Chlamydophila pneumoniae is an obligate intracellular bacterium that replicates in a biphasic life cycle within eukaryotic host cells. Four published genomes revealed an identity of > 99 %. This remarkable finding raised questions about the existence of distinguishable genotypes in correlation with geographical and anatomical origin.C. pneumoniae by analysing synonymous single nucleotide polymorphisms (sSNPs) that are under reduced selection pressure. We conducted an in silico analysis of the four sequenced genomes, chose 232 representative sSNPs and analysed the loci in 38 C. pneumoniae isolates. We identified 15 different genotypes that were separated in four major clusters. Clusters were not associated with anatomical or geographical origin. However, animal lineages are basal on the C. pneumomiae phylogeny, suggesting a recent transmission to humans through successive bottlenecks some 150,000 years ago. A lack of detectable variation in 17 isolates emphasizes the extraordinary genetic conservation of this species and the high clonality of the population. Moreover, the largest cluster, which encompasses 80% of all analysed strains, is an extremely young clade, that went through an important population expansion some 3,300 years ago.We studied the genetic diversity of C. pneumoniae.sSNPs have proven useful as a sensitive marker to gain new insights into genetic diversity, population structure and evolutionary history of  Chlamydiales evolved from their free-living ancestors about 500–1000 million years ago (mya) [C. trachomatis and C. pneumoniae 50–200 mya proceeds the appearance of Homo sapiens and, thus, each lineage possessed sufficient biological potential to exploit new hosts [Chlamydiales; less than 1 % of the total gene number was estimated to result from HGT [Chlamydiaceae genome represent orthologs with other Chlamydiaceae species [The order go (mya) . The divfrom HGT . Instead species -6. DespiChlamydophila pneumoniae deserves medical attention as an important respiratory pathogen causing about 10 % of community acquired pneumonias and upper respiratory tract infections like bronchitis, pharyngitis and sinusitis [C. pneumoniae has been isolated in the brain and is associated with Alzheimer's disease and Multiple Sclerosis [inusitis ,8. Virtuinusitis . Viable inusitis . Moreoveclerosis ,13.C. trachomatis, no biovars or pathotypes have been described so far for C. pneumoniae, despite of the broad range of disease associations and sites of infection. The genomic sequences of the four isolates AR-39, CWL-029, J138 and TW-183 show a remarkable level of gene conservation and synteny. Overall identity between isolates is > 99% [C. pneumoniae has the highest potential for recombination among fully sequenced Chlamydiaceae and that the majority of recombination hotspots of the C. pneumoniae genome are concentrated in a family of polymorphic proteins [C. pneumoniae hence SNPs are among the most sensitive phylogenetic markers to reconstruct the evolutionary history of this species. SNPs occur as two classes of substitutions within genes, referred to as synonymous and non-synonymous SNPs . nsSNPs result in amino acid replacement and are targets of evolutionary selection. sSNPs make a difference with respect to the codon usage but do not alter the protein sequence and are therefore evolutionary almost neutral. This qualifies sSNPs as a valuable target for population genetic studies. SNPs have been successfully used to reconstruct the phylogeny of the M. tuberculosis complex [B. anthracis isolates [C. pneumoniae to answer questions about the homology of geographically and anatomically different isolates and the existence of distinguishable genotypes. We first conducted an in silico analysis of sSNPs and nsSNPs of the four sequenced C. pneumoniae isolates. To reduce the potential ascertainment bias caused by deriving the SNP events from a limited set of only four completely sequenced genomes, the SNPs selected for sequencing should be as independent as possible. Compared to non-coding and non-synonymous SNPs, synonymous SNPs provide evolutionary almost neutral, abundant and equally distributed sequence markers in the C. pneumoniae genomes. We therefore chose a representative subset of 232 sSNPs and analysed the loci in 38 C. pneumoniae isolates. As chlamydiae are difficult to propagate the available collection is the largest that has even been analysed. By this approach, we were able to gain new insights into the genetic diversity, the population structure and evolutionary history of C. pneumoniae.In contrast to is > 99% -6. The gproteins . The hig complex  and B. aisolates . In the Comparative genome analysis of the four sequenced reference strains revealed a total of 688 SNPs within the 1.2 mega base genome. The ratio of transitions:transversions has been determined at 2.7:1 for synonymous SNPs, 1.4:1 for nonsynonymous SNPs, 1: 1.07 for SNPs in intergenic regions and 1:2 for RNA genes. SNPs of all classes were homogeneously distributed over the whole genome . Using different phylogenetic reconstructions  we obtained the same tree topologies where the animal isolates are basal and the human isolates are derived, suggesting a zoonosis scenario for C. pneumoniae  and cluster IV is 95.2 (± 7.4) mutations, this translates into 0.14% sequence divergence, which suggests that these two lineages separated some 150,000 years ago. This analysis is based on the E. coli clock rate. We are aware that the evolutionary clock of obligate intracellular organisms might run faster; that would even reduce the calculated age of clades.The phylogeny of 38 C. pneumomiae (figure P = 0.879). Based on the stepwise expansion model, the effective population size of this lineage increased by a factor of 1,000 (θ0 = 0.002 and θ1 = 2.437). Assuming a synonymous mutation rate (μ) of 9 × 10-9 per nucleotide per year and a τ-value of 4.967 , the expansion event took place about 3,300 years ago.We computed frequencies of pairwise differences between the large cluster IV haplotypes (the mismatch distribution of the raw data) to evaluate the hypothesis of recent population growth in e figure . The disC. pneumoniae isolates and to allocate them to the described clusters. A subset of sSNPs was defined that discriminates between the genotypes. Additional file C. pneumoniae isolates. A total of 12 different SNPs (four to five SNPs per isolate) are sufficient to identify these isolates or to relate a new isolate to the characterized ones.SNP data revealed to be useful to genotype new C. pneumoniae isolates based on 232 sSNPs displays the genetic variability of the species. The topology ot the phylogenetic tree of the four reference isolates . In an environment of strong selective pressure one could expect that the number of sSNPs would exceed the number of nsSNPs because the latter are purged from the population by purifying selection. To solve this apparent contradiction we determined the likelihood for a single nucleotide exchange to cause a nsSNP or sSNP for the CWL-029 genome. Absence of any selection would result in a ratio of 3.4:1 between nsSNPs and sSNPs. Thus, the reduced number of observed nsSNP in comparison to the theoretically expected one is evidence for strong selective pressure within the intracellular niche.C. pneumoniae differs from C. trachomatis, which shows a significant amount of genetic variation between serovars within the "plasticity zone" around the origin of replication [C. pneumoniae species [We further found that all SNPs are homogeneously distributed around the genome figure . In thislication ,23. No c species .C. pneumoniae that remained undetected by other methods. Analysing a good portion of the worldwide available isolates, we were able to divide C. pneumoniae into different genotypes. Nevertheless, a lack of detectable variation in some isolates emphasizes the extraordinary genetic conservation of this species and the high clonality of the population. High selection pressure within the intracellular niche and an evolutionary bottleneck as a consequence of the adaptation to the human or animal host might have contributed to the stability of the genome.In summary, we have shown that sSNPs are a sensitive tool to reveal the genetic diversity and past demography of C. pneumoniae isolates of different geographical and anatomical origin were analysed. Table 38 To identify SNPs within the four sequenced isolates, we implemented a Java program which performed the tasks of calculating sequence alignments and extracting the SNPs from the alignments. The genomic sequences and annotations were downloaded from the Genbank database . StartinAccording to the locations of sSNPs 189 loci evenly distributed around the whole genome were chosen for further analysis. The adjacent up- and downstream region (approx. 500 bp) of each SNP was amplified by PCR  to estimate the time of the expansion (t) using the equation τ = 2μt, where μ is the neutral mutation rate for the locus. The confidence intervals of τ were calculated using a parametric bootstrap approach [To determine whether some expansion. For expapproach . Mismatcapproach .sSNP: synonymous single nucleotide polymorphismnsSNP: non-synonymous single nucleotide polymorphismmya: million years agoHGT: horizontal gene transferLBA: long branch attractionPBMC: polymorphic blood mononuclear cellsthe author(s) declares that there are no competing interests.TR carried out all bioinformatics and drafted the manuscript. SO and SS carried out the PCR and sequencing. MG, WS and JR drafted the manuscript. MM isolated a majority of the isolates. TW carried out the phylogenetic analysis. JG conceived, designed and coordinated the study and drafted the manuscript. All authors read and approved the final manuscript.Genes containing synonymous or non synonymous polymorphisms. This table list the genes and protein IDs containing synonymous or non synonymous polymorphisms as identified in this study.Click here for fileC. pneumoniae isolates by different SNPs. This figure allows for the identification of C. pneumoniae isolates by different SNPs. Numbers indicate SNP positions within the CWL-029 genome. The first branches are identified by two SNPs. The primers for identification for the isolates can be identified in additional file Algorithm for identification of Click here for filePrimers. This table lists the primers used for amplification of SNP containing fragments.Click here for fileSNP analysis raw data. This data give the detected base for each isolate and each polymorphic locus.Click here for fileConcatenated alignment for phylogenetic reconstruction. This file provides the concatenated alignment of the SNP sequence data which was used for phylogenetic reconstruction.Click here for file


Recent technological advances have made it possible to efficiently genotype large numbers of single nucleotide polymorphisms (SNPs) in livestock species, allowing the production of high-density linkage maps. Such maps can be used for quality control of other SNPs and for fine mapping of quantitative trait loci (QTL) via linkage disequilibrium (LD).A high-density bovine linkage map was constructed using three types of markers. The genotypic information was obtained from 294 microsatellites, three milk protein haplotypes and 6769 SNPs. The map was constructed by combining genetic (linkage) and physical information in an iterative mapping process. Markers were mapped to 3,155 unique positions; the 6,924 autosomal markers were mapped to 3,078 unique positions and the 123 non-pseudoautosomal and 19 pseudoautosomal sex chromosome markers were mapped to 62 and 15 unique positions, respectively. The linkage map had a total length of 3,249 cM. For the autosomes the average genetic distance between adjacent markers was 0.449 cM, the genetic distance between unique map positions was 1.01 cM and the average genetic distance (cM) per Mb was 1.25.There is a high concordance between the order of the SNPs in our linkage map and their physical positions on the most recent bovine genome sequence assembly (Btau 4.0). The linkage maps provide support for fine mapping projects and LD studies in bovine populations. Additionally, the linkage map may help to resolve positions of unassigned portions of the bovine genome. Advances in technology have dramatically increased the ability to cost-effectively genotype a large number of SNPs in humans and farm animals ,2. The mHistorically a diverse variety of methodologies and procedures have been used to order bovine chromosomal segments -16. A phThe objective of this work is to present a high-density bovine linkage map (HDBLM) that combines a low-density microsatellite based linkage map (LDM) with SNPs from the Affymetrix GeneChip™ Bovine Mapping 10K SNP kit  . ResultsGenotypes were received from Affymetrix  for 9,713 SNPs with an average call rate of 99.25% for the 10K SNP panel. A total of 1,891 SNPs were removed for the following reasons: departure from Hardy-Weinberg Equilibrium (HWE) (120), more than 50 inheritance inconsistencies (260), having an allele with frequency lower than 5% , and less than 10 informative meioses (17)  Table . The lin-5); the correlation of chromosome size to recombination distance was -0.66.On average the genetic distance per unit of physical distance (cM/Mb) was 1.25 Table . ChromosWe were unable to assign 2,946 of the 9,713 SNPs to the linkage map. Of the 7,822 SNPs that passed quality control, 7,510 SNPs were allocated to a confirmed chromosome. Six hundred and fifty two SNPs that had an assigned chromosome were not mapped because a unique map position could not be found and their inclusion based on physical position served to increase the length of linkage map above the defined threshold. There were 91 SNPs with unknown physical position, thus preventing their insertion analysis.There was not complete concordance in marker order between the linkage and physical maps Figure . The aveThe linkage map presented in this paper is the most dense map to date for cattle; the relatively high number of informative meioses per available SNP represented in the 10K SNP panel is greater than that reported by Snelling et al.  thus enaThe addition of 6,767 SNPs from the 10K SNP panel to the low-density microsatellite-based maps (LDM) resulted in both expansion and additional coverage of the linkage maps. The expansion was explained by an increase in genetic distance from proximal to distal markers of LDMs. This additional coverage was measured by an increase in genetic distances from the placement of the last SNPs of the 10K SNP panel to the proximal and the distal marker of a LDM. The magnitude of the expansion was of 80.4 cM and the coverage increased by 338.2 cM. The high reliability of the map presented here was made possible because of a high accuracy of genotyping, thorough pre-screening of the genotypic data for inconsistencies , relatively high numbers of informative meioses and the ability to place orphan and displaced SNPs. Hence this map will be useful to monitor the bovine genome assembly. Using the approach applied by Breen et al. , a map rThe observation that chromosome size increases the average recombination rate was consistent with other studies ,31. The The 7K-linkage map presented here has substantially improved on the previously incomplete assignment of SNPs from the 10K SNP panel, and has reordered SNPs that had been wrongly assigned. Thus, our linkage map has shown utility for identifying errors in the current sequence assembly of the bovine genome. In addition, the markers and linkage map will be valuable for fine mapping of QTL ,34.The assignment of SNPs to a chromosome from the 10K SNP panel was incomplete and some of their SNPs were wrongly assigned. The assigning and re-assigning of orphan and displaced SNPs to a chromosome and the further placement of these SNPs to unique positions in the linkage during mapping rounds 2–4 was based totally on linkage information. The inclusion of SNPs with up to 50 mis-inheritances in the construction of linkage maps did not have an effect on recombination distances. The markers and linkage map presented in this paper will be useful in the fine mapping of QTL using LD methods ,34. HoweUsing a unique animal resource, 7066 bovine genetic markers were positioned in our linkage map. Approximately 90% (6767 out of 7510) of the SNPs that passed quality control testing from the 10K SNP panel were placed on the linkage map  from the experiment were genotyped by external laboratories according to standard practices for fluorescent dye-labelled primers, utilising Applied Biosystems 3100 genetic analysers  for 294 microsatellites; three milk protein haplotypes: 1) Alpha s1 casein (CSN1S1) formed by A_CAS_41_26 and AS_CAS_192; located at 6517 and 17807 base pairs (bp) of locus X59856  respectively, 2) Kappa casein (CSN3) formed by K_CAS_148, located at 5345 bp of locus X14908 (accession number X14908) and 3) Beta casein (CSN2) formed by B_CAS_37, B_CAS_67, B_CAS_106 and B_CAS_122, located at 690, 8101, 8219, and 8267 bp of locus X14711 (accession number X14711) respectively [1 sires were screened for approximate 500 microsatellites. Where four out of six sires were heterozygous, the markers were used. The 10K SNP panel was genotyped 12 months later than the other markers.In total, 1679 animals  and was therefore a female-specific map. The F2 daughters' genotypes were comprised of maternally-derived alleles as well as paternally- (F1 sire) inherited haplotypes. The maternally-inherited alleles were derived by subtracting the maternally-inherited haplotypes from the progeny genotypes as follows. Because recombination is not possible for the haploid sex chromosome in males, these maternally-inherited haplotypes represented entire  chromosomes. This in turn enabled the maternally-inherited haplotype to be determined in the F2. As for their F2 daughters, the F1 dams' chromosome-long haplotypes were known. This is because their sires  were genotyped. Therefore the F1 dams' phases were known, increasing the ability to observe recombination events amongst their F2 offspring. Our linkage map is based on a two-generation pedigree and it could be further enhanced using a three-generation pedigree. The number of animals involved in the pedigree structure, number of markers involved in map construction and limitations in hardware capability limited the use of a three-generation pedigree.The non-pseudoautosomal part of the sex chromosome was constructed differently; it utilized maternally-derived genotypes  and hence resulted in a low-density microsatellite-based linkage map. Subsequent rounds incorporated SNPs from the 10K SNP panel and enabled the construction of high-density linkage maps.The LDM was constructed based on 294 microsatellites, three milk protein haplotypes and two gene-based SNPs Figure . ConstruThe 10K SNP panel did not have complete assignment of SNPs to a specific chromosome. Of the 7822 SNPs available from the 10K SNP panel, 1189 (orphan SNPs) were not initially assigned to a chromosome. Using the mapping information from mapping round 1, CRI-MAP V.2.4 (TWOPOINT option) ,44, 1053The MultiMap  parameteFor each bovine chromosome, three low-density linkage maps were constructed: 1) low-density microsatellite linkage map (LD1) Figure , 2) low-The low-density linkage maps (LD1–LD3) comprise a mix of common markers (microsatellites as well as SNPs) and differ from each other only in SNPs from the 10K SNP panel. The three separate low-density maps  were integrated into one linkage map termed ARTIFICIAL LINKAGE MAP – I (ALMI). The integration procedure was performed observing the following rules: markers that appeared in more than one of the three linkage maps were anchored; markers that occurred only in one of the low-density linkage maps were integrated into the ALMI, retaining their original order with respect to other markers within their own low-density linkage map. The resulting ALMI had a greater number of markers than the individual low-density linkage maps (LD1–LD3). There were no inconsistencies in SNP order among the three different low-density maps. In some cases, the integration of a marker was difficult due to the ambiguous positions where it could be placed. However, this had no impact on the linkage map because MultiMap  was ableSNPs not mapped during mapping round 2 were brought into the linkage map using an iterative procedure with the AMLI used as the framework map. To enter the map, the position for the SNPs had to exceed a LOD score of two with the Flips Option set to two. The proposed placements suggested by MultiMap  for the This round consisted of mapping the 779 re-assigned SNPs, followed by one further round of mapping for all SNPs from the 10K SNP panel that had not been placed during mapping round 3. The mapping criteria were same as in mapping round 3.The remaining unmapped SNPs from the 10K SNP panel after mapping round 4 were inserted into the linkage map at a position where they were neighbouring the SNP with the closest physical position. Initially, the physical positions for SNPs were obtained from the bovine assembly Btau_3.1 . The finRecombination distances and marker physical positions (obtained from bovine genome assembly (Btau 4.0) ) were usJA construction of bovine autosomal chromosomes 2–13, 15–29 linkage maps and writing of manuscript. MK provided bioinformatics support. PF construction of bovine sex chromosome linkage map. WC construction of bovine chromosomes 1 and 14 linkage maps. RS initial data cleaning and analysis of results.Unmapped SNPs from the 10K SNP panel. Excel spreadsheet containing unmapped SNPs from the 10K SNP panel and the reason why the marker was not mapped.Click here for fileLinkage map. Excel spreadsheet containing linkage maps of bovine chromosomes. Data include chromosome, marker name, and marker reference: DataBase and ID, marker type, informative meioses, Kosambi distance, position in chromosome in centimorgans, marker physical position (Bovine genome sequence assembly (Btau 4.0) [tau 4.0) , informaClick here for fileSummary final SNP status from the 10K SNP panel. Excel spreadsheet containing total numbers of mapped, available, removed for quality control reasons, and unmapped SNPs from the 10K SNP panel.Click here for fileInformation on pedigree structure. Microsoft Word document presenting number of animals forming the pedigree structure utilized as the animal population in the map construction.Click here for file


Bos taurus. The extent of LD was estimated for 370 and 186 single nucleotide polymorphism markers on BTA19 and 29 respectively using the square of the correlation coefficient (r2) among alleles at pairs of loci. A comparison of the extent of LD found that the decline of LD followed a similar pattern in both breeds. We observed long-range LD and found that LD dissipates to background levels at a locus separation of about 20 Mb on both chromosomes. Along each chromosome, patterns of LD were variable in both breeds. We find that a minimum of 30 000 informative and evenly spaced markers would be required for whole-genome association studies in cattle. In addition, we have identified chromosomal regions that show some evidence of selection for economically important traits in Angus and Holstein cattle. The results of this study are of importance for the design and application of association studies.The objective of this study was to quantify the extent of linkage disequilibrium (LD) on bovine chromosomes 19 and 29 and to study the pattern of selection signatures in beef and dairy breeds (Angus and Holstein) of  Linkage disequilibrium (LD) is the non-random association of alleles at different loci. If two alleles at two different loci are in LD, combinations of alleles within haplotypes occur at frequencies that differ from that expected under the hypothesis of independence. An association between the genetic variation at a locus and a phenotype indicates that either the genetic variation at that locus directly affects the phenotype of interest or the locus is in LD with the causal mutation . The feaD′. The distance over which LD is likely to be useful for association mapping was found to be 13.3 Mb confirming that the range of LD is extensive in Holstein–Friesian dairy cattle. Bos taurus and Bos indicus subspecies using 2670 SNP markers and observed that the extent of LD (estimated using r2) available for association analysis does not exceed 500 kb. The differences in the extent of LD between the study by D′ vs. r2. D′ has been reported to overestimate the extent of LD  using dense SNP markers. We have chosen BTA19 and BTA29 as candidate chromosomes for mapping because QTL for several economically important traits has been identified on these chromosomes  and Holstein  cattle. To maximize the genetic diversity within each sampled population, families were selected to span the diversity of each breed. Three-generation families were sampled so that chromosomes could be phased using linkage information. The general family structure consisted of a grandparent, parent and three or more progeny.DNA was collected from Angus (build 2.0 (ftp://ftp.hgsc.bcm.tmc.edu/pub/data/Btaurus/snp). The markers were genotyped within each population of beef and dairy animals using the Illumina BeadStation 500G genotyping system  was run using a two-way contingency table of loci against breeds.A total of 1001 and 535 evenly spaced SNP markers for BTA19 and 29 respectively were chosen from bovine sequence blast (Genomic sequence coordinates for SNPs were obtained by performing genoprob v2.0(genoprob estimates the probability that a genotype is correct (pGmx) and the order (phase) of the allele is correct (oGmx) conditional on the pedigree, locus order and map distances. For the LD analysis, we excluded all genotypes with pGmx ≤ 0.95, but did not put any constraint on oGmx. The summary of average genotype and order probabilities for each breed is shown in We used prob v2.0. Sire 26r2) between marker alleles using gold  using a five-locus sliding window for both chromosomes and for each pair of averages; we subtracted the mean Angus allele frequency from that for Holstein. We plotted mean allele frequency differences against the location of the third locus within the five-locus window. To establish whether the allele frequency difference between the breeds differed significantly from zero and thus was putatively indicative of a selection signature, we performed 100 000 and 1 000 000 allele-frequency-against-locus permutation tests for BTA19 and BTA29 respectively to empirically identify the 5% significance level thresholds. To confirm the chromosomal regions identified using the sliding-window approach, we performed a chromosome-wide scan to detect regions showing evidence of selection using a Web-based tool to compute the extended haplotype homozygosity (EHH) statistic . First, P<0.001) for its MAF distribution. The presence of a uniform distribution of SNP MAFs is because of the ascertainment bias in SNP discovery and does not represent the true distribution of SNP MAF in the genome, which is more appropriately modelled by a gamma distribution. Any difference between the MAF distributions probably reflects a breed of ascertainment effect (i.e. the SNPs were discovered because they were the most common SNPs on these chromosomes in Holstein), which would lead to an excess of high MAF SNPs in one breed and an excess of low MAF in the second. Therefore, the difference probably has no biological significance other than identifying the breed of SNP discovery. The distribution of MAF for SNPs used in the estimation of selection signatures using the five-locus sliding-window approach (results not shown) was not different from The average MAF for SNPs on BTA19 was 0.27 for both Angus and Holstein, but was 0.25 and 0.27, respectively on BTA29. The distribution of MAFs for SNPs used in the LD analyses in both breeds is shown in http://genomes.sapac.edu.au/bovineqtl/index.html, http://www.animalgenome.org/QTLdb/cattle.html, http://www.vetsci.usyd.edu.au/reprogen/QTL_Map/) were used to identify the chromosomal coordinates of published QTL in beef and dairy cattle on BTA19 and 29. Markers within the reported QTL regions were aligned to the third draft of the bovine genome sequence assembly (Btau 3.1) to obtain the approximate position of these QTL in Mb, and these are reported in Details of the allelic frequencies for both breeds are provided in bly Btau .1 to obtD′ as a measure of LD because we used r2. We compared our study with that of r2 and found similar results for Angus and Holstein data. For example, at intermarker distances of 5, 100 and 500 kb in Holstein, the r2-values in our study were 0.6, 0.26 and 0.1 compared to 0.53, 0.23 and 0.1 in r2-values for BTA19 in Bos taurus breeds. Our study shows that at a physical distance of 100 kb, the average r2-value is 0.23–0.26. We can assume that any QTL we seek will be at most in the middle of the interval and therefore no more than 50 kb away from any marker. Hence, the average r2 between these markers and a QTL located at the mid-interval is approximately 0.3. This indicates that there should be an informative marker every 100 kb to achieve a moderate LD  for genome-wide association studies. Because the bovine genome is approximately 3 Gb, we would need a minimum of 30 000 evenly spaced and informative markers to perform a whole-genome association study, which agrees with Bos taurus cattle. The information generated from this study has important implications for the design and application of association studies in cattle populations.Graphical representation of the patterns of LD shows regions of high and low LD across the chromosomes in both breeds. A clear difference in the pattern of LD is observed in Angus and Holstein –S13. For


Solanum lycopersicum L.) has narrow genetic diversity that makes it difficult to identify polymorphisms between elite germplasm. We explored array-based single feature polymorphism (SFP) discovery as a high-throughput approach for marker development in cultivated tomato.Cultivated tomato  increased dramatically at α ≤ 10-6. Trough sequence validation, we identified 279 SNPs and 27 InDels in 111 loci. Sixty loci contained ≥ 2 SNPs per locus. We used a subset of validated SNPs for genetic diversity analysis of 92 tomato varieties and accessions. Pairwise estimation of θ (Fst) suggested significant differentiation between collections of fresh-market, processing, vintage, Latin American (landrace), and S. pimpinellifolium accessions. The fresh-market and processing groups displayed high genetic diversity relative to vintage and landrace groups. Furthermore, the patterns of SNP variation indicated that domestication and early breeding practices have led to progressive genetic bottlenecks while modern breeding practices have reintroduced genetic variation into the crop from wild species. Finally, we examined the ratio of non-synonymous (Ka) to synonymous substitutions (Ks) for 20 loci with multiple SNPs (≥ 4 per locus). Six of 20 loci showed ratios of Ka/Ks ≥ 0.9.Three varieties, FL7600 (fresh-market), OH9242 (processing), and PI114490 (cherry) were used as a source of genomic DNA for hybridization to oligonucleotide arrays. Identification of SFPs was based on outlier detection using regression analysis of normalized hybridization data within a probe set for each gene. A subset of 189 putative SFPs was sequenced for validation. The rate of validation depended on the desired level of significance (α) used to define the confidence interval (CI), and ranged from 76% for polymorphisms identified at α ≤ 10Array-based SFP discovery was an efficient method to identify a large number of molecular markers for genetics and breeding in elite tomato germplasm. Patterns of sequence variation across five major tomato groups provided insight into to the effect of human selection on genetic variation. Solanum lycopersicum and a wild relative, S. pennellii  and the upper level of the confidence interval a + b*X + t(α/2) * √ [(1 + h) * MSE], with the value of the t statistic changed to generate a CI at the desired level of significance (α). Because we defined both upper and lower confidence limits, the t statistic was adjusted for the one-tailed test by choosing t for (α/2).The hybridization data for perfect match (PM) probes was extracted from the raw MicroStation Cell Library (CEL) files containing data for FL7600, OH9242, and PI114490. Three files representing three independent hybridizations were available for each variety. Following extraction, data were background corrected and quantile normalized using the Bioconductor package implemented in the R language [2, 50 μM of each dNTP, 0.1 μM of each forward and reverse primers, 20 ng of DNA template, and 1 unit of Taq DNA polymerase. Amplification was performed in a thermocycler  programmed for 3 min at 94°C followed by 40 cycles of 45 s at 94°C, 45 s at a suitable annealing temperature between 56 and 60°C, and 1 min 45 s at 72°C, followed by an extended incubation for 6 min at 72°C. PCR products were purified using QIAquick PCR purification columns and QIAEX II Gel Extraction kits  or simply by ethanol precipitation. Purified PCR products were sequenced using the Big-Dye Termination cycle sequencing reactions and an ABI Prism 3100xl sequencer . Sequencing was performed in the forward and reverse directions for each of the PCR products for two or three varieties of tomato. The Pregap4 module of the Staden sequence analysis package [FI855394 to FI855581).Primers flanking target regions of 210 randomly-selected potential SFPs were designed using BatchPrimer3 v1.0 software  with the package  was usedS. pimpinellifolium, based on pairwise θ (Fst) according to Weir and Cockerham [Fst) and heterozygosity were obtained using Microsatellite analyzer (MSA) V4.05 [P-value for the pairwise θ was based on 10,000 permutations and a bonferoni correction. Expected heterozygosity was calculated based on a single locus for each group and values for 46 markers were then averaged to estimate heterozygosity across all markers.Genetic differentiation was assessed between the five groups, fresh-market, processing, vintage, landrace, and ockerham . The 92 ockerham ,45 or mRA) V4.05 . The P-vWe investigated the distribution of haplotypes in the tomato collection directly by sequencing or indirectly by SNP genotyping. Loci Le006861 and Le016258 were investigated by direct sequencing for the 31 varieties and accessions. As we identified only two haplotypes within cultivated tomatoes, subsequent haplotyping was investigated through SNP genotyping. The loci Le011957, Le013904, and Le013946 each contain ≥ 4 SNPs. Two SNP markers were developed for each of the three loci and SNP detection for the 82 varieties and accessions was performed with the Luminex 200 system as described above.. The two sequences that contained both alleles at each SNP were trimmed so that no stop codons were present. DNA sequences were aligned using ClustalX v1.8 [In order to identify coding sequences for loci with ≥ 4 SNPs, we used Basic Local Alignment Searches (BLAST) against the tomato EST database. Alignments were used to identify and remove intron sequences, and open reading frames within the exon sequence were subsequently identified using ORF finder alX v1.8 , and thealX v1.8 ,48.SCS, extracted data and performed background correction and normalization, improved statistical models, identified putative SFPs, performed sequence validation, and drafted the manuscript. MDR, assisted in the analysis of sequence data and manuscript preparation. CC and TZ performed microarray hybridizations. DMF, developed statistical models, designed and supervised the research.All authors have read and approved the final manuscript.SNP variation for three tomato varieties, FL7600, OH9242, and PI114490 used in SFP discovery. The data includes locus names and SNP positions, flanking sequences, and primers.Click here for fileInDel variation for three tomato varieties, FL7600, OH9242, and PI114490 used in SFP discovery. The data includes locus names and InDel positions, flanking sequences, and primers.Click here for fileDescription and SNP genotypes of plant materials used in this study. The data includes categorical details of 92 tomato varieties and accessions with genotypes at 46 loci.Click here for fileSummary of allelic diversity analysis at 51 loci. The summary shows allelic diversity of 92 tomato varieties and accessions divided into five groups, fresh-market, processing, vintage, landrace, and wild species.Click here for file


Based on a "training" sample of 1,042 subjects genotyped for 5,728 single-nucleotide polymorphisms (SNPs) of a conventional 0.4-Mb genome scan and a "test" sample of 746 subjects genotyped for 545,080 SNPs on a 500k-chip, we investigated the extent to which the subjects' immunoglobulin M levels can be reproducibly predicted from a multilocus genotype. We were specifically interested in the reproducibility of predictors across populations  and across SNP sets  because this is a prerequisite for clinical application. For the training sample, neural network (NN) analysis yielded classifiers that predicted immunoglobulin M levels from the subjects' multilocus genotypes at acceptable error rates through a configuration of 15 genomic loci (61 SNPs). With the test sample (746 subjects) we addressed the question of reproducibility across populations and across SNP sets by means of a novel "competitive SNP set" approach. However, the chip data contained several sources of distortion, including greatly elevated noise levels and artifact-prone SNP regions, thus complicating attempts to verify the reproducibility of NN predictors. Though 5 of 15 genomic loci from the training samples appeared to be reproducible, the NN classifiers derived so far from the test samples are insufficiently compatible with the training samples. Nonetheless, our results are promising enough to justify further investigations. Because the underlying algorithm can easily be split into parallel tasks, the proposed "competitive SNP set" approach has turned out to be well suited for computers with today's 64-bit multiprocessor architectures and to offer a valuable extension to genome-wide association analyses. In this investigation we focused on the immunoglobulin M (IgM) phenotype because a heritable malfunction of the inflammatory response system has been linked to various complex diseases. The "natural" antibody IgM displays a high within-pair concordance in monozygotic twins in the range of 0.849 ± 0.091, while chronically elevated IgM levels develop years before the first clinical symptoms occur [Standard (logistic) regression connects genotype with phenotype in a direct way, thus greatly simplifying biology. In fact, genes code for proteins or RNA ("gene products"), which may interact in a variety of ways and influence the phenotype only after a cascade of intermediate steps. Molecular-genetic NNs generalize standard regression analysis in a very natural way by 1) implementing multistage gene products through one or more intermediate "layer(s)"  using the method of gradient descent. The basic algorithm is:yi observed   (i) output:   j = 1,2,...Nj)(j) hidden layer:   sk = xk   xkobserved   (k) input:   p)   improvements:   xk denote observed genotypes, yi observed phenotypes, σ the activation function of sigmoid-type: R → , α the learning rate, and p the number of subjects. This algorithm can easily be adapted to the requirements of specific genetic analyses.where k-fold cross-validation, the data are split into k roughly equal parts, and k-1 partitions are used for training, while one partition is used for testing. This process is repeated until each partition has served as a test set, so that k estimates of prediction error are generated. The resulting prediction error is approximately unbiased for the "true" error if k is sufficiently large .Results derived through the standard NN approach, which uses 80% of samples for training and the remaining 20% for testing, tend to be over-optimistic, in particular if genotype errors and missing data are present. Therefore, in the Our training sample comprised 511 nuclear and multiplex families with 1,073 subjects ascertained through index cases with a diagnosis of rheumatoid arthritis. All subjects were genotyped for 5,728 specifically selected SNPs of a conventionally designed genome scan resolving an average 0.4-Mb inter-marker distance (GAW15) [In a first step ("screening") we looked for clusters of at least three SNPs within a 0.5-Mb region that contributed to the observed IgM distribution in terms of NN performance rates above a prespecified threshold under the constraint of a ten-fold cross-validation. The threshold was chosen in such a way that less than 10% of the SNPs under investigation entered the subsequent optimization phase that relied on the resulting clusters as SNP "pool". In the second step, we constructed NN classifiers in a systematic way by iteratively adding or removing genomic loci from the SNP "pool" and fitting the NN model to the 1,042 observations under the constraint of a ten-fold cross-validation. The number of correctly classified subjects, averaged across the ten solutions, served as an optimization criterion. Our NN approach included a single intermediate layer with the number of gene products equalling the number of genomic loci, while a single output node formed the one-dimensional phenotype  overall performance for each optimization step. The optimization stopped when a plateau was reached at a rate of 77.3% (± 0.636) correctly classified subjects  were included in the 500k-chip used with the test samples. In most cases, however, at least 8 SNPs were found in the ± 0.1 Mb surrounding interval, except for 121 genomic regions (2.1%) insufficiently covered by the chip. As for informativeness, 33.4% of the chip SNPs displayed insufficient allelic variation, essentially raising the overall "noise" level. By contrast, this latter rate was as low as 4.4% for the training set. IgM prevalences showed significant differences between training sample  and test sample  by design. These differences, however, were irrelevant for the envisaged replication analyses.k of the 5,728 SNPs of our training sample were used to define surrounding Xk ± 0.1 Mb intervals . Typically 50 to 80 SNPs of the 500k-chip were located in these intervals and served as pool for selecting eight "optimal" SNPs in terms of informativeness and vicinity to the original loci at Xk . Finally, six subsets of 5,728 SNPs each were constructed by randomly combining SNPs from each interval , (20: 22347-24962), and 2) stable across five out of six SNP sets (3: 127908352-127934624), (7: 64334980-64399828), (10: 42184144-42240840), (12: 132000536-132033000), (17: 524930-565912), (19: 18062756-18176628). In consequence, the NN classifiers so far derived from the test samples showed insufficient compatibility with those of the training samples and still require improvements.When applied separately to the six competitive SNP sets, the screening procedure of our two-stage optimization algorithm yielded 55 to 74 clusters of at least three SNPs within 0.5-Mb regions. This is a somewhat smaller yield compared to that of the training sample, yet a presumable consequence of the lower allelic variability found in the chip data. Subsequent optimization steps yielded relatively reproducible results (across four out of six SNP sets) for 5 of the 15 genomic loci derived from the training samples , whereas the most stable regions for the test samples appeared to be mere artifacts inherent in the 500 k chip: 1) across all six SNP sets  interactions between genes and between gene products play a central role . While the ultimate goal of molecular-genetic research is the detection of "causality", clinicians are also interested in reliable classification and prediction through "objective" laboratory methods. Because the underlying algorithm can easily be split into parallel tasks, the proposed "competitive SNP set" approach is well suited for computers with today's 64-bit multiprocessor architectures, and thus a valuable extension of standard genome-wide association analyses.GAW: Genetic Analysis Workshop; IgM: Immunoglobulin M; NN: Neural network; SNP: Single-nucleotide polymorphism.The authors declare that they have no competing interests.All three authors contributed equally to the design of this project and its practical realization; HHS conducted the CPU-intensive computations on a large-scale computer system.


Extended and validated CRLMM is shown to be more accurate than the Affymetrix default programs, and datasets and methods for validation are presented that can serve as standard benchmarks by which future SNP chip calling algorithms can be measured. Multiple algorithms have been developed for the purpose of calling single nucleotide polymorphisms (SNPs) from Affymetrix microarrays. We extend and validate the algorithm CRLMM, which incorporates HapMap information within an empirical Bayes framework. We find CRLMM to be more accurate than the Affymetrix default programs (BRLMM and Birdseed). Also, we tie our call confidence metric to percent accuracy. We intend that our validation datasets and methods, refered to as SNPaffycomp, serve as standard benchmarks for future SNP calling algorithms. Genome-wide association studies hold great promise in discovering genes underlying complex, heritable disorders for which less powerful study designs have failed in the past -3. Much ® Human Mapping 100K and 500K arrays are beginning to be widely used in association studies, and the 6.0 array with >900,000 SNPs has recently been introduced. At these genotype densities, association studies are theoretically well-powered to detect variants of small phenotypic effect in samples involving hundreds to thousands of subjects [Single nucleotide polymorphism (SNP) microarrays represent a key technology allowing for the high throughput genotyping necessary to assess genome-wide variation and conduct association studies -9. Over subjects -16.per se but rather the inadequacy of current SNP calling programs to extract the greatest information from the raw data and, more importantly, to quantify SNP quality, so that unreliable SNPs may be eliminated from further analysis.Practically though, the use of SNP microarrays in association studies has not been entirely straightforward. Genotyping errors, even at a low rate, are known to produce large numbers of putative disease loci, which upon further investigation are found to be false positives. Work by Mitchell and colleagues  suggestsIn general, genotyping algorithms make a call  for a SNP of each sample assuming diploids. Typically, a confidence measure is also attached to each genotype call. The user can then choose a level of confidence required for a call to be dropped. One of the first algorithms designed for calling SNPs was Adaptive Background genotype Calling Scheme (ABACUS) . OriginaRecently, Rabbee and Speed  developead hoc Bayesian manner; clusters populated with few data points, because of low minor allele frequency say, draw more influence from the prior. Because the laboratory effect resulted in too much across-study-variability, Affymetrix did not use HapMap as training. With their most recent product, the 6.0 array, Affymetrix provides yet another algorithm: Birdseed [Affymetrix has recently introduced a new algorithm, Bayesian Robust Linear Model with Mahalanobis Distance Classifier (BRLMM) , which iBirdseed .In the last year, Carvalho and colleagues  developeThe goal in SNP calling algorithm design, thus far, has centred solely on increasing the number of SNPs that can be called with high confidence of accuracy. Little attention has been paid to developing measures of confidence. Each method provides its own metric with no standardization across algorithms. Worse, none of the metrics are explicitly linked to per-SNP accuracy. Questions as to whether metrics from the same algorithm translate to different accuracies based on the quality of the chip experiment remain open. Geneticists hoping to set an accuracy threshold to take SNPs forward for further genetic analysis are left in the dark.The first goal of this paper is to describe and validate new features of our algorithm for SNP calling. Treatment of the input data with SNP Robust Multiarray Average (RMA) does not completely eliminate laboratory specific effects; we extend CRLMM to include a recalibration step using the original Bayesian framework to adjust clusters to account for these residual effects. We have also added a procedure that explicitly ties metrics of call confidence to per-SNP accuracy in a manner robust to chip-run quality. Details of both novel features can be found in the Materials and methods section. Because we are the developers of CRLMM, and have no plans of maintaining the original version, we avoid the use of yet another acronym and refer to our new algorithm as CRLMM as well. A software implementation of CRLMM is freely available through the oligo package at Bioconductor ,26, an oaffycomp [Our second goal is to describe validation benchmarks to be used in the future by other software developers for comparison purposes, as has been done for expression array algorithms with affycomp . Beyond In the Results section we use our validation benchmarks to compare our new method (CRLMM) to the most widely used algorithm to date (BRLMM). We find that CRLMM provides more accurate genotype calls across datasets; at a high per-call accuracy, the drop rate of BRLMM is substantially higher than that of CRLMM across multiple datasets. Furthermore, CRLMM offers substantially improved estimates of accuracy. A less comprehensive comparison between CRLMM and Birdseed is included in the Discussion. Note that as more 6.0 data becomes publicly available, we will readily perform all our assessments on the new data and publish results on our SNPaffycomp website .The development of CRLMM involved training on the high quality Affymetrix HapMap array sets using HapMap Project genotypes as the correct calls . CRLMM aOne may critique the above result by pointing out CRLMM has an unfair advantage over BRLMM for the datasets used; CRLMM was trained on data generated from HapMap individuals. Moreover, the calls from the HapMap Project are known to have an error rate of their own. To examine whether CRLMM outperforms BRLMM for array data from other individuals, the two algorithms were applied to a set including both multiple replicate and trio samples on the Xba and Nsp chips of the 100K and 500K arrays, respectively. The replicate and trio files were run together because running the former files exclusively would be highly artificial, making results ungeneralizable. The gold standards for the replicate data were the majority call among high quality chips. Accuracy was then defined as agreement with these consensus sets, which were generated separately for each dataset and calling method. The trio data were scored by tabulating the fraction of SNP trios  not violating Mendelian inheritance among those without any dropped calls. As with the HapMap validation, the accuracy for these two types of files was examined at different drop rates. The ADPs of both the replicates and trios demonstrate that CRLMM makes calls more accurately than BRLMM on non-HapMap sets Figure . These dAn alternative, quantitative presentation of the same data above can be found in Table CRLMM allows for the identification of these poor quality chips. It is well known that the inclusion of poor quality chips in a dataset may distort calling algorithms to such a degree that mistaken calls are made even on high quality chips. Therefore the identification and exclusion of poor quality chips is vital in any analysis. In this regard, BRLMM proves to be inadequate; using a summary statistic based on BRLMM confidence metrics will not accurately reflect the chip quality. As an example, consider one of the samples in the Affymetrix first pass Sty data; measured against the HapMap as the gold standard, it has an average accuracy less than 33% whether it is called by BRLMM or CRLMM. This degree of accuracy can be achieved by guessing, which implies that no information is provided by the array. Yet, Figure Not only is the BRLMM confidence metric invalid for poor quality chips, it corresponds to different accuracies from dataset to dataset. Figure Ultimately, the end-user must have a per-call measure of accuracy to identify which SNPs to exclude from further analysis. Labs using BRLMM are left to their own devices to connect the program's confidence metric to accuracy. In Figure The first step in a useful genotyping algorithm is transforming raw data into genotype calls. As we have demonstrated, the quality of these calls can vary. A second step in a useful genotyping algorithm is to quantify how certain we are about our calls. We have improved our previous algorithm  as a solAcross datasets from multiple laboratories and by different methods of validation, CRLMM is just as, and in many cases more, accurate than BRLMM in calling genotypes from Affymetrix SNP chips. This result is due in large part to the utilization of HapMap information in making calls. Figure An apparent improvement of BRLMM over DM is that the former no longer has a propensity to drop heterozygous calls at a given confidence threshold. To assure equal drop rates for homozygous and heterozygous calls, we found that Affymetrix altered the confidence metric. The end result is that the metric corresponds to different accuracies for homozygous as compared to heterozygous calls Figure . We alsoIt is true that the use of a single cut-off for CRLMM derived data will incur more heterozygous drop-out , a result that may bias distal analyses. Nevertheless, uniform drop rates can still be easily achieved by choosing a more stringent confidence cut-off for homozygotes; in this way, calls can be both above a pre-specified accuracy threshold and have equivalent drop rates between homozygotes and heterozygotes. One may wonder whether these steps can be legitimately applied to results from an algorithm, since calls and confidence metrics are not made with absolute certainty. Figure S4 from Additional data file 1 demonstrates this concern to be largely unfounded. Figure S5 from Additional data file 1 shows that even after forcing drop rates to be the same between heterozygotes and homozygotes, CRLMM results at different accuracy thresholds are still more accurate than BRLMM. In the end, we do not view the equalization of drop rates to be a definitive solution to this problem; rather, modifications to statistical methodologies for association and linkage studies are required to account for this factor.Just as important as accuracy are assessments of quality for both chips and calls. We have shown that the BRLMM confidence metric is inadequate for chip quality determination, corresponds to different accuracies from dataset to dataset, and even has different meanings for homozygotes and heterozygotes on the same chip. Regarding the assessment of chip quality, it should be noted that Affymetrix recommends running DM to eliminate poor quality chips prior to using BRLMM . For CRLIn theory, a confidence metric may be defined over any space. One may believe that so long as it proves to be stable over datasets and call types, it will be of optimal utility. All an end-user needs to do is consult an ADP plot to set an accuracy threshold over which SNPs can be taken forward for further analysis. This notion is false. The reason is that the accuracy so calculated is an average of individual SNPs with corresponding variable accuracy. Within this range, there will be a significant portion of individual SNPs of lower accuracy than the average value. To address this problem, we made CRLMM's confidence metric per-call accuracy. We feel methodologies developed in the future will be of greater utility if this convention is followed.It is not inconceivable that new methods of genotype calling that surpass CRLMM will be devised. Moreover, Affymetrix will continue to design new SNP arrays; adaptations of old algorithms for these chips will require additional validation. Algorithms of the future may prove to be more accurate, as demonstrated by ADPs, and have confidence metrics even more reflective of true accuracy, as exhibited by ACPs. In fact, the most important aspect of this work is laying the ground work for a standard set of assessments by which different methodologies may be measured against each other. Indeed, we already have included comparisons of CRLMM to Birdseed . Figure The datasets used in this study is freely available at our SNPaffycomp website  at whichWe used several HapMap datasets representing runs of varying quality, different labs, and different times. The publicly available high quality HapMap Project data are on 269 individuals genotyped on 100K arrays  by AffymAnother two sets of 500K chip results from 95 and 152 HapMap individuals were provided to us by Affymetrix and the Broad Institute, respectively; these data differed from the aforementioned sets principally in that they were derived from first pass runs of variable albeit typical quality. Two 6.0 array datasets, from 44 and 96 HapMap samples, were run at the Chakravarti laboratory for testing purposes.The other Chakravarti laboratrory datasets were generated from non-HapMap individuals. A replicate dataset composed of 40 50K Xba chips (part of the 100K chipset) run on a single DNA sample were used as well as 30 trios. Eight of the 40 replicates were of low quality as assessed by SNR. Similarly for the 250K Nsp chip (part of the 500K chipset), the Chakravarti lab provided a replicate dataset of 16 chips and 30 trios. All 16 replicates were of high quality. All trio data for both the Xba and Nsp chips were of high quality. Replicates were performed with the same DNA prep but all subsequent steps - ligation, amplification, fragmentation and hybridization - were repeated on different chips.The HapMap sample gold standard against which outputs from either BRLMM or CRLMM are compared is derived from the final HapMap Project genotype calls (data release 22) made on a number of platforms other than Affymetrix arrays . Only a For the replicate data, high quality chips  are used in deriving the gold standard. For each SNP, calls for the three genotypes are tallied across the replicates. The genotype comprising greater than 50% of the calls is designated the gold standard call. SNPs not meeting this criterion are excluded from validation. Distinct gold standards are generated for each chip and method.In both the HapMap and replicate data validation, accuracy versus dropped rate curves are plotted. Each point on the plots represents the mean number of SNPs correctly called ignoring a specified percent of the lowest quality SNPs. Quality is assessed by program specific metrics of confidence, that is, 1 - ratio distance and percent accuracy for BRLMM and CRLMM, respectively.For trio data, family structures are exploited to measure the accuracy of genotype calling. The number of Mendelian errors is tabulated at a given dropped call rate. The resulting value is subtracted from and divided by the number of SNP trios that have no dropped calls to give an accuracy rate.et al. [2 of the quantity of DNA in the target sample associated with alleles A and B. Sense and antisense information are kept separately to allow the correct calling of the genotype by one strand when the other is non-informative [A,-, θB,-, θA,+, θB,+, and transform them into the log ratio M- = θA,- - θB,- and M+ = θA,+ - θB,+ and average log intensities S- = /2 and S+ = /2.A brief summary of the pre-processing and genotyping algorithms is presented here; for a more technical treatment, see Carvalho et al. . Startinet al. . The resormative . We denoi from sample j by Mi,j with sense and antisense orientation denoted by s = +,-. We code the genotypes by k = 1, 2, 3 for AA, AB, and BB, respectively. In general, M values impart strong discriminatory power  algorithm. Examples of the estimated fL and fS are included in Figure f1 will be larger than the standard deviation of errors e.with M values. The utility of the hierarchical model is most apparent for SNP regions for which there are few observations in the training step. Using empirically derived priors for the centers and scales of the genotype regions, we give a closed form empirical Bayes solution to predict centers and scales for cases with few or no observations.The fitted models can also be used to obtain genotype calls by estimating and maximizing the probability of each class for each SNP. However, for all SNPs for which we have HapMap calls (available for about 96% of SNPs on the arrays), we use a supervised learning approach, which yields more accurate genotype calls. We use these calls to define 'known' genotypes, which in turn permits us to define a training set. For SNPs with no HapMap calls we use the estimates from the model described above to define the 'known' classes. With the training data in place we use a two-stage hierarchical model and give likelihood-based closed-form definitions of the genotype regions. For each SNP, we define two-dimensional genotype regions based on the sense and antisense Zi,j be the unknown genotype for SNP i on sample j. Figures Let Xi,j,s and fj,k are as above but with the j and s notation re-introduced, mi,k,s is the SNP-specific shift from the typical genotype region centers, and ei,j,k,s represents measurement error. We expect different samples to have different biases; thus, the effects function f now depends on j. Notice that the SNP-specific covariates X also depend on the sample because the average signal S may vary from sample to sample. The 'm's represent the cluster center shifts not accounted for by the covariates included in X. To define the first level of our model, we denote the vector of SNP-specific region centers with mi = . We model the distribution of this vector with a multivariate normal distribution. Notice that, by definition, m is centered at 0, since the mean levels of the three genotypes are absorbed into f. The second level of the model, the variability seen within the genotypes for each SNP, is described by the 'e's. We assume these to be independent (conditioned on genotype Z) normally distributed random variables across samples and SNPs. We use an inverse chi-squared prior to improve estimates of the variance structure when not enough data are available. Because the large number of SNPs permit us to estimate the fjs precisely, for simplicity, we treat them as known. With this estimate of fj in place for each sample, all we need to make our likelihood-based genotype calls are estimates of the centers and scales. The key idea is to consider the HapMap calls as known genotypes and use this information to obtain maximum likelihood estimates. A second step is to update these estimates with posterior means derived from the hierarchical model. The mathematical details are described in Carvalho et al. [Here, o et al. .Mi,j,+ and Mi,j,-. Notice that these M values can come from any study, and we will use the centers and scales, estimated from the HapMap data. We do this by forming a likelihood based distance function based on the mixture model described above. Our prediction is the genotype k that minimizes the negative log-likelihood. Furthermore, the log likelihood ratio test serves as a predictor of confidence accuracy.The next step is to make a genotype call and calculate a confidence measure for any given pair (sense and antisense) of observed log-ratios: et al. [Although our pre-processing procedure greatly improves comparability across lab/studies, some slight differences in cluster centers appear to persist. For this reason we add an extra step to the algorithm described in Carvalho et al. . After oAs a measure of chip quality we define a SNR that captures the difference of the intensity values among the genotype clusters across all SNPs of a given chip. The fitted models described in the previous section are used to define our metric:e overshadow the signal f1 can be appreciated by noticing the loss of three distinct horizontal bands, corresponding to the three genotypes as seen in Figure Figure The measures of confidence of existing calling algorithms are, by and large, all based on a similar approach. Specifically, once genotype regions are defined for a given SNP, the measure of confidence for a given call is derived from the distance between: the point related to the call; and the region centers. This is the basis of the distance ratio used in BRLMM  as well For chip quality, we use the aforementioned metric SNR. To capture SNP quality, we quantify the distance between the regions for the three genotypes. A variety of factors can affect the efficacy of the feature probes in determining the genotype for a given SNP. Clearly, SNPs with greater separation of regions can be called more accurately Figure . We captFinally, we noted that accuracy is slightly different for homozygous and heterozygous genotypes. Heterozygous genotypes appear to be more difficult to call because their corresponding regions are more liable to be abutting the two homozygous regions. Therefore, a greater proportion of heterozygous points are close to the periphery of homozygous regions, where they are incorrectly called.To provide a useful prediction of accuracy that takes all these measures into account, we perform a logistic regression:fs are smooth and model them with cubic splines. We fit this model on the high quality HapMap dataset to obtain estimates of f1 and f2. The model was fit to heterozygous and homozygous calls separately. The resultant covariates are used in new datasets to calculate per-SNP accuracy.We assume the To ascertain the robustness of the confidence metric from datasets generated from different labs and of different quality, we plot accuracy versus confidence threshold. The thresholds correspond to values used to achieve the specified dropped rates in the ADPs. Confidence metrics that are more robust will produce plots with less outward fanning.Another method of validating confidence metrics is to divide calls among quantiles according to the metric and plot average accuracy against the confidence metric for each quantile . Ideally, curves stratified by homozygote and heterzygote calls should not deviate from one another. Confidence metrics that accurately predicted per-call accuracy produce curves that lie on the diagonal.ABACUS, Adaptive Background genotype Calling Scheme; ACP, average accuracy versus quantile confidence metric plot; ADP, accuracy versus drop rate plot; BRLMM, Bayesian Robust Linear Model with Mahalanobis Distance Classifier; CRLMM, Corrected Robust Linear Model with Mahalanobis Distance Classifier; DM, Dynamic Model; MPAM, Modified Partitioning Around Medoids; RLMM, Robust Linear Model with Mahalanobis Distance Classifier; RMA, Robust Multiarray Average; SNP, single nucleotide polymorphism; SNR, signal to noise ratio.SL and RAI conceived the study, developed the software, and drafted the manuscript. AC conceived the study, provided the data, and revised the manuscript. BC conceived the study, developed the software, and revised the manuscript. DEA conceived the design and revised the manuscript. DJC revised the manuscript.The following additional data are available. Additional data file Supplemental figuresClick here for file


Sus scrofa chromosomes 2 and 12, as well as the fifth region around 67 cM on chromosome 2, respectively. In total, 151 out of 416 SNPs discovered were genotyped successfully. Using a family-based analysis we found that four regions surrounding ELF5, KIF18A, COL23A1 on chromosome 2, and NPTX1 on chromosome 12, respectively, may contain the genetic variants important for the development of the scrotal hernia in pigs. These findings were replicated in another case-control dataset. The SNPs around the ELF5 region were in high linkage disequilibrium with each other, and a haplotype containing SNPs from ELF5 and CAT was highly significantly associated with hernia development. Extensive re-sequencing work focused on the KIF18A gene did not detect any further SNPs with extensive association signals. These genes may be involved in the estrogen receptor signaling pathway (KIF18A and NPTX1), the epithelial-mesenchymal transition (ELF5) and the collagen metabolism pathway (COL23A1), which are associated with the important molecular characteristics of hernia pathophysiology. Further investigation on the molecular mechanisms of these genes may provide more molecular clues on hernia development in pigs.Scrotal hernia in pigs is a complex trait likely affected by genetic and environmental factors. A large-scale association analysis of positional and functional candidate genes was conducted in four previously identified genomic regions linked to hernia susceptibility on  However, the molecular mechanism of etiology remains elusive Most of the inguinal and scrotal hernias  have now been frequently applied in the search for human disease susceptibility loci Sus scrofa chromosomes (SSC) 2 and 12 have been replicated in seven other independent paternal families derived from three commercial Pietrain-based pig lines Our previous genome-wide linkage studies have identified several genomic regions related to the hernia incidence in Pietrain-based lines Since the pig whole genome sequencing project is not yet completed, we had to use all available sequence information in pigs, as well as the comparative information from the human genome. Taking into consideration the practical concerns of experimental power in the design of GWA and candidate gene association analyses in human disease studies HIPK3) and the complement regulatory protein CD59 molecule (CD59) in region II, the cathepsin F (CTSF) in region I, and the mitochondrial lon peptidase 1 (LONP1) (COL23A1) and the ADAM metallopeptidase with thrombospondin type 1 motif, 2 (ADAMTS2), which likely map to the region surrounding 67 cM on SSC2 potentially linked to hernia susceptibility , we blasted them against the NCBI nucleotide database, and selected a first set of four important candidate genes distributed in the three specific sub-regions, i.e. the homeodomain interacting protein kinase 3 ( (LONP1) . Then ne (LONP1) , and the (LONP1) . This futibility .In order to give a better coverage over the interesting regions, we have chosen 99 genes in total, both functionally and positionally, from the five interesting regions linked with scrotal hernia in pigs on SSC2 and SSC12. For these 99 genes, we discovered 451 SNPs (dbSNP accession numbers: 107794144–107794595)  . Due to the cost and development of multiplexes, we have assayed 181 SNPs from 89 genes in six iPLEXes by the Sequenom platform . After quality evaluation of the genotype data, 151 SNPs (dbSNP accession numbers: 107794444–107794595) are in Hardy-Weinberg equilibrium (p>0.01), with acceptable genotyping success rates (>85%), and no deviation of Mendelian inheritance existed in our pig families for these SNPs. We adopted the iterative strategy for the practical genotyping procedure. The genotype data were analyzed after one round of genotyping, and then based on the results we got, we chose new SNPs for new assays, according to their positions and functional importance, as well as the requirement of maximizing the number of genes included in the iPLEXes to be studied to increase the marker coverage density.EFEMP2) located in region I , HIPK3 and the kinesin family member 18A (KIF18A), were highly significantly associated with scrotal hernia (p<0.01) .In the family-based Pietrain lines, we found several SNPs in the four regions on SSC2 were significantly associated with scrotal hernia incidence, respectively, after single marker association analysis . One SNPregion I , one SNPectively . Interes(p<0.01) . Even af(p<0.01) , and the(p<0.01) . FurtherELF5 (1 MB region) and KIF18A (6 MB region) , except exon 1 of KIF18A, which were found in an unordered sequenced BAC clone CH242-13K2 on chromosome 2 (NCBI accession number: CU633215), and genotyped 14 SNPs from this gene. However, the newly genotyped SNPs from KIF18A were not significantly associated with scrotal hernia (p>0.05). In the catalase gene (CAT), which is very close to ELF5 physically and oriented differently in human, three new SNPs, two in the 5′-untranslated region (UTR), CAT-5U-5 and CAT-5U-1, and one in intron 11, CAT-E11-1, were found to be associated significantly with scrotal hernia. The SNP CAT-E11-1 was still significant after multiple testing procedure (p<0.05). It seems that the closer the SNP to ELF5, the stronger the association (AAAAGCA) now composed of seven SNPs from ELF5 and CAT, ELF5-1, ELF5-3, ELF5-5, ELF5-8, CAT-E11-1, CAT-5U-5 and CAT-5U-1, is highly significantly associated with scrotal hernia (p = 9.0E-04)  . At thisociation . In addi9.0E-04) , which ePYCR1) and the neuronal pentraxin I (NPTX1), PYCR1-1, PYCR1-E4-E5, NPTX1-3 and NPTX1-4-2, were highly significantly associated with scrotal hernia (p<0.0001), respectively. The significance of the association signal is higher for these SNPs than those on SSC2. We observed low LD between them, as well for the remaining SNPs genotyped in this region.On SSC12, we focused on the first 20 cM region which supports a hernia QTL, and several genes in this region were found to be significantly associated with scrotal hernia , Table 1ELF5 (ELF5-1 and ELF5-5) were highly significantly associated with scrotal hernia (p<0.001). Two SNPs in CAT (CAT-E11-1 and CAT-5U-1) were significantly associated (p<0.01), and another SNP, CAT-5U-5, at p<0.05 level. The SNP in HIPK3 was not associated any more. The same SNP in intron 3 of KIF18A (KIF18A-E3-3) was associated with scrotal hernia, like in the family-based analysis (p<0.01). The SNP in COL23A1 (COL23A1-E2) was highly significantly associated with hernias (p<0.001) , and another one, NPTX1-3, highly significant (p<0.001) also. However, SNPs from PYCR1 were not significant in case control samples.We further analyzed the case-control dataset, composed of an admixture of several pig lines, two Duroc lines, two Landrace lines, one Large White line, and crossbred animals . This dataset gave the replication of the same significant association results as that in the family-based dataset, and seemed to demarcate the region associated with scrotal hernia . On SSC2p<0.001) . On SSC1ELF5, KIF18A, COL23A1 on SSC2 and NPTX1 on SSC12, respectively, may contain the genetic variants important for the development of the scrotal hernia in pigs.After comprehensive SNP discovery and genotyping by using first a family-based analysis, which was replicated in another case-control dataset, we found that four regions surrounding The mapping of causal variants for complex disease traits requires the correctly assigned physical order and orientation of genomic sequences, and genetic relationship among genetic markers in specific genomic regions , which is still under development for pig genomics researchers now Here, family-based analysis was first used to identify the four interesting regions, and then a case-control analysis similar to an admixture mapping method was used to replicate the results. Admixture mapping, or mapping by admixture linkage disequilibrium, is widely used in human disease studies now, to track the common underlying genetic factor(s) for different populations. It needs fewer markers, is more robust to allelic heterogeneity, and can achieve higher mapping resolution than traditional linkage studies KIF18A and NPTX1), collagen metabolism (COL23A1), and the epithelial-mesenchymal transition (EMT) (ELF5). These were three of the original five pathways that had been previously suggested. When treated with ICI 182,780 (the anti-estrogen fulvestrant), KIF18A, as a motor protein, was found to be able to bind to ERα, elicit the estrogen non-genomic response and activate the ERK-pathway as well NPTX1 was observed after ICI 182,780 treatment on rat efferent ductules, along with the altered expression of genes related to extracellular matrix organization, such as matrix metalloproteinase 7 (MMP7) ESR1 and ESR2 with scrotal hernia were not found using the same datasets KIF18A and NPTX1, as they could possibly be linked to estrogen, could also affect the function of estrogen, or be affected, and thus involved in the abnormal development of scrotal hernia, though the exact molecular mechanism is still unclear.Interestingly enough, genes fine mapped to be associated with scrotal hernia are implied in the pathway regulated by the estrogen receptors , Iowa State University.We collected 1,467 pigs born between 1991 and 2002, from 6 commercial pig breeding lines  and pigs from crossbred herds, which were classified as affected or unaffected for scrotal hernias. These pigs can be divided into two different datasets with regard to with or without family information. The family-based Pietrain lines had 946 individuals from 298 pig nuclear families, including unaffected sires, dams, and male offspring (90% affected). The case-control dataset was composed of 100 unaffected and 421 affected males, which included animals derived from two Duroc lines, two Landrace lines, one Large White line, and crossbred animals . We extracted the genomic DNA of all animals, and adjusted the DNA concentration to 12.5 ng/µl.Homo sapiens (HSA) chromosomes 5, 11 and 19, and SSC12 to HSA17 (www.nature.com/nature/supplements/collections/humangenome/chromosomes/index.html). We further constructed the gene networks by putting them into PubGene (www.pubgene.org), not only to examine their relationships, but also to select more relevant candidate genes.We selected the positional and biological candidate genes after text mining the published data resources and literature. These genes were extensively examined for their comparative locations on pig chromosomes 2 and 12 . In addito HSA17 , gene inwww.sanger.ac.uk/cgi-bin/humace/clone_status?speciesPig) was also checked and the sequences of whole BAC clones in the interesting regions were downloaded, and annotated by blasting against human genome sequence (http://frodo.wi.mit.edu/).The latest information from the pig genome sequencing project from the Sanger Center  were used for single nucleotide polymorphism (SNP) discovery. After sequencing the PCR products, the sequences were aligned and compared using Sequencher software version 3.0 . Some of the potential SNPs were confirmed by restriction fragment length polymorphism (PCR-RFLP) tests using suitable restriction endonucleases .The SNP genotyping was carried out commercially on the Sequenom MassARRAY® system. Two positive controls, two negative controls and one blank were put on the same plate for the purpose of quality control. Multiplex primers were designed using AssayDesign 3.1. After PCR amplification, samples were then spotted onto a 384 well SpectroChip® by a robot dispenser, which enabled an automated readout by laser excitation in a compact MALDI-TOF mass spectrometer. SNP genotypes were collected by the Typer 3.4 Software. Each SNP was checked to estimate the Hardy-Weinberg equilibrium status and the minor allele frequency (MAF).2), as well as the haplotype association analyses.All SNPs were analyzed individually by the transmission disequilibrium tests (TDT) on the family-trios and the case-control data. Three software packages widely used in human disease studies, FBAT (v2.0.2c), Haploview (v4.0), and PLINK (v1.02), were employed in this study http://www.r-project.org) to evaluate the expected ratio of erroneous rejections of the null hypothesis to the total number of rejected hypotheses among all the genes or SNPs analyzed Given that a large number of SNPs were genotyped, we evaluated the robustness of our results using the false discovery rate (FDR). FDR values (p<.20) were calculated using package ‘multtest’ in R  and Sus scrofa, slight modification has been made for SSC2 and SSC12. On SSC2, three regions(Regions I–III) have been implied in pig hernia development. Markers defining these three regions were used to find genes HIPK3, CD59, CTSF and LONP1 (gene symbols in red), by BLAST using directly the marker sequence or BAC end sequence of those BACs containing the marker. Within the genomic region at the beginning of SSC12, 30 genes have been selected [S1–S8]. Only partial lists of the selected genes are shown here.(0.12 MB DOC)Click here for additional data file.Figure S2www.pubgene.org), not only to examine their relationships, but also to select out more relevant candidate genes in our target genomic regions.Gene network constructed for positional candidate genes. The gene networks were constructed by putting the selected candidate genes in the interesting genomic regions for porcine scrotal hernia into PubGene website ((0.59 MB DOC)Click here for additional data file.Table S1Positional Candidate Genes selected on SSC2 and SSC12.(0.08 MB XLS)Click here for additional data file.Table S2Association results for fine mapping regions linked to scrotal hernia in pigs .(0.07 MB DOC)Click here for additional data file.


The extent of linkage disequilibrium (LD) within a population determines the number of markers that will be required for successful association mapping and marker-assisted selection. Most studies on LD in cattle reported to date are based on microsatellite markers or small numbers of single nucleotide polymorphisms (SNPs) covering one or only a few chromosomes. This is the first comprehensive study on the extent of LD in cattle by analyzing data on 1,546 Holstein-Friesian bulls genotyped for 15,036 SNP markers covering all regions of all autosomes. Furthermore, most studies in cattle have used relatively small sample sizes and, consequently, may have had biased estimates of measures commonly used to describe LD. We examine minimum sample sizes required to estimate LD without bias and loss in accuracy. Finally, relatively little information is available on comparative LD structures including other mammalian species such as human and mouse, and we compare LD structure in cattle with public-domain data from both human and mouse.D', Dvol and r2, for 1,566,890 syntenic SNP pairs and a sample of 365,400 non-syntenic pairs. Mean D' is 0.189 among syntenic SNPs, and 0.105 among non-syntenic SNPs; mean r2 is 0.024 among syntenic SNPs and 0.0032 among non-syntenic SNPs. All three measures of LD for syntenic pairs decline with distance; the decline is much steeper for r2 than for D' and Dvol. The value of D' and Dvol are quite similar. Significant LD in cattle extends to 40 kb (when estimated as r2) and 8.2 Mb (when estimated as D'). The mean values for LD at large physical distances are close to those for non-syntenic SNPs. Minor allelic frequency threshold affects the distribution and extent of LD. For unbiased and accurate estimates of LD across marker intervals spanning < 1 kb to > 50 Mb, minimum sample sizes of 400 (for D') and 75 (for r2) are required. The bias due to small samples sizes increases with inter-marker interval. LD in cattle is much less extensive than in a mouse population created from crossing inbred lines, and more extensive than in humans.We computed three LD estimates, r2 > 0.19) and up to 300,000 markers at 10 kb intervals for a high power genome scan (median r2 > 0.62). For estimation of LD by D' and Dvol with sufficient precision, a sample size of at least 400 is required, whereas for r2 a minimum sample of 75 is adequate.For association mapping in Holstein-Friesian cattle, for a given design, at least one SNP is required for each 40 kb, giving a total requirement of at least 75,000 SNPs for a low power whole-genome scan (median  Recent developments in high-throughput genotyping technology -3 and thD' and r2, each with different statistical properties  wa waD' [r2 mapping  defined  package  in the R package . A randoD' (Dvol) as per the formulae given and implemented in the C codes of Chen et al. [Dvol from the four haplotype counts. Four haplotype counts were computed with GOLD [We also computed a volume measure of n et al. . The proith GOLD .To examine the effect of sample size on the accuracy of estimation of LD parameters, samples of 25, 50, 75, 100, 150, 200, 300, 400, and 500 were randomly drawn from the total sample of 1,000 analysed in this study Table . The effMSK conceived the study, contributed in its design, data collection, analysis and was the primary author for assembling the manuscript. ARC participated in interpretation and manuscript preparation. FWN participated in data interpretation and manuscript preparation. KRZ contributed in generating SNP data and preliminary data analyses. JALC organized DNA sample preparation and participated in SNP discovery and SNP genotyping. WB contributed in SNP discovery and provided bioinformatic support in SNP positioning. RDS provided samples and edited the manuscript. JFT provided samples and edited the manuscript. HWR is project leader and contributed in project concept, design, interpretation and manuscript preparation.Figure S1. Frequency distribution of MAF for 9,195 SNPs used in the LD analysis. Figure S2. Comparison of Dvol and D' computed for non-syntenic SNP pairs. Figure S3. Relationship between mean minor allelic frequency (MAF) and D' for non-syntenic SNP pairs. Figure S4. Relationship between mean minor allelic frequency and r2 for non-syntenic SNP pairs. Figure S5. Relationship between mean minor allelic frequency and Dvol for non-syntenic SNP pairs. Figure S6. Mean D' at different physical distances pooled over all the autosomes and estimated using different sample sizes. The numbers with n_ in legend are the sample sizes randomly drawn from a total of 1000. The n_1546 are all the bulls genotyped in the present study. Figure S7. Mean r2 at different physical distances pooled over all autosomes and estimated using different sample sizes. The numbers with n_ in legend are the sample sizes randomly drawn from a total of 1000. The n_1546 are all the bulls genotyped in the present study.Click here for fileFigures S8. Positions and MAF of the SNPs included in present studies.Click here for fileTable S1. Mean linkage disequilibrium among syntenic SNPs over different map distances on each autosome (1–29).Click here for fileFigure S9. Distribution of D' between SNP pairs in relation to the physical distance between loci (Mb), for individual autosomes (1–29). The red line shows average D' in each 500 kb sliding window. The blue line shows the theoretical distribution from the Malécot model.Click here for fileFigure S10. Distribution of r2 between SNP pairs in relation to the physical distance (Mb) between loci, for individual autosomes (1–29). The red line shows average r2 in each 500 kb sliding window.Click here for fileFigure S11. Linkage disequilibrium (D') between SNPs on all the bovine autosomal chromosomes (BTA1-BTA29) presented in the form of heatmap of D' . All SNP with the MAF of less than 0.05 and showing deviation from HWE were excluded in the LD measurement. These figures were prepared by Haploview software. Here bright red color indicates D' = 1 with LOD ≥ 2, blue D' = 1 with LOD < 2, shades of pink/red D' < 1 with LOD ≥ 2 and white D' < 1 with LOD < 2.Click here for fileFigure S12. Linkage disequilibrium (r2) between SNPs on all the bovine autosomal chromosomes (BTA1-BTA29) presented in the form of heatmap of r2 . All SNP with the MAF of less than 0.05 and showing deviation from HWE were excluded in the LD measurement. These figures were prepared by Haploview software. Here black color indicates r2 = 1, shades of grey 0 <r2 < 1 and white r2 = 0.Click here for fileTable S2. Comparison of pair-wise estimates D' and Dvol for two chromosomes obtained from different sample sizes and compared against a reference sample of 1000 animals.Click here for file


We found that in this sample, adjusting for the continuous PCs or adjusting for discrete clusters identified using the PCs adequately accounts for the case-control population structure, but that a recently proposed randomization test performs poorly.Population structure occurs when a sample is composed of individuals with different ancestries and can result in excess type I error in genome-wide association studies. Genome-wide principal-component analysis (PCA) has become a popular method for identifying and adjusting for subtle population structure in association studies. Using the Genetic Analysis Workshop 16 (GAW16) NARAC data, we explore two unresolved issues concerning the use of genome-wide PCA to account for population structure in genetic associations studies: the choice of single-nucleotide polymorphism (SNP) subset and the choice of adjustment model. We computed PCs for subsets of genome-wide SNPs with varying levels of LD. The first two PCs were similar for all subsets and the first three PCs were associated with case status for all subsets. When the PCs associated with case status were included as covariates in an association model, the reduction in genomic inflation factor was similar for all SNP sets. Several models have been proposed to account for structure using PCs, but it is not yet clear whether the different methods will result in substantively different results for association studies with individuals of European descent. We compared genome-wide association  Cases for the North American Rheumatoid Arthritis Consortium (NARAC) study were collected across the United States; controls were collected from Long Island, NY . BecauseSeveral methods have been developed to detect and adjust for population structure, including genomic control , structuWhen limited numbers of SNPs are available to compute the PCs, each SNP will contribute a greater proportion of information to the PCA. In this situation, SNPs in strong linkage disequilibrium (LD) may have excessive influence on one or a set of PCs . HoweverIn addition to the controversy concerning choice of SNPs for PCA, investigators have proposed several methods to adjust for the structure. The developers of the computer program EIGENSTRAT recommend adjustment for the PCs as linear covariates in an association model, while other investigators have proposed approaches that cluster individuals, arguing that the PCs, phenotype, and allele frequencies may not be linearly related, particularly when the PCs create distinct clusters . Kimmel To address these questions, we compare six choices for subsetting SNPs for PCA, and four methods for adjusting for structure identified using PCA, as well as no adjustment and genomic control, using the NARAC rheumatoid arthritis (RA) genome-wide SNP data provided by GAW16. Our goal is to determine to what extent SNP choice and method of population structure adjustment affects the results of the genome-wide association study, measured by the genomic control inflation factor, and by specific association results for previously reported positive associations between SNPs and RA -13.p < 10-6), ≥ 2% missing genotypes, or minor allele frequency <0.01, leaving a total of 470,943 autosomal SNPs.GAW16 NARAC samples  were genotyped using the Illumina 550 k chip. All subjects had fewer than 10% missing genotypes. Seven subjects had X chromosome heterozygosity incompatible with their phenotypic sex   and werer2 > 0.4 . For association Models (C), (D), and (E) we used the partitioning around medoids algorithm  for exploration of adjustment methods . We determined the distributions of genome-wide s2476601 ,12 and rs2476601  with dision in R ) to clusion in R .n = 77), 0.11 (n = 68), 0.12 (n = 460), 0.37 (n = 230), and 0.63 (1120). One million samplings were used to assess significance.Logistic regression analyses [Models (A)-(D)] were performed using the computer program PLINK  and analp < 0.05; Table r2 > 0.94). For each of the remaining S3 PCs, we display in Table r2 > 0.15. Subset S3 PC3 and PC4 were highly correlated with the same PCs for subsets S4-S6 (r2: 0.83-0.98). For Subset S2, PC3 and PC4 appear to take the place of S3 PC3. The correlations between the S3 PCs and PC3-PC10 obtained from the minimally filtered SNP Set S1 are weaker and less consistent. We examined the SNP weights to understand why some PCs in different subsets are not strongly correlated. SNPs in the MHC region on chromosome 6 were assigned large weights for PCs 1 and 2 in the S1 subset. Removal of SNPs in this region , or general removal of SNPs in LD across the genome  reduced the problem of extreme weights on chromosome 6 , which is known for strong structure in European populations [For each SNP subset, PC1-3 are highly associated with case status  level without reducing power for true positive associations. The genomic control inflation factor (λ), defined as the median association test statistic across all SNPs divided by its expected value, is an overall indicator of the inflation in the test statistics . We compLinear adjustment for PCs to account for population structure has been shown to have lower power and higher type I error than alternatives in some situations ,10, therFor the genome-wide results excluding the MHC region, we compared the genomic control inflation factor (λ) for adjustment Methods (B)-(E) to the unadjusted Model (A). The inflation factor was 1.24 for the unadjusted logistic regression, 1.03-1.04 for the PC-adjusted logistic regression analyses, and 1.15 for the PSAT analysis (Figure We performed PCA for six subsets of genome-wide SNPs; the first two PCs were similar for all subsets, and were highly associated with RA case status. The third and fourth PCs were very similar for SNP sets that either removed all SNPs in LD or removed the chromosome 8 inversion region known for high LD. SNP sets that included all SNPs had SNPs with extreme weights for some PCs. In this data set, structure adjustment using PCs obtained from any of the SNP subsets similarly reduces the genomic inflation factor. However, SNP sets that result in PCs that over-weight a genomic region may be prone to loss of power because strong PC weights on a true-positive SNP may adjust away the SNP effect.We evaluated four methods of adjusting for structure using PCs. Adjustment for the four PCs associated with case status as linear covariates or for the cluster assignments in logistic regression substantially reduces both the genomic inflation factor and the significance level of the most highly associated SNPs Figure , suggestExploring existing genome-wide association study data allows us to gain experience with methods for adjusting for population structure under realistic conditions, including variable LD due to inversion polymorphisms and regions of highly associated SNPs. We have found that population structure exists in the GAW16 RA data, and that the structure is associated with case status. Therefore, the association tests are potentially subject to confounding by genetic ancestry. In this sample, we have determined that the choice of SNP subset for PCA does not substantively affect the resulting reduction in genomic inflation factor for genome-wide association analyses. However, for some SNP subsets, the PCs associated with case status and used in the structure adjustment heavily weighted small numbers of SNPs, which could lead to reduced power to detect true associations in those genomic regions. Thus, the SNP set selected could have an impact on power. We recommend that in practice, for studies with modest levels of structure such as that expected when subjects are all of European ancestry, investigators check the PCs that will be used for structure adjustment for extreme weights and choose alternative SNP sets if any genomic regions are heavily weighted.GAW16: Genetic Analysis Workshop 16; LD: Linkage disequilibrium; NARAC: North American Rheumatoid Arthritis Consortium; PC: Principal component; PCA: Principal-component analysis; PSAT: Population structure association test; RA: Rheumatoid arthritis; SNP: Single-nucleotide polymorphism.The authors declare that they have no competing interests.GMP and NT participated in carrying out the analysis, interpreting the data, and drafting the manuscript. KLL participated in interpreting the data and drafting and critically revising the manuscript. All authors read and approved the final manuscript.