Genetics of complex disease:
Gespeichert in:
| Hauptverfasser: | , , , |
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| Format: | Buch |
| Sprache: | English |
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London ; New York
Garland Science
[2016]
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| Online-Zugang: | Inhaltsverzeichnis |
| ISBN: | 9780815344919 |
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| 245 | 1 | 0 | |a Genetics of complex disease |c Peter Donaldson, Ann Daly, Luca Ermini, Debra Bevitt |
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Contents
Preface v
Acknowledgments viii
1 Genetic Diversity 1
1.1 Genetic Terminology 2
The use of the terms genes and alleles varies, though they do have precise definitions 2
1.2 Genetic Variation 5
Genetic variation can be measured by several methods 6
Alleles on the same chromosome are physically linked and inherited as haplotypes 7
Linkage disequilibrium promotes conservation of haplotypes in populations 8
13 Genetics and Evolution 9
Mutation is the major cause of genetic variation 10
Genetic variation caused by mutation alters allele frequencies in populations 11
Migration and dispersal cause gene flow 12
Allele frequencies can change randomly via genetic drift 13
The thrifty gene hypothesis 16
Natural selection acting on different levels of fitness affects the gene pool 16
1.4 Calculating Genetic Diversity: Determining Population Variability 19
Genotype and allele frequencies illustrate genetic diversity 19
Allele frequency refers to the numbers of alleles present in a population 20
Heterozygosity provides a quantitative estimation of genetic variation 21
The HWP is a complex but essential concept in population genetics 21
Calculating expected genotype frequencies using the HWP 22
Different populations may have different allele frequencies 22
1.5 Population Size and Structure 26
Breeding population size is important in evolution 26
Genetic variation is not always uniform in a population 26
Wahlund s principle 27
1.6 The Mitochondrial Genome 27
1.7 Gene Expression and Phenotype 29
Genetic variation is manifested in the phenotype 29
Phenotypes are influenced by the environment 29
1.8 Epigenetics 30
x Contents
1.9 Genomic Imprinting 31
Conclusions 31
Further Reading 33
2 Defining Complex Disease 35
2.1 Definition of a Genetically Complex Disease 36
To fully understand complex disease it is important to deconstruct this definition 36
2.2 Chromosomal Diseases 40
Changes in chromosome number cause serious genetic diseases 40
Changes in chromosome structure can cause serious illness 41
2.3 Mendelian Diseases 43
Mendelian diseases involve a single gene and show simple patterns of inheritance 43
Penetrance is an important difference between Mendelian and complex diseases 45
Some diseases have both Mendelian and complex characteristics 47
Modifier genes may also confuse the picture 47
Mendelian traits can. be studied in families 47
There are complications to Mendelian diseases 48
2.4 Variation in The Mitochondrial Genome is Associated with Disease 50
Variation in the mtDNA has been widely associated and linked with many different diseases 52
2.5 De Novo Mutations and Human Disease 53
2.6 Three Different Types of Complex Disease 54
Studying complex disease is different from studying Mendelian disease 54
Monogenic complex diseases involve a single risk allele 55
Oligogenic complex diseases involve several alleles 56
Polygenic complex diseases involve many risk alleles 56
2.7 Alzheimer's Disease May be a Monogenic Complex Disease 57
2.8 HSCR - An Oligogenic Complex Disease 58
Sporadic HSCR illustrates the oligogenic model for complex disease 58
2.9 Crohn's Disease is Mostly a Polygenic Complex Disease 61
Early studies of Crohn’s disease suggested a number of locations for risk alleles 61
Genetic variations in the human equivalent of the plant nod2 gene (CARDIS) were the first
identified and confirmed Crohn’s disease risk alleles 62
Genetic variations in other immune regulatory genes are important risk factors in
Crohn’s disease 64
The Wellcome Trust Case Control Consortium (WTCCCl): Crohn’s disease 64
The current number of risk alleles for Crohn’s disease may be as high as 163 66
2.10 Applying Disease Models to Populations 67
Conclusions 67
Further Reading 69
3 How to investigate Complex Disease Genetics 73
3.1 Planning Stage 1: Gathering the Basic Knowledge 73
Incidence and prevalence are measures of how common a disease is 74
Contents xi
Incidence and prevalence can be very different or very similar depending on the prognosis
for the disease 75
Incidence and prevalence of disease may vary in different populations 76
What is the evidence for a genetic component to the disease? 76
What is known about the disease pathology? 80
Before we get down to the hard business of study planning there are one or two other
questions that it is important to ask 82
3.2 Planning Stage 2: Choosing a Strategy 84
Two basic strategies for identifying risk alleles in complex disease 84
In terms of the history of genetic studies in complex disease there are two main periods:
pre- and post-genome 84
Each of these two strategies has a substrategy 88
3.3 Good and Bad Practice 93
Accurately identifying true disease susceptibility alleles in GWAS (and other association
studies) is dependent on sample size 93
Case selection can introduce bias into a study 94
It is important to consider whether we are studying a disease, a syndrome, or a trait within
a disease subgroup 94
Selection of appropriate controls is equally important in any study 94
Errors in the laboratory and in sample handling can also introduce bias into a study 96
Statistical analysis is the key in any study of complex disease 96
SNP chip selection is an important factor to consider in study design 96
Unfortunately publication bias does occur 97
Replication in an independent sample is crucial for ail association studies, especially GWAS 98
3.4 New Technologies and the Future 100
The technological advances of the past decade have had a major impact on research into
the genetics of complex disease and the rate of change is going to increase 100
New developments will come from the ENCODE project, and will also involve more
epigenetics and imputation analysis 100
The real debate about the future of complex disease research lies not in the genetics itself,
but downstream from the genetics 101
Conclusions 101
Further Reading 103
4 Why Investigate Complex Disease Genetics? 105
4.1 Why Do We Investigate Complex Disease? 106
Complex diseases do not conform to simple patterns of inheritance 106
The HGP in research into genetically complex disease 107
4.2 Disease Diagnosis 108
Early studies on the genetics of ankylosing spondylitis indicated what could be achieved
in terms of differential diagnosis in the post-genome era 108
Genetic associations in complex disease confer small risks 110
4.3 Patient Treatment/Management and Care 110
Identifying risk alleles that predict onset of complex diseases may enable patients to
make beneficial lifestyle changes 111
xii Contents
Predicting disease severity through genetic analysis may have clinical significance in
terms of patient management 1 11
Common genetic variation may predict response to treatment and be critical in patient care 113
Onset, severity, and response to treatment are all part of patient management 113
4.4 Disease Pathogenesis 114
Early studies offered potential insight into the biology of ankylosing spondylitis 113
Later GWAS have offered even further insight into the biology of ankylosing spondylitis 116
Rheumatoid arthritis has many strong genetic associations, some of which can be used to
help us unravel the pathogenesis of this disease 116
Bipolar disease is a disease for which there are many weak genetic associations, but few
strong consistent associations 122
Coronary artery disease is the most common cause of death in the developed world 127
4.5 What about the Other Diseases? 136
Conclusions 137
Further Reading 138
5 Statistical Analysis in Complex Disease: Study Planning and
Data Handling 141
5.1 Linkage Analysis 142
The LOD score is a measure of significance of linkage between a trait and a marker allele 144
5.2 The Basic Statistical Concepts of Association Analysis and their Application
in Study Design 145
In statistical terms, there are two different hypotheses to consider in the analysis of genetic
association studies: a null hypothesis and an alternative hypothesis 146
5.3 Statistical Error, Power, and P Values 146
Making the right decision and avoiding errors in the hypothesis testing 146
The likelihood of detecting a significant difference in an association study is directly related to
sample size 147
Probability (P) values are simply statements of the probability that the observed differences
between two groups could have arisen by chance 148
5.4 The Basic Statistical Considerations for Analysis of Case Control Association Studies
and their Application to Data Collection and Analysis 151
Departures from HWE can have different causes 151
Pearsons X2 and Fishers exact test are used to assess the departure from the null hypothesis 152
Fisher’s exact test calculates the exact probability (P) of observing the distribution seen in the
contingency table 154
The Cochran-Armitage test looks for a trend for a difference between cases and controls
across the ordered genotypes in the table 155
Ihere is no simple answer to the question of which test to choose 1 57
Data may also be analyzed assuming a predefined genetic model 157
Logistic regression is frequently used in association studies 160
The pitfalls and problems of GWAS 162
5.5 Howto Interpret a GWAS 165
There are several ways to interpret statistically significant genetic associations 165
There are several diagnostic plots that can be used for the visualization of genome-wide
association results 165
Contents xiii
Linkage disequilibrium is a useful tool in association studies provided you know how to
handle it 167
The ability to detect a significant association through linkage disequilibrium can increase
the power of an association study 167
Most association analyses identify multiple SNPs, other genetic variants, and haplotypes 170
Conclusions 171
Further Reading 172
6 The Major Histocompatibility Complex 175
6.1 Histocompatibility 176
The idea of histocompatibility first started with blood groups 176
The MHC-encoded HLA antigens are the second major histocompatibility group 176
Naming the HLA antigens and alleles up to and including the early molecular genotyping era 177
The current naming system for HLA alleles and genes allows for a greater level of resolution
to be reported 183
The MHC encodes a cornucopia of genetic diversity within the HLA genes 184
Comparing the levels of genetic diversity at DR with those at DQ can make DQ look like
a poor relation 185
HLA class II molecules can be expressed in tram or in cis 187
The final groups of genes that need to be considered are those called pseudogenes,
gene fragments, and null alleles 188
6.2 The Extended Human MHC MAP 189
6.3 Molecular Structure of HLA Class I and Class II 191
X-ray crystallography of HLA-A2 revealed the full structure and much about the function
of HLA class I 191
The X-ray crystallography structure of HLA class II structure revealed the critical difference
between class I and class II 192
6.4 Immune Function of HLA Class I and Class II 193
Class I molecules have distinct features 193
HLA class II is different to class I 193
HLA class I and class II have important similarities 194
HLA class I and antigen engagement in the cell is different from HLA class II 194
HLA class II and antigen engagement in the cell is different 195
6.5 HLA Class I and Disease 196
Hemochromatosis is an example of a Mendelian disease which maps within the xMHC 196
Psoriasis proves the point that HLA-C is an important locus to consider in genetic studies
of the MHC 196
Type I versus type II psoriasis 197
Before we leave HLA class I we need to consider Bw4 and Bw6 197
6.6 HLA Class II and Disease 197
Severe or cataplectic narcolepsy has one of strongest HLA associations ever reported 197
There are different functional interpretations of the HLA association with narcolepsy 198
Multiple sclerosis is a disease with a strong genetic association with HLA class II 199
HLA class II and autoimmune liver disease 201
xiv Contents
AIH is a relatively rare classical autoimmune disease of the liver 202
PSC is not a classical autoimmune disease 207
PBC is an autoimmune liver disease with a genetic component 210
6.7 Comparing the HLA Associations of the Three Liver Diseases 213
6.8 Non-HLA MHC Genes and Disease 213
The MHC class III region complement, MICA, and TNFA genes in complex disease 214
6.9 A Single Gene or a Risk Portfolio 216
A single gene may explain MHC- encoded genetic susceptibility to disease 216
Alternatively there is always room for a second bite of the cherry: a multihit hypothesis 217
6.10 How to Compare and Critically Evaluate Contrasting Studies 218
Knowing history is important when we critically review and design studies 218
Conclusions 219
Further Reading 221
7 Genetics of Infectious Disease 223
7.1 The Infection Process and Disease 223
Mechanisms of infection vary widely but common steps in the process can be identified 224
The immune response combats infectious disease 224
Individuals infected by the same pathogen may experience different outcomes 225
7.2 Heritability of Resistance and Susceptibility to Infectious Disease 225
Different populations infected by the same pathogen may experience different outcomes 225
Leprosy and tuberculosis were once believed to be inherited diseases 226
Adoption studies indicate that susceptibility to infectious disease has a heritable component 227
Rare monogenic defects in immunity can cause primary immune deficiencies 227
7.3 Identifying Alleles that Affect Risk of Susceptibility and Resistance to Infectious
Disease 228
Risk alleles can be identified using a hypothesis-driven or genome- wide approach 228
The outcome of infectious disease being tested must be clearly defined 229
7.4 Malaria 229
The life cycle of the Plasmodium protozoa is complex 229
Hemoglobinopathies confer resistance to malaria 230
Haldanes malaria hypothesis proposed that thalassemia confers protection against malaria 232
Allison demonstrated that sickle cell trait confers resistance to P. falciparum 232
Studies on Pacific Island populations provided experimental evidence that thalassemia confers
protection from malaria 233
The mechanism of resistance to malaria conferred by hemoglobinopathies is still not fully
understood 233
Resistance to malaria conferred by HbS and thalassemia is a complex genetic trait 235
Other malaria resistance alleles have been identified via epidemiological or hypothesis-driven
studies 235
GWAS suggest that polymorphisms in immunity-related genes may affect outcome of
Plasmodium infection 235
GWAS searching for malaria resistance alleles highlight the challenges of GWAS in African
populations 235
Contents xv
7.5 HIV-1 237
C-C chemokine receptor 5 (CCR5) acts as a co-receptor for HIV-1 in the early stages of
infection 237
Some individuals are naturally resistant to HIV infection 238
A 32-bp deletion in the CCR5 gene confers resistance to HIV-1 infection 239
Selection pressure by HIV-1 cannot account for the high frequency of CC/?5-A32 in the
northern European population 240
CCR5- A32 affects the outcome of infection by West Nile virus 240
CCR5-A32 cannot account for all HIV-1 resistance 241
CCR5 promoter polymorphisms affect HIV-1 control 242
CCR3/CCR2 haplotypes have a complex effect on HIV-1 control 242
Polymorphisms in chemokine receptor ligand genes influence HIV-1 control 244
Polymorphisms in HLA genes affect outcome of HIV infection 245
HLA class I homozygosity is not always bad news 246
GWAS confirms the protective role of HLA-B in HIV-1 infection 247
Amino acids in the HLA-B binding groove are associated with HIV-1 control 248
GWAS revealed, for the first time, association of HLA-C with HIV-1 control 248
Some SNPs previously implicated in HIV-1 control have not yet been confirmed
by GWAS 249
Conclusions 249
Further Reading 251
8 Pharmacogenetics 253
8.1 Definition and a Brief History of Pharmacogenetics 254
8.2 Cytochrome P450 255
There is a clear relationship between genotype and phenotype for several forms of
cytochrome P450 255
The conversion of the analgesic drug codeine, which is administered as a pro-drug and
is activated to morphine by CYP2D6, is of clinical importance 258
The cytochrome P450 CYP2C9 metabolizes warfarin — a very widely used drug 259
CYP2C19 activates clopidogrel - a drug widely used to prevent strokes and heart attacks 259
8.3 Other Drug-Metabolizing Enzymes and Transporters 261
For phase II conjugation reactions, the UDP glucuronosyltransferase family makes the
largest contribution 261
Methyl transferases are also important in phase II drug metabolism 261
Polymorphisms in drug transporters also play a role in pharmacogenetics 263
8.4 Drug Targets 263
The relationship between VKOR and coumarin anticoagulants is one of the most consistently
reported genetic associations involving drug targets unrelated to cancer 263
The efficacy of p-adrenergic receptor agonists widely used in the treatment of allergies may
also be genetically determined 264
8.5 Adverse Drug Reactions 266
HLA genotype is a potent determinant of susceptibility to several different types of adverse drug reactions
266
xvi Contents
The anti-human immunodeficiency virus (HIV-1) drug Abacavir gives rise to hypersensitivity
in some patients 267
Drug-induced liver injury is a rare, but clinically important problem 267
There are many other susceptibility factors for serious adverse drug reactions 269
Adverse reactions to commonly used statins provide a key example of non-HLA-related
adverse drug reactions 270
Cardiotoxicity reactions to drugs do not appear to involve an immune or inflammatory
response 270
Conclusions 271
Further Reading 273
9 Cancer as a Complex Disease: Genetic Factors Affecting Cancer
Susceptibility and Cancer Treatment 275
9.1 Defining Cancer 278
9.2 Cancer as a Complex Disease 280
Early studies of cancer found evidence of genetic associations with risk 280
GWAS has revolutionized the search for cancer-promoting alleles in non-familial cancers 281
9.3 Genetic Risk Factors for Particular Cancers Detected by GWAS 281
GWAS has identified a number of biologically plausible genetic risk factors for breast cancer 281 Novel insights into lung cancer involving the target for nicotine were detected by GWAS 283
A large number of genetic risk factors for prostate cancer have been revealed by GWAS 283
9.4 General Cancer Risk Loci Detected by GWAS 285
9.5 Previously Established Cancer Risk Factors Confirmed by GWAS 286
Alcohol, smoking, and chemical exposure increase the risk of cancer 286
9.6 Individualizing Drug Treatment Based on Tumor Genotype 288
Newly developed drugs inhibit the function of mutated proteins in cancer cells 288
Specific antibodies can target tumor- specific proteins and inhibit tumor growth 289
Epigenetic changes in the tumor involving methylation may affect response to conventional
drug treatments 290
Gene expression profiling may enable personalized cancer treatment 290
Before we close the chapter on cancer it is important to recognize that there are many forms
of this disease 291
Conclusions 292
Further Reading 294
10 Genetic Studies on Susceptibility to Diabetes 295
10.1 Diabetes Mellitus 295
10.2 Genetics of T1D 297
10.3 Early Genetic Studies in T1D 297
HLA class II genotype is the strongest genetic risk factor for T1D 298
Not all of the risk for T1D above may be associated with the DQB allele or HLA class II 299
Other genetic risk factors for T1D include the genotype for the insulin gene 300
Candidate gene studies have identified a number of other non-MHC associations with TlD 300
Contents xvii
10.4 GWAS Studies in T1D 303
The 2007 WTCCC1 study was one of the first GWAS in Tl D 303
Following the introduction of GWAS in 2007, research has resulted in the identification of
at least 40 further potential T1D alleles 305
10.5 Early Genetics of T2D 306
There have been different interpretations of the associations with PPARG, KCNJ11, and
TCF7L2 307
10.6 GWAS Studies in Type T2D 307
Examples from the WTCCC1 study 308
Other risk alleles for T2D from other studies 309
10.7 The Future of Genetics in T2D 310
Future prospects in T2D research involve genome sequencing 310
Epigenetics may be important in diabetes 310
10.8 Genetics of Monogenic Diabetes 311
Conclusions 312
Further Reading 314
11 Ethical, Social, and Personal Consequences 315
11.1 Defining Ethics 316
There are philosophical arguments for and against ethical constraint in biomedical research 316 What are the practical ethical implications in the study of genetics of complex disease? 317
11.2 Ethics in Genetics: What We Can Learn from the Past? 318
The consequence of the Eugenics Movement and the ideas it spread were extremely
bad news for the developing science of genetics 318
11.3 Looking into the Future Use of Genetic Data 321
Genetic studies of complex disease will have a major impact on clinical medicine 321
The potential personal impact of data from studies in complex disease is considerable 322
11.4 Who Does the Data Belong to? Interacting with Commerce 329
Do I own my genome? 330
11.5 Who Should be Able to Access the Data? 332
Conclusions 332
Further Reading 334
12 Sequencing Technology and the Future of Complex Disease Genetics 337
12.1 DNA Sequencing: The Past, Present, and Future 338
The development of DNA sequencing using the Sanger sequencing technique opened
the way to sequencing the genome 338
The new era: next-generation DNA sequencing 340
The upcoming era: third-generation sequencing 343
12.2 The Future of NGS in Clinical Practice and Research 347
Using NGS will enable high- resolution genotyping for SNPs in complex disease 348
Using NGS will enable better identification of CNVs 350
Sequencing the RNA transcript and the whole transcriptome is an alternative way forward 350
xviii Contents
12.3 Whole-Genome Versus Exome Sequencing 350
12.4 The Next Generations of Genome/Exome-Wide Association Studies 351
Missing and non-genotyped SNP data can be imputed using large databases and known
patterns of linkage disequilibrium 352
G WAS identifies both synthetic (false) associations and direct (real) associations 353
The importance of linking genotype to phenotype 353
Genotyping on new arrays provides a focus and higher level of resolution for GWAS 354
Different forms of NGS technology will impact on how GWAS is used 354
12.5 Epigenetics: A Complimentary Strategy in Complex Disease Studies 357
12.6 Metagenomics and the Bacterial Genome 357
To put metagenomics into context, we need to consider the impact it may have 359
12.7 Major Ongoing International Genome Projects 360
HapMap is a project with major significance in current research, especially GWAS 360
The 1000 Genomes Project has major potential in studies of complex disease 362
ENCODE will help to link genotype to phenotype in complex disease 363
12.8 Systems Biology 364
Considering systems biology allows us to look into the future 364
Conclusions 366
Further Reading 368
Glossary 373
Index 397
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| discipline | Biologie Medizin |
| format | Book |
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| id | DE-604.BV043516335 |
| illustrated | Not Illustrated |
| indexdate | 2024-08-14T00:53:50Z |
| institution | BVB |
| isbn | 9780815344919 |
| language | English |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-028932380 |
| oclc_num | 915378036 |
| open_access_boolean | |
| owner | DE-355 DE-BY-UBR DE-11 |
| owner_facet | DE-355 DE-BY-UBR DE-11 |
| publishDate | 2016 |
| publishDateSearch | 2016 |
| publishDateSort | 2016 |
| publisher | Garland Science |
| record_format | marc |
| spelling | Donaldson, Peter 1934- Verfasser aut Genetics of complex disease Peter Donaldson, Ann Daly, Luca Ermini, Debra Bevitt London ; New York Garland Science [2016] © 2016 txt rdacontent n rdamedia nc rdacarrier Molekulargenetik (DE-588)4039987-4 gnd rswk-swf Krankheit (DE-588)4032844-2 gnd rswk-swf Krankheit (DE-588)4032844-2 s Molekulargenetik (DE-588)4039987-4 s b DE-604 Daly, Ann Verfasser aut Ermini, Luca Verfasser aut Bevitt, Debra Verfasser aut Digitalisierung UB Regensburg - ADAM Catalogue Enrichment application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=028932380&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis |
| spellingShingle | Donaldson, Peter 1934- Daly, Ann Ermini, Luca Bevitt, Debra Genetics of complex disease Molekulargenetik (DE-588)4039987-4 gnd Krankheit (DE-588)4032844-2 gnd |
| subject_GND | (DE-588)4039987-4 (DE-588)4032844-2 |
| title | Genetics of complex disease |
| title_auth | Genetics of complex disease |
| title_exact_search | Genetics of complex disease |
| title_full | Genetics of complex disease Peter Donaldson, Ann Daly, Luca Ermini, Debra Bevitt |
| title_fullStr | Genetics of complex disease Peter Donaldson, Ann Daly, Luca Ermini, Debra Bevitt |
| title_full_unstemmed | Genetics of complex disease Peter Donaldson, Ann Daly, Luca Ermini, Debra Bevitt |
| title_short | Genetics of complex disease |
| title_sort | genetics of complex disease |
| topic | Molekulargenetik (DE-588)4039987-4 gnd Krankheit (DE-588)4032844-2 gnd |
| topic_facet | Molekulargenetik Krankheit |
| url | http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=028932380&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA |
| work_keys_str_mv | AT donaldsonpeter geneticsofcomplexdisease AT dalyann geneticsofcomplexdisease AT erminiluca geneticsofcomplexdisease AT bevittdebra geneticsofcomplexdisease |