Two discoveries that will shape the 21st century Deciphering the Human Genome Lifebook.dat – the database required to make a human being Production of Human Embryonic Stem Cell Lines Formlife.exe – the executable program to make a human being Cloning of Human Beings (a negative utopia) Copy.exe – a possibility of pertuating ourselves?
Diakonie Publik 3/2001 Blueprint of Life Cell Nucleus Chromosom Gene DNA Gene-product: Protein
Informationsübertragung bei der zellteilung Cell division: Transfer of Information!
DNA as carrier of Information H O Minor groove C in phosphate ester chain C and N in bases P Major groove Base pairs Sugar phosphate backbone 3‘ 5‘
Human ß-Globin, Segment ... TAAGCCAGTG CCAGAAGAGC CAAGGACAGG TACGGCTGTC ATCACTTAGA CCTCACCCTG TGGAGCCACA CCCTAGGGTT GGCCAATCTA CTCCCAGGAG CAGGGAGGGC AGGAGCCAGG GCTGGGCATA AAAGTCAGGG CAGAGCCATC TATTGCTTAC ATTTGCTTCT GACACAACTG TGTTCACTAG CAACCTCAAA CAGACACCAT GGTGCACCTG ACTCCTGAGG AGAAGTCTGC CGTTACTGCC CTGTGGGGCA AGGTGAACGT GGATGAAGTT GGTGGTGAGG CCCTGGGCAG GTTGGTATCA AGGTTACAAG ACAGGTTTAA GGAGACCAAT AGAAACTGGG CATGTGGAGA CAGAGAAGAC TCTTGGGTTT CTGATAGGCA CTGACTCTCT CTGCCTATTG GTCTATTTTC CCACCCTTAG GCTGCTGGTG GTCTACCCTT GGACCCAGAG GTTCTTTGAG TCCTTTGGGG ATCTGTCCAC TCCTGATGCT GTTATGGGCA ACCCTAAGGT GAAGGCTCAT GGCAAGAAAG ...
Human ß-Globin, Exon1, Segment ...GTG CAC CTG ACT CCT GAG GAG Val His Leu Thr Pro Glu Glu AAG TCT GCC GTT ACT GCC CTG Lys Ser Ala Val Thr Ala Leu TGG GGC AAG GTG AAC GTG ... Trp Gly Lys Val Asn Val + 126 further AS ! TGA Stop !!!
Genomic Library of Mankind • 46 Chromosomes 2 * 3,2 billion letters • 30 000 – 40 000 Genes • ca. 99 % not protein-coding (excess of void information) • Man/Chimpanzee 1-2% global text difference (ca. 120 Mio Letters) i.e. in every line of the lifebook about 1-2 „misprints“ • enormous repetitive segments • Retroviral traces (hundreds of thousand items) – the human genome is a museum of virus infections !
Genomic Library of Individual Person • ca. 2 Mio differences (SNPs) (between non-related persons) • ca. 60 000 of them in coding regions • ca. 10 000 genetic defects (each individual carries disposition for about 5 defects) • every 500 -2000 letters a variation • (i.e. on every page of the life book a few „misprints“
Evolutionary Traces in the Genome • 25 % of the human genome are „deserts" • ca. 50 % are repetitions • among them ca. 45 % „jumping copies", (silent since millions of years)
Genomic Non-sense but important identification tag! Person 1 : CA CA CA CA CA CA CA 7 repeats no!! (Father ?) CA CA CA CA 4 repeats Person 2: CA CA CA CA CA CA CA CA 8 repeats (Father ?) CA CA CA 3 repeats Person 3: CA CA CA CA CA CA CA CA 8 repeats (Father ?) CA CA 2 repeats !! Person 4: CA CA CA CA CA CA CA CA 8 repeats (Mother) CA CA CA CA CA CA CA 7 repeats !! Person 5: CA CA CA CA CA CA CA 7 repeats (Child) CA CA 2 repeats
Genetic prediction: Scope It can establish a diagnosis It can predict the future It provides implied information on related individuals
Genetic Diagnosis and Prediction: Scale „Within the next decade genetic testing will be used widely for predictive testing in healthy people and for diagnosis and management of patients.“ Bell J (1998) „New Genetics in Clinical Practice“, Brit. Med. J. 316 , 618-620
Genetic Prediction: Upper Limit of Determination Concordance of traits in monozygotic twins: Rare mendelian diseases: up to 100% Frequent complex diseases: 30 – 70% Relevant prediction is in terms of probability rather than of certainty (maybe useful for the insurance company, but of limited use for the individual) There is always considerable non-genetic variability!
Genetic Diagnosis and Prediction: Tests of What ? Non-inherited genetic traits : Chromosomal Aneuploidy e.g. Down syndroma, Klinefelter syndroma Mendelian diseases : about 1500 out of 5000 may be diagnosed dominant mode of inheritance: in every generation of family recessive mode of inheritance: in one family “out of the blue“ Complex (multifactorial) diseases : about 40 genes may contribute (example of cardiovascular disease)
Genetic Diagnosis and Prediction: Tests on Whom ? • Partners Purposes (with example): • Embryo in vitro • Embryo in utero Prediction of disorder: Huntington • Fetus before birth Selection between alternatives: immune • Newborn Prevention of disease: PKU • Child • Adult
Examples of Mendelian Diseases Huntington (D) BRCA (breast cancer, D) Cystic fibrosis Thrombophilia Porphyria Haemochromatosis Myotonic dystrophy Duchenne muscular dystrophy (sex-linked) Phenylketonuria Galactosemia Thalassemia Congenital hypothyrioidism
Examples of Complex Diseases with Genetic Contribution Diabetes type 1 Diabetes type 2 Breat cancer Colon cancer Prostate cancer Alzheimer´s dementia (early onset form) Multiple sclerosis Bipolar disorder Schizophrenia Autism Familial Parkinson disease
Risk prediction of Complex Disease:GRR Genotype Relative Risk (GRR) = Frequency of disease in carriers of variant allele Frequency of disease in carriers of normal allele GRR > 50 single gene disorder with high penetrance 4-50 oligogenic disorder <4 polygenic factors (complex disease)
Risk prediction of Complex Disease: Bad Test Not even a high GRR guarantees a good test if Frequency (of risk allele) > frequency (disease in population) HLA-B27 other allele Sum Healthy persons 985 8 898 9 983 Spondylitis ankylosans (Mb. Bekhterev) 15 2 17 All persons 1 000 9 000 10 000 Frequency of disease: 17 / 10 000 = 0.17% Frequency of gene variant: 1000 / 10 000 = 10% GRR = 15/17 divided by 985/9983 = 8.9 Frequency of gene variant in disease: 15/17 = 88% Frequency of gene variant in healthy: 9845 / 9983 = 10% Test prediction on risky gene : 15/1000 diseased persons = 1.5% Rate of false positives: 98.5% Rate of false negatives: 0.02%
Is genetic prediction of disease possible? Monogenic case , rare, high GRR, high penetrance: yes, if genotype is moderately specific Polygenic case and multifactorial causation, low GRR: only statistical prediction in large populations samples Population screening instead of individual diagnosis: • Screening for heterozygotes for recessive disorder • Screening of fetuses or newborns for necessary therapy • Screening for predictive genotype in frequent disease (breast cancer) • Any screening runs the following risks: False positives, if genotype is not the specific causative factor False negatives, if genotype is not the only causative factor
Metabolic Network of Lipoproteins surface remnants (LRP&HSPG) LDLRec B 100 B 100 A C E A C E B 100 scavenger HL VLDL IDL LDL liver LPL HDLrec CETP/HL cubulin nasc periph. cholesterol A C E A C E LCAT HDL 2 HDL 3 disc LCAT (LPL) SRB1 - CETP/HL periph. cholesterol A C H B 48 E B 48 HL LPL Chylo LRP Chylo remnants intestine HL scavenger surface remnants LDLRec 15/03/01 (on LPL def.)
Cholesterol as risk factor Heritability is 50% - even the best prediction can reduce the variation of individual values by 50% Variation in more than 20 genes are responsible for the normal status of cholesterol (clinical LDL/HDL ratio). Each can contribute around 2% Individual prediction will be useless Statistical prediction for groups will be possible provided that the complexity of genetic causation is not too high Test makes sense only if there are genotypes with normal cholesterol levels in young, but high levels at advanced age
Risk and individual disease Only part of all individuals with risk status will actually become ill Every person belongs to several risk groups for a chronic disease with genetic component. Thus in principle everybody should pay a risk surcharge For all persons this will cancel Provided that equity of information is established between insurance and client!
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