Bernard Keavney Institute of Human Genetics University of Newcastle, UK. Recent developments in genetic epidemiology relevant to PURE

Objectives Brief revision of some genetic “basics” Developments in genetic markers and genotyping technology Ethnicity, genetic variation and disease The potential impact of rare variants on common diseases: epidemiological and technological challenges.

Monogenic HCM, LQTS (disease genes) Genetic contribution to cardiovascular diseases genes environment (large-effect susceptibility genes) oligogenic Non-genetic Congenital HD Hypertension T II DM Atherosclerosis (small-effect susceptibility genes) polygenic

Common variants which affect human diseases HLA: Autoimmunity and infection APOE4: Alzheimer’s, CHD, lipids FV Leiden: Venous thrombosis PPARG: Type II Diabetes KCJN11: Type II Diabetes PTPN22: RhA, Type 1 Diabetes Insulin: Type I Diabetes NOD2: Crohn’s disease CF-H: Age-related MD RET: Hirschprung disease

Candidate gene association studies: a uniquely non-replicable area of science Six of 166 replicated in >75% of studies (4%)* Study sizes too small Statistical significance levels not stringent enough Meta-analyses: problem of publication bias Most conducted in urban Western Caucasian populations Minimal environmental heterogeneity within individual studies Minimal amount of “gene space” tested *Hirschhorn et al. Genet. Med. 2002

Genome figures The human genome: 3,200,000,000 base pairs 5% gene coding regions (1% expressed sequence) Noncoding regulatory elements are situated near genes 20,000 genes Any two genomes: 99.9% identical 3.2M differences between any two individuals 11,000,000 sites vary in at least 1% of the world’s population (Polymorphisms) Every site compatible with life has been mutated several times in this generation alone

Single nucleotide polymorphisms (SNPs): the mapping tool for association studies CAACTGTGTAGGTTGAG CAACTGTGTTGGTTGAG Between 2000 and million SNPs have been identified. For mapping, focus hitherto on common SNPs (MAF > 0.05): ancient power to detect given effect greater 90% of human variation is due to common alleles Most common variants are found in all world populations Technology to find rare variants has not been available thus far Expect one common SNP every ~600 bp Total of 7M genomewide……Which ones to type? And how many? Coding (amino acid change) Minority Noncoding Some regulatory

SNPs in dbSNP

The degree of association between a disease allele and a marker allele determines power Disease Causal SNP Marker SNP Testing two associations in one. DHDHABBADHDHABBA The arrangement of two or more alleles on a chromosome is called a haplotype Locus 1 Locus 2

The degree of association between a disease allele and a marker allele determines power Disease Causal SNP Marker SNP Testing two associations in one. DHDHABBADHDHABBA The arrangement of two or more alleles on a chromosome is called a haplotype Locus 1 Locus 2

Chromosomes are mosaics reflecting ancestral haplotypes

ACE gene diagram Position of 10 polymorphisms typed at the ACE locus 2 10 haplotypes could be generated from these genotypes

T A T A T C G I A 3 T A T A T T G I A 3 T A T A T C A I A 3 C C C T C C A D G 2 C C C T C C G D G 2 C A D G 2T A C A T C A D G 2T A T A T. Clade A Clade B Clade C X Keavney et al 1998

Oct 2005: Characterisation of most of the common genetic variation present genomewide in four world populations

HapMap project Phase I: 1 common SNP (MAF>0.05) every 5 Kb in 269 DNA samples (1 million SNPs) Yoruba from Ibadan, Nigeria European ancestry from Utah, US Han Chinese from Beijing Japanese from Tokyo 10 x 500Kb regions Resequenced in 48 individuals All SNPs genotyped in 269 samples Phase II : 4 million common SNPs Goal: to assess feasibility of whole-genome association studies and provide the “road map”of SNPs to type

HapMap phase I data

Recombination rates, haplotype lengths and gene location Chromosome 9q13

The POMC gene Intron 1 (3709bp) Exon 1 (85bp) Exon 2 (151bp) Intron 2 (2887bp) Exon 3 (833bp) RsaIC1032GC8246T There are no common polymorphisms in the translated sequence 5’ Baker et al Diabetes 2005

WHR adjusted for age, sex, smoking, alcohol, exercise, with or without BMI Difference 0.2 SD per allele. P=0.003 for C1032G; p=NS for RsaI N=1426 P< Means (95% CIs) Baker et al. Diabetes 2005

Genome-wide association studies are feasible: HapMap data

Chip-based genotyping provides the possibility to type 500,000 SNPs in a single individual today. Chip-based WGA study using 116,204 SNPs identified the role of Factor H in AMD (Klein et al. April 2005)

The within-population component of genetic variation accounts for most of human genetic diversity Rosenberg et al. Science individuals from 52 populations; 377 autosomal microsatellites 47% of 4199 alleles present in all regions 7% alleles region-specific; median q=0.01

Few SNPs rare in one panel are common in another HapMap 2005

Ioannidis et al. Nat Genet Heterogeneity of allele frequencies and disease O.R.s in meta-analyses of 43 gene-disease associations I 2 =75% shown by red line

Disease-causing variants: common or rare alleles? With a few exceptions (e.g. ACE I/D and plasma ACE) this is empirically confirmed

20Kb shown All common haplotypes at LEP are captured by these markers C538T is a rare allele (q<0.01) Leptin gene polymorphisms and cardiovascular risk Gaukrodger et al. 2005

LEP C538T polymorphism, arterial stiffness and carotid IMT TraitEstimate (SE)95% CI Pulse pressureDisplacement*1.00 (0.31)0.39 – 1.61 Polygenic h2 $ 0.24 (0.06)0.12 – 0.36 Mean IMTDisplacement0.90 (0.36)0.19 – 1.61 Polygenic h20.20 (0.07)0.06 – 0.34 Residual correlation 0.13 (0.04)0.04 – 0.21 Gaukrodger et al. JMG 2005

Rare alleles with large effect contribute to HDL cholesterol variation in the “normal range” APOA1 ABCA1 LCAT Sequenced Coding Region 128 High HDLC (>95%) 128 Low HDLC (<5%) Low HDLC High HDLC Var +213 Var Variants affected function Replicated in 2 nd population No association between HDLC and common variants in these genes 1/6 of those with HDLC <5% had a mutation These would be missed by a “common variant only” strategy Cohen et al. Science 2004

High-throughput sequencing technologies from September 2005 issues of “Science” and “Nature”

Conclusions Technological progress is very rapid: prospect of WGA scans on large numbers of samples in near future Many studies (eg UK Biobank) focus on gene- environment interaction but often environmental heterogeneity is minimal There remains a pressing need to describe and validate genetic associations with CVD in populations other than US and Western European Caucasians