Large-Scale Integration of Human Genetic and Physical Maps

Nievergelt, Caroline M.; Smith, Douglas W.; Kohlenberg, J. Bradley; Schork, Nicholas J.

doi:10.1101/gr.1475304

Cited by 42 publications

(41 citation statements)

References 21 publications

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“…The first was an inversion of a 12 cM region on chromosome 8 in the genetic marker map, which has since been published, 17 and 1 marker, D5S1359, which led to a change in the maximum multipoint LOD score on chromosome 5 (LODϭ2.12 with D5S1359 included; LODϭ2.50 with D5S1359 excluded). This marker is not in the Rutger's map, and had been selected according to position at Southampton (http://cedar.genetics.soton.ac.uk/public_html/LDB2000/ release.html), which we have not been able to further validate.…”

Section: Effect Of Different Marker Maps On Lod Scoresmentioning

confidence: 99%

Increased Support for Linkage of a Novel Locus on Chromosome 5q13 for Essential Hypertension in the British Genetics of Hypertension Study

et al. 2006

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Abstract-Human hypertension arises from a combination of genetic factors and lifestyle influences. With cardiovascular disease set to become the number 1 cause of death worldwide, it is important to understand the etiologic mechanisms for hypertension, because these might provide new routes to improved treatment. The British Genetics of Hypertension Study has recently published a primary genome screen that identified 4 chromosomal regions of interest. We have now genotyped additional markers to confirm the most promising regions for follow-up studies. Thirty-four additional microsatellites were genotyped in our severely hypertensive affected sibling pair resource (now 1635 families with 2142 affected sibling pairs), leading to a substantial increase in information content in the regions of interest. We found increased support for linkage of chromosome 5q13 to human hypertension (multipoint logarithm of oddsϭ2.50) with 3 adjacent markers yielding single point logarithm of odds scores of 3.22, 2.84, and 2.51. The placement of additional markers on 2q, 6q, and 9q diminished support for linkage in these regions. However, the addition of new data and families identified novel regions of interest on chromosomes 1q and 11q. The 3 positive markers in the chromosome 5 region were also genotyped in 712 distinct parent-offspring trios with the same severe phenotype to replicate linkage and association. Borderline support for replication was found (Pϭ0.07). We found increased evidence for linkage and borderline-significant evidence for association for a hypertension susceptibility locus on chromosome 5q13 that is worthy of detailed fine mapping and assessment of candidate genes. (Hypertension. 2006;48:105-111.)

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Section: Effect Of Different Marker Maps On Lod Scoresmentioning

confidence: 99%

Increased Support for Linkage of a Novel Locus on Chromosome 5q13 for Essential Hypertension in the British Genetics of Hypertension Study

et al. 2006

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“…The user provides intermarker recombination fractions or genetic map distances obtained from genetic maps [1,3] or through interpolation. If no genetic map is available for the markers of interest, SNP marker loci can be ordered based upon their sequence-based physical map position and then interpolated onto a genetic map -for example, the Rutgers Combined Linkage-Physical Map [3] or the DeCode genetic map [13,15] .…”

Section: Methodsmentioning

confidence: 99%

SimPed: A Simulation Program to Generate Haplotype and Genotype Data for Pedigree Structures

2005

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With the widespread availability of SNP genotype data, there is great interest in analyzing pedigree haplotype data. Intermarker linkage disequilibrium for microsatellite markers is usually low due to their physical distance; however, for dense maps of SNP markers, there can be strong linkage disequilibrium between marker loci. Linkage analysis (parametric and nonparametric) and family-based association studies are currently being carried out using dense maps of SNP marker loci. Monte Carlo methods are often used for both linkage and association studies; however, to date there are no programs available which can generate haplotype and/or genotype data consisting of a large number of loci for pedigree structures. SimPed is a program that quickly generates haplotype and/or genotype data for pedigrees of virtually any size and complexity. Marker data either in linkage disequilibrium or equilibrium can be generated for greater than 20,000 diallelic or multiallelic marker loci. Haplotypes and/or genotypes are generated for pedigree structures using specified genetic map distances and haplotype and/or allele frequencies. The simulated data generated by SimPed is useful for a variety of purposes, including evaluating methods that estimate haplotype frequencies for pedigree data, evaluating type I error due to intermarker linkage disequilibrium and estimating empirical p values for linkage and family-based association studies.

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“…Genetic maps, however, are not known with certainty. Comparison studies of sequence-based physical maps and genetic maps have revealed discrepancies in the order of some markers (Matise et al 2002;Nievergelt et al 2004). Differences in marker order and marker location between published genetic maps have also been found (Nievergelt et al 2004).…”

mentioning

confidence: 96%

“…Comparison studies of sequence-based physical maps and genetic maps have revealed discrepancies in the order of some markers (Matise et al 2002;Nievergelt et al 2004). Differences in marker order and marker location between published genetic maps have also been found (Nievergelt et al 2004). These inaccuracies can seriously bias linkage findings from genetic epidemiologic studies (Buetow 1991;Halpern and Whittemore 1999;Daw et al 2000).…”

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confidence: 99%

A Novel Markov Chain Monte Carlo Approach for Constructing Accurate Meiotic Maps

George

2005

Genetics

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Mapping markers from linkage data continues to be a task performed in many genetic epidemiological studies. Data collected in a study may be used to refine published map estimates and a study may use markers that do not appear in any published map. Furthermore, inaccuracies in meiotic maps can seriously bias linkage findings. To make best use of the available marker information, multilocus linkage analyses are performed. However, two computational issues greatly limit the number of markers currently mapped jointly; the number of candidate marker orders increases exponentially with marker number and computing exact multilocus likelihoods on general pedigrees is computationally demanding. In this article, a new Markov chain Monte Carlo (MCMC) approach that solves both these computational problems is presented. The MCMC approach allows many markers to be mapped jointly, using data observed on general pedigrees with unobserved individuals. The performance of the new mapping procedure is demonstrated through the analysis of simulated and real data. The MCMC procedure performs extremely well, even when there are millions of candidate orders, and gives results superior to those of CRI-MAP.

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Large-Scale Integration of Human Genetic and Physical Maps

Cited by 42 publications

References 21 publications

Increased Support for Linkage of a Novel Locus on Chromosome 5q13 for Essential Hypertension in the British Genetics of Hypertension Study

Increased Support for Linkage of a Novel Locus on Chromosome 5q13 for Essential Hypertension in the British Genetics of Hypertension Study

SimPed: A Simulation Program to Generate Haplotype and Genotype Data for Pedigree Structures

A Novel Markov Chain Monte Carlo Approach for Constructing Accurate Meiotic Maps

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