maintained with a 12 hour day/night cycle in a specific pathogen-free (SPF) facility by trained, qualified animal technicians. General husbandry was carried out on a daily basis. Animals had free access to food and water throughout the experiment. The experimental design was vetted and approved by the Home Office (project licence number: PPL 70/4351).Transmissions were conducted in a category 3 biohazard containment unit using either halothane/oxygen or isoflourane/oxygen for anaesthesia purposes.Animals were checked daily for neurological symptoms by a research staff member trained and approved in animal handling by the Animal Procedures department of the Home Office. Onset of disease was taken as the onset of three or more concurrent neurological signs. Mice were sacrificed at a defined endpoint of two weeks post diagnosis.The BXD mouse set is derived from an F2 intercross between C57BL/6 and DBA/2J progenitors. 26 successive generations of brother-sister mating result in the generation of inbred mouselines that are homozygous at virtually every locus ic transmissions were heavily prioritised in the F2 study. This was to ensure sufficient numbers to allow meaningful comparisons with previous studies.Group sizes for the study are given in Adult female mice aged 10–12 weeks old were used. Female mice were used in preference to males as, although group-housing by sex is the only feasible way to maintain a cohort of this size, severe injury and death in males caused by fighting was very frequently observed in a smaller pilot study. The small standard deviations associated with incubation time in BXD mice see  suggesteAge-matched controls were given uninfected brain homogenate at the same dosage and volume as those administered infectious material. Group sizes for controls ranged between 5 and 10. There was no effect of inoculation batch on incubation time for any experiment.ic. Brains were harvested at terminal stages of disease.BSE affected bovine brain was provided by Central Veterinary Laboratories, Weybridge, UK. Cloned ME7 mouse-adapted scrapie was obtained from BBSRC, UK. Me7 was propagated by passage in C57 animals inoculated ic) inoculations. Intra-peritoneal (ip) inoculations used either 100 µl of 10% w/v BSE or 30 µl of 0.1% w/v Me7. Injections were given in the intraperitoneal cavity of the abdominal region. Experimental controls for ic and ip transmissions were inoculated under the same experimental conditions using identical quantities of uninfected mouse brain material.Transmissions were conducted in a category 3 biohazard containment unit. Anaesthesia was carried out using halothane/oxygen. 30 µl of 1% w/v infective brain homogenate was injected into the right parietal lobe for all intra-cerebral , tremor, wobbly gait and foot clasping sc positivity in the medulla was limited to 40% of brain tissues examined. However these animals were recorded as being infected, on the basis of the following:All neuropathology was performed by the department of Neuropathology at the Institute of Psychiatry, London, UK. Brains of terminally ill animals were removed, fixed in formaldehyde/saline and processed for either haematoxylin and eosin staining or immuno-histochemical staining of PrP deposits using SP40 anti-PrP polyclonal antibody. Confirmation of disease in each of the experimental groups has been achieved by either establishing the existence of PrP-positive amyloid deposits Neuronal vacuolation was observed in the medulla of all affected brains.No evidence was found of any inflammatory response or other superimposed disease effects.Neither injected nor non-injected age-matched controls displayed TSE symptoms .Scoring was performed blind with respect to affective status (ie. case or control). TSE-related vacuoles were found only in the brains of prion-inoculated miceThere is no evidence in our data to suggest that the plaque-negative animals form a distinct subgroup in terms of incubation periods (Mann-Whitney U test).Basic statistical analyses were carried out using SPSS v13 (LEAD technologies) and MiniTab (MiniTab Inc.).Internal consistency (or reliability) of the data was estimated in SPSS using the Split-Half method AV) while, the mean within-strain variance estimates mean environmental variance (EV). Thus, an estimate of narrow-sense heritability (h2n) may be obtained using the formula Genetic correlation was investigated in the BXD study by Pearson's correlation of BXD means across traits. This has allowed exploration of genetic overlap between the traits. Calculation of heritability estimated the contribution of genetics overall to each transmission phenotype. Being that members of each BXD line are genetically identical, any variation within each line is ascribed to environmental variance, whereas variance between strains is genetic. One half of the variance of means between the BXD lines estimates additive genetic variance . An unconstrained regression model was used with the strength of linkage in each region expressed as a likelihood ratio statistic.For F2s interval mapping was performed using consensus map distances from the Mouse Genome database . In order to remove any bias from the computed statistics C57BL/6 and DBA2/J data were excluded from permutation and from simple and composite mapping. Two levels of significance are used to assess the strength of linkage or association in genomewide studies:suggestive threshold represents the approximate value of the Likelihood Ratio Statistic (LRS) that would be expected to yield on average one false positive per genome scan significant threshold represents the approximate LRS value that would be expected to yield on average one false positive every twenty genome scans The www.genenetwork.org, was used to perform initial interval mapping. This was followed up with a comprehensive analysis of genome-wide linkage using Mapmanager QTXvb20 The WebQTL interface, located at http://www.informatics.jax.org) and the Roche database (http://mousesnp.roche.com). Markers 10 cM either side of each candidate peak were chosen ip analysis..An advantage of the RI method is that the BXDs are already genetically characterised at over 4000 markers, thus BXD mice require no additional genotyping. For each BXD region nominated for F2 follow-up, markers were chosen on the basis of location and heterozygosity between C57 and DBA progenitors from the Mouse Genome Database (2 (3 mM) and forward/reverse primers (1 picoMolar of each). The 5′ end of each forward primer was fluorescently labelled with either Hex, Fam  or Ned . All reverse primers were obtained from MWG Biotech.DNA was extracted from 1 cm F2 tail samples prior to transmissions using a commercially available kit . 10 ng of DNA (in 5 µl of double-distilled water) was used as the template in 20 µl polymerase chain reactions (PCR). Reactions were carried out in 96-well plates using a PTC-200 thermocycler . The reaction mix consisted of dNTPs (200 µM), AmpliTaq-Gold and provided buffer (Applied Biosystems), MgClThe reaction sequence was as follows: 94°C for 5 minutes; 94°C for 30 seconds, 15 seconds at annealing temperature, 72°C for 30 seconds for 35–40 cycles; 72°C for 5 minutes.PCR products were run in multiplex on a 3100 genetic analyser (Applied Biosystems). Pooled sample were heat denatured in formamide  for 2 minutes at 94°C and subsequently snap frozen before being run using a 5 second injection time using a Rox GS-500 size standard (Applied Biosystems). Resolution of individual products within the multiplex required a minimum fluorescence signal of not less than 1000 units per PCR product. The software ‘Genotyper’ (Applied Biosystems) was used to analyse and genotype the output data.2). Conversion of r to Fisher's Z (Zr) is recommended Implementation of Random-Effects meta-analysis formulae was performed in Microsoft Excel spreadsheets. The effect size r was derived from the estimated variances calculated in Mapmanager,  are accounted for by assigning positive and negative Zr signs for C57BL/6 and DBA/2J alleles respectively.Calculation of heterogeneity was performed using equations appropriate to the number of studies being combined Equation 4 returns a standard normal deviation (Z) score for which the corresponding p value was obtained using a standard conversion table. This statistic was converted to a The between-study variance is an expression of the overall variance between two or more studies, taking into account all of its possible sources. ic and BSE ip). When BXD and F2 data are initially combined, the constant c is calculated using fixed-effect weights as this step occurs before random effects weights are calculated. This was applied in stages so that heterogeneity was first calculated between BXD and F2s for the same trait, and then between traits making up each measure. For example, the measure ‘BSE’ comprises BSE ic and Me7 ip) random effect weights (w*) are used:For subsequent combining between transmissions Click here for additional data file.Table S2−4), **Significant (P<2.6×10-5).Summary of effect sizes derived from Random-Effects meta-analysis procedure. Weighted mean r's for combined BXD and F2s data, r± s.e.m see . Convers(0.03 MB DOC)Click here for additional data file.Table S3−4), **Significant (P<2.6×10−5).Derived meta-analysis effect sizes summarised by agent/route. Weighted mean r (± s.e.m) for combined BXD and F2s data which is subsequently combined by route and by agent see . *Sugges(0.04 MB DOC)Click here for additional data file.