Evaluation of linkage disequilibrium and its effect on non-parametric multipoint linkage analysis using two high density single-nucleotide polymorphism mapping panels

Genotype data from the Illumina Linkage III SNP panel (n = 4,720 SNPs) and the Affymetrix 10 k mapping array (n = 11,120 SNPs) were used to test the effects of linkage disequilibrium (LD) between SNPs in a linkage analysis in the Collaborative Study on the Genetics of Alcoholism pedigree collection (143 pedigrees; 1,614 individuals). The average r2 between adjacent markers across the genetic map was 0.099 +/- 0.003 in the Illumina III panel and 0.17 +/- 0.003 in the Affymetrix 10 k array. In order to determine the effect of LD between marker loci in a nonparametric multipoint linkage analysis, markers in strong LD with another marker (r2 > 0.40) were removed (n = 471 loci in the Illumina panel; n = 1,804 loci in the Affymetrix panel) and the linkage analysis results were compared to the results using the entire marker sets. In all analyses using the ALDX1 phenotype, 8 linkage regions on 5 chromosomes (2, 7, 10, 11, X) were detected (peak markers p < 0.01), and the Illumina panel detected an additional region on chromosome 6. Analysis of the same pedigree set and ALDX1 phenotype using short tandem repeat markers (STRs) resulted in 3 linkage regions on 3 chromosomes (peak markers p < 0.01). These results suggest that in this pedigree set, LD between loci with spacing similar to the SNP panels tested may not significantly affect the overall detection of linkage regions in a genome scan. Moreover, since the data quality and information content are greatly improved in the SNP panels over STR genotyping methods, new linkage regions may be identified due to higher information content and data quality in a dense SNP linkage panel.     

Handling marker-marker linkage disequilibrium: pedigree analysis with clustered markers

Single-nucleotide polymorphisms (SNPs) are rapidly replacing microsatellites as the markers of choice for genetic linkage studies and many other studies of human pedigrees. Here, we describe an efficient approach for modeling linkage disequilibrium (LD) between markers during multipoint analysis of human pedigrees. Using a gene-counting algorithm suitable for pedigree data, our approach enables rapid estimation of allele and haplotype frequencies within clusters of tightly linked markers. In addition, with the use of a hidden Markov model, our approach allows for multipoint pedigree analysis with large numbers of SNP markers organized into clusters of markers in LD. Simulation results show that our approach resolves previously described biases in multipoint linkage analysis with SNPs that are in LD. An updated version of the freely available Merlin software package uses the approach described here to perform many common pedigree analyses, including haplotyping and haplotype frequency estimation, parametric and nonparametric multipoint linkage analysis of discrete traits, variance-components and regression-based analysis of quantitative traits, calculation of identity-by-descent or kinship coefficients, and case selection for follow-up association studies. To illustrate the possibilities, we examine a data set that provides evidence of linkage of psoriasis to chromosome 17.     

Single-nucleotide polymorphism versus microsatellite markers in a combined linkage and segregation analysis of a quantitative trait

Increasingly, single-nucleotide polymorphism (SNP) markers are being used in preference to microsatellite markers. However, methods developed for microsatellites may be problematic when applied to SNP markers. We evaluated the results of using SNPs vs. microsatellites in Monte Carlo Markov chain (MCMC) oligogenic combined segregation and linkage analysis methods. These methods were developed with microsatellite markers in mind. We selected chromosome 7 from the Collaborative Study on the Genetics of Alcoholism dataset for analysis because linkage to an electrophysiological trait had been reported there. We found linkage in the same region of chromosome 7 with the Affymetrix SNP data, the Illumina SNP data, and the microsatellite marker data. The MCMC sampler appears to mix with both types of data. The sampler implemented in this MCMC oligogenic combined segregation and linkage analysis appears to handle SNP data as well as microsatellite data and it is possible that the localizations with the SNP data are better.     

An x chromosome association scan of the Norfolk Island genetic isolate provides evidence for a novel migraine susceptibility locus at Xq12

Migraine is a common and debilitating neurovascular disorder with a complex envirogenomic aetiology. Numerous studies have demonstrated a preponderance of women affected with migraine and previous pedigree linkage studies in our laboratory have identified susceptibility loci on chromosome Xq24-Xq28. In this study we have used the genetic isolate of Norfolk Island to further analyse the X chromosome for migraine susceptibility loci.An association approach was employed to analyse 14,124 SNPs spanning the entire X chromosome. Genotype data from 288 individuals comprising a large core-pedigree, of which 76 were affected with migraine, were analysed. Although no SNP reached chromosome-wide significance (empirical α = 1 × 10(-5)) ranking by P-value revealed two primary clusters of SNPs in the top 25. A 10 SNP cluster represents a novel migraine susceptibility locus at Xq12 whilst a 11 SNP cluster represents a previously identified migraine susceptibility locus at Xq27. The strongest association at Xq12 was seen for rs599958 (OR = 1.75, P = 8.92 × 10(-4)), whilst at Xq27 the strongest association was for rs6525667 (OR = 1.53, P = 1.65 × 10(-4)). Further analysis of SNPs at these loci was performed in 5,122 migraineurs from the Women's Genome Health Study and provided additional evidence for association at the novel Xq12 locus (P<0.05).Overall, this study provides evidence for a novel migraine susceptibility locus on Xq12. The strongest effect SNP (rs102834, joint P = 1.63 × 10(-5)) is located within the 5'UTR of the HEPH gene, which is involved in iron homeostasis in the brain and may represent a novel pathway for involvement in migraine pathogenesis.     

Identification of linked regions using high-density SNP genotype data in linkage analysis

MOTIVATION: With the knowledge of large number of SNPs in human genome and the fast development in high-throughput genotyping technologies, identification of linked regions in linkage analysis through allele sharing status determination will play an ever important role, while consideration of recombination fractions becomes unnecessary. RESULTS: In this study, we have developed a rule-based program that identifies linked regions for underlined diseases using allele sharing information among family members. Our program uses high-density SNP genotype data and works in the face of genotyping errors. It works on nuclear family structures with two or more siblings. The program graphically displays allele sharing status for all members in a pedigree and identifies regions that are potentially linked to the underlined diseases according to user-specified inheritance mode and penetrance. Extensive simulations based on the chi(2) model for recombination show that our program identifies linked regions with high sensitivity and accuracy. Graphical display of allele sharing status helps to detect misspecification of inheritance mode and penetrance, as well as mislabeling or misdiagnosis. Allele sharing determination may represent the future direction of linkage analysis due to its better adaptation to high-density SNP genotyping data. AVAILABILITY: http://paed.hku.hk/uploadarea/yangwl/html/index.html     

Comparison of the power between microsatellite and single-nucleotide polymorphism markers for linkage and linkage disequilibrium mapping of an electrophysiological phenotype

We performed linkage and linkage disequilibrium (LD) mapping analyses to compare the power between microsatellite and single nucleotide polymorphism (SNP) markers. Chromosome-wide analyses were performed for a quantitative electrophysiological phenotype, ttth1, on chromosome 7. Multipoint analysis of microsatellite markers using the variance component (VC) method showed the highest LOD score of 4.20 at 162 cM, near D7S509 (163.7 cM). Two-point analysis of SNPs using the VC method yielded the highest LOD score of 3.98 in the Illumina SNP data and 3.45 in the Affymetrix SNP data around 152-153 cM. In family-based single SNP and SNP haplotype LD analysis, we identified seven SNPs associated with ttth1. We searched for any potential candidate genes in the location of the seven SNPs. The SNPs rs1476640 and rs768055 are located in the FLJ40852 gene (a hypothetical protein), and SNP rs1859646 is located in the TAS2R5 gene (a taste receptor). The other four SNPs are not located in any known or annotated genes. We found the high density SNP scan to be superior to microsatellites because it is effective in downstream fine mapping due to a better defined linkage region. Our study proves the utility of high density SNP in genome-wide mapping studies.     

Genome scan linkage analysis comparing microsatellites and single-nucleotide polymorphisms markers for two measures of alcoholism in chromosomes 1, 4, and 7

Background

We analyzed 143 pedigrees (364 nuclear families) in the Collaborative Study on the Genetics of Alcoholism (COGA) data provided to the participants in the Genetic Analysis Workshop 14 (GAW14) with the goal of comparing results obtained from genome linkage analysis using microsatellite and with results obtained using SNP markers for two measures of alcoholism (maximum number of drinks -MAXDRINK and an electrophysiological measure from EEG -TTTH1). First, we constructed haplotype blocks by using the entire set of single-nucleotide polymorphisms (SNP) in chromosomes 1, 4, and 7. These chromosomes have shown linkage signals for MAXDRINK or EEG-TTTH1 in previous reports. Second, we randomly selected one, two, three, four, and five SNPs from each block (referred to as Rep1 – Rep5, respectively) to conduct linkage analysis using variance component approach. Finally, results of all SNP analyses were compared with those obtained using microsatellite markers.

Results

The LOD scores obtained from SNPs were slightly higher but the curves were not radically different from those obtained from microsatellite analyses. The peaks of linkage regions from SNP sets were slightly shifted to the left when compared to those from microsatellite markers. The reduced sets of SNPs provide signals in the same linkage regions but with a smaller LOD score suggesting a significant impact of the decrease in information content on linkage results. The widths of 1 LOD support interval of linkage regions from SNP sets were smaller when compared to those of microsatellite markers. However, two linkage regions obtained from the microsatellite linkage analysis on chromosome 7 for LOG of TTTH1 were not detected in the SNP based analyses.

Conclusion

The linkage results from SNPs showed narrower linkage regions and slightly higher LOD scores when compared to those of microsatellite markers. The different builds of the genetic maps used in microsatellite and SNPs markers or/and errors in genotyping may account for the microsatellite linkage signals on chromosome 7 that were not identified using SNPs. Also, unresolved map issues between SNPs and microsatellite markers may be partly responsible for the shifted linkage peaks when comparing the two types of markers.

     

Loss of FBXO7 (PARK15) results in reduced proteasome activity and models a parkinsonism‐like phenotype in mice

Mutations in the FBXO7 (PARK15) gene have been implicated in a juvenile form of parkinsonism termed parkinsonian pyramidal syndrome (PPS), characterized by Parkinsonian symptoms and pyramidal tract signs. FBXO7 (F‐box protein only 7) is a subunit of the SCF (SKP1/cullin‐1/F‐box protein) E3 ubiquitin ligase complex, but its relevance and function in neurons remain to be elucidated. Here, we report that the E3 ligase FBXO7‐SCF binds to and ubiquitinates the proteasomal subunit PSMA2. In addition, we show that FBXO7 is a proteasome‐associated protein involved in proteasome assembly. In FBXO7 knockout mice, we find reduced proteasome activity and early‐onset motor deficits together with premature death. In addition, we demonstrate that NEX (neuronal helix–loop–helix protein‐1)‐Cre‐induced deletion of the FBXO7 gene in forebrain neurons or the loss of FBXO7 in tyrosine hydroxylase (TH)‐positive neurons results in motor defects, reminiscent of the phenotype in PARK15 patients. Taken together, our study establishes a vital role for FBXO7 in neurons, which is required for proper motor control and accentuates the importance of FBXO7 in proteasome function.     

Genetic analysis of the maximum drinks phenotype

Using data provided by the Collaborative Study on the Genetics of Alcoholism we studied the genetics of a quantitative trait: the maximum number of drinks consumed in a 24-hour period. A two-stage method was used. First, linkage analysis was performed, followed by association analysis in regions where linkage was detected. Additionally, the extent of linkage disequilibrium among single-nucleotide polymorphisms (SNP) associated with the phenotype was assessed. Linkage to chromosomes 2 and 7 was detected, and follow-up association analysis found multiple trait-associated SNPs in the chromosome 7 linkage region. Chromosome 4, which has been implicated in previous studies of the maximum drinks phenotype, did not pass our threshold for linkage evidence in stage 1, but secondary analyses of this chromosome indicated modest evidence for both linkage and association. The evidence suggests that chromosome 7 may harbor an additional locus influencing the maximum drinks consumption phenotype.     

An interactional network of genes involved in chitin synthesis in Saccharomyces cerevisiae

Background

The identification of disease-associated genes using single nucleotide polymorphisms (SNPs) has been increasingly reported. In particular, the Affymetrix Mapping 10 K SNP microarray platform uses one PCR primer to amplify the DNA samples and determine the genotype of more than 10,000 SNPs in the human genome. This provides the opportunity for large scale, rapid and cost-effective genotyping assays for linkage analysis. However, the analysis of such datasets is nontrivial because of the large number of markers, and visualizing the linkage scores in the context of genome maps remains less automated using the current linkage analysis software packages. For example, the haplotyping results are commonly represented in the text format.

Results

Here we report the development of a novel software tool called CompareLinkage for automated formatting of the Affymetrix Mapping 10 K genotype data into the "Linkage" format and the subsequent analysis with multi-point linkage software programs such as Merlin and Allegro. The new software has the ability to visualize the results for all these programs in dChip in the context of genome annotations and cytoband information. In addition we implemented a variant of the Lander-Green algorithm in the dChipLinkage module of dChip software (V1.3) to perform parametric linkage analysis and haplotyping of SNP array data. These functions are integrated with the existing modules of dChip to visualize SNP genotype data together with LOD score curves. We have analyzed three families with recessive and dominant diseases using the new software programs and the comparison results are presented and discussed.

Conclusions

The CompareLinkage and dChipLinkage software packages are freely available. They provide the visualization tools for high-density oligonucleotide SNP array data, as well as the automated functions for formatting SNP array data for the linkage analysis programs Merlin and Allegro and calling these programs for linkage analysis. The results can be visualized in dChip in the context of genes and cytobands. In addition, a variant of the Lander-Green algorithm is provided that allows parametric linkage analysis and haplotyping.     

骨质疏松症易感基因BDNF的遗传学关联分析及功能研究

为探索脑源性神经营养因子BDNF基因与中国汉族人群骨密度(bone mineral density, BMD)的关系,解析该基因调节骨质疏松症的功能机制,进而为中国汉族人群的骨质疏松症的防治提供依据。本研究从我国陕西地区征集了1300例汉族样本,并测量髋部/脊椎骨密度。选取BDNF基因上的14个标签SNPs进行基因分型,与1300例样本BMD进行关联分析,发现8个SNPs与髋部/脊椎BMD显著关联(P < 0.05)。其中,SNP rs16917237同时与髋部和脊椎骨密度关联,经Bonferroni校正后仍表现出显著性(0.05/14 = 0.0036)。整合连锁不平衡和单体型分析、表观功能注释、表达数量基因座分析、代谢通路分析进一步探索BDNF基因调节骨质疏松症的机制。构建小鼠前成骨细胞系(MC3T3-E1)人骨形态形成蛋白(rhBMP-2)诱导分化模型,利用小干扰RNA(siRNA)敲除BDNF。结果显示：14个SNPs位于同一单体型内;rs16917237在成骨细胞中表现出较强的激活型组蛋白H3K4me1、H3K4me3、H3K27ac修饰信号以及P300结合信号,表明其在成骨细胞中可能具有调控活性;rs16917237在11个组织中均能显著影响BDNF基因表达;BDNF基因位于与成骨细胞增殖分化相关的MAPK通路中;BDNF敲低能够显著降低MAPK通路中与成骨分化相关的CREB 基因mRNA和蛋白表达水平,提示其可能通过调控CREB表达进而影响成骨分化。生物信息学分析和功能实验结果一致,表明BDNF基因可能是影响骨质疏松症的重要功能基因。     

A Genome Scan Conducted in a Multigenerational Pedigree with Convergent Strabismus Supports a Complex Genetic Determinism

A genome-wide linkage scan was conducted in a Northern-European multigenerational pedigree with nine of 40 related members affected with concomitant strabismus. Twenty-seven members of the pedigree including all affected individuals were genotyped using a SNP array interrogating > 300,000 common SNPs. We conducted parametric and non-parametric linkage analyses assuming segregation of an autosomal dominant mutation, yet allowing for incomplete penetrance and phenocopies. We detected two chromosome regions with near-suggestive evidence for linkage, respectively on chromosomes 8 and 18. The chromosome 8 linkage implied a penetrance of 0.80 and a rate of phenocopy of 0.11, while the chromosome 18 linkage implied a penetrance of 0.64 and a rate of phenocopy of 0. Our analysis excludes a simple genetic determinism of strabismus in this pedigree.     

The identification of SNPs with indeterminate positions using the Equine SNP50 BeadChip

We have used linkage disequilibrium (LD) to identify single nucleotide polymorphisms (SNPs) on the Illumina Equine SNP50 BeadChip, which may be incorrectly positioned on the genome map. A total of 1201 Thoroughbred horses were genotyped using the Illumina Equine SNP50 BeadChip. LD was evaluated in a pairwise fashion between all autosomal SNPs, both within and across chromosomes. Filters were then applied to the data, firstly to identify SNPs that may have been mapped to the wrong chromosome and secondly to identify SNPs that may have been incorrectly positioned within chromosomes. We identified a single SNP on ECA28, which showed low LD with neighbouring SNPs but considerable LD with a group of SNPs on ECA10. Furthermore, a cluster of SNPs on ECA5 showed unusually low LD with surrounding SNPs. A total of 39 SNPs met the criteria for unusual within-chromosome LD. The results of this study indicate that some SNPs may be misplaced. This finding is significant, as misplaced SNPs may lead to difficulties in the application of genomic methods, such as homozygosity mapping, for which SNP order is important.     

Linkage analysis of complex diseases using microsatellites and single-nucleotide polymorphisms: application to alcoholism

The efficacy of linkage studies using microsatellites and single-nucleotide polymorphisms (SNPs) was evaluated. Analyzed data were supplied by the Collaborative Study on the Genetics of Alcoholism (COGA). Alcoholism was analyzed together with a simulated trait caused by a gene of known position, through a nonparametric linkage test (NPL). For the alcoholism trait, four densities of SNPs (1 SNP per 0.2 cM, 0.5 cM, 1 cM and 2 cM) showed higher peaks of NPL z scores and smaller significant p-values than the usual 10-cM density of microsatellites. However, the two highest densities of SNPs had unstable z score signals, and therefore were difficult to interpret. Analyzing a simulated trait with the same markers in the same pedigrees, we confirmed the higher power of all four densities of SNPs compared to the 10-cM microsatellites panel, although the existence of other confounding peaks was confirmed for maps that are denser than 1 SNP/cM. We further showed that estimating the gene position using SNPs is far less biased than using the usual panel of microsatellites (biases of 0-2 cM for SNPs vs. 8.9 cM for microsatellites). We conclude that using dense maps of SNPs in linkage analysis is more powerful and less biased than using the 10-cM maps of microsatellites. However, linkage signals can be unstable and difficult to interpret when several SNPs are genotyped per centimorgan. The power and accuracy of 1 SNP/cM or 1 SNP/2 cM may be sufficient in a genome-wide linkage scan while denser maps may be most useful in fine-gene mapping studies exploiting linkage disequilibrium.     

Accuracy of genotype imputation in sheep breeds

Although genomic selection offers the prospect of improving the rate of genetic gain in meat, wool and dairy sheep breeding programs, the key constraint is likely to be the cost of genotyping. Potentially, this constraint can be overcome by genotyping selection candidates for a low density (low cost) panel of SNPs with sparse genotype coverage, imputing a much higher density of SNP genotypes using a densely genotyped reference population. These imputed genotypes would then be used with a prediction equation to produce genomic estimated breeding values. In the future, it may also be desirable to impute very dense marker genotypes or even whole genome re‐sequence data from moderate density SNP panels. Such a strategy could lead to an accurate prediction of genomic estimated breeding values across breeds, for example. We used genotypes from 48 640 (50K) SNPs genotyped in four sheep breeds to investigate both the accuracy of imputation of the 50K SNPs from low density SNP panels, as well as prospects for imputing very dense or whole genome re‐sequence data from the 50K SNPs (by leaving out a small number of the 50K SNPs at random). Accuracy of imputation was low if the sparse panel had less than 5000 (5K) markers. Across breeds, it was clear that the accuracy of imputing from sparse marker panels to 50K was higher if the genetic diversity within a breed was lower, such that relationships among animals in that breed were higher. The accuracy of imputation from sparse genotypes to 50K genotypes was higher when the imputation was performed within breed rather than when pooling all the data, despite the fact that the pooled reference set was much larger. For Border Leicesters, Poll Dorsets and White Suffolks, 5K sparse genotypes were sufficient to impute 50K with 80% accuracy. For Merinos, the accuracy of imputing 50K from 5K was lower at 71%, despite a large number of animals with full genotypes (2215) being used as a reference. For all breeds, the relationship of individuals to the reference explained up to 64% of the variation in accuracy of imputation, demonstrating that accuracy of imputation can be increased if sires and other ancestors of the individuals to be imputed are included in the reference population. The accuracy of imputation could also be increased if pedigree information was available and was used in tracking inheritance of large chromosome segments within families. In our study, we only considered methods of imputation based on population‐wide linkage disequilibrium (largely because the pedigree for some of the populations was incomplete). Finally, in the scenarios designed to mimic imputation of high density or whole genome re‐sequence data from the 50K panel, the accuracy of imputation was much higher (86–96%). This is promising, suggesting that in silico genome re‐sequencing is possible in sheep if a suitable pool of key ancestors is sequenced for each breed.     

The Wheat 660K SNP array demonstrates great potential for marker‐assisted selection in polyploid wheat

The rapid development and application of molecular marker assays have facilitated genomic selection and genome‐wide linkage and association studies in wheat breeding. Although PCR‐based markers (e.g. simple sequence repeats and functional markers) and genotyping by sequencing have contributed greatly to gene discovery and marker‐assisted selection, the release of a more accurate and complete bread wheat reference genome has resulted in the design of single‐nucleotide polymorphism (SNP) arrays based on different densities or application targets. Here, we evaluated seven types of wheat SNP arrays in terms of their SNP number, distribution, density, associated genes, heterozygosity and application. The results suggested that the Wheat 660K SNP array contained the highest percentage (99.05%) of genome‐specific SNPs with reliable physical positions. SNP density analysis indicated that the SNPs were almost evenly distributed across the whole genome. In addition, 229 266 SNPs in the Wheat 660K SNP array were located in 66 834 annotated gene or promoter intervals. The annotated genes revealed by the Wheat 660K SNP array almost covered all genes revealed by the Wheat 35K (97.44%), 55K (99.73%), 90K (86.9%) and 820K (85.3%) SNP arrays. Therefore, the Wheat 660K SNP array could act as a substitute for other 6 arrays and shows promise for a wide range of possible applications. In summary, the Wheat 660K SNP array is reliable and cost‐effective and may be the best choice for targeted genotyping and marker‐assisted selection in wheat genetic improvement.     

Pathway analysis of genome-wide association study for bone mineral density

The aim of this study was to identify the candidate causal single nucleotide polymorphisms (SNPs) and candidate causal mechanisms that contribute to bone mineral density (BMD) and to generate a SNP to gene to pathway hypothesis using an analytical pathway-based approach. We used hip BMD GWAS data of the genotypes of 301,019 SNPs in 5,715 Europeans. ICSNPathway (identify candidate causal SNPs and pathways) analysis was applied to the BMD GWAS dataset. The first stage involved the pre-selection of candidate causal SNPs by linkage disequilibrium analysis and the functional SNP annotation of the most significant SNPs found. The second stage involved the annotation of biological mechanisms for the pre-selected candidate causal SNPs using improved-gene set enrichment analysis. ICSNPathway analysis identified seven candidate SNPs, eight candidate pathways, and seven hypothetical biological mechanisms. Eight pathways are as follows; gamma-hexachlorocyclohexane degradation (nominal p-value < 0.001, false discovery rate (FDR) <0.001), regulation of the smoothened signaling pathway (nominal p-value < 0.001, FDR = 0.016), TACI and BCMA stimulation of B cell immune response (nominal p-value < 0.001, FDR = 0.021), endonuclease activity (nominal p-value = 0.001, FDR = 0,026), regulation of defense response to virus (nominal p-value = 0.001, FDR = 0.028), serine_type_endopeptidase_inhibitor_activity (nominal p-value = 0.001, FDR = 0.044), endoribonuclease activity (nominal p-value = 0.002, FDR = 0.045), and myeloid leukocyte differentiation (nominal p-value = 0.001, FDR = 0.050). The most significant causal pathway was gamma-hexachlorocyclohexane degradation. CYP3A5, PON2, PON3, CMBL, PON1, ALPL, CYP3A43, CYP3A7, ACP6, ACPP, and ALPI (p < 0.05) are involved in the pathway of gamma-hexachlorocyclohexane degradation. Further examination of the gene contents revealed that DBR1, DICER1, EXO1, FEN1, POP1, POP4, RPP30, and RPP38 were involved in 2 of the 8 pathways (p < 0.05). By applying ICSNPathway analysis to BMD GWAS data, we identified seven candidate SNPs and eight pathways involving gamma-hexachlorocyclohexane degradation, which may contribute to low BMD.     

Whole-genome scan, in a complex disease, using 11,245 single-nucleotide polymorphisms: comparison with microsatellites

Despite the theoretical evidence of the utility of single-nucleotide polymorphisms (SNPs) for linkage analysis, no whole-genome scans of a complex disease have yet been published to directly compare SNPs with microsatellites. Here, we describe a whole-genome screen of 157 families with multiple cases of rheumatoid arthritis (RA), performed using 11,245 genomewide SNPs. The results were compared with those from a 10-cM microsatellite scan in the same cohort. The SNP analysis detected HLA*DRB1, the major RA susceptibility locus (P=.00004), with a linkage interval of 31 cM, compared with a 50-cM linkage interval detected by the microsatellite scan. In addition, four loci were detected at a nominal significance level (P<.05) in the SNP linkage analysis; these were not observed in the microsatellite scan. We demonstrate that variation in information content was the main factor contributing to observed differences in the two scans, with the SNPs providing significantly higher information content than the microsatellites. Reducing the number of SNPs in the marker set to 3,300 (1-cM spacing) caused several loci to drop below nominal significance levels, suggesting that decreases in information content can have significant effects on linkage results. In contrast, differences in maps employed in the analysis, the low detectable rate of genotyping error, and the presence of moderate linkage disequilibrium between markers did not significantly affect the results. We have demonstrated the utility of a dense SNP map for performing linkage analysis in a late-age-at-onset disease, where DNA from parents is not always available. The high SNP density allows loci to be defined more precisely and provides a partial scaffold for association studies, substantially reducing the resource requirement for gene-mapping studies.     

High-density single nucleotide polymorphism screen in a large multiplex neural tube defect family refines linkage to loci at 7p21.1-pter and 2q33.1-q35

BACKGROUND: Neural tube defects (NTDs) are considered complex, with both genetic and environmental factors implicated. To date, no major causative genes have been identified in humans despite several investigations. The first genomewide screen in NTDs demonstrated evidence of linkage to chromosomes 7 and 10. This screen included 44 multiplex families and consisted of 402 microsatellite markers spaced approximately 10 cM apart. Further investigation of the genomic screen data identified a single large multiplex family, pedigree 8776, as primarily driving the linkage results on chromosome 7. METHODS: To investigate this family more thoroughly, a high-density single nucleotide polymorphism (SNP) screen was performed. Two-point and multipoint linkage analyses were performed using both parametric and nonparametric methods. RESULTS: For both the microsatellite and SNP markers, linkage analysis suggested the involvement of a locus or loci proximal to the telomeric regions of chromosomes 2q and 7p, with both regions generating a LOD* score of 3.0 using a nonparametric identity by descent relative sharing method. CONCLUSIONS: The regions with the strongest evidence for linkage map proximal to the telomeres on these two chromosomes. In addition to mutations and/or variants in a major gene, these loci may harbor a microdeletion and/or translocation; potentially, polygenic factors may also be involved. This single family may be promising for narrowing the search for NTD susceptibility genes.