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Linkage disequilibrium maps and disease-association mapping
No full textOver the last few years, association mapping of disease genes has developed into one of the most dynamic research areas of human genetics. It focuses on identifying functional polymorphisms that predispose to complex diseases. Population-based approaches are concerned with exploiting linkage disequilibrium (LD) between single-nucleotide polymorphism (SNPs) and disease-predisposing loci. The utility of SNPs in association mapping is now well established and the interest in this field has been escalated by the discovery of millions of SNPs across the genome. This chapter reviews an association-mapping method that utilizes metric LD maps in LD units and employs a composite likelihood approach to combine information from all single SNP tests. It applies a model that incorporates a parameter for the location of the causal polymorphism. A proof-of-principle application of this method to a small region is given and its potential properties to large-scale datasets are discussed
Effects of single SNPs, haplotypes, and whole-genome LD maps on accuracy of association mapping
No full textWe describe an association mapping approach that utilizes linkage disequilibrium (LD) maps in LD units (LDU). This method uses composite likelihood to combine information from all single marker tests, and applies a model with a parameter for the location of the causal polymorphism. Previous analyses of the poor drug metabolizer phenotype provided evidence of the substantial utility of LDU maps for disease gene association mapping. Using LDU locations for the 27 single nucleotide polymorphisms (SNPs) flanking the CYP2D6 gene on chromosome 22, the most common functional polymorphism within the gene was located at 15 kb from its true location. Here, we examine the performance of this mapping approach by exploiting the high-density LDU map constructed from the HapMap data. Expressing the locations of the 27 SNPs in LDU from the HapMap LDU map, analysis yielded an estimated location that is only 0.3 kb away from the CYP2D6 gene. This supports the use of the high marker density HapMap-derived LDU map for association mapping even though it is derived from a much smaller number of individuals compared to the CYP2D6 sample. We also examine the performance of 2-SNP haplotypes. Using the same modelling procedures and composite likelihood as for single SNPs, the haplotype data provided much poorer localization compared to single SNP analysis. Haplotypes generate more autocorrelation through multiple inclusions of the same SNPs, which could inflate significance in association studies. The results of the present study demonstrate the great potential of the genome HapMap LDU maps for high-resolution mapping of complex phenotypes
Searching for candidate genes for asthma using combined linkage and segregation analysis
No full textAllelic association and disease mapping
No full textThe application of allelic association to map genes for complex traits, particularly using high-density maps of single nucleotide polymorphisms in candidate regions, is an area of very active research. Here we present some aspects of the methodology and applications to both major gene mapping, which illustrates the effectiveness of the method, and oligogenes, where methods are still in flux and for which there have been relatively few successes to date. Several important considerations emerge, including the selection of the optimal metric for measuring association and the importance of modelling the decline in association with distance given the variability in association in a candidate region. The Malecot model of association with distance is shown to have a resolution of greater than 50 kilobases but the available evidence suggests that considerably higher resolution might be achieved with dense single nucleotide polymorphism (SNP) maps
Positional cloning by linkage disequilibrium
No full textRecently, metric linkage disequilibrium (LD) maps that assign an LD unit (LDU) location for each marker have been developed (Maniatis et al. 2002). Here we present a multiple pairwise method for positional cloning by LD within a composite likelihood framework and investigate the operating characteristics of maps in physical units (kb) and LDU for two bodies of data (Daly et al. 2001; Jeffreys et al. 2001) on which current ideas of blocks are based. False-negative indications of a disease locus (type II error) were examined by selecting one single-nucleotide polymorphism (SNP) at a time as causal and taking its allelic count (0, 1, or 2, for the three genotypes) as a pseudophenotype, Y. By use of regression and correlation, association between every pseudophenotype and the allelic count of each SNP locus (X) was based on an adaptation of the Malecot model, which includes a parameter for location of the putative gene. By expressing locations in kb or LDU, greater power for localization was observed when the LDU map was fitted. The efficiency of the kb map, relative to the LDU map, to describe LD varied from a maximum of 0.87 to a minimum of 0.36, with a mean of 0.62. False-positive indications of a disease locus (type I error) were examined by simulating an unlinked causal SNP and the allele count was used as a pseudophenotype. The type I error was in good agreement with Wald's likelihood theorem for both metrics and all models that were tested. Unlike tests that select only the most significant marker, haplotype, or haploset, these methods are robust to large numbers of markers in a candidate region. Contrary to predictions from tagging SNPs that retain haplotype diversity, the sample with smaller size but greater SNP density gave less error. The locations of causal SNPs were estimated with the same precision in blocks and steps, suggesting that block definition may be less useful than anticipated for mapping a causal SNP. These results provide a guide to efficient positional cloning by SNPs and a benchmark against which the power of positional cloning by haplotype-based alternatives may be measured
Linkage disequilibrium in human populations
No full textWhereas the human linkage map appears on limited evidence to be constant over populations, maps of linkage disequilibrium (LD) vary among populations that differ in gene history. The greatest difference is between populations of sub-Saharan origin and populations remotely derived from Africa after a major bottleneck that reduced their heterozygosity and altered their Malecot parameters, increasing the intercept M that reflects association in founders and decreasing the exponential decline . Variation among populations within this ethnic dichotomy is much smaller. These observations validate use of a cosmopolitan LD map based on a sizeable sample representing a large population reliably typed for markers at high density. Then an LD map for a region or isolate within an ethnic group may be created by fitting the sample LD to the cosmopolitan map, estimating Malecot parameters simultaneously. The cosmopolitan map scaled by recovers 95% of the information that a local map at the same density gives and therefore more than the information in a low-resolution local map. Relative to a Eurasian cosmopolitan map the scaling factors are estimated to be 0.82 for isolates of European descent, 1.53 for Yorubans, and 1.74 for African Americans. These observations are consistent with a common bottleneck (perhaps but not necessarily speciation) 173,500 years ago, if the bottleneck associated with migration out of Africa was 100,000 years ago. Eurasian populations (especially isolates with numerous cases) are efficient for genome scans, and populations of recent African origin (such as African Americans) are efficient for identification of causal polymorphisms within a candidate sequence
Properties of linkage disequilibrium (LD) maps
No full textA linkage disequilibrium map is expressed in linkage disequilibrium (LD) units (LDU) discriminating blocks of conserved LD that have additive distances and locations monotonic with physical (kb) and genetic (cM) maps. There is remarkable agreement between LDU steps and sites of meiotic recombination in the one body of data informative for crossing over, and good agreement with another method that defines blocks without assigning an LD location to each marker. The map may be constructed from haplotypes or diplotypes, and efficiency estimated from the empirical variance of LD is substantially greater for the metric based on evolutionary theory than for the absolute correlation r, and for the LD map compared with its physical counterpart. The empirical variance is nearly three times as great for the worst alternative (r and kb map) as for the most efficient approach (and LD map). According to the empirical variances, blocks are best defined by zero distance between included markers. Because block size is algorithm-dependent and highly variable, the number of markers required for positional cloning is minimized by uniform spacing on the LD map, which is estimated to have 1 LDU per locus, but with much variation among regions. No alternative representation of linkage disequilibrium (some of which are loosely called maps) has these properties, suggesting that LD maps are optimal for positional cloning of genes determining disease susceptibility
Genome scanning by composite likelihood
No full textAmbitious programs have recently been advocated or launched to create genomewide databases for meta-analysis of association between DNA markers and phenotypes of medical and/or social concern. A necessary but not sufficient condition for success in association mapping is that the data give accurate estimates of both genomic location and its standard error, which are provided for multifactorial phenotypes by composite likelihood. That class includes the Malecot model, which we here apply with an illustrative example. This preliminary analysis leads to five inferences: permutation of cases and controls provides a test of association free of autocorrelation; two hypotheses give similar estimates, but one is consistently more accurate; estimation of the false-discovery rate is extended to causal genes in a small proportion of regions; the minimal data for successful meta-analysis are inferred; and power is robust for all genomic factors except minor-allele frequency. An extension to meta-analysis is proposed. Other approaches to genome scanning and meta-analysis should, if possible, be similarly extended so that their operating characteristics can be compared
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
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