Genetic discovery in a million people - where do we go from here? - UK Biobank
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Genetic discovery in a million people –
where do we go from here?
Cristen Willer, PhD
Associate Professor
Frank N. Wilson Professor of Cardiovascular Medicine
Department of Internal Medicine
Department of Human Genetics
Department of Computational Medicine and Bioinformatics
“The team, the team, the team”
- Bo Schembechler
Sarah Graham Jonas Nielsen
Brooke Wolford Wei Zhou
Ida Surakka Whitney Hornsby
Why do we study the genetics of human diseases and traits?
Why do we study the genetics of
human diseases and traits?
So people can live healthy,
active, long lives -- avoiding
premature death due to heart
disease
Hopefully, genes involved in the trait, identified through naturally occurring
variation in humans, can become leads for prevention and treatment.
Lastly, perhaps we can predict who would most benefit from preventive lifestyle
changes, medical screening, or treatment.
Genetics for improved treatment of disease
Biobank
Statistical rigor Clinical
trials
Enrollment
New
therapeutics
QC
PNPLA5
APOE
Experimental
model
systems
Biobanks amortize the cost of GWAS across traits Genotypes x phenotypes
HUNT GWAS of 1,400 traits (N~70k)
Low-pass genomes of 2,202
Combined with HRC reference
+
Imputed into ~70k HUNT study 1,400 diseases and
quantitative traits
~3 days in the cloud Built a results viewer
http://pheweb.sph.umich.edu:5003/pheno/594.1
Colorectal Cancer (N=4,562 cases)
BOLT-LMM
SAIGE Logistic
Saddlepoint
approximati
mixed
Scalable and Accurate model
on
Implementation of Sample
Unbalanced
case-control
relatedness
GEneralized mixed model SAIGE
ratio
Optimization
strategies
Large scale data
• SAIGE is implemented as an open-source R package available at
• https://github.com/weizhouUMICH/SAIGE/
• The GWAS results for 1,403 binary phenotypes (3 days) with the
PheCodes in UK Biobank using SAIGE are currently available:
• https://www.dropbox.com/sh/wuj4y8wsqjz78om/AAACfAJK54Ktvn
zSTAoaZTLma?dl=0
• Michigan PheWeb http://pheweb.sph.umich.edu/UKBiobank
Zhou et al., Nature Genetics, 2018 Shawn Lee & Wei Zhou
Atrial fibrillation – basic mechanisms
• Irregularities in heart beat
• 62% heritability in twin study (Christophersen, Circ Arrhythm Electrophysiol, 2009)
Jonas Nielsen
• As recently as 2010, arrhythmias were primarily thought to be caused by ion channel dysfunction
Meta-analysis for Atrial Fibrillation Nielsen et al., Nat Genet 2018
deCODE DiscovEHR/MyCODE MGI
European ancestry (USA) European ancestry (USA)
Statistical Genetics
Iceland
13,471 AF cases 6,679 AF cases 1,226 AF cases 60,620 AF
358,161 controls 41,803 controls 11,049 controls cases
HUNT-MI UK biobank AFGen 970,216
Norway European Mostly European controls
6,493 AF cases 14,820 AF cases 17,931 AF cases
63,142 controls 380,919 controls 115,142 controls
163 independent risk variants at 111 loci
Prioritized 163 functional candidate genes likely to be involved in AFAtrial fibrillation GWAS
(111 loci, 80 novel loci)
PITX2
7x10-443
Nielsen et al., Nat Genet 2018Candidate functional genes
by biological function
Cardiac and Skeletal Muscle Function
TFs cardiac development
AKAP6, COL25A, CFL2, DPT, MYH6,
EPHA3, GTF2I, HAND2, NAV2, NKX2-5,
MYH7, MYO18B, MYO1C, MYOCD, MYOT,
PITX2, SLIT3, SOX15, SOX5, TBX5, TGFB3
MYOZ1, MYPN, PKP2, RBM20, SGCA,
SSPN, SYNPO2L, TTN, TTN-AS, WIPF1
Intracellular calcium handling in heart Cardiac ion channels
CALU, CAMK2D, CASQ2, PLN, S100A7A GRIK4, KCNC2, KCND3, KCNH2, KCNJ5,
KCNN2, KCNN3, SCN10A, SCN5A, SLC9B1
Angiogenesis Hormone signaling
TNFSF12, TNFSF12-TNFSF13 ESR2, IGF1R, JMJD1C, NR3C1, THRB1
Congenital heart defects
MYH6, NKX2-5, PITX2, TBC1D32, TBX5
Nielsen et al., Nat Genet 2018PheWAS demonstrates phenotypes correlated with AF
Turn genetic association into biology
RoadMap Epigenomics and ENCODE have catalogued regions of open chromatin for
many tissue types
Jonas NielsenPathways enriched for AF genes include
failure of heart looping and abnormal
heart development
Results from DEPICT Nielsen et al., Nat Genet 2018AF associated variants show enrichment in regions of
open chromatin in fetal heart tissue
Analyses performed using GREGOR and GARFIELD Nielsen et al., Nat Genet 2018AF Association at the MYH6/MYH7 locus
rs422068, intronic to MYH6Rabbit hearts with mechanically induced HF demonstrate arrhythmia and increased expression of MYH7 Todd Herron, José Jalife
Life-time risk of AF based on genetic risk score
Nielsen et al., AJHG, 2018Biobanks allow us to study many phenotypes
157 loci for estimated
Glomerular Filtration
Genotypes x Rate (eGFR)
phenotypes Rare LOF indel 53 new loci
42% risk of fracture to carriers Sex-specific effects
(32% risk for BMD < 2 SD)
Thyroid Stimulating
Hormone
66 loci – 28 novel
Pleiotropy
12 Liver-related blood traits
89 coding variants (11 LoF)
17 have impact > 1 SDGlobal Lipids Genetics Consortium
22Goal: longer, healthier lives (prevent premature death)
Where do I think the future lies?
1. Genetic discovery
• Dual purposes: pharmaceutical targets and identifying high-risk
individuals
• Focus on coding variation has proven useful
2. Functional fine-mapping by encouraging collaboration between
genetics discovery and molecular biology (iPSC, animal models,
GTEx, single-cell)
3. Prediction/prevention
4. Return results to participants (large effect variants & polygenic
risk scores) to improve uptake of lifestyle intervention (?)How to make UK biobank even more useful! • Quantify accuracy of any unusual measurements to standard clinical tests (i.e. blood lipid levels, eGFR, heel estimated bone mineral density, etc.) • Self-reported family history information for more phenotypes • Specialty clinic ascertainment (cardiac surgery, for example, for bicuspid aortic valve) • Imputation of more variants from TOPMed • Full exome or genome sequencing (and reference sequences for imputation into other cohorts)
Acknowledgements
HUNT-MI Working Team DiscovEHR/MyCode Functional collaborators
Kristian Hveem Jonas Nielsen Regeneron Y. Eugene Chen
Lars Fritsche Wei Zhou Tanya Teslovich Bo Yang
Oddgeir Holmen Sarah Graham Aris Baras Todd Herron
Maiken Gabrielsen Ida Surakka Shane McCarthy Pepe José Jalife
Anne Heidi Skogholt Brooke Wolford
Hyun Min Kang Geisinger GLGC
MGI Shawn Lee David Carey Sek Kathiresan
Chad Brummett Mike Boehnke
Michael Mathis deCODE Rabbit Model Gina Peloso
Sachin Kheterpal Kari Stefansson Todd Herron Pradeep Natajaran
Daniel Gudbjartsson Jose Jalife All the GLGC cohorts
AFGen Consortium Hilma Holm
Patrick Ellinor Rosa Thorolfsdottir
25
cristen@umich.eduA “proxy case” is an unaffected first or second
degree relative of a case
F=1 F=0 F=0.5 F=0
&
A & C
F=1 F=0 F=1 F=0.5 F=0
& & &
B D
Brooke
Liu & Pickrell, Nat Genet, 2017 “GWAS by proxy” Wolford 26Unaffected relatives of cases have
intermediate risk allele frequency
27Unaffected relatives of cases have
intermediate risk allele frequency
28Power improves after modeling
unaffected
1,000 cases
simulations at MAF 0.1
Power
A - Standard GWAS 1.00
B - Exclude unaffected
Power at alpha=5e−8
0.75
scheme
relatives of cases A
0.50 B
C
C – Exclude cases (GWAX) D
0.25
D – Model unaffected
0.00
relatives of cases with 0.5 1.0 1.1 1.2 1.3
liability Odds Ratio
29BOLT-LMM: Linear Mixed Model
Venous thromboembolism
(VTE)
• 2,325 Cases
• 65,294 Controls
• Case: Control = 0.036
λall = 1.047
GMMAT: Logistic Mixed Model
λall = 1.015
SPA-GMMAT: Logistic Mixed Model + SPA tests
λall = 1.015
30Simultaneous consideration of lipid-lowering variants that are protective against liver disease
Figure 1. ZNF529 silencing induces LDLR expression and LDL-C uptake.
LoF carriers and bone mineral density
Frameshift indel in MEPE
0.8% frequency in Norway
Impact ↓ -0.5 SD on BMD
↑ Fracture risk
OR 1.4 – 1.8 (p ~ 10-5)
Carrier fracture risk: 42%
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