The mutation rate is known to vary between adjacent sites within the human genome as a consequence of context, the most well-studied example being the influence of CpG dinucelotides. We investigated whether there is additional variation by testing whether there is an excess of sites at which both humans and chimpanzees have a single-nucleotide polymorphism (SNP). We found a highly significant excess of such sites, and we demonstrated that this excess is not due to neighbouring nucleotide effects, ancestral polymorphism, or natural selection. We therefore infer that there is cryptic variation in the mutation rate. However, although this variation in the mutation rate is not associated with the adjacent nucleotides, we show that there are highly nonrandom patterns of nucleotides that extend ∼80 base pairs on either side of sites with coincident SNPs, suggesting that there are extensive and complex context effects. Finally, we estimate the level of variation needed to produce the excess of coincident SNPs and show that there is a similar, or higher, level of variation in the mutation rate associated with this cryptic process than there is associated with adjacent nucleotides, including the CpG effect. We conclude that there is substantial variation in the mutation that has, until now, been hidden from view. Understanding the process of mutation is important, not only mechanistically, but also because it has implications for the analysis of sequence evolution and population genetic inference. The mutation rate is known to differ between sites within the human genome. The most dramatic example of this is when a C is followed by G; both the C and G nucleotides have a rate of mutation that is between 10- and 20-fold higher than the rate at other sites. In addition, is it known that the mutation rate may be influenced by the nucleotides flanking the site. Here we show that there is also very substantial variation in the mutation rate that is not associated with the flanking nucleotides, or the CpG effect. Although this variation does not depend upon the adjacent nucleotides, there are nonrandom patterns of nucleotides surrounding sites that appear to be hypermutable, suggesting there are complex context effects that influence the mutation rate. Substantial variation is seen in the rate of mutation among different sites in the human genome, which is not associated with mutable CpG sites or due to simple context effects. The mutation rate is thought to vary across the human genome on several different scales. At the chromosomal level, the Y chromosome evolves faster than the autosomes, which evolve faster than the X chromosome ,2. This Although variation in the mutation rate has been well-characterised in terms of adjacent nucleotides ,9,11, itp < 0.0001 under the null hypothesis that the ratio is 1). This excess is not due to our inability to correct for CpG effects; if we remove CpG dinucleotides from the analysis, we observe 5,028 coincident SNPs but would only expect 2,533 taking into account simple context effects (ratio = 1.98 (0.03); p < 0.0001). If we look at the pattern of coincident SNPs, it is evident that almost all the excess is due to the same SNP being present in both humans and chimpanzees, with A-T/A-T SNPs being dramatically over-represented  with the chimpanzee SNP in the central position and a human SNP elsewhere within the alignment. Of these alignments, 11,571 have the human and chimpanzee SNP at the same position ; we refeAlthough the excess of coincident SNPs is consistent with variation in the mutation rate that is not associated with simple context, there are several other explanations that warrant consideration.In correcting for simple context effects, we have also made two assumptions; we have assumed that the pattern of mutation is the same on the two strands of the DNA duplex, and we have assumed that context effects are the same across the genome. As a consequence of these assumptions, we could be underestimating the expected number of coincident SNPs. For example, let us imagine that the triplet AAA has a high mutation rate on one strand, say the transcribed strand, and a low mutation rate on the other strand, but that the pattern is the opposite for the triplet CCC . Because the relative mutation rates of AAA and CCC depend on which strand we are considering, we would tend to underestimate the expected number of coincident SNPs.The pattern of mutation is known to differ between the two DNA strands in a manner that depends on transcription ,13. Hower = 1.00 for all triplets, r = 0.85 without triplets containing CpGs; r = 0.99 for all triplets, r = 0.75 without triplets containing CpGs; In agreement with Green et al. , we obser = 0.99 using all triplets; r = 0.88 excluding triplets involving CpGs; The excess of coincident SNPs could also be due to variation in the pattern of mutation across the genome for reasons similar to those given for strand asymmetry; if the relative rate at which each triplet mutates differs between genomic regions, then we will underestimate the expected number of coincident SNPs. Since such variation in the pattern of mutation might be expected to generate differences in base composition, we divided our dataset of alignments according to their GC content and estimated the mutation rate of the central nucleotide in each triplet in the chimpanzee sequence using the human sequence to infer the ancestral sequence. The relative rates of mutation inferred from the sequences in the upper and low GC content quartiles are highly correlated to each other (p < 0.001) or non-CpG sites (1.51 (0.26); and p < 0.05). Second, the pattern of coincident SNPs  , as oppoent SNPs  is inconIt is also possible for the apparent excess of coincident SNPs to be due to selection; if some regions of the genome are under selection, then we expect them to have a low density of SNPs, because many SNPs will be removed as they are deleterious. As a consequence, SNPs will be clustered between these regions, causing an apparent excess of coincident SNPs. This seems an unlikely explanation, since the vast majority of our data is intergenic and intronic , and although selection is known to act within these regions, it is thought to only affect a small percentage of sites –17. FurtIt therefore seems that the excess of coincident SNPs is a consequence of variation in the mutation rate that is not associated with simple context effects, variation in these context effects between strands or regions of the genome, or natural selection. The question therefore arises whether the variation in the mutation rate is associated with other contexts that are distant from the target site, degenerate in nature, or sufficiently complex to be difficult to discern. It should be noted that simple context effects beyond the adjacent nucleotides  are not responsible for the excess. Although these effects exist , they arp < 0.0001). Considering pentamers, rather than triplets, also fails to reveal any context that is associated with coincident SNPs, except for the α-polymerase pause site motif, TG(A/G)(A/G)(G/T)(A/C), which has been suggested as a hypermutable motif [To investigate whether there are other, more complex context effects, we tabulated the frequency of each triplet at each site in the alignments containing coincident SNPs, and a similar-sized dataset of alignments with noncoincident SNPs. Surprisingly, we found significant heterogeneity in triplet frequencies that extends to about 80 bp on either side of the coincident SNP A; i.e., le motif ,19. HoweTo quantify the level of cryptic variation in the mutation rate, we fit two models to the ratio of the observed number of coincident SNPs over the number expected with simple context effects. In the first model, we assumed that the variation in the mutation rate was log-normally distributed; in the second, we assumed that there were two types of sites—normal and hypermutable. These models give qualitatively similar estimates of the variation, so we only discuss the log-normal model in detail, because this is a model with a single parameter  phylogenies  and altHowever, although many of the hot spots in mtDNA appear to be due to strand slippage–type mutational mechanisms ,27, thisIt has also been shown recently that the mutation rate is elevated close to insertion and deletion mutations in the nuclear genomes of several eukaryotes, including humans . HoweverOver the last few years, DNA sequence analysis has revealed that the mutation process is highly complex, varying between different parts of the genome and between different sites. Unfortunately we do not yet understand many of these patterns.−30, mismatch score = −1, and simple sequence filter off. We retained those alignments, which were 101 bp in length, and in which the human or chimpanzee sequence showed identity at 96 sites if the SNPs were coincident, or 94 sites if they were not coincident. We adjusted the number of matches required to control for the fact that if the SNPs are not coincident, then there must be two extra mismatches. We randomly chose one alignment if a chimpanzee SNP matched more than one human SNP at the levels of identity we set; we obtained very similar results removing these cases from the analysis. The alignments were trimmed to 40 bp on either side of the central chimpanzee SNP because there is a slight bias away from finding human SNPs at the edges of the chimpanzee query sequence. This bias occurs because SNPs, being classed as mismatches, tend to cause BLAST to prematurely terminate the alignment. To perform the analysis of triplet frequencies, we downloaded an extended flanking sequence for the chimpanzee SNPs analysed.We downloaded human and chimpanzee SNPs from dbSNP build 126. Dividing the data into chromosomes, we BLASTed each chimpanzee SNP, along with 50 bp of flanking DNA on either side of the SNP, against a database of human SNPs. We set the BLAST parameters as follows; e-value = 1 × 10The macaque SNPs were kindly provided by Dr. Ripan Malhi . We repeSites were designated as CpG if the site, or any of the SNPs at the site, would yield a CpG dinucleotide.xyzn, where x, y, and z can be T, C, A, or G, in the chimpanzee sequence in the alignments, along with the number of chimp triplets that have a human SNP opposite the central nucleotide, xyz.Hsnpn. From these, we can estimate the probability of observing a human SNP opposite a chimpanzee triplet in our alignments: xyz = nxyz.Hsnp / nxyzp. We can also calculate the frequency of each triplet in the chimpanzee sequences: xyz = nxyz/fΣxyzn To calculate the probability that the human and chimpanzee SNPs are coincident, we need to take into account that there are two alleles in the chimpanzee SNPs, and the triplets they are a part of will have different probabilities of having a human SNP opposite them. If we knew the relative frequencies of the chimpanzee alleles, we could calculate the chance of a coincident SNP as y pxyzg + (1– yg)xy'zp where y and y' are the two chimpanzee alleles and yg is the frequency of the y allele. However, we do not have allele frequency information, so we estimated the relative probabilities of each of the two ancestral states for the chimpanzee SNP, since the ancestral allele is likely to be at a higher frequency in the population. For example, let us imagine we have a CYC SNP—i.e., a Y SNP surrounded by C on both sides. The ancestral triplet could have been CCC or CTC. The probability that the SNP was generated from a CCC can be estimated as mCCC= fCCCrCCC/(fCCCrCCC + fCTCrCTC) where xyzr is the rate at which triplet XYZ generates a SNP in the central position of the triplet. We estimate xyzr by orienting the chimp SNPs using the human sequence, excluding coincident SNPs and SNPs for which the human nucleotide is different to both chimp alleles; let xyz.Csnps be the number of chimp triplets that are inferred to have generated a SNP, then xyzr = xyz,Csnp/nxyzs. The expected number of coincident SNPs in each alignment is then, using the above example, (mCCCpCCC + mCTCpCTC)/Σxyzp, where the summation is across all the triplets in the alignment. The total number of expected coincident SNPs was simply the sum across alignments.We estimated the expected number of coincident SNPs, taking into account the effects of adjacent nucleotides on the rate of mutation, what we term “simple” context effects, as follows. Our data consist of a set of alignments in which we have both a human and a chimpanzee SNP. We start by tabulating the numbers of each triplet, We used two methods to calculate the standard error for the ratio of the observed number of coincident SNPs over the expected number: we bootstrapped the data by alignment and then summed the observed and expected values across the bootstrapped datasets. However, it turned out that this was very closely approximated by assuming that the observed number of coincident SNPs was Poisson distributed and the expected value was known with no error; these are the standard errors we present.We performed a number of simulations to check that the BLAST analysis was not biased and that our method to estimate the number of coincident SNPs under simple context effects worked well. In each simulation, we evolved human genomic sequences under a mutation pattern, in which the mutation rate depended on the adjacent nucleotides, to generate a simulated human and chimpanzee sequence. Into these we introduced SNPs according to the same mutation pattern at the density found in dbSNP—one SNP every 266 bp in humans and every 2,128 bp in chimp. We then constructed a BLAST database of ∼140,000 human SNPs with 100 bp of flanking DNA sequence, and a query dataset of ∼18,000 chimpanzee SNPs with 50 bp of flanking DNA. We ran the BLAST analysis and analysed the output exactly as we had with the real data. We ran simulations in which we had no mutation bias and datasets in which the mutation rate of all triplets was the same except for triplets containing CpGs, which had a mutation rate 10, 15, or 20 times the background rate. We ran a set of simulations in which we had 0%, 1%, and 2% divergence. Our method works well at all divergences and under all mutation patterns, except when the CpG rate is very high, where the method tends to underestimate the expected number of coincident SNPs . Surprishttp://www.ncbi.nlm.nih.gov/projects/geo/). We normalized the results from the mouse and rat arrays separately using the RMA algorithm [To investigate strand asymmetry, we estimated the mutation rate of the central nucleotide in each triplet by tabulating the number of times each triplet contained a SNP. The direction of mutation was inferred from the frequency; i.e., the minority allele was judged to be the new mutation. We inferred mutation rates across 964 human genes from the Seattle SNPs  and Envilgorithm  as implelgorithm . We judglgorithm .h and μc, respectively; if μh and μc are small, the probability at a particular site will be γμh and γμc, where γ is the relative rate of mutation. Let us assume that γ takes some distribution D(γ) which has a mean of one. The expected number of coincident SNPs isWe estimated the variation in the mutation rate as follows. We start by assuming there is no divergence between humans and chimpanzees so a hypermutable site in humans will also be hypermutable in chimpanzees. Let the average probability of detecting a SNP at a site in humans and chimpanzees be μIf there is no variation in the mutation rate then this reduces tosuch that the ratio of the number of coincident SNPs, over the number expected with no variation, isan equation which only depends upon the distribution of γ. We assume that γ is either log-normally distributed, or that it has a two state distribution in which sites can either be hypermutable or normal (see Protocol S1). We estimate the parameters of the distribution of γ by considering the ratio of the observed number of SNPs over the number expected with simple context effects .v be the average rate of mutation per unit time in both humans and chimpanzees. Consider a site, in the ancestor of humans and chimpanzees, that currently has a mutation rate vγ. The probability that the site will remain unchanged along both the human and chimpanzee lineage isThis model is unrealistic, because we assume that a site does not change its mutation rate; however, hypermutable sites are more likely to change, and this may lead them to become nonhypermutable. Under the log-normal model, we assume that once a site changes, its mutation rate is drawn randomly from the log-normal distribution. Let t is the time since humans and chimpanzees diverged. The probability that such a site will produce a coincident SNP iswhere If the site changes in one of the lineages, then the mutation rates in the two lineages become independent of one another; since the mean of a product is the product of the means, when two random variables are independent, the probability of a coincident SNP at a site which has undergone at least one substitution isP0, as given by The expected number of SNPs with no variation in the mutation rate is still vt, which is the average divergence between humans and chimpanzees and the distribution of γ. Since we set the average of the log-normal distribution to one, we need only find the shape parameter of the log-normal distribution.This equation depends on the compound parameter 2To estimate the variance associated with simple context effects, we calculated the mutation rate of each triplet as above, when correcting simple context effects. We then scaled the mutation rates so the mean across triplets, taking into account their frequencies in the genome, had a mean of one. We then calculated the variance. This can be compared directly to the variance of the log-normal distribution which we had also constrained to have a mean of one. We weighted the variance estimates from the CpG and non-CpG sites by the relative frequency of the sites.Figure S1The slight deficit of human SNPs adjacent to the chimpanzee is caused by the adjacent sites being more likely to be inferred to be within a CpG because the chimp SNP might contain either C or G. For example, if the human SNP at +1 is G/A and the chimp SNP is C/G, this would be called a potential CpG site and excluded.(56 KB PDF)Click here for additional data file.Figure S2(A) All genes and (B) genes expressed in the testes.(35 KB PDF)Click here for additional data file.Figure S3(28 KB PDF)Click here for additional data file.Table S1The table shows the number of times a particular SNP in humans is found opposite a particular SNP in chimpanzees, and the observed-over-expected ratio excluding CpG sites. Note that some of the observed values are greater than when we included CpG dinucleotides. This is because we re-ran the analysis and when a chimp SNP had matched multiple human sequences, we chose a sequence in which the human SNP was not involved in a CpG. Ratios are omitted when the expected value was less than 20.(61 KB DOC)Click here for additional data file.Table S2(27 KB DOC)Click here for additional data file.Table S3(51 KB DOC)Click here for additional data file.Table S4(27 KB DOC)Click here for additional data file.Text S1(28 KB PDF)Click here for additional data file.


As well as EDG1, the titin (TTN) gene, involved in myofibrillogenesis, has been previously shown to possess expression difference in musculus longissimus muscle between low-marbled and high-marbled steer groups, and to be located within genomic region of a quantitative trait locus for marbling. Thus TTN was considered as a positional functional candidate for the gene responsible for marbling. In this study, we explored SNP in TTN and analyzed association of the SNP with marbling.Marbling defined by the amount and distribution of intramuscular fat is an economically important trait of beef cattle in Japan. We have recently reported that single nucleotide polymorphisms (SNPs) in the TTN, referred to as g.231054C>T, was the only difference detected between high- and low-marbled steer groups. The SNP was associated with marbling in 3 experiments using 101 sires (P = 0.004), 848 paternal half-sib progeny steers from 5 sires heterozygous for the g.231054C>T (P = 0.046), and 820 paternal half-sib progeny steers from 3 sires homozygous for C allele at the g.231054C>T (P = 0.051), in Japanese Black beef cattle. The effect of genotypes of the SNP on subcutaneous fat thickness was not statistically significant (P > 0.05).A SNP in the promoter region of EDG1 SNPs, the TTN SNP polymorphism is associated with marbling and may be useful for effective marker-assisted selection to increase the levels of marbling in Japanese Black beef cattle. Further replicate studies will be needed to confirm the allelic association observed here, and to expand the results to evaluate all possible genotypic combinations of alleles.These findings suggest that in addition to the  Marbling has an important effect on the economics of beef production -4. Thus,endothelial differentiation, sphingolipid G-protein-coupled receptor, 1 (EDG1) gene, which is involved in blood vessel formation  including 101,593 bp, and obtained 5,046,004-bp bovine genomic sequence [GenBank:NW_001494580] containing the TTN gene. By comparing the genomic and cDNA sequences, we showed that the bovine TTN gene spans 276,648 bp and comprises 211 bp of the first exon, 1,315 bp of the last exon, 100,067 bp of 313 internal exons, and 175,055 bp of 314 introns.We screened the NCBI bovine genome sequence database  with the publicly available bovine TTN gene expression patterns [TTN gene.Two Holstein steers and two somatic nuclear-derived cloned steers  from a Jpatterns . The detpatterns . We usedTTN gene between two low-marbled Holstein steers and two high-marbled cloned steers , and 1 × Go Taq buffer (Promega). The PCR conditions were carried out as follows: 94°C for 2 min, 35 cycles of 94°C for 30 s, the annealing temperature indicated in Table PCR primers were designed to target ~7-kb proximal promoter and the first and last exon regions, to screen polymorphisms in the rs Table . We seleTTN gene for the 4 DNA samples. This sequence analysis revealed only one SNP in the TTN gene between the 2 steer groups: a C to T transition located 652 bp upstream of the putative transcription initiation site in the promoter region (g.231054C>T). The g.231054C>T has been submitted to the dbSNP with ss accession number 120037472. The two Holstein steers were homozygous for C allele at the g.231054C>T site, whereas the two cloned steers heterozygous for C and T alleles at the g.231054C>T site.We sequenced the 6,615-bp 5' flanking region, the 211-bp first exon region, and the 1,315-bp last exon region of the g.231054C>T with marbling and subcutaneous fat thickness. We used 101 Japanese Black sires in experiment 1. The sires used to be or are used in the Oita Prefectural Institute of Animal Industry . There was no strong bias for a specific father or a specific maternal grandfather of the sires, and the sire panel likely represents a variety of the sire lines. In experiment 2, 848 paternal half-sib Japanese Black progeny steers (55 to 445 steers per sire) produced from 5 sires heterozygous for the g.231054C>T, with dams considered to represent a random sample of the female population, were used. We used 820 paternal half-sib Japanese Black progeny steers (88 to 535 steers per sire) produced from 3 sires homozygous for C allele at the g.231054C>T, with dams considered to be a random mating population, in experiment 3. These progeny steers (in experiments 2 and 3) were fattened, and shipped to the carcass market in the Oita prefecture. Semen or blood of the sires and adipose tissues of the progeny steers were collected for SNP genotyping. DNA samples were prepared from the materials according to standard protocols.We performed 3 experiments for association of the et al. [The predicted breeding values of the sires and the progeny steers for marbling score and subcutaneous fat thickness were obtained from the Oita recording system for beef cattle reported by Sasaki et al. , and uselongissimus muscle from its chine bone end.Marbling score and subcutaneous fat thickness were measured on carcasses dissected at the sixth and seventh rib section, according to the Japanese meat grading system by certified graders from the Japan Meat Grading Association  . MarblinP < 0.001). Random effects included additive genetic effect of the individuals; that is, the animal model was adopted to predict the breeding values.Data were analyzed by the REML method using the MTDFREML programs , and genThis study conformed to the guidelines for animal experimentation of the Graduate School of Agriculture, Kyoto University .g.231054C>T was genotyped using PCR-restriction fragment length polymorphism (RFLP) method. PCR primers used for PCR-RFLP were 5'-TCATCTCCTAACTACTTCCCA-3' and 5'-ACAAAATCTGAACCTGGCTT-3' . PCR amplifications were carried out as described in SNP detection section, using a final volume of 20 μl and the annealing temperature of 55°C. An aliquot of PCR-amplified products was digested at 37°C for 1 h with restriction enzyme HpyCH4III, and electrophoresed on a 3.0% agarose gel. Agarose gels were stained with ethidium bromide and photographed under an ultraviolet light. Using this method, 227-bp PCR fragments containing the SNP site were amplified, and HpyCH4III-digested into 36- and 191-bp fragments at the C allele, but not at the T allele: the CC homozygotes, the TT homozygotes, and the CT heterozygotes resulted in 2 bands (36 and 191 bp), 1 band (227 bp), and 3 bands .The g.231054C>T revealed 50 animals homozygous for C allele, 47 animals heterozygous for C allele and T allele, and 4 animals homozygous for T allele. The 4 TT homozygotes were too small sample size to give reliable estimates, and then excluded from our statistical analysis. Statistically significant differences among the genotypes of the SNP were detected for marbling (P = 0.004), but not for subcutaneous fat thickness (P = 0.984), by the analysis with the model that included the SNP genotype as the fixed effect and the sire (father of the sire) as the random effect  in the model that included the SNP genotype and the sire as the fixed effects and their interaction, and was excluded from our statistical model. In the model without the interaction effect, the SNP genotype had the statistically significant effect on marbling (P = 0.046), but not subcutaneous fat thickness (P = 0.749)  in the model that included the SNP genotype and the sire as the fixed effects and their interaction, and was excluded from our statistical model. In the model without the interaction effect, the SNP genotype effect reached marginal significance (P = 0.051) for marbling, but not for subcutaneous fat thickness (P = 0.310) .Based on 3 experiments, we showed that the g.231054C>T with marbling, together with TTN expression difference between low-marbled  and high-marbled steer groups  and co-localization of the marbling quantitative trait locus with the TTN, we can hypothesize that the SNP in the promoter region might have an impact on TTN expression and also marbling by affecting TTN promoter activity. However, the SNP is not located within any of as yet identified canonical sequences involved in gene transcription. Thus, a more likely event is that the TTN SNP is in linkage disequilibrium with an unidentified and truly relevant mutation, rather than functional and a causal mutation for marbling.Based on the association of the P > 0.05) for subcutaneous fat thickness. Further, the marbling quantitative trait locus corresponding to chromosomal position of the TTN did not have statistically significant effect on subcutaneous fat thickness [TTN SNP is not associated with subcutaneous fat thickness in Japanese Black beef cattle. This might be supported by the fact that Japanese Black breed exhibits low genetic correlation between marbling and subcutaneous fat thickness [The effect of genotypes of the SNP was not statistically significant (hickness ,11. Thushickness .EDG1 gene were associated with marbling in Japanese Black breed [Several previous studies have reported polymorphisms associated with beef marbling using beef cattle breeds other than Japanese Black -27. We hck breed . Thus, oTTN SNP obtained in this study, as well as the EDG1 SNPs, may be applied to effective marker-assisted selection to increase the levels of marbling in Japanese Black beef cattle. Further replicate studies will be needed to confirm the allelic association observed here, and to expand the results to evaluate all possible genotypic combinations of alleles.The information on the The authors declare that they have no competing interests.TY carried out the statistical analyses and drafted the manuscript. SS, SS, SY, and YT carried out the SNP detection and genotyping. MM, HM, FM, and TF participated in the sample and data collection. TM carried out further statistical analyses. YS participated in the design and coordination of the study and helped to draft the manuscript. All authors read and approved the final manuscript.


We developed an efficient pipeline to analyze genome-wide association study single nucleotide polymorphism scan results. Purl scripts were used to convert genotypes called using the BRLMM algorithm into a modified PB format. We computed summary statistics characteristic of our case and control populations including allele counts, missing values, heterozygosity, measures of compliance with Hardy-Weinberg equilibrium, and several population difference statistics. In addition, we computed association tests, including exact tests of association for genotypes, alleles, the Cochran-Armitage linear trend test, and dominant, recessive, and overdominant models at every single nucleotide polymorphism (SNP). In addition, pairwise linkage disequilbrium statistics were elaborated, using the command line version of HaploView, which was possible by writing a reformatting script. Additional Perl scripts permit loading the results into a MySQL database conjoined with a Generic Genome Browser (gbrowse) for comprehensive visualization. This browser incorporates a download feature that provides actual case and control genotypes to users in associated genomic regions. Thus, re-analysis “on the fly” is possible for casual browser users from anywhere on the Internet. In addition, newer platforms provide detailed typing for both common and rare copy number variations (CNVs) in the human genome.Genome-wide polymorphism and copy number variation arrays are an accepted and standardized approach to disease association mapping, which is gaining in popularity in the genetic research community. Nearly two hundred genome-wide association studies (GWAS) have now been cataloged by the National Human Genome Research Institute of the National Institutes of Health  single nucleotide polymorphism (SNP) platform; and Affymetrix has released a 6.0 version that contains over 900,000 SNPs  and approximately 950,000 CNV probes. Each platform provides copious genetic data on every typed research subject.The mass application of the 500 K array around the world has produced an enormous amount of data susceptible to independent and creative analysis in many locations. Both population history studies and association analysis can be carried forward with these data. The ease and availability of analysis can contribute immensely into gaining new insights from the data provided from dense SNP and CNV panels.A methodologic pipeline for analysis of the microarray genotype calls, performed directly on a Unix platform, using an average PC-Linux or Mac computer, avoids cumbersome and time-consuming statistical software programming and permits rapid conclusions to be drawn about population structure and associations. Here, we provide methodology and detailed instructions for implementing the analysis of GWAS results.The main feature of our analytical pipeline is the concentration of all available information into a single text file. We call it a modified PB file. This single file comprises structured SNP calls, confidence scores, and references for all mapped SNPs for hundreds or thousands of genotyping microarrays.http://pga.gs.washington.edu) to easily represent SNP information for groups of subjects over a given span of reference sequences. Our modified PB format has an inherent feature of easy adaptability to the descriptive applications and tools written by Ross Lazarus and publicly available at http://innateimmunity.net.Our modified PB format is based on the Prettybase format developed by the SeattleSNPs project , genotype call for the first allele, and genotype call for the second allele.A missing genotype, if present, is denoted N N in the third and fourth columns.The first letter of a PID indicates the population to which this subject belongs.We added some other characteristics to the Prettybase format, thus rendering it into the modified PB format. The first of these characteristics is the 11-digit chromosome-displacement field (ChrDisp) specifying the unique position of the SNP in the human genome. The first two digits contain the chromosome number padded with leading zeros, if necessary, while the last nine digits contain the base pair position (again padded with leading zeros). See  Also, a The modified PB file can be sorted with a simple “sort” command , which will group the record lines into blocks of the same SNP, sorted according their position in the genome. Each block, in turn, is sorted according to the PID , Panel CThis modified PB file can have different numbers of fields and lines; a variety of chromosome-displacements or population specifications. Thus, we define a “regular” modified PB as a file that contains an equal number of persons per SNP block and equal number of fields per record. In general, we always refer to that type of modified PB file, since the number of the records equals the product of the number of SNPs multiplied by the number of genotyped research subjects.The modified PB-file pipeline is used in our laboratory and is the backbone for a recent study of genetic variation in Ashkenazi Jews  and one Two sources of input data were provided to us from Affymetrix 500 K scans:Data: CEL files from the Affymetrix GeneChip reader from a genotyping facility: The CEL files are binary files containing the fluorescence intensities for each probe on the microarray. Those files together with SNP-specific annotation files obtained from the Affymetrix website were processed in two steps into a modified PB file within a few hours.Step 1: Running BRLMM genotyping algorithmapt-probeset-genotype was used. It is part of the open-source Affymetrix Power Tools (APT) software package  or were discarded. It was important to use dbSNP identifiers instead of Affymetrix IDs for further processing with annotations. While each SNP is undoubtedly identified by its dbSNP ID, it can have more than one Affymetrix ID depending upon the probeset used to detect it. Thus, different Affymetrix IDs on different chip releases may detect the same dbSNP ID. For Affymetrix genotyping microarrays, a Perl script was used to convert all data into a single modified PB file with seven columns.Optionally, a LINKAGE-format individuals’ file could be used to select for PIDs.Annotations: A NetAffx annotation information file that provides consolidated data from multiple public sources, including probe sequences, gene annotations, and extensive annotation for each SNP  is requiFor the processing of Illumna GoldenGate or Infinium data, A Perl script was constructed that permitted flat BeadStudio V3.x “Full Data Table Output” files to be converted to modified PB format through accurate parsing and reformatting.Language Reference (Procedures Guide (ftp://ftp.ncifcrf.gov/pub/users/goldb).SAS software  version 9.1.3, running on a SUSE Linux platform, converted the PB files into SAS data sets for further analysis. Commands and instructions were written according to the eference  and Proces Guide  and are D), inbreeding coefficient (FIS), and the test statistic and P-value for the chi-square test for Hardy-Weinberg equilibrium. The genotype counts were used to compute exact P-values by the method of Wiggington et al. (2005). The direction of H-W deviation was also assessed.The genotypes were recoded so that the most common allele in a reference population  was assigned a value of 0 and the other allele  was assigned a value of 1. For each marker in each population summary statistics were computed that included the number of observed alleles (1 or 2), the allele and genotype counts and frequencies, the Hardy-Weinberg (H-W) disequilibrium coefficient (FST) (DS) (n) for assignment statistic of Several genetic distance statistics were computed for the populations including the Fixation Index of the Subpopulation within the Total (FST)  and Nei’ST) (DS) , 1978. IST) (DS)  and the Three chi-square tests comparing marker allele and/or genotype frequencies were implemented with SAS software. They consist of the allele test , the genotype test (Pearson chi-square test on table of genotype counts by disease status), and the trend test (Armitage test on table of genotype counts by disease status). Equivalent exact tests were implemented through SAS PROC FREQ. In addition, Fisher’s exact test was used to test recessive and dominant models. Odds ratios were computed for the allele test by PROC ALLELE and for the trend test by PROC LOGISTIC. The genetic distance and association statistics are summarized in a table with one line per SNP. .Genome-wide association studies require team-work between disparate centers, laboratories, and technologies. Samples located in one center may be received in another, typed in a third and analyzed in a fourth location, often across continents. Today, in spite of rapid Internet connections, the transfer of hundreds of gigabytes of row genotype data can be a serious time and security challenge. While the newest hardware configurations of PC and Mac computers could be found in nearly every research institution, this is not the case with expensive proprietary software, SNP genotyping arrays or medical samples. Therefore, we proposed a method for standard assembly and quick extraction of genotype information from row data. This method provides easy transportability, a standardized, easily understandable and robust format that would be advantageous for any SNP-typing or mapping association study.Our analytic platform allows collection of a huge amount of data in a short amount of time. Thus, the modified PB file can be promptly forwarded for further statistical analysis.As part of our effort, Perl scripts were written which converted and supplied the modified PB for analysis using SAS, Haploview, PHASE and the R platform. This permitted the generation of three different types of statistics from the data assembled in the PB:FST), Entropy index, Nei’s Gene substitution Distance Measure (DS), Kullback-Leibler Average Distance measure, Informativeness for assignment (In), etc.;Population differentiation statistics: Fixation index  and control (L in first PID column) populations, and seThe central element of our analysis is the modified PB text file. This single file summarizes all data from the genotype calls and confidence scores of many chips and individuals (grouped into populations). The file can be zipped and transported, and may be used as input for many types of analysis. Furthermore, with the wide availability of hard disks of 1.5 terabyte capacity, data may be analyzed all at once on PC-Linux and Macintosh computers using only UNIX, Perl, MySQL, R or other General Public License (GPL) software. This will permit users to avoid high-performance hardware and expensive proprietary software. The file can also be parsed and dispersed into 24 chromosome files  for individual chromosomal analysis. In our experience, although the Windows XP operating system is user-friendly compared to UNIX, the restrictions it puts on the size of input data for its basic applications  render it impractical for this volume of data analysis.On a Linux PC with 2 gigabytes of Random Access Memory (RAM), the process of building a modified PB file from Affymetrix 500 K CEL files for few hundred individuals may take 1–2 hours. This time estimate accounts for a minimum of other processes running on the PC. Although this is an imprecise performance measure, this processing time frame estimate should provide potential users with a “rule of thumb guide” for processing time requirements of the pipeline we have described.Table S1


Association tests were performed for individual SNPs and jointly with the CFH SNP Y402H. Analyses for interaction were also performed.We genotyped three SNPs, rs10490924 and rs11200638, is D'=0.8 and all four possible haplotypes of the two SNPs were detected in the samples. The allelic association test for rs11200638 on the promoter of HTRA1 yielded p-values less than 10−10 for geographic atrophy, less than 10−16 for neovascularization, and less than 10−19 for the pooled phenotypes . Disease risk is conferred in a dosage-dependent fashion. Similar figures were observed for the LOC387715/ARMS2 SNP. No interaction was detected between either between the 10q26 SNPs or the CFH SNP.The linkage disequilibrium measure for two SNPs on 10q26, HTRA1 and LOC387715/ARMS2 SNP appear to contribute equally to disease risk  with no evidence of interaction with CFH.This is the first analysis to show that the two 10q26 SNPs are not in complete linkage disequilibrium. Our studies however show that both the  The most consistently identified genetic variants lie within the regulators of complement activation (RCA) locus on chromosome 1, principally the gene  H (CFH) , and the H (CFH) -6. Assocrs10490924 and rs11200638 are in almost complete linkage disequilibrium in all cohorts examined thus far (D' >0.98) [HTRA1, a gene that encodes a heat shock serine protease found in retinal tissues, produces a change in the expression level of the gene [Intriguingly, both ' >0.98) ,12,13, lthe gene ,8. Interthe gene .LOC387715/ARMS2 has been suggested to encode a mitochondrial-associated protein that is also found in the retina. The polymorphism which results in the substitution of serine-for-alanine at position 69 may result in misfolding of the protein [Most recently, HTRA1 rs11200638 SNP, the LOC387715/ARMS2 SNP rs10490924 (A69S), and the CFH SNP rs1061170 (Y402H).In this article, we present further analyses of the 10q26 polymorphisms in a Caucasian population from the USA with advanced AMD  and assess joint effects of the Sporadic advanced cases  and controls  together with 205 patients with CNV, 20 with both GA and CNV, and 171 age-matched examined controls were ascertained. Informed consent was obtained from all participants, and the procedures used conformed to the tenets of the Declaration of Helsinki.rs1061170, rs10490924, and rs11200638 was performed as described previously [rs10490924 forward: 5′-GGT GGT TCC TGT GTC CTT CA-3′, reverse: 5′-GGG GTA AGG CCT GAT CAT CT-3′; rs11200638 forward: 5′-CGG ATG CAC CAA AGA TTC TCC-3′, reverse: 5′-TTC GCG TCC TTC AAA CTA ATG G-3′). Following amplification, genotype determination was performed on the PCR products using either temperature gradient capillary electrophoresis  or through direct sequencing using CEQ2000XL DNA analysis system .Genotyping of 2 values in the entire sample and controls alone were calculated to identify possible genotyping errors. No extreme deviations  were observed (2 tests with one degree of freedom for association were performed. Odds ratios (OR) and their respective confidence intervals were calculated, [Hardy–Weinberg Equilibrium (HWE) χobserved . Linkageculated, . Both SNrs11200638 and complement factor H (CFH) Y402H, previously genotyped) were calculated using standard methods [CFH Y402H and rs11200638 as independent variables [i controlled for marker Xj) are formulated as logit(p)=β0 + β1X1 and logit(p)=β0 + β1X1 + β2X2, respectively. For highly correlated SNPs (rs10490924 and rs11200638), an interaction term was incorporated in the model: logit(p)=β0 + β1X1 + β2X2 + β3X1×X2.Joint ORs for two SNPs  test for association with two variables was used . Four geHTRA1 rs11200638 SNP showed strong disease association with both advanced forms of AMD, p<10−10 (geographic atrophy), p<10−16 (neovascularization), and p<10−20 for pooled phenotypes. The LOC387715/ARMS2 rs10490924 SNP showed similar levels of association. Strong association is also confirmed between disease status and the CFH SNP rs1061170 , though not in complete LD as all possible diplotypes were found in the population. rs10490924 ‘T’ + rs11200638 ‘A’ than controls.The two 10q26 SNPs, HTRA1 and CFH SNPs. Odds ratios of having the disease rise to almost 200 in individuals who are homozygous for the risk alleles in both genes. Similar odds ratios are observed for the LOC387715/ARMS2 and CFH SNP  and rs10490924 (LOC387715/ARMS2 A69S), independent of the CFH Y402H polymorphism. In all previous papers which examined linkage disequilibrium in the 10q26 region, these two SNPs have been reported to be in almost complete linkage disequilibrium [CFH SNP, were not significantly different for either of the 10q26 SNPs genotyped in our cohort. Each of the two SNPs also confers similar odd ratios when combined with the CFH SNP. Neither SNP was more clearly associated with disease status nor with advanced AMD phenotype though arguably if our cohort is enlarged substantially it might be possible to distinguish which SNP contribute the most risk.Our data confirm the association between advanced AMD (both geographic atrophy and CNV) and the 10q26 SNPs ilibrium . This waCFH and the region on chromosome 10q26. Our analyses are in agreement with this. Thus it seems likely that CFH and 10q26 contribute independently to AMD development, at least at the genomic level.Previous studies  have notHTRA1 and/or LOC387715/ARMS2 or a 10q26 haplotype that expands more than two SNPs play a crucial role in determining the advanced AMD phenotypes. Functional consequences from the polymorphisms of LOC387715/ARMS2 or HTRA1, or even CFH, have yet to be determined. Our current data indicate that in a Caucasian population from the USA, the HTRA1 promoter SNP is strongly associated with advanced AMD but appears to exert its effect independently of CFH.There remains an enormous amount to be learned about the genetic etiology of AMD. Nonetheless, there is little question that either


Epha2 in two independent strains of mice developed progressive cortical cataract. Retroillumination revealed development of cortical vacuoles at one month of age; visible cataract appeared around three months, which progressed to mature cataract by six months. EPHA2 protein expression in the lens is spatially and temporally regulated. It is low in anterior epithelial cells, upregulated as the cells enter differentiation at the equator, strongly expressed in the cortical fiber cells, but absent in the nuclei. Deletion of Epha2 caused a significant increase in the expression of HSP25 (murine homologue of human HSP27) before the onset of cataract. The overexpressed HSP25 was in an underphosphorylated form, indicating excessive cellular stress and protein misfolding. The orthologous human EPHA2 gene on chromosome 1p36 was tested in three independent worldwide Caucasian populations for allelic association with cortical cataract. Common variants in EPHA2 were found that showed significant association with cortical cataract, and rs6678616 was the most significant in meta-analyses. In addition, we sequenced exons of EPHA2 in linked families and identified a new missense mutation, Arg721Gln, in the protein kinase domain that significantly alters EPHA2 functions in cellular and biochemical assays. Thus, converging evidence from humans and mice suggests that EPHA2 is important in maintaining lens clarity with age.Age-related cataract is a major cause of blindness worldwide, and cortical cataract is the second most prevalent type of age-related cataract. Although a significant fraction of age-related cataract is heritable, the genetic basis remains to be elucidated. We report that homozygous deletion of  EPHA2 is associated with cortical cataract. EPHA2 is expressed in mouse and human cortical lens fiber cells, and homozygous deletion of Epha2 in two independent strains of mice led to development of cataract that progressed with age. Common and rare variants including a missense mutation in the EPHA2 gene were associated for cortical cataract in three different Caucasian populations. Our study identified EPHA2 as a gene for human age-related cataract and established Epha2 knockout mice as a model for progressive cortical cataract.Cataract is the leading cause of blindness. Cataract may form at any age, but the peak incidence is bimodal—in the perinatal period or later than 50 years of age. The early onset forms follow Mendelian inheritance patterns and are rare. Age-related cataract accounts for 18 million cases of blindness and 59 million cases of reduced vision worldwide. Among three types of age-related cataract, cortical cataract is known to be highly heritable, although few genes have been linked to its etiology. We report here that  Cataract is the leading cause of visual impairment worldwide, with approximately 37 million people affected, accounting for 48% of global blindness Age-related cataract has consistently been attributed to risk factors such as old age, female gender, diabetes, hypertension, smoking, UV light exposure, and heavy alcohol intake EPHA2) that resides under the linkage peak on 1p36 was selected as a candidate gene because knockout mice for Epha2 developed age-related cortical cataract. In this study, we extensively examined characteristics of the EPHA2 protein using knockout mice. We investigated allelic association by re-sequencing and genotyping of single nucleotide polymorphisms (SNPs) in coding and non-coding conserved regions in large population-based human studies.A genome-wide scan for cortical cataract after adjusting for covariates in a subset of the BDES showed evidence of linkage in 1p36 Epha2 gene, we observed development of progressive cortical cataract in a significant fraction of homozygous knockout mice. This line of knockout mice (−/−Epha2) with deletion of exons 6–17 was generated using a secretory gene trapping strategy −/−Epha2 mice appeared by five months, which was confirmed as cataract by slit lamp examination  cohort  and one new non-coding variant (A/G in intron 12-13) (-/G) and seven other coding variants, including rs6678618 (Ala190Ala), rs6678616 (Leu191Leu), rs35484156 (Ser277Leu), rs2230597 (Pro329Pro), rs55655135 (Leu632Leu), rs10907223 (Leu661Leu), and rs3754334 (Ile958Ile). These polymorphisms were previously reported in public databases.The human ) cohort , which wfamilies . We idenn 12-13) . These vArg721Gln and rs35484156) and two synonymous variants (Ile779Ile and rs6678616) were pursued by genotyping all available individuals  in the BDES. We performed extensive quality control metrics for the final model containing Arg721Gln in the discovery dataset to confirm both the consistency and the validity of the association result . Since we did not have Arg721Gln ‘AA’ homozygotes in our data, we did not assess the effect size for a recessive model. Using the severe cortical cataract trait the association signal at Arg721Gln was less significant , and we believe that 0.1%–0.2% represents a true population estimate for this rare variant. The risk allele has 78% penetrance in heterozygous individuals whose age is 70 years or more. We were unable to test association for the Arg721Gln in the replication sets because either the rare allele was not present (UKTS) or its frequency was too small (BMES) to be used in the statistical models.The sequencing data for S family . We weredatasets , the Unidatasets . The freand UKTS ; increasArg721Gln mutation changes arginine, a positively charged amino acid, to glutamine. Examination of the EPHA2 kinase domain crystal structure Arg721Gln mutation may disrupt the salt bridge and affect its conformation and function. To test this possibility, wild type (WT) and Arg721Gln mutant EPHA2 were expressed in HEK 293 cells that have low endogenous EPHA2 Arg721Gln-EPHA2 were significantly growth-inhibited cells by ephrin-A1, whereas WT-EPHA2 expressing cells were refractory  cells derived from −/−Epha2 embryos, affecting about 40% of total cell populations , one at the 5′ and another at the 3′ end that were relatively independent; the LD in the BDES, BMES, and UKTS was very similar to that in the CEU HapMap sample (2) between SNPs in 5′ and 3′ LD blocks did not exceed 0.5 in all three datasets , either with the full range of cortical cataract scores or with severe cortical cataract. Except for rs6678616, P values at the other five SNPs  were smaller when using the categorical yes/no definition of cortical cataract, despite the decrease in sample size  or unaffected , and eliminating all those with intermediate scores. If the whole range of scores was considered, the average cortical score in the BDES, UKTS, and BMES was 7.6%, 16.3%, and 3.2%, respectively, while the proportions with severe cortical cataract in the BDES, UKTS, and BMES were 10%, 29%, and 3%, respectively. Prior to association analysis, the quantitative trait of percent of lens affected and the binary trait, affected with cataract (yes/no), were adjusted for significant covariates using regression. The values obtained after regression  were inverse rank transformed to normalize their distribution. Association testing was performed on values of the trait before and after inverse rank transformation. Multiple genetic models  were tested for each SNP , but forple size . Differe−4). In the UKTS we also identified significant association for severe cortical cataract with another rare variant Ile779Ile (P = 3×10−5). Rigorous diagnostic testing of the final models in the two family datasets (BDES and UKTS) confirmed that the results were valid  with C-A as the risk haplotype  and explained 6% of the total variance in cortical cataract scores. This result was consistent with the single SNP association tests in the two family studies  and G-G as the protective haplotype  at markers rs7548209-rs3754334 (r2 = 0.8), suggesting that the ‘C’ allele at rs7548209 causes increased risk; this haplotype explained 2% of the variance in the quantitative trait for cortical cataract. In total, these haplotypes accounted for 8% of the total variation. The risk alleles in the haplotype association test above are similar to those in the single SNP association in the two family datasets  and rs6678616 (P = 1×10−4), respectively (−9) and rs3768293 (P = 0.0037) were the most significant markers when combining the two family datasets and when combining all three datasets, respectively (Joint (meta-)analysis was conducted to combine the results of the two family studies and then to combine all three studies . The mosectively . There wectively . For sevectively . We noteectively . Neverthectively . Diversiectively . As a reectively . Howeverectively , arguingEPHA2 is essential in maintaining the clarity of crystalline lens in aging mice and is associated with age-related cortical cataract in humans. EPHA2 protein is expressed in the cortical lens fiber cells, and homozygous deletion of Epha2 in mice caused progressive cortical cataract. At the molecular level, cataractogenesis was preceded by accumulation of underphosphorylated HSP25, indicating elevated stresses and protein misfolding in Epha2−/− lens. In human studies involving three independent Caucasian populations, we discovered common polymorphisms, as well as a rare variant that significantly altered the function of EPHA2 kinase activities and cellular functions. Therefore, converging evidence from genetically engineered mice and human population studies, coupled with in vitro cell-based assays, strongly suggest that EPHA2 is the first major genes associated with the common form of cataract. The involvement of a receptor tyrosine kinase in cortical cataract etiology points to future directions in designing new preventive and therapeutic approaches for cortical cataract by targeting EPHA2.We report here that Epha2 knockout mice is the kinked tails, which develops during early embryogenesis Epha2 knockout mice generated by the secretory trapping strategy Prior to this study, the only noticeable phenotype reported in Epha2 knockout mice that were extensively investigated for potential molecular implications in cataractogenesis. This effort provided confidence in subsequent studies pursuing causal variants in human populations. Second, we investigated association with polymorphisms of EPHA2 in worldwide Caucasian populations from the United States, United Kingdom, and Australia. Phenotypic measurements were comparable among the three datasets, but there was variability in the extent of severity of cataracts in these populations. The most severe forms of cataract were associated with rare variants that were unique to each population and there was little replication between populations. Common variants could be replicated across populations with a greater degree of certainty. To ensure validity of the results, we extensively examined diagnostics of the final models in the family data. A candidate gene study using family data, unlike a genome-wide association study using unrelated data, requires special techniques to ensure the validity, i.e. normality of the model residuals expected asymptotically and required for the P values that presume normality to be valid. We applied the George-Elston transformation to both sides of the regression equation in the association tests There are several unique approaches in our study compared with other studies to identify genes involved in complex disease. First, we utilized two independent lines of Arg721Gln, residing in the protein kinase domain of EPHA2. Moreover, the variant showed increased spontaneous activation in the absence of ligand stimulation, suggesting that the coding variant alters the spontaneous and ligand-triggered cellular signaling. The altered signaling is associated with enhanced growth inhibition by ligand, and stochastic intracellular retention in primary fibroblasts. The reason for the cytoplasmic retention in only some, but not all cells expressing the mutant is not clear at present. Regardless of the mechanisms, cytoplasmic trapping is likely to interrupt both signaling and structural functions of EPHA2/ephrinA system in the lens fiber cells. Because EphA2 is essential for maintaining lens clarity as evidenced by cataractogenesis in homozygous knockout mice, functional alterations in EphA2 is likely to impair its physiological roles in the lens, predisposing the carriers to increased risk of cataract development.Our studies led to the successful identification of a non-synonymous variant, EPHA2 could be involved in predisposition to cataractogenesis in the general population. The most significant common variant for the quantitative cortical cataract trait was rs6678616 in the region coding for the ligand binding domain of the EPHA2 gene. Bioinformatic investigation revealed that rs6678616 is located in an exonic splicing enhancer element, and nucleotide changes of the SNP may potentially affect the affinity of splicing enhancer factors up to 14-fold (data not shown). The most significant SNP, rs7548209, for severe cortical cataract is present in a downstream conserved non-coding region of the EPHA2 gene. Characteristics of this variant are not known. It is possible that such a variant may affect EPHA2 expression during aging.In addition to this rare variant, common polymorphisms were also discovered and replicated in worldwide Caucasian populations, suggesting that Efna5 encoding a ligand (ephrinA5) for EPHA2 also developed cataract EPHA2, although none of rare variants were functionally characterized EPHA2 and the much more common age-related cataract, based on large human population studies and genetically engineered mice.While this manuscript was under preparation, it was reported that mice with homozygous deletion of Epha2 knockout mice suggests an important role of EPHA2 kinase in cataractogenesis during aging. Previous studies have shown that EPHA2 regulates epithelial cell morphogenesis and homeostasis in part by interacting with components of adherens and tight junctions, such as claudins and cadherins Since aging was the most important risk factor for age-related cataract, age-dependent reduction of EPHA2 protein expression in normal lens and age-dependent deterioration of lens clarity in EPH and EFN genes may be potentially involved in a broad spectrum of age-related eye and neurodegenerative diseases. Further functional investigation of these genes will elucidate pathogenic mechanisms leading to age-related cataract and other late onset diseases.Recent studies show that there are common genes and pathways among late onset neurodegenerative diseases, including Alzheimer's disease, multiple sclerosis, diabetes, and age-related eye disease. Genes involved in neurodegenerative diseases, kinesin light chain 1 EPHA2 has an important role in maintaining lens transparency. While genetic predisposition is known to be a major contributing factor to age-related cortical cataract, our study has identified and characterized genetic variants of the EPHA2 gene in human, and both common and rare variants confer risks for cortical cataract.In summary, converging evidence from mouse and humans demonstrates that Epha2 knockout mice was generated through secretory gene trapping as described previously +/+Epha2, +/−Epha2, and −/−Epha2 mice that were used in subsequent studies. Another line of mice on C57Bl/6 background was provided by Drs. M. Asano and Y. Iwakura Epha2, leading to abolishment of EPHA2 expression.The KST085 line of All procedures involving mice were performed in accordance with guidelines set forth by the American Association for Accreditation of Laboratory Animal Care and the USPHS “Policy on Humane Care and Use of Laboratory Animals.” Studies were approved and supervised by The Case Western Reserve University Institutional Animal Care and Use Committee.For histological analyses, mouse eyes were enucleated and fixed in neutral buffered 10% formalin solution for 24 hours. Eyes were dehydrated through a graded alcohol series and embedded in paraffin. Sections (5 µm) were cut through papillary-optic nerve axis and stained with hematoxylin and eosin (H&E).4Cl and permeabilized with 0.3% NP40 for 10 minutes. The sections were then incubated with primary antibodies at room temperature for 1 hour followed by detection with donkey secondary antibodies conjugated with FITC or Texas-red  at room temperature for 30 minutes.For immunofluorescence analyses, postnatal day 14 eyes were dissected, fixed with 4% paraformaldehyde for 25 min. Eyes were washed with ice cold Phosphate Buffered Saline (PBS) and kept in 15% sucrose (prepared in PBS) overnight. Tissue freezing medium  embedded eyes were cut on a cryostat , and air dried onto Superfrost Plus slides (Fisher Scientific). After washing with PBS, the sections were blocked with 50 mmol/L NHEPHA2 and Ephrin-A1 proteins were detected in human and mouse lens paraffin-embedded sections. Briefly, sections were deparaffinized, hydrated, and immersed in citrate buffer  for 10 minutes at 95°C. Primary antibodies were incubated with samples and then detected by Biotinylated secondary antibody and avidin–biotin–peroxidase system (Vector). Color immunostaining was revealed using diaminobenzidine (Vector). Images were taken using a fluorescent Leica DM-IRE2 microscope equipped with a SPOT RT-SE digital camera (Diagnostic Instrument) and analyzed with MetaMorph software 6.1r4 .Antibodies used include: goat anti-mouse EPHA2 ectodomain, goat anti-human EPHA2 , rabbit anti-EPHA2 and anti-ephrin-A1, goat anti-HSP25 and mouse anti-phospho-ERK, rabbit anti-ERK , rabbit anti-phospho-HSP25, anti-phospho-AKT, anti-Akt, anti-GAPDH , mouse monoclonal anti-N-cadherin (BD Biosciences). Rabbit-anti-α,β,γ-crystallin were kindly provided by Dr. Zigler .3VO4. Then samples were centrifuged at 13,000 g for 10 minutes at 4°C. Protein concentrations in supernatant were assessed using the bicinchoninic acid protein assay kit . Equal amounts of protein extracts were resolved by 4–20% SDS-PAGE and electrotransferred onto polyvinylidene difluoride membranes , which were then blotted with the indicated antibodies.Lenses were dissected from eyes and homogenized in ice-cold lysis buffer containing 20 mmol/L Tris (pH 7.4), 125 mmol/L NaCl, 10% glycerol, 1% Triton X-100,0.5% DCA, 0.1% SDS, 20 mmol/L NaF, 1 mmol/L phenylmethylsulfonyl fluoride, µg/mL aprotinin, 4 µg/mL leupeptin, and 1 mmol/L NaMice were anesthetized by an intramuscular injection of Tribromoethanol at 200 mg/kg body weight followed by induction of mydriasis with tropicamide (1%) and phenylephrine (10%). The lenses were examined with slit lamp and the observations were recorded by digital photography.The Beaver Dam Eye Study (BDES) is a population-based cohort study for age-related ocular diseases and was used as a discovery sample set. Informed consent was obtained from the participants following a protocol approved by the Institutional Review Board (IRB) of the University of Wisconsin; the tenets of the Declaration of Helsinki were observed. The population, the methodology, and the baseline parameters are described in detail elsewhere Retroillumination photographs were used to quantify cortical cataract. The lens photographs were graded based on the Wisconsin Cataract Grading System age) and highest body mass index , and ordinal variables such as gender , diabetes status , history of heavy alcohol intake , history of cigarette smoking, defined as more than 100 cigarettes smoked during the lifetime  , and age-related macular degeneration affection score  age and gender, and the binary cortical cataract was predicted by age and 2age. We used residuals from each final model as the initial trait in our association analysis.We adjusted for covariates that were previously identified by epidemiologic studies The United Kingdom Twin Eye Study (UKTS) consists of a total of 506 white female twin pairs (226 monozygotic and 280 dizygotic), between ages of 49 and 79 from the UK Adult Twin Registry In UKTS, cortical cataract scores were graded from 0 to 5 in decimalized steps, which could be converted to percent of the lens affected on multiplying by a factor of 20. In the digital grading procedure, photos taken from participants were stored using a digitized retroillumination camera system and analyzed using the Wilmer Automated Cortical Cataract Grading System age (years), average alcohol intake from current and previous periods , history of smoking , and average systolic blood pressure from initial and subsequent measurements . Diabetic status was not considered because of the large amount of missing data (percent missing = 52.3%). When testing significant covariates, we considered polynomial functions (e.g. age2) as well as first-order interactions between any two covariates. We selected the final models for the quantitative and binary traits using stepwise linear and logistic regression models, respectively. The final model for the quantitative cortical cataract trait consisted of smoke, sysbp2, interaction between age and bmi, interaction between smoke and bmi, interaction between smoke and alcohol, and interaction between sysbp and alcohol. The binary severe cortical trait was predicted only by age, age2, smoke, sysbp2, alcohol2, interaction between age and bmi, interaction between age and alcohol, interaction between alcohol and bmi, and interaction between smoke and alcohol. The residuals for the severe binary and quantitative cortical cataract traits were used in the association analyses.To adjust for risk factors in the UKTS, the following covariates were tested: The Blue Mountains Eye Study (BMES) collected data in a suburban Australian population aged 49 years and older at baseline age) and systolic blood pressure , and the ordinal variables gender , hypertension status , history of heavy smoking, defined as more than 100 cigarettes smoked during the lifetime , and age-related macular degeneration affection score . We selected the final models for the quantitative and binary traits using stepwise linear and logistic regression models, respectively. The quantitative cortical cataract score was predicted by gender and age. The binary severe cortical cataract trait was predicted by smoke, sysbp, interaction between age and gender, interaction between age and sysbp, and interaction between sysbp and smoke. We used residuals from each final model as the trait in our association analysis, for both the severe binary and quantitative cortical cataract trait.All the definitions for cataract traits determined in the BMES followed the same grading protocol as that used in the BDES. Related individuals were excluded (N = 99) by only keeping the oldest individual from each family. The oldest individuals were selected because they were likely to bear the most amount of phenotypic information. A total of 1470 individuals were available with DNA and phenotypic data after removing related individuals. We considered as risk factors continuous variables such as age , 15 were unaffected family members with no evidence of cataract formation  at ≥70 years of age, representing 18 families. Our rationale for choosing this set of individuals for re-sequencing was that they were previously shown to be linked to markers at 1p36 To identify linked families, sib pairs from the SIBPAL program (version 5.3.1) in the candidate region (1p36) were ranked for linkage informativity EPHA2 exons along with 50–100 base pairs of the surrounding intronic junctions were sequenced with the polymerase chain reaction (PCR) with primers designed using the PrimerSelect program (version 4.05) in the DNASTAR software (All 17 software . For eac2), and phylogenetic mining. Based on the results of re-sequencing, three rare coding variants and a common SNP were selected for follow up in the remaining individuals of the discovery data. For the tag-based approach, linkage disequilibrium (LD) in EPHA2 was investigated by examining SNPs from 10 Kb upstream (+10 Kb) to 10 Kb downstream (−10 Kb) of the gene  in HapMap data  using the HAPLOVIEW program (version 3.1). We selected at least one SNP in each block based on HapMap data to get four tagging SNPs.We selected SNPs for full-scale genotyping via three strategies: re-sequencing, tag-based selection using the linkage disequilibrium (rEPHA2 sequences from human (Homo sapiens) and mouse (Mus musculus) were aligned using zPicture. We used window size width of ≥100 bp with ≥70% identity between human and mouse EPHA2 by comparing the human EPHA2 gene as a reference with the orthologous sequences in two primates, the chimpanzee (Pan troglodytes) and the rhesus monkey (Macaca mulatta) using eShadow Phylogenetic footprinting Based on the hybrid selection strategy for SNPs, we first selected four non-redundant tag based SNPs that had MAF>5%. These SNPs were supplemented with 11 conserved non-coding SNPs identified through phylogenetic approaches. Finally, a set of intriguing coding and regulatory SNPs (N = 4) identified through re-sequencing were also genotyped. In total, 19 SNPs were genotyped in all three datasets, the BDES, UKTS, and BMES.We used two different techniques, TaqMan and SNPlex assays, to interrogate SNPs. The genotyping procedure was performed according to the manufacturer's protocol. Clustering algorithms for genotype assignment used three quality control steps: (1) removing wells  with a significant number of outlier SNPs, (2) setting a SNP pass value by assigning a pass or fail status for each SNP, and (3) for each passing SNP, a confidence value was calculated . A confidence value of at least 95% was required to retain a genotype call. We further cleaned the data by removing individuals with low quality DNA across the SNPs . We calculated the error rate and no-call rate using 5% replicate plating of DNA prior to these filtering steps. The average error rate and no-call rate for SNPlex genotyping was 0.7% and 3.9%, respectively. The average error rate and no-call rate for TaqMan genotyping was 0.3% and 3.7%, respectively. Cleaned data were used in association analyses.All genotyped SNPs met Hardy-Weinberg equilibrium with a nominal P>0.05. We estimated allele frequencies using the FREQ program in S.A.G.E. (version 5.4.2), and 95% confidence intervals were reported . The propolygenic, marital, family, and sibling effects as well as for individual random effects, and that transforms both sides of the regression equation thi pedigree member is i) = h(fi)+εih are median unbiased estimates g), additive, dominant, and recessive with respect to a reference allele, were assessed at each SNP. The minor allele was selected as the reference allele. For example, a SNP in an additive model was coded as 0, 1, or 2 according to the number of reference alleles, whereas it was coded as 0 or 1 in a dominant model according to the absence or presence of the reference allele.All genotyped SNPs were tested for association using the ASSOC program in S.A.G.E. (version 5.3.1). The method in ASSOC is a maximum likelihood regression-based approach that allows appropriate handling of familial correlations, estimating as necessary variance components for sibling and random effects, along with a genetic model for a SNP, were included in the final analysis. For each SNP, the baseline model without the SNP covariate and an alternate model with the SNP covariate were contrasted. The likelihood was maximized with respect to the null model containing the variance components and the baseline parameter α, and the alternate model containing the same variance components and both α and β. We performed likelihood ratio tests by comparing twice the difference between two log likelihoods with a chi-square distribution, and Wald tests by dividing each estimate of β by its standard error, estimated by numerical double differentiation of the log likelihood, and comparing the quotient to a standard normal distribution. Thus we obtained two P values that would be asymptotically identical.To find the best model for the familial correlation structure, each familial variance component was tested in the model by itself and in combination with others. The most significant, but parsimonious, model was retained as the “null” model against which was contrasted models containing the SNPs as covariates. For the two family datasets BDES and UKTS, the variance components for i)−h(α+βgi)h(y]) in a final model that included the most significant SNP by plotting a normal quantile-quantile  plot. Regardless of the distribution of y, normality of the residuals is required for the association result to be valid. We also conducted association analyses on the normalized traits, forcing the marginal distribution of the analyzed trait to be about normally distributed under the null hypothesis, by taking the inverse standard normal transformation of the empirical quantile that was obtained from the formula [r(y)−1/3] divided by (n+1/3), where r(y) is the rank of yWe examined the residuals  After single SNP association tests, for which the P values were moderate, we conducted haplotype-based association tests in the unrelated data in order to explore the improvement in significance. Using the five most significantly associated SNPs , moving windows of different sizes (sizes 2 to 5) were applied using the PLINK program (version 1.0.1). The PLINK program probabilistically infers haplotypes for each individual based on the standard expectation-maximization phasing iβ) are heterogeneous among studies and follow a normal distribution with mean k is the number of studies. Under the null hypothesis (th95 percentile of a chi-squared distribution. We used effect sizes (iβ) and sampling variances . We report N, total number of analyzable valid individuals, Z-scores, and overall P values in the combined dataset.We conducted meta-analysis using the METAL program, which calculates an overall z-statistic and P value from the weighted average of the individual statistics, accounting for sample size by calculating the square-root of the number of individuals examined in each sample  Anterior view of an intact Epha2−/− lens stained with X-gal to detect LacZ reporter gene expression. With 3-hour short-term staining, expression in epithelial cells on lens surface but not in deeper fiber cells was detected. (C) X-gal staining of frozen sagittal section showing LacZ reporter expression pattern. Inset is shown in (D). Scale bars: 1 mm for B and C; 10 µm for D. (E) Staining of an Epha2−/− lens using an antibody recognizing the ectodomain of EPHA2. Because part of the EPHA2 ectodomain was fused to LacZ reporter gene, the staining pattern is similar to that of X-gal as in D; both were trapped in the secretory vesicles. F) EPHA2 protein expression in the isolated lens cortex and nuclei. Note the age-dependent decrease in EPHA2 expression. Same amounts of protein extracts were loaded as indicated by Ponceau S staining.Morphological, histochemical and biochemical characterization of wild type and mutant mouse lens. (A) H&E sections of lenses from eight-month- and two-week-old mice. Note abnormal structure of the (2.87 MB TIF)Click here for additional data file.Figure S2EPHA2 expression in human lens. (A) Lysates were prepared from two 56 and 60 year old human lenses and blotted for EPHA2. (B–D) EPHA2 staining of human lens: Images from sagittal sections at anterior (B) and posterior (C) regions and coronal section near the equator (D) are shown. (E) Negative control without primary antibody. Scale bars: 5 µm.(4.16 MB TIF)Click here for additional data file.Figure S3Epha2 deletion did not cause significant changes in crystallin expression until development of mature cataract. Each lens from the indicated age was extracted with RIPA buffer first. After centrifugation, the supernatant was collected as soluble fraction. The pellet was directly dissolved in protein gel loading solution containing 2% SDS. The soluble and insoluble fractions were separated and blotted with the indicated antibodies. (B) Epha2 deletion did not significantly alter the ERK1/2 and Akt kinase activities. Total lens proteins were extracted with RIPA buffer and subject to immunoblot with the indicated antibodies.Crystallin expression and activation status of Akt and ERK1/2 kinases in wild type and mutant mice. (A) (1.29 MB TIF)Click here for additional data file.Figure S4Normal Quantile-Quantile (Q-Q) plots from residuals under the dominant model. The first column is the residuals from the initial trait without any transformations (NOTF), the second column is the residuals from the initial trait with the George-Elston transformation (GETF), and the third column is the inverse normal transformed adjusted data with the George-Elston transformation (INV-GETF). (A–B) Normal Q-Q plots from residuals of the Arg721Gln variant were plotted for the BDES. (A) The plots were drawn for cortical cataract. (B) The plots were drawn for severe cortical cataract. (C–D) Normal Q-Q plots from residuals of rs7548209 were plotted for the UKTS. (C) The plots were drawn for cortical cataract. (D) The plots were drawn for severe cortical cataract.(0.76 MB TIF)Click here for additional data file.Figure S5Retroillumination images of normal human lens vs. lens with cortical cataract. A normal human lens. (A) compared with cortical cataract lens from the Beaver Dams Eye Studies (BDES) (B) and Blue Mountain Eye Studies (BMES) (C). (D) A panel of retroillumination images from UKTS going from normal in the lower right corner to varying degrees of cortical cataract. The lens in the bottom left panel is the most severe.(9.61 MB TIF)Click here for additional data file.Figure S6EPHA2. Each LD plot is based on correlation coefficient of LD (r2) in the HapMap CEU (A), BDES (B), UKTS (C), and BMES (D) datasets.Plots of linkage disequilibrium (LD) for (2.84 MB TIF)Click here for additional data file.Table S1EPHA2 gene re-sequencing.Exonic primers and their amplification conditions for (0.05 MB DOC)Click here for additional data file.Table S2Completeness of SNP genotyping and other quality metrics.(0.07 MB DOC)Click here for additional data file.Table S3Single SNP association using ASSOC in the BDES family data.(0.14 MB DOC)Click here for additional data file.Table S4Single SNP association using ASSOC in the UKTS family data.(0.14 MB DOC)Click here for additional data file.Table S5Association results using PLINK in the BMES unrelated data. (A) P values from single SNP association under the dominant model. (B) P values from single SNP trend test for the severe cases and controls with age at least 70 years in the BMES. (C) Haplotype association for the binary and quantitative cortical cataract trait in BMES.(0.10 MB DOC)Click here for additional data file.Table S6Heterogeneity test and meta-analysis using METAL (A to D). Three genetic models  were tested for each SNP. QE-P: P values associated with the Q statistic; Zscore: Z-score from meta-analysis; N: the total number of individuals in the meta-analysis; Meta-P: P values from meta-analysis. (A) Heterogeneity test (Q test) and meta-analysis for the quantitative cortical cataract from the BDES and UKTS. (B) Heterogeneity test (Q test) and meta-analysis for severe cortical cataract from the BDES and UKTS. (C) Heterogeneity test (Q test) and meta-analysis from all three datasets  under the dominant model.(0.23 MB DOC)Click here for additional data file.


These methods precisely identified 12 candidate intervals, which are shared by all 5 affected individuals. Our study illustrates how genetic information can be maximized using readily available tools as a first step in mapping single-gene disorders in small families.The identification of genes for monogenic disorders has proven to be highly effective for understanding disease mechanisms, pathways and gene function in humans. Nevertheless, while thousands of Mendelian disorders have not yet been mapped there has been a trend away from studying single-gene disorders. In part, this is due to the fact that many of the remaining single-gene families are not large enough to map the disease locus to a single site in the genome. New tools and approaches are needed to allow researchers to effectively tap into this genetic gold-mine. Towards this goal, we have used haploid cell lines to experimentally validate the use of high-density single nucleotide polymorphism (SNP) arrays to define genome-wide haplotypes and candidate regions, using a small amyotrophic lateral sclerosis (ALS) family as a prototype. Specifically, we used haploid-cell lines to determine if high-density SNP arrays accurately predict haplotypes across entire chromosomes and show that haplotype information significantly enhances the genetic information in small families. Panels of haploid-cell lines were generated and a 5 centimorgan (cM) short tandem repeat polymorphism (STRP) genome scan was performed. Experimentally derived haplotypes for entire chromosomes were used to directly identify regions of the genome identical-by-descent in 5 affected individuals. Comparisons between experimentally determined and  The etiology of most cases of ALS remains obscure, with proposed mechanisms including viral, autoimmune, excitotoxic, metabolic/mitochondrial, toxic or apoptotic processes, protein misfolding or altered axonal transport Cu/Zn superoxide dismutase (SOD1) gene cause ALS1 vesicle associated membrane protein (VAMP)-associated protein B (VAPB) (ALS8) TAR DNA binding protein (TARDBP)fused in sarcoma/translated in liposarcoma (FUS/TLS)Although ALS usually occurs sporadically, without family history (sALS), ∼10% of cases are familial (fALS), generally with an autosomal-dominant pattern of inheritance While these discoveries have been important for increasing our understanding of the causes of ALS and for developing and testing various treatment strategies, our understanding of the molecular underpinnings of ALS is still in its infancy and identifying additional mutations with forms of ALS that are clinically similar to sALS is likely to clarify the molecular pathways involved in these diseases. However, large families with dominantly inherited ALS are difficult to study because the lethality of the disease limits the ability to obtain DNA from affected individuals. Furthermore, family members can be reluctant to participate in research studies because they do not want to consider the possibility that they or their children might be at risk. For these reasons, the novel ALS family (ALS-A) we have been studying for the past 19 years is of significant scientific importance . We have∧5). Conversely, if completely informative, unambiguously defined haplotypes should allow ∼97% of the genome which is not shared by the affected individuals to be excluded. A limitation of traditional linkage analysis, which is illustrated by the results of an 777 STRP marker screen and conventional multipoint analysis on the ALS-A family, is that there is a significant disparity between the theoretical portion of the genome that should be excluded from containing the ALS-A locus (>97%), and the portion of the genome actually excluded (67%). This disparity results because the markers are not fully informative and haplotypes can not be distinguished.A pedigree of the ALS-A family is shown in Although haplotype analysis is often used to precisely follow the segregation of small chromosomal regions, recombination and uninformative markers have historically made establishing unequivocal haplotypes for large chromosomal regions impossible. To test if obtaining accurate chromosomal haplotypes can be achieved using high-density SNP arrays and if this would provide more complete segregation information, we directly compared high-density SNP genotype analysis with experimentally determined haplotypes ascertained using a panel of haploid mouse-human hybrid cell lines generated from ALS-A family members Experimental haplotypes were established by generating panels of 18–37 haploid cell lines for eight family members including five affecteds. A 777 STRP marker genome scan was performed on DNA from the haploid cell lines, as well as diploid lymphocyte DNA. Genotyping results from chromosome separated cell lines were used to directly define haplotypes for entire chromosomes. in silico SNP haplotypes predicted by Allegro As a second and parallel approach to maximize the genetic information from this small ALS family, we investigated the utility of high density SNP arrays to predict shared haplotypes. While SNP arrays have typically been used to examine heterogeneous DNA samples in association studies, we sought to determine whether this technology would be successful in large scale haplotype reconstruction in a small family with ethnically similar individuals. By comparing the haplotypes that were experimentally derived using haploid cells lines, we were able to test the accuracy of in silico and were compared with the experimentally defined haplotypes from the haploid cell lines. Evaluation and comparison of recombination points revealed that the SNP arrays were able to precisely and accurately reconstruct haplotypes over large chromosomal regions. Diploid DNA samples from fourteen members of the ALS-A family were analyzed using the GeneChip™ Human Mapping 100 K Set (Affymetrix) and the resultant data were analyzed using the linkage program Allegro Haplotypes determined from the SNP analysis were examined and regions shared between all five affected individuals were then defined across the entire genome. The SNP method identified a total of 10 shared regions (red), eight of which were detected using the haploid mapping approach and an additional 2 regions that were not previously identified , Table 1In addition to examining the utility and accuracy of predicted SNP haplotypes, we also investigated the power of nonparametric or model free linkage analysis to detect shared chromosomal regions. The non-parametric linkage analyses , which perform statistical analysis of an increase in the number of alleles shared among the affected individuals with respect to identity by descent (IBD), weigh the genotypes of affected individuals rather than those of unaffected individuals; therefore all shared regions have equally high allele sharing LOD and NPL scores and can be easily identified. Using the linkage program Allegro As a final step, additional pedigree analysis was done to prioritize the candidate gene regions to those shared among affected individuals but absent from older unaffected family members. Although the range in disease onset is broad in the ALS-A family (35–73 years), two unaffected elderly individuals, II∶3 and II∶9, had no evidence of the disease; neither had signs or symptoms of ALS when examined . Additionally, individual II∶3 had no signs of ALS when interviewed at age 76, and prior to dying from Alzheimer's disease at age 90 y, individual II∶9 did not demonstrate ALS symptoms according to relatives or medical records. There was no evidence of frontotemporal dementia in the family, including II∶9, who had onset of dementia in the late 80 s, no unusual behavior while dementing, and responded to anticholinesterase medication. Although neither II∶3 and II∶9 showed signs of ALS, the possibility that they carry/carried the disease gene can not be excluded. However, the age of these individuals make the risk that they carry the ALS-A gene quite low, and hence make the regions of the genome that they do not share with the affected individuals more likely to contain the gene. Similarly, regions shared by all five affected individuals and II∶3 or II∶9 have a lower probability of containing the ALS-A gene. Using this approach we have prioritized the candidate regions into three categories (RI-1-3), with RI-1 being the most likely . Three oIn linkage studies of large families with Mendelian disorders LOD score analysis is an effective method to define the disease locus i.e. the single region of the genome that is shared among affected individuals. However, in small families with limited numbers of meioses, traditional LOD score analysis using STRP markers is less informative, often showing suggestive LOD scores for regions of the genome that should be excluded and missing other regions that may contain the gene. We have explored methods to maximize the amount of genetic information that can be derived from a small family with an autosomal dominant form of ALS and show that analysis of high density SNP haplotypes are able to precisely define nearly all regions of the genome that are shared among the affected individuals. Specifically, we used haploid cell lines to experimentally demonstrate that high density SNP arrays can be used to accurately define chromosomal haplotypes, precisely identify shared candidate regions among affecteds and exclude the remaining regions of genome that are not shared. Additionally, we show that SNP genotypes can be accurately analyzed by nonparametric linkage programs and validate the power of model free analysis to detect shared regions in small families.Over the past 20 years, genetic investigations using families with monogenic disorders have provided unparalleled information into gene function, normal and pathogenic pathways and disease mechanisms Towards this effort, we have mapped a rare familial form of ALS that is clinically similar to sALS. Candidate regions for the ALS-A gene span 142 Mb of DNA located in twelve intervals. Three of these regions/subregions, which span ∼23 Mb, are higher priority regions for future studies because they are not shared in older unaffected members of the ALS-A family. While the following paragraphs describe the next steps in identifying the ALS-A gene, similar strategies would be generally applicable to mapping efforts of any single gene disorder in any similarly small family. First, newly identified candidate regions can be refined and prioritized, additional families with similar diseases can be screened for linkage to separate candidate loci and genes of interest can be sequenced.TARDBP gene. Mutations in this gene cause a phenocopy of sALS Comparisons of known ALS loci with our newly defined candidate region show several regions of overlap or possible overlap. For example, an ambiguous region defined by the haploid cell lines at 1p36.21 contains the recently identified The disease spectrum for both ALS and FTD are still evolving, and families linked to the ALS-FTD locus on chromosome 9 have members that present with pure ALS, pure FTD or both To further refine the ALS-A locus we propose additional independent linkage studies using other small ALS families with clinically similar forms of ALS. While many of these additional families will be even smaller than the ALS-A family, our mapping study will enable other groups to determine if ALS families they have collected share any of the candidate ALS-A regions we describe here. Multiple families with linkage to one of these regions would provide additional support for prioritizing specific regions for detailed gene cloning efforts. Additionally, haplotype conservation could indicate the presence of an ancestral mutation and potentially pinpoint regions of special interest for sequencing.superoxide dismutase 3 (SOD3) located within the 4p15.2-p14 region, due to the similarity with SOD1, although no mutations were detected in the exons, exon/intron boundaries or upstream sequence. While careful examination of known genes will not be as complicated in small gene poor areas (chr.4p), this process will be more difficult in large gene rich regions, such as the shared interval on chromosome 6p. Therefore, in addition to targeted gene sequencing, brute force sequencing of entire shared regions may be appropriate.As a complementary approach, we have begun examining candidate genes within shared regions, initially focusing on genes encoding proteins expressed within the CNS or that act in pathways implicated in ALS. For example, we sequenced In summary, we have demonstrated through experimentally derived haplotypes using haploid DNA that dense genomic SNP arrays can accurately define chromosomal haplotypes in small families. Additionally, we experimentally validate the power and effectiveness of model free linkage analysis of SNP genotypes in the detection of the shared candidate regions. Application of this rapidly improving technology will enable genetic investigations of a whole class of families with Mendelian disorders that have typically been ignored by the scientific community due to their size. By changing the mapping paradigm from the idea that families with Mendelian disorders need to be large enough to map the gene to a single site in the genome, to include the concept that small families can be useful if all regions that could contain the gene are identified, we can begin to use a valuable and virtually untapped resource. Improving the power of mapping single-gene disorders will uncover new disease pathways and mechanisms and clarify the pathogenesis of devastating disorders like ALS.This study was conducted according to the principles expressed in the Declaration of Helsinki. All subjects participating in this study signed an informed consent form approved by the Human Subjects Committee at the University of Minnesota. DNA was extracted from peripheral venous blood using the Gentra Puregene blood kit .Panels of haploid mouse/human hybrid cell lines were generated at GMP Genetics for eight ALS-A family members  by electA 777 STRP marker high-density genome screen was performed on both diploid and haploid DNA from members of the ALS-A family using fluorescent technology . The worHaplotypes for each individual were directly determined along entire chromosomes by analyzing the genotypes of cell lines haploid for each chromosome, including the 22 autosomes and the X chromosome. The diploid DNA was used as an internal control for each patient and cell lines monosomic for each chromosome were selected for further analysis so that each homologue was represented. For the eight patients in which haploid cell lines were established, phase was determined for each chromosome using the two independently represented homologues. However, rarely both homologues were not present in the monosomic state, in which case haplotypes were defined by comparing genotypes of the available haploid cell line with the diploid DNA.Linkage analysis of the 22 autosomes and the X chromosome were performed using the computer program VITESSE version 2.0 High-density SNP based linkage analysis was conducted using the GeneChip™ Human Mapping 100 K Set . SNP genotype data were extracted with the following “cut-off values”: call rate of control samples >0.95, p value of HWE (Hardy-Weinberg equilibrium test) in control samples >0.05, MAF  of control samples >0, and confidence scores of all family members <0.1. Control samples were collected from 24 healthy Japanese individuals. SNPs were selected to keep the inter-marker distance at approximately 100 kb. Parametric and non-parametric multipoint linkage analyses were performed using the Allegro version 2.0 program Figure S1Parametric LOD Score Analysis of the SNP Genotypes. Parametric LOD Score Analysis of the SNP Genotypes. The SNP genotype data were analyzed by Allegro and parametric LOD scores were generated for each autosomal chromosome. Chromosomal location is shown along the X-axis.(2.38 MB EPS)Click here for additional data file.