Ermerging Genomics Technologies in Research of Complex Traits and - PowerPoint PPT Presentation

Power of Programming, Munich, D, 14 March 2014 Ermerging Genomics Technologies in Research of Complex Traits and Diseases André G Uitterlinden Genetic Laboratory Department of Internal Medicine Department of Epidemiology Department of Clinical Chemistry www.glimdna.org Note: for non-commercial purposes only

RNA • Dynamic • Instable • Tissue specific regulation • Quantitative measurement Clinical+ Biological Relevance

AGGAGTCTGACTGACCATTGGACTAGGGGATTGACCAGTAGGCTGCGATTCGGATGCGGATTGACGATTAAAAAGGATTACGATT AGCTGTGACGTGCAGGATGCTGCGATGCTGGACTGAACGCCCCCCGGGCTTCTTTATTAGCTGCTGACGTGCCAGATGCTGAC HUMAN DNA IS HIGHLY VARIABLE GTGCAGTGCGGCTGACGGTGCTTACCTGGATCGGATGCTACCAGTCGATCGATCGATCGTAGCGTAGCGTATGCTAGCTAGTGAT CGATGCTAGTAGCTAGCTAGCTGATCGATCATCGATCGTAGCTAGCTAGCTAGCTAGCTGATCGATCGATGCTAGCTAGCTAGCTA GTCATCTGTGGTGGGGGGTTAAATGCGATTGCCGCTAGCTAGAACAAAATAGCGGTATTTTGGGGAGTCTGACTGACCATTGGAC TAGGGGATTGACCAGTAGGCTGCGATTCGGATGCGGATTGACGATTAAAAAGGATTACGATTAGCTGTGACGTGCAGGATGCTGC GATGCTGGACTGAACGCCCCCCGGGCTTCTTTATTAGCTGCTGACGTGCCAGATGCTGACGTGCAGTGCGGCTGACGGAGTCT DNA Variants are: “SNP=Single Nucleotide Polymorphism” GACTGACCATTGGACTAGGGGATTGACCAGTAGGCTGCGATTCGGATGCGGATTGACGATTAAAAAGGATTACGATTAGCTGTGA CGTGCAGGATGCTGCGATGCTGGACTGAACGCCCCCCGGGCTTCTTTATTAGCTGCTGACGTGCCAGATGCTGACGTGCAGTG *Frequent in the Genome: CGGCTGACGGTGCTTACCTGGATCGGATGCTACCAGTCGATCGATCGATCGTAGCGTAGCGTATGCTAGCTAGTGATCGATGCTA GTAGCTAGCTAGCTGATCGATCATCGATCGTAGCTAGCTAGCTAGCTAGCTGATCGATCGATGCTAGCTAGCTAGCTAGTCATCTGT - >75 million (?) variable loci in genome (~2%) GGTGGGGGGTTAAATGCGATTGCCGCTAGCTAGAACAAAATAGCGGTATTTTGGGGAGTCTGACTGACCATTGGACTAGGGGATT GACCAGTA G GCTGCGATTCGGATGCGGATTGACGATTAAAAAGGATTACGATTAGCTGTGACGTGCAGGATGCTGCGATGCTGGA - “SNPs” , in/del, CNV, VNTR CTGAACGCCCCTCGGGCTTCTTTATTAGCTGCTGACGTGCCAGATGCTGACGTGCAGTGAGGAGTCTGACTGACCATTGGACTA GGGGATTGACCAGTAGGCTGCGATTCGGATGCGGATTGACGATTAAAAAGGATTACGATTAGCTGTGACGTGCAGGATGCTGCGA - dbSNP, HapMap, 1KG, “local” NGS efforts,.. TGCTGGACTGAACGCCCCCCGGGCTTCTTTATTAGCTGCTGACGTGCCAGATGCTGACGTGCAGTGCGGCTGACGGTGCTTAC “IN/DEL=Insertion Deletion” CTGGATCGGATGCTACCAGTCGATCGATCGATCGTAGCGTAGCGTATGCTAGCTAGTGATCGATGCTAGTAGCTAGCT A GCTGATC GATCATCGATAACCG TAT AAGGGCTAGCTAGCTGATCGATCGATGCTAGCTAGCTAGCTAGTCATCTGTGGTGGGGGGTTAAATGC GATTGCCGCTAGCTAGAACAAAATAGCGGTATTTTGGCGGCTGACGGTGCTTACCTGGATCGGATGCTACCAGTCGATCGATCGA *Frequent in the Population: TCGTAGCGTAGCGTATGCTAGCTAGTGATCGATGCTAGTAAGGAGTCTGACTGACCATTGGACTAGGGGATTGACCAGTAGGCTG CGATTCGGATGCGGATTGACGATTAAAAAGGATTACGATTAGCTGTGACGTGCAGGATGCTGCGATGCTGGACTGAACGCCCCC CGGGCTTCTTTATTAGCTGCTGACGTGCCAGATGCTGACGTGCAGTGCGGCTGACGGTGCTTACCTGGATCGGATGCTACCAGT > 5 % = common polymorphism “CNV=Copy Number Variation” CGATCGATCGATCGTAGCGTAGCGTATGCTAGCTAGTGATCGATGCTAGTAGCTAGCTAGCTGATCGATCATCGATCGTAGCTAGC TAGCTAGCTAGCTGATCGATCGATGCTAGCTAGCTAGCTAGTCATCTGTGGTGGGGGGTTAAATGCGATTGCCGCTAGCTAGAAC 1 – 5 % = less common variant AAAATAGC GGTATTTTGGAGGAGTCTGACTGACCATTGGACTAGGGGATTGACCAGTAGGCTGCGATTCGGATGCGGATTGAC GATTAAAAAGGAT TACGATTAGCTGTGACGTGCAGGATGCTGCGATGCTGGACTGAACGCCCCCCGGGCTTCTTTATTAGCTGCT < 1 % = rare variant/mutation GACGTGCCAGATGCTGACGTGCAGTGCGGCTGACGGTGCTTACCTGGATCGGATGCTACCAGTCGATCGATCGATCGTAGCGTA GCGTATGCTAGCTAGTGATCGATGCTAGTAGCTAGCTAGCTGATCGATCATCGATCGTAGCTAGCTAGCTAGCTAGCTGATCGATC GATGCTAGCTAGCTAGCTAGTCATCTGTGGTGGGGGGTTAAATGCGATTGCCGCTAGCTAGAACAAAATAGCGGTATTTTGGGCTA GCTAGCTGATCGATCATCGATCGTAGCTAGCTAGCTAGCTAGCTGATCGATCGATGCTAGCTAGCTAGCTAGTCATCTGTGGTGGG GGGTTAAATG CACACACACACACACACACACACACACACACACA GATTGCCGCTAGCTAGAACAAAATAGCGGTATTTTGGGGT GCTTACCTGGATCGGATGCTACCAGTCGATCGATCGATCGTAGCGTAGCGTATGCTAGCTAGTGATCGATGCTAGTAGCTAGCTAG CTGATCGATCATCGATCGTAGCTAGCTAGCTAGCTAGCTGATCGATCGATGCTAGCTAGCTAGCTAGTCATCTGTGGTGGGGGGTT “VNTR=Variable Nunber of Repeats” AAATGCGATTGCCGCTAGCTAGAACAAAATAGCGGTATTTTGGAGGAGTCTGACTGACCATTGGACTAGGGGATTGACCAGTAGG CTGCGATTCGGATGCGGATTGACGATTAAAAAGGATTACGATTAGCTGTGACGTGCAGGATGCTGCGATGCTGGACTGAACGCCC CCCGGGCTTCTTTATTAGCTGCTGACGTGCCAGATGCTGACGTGCAGTGCGGCTGACGGTGCTTACCTGGATCGGATGCTACCA GTCGATCGATCGATCGTAGCGTAGCGTATGCTAGCTAGTGATCGATGCTAGTAGCTAGCTAGCTGATCGA

The influence of “technology-push” Time needed for genotyping 1 SNP in 7.000 DNA samples of the Rotterdam Study 1996 6 months : RFLP, Epp tubes 1999 3 months: RFLP, 96-well plates 2001 1 week: SBE, 384-well plates 2003 1 day: Taqman (manual) 2004 6 hrs : Taqman, Caliper pipetting robot 2005 3 hrs: Taqman, Deerac, “Fast” PCR 2007 6 sec: Illumina 550K array, 600 DNAs/week 2010 < 0.0006 sec: Illumina HiSeq2000 Sequencers

Human Ageing Research: Bone as an Example... Maternal genotype Environmental factors Ageing Paternal genotype Peak BMD Bone Loss Bone Osteoporosis: growth Low BMD, fractures men BMD women GenR ERGO/Rotterdam Study AGGO EPOS CALEUR DNA collections bone endpoints 75 25 50 100 Age (yr)

Osteoporotic fracture is a “complex” phenotype: Hip fx Fracture Risk Wrist fx Clinical Expression: Vertebral fx etc. Risk Factors : Bone Strength Impact Force Fall Risk BMD Quality Geometry DNA mutations and polymorphisms Environmental factors : diet, exercise, sun exposure, ... +Age, Sex, Age-at-Menopause, Height, OA, etc.

Environmental influences can differ between populations ! Geographical distance: <100km HOLLAND BELGIUM Foto: Barbara Obermayer-Pietsch Foto: Stuart Ralston > 1100 mg/day < 500 mg/day Dietary Calcium intake

Genetic Architecture of Diseases/Traits : Study designs to identify “risk” alleles common, complex rare, monogenic Linkage Analysis in pedigrees big Effect Size Next-Generation High-Throughput small Sequencing Genome-Wide Association Study rare common Frequency Genetic Variant

Genome-Wide Association Study (GWAS) DNA collection : e.g. 1000 cases vs. 1000 controls DATA ANALYSIS (e.g., PLINK): Each dot is one SNP in, e.g, 2000 subjects AA AB BB Illumina Affymetrix AA→ SNP 1 14 18 X 1 2 3 4 5 6 7 8 10 12 BB→ SNP 2 AA Chromosomes AB→ SNP 3 . . BB . . Select SNPs Combine GWAS . . AB AB→ SNP 550,000 Replication - Effects per SNP are usually small - We are looking at common variants Meta-Analysis of all data

A “Dubai”plot: GWAS of human iris colour P < 1.10 -206 HERC2/OCA2 gene n = 5974 P - value (-log 10) 12 kb on Chr. 15q11 Chromosome / position Rotterdam Study: Kayser et al, Am J Hum Genet, 2008

A “Holland”plot: GWAS for BMD in the Rotterdam Study LUMBAR SPINE BMD 5 x 10 -8 • Rotterdam Study • ERF Study N=5,000 • Twins UK • deCODE Genetics Rivadeneira et al., Nat Genet., 2009 • Framingham Study

A real Manhattan plot: “height” in the GIANT consortium - 180,000 subjects - 180 loci identified - 10-15% variance explained 5 x 10 -8 Lango, Estrada, Rivadeneira et al., Nature, 2010

Grades of Evidence - Collaborative prospective meta-analysis of individual Very Good level data in consortia - Meta-analysis of published data - >2 large studies (n > 1000 each) - 1-3 smaller studies Not so Good - 1 small study (n<500)

EUROPE by prejudice.…….(according to USA) (From: Yanko Tsvetkov, alphadesigner.com)

The he GEFOS/GE OS/GENOM NOMOS OS con onsortium ortium Number of subjects: GENOMOS: >150,000 of which GWAS: 40,000 www.gefos.org = G GENOMOS study dy popul ulat ation on = i idem m + GWAS www.genomos.eu = i idem, m, under er negot otiati ation on / i in devel elopm opment ent

GEFOS HYPOTHESIS-FREE GWAS: AS SAMPLE SIZE INCREASES, GENOME-WIDE SIGNIFICANT SIGNALS BECOME GRADUALLY EVIDENT LUMBAR SPINE BMD 5 x 10 -8 • Rotterdam Study • ERF Study N=5,000 • Twins UK • deCODE Genetics Rivadeneira et al., Nat Genet., 2009 • Framingham Study

LUMBAR SPINE BMD LRP5 5 x 10 -8 • Rotterdam Study • ERF Study N=6,200 • Twins UK • deCODE Genetics Rivadeneira et al., Nat Genet., 2009 • Framingham Study

LUMBAR SPINE BMD LRP5 5 x 10 -8 • Rotterdam Study • ERF Study N=8,500 • Twins UK • deCODE Genetics • Framingham Study Rivadeneira et al., Nat Genet., 2009

LUMBAR SPINE BMD RANK-L C6ôrf10 OPG 1p36 LRP5 5 x 10 -8 MHC • Rotterdam Study • ERF Study N=15,000 • Twins UK • deCODE Genetics • Framingham Study Rivadeneira et al., Nat Genet., 2009

LUMBAR SPINE BMD RANK-L C6ôrf10 OPG 1p36 SP7 LRP5 5 x 10 -8 • Rotterdam Study • ERF Study N=19,125 • Twins UK • deCODE Genetics • Framingham Study Rivadeneira et al., Nat Genet., 2009

GWAS issues: *GWAS hits are just a start to find causal genes/variant(s) *Follow-up research per individual locus *GWAS creates new genome annotation/function/biology *Small effect size does NOT mean small biological relevance Willer et al., Nature Genetics, jan 2009: 145 authors

Published Genome-Wide Associations through 12/2012 P ublished GWA at p≤5X10 -8 for 17 trait categories As of 11/19/13, the catalog includes 1751 publications and 11,912 SNPs. With current GWAS efforts we have: *Genotyped only 0.3% of nucleotides in the human genome *Selected for “Universal/Cosmopolitan” variants *Explained 2-30% of genetic variance per disease (some exceptions) *not analysed many more phenotypes NHGRI GWA Catalog www.genome.gov/GWAStudies www.ebi.ac.uk/fgpt/gwas/

What are eQTLs?  e xpression Q uantitative T rait L oci  genomic variations that explain expression traits