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[1.] Analyse:Ae/Fragment 001 01 - Diskussion
Bearbeitet: 6. May 2016, 14:32 Graf Isolan
Erstellt: 6. May 2016, 14:14 (Graf Isolan)
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1 Introduction

1.1 Genetic background of diseases

Diseases with a genetic component, like other phenotypic traits, are usually distinguished as being either Mendelian or complex. Mendelian traits are characterized by well-defined phenotypes, one or two genetic disease loci with high penetrance, a small phenocopy rate and usually small susceptibility allele frequencies. This clear genotype-phenotype relation results in a clear pattern of inheritance. Mendelian diseases are usually rare in the population. Complex traits show a less clear relationship between genotype and phenotype due to two or more of the following characteristics: ill-defined phenotypes, incomplete penetrance, high phenocopy rate, genetic heterogeneity, oligogenic or polygenic inheritance, epistasis, mitochondrial inheritance, imprinting, and an often large contribution of environmental influences (Lander and Schork, 1994; Belmont and Leal, 2005; Gulcher and Stefansson, 2006).

Unfortunately, almost all common, non-infectious diseases have a genetic component and fall into the category of complex traits. Examples are heart disease, cancer, arthritis, asthma, diabetes, hypertension, lipid metabolism disorders, some forms of Alzheimer’s disease, and depression.


Belmont, J. W. & Leal, S. M. (2005) Complex phenotypes and complex genetics: an introduction to genetic studies of complex traits. Curr Atheroscler Rep, 7(3): 180–187.

Gulcher, J. & Stefansson, K. (2006) Positional cloning: complex cardiovascular traits. Methods Mol Med, 128: 137–152.

Lander, E. S. & Schork, N. J. (1994) Genetic dissection of complex traits. Science, 265(5181): 2037–2048.

Chapter 1

Introduction

1.1 Genetic background of diseases

[...] Diseases with a genetic component, like other phenotypic traits, are usually distinguished as being either Mendelian or complex. Mendelian traits are characterized by well-defined phenotypes, one or two genetic disease loci with high penetrance, a small phenocopy rate and usually small susceptibility allele frequencies. This clear genotype-phenotype relation results in a clear pattern of inheritance. Mendelian diseases are usually rare in the population.

Complex traits show a less clear relationship between genotype and phenotype due to two or more of the following characteristics: ill-defined phenotypes, incomplete penetrance, high phenocopy rate, genetic heterogeneity, oligogenic or polygenic inheritance, epistasis, mitochondrial inheritance, imprinting, and an often large contribution of environmental influences [85].

Unfortunately, most common diseases in humans resemble complex traits. Examples are hypertension, lipid metabolism disorders, some forms of Alzheimer’s disease, and depression.


[85] E. S. Lander and N. J. Schork. Genetic dissection of complex traits. Science, 265(5181): 2037–48, Sep 30 1994.

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[2.] Analyse:Ae/Fragment 002 20 - Diskussion
Bearbeitet: 6. May 2016, 12:20 Graf Isolan
Erstellt: 6. May 2016, 12:15 (Graf Isolan)
Ae, Fragment, KomplettPlagiat, Lou 2008, SMWFragment, Schutzlevel, ZuSichten

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Although 99.9% of the human genomes are identical between people, there are still millions of differences among the 3.2 billion base pairs (Kruglyak and Nickerson, 2001). These genetic variations can cause phenotypic variations among people and are potentially associated with traits or diseases. Genetic markers, which are nucleotide variants with known positions, are often used for human disease analyses. Several types of markers exist, such as Restriction Fragment Length Polymorphisms (RFLP’s), microsatellites, and single nucleotide polymorphisms (SNPs). Markers can be used to construct a genetic map, which can be used as a reference for disease-[gene mapping (Dib et al., 1996).]

Dib, C., Fauré, S., Fizames, C., Samson, D., Drouot, N., Vignal, A., Millasseau, P., Marc, S., Hazan, J., Seboun, E. & others (1996) A comprehensive genetic map of the human genome based on 5,264 microsatellites. Nature, 380(6570): 152–154.

Kruglyak, L. & Nickerson, D. A. (2001) Variation is the spice of life. Nat Genet, 27(3): 234–236.

Across the human genome, about 99.9% of the genome is identical between people, but there are still millions of differences among the 3.2 billion base pairs (Kruglyak & Nickerson 2001). These genetic variations can cause phenotypic variation among people and are potentially associated with traits or diseases. Genetic markers, which are nucleotide variants with known positions, are often used for human disease analyses. There exist several types of genetic markers, for example, Restriction Fragment Length Polymorphisms (RFLP’s), microsatellites, and single nucleotide polymorphisms (SNPs). Markers can be used to construct a genetic map, which can be used as a reference for disease-gene mapping (Dib et al. 1996).

Dib, C., S. Faure, C. Fizames, D. Samson, N. Drouot et al. 1996 A comprehensive genetic map of the human genome based on 5,264 microsatellites. Nature 380: 152-154.

Kruglyak, L., and D. A. Nickerson, 2001 Variation is the spice of life. Nature Genetics 27: 234-236.

Anmerkungen

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[3.] Analyse:Ae/Fragment 003 02 - Diskussion
Bearbeitet: 6. May 2016, 12:46 Graf Isolan
Erstellt: 6. May 2016, 12:36 (Graf Isolan)
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Botstein et al. (1980) proposed the concept using RFLP’s as the markers to construct a genetic map. Later, genetic maps were constructed using denser microsatellites (Murray et al., 1994; Dib et al., 1996). SNPs, which usually contain two alleles, have drawn significant attention as markers for genetic disease-mapping studies due to their high abundance across the human genome (Kruglyak, 1997; Sachidanandam et al., 2001). It was estimated

that there are around 7.1 million SNPs with a minimal allele frequency of at least 0.05 in the human population (Kruglyak and Nickerson, 2001).


Dib, C., Fauré, S., Fizames, C., Samson, D., Drouot, N., Vignal, A., Millasseau, P., Marc, S., Hazan, J., Seboun, E. & others (1996) A comprehensive genetic map of the human genome based on 5,264 microsatellites. Nature, 380(6570): 152–154.

Kruglyak, L. (1997) The use of a genetic map of biallelic markers in linkage studies. Nat Genet, 17(1): 21–24.

Kruglyak, L. & Nickerson, D. A. (2001) Variation is the spice of life. Nat Genet, 27(3): 234–236.

Murray, J. C., Buetow, K. H., Weber, J. L., Ludwigsen, S., Scherpbier-Heddema, T., Manion, F., Quillen, J., Sheffield, V. C., Sunden, S., Duyk, G. M. & others (1994) A comprehensive human linkage map with centimorgan density. Science, 265(5181): 2049–2054.

Sachidanandam, R., Weissman, D., Schmidt, S. C., Kakol, J. M., Stein, L. D., Marth, G., Sherry, S., Mullikin, J. C., Mortimore, B. J.,Willey, D. L. & others (2001) A map of human genome sequence variation containing 1.42 million single nucleotide polymorphisms. Nature, 409(6822): 928–933.

Botstein et al. (Botstein et al. 1980) proposed the concept using RFLP’s as the markers to construct a genetic map. Later, denser microsatellites were used to construct genetic maps (Murray et al. 1994; Dib et al. 1996). SNPs, which usually contain two alleles, have become the standard polymorphisms nowadays for higher resolution genetic disease-mapping due to their abundance throughout the human genome (Kruglyak 1997; Sachidanandam et al. 2001). It was estimated that there are around 7.1 million SNPs with a minimal allele frequency of at least 0.05 in the human population (Kruglyak & Nickerson 2001).

Dib, C., S. Faure, C. Fizames, D. Samson, N. Drouot et al. 1996 A comprehensive genetic map of the human genome based on 5,264 microsatellites. Nature 380: 152-154.

Kruglyak, L., 1997 The use of a genetic map of biallelic markers in linkage studies. Nature Genetics 17: 21-24.

Kruglyak, L., and D. A. Nickerson, 2001 Variation is the spice of life. Nature Genetics 27: 234-236.

Murray, J. C., K. H. Buetow, J. L. Weber, S. Ludwigsen, T. Scherpbierheddema et al. 1994 A Comprehensive Human Linkage with Centimorgan Density. Science 265: 2049-2054.

Sachidanandam, R., D. Weissman, S. C. Schmidt, J. M. Kakol, L. D. Stein et al. 2001 A map of human genome sequence variation containing 1.42 million single nucleotide polymorphisms. Nature 409: 928-933.

Anmerkungen

No source given, nothing is marked as a citation. The references are identical.

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[4.] Analyse:Ae/Fragment 005 08 - Diskussion
Bearbeitet: 6. May 2016, 13:32 Graf Isolan
Erstellt: 6. May 2016, 13:26 (Graf Isolan)
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The association test can be more powerful than the linkage test, and it requires fewer samples than linkage analysis to achieve the same power for common complex diseases (Risch and Merikangas, 1996).

Association analysis tests whether the disease and marker alleles are in LD. Disease phenotypes are used for association analyses instead of disease loci since, in general, the disease loci are unknown (Weiss and Terwilliger, 2000). LD generally spans only small distances, and the markers used for association analysis are often very tightly spaced. Therefore, association analysis provides a higher resolution for locating disease genes than linkage analysis. A common strategy for identifying complex disease genes is to conduct linkage analyses first and then follow significant results with tests for association at a denser panel of markers in an attempt to further localize the disease gene (Cardon and Bell, 2001).

Two main categories of statistical methods, population-based (case-control and case cohort studies) and family-based studies, are often used for association analysis (Laird and Lange, 2006). Population-based analysis requires samples to be independently collected. It compares the differences of distributions of allele frequencies between the affected individuals (cases) and unaffected individuals (controls) (Risch, 2000). A contingency table can be created and the Pearson chi-squared statistic or [Fisher’s exact test can be used to test for association.]


Cardon, L. R. & Bell, J. I. (2001) Association study designs for complex diseases. Nat Rev Genet, 2(2): 91–99.

Laird, N. M. & Lange, C. (2006) Family-based designs in the age of large-scale gene-association studies. Nat Rev Genet, 7(5): 385–394.

Risch, N. & Merikangas, K. (1996) The future of genetic studies of complex human diseases. Science, 273(5281): 1516–1517.

Risch, N. J. (2000) Searching for genetic determinants in the new millennium. Nature, 405: 847–856.

Weiss, K. M. & Terwilliger, J. D. (2000) How many diseases does it take to map a gene with SNPs? Nat Genet, 26(2): 151–157.

[Page 6]

The association test can be more powerful than the linkage test, and it requires fewer samples than linkage analysis to achieve the same power for common complex diseases (Risch & Merikangas 1996).

Association analysis tests whether the disease and marker alleles are in linkage disequilibrium (LD). Disease phenotypes are used for association analyses instead of disease loci since, in general, the disease loci are unknown (Weiss & Terwilliger 2000). LD generally spans only small distances, and the markers used for association analysis are often very tightly spaced. Therefore, association analysis provides a higher resolution for locating disease genes than linkage analysis. A very common strategy in the past for identifying complex disease genes is to conduct linkage analyses first and then follow significant results with tests for association at a denser panel of markers in an attempt to further localize the disease gene (Cardon & Bell 2001).

In the terms of samples, there are two types of statistical methods for association analysis, population-based (case-control and case-cohort studies) and family-based studies (Laird & Lange 2006).

1.3.1 Population-based association analysis

Population-based analysis requires samples to be independently collected. It compares the differences of distributions of allele frequencies between the affected individuals (cases)

[Page 7]

and unaffected individuals (controls) (Risch 2000). A contingency table can be created and the Pearson chi-squared statistic or Fisher’s exact test can be used to test for association.


Cardon, L. R., and J. I. Bell, 2001 Association study designs for complex diseases. Nat.Rev.Genet. 2: 91-99.

Laird, N. M., and C. Lange, 2006 Family-based designs in the age of large-scale gene-association studies. Nat.Rev.Genet. 7: 385-394.

Risch, N., and K. Merikangas, 1996 The future of genetic studies of complex human diseases. Science 273: 1516-1517.

Risch, N. J., 2000 Searching for genetic determinants in the new millennium. Nature 405: 847-856.

Weiss, K. M., and J. D. Terwilliger, 2000 How many diseases does it take to map a gene with SNPs? Nature Genetics 26: 151-157.

Anmerkungen

No source given, nothing is marked as a citation. The references are identical.

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[5.] Analyse:Ae/Fragment 006 01 - Diskussion
Bearbeitet: 6. May 2016, 13:51 Graf Isolan
Erstellt: 6. May 2016, 13:46 (Graf Isolan)
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Regression-based analyses such as logistic regression can also be used in the case-control test (Agresti, 2002). The main limitation of the case-control analysis is that the presence of confounding effects in the samples could cause a high false positive rate in the analysis (Risch, 2000; Devlin et al., 2001). For example, population admixture and population substructure can cause confounding, which can produce association between unlinked loci (Ewens and Spielman, 1995).

Three major types of approaches were proposed to solve this problem: genomic control (GC) (Devlin and Roeder, 1999; Devlin et al., 2001), structured analysis (SA) (Prichard [sic] et al., 2000) and EIGENSTRAT (Price et al., 2006).


Agresti, A. (2002) Categorical data analysis. Wiley, New York.

Devlin, B. & Roeder, K. (1999) Genomic control for association studies. Biometrics, 55(4): 997–1004.

Devlin, B., Roeder, K. & Wasserman, L. (2001) Genomic control, a new approach to genetic-based association studies. Theor Popul Biol, 60(3): 155–166.

Ewens, W. J. & Spielman, R. S. (1995) The transmission/disequilibrium test: history, subdivision, and admixture. Am J Hum Genet, 57(2): 455–464.

Price, A. L., Patterson, N. J., Plenge, R. M., Weinblatt, M. E., Shadick, N. A. & Reich, D. (2006) Principal components analysis corrects for stratification in genome-wide association studies. Nat Genet, 38: 904–909.

Prichard [sic], B. N., Graham, B. R. & Cruickshank, J. M. (2000) New approaches to the uses of beta blocking drugs in hypertension. J Hum Hypertens, 14 Suppl 1: S63–S68.

Risch, N. J. (2000) Searching for genetic determinants in the new millennium. Nature, 405: 847–856.

Regression-based analyses such as logistic regression can also be used in the case-control test (Agresti 2002). The major concern of the case-control analysis is that the presence of confounding effects in the samples could give rise to a high false positive rate in the analysis (Risch 2000; Devlin et al. 2001). For example, population admixture and population substructure can produce association between unlinked loci (Ewens & Spielman 1995). Two major types of approaches were proposed to solve this problem: genomic control (GC) (Devlin & Roeder 1999; Devlin et al. 2001) and structured analysis (SA) (Pritchard et al. 2000).

Agresti A., 2002 Categorical data analysis. Wiley, New York.

Devlin, B., and K. Roeder, 1999 Genomic control for association studies. American Journal of Human Genetics 65: A83.

Devlin, B., K. Roeder, and L. Wasserman, 2001 Genomic control, a new approach to genetic-based association studies. Theoretical Population Biology 60: 155-166.

Ewens, W. J., and R. S. Spielman, 1995 The Transmission Disequilibrium Test - History, Subdivision and Admixture. American Journal of Human Genetics 57: 455-464.

Pritchard, J. K., M. Stephens, and P. Donnelly, 2000 Inference of population structure using multilocus genotype data. Genetics 155: 945-959.

Risch, N. J., 2000 Searching for genetic determinants in the new millennium. Nature 405: 847-856.

Anmerkungen

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[6.] Analyse:Ae/Fragment 015 05 - Diskussion
Bearbeitet: 5. May 2016, 23:49 Graf Isolan
Erstellt: 5. May 2016, 23:22 (Graf Isolan)
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The fundamental idea of Bayesian inference is that both the model parameters (θ) and the observed data are considered as random variables and are modeled using probability distributions (Gelman et al., 1995). The parameters are given a prior distribution, P(θ), then through the likelihood function, P(Y/θ), the parameter can be estimated from the posterior density, P(θ/Y) ∝ P(θ)P(Y/θ). All of the above four methods therefore treat the unknown haplotypes of each individual as random variables. The main dierence [sic] between using the EM algorithm and a Bayesian method to do haplotype inference is whether the haplotype frequencies in the population are treated as random variables or not. Another important common aspect of these above four methods is that they all used Markov Chain Monte Carlo (MCMC) methods to sample from the posterior distribution.

Gelman, A., Carlin, J. B., Stern, H. S. & Rubin, D. B. (1995) Bayesian Data Analysis. Chapman and Hall, Canberra.

The fundamental idea of Bayesian inference is that both the model parameters (θ) and the observed data are considered as random variables and are modeled using probability distributions (Gelman et al. 1995). The parameters are given a prior distribution, P(θ), then through the likelihood function, P(Y|θ), the parameter can be estimated from the posterior density, P(θ|Y) ∝ P(θ)P(Y|θ). All of the above four methods therefore treat the unknown haplotypes of each individual as random variables. The main difference between using the EM algorithm and a Bayesian method to do haplotype inference is whether the haplotype frequencies in the population are treated as random variables or not. Another important common aspect of these above four methods is that they all used Markov Chain Monte Carlo (MCMC) methods to sample from the posterior distribution.

Gelman, A., Carlin, J. B., Stern, H. S. and Rubin, D. B. (1995). Bayesian Data Analysis, 1st edn, Chapman & Hall, Canberra.

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