Parameter Estimation and Quantitative Parametric Linkage Analysis with GENEHUNTER-QMOD
Abstract: Objective: We present a parametric method for linkage analysis of quantitative phenotypes. The method provides a test for linkage as well as an estimate of different phenotype parameters. We have implemented our new method in the program GENEHUNTER-QMOD and evaluated its properties by performing simulations. Methods: The phenotype is modeled as a normally distributed variable, with a separate distribution for each genotype. Parameter estimates are obtained by maximizing the LOD score over the normal distributi… Show more
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Cited by 3 publications
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Parametric or model‐based linkage analysis assumes that models describing both the trait and genetic marker loci are known without error, although sensitivity analysis approaches allow one to account for uncertainty in the trait model. Nonparametric, model‐free or weakly parametric linkage methods make fewer assumptions about the trait model. Family‐based linkage analysis in general is most powerful to detect genetic variants that have large effects on risk of a disease or variation of a quantitative trait. These variants tend to be rare in the population for any disease which is likely to have undergone negative selection over evolutionary time. Linkage studies are more powerful than genome‐wide association studies (GWAS) to detect genes with rare, high penetrance risk variants, whereas GWAS are more powerful to detect common risk variants which tend to have small individual effects on risk. Thus, both linkage and association analysis strategies are useful in the era of whole‐genome sequencing. Both parametric and nonparametric linkage methods are useful for detecting regions of the genome harbouring high penetrance risk variants which are of particular interest for precision medicine. Key Concepts Linkage analysis evaluates the likelihood that a specific allele or haplotype of alleles co‐segregates with a disease or trait in a family or group of families. Linkage analysis searches for evidence of a violation of Mendel's Law of Independent Assortment of alleles at two loci to the offspring. Linkage is due to the fact that long stretches of DNA on a specific chromosome are transmitted together to an offspring. Genetic loci on different chromosomes are not linked and do segregate independently to offspring. Genetic loci on the same chromosome that are close together show some evidence of linkage, but loci that are far apart on the same chromosome may not show evidence of linkage due to the fact that chiasmata occur at the four‐strand stage of meiosis, which results in recombination between the chromatids of the parental pair of chromosomes. Linkage is a measure of the probability of recombination between two loci (such as a risk allele for a disease and a genetic variant somewhere in the genome) given the observed genotypes of the parents and offspring. In parametric linkage, a model is assumed that specifies the mode of inheritance, allele frequency and penetrance of all genotypes for the disease locus (probability of a given value of a quantitative trait) plus the mode of inheritance, allele frequency and probability of observing each laboratory phenotype given genotype for the genotyped marker loci (usually codominant and 100% for modern single‐nucleotide polymorphism and DNA sequencing genotypes). In nonparametric linkage, no specific assumptions are made about the trait model, but the genotyped marker locus model must be fully described as above for parametric linkage. The power of these two approaches depends on many different factors, but both depend on the observation of large numbers of meioses; so, large families with multiple affected individuals (or a wide range of quantitative trait values) in which all individuals have both phenotype and genotype data are most powerful. Linkage analyses are particularly useful for detecting genetic variants with large effects on risk of disease or large effects on the variance of a quantitative trait, which are of particular utility for precision medicine.
Parametric or model‐based linkage analysis assumes that models describing both the trait and genetic marker loci are known without error, although sensitivity analysis approaches allow one to account for uncertainty in the trait model. Nonparametric, model‐free or weakly parametric linkage methods make fewer assumptions about the trait model. Family‐based linkage analysis in general is most powerful to detect genetic variants that have large effects on risk of a disease or variation of a quantitative trait. These variants tend to be rare in the population for any disease which is likely to have undergone negative selection over evolutionary time. Linkage studies are more powerful than genome‐wide association studies (GWAS) to detect genes with rare, high penetrance risk variants, whereas GWAS are more powerful to detect common risk variants which tend to have small individual effects on risk. Thus, both linkage and association analysis strategies are useful in the era of whole‐genome sequencing. Both parametric and nonparametric linkage methods are useful for detecting regions of the genome harbouring high penetrance risk variants which are of particular interest for precision medicine. Key Concepts Linkage analysis evaluates the likelihood that a specific allele or haplotype of alleles co‐segregates with a disease or trait in a family or group of families. Linkage analysis searches for evidence of a violation of Mendel's Law of Independent Assortment of alleles at two loci to the offspring. Linkage is due to the fact that long stretches of DNA on a specific chromosome are transmitted together to an offspring. Genetic loci on different chromosomes are not linked and do segregate independently to offspring. Genetic loci on the same chromosome that are close together show some evidence of linkage, but loci that are far apart on the same chromosome may not show evidence of linkage due to the fact that chiasmata occur at the four‐strand stage of meiosis, which results in recombination between the chromatids of the parental pair of chromosomes. Linkage is a measure of the probability of recombination between two loci (such as a risk allele for a disease and a genetic variant somewhere in the genome) given the observed genotypes of the parents and offspring. In parametric linkage, a model is assumed that specifies the mode of inheritance, allele frequency and penetrance of all genotypes for the disease locus (probability of a given value of a quantitative trait) plus the mode of inheritance, allele frequency and probability of observing each laboratory phenotype given genotype for the genotyped marker loci (usually codominant and 100% for modern single‐nucleotide polymorphism and DNA sequencing genotypes). In nonparametric linkage, no specific assumptions are made about the trait model, but the genotyped marker locus model must be fully described as above for parametric linkage. The power of these two approaches depends on many different factors, but both depend on the observation of large numbers of meioses; so, large families with multiple affected individuals (or a wide range of quantitative trait values) in which all individuals have both phenotype and genotype data are most powerful. Linkage analyses are particularly useful for detecting genetic variants with large effects on risk of disease or large effects on the variance of a quantitative trait, which are of particular utility for precision medicine.
Parametric or model‐based linkage analysis assumes that models describing both the trait and genetic marker loci are known without error, although sensitivity analysis approaches allow one to account for uncertainty in the trait model. Nonparametric, model‐free or weakly parametric linkage methods make fewer assumptions about the trait model. Family‐based linkage analysis in general is most powerful to detect genetic variants that have large effects on risk of a disease or variation of a quantitative trait. These variants tend to be rare in the population for any disease which is likely to have undergone negative selection over evolutionary time. Linkage studies are more powerful than genome‐wide association studies (GWAS) to detect genes with rare, high penetrance risk variants, whereas GWAS are more powerful to detect common risk variants which tend to have small individual effects on risk. Thus, both linkage and association analysis strategies are useful in the era of whole‐genome sequencing. Both parametric and nonparametric linkage methods are useful for detecting regions of the genome harbouring high penetrance risk variants which are of particular interest for precision medicine. Key Concepts Linkage analysis evaluates the likelihood that a specific allele or haplotype of alleles co‐segregates with a disease or trait in a family or group of families. Linkage analysis searches for evidence of a violation of Mendel's Law of Independent Assortment of alleles at two loci to the offspring. Linkage is due to the fact that long stretches of DNA on a specific chromosome are transmitted together to an offspring. Genetic loci on different chromosomes are not linked and do segregate independently to offspring. Genetic loci on the same chromosome that are close together show some evidence of linkage, but loci that are far apart on the same chromosome may not show evidence of linkage due to the fact that chiasmata occur at the four‐strand stage of meiosis, which results in recombination between the chromatids of the parental pair of chromosomes. Linkage is a measure of the probability of recombination between two loci (such as a risk allele for a disease and a genetic variant somewhere in the genome) given the observed genotypes of the parents and offspring. In parametric linkage, a model is assumed that specifies the mode of inheritance, allele frequency and penetrance of all genotypes for the disease locus (probability of a given value of a quantitative trait) plus the mode of inheritance, allele frequency and probability of observing each laboratory phenotype given genotype for the genotyped marker loci (usually codominant and 100% for modern single‐nucleotide polymorphism and DNA sequencing genotypes). In nonparametric linkage, no specific assumptions are made about the trait model, but the genotyped marker locus model must be fully described as above for parametric linkage. The power of these two approaches depends on many different factors, but both depend on the observation of large numbers of meioses; so, large families with multiple affected individuals (or a wide range of quantitative trait values) in which all individuals have both phenotype and genotype data are most powerful. Linkage analyses are particularly useful for detecting genetic variants with large effects on risk of disease or large effects on the variance of a quantitative trait, which are of particular utility for precision medicine.
Joint Linkage and Association Analysis Using GENEHUNTER-MODSCORE with an Application to Familial Pancreatic Cancer
Introduction: Joint linkage and association (JLA) analysis combines two disease gene mapping strategies: linkage information contained in families and association information contained in populations. Such a JLA analysis can increase mapping power, especially when the evidence for both linkage and association is low to moderate. Similarly, an association analysis based on haplotypes instead of single markers can increase mapping power when the association pattern is complex. Methods: In this paper, we present an extension to the GENEHUNTER-MODSCORE software package that enables a JLA analysis based on haplotypes and uses information from arbitrary pedigree types and unrelated individuals. Our new JLA method is an extension of the MOD score approach for linkage analysis, which allows the estimation of trait-model and linkage disequilibrium (LD) parameters, i.e., penetrance, disease-allele frequency, and haplotype frequencies. LD is modelled between alleles at a single diallelic disease locus and up to three diallelic test markers. Linkage information is contributed by additional multi-allelic flanking markers. We investigated the statistical properties of our JLA implementation using extensive simulations, and we compared our approach to another commonly used single-marker JLA test. To demonstrate the applicability of our new method in practice, we analyzed pedigree data from the German National Case Collection for Familial Pancreatic Cancer (FaPaCa). Results: Based on the simulated data, we demonstrated the validity of our JLA MOD score analysis implementation and identified scenarios in which haplotype-based tests outperformed the single-marker test. The estimated trait-model and LD parameters were in good accordance with the simulated values. Our method outperformed another commonly used JLA single-marker test when the LD pattern was complex. The exploratory analysis of the FaPaCa families led to the identification of a promising genetic region on chromosome 22q13.33, which can serve as a starting point for future mutation analysis and molecular research in pancreatic cancer. Conclusion: Our newly proposed JLA MOD score method proves to be a valuable gene mapping and characterization tool, especially when either linkage or association information alone provide insufficient power to identify the disease-causing genetic variants.
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