Introduction to Genetic Epidemiology CM van Duijn Genetic Epidemiology Unit
Gene Discovery • Basic principles • Candidate gene studies • Genome screening • Genome sequencing • Genetic architecture disease
Rationale Genetic Epidemiology Gene ⇒ Protein ⇒ Disease
Genetic code AGGAGTCCAAAGCGCGCAGTGCGCAGCGCGCA CCAGTCGTGACTCCAAAGCGATTCGATAGCAAC CCGATCCTATGAGGGCGCAGGAGTCCAAAGCGC GCAGTGCGCGAGAGGAGTCGGAGTCCGGCAATT GCCCAATGCCGATCGAACGACGTAACCGACTTA GGCCAGAGAGCTAGCGATCCGACTCTAAGAGCA GCTAAAGACTCCAAAGCGATTCGATAGCAACCC GCCGATCGAAGGAGTCCAAAGTCGGAGTCCGGC AACAGTCGTTGCCCAATGCCGGCGATTCGAATC GAACGACGTAACGGCAACAGTCGTGACTTGCCC AATGCCCGACCAGTCGTGACACTCCAAAGTGCC CAATGCCGATCCGATTCGATAGCACCAATGCCGA TCCAAACGAACGACGTCCAAAAACCGACTT
Genetic code AGGAGTCCAAAGCGCGCAGTGCGCAGCGCGCA CCAGTCGTGACTCCAAAGCGATTCGATAGCAAC CCGATCCTATGAGGGCGCAGGAGTCCAAAGCGC GCAGTGCGCGAGAGGAGTCGGAGTCCGGCAATT GCCCAATGCCGATCGAACGACGTAACCGACTTA GGCCAGAGAGCTAGCGATCCGACTCTAAGAGCA GCTAAAGACTCCAAAGCGATTCGATAGCAACCC GCCGATCGAAGGAGGCCAAAGTCGGAGTCCGG CAACAGTCGTTGCCCAATGCCGGCGATTCGAATC GAACGACGTAACGGCAACAGTCGTGACTTGCCC AATGCCCGACCAGTCGTGACACTCCAAAGTGCC CAATGCCGATCCGATTCGATAGCACCAATGCCGA TCCAAACGAACGACGTCCAAAAACCGACTT
Founder chromosome with disease associated mutation Mutation = change in base pair
Basic Rationale A mutation/polymorphism causally related to the disease should be found more often in affected than unaffected individuals
Recombination
Recombination
Mutation A A A A A A A A A A A A A A A
Founder Mutation chromosome with disease associated mutation Region that is identical by descent (IBD) including the disease locus (haplotype)
Association Look for the hayfork in stead of the needle
Basic Rationale A mutation/polymorphism not causally related to disease, but close to the disease gene should also be found more often in affected than unaffected individuals
Approaches to Gene Finding (indirect) Candidate gene ? Public health, Clinical decision ? gene protein disease ? Genome screen gene protein disease ? New drug targets & biomarkers
Gene Discovery • Basic principles • Candidate gene studies • Genome screening • Genome sequencing • Genetic architecture disease
Candidate gene approach • Not translated into protein • May determine level protein Unknown disease Promoter mutation,e.g. change Gene in base pair • May determine function protein • May determine level protein
Genetic code AGGAGTCCAAAGCGCGCAGTGCGCAGCGCGCA CCAGTCGTGACTCCAAAGCGATTCGATAGCAAC CCGATCCTATGAGGGCGCAGGAGTCCAAAGCGC GCAGTGCGCGAGAGGAGTCGGAGTCCGGCAATT GCCCAATGCCGATCGAACGACGTAACCGACTTA GGCCAGAGAGCTAGCGATCCGACTCTAAGAGCA GCTAAAGACTCCAAAGCGATTCGATAGCAACCC GCCGATCGAAGGAGTCCAAAGTCGGAGTCCGGC AACAGTCGTTGCCCAATGCCGGCGATTCGAATC GAACGACGTAACGGCAACAGTCGTGACTTGCCC AATGCCCGACCAGTCGTGACACTCCAAAGTGCC CAATGCCGATCCGATTCGATAGCACCAATGCCGA TCCAAACGAACGACGTCCAAAAACCGACTT
Diversity Genes APOE COL2A1 Base pairs 3597 31 001 Amino Acids 299 1418 Exons 4 54
Candidate gene approach Select markers in gene or its promoter using literature and bioinformatics and test these in affected and unaffected subjects Marker Allele 1 12 2 A 3 9 4 2 5 3
Genetic Markers (SNPs) • Flag a locus on chromosome • May be located in / out gene • May be located in / out exon
Example: Alzheimer’s disease (AD) 21
Pathology Alzheimer’s disease (AD) Senile plaques - amyloid A β APP Neurofibrillary tangles - tau MAPT A β amyloid angiopathy 22
Genetic variations in MAPT 23
Rotterdam Study • 12,000 subjects aged 55 + years who have been followed for 15 years • Screening for major diseases and risk factors ever 5 years • 700 patients with Alzheimer’s disease • Genotyping: Taqman / Illumina 500 k • Basically compare the frequency of rare variants in cases and controls 24
# Marker Position Frequency minor allele Cases Controls 1 hCV2536908 40526680 0.2371 0.2099 2 hCV341577 40538554 0.4454 0.4146 p<0.02 3 hCV9254243 40571807 0.3683 0.3736 4 hCV2032862 40598477 0.233 0.2813 p<0.01 5 hCV2032865 40603713 0.4187 0.4999 6 hCV2554844 40717672 0.4968 0.4938 7 hCV2541205 40828104 0.4708 0.4389 8 hCV2265271 41070456 0.1755 0.2126 9 hCV2544843 41235818 0.4495 0.3671 10 hCV2257689 41241147 0.459 0.3671 11 hCV2544830 41256855 0.446 0.4631 12 hCV2257669 41301901 0.1837 0.2049 13 hCV7450857 41340226 0.1887 0.235 14 hCV3202946 41350591 0.1347 0.1357 15 hCV3202949 41352389 0.4547 0.4368 16 hCV1016016 41375573 0.383 0.3536 17 hCV3202956 41381748 0.1863 0.2346 18 hCV7563692 41407682 0.1808 0.2135 19 hCV3202960 41424176 0.1695 0.1446 20 hCV2042903 41424329 0.2682 0.3078 21 hCV11936104 41439239 0.1734 0.2376 22 hCV2560317 41461242 0.4803 0.4819 23 hCV2264293 41465690 0.194 0.2194 24 hCV2560314 41472690 0.4335 0.4405 25 hCV11936132 41497167 0.1745 0.2074 26 hCV15858203 41511550 0.1912 0.2188 27 hCV7563831 41551932 0.1776 0.2084 28 hCV2560260 41560151 0.1659 0.1250 29 hCV338624 41604276 0.1565 0.1125 30 hCV2598655 41615467 0.1543 0.1325 31 hCV2554114 42150418 0.2083 0.2013 32 hCV2261778 42164185 0.1703 0.2013 33 hCV2261785 42184098 0.1733 0.2049 25 34 hCV2261819 42220763 0.1740 0.2063
Multiple Testing A large a number of tests are performed with no strong a priori hypothesis There is no a priori hypothesis which allele There is no a priori hypothesis about the direction of the effect: increase or decrease in risk 26
Multiple Testing Test1 Test2 ok ok 0.95*0.95=0.90 wrong ok 1-0.90=0.10 instead of 0.05 ok wrong wrong wrong If you test with p = 0.05/2, the probability of at least 1 false + Is 1- 0.975 2 = 0.95 (Bonferroni correction) If you do 34 tests the probability of at least 1 false + Is 1- 0.95 34 = 1 => adjust p-value 0.05/34 = 1.4*10 -3 27
# Marker Position Frequency minor allele Cases Controls 1 hCV2536908 40526680 0.2371 0.2099 2 hCV341577 40538554 0.4454 0.4146 p<0.02 NOT SIGNIFICANT 3 hCV9254243 40571807 0.3683 0.3736 4 hCV2032862 40598477 0.233 0.2813 p<0.01 NOT SIGNIFICANT 5 hCV2032865 40603713 0.4187 0.4999 6 hCV2554844 40717672 0.4968 0.4938 7 hCV2541205 40828104 0.4708 0.4389 8 hCV2265271 41070456 0.1755 0.2126 9 hCV2544843 41235818 0.4495 0.3671 10 hCV2257689 41241147 0.459 0.3671 11 hCV2544830 41256855 0.446 0.4631 12 hCV2257669 41301901 0.1837 0.2049 13 hCV7450857 41340226 0.1887 0.235 14 hCV3202946 41350591 0.1347 0.1357 15 hCV3202949 41352389 0.4547 0.4368 16 hCV1016016 41375573 0.383 0.3536 17 hCV3202956 41381748 0.1863 0.2346 18 hCV7563692 41407682 0.1808 0.2135 19 hCV3202960 41424176 0.1695 0.1446 20 hCV2042903 41424329 0.2682 0.3078 21 hCV11936104 41439239 0.1734 0.2376 22 hCV2560317 41461242 0.4803 0.4819 23 hCV2264293 41465690 0.194 0.2194 24 hCV2560314 41472690 0.4335 0.4405 25 hCV11936132 41497167 0.1745 0.2074 26 hCV15858203 41511550 0.1912 0.2188 27 hCV7563831 41551932 0.1776 0.2084 28 hCV2560260 41560151 0.1659 0.1250 29 hCV338624 41604276 0.1565 0.1125 30 hCV2598655 41615467 0.1543 0.1325 31 hCV2554114 42150418 0.2083 0.2013 32 hCV2261778 42164185 0.1703 0.2013 28 33 hCV2261785 42184098 0.1733 0.2049 34 hCV2261819 42220763 0 1740 0 2063
Gene Discovery • Basic principles • Candidate gene studies • Genome screening • Genome sequencing • Genetic architecture disease
Human Genome • 3 billion base pairs • Average size gene: 30,000 base pairs • Genes make up <10% DNA
8 8 Founder 4 chromosome with 3 disease associated 3 mutation 6 4 2 7 6 5 4 1 3 5 2 1 9 4 6 2 1 3 6 3 7 Region that is identical 3 3 3 3 3 3 by descent (IBD) 6 6 5 6 6 6 including the disease 1 8 3 4 5 1 locus 4 5 7 1 3 5 1 3 2 5 8 7 Chromosomes from “apparently unrelated” individuals with a certain trait
Genome screen Select markers covering the full genome and test these in patients and controls or families Marker Allele 1 13 2 A 3 4 Unknown disease 4 9 mutation,e.g. 5 10 change in base 6 I pair 7 3
How many markers do you need? Marker Allele 1 13 2 A 3 4 Unknown Marker 3 or 4 should disease flag the block of DNA 4 9 mutation 5 10 6 I 7 3
High-density genotyping >3,500,000 SNPs to: validate SNPs, determine frequency, assays determine the correlation structure of alleles and number of independent haplotypes ENCODE: sequencing 10 typical 500kb regions
General population • About 500 000 are found in Caucasians • This yields a threshold for significance of 0.05/500,000 = 5*10 -8
How many patients do you need? Allele frequency and odds ratio determine the number of patients and 1.2 controls needed 1.3 1.5 Common variants 2 are easier to find with association than rare ones Wang et al., Nat RevGen, 2005 36
Genome wide association analyses (GWAs) of LDL cholesterol: p-plot
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