1 / 58

UM BDSI June 21, 2019

UM BDSI June 21, 2019. Early years. Born May 16, 1956 in Eugene, Oregon Father George : printer, small business owner Mother Chari : homemaker, volunteer Parents active in community (Y, Active Club , Rotary , Blood Bank , Republican Party, …)

vito
Télécharger la présentation

UM BDSI June 21, 2019

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. UM BDSI June 21, 2019

  2. Early years • Born May 16, 1956 in Eugene, Oregon • Father George: printer, small business owner • Mother Chari: homemaker, volunteer • Parents active in community (Y, Active Club , Rotary, Blood Bank, Republican Party, …) • Siblings: Richard (1958-88), Barb (1961-present)

  3. College • U Oregon Honors College, Math, 1973-77 • Broad liberal arts education: lots of mostly pure math, physical sciences, but also History of Ideas, Europe since 1789, Novels of Kurt Vonnegut, … • Plan A: Math major obvious choice, but by junior year of college: what will I do after I finish college?? • Plan B: Law school at Stanford, UC Berkeley, Oregon? • Two critical pieces of advice (10 minute conversations): • apply for Fulbright scholarship (Ed Diller) • study quantitative biology (Bill Bradshaw) Mike: I haven't had biology since 7th grade. Bill: Who cares?

  4. Postgraduate year in Germany 1977-78 • Goethe Institute in Blaubeuren • Albert-Ludwigs-Universitӓt Freiburg • Analysis, topology, American history, … • No real evaluations, lots of travel • Pan C: decided who care's? • GREs in Heidelberg, October 1977 • Read Keeton: "Biological Science" and discovered genetics (and ecology, epidemiology, …) • Considered UCLA and U Washington; chose UCLA

  5. Graduate school at UCLA 1978-1983 • Biomathematics: see also R Little, J Taylor, T Johnson • Advisors: Ken Lange and Anne Spence • Academic siblings: Neil Risch, Dan Weeks • Academic grandfather: Robert Elston • Key event: Henry Tuckwell visited and started a journal club, said I should give the first talk; yikes!

  6. Why statistical genetics? • First term at UCLA took computational statistics with Bob Jennrich; needed project • Ken had just gotten first NIH grant; offered to pay me to write first version of MENDEL • Good project, added to $325/month NIH traineeship • Liked Ken, liked project • Chose statistics to do genetics with Ken and because I liked probability and computing

  7. Summer internship at Cedars-Sinai • Ken went to Boston on sabbatical • Anne Spence helped find me a summer job with with Richard Gatti at Cedars-Sinai hospital • Developed method to classify HLA-D genotypes (now recognized as multigene family!), wrote my first first-author paper (with lots of help from Dick) • Played (Colossal Cave) Adventure: "You are standing at the end of a road before a small brick building. Around you is a forest. A small stream flows out of the building and down a gully…"

  8. Graduate school not always just about learning • The most important events of my life occurred May 10 to September 21, 1980 • Henry's advice: don't get married (some advice should not be taken!) • Ken's advice: any time you go anywhere (both of you) will visit and give talks: Salt Lake City, Cincinnati, Indianapolis, Berkeley, Portland, Ann Arbor

  9. While in graduate school at UCLA • Lots of coursework • Qualifying exams in biomathematics, genetics • RA/programmer; TA for population genetics • Learned to collaborate, write, present (better) • Platform presentations at ASHG in 1981, 1982 • 9 papers (4 first author), 7 with Ken • Postdoc offers from Mary Claire King, Jerry Rotter, Michael Conneally, Mark Skolnick

  10. Choosing Michigan • ASHG in 1982 in Detroit • Ken arranged for Betsy and me to give talks at U-M • Came back in April 1983 for actual job interviews • Choose Michigan over NIH, postdocs • Reasons: • best sum of two jobs in the same place • great university, outstanding human genetics • strong welcome from Biostatistics and other faculty: Charlie Sing, Pat Peyser • felt at home; a place if things went well we might stay a few years (>35 years later )

  11. Early years at Michigan • Started January 1, 1984 • Coldest winter in 50 years; -21oF 3 weeks after arrival • 12th faculty member in department • First two years summer months supported by Charlie • Daily faculty lunches helped with easy integration into department, SPH, U-M • Morton Brown, Pat Peyserimmediate mentors; Morton and Raya Brown made us part of their family

  12. Early years at Michigan • Established methods research independence (without Ken) • Learned to do applied research (most people call it science): Alzheimer disease, neurofibromatosis, breast cancer, lipids, blood pressure, human mutation • Learned to teach: in first 3½ years at U-M, I taught 11 courses, 7 of them different • 601 (now 602): Statistical inference • 666: Statistical models and numerical methods in human genetics • 500/503 (now 501): Introduction to biostatistics • 680: Stochastic processes • 511: Statistical computer packages (OJOC) • 580: Mathematical modeling in clinical research (OJOC)

  13. Early years at Michigan: our three sons • David Foxman Boehnke, Sept 23, 1985 • Kevin Foxman Boehnke, April 23, 1987 • Richard Foxman Boehnke, July 2, 1989 • "You know Mike, three kids is a lot more work than two." Galen Shorack, April 1989 (TIMELY mentoring is valuable)

  14. First key methods paper on my own Basis for NIH grant HG000376 in 1988; start of continuous methods funding for >30 years Basis for Lynn Ploughman dissertation, my first PhD student

  15. Other early methods research 1986-1994 • Power of linkage studies for complex traits (with Lynn Ploughman) • Optimal design for human family studies (5 papers) • Statistical methods for radiation hybrid mapping: • 8 methods papers (+6 science papers) • focus of Kathryn Lunettadissertation, a paper in Heather Stringham's dissertation • 1992 paper (with Ken) won Snedecoraward • Allele frequency estimation in pedigrees • Limits of resolution of genetic linkage studies

  16. Getting started in human gene mapping GENOMICS 1, 361-363 (1987) Linkage Analysis of von Recklinghausen Neurofibromatosis to DNA Markers on Chromosome 17 S.R.DIEHL, M. BOEHNKE, R.P.ERICKSON, A.B.BAXTER, M.A.BRUCE, J.L.LIEBERMAN, D.J.PLATT, L.M.PLOUGHMAN, K.A.SEILER, A.M.SWEET, AND F.S.COLLINS best collaborators ----------- “Whoever has the most toys wins” 1980s bumper sticker Francis Collins moved to U-M in 1984, same year I did

  17. IBD5 NOD2 PPAR KCNJ11 CTLA4 PTPN22 2000 2001 2002 2003 2004 HMGA2 GDF5-UQCC HMPG JAZF1 CDC123 ADAMTS9 THADA WSF1 LOXL1 IL7R TRAF1/C5 STAT4 ABCG8 GALNT2 PSRC1 NCAN TBL2 TRIB1 KCTD10 ANGLPT3 GRIN3A MEIS1 LBXCOR1 BTBD9 C3 8q24 ORMDL3 4q25 TCF2 GCKR FTO C12orf30 ERBB3 KIAA0350 CD226 16p13 PTPN2 SH2B3 FGFR2 TNRC9 MAP3K1 LSP1 8q24 CDKN2B/A 8q24 #2 8q24 #3 8q24 #4 8q24 #5 8q24 #6 ATG16L1 5p13 10q21 IRGMNKX2-3 IL12B 3p21 1q24 PTPN2 TCF2 CDKN2B/A IGF2BP2 CDKAL1 HHEX SLC30A8 Early (lack of) progress in mapping genes for complex traits Cholesterol Obesity Myocardial infarction QT interval Atrial fibrilliation Type 2 diabetes Prostate cancer Breast cancer Colon cancer Height Age related macular degeneration Crohn’s disease Type 1 diabetes Systemic lupus erythematosus Asthma Restless leg syndrome Gallstone disease Multiple sclerosis Rheumatoid arthritis Glaucoma NOS1AP IFIH1 PCSK9 CFB/C2 LOC387715 8q24 IL23R TCF7L2 CD25 IRF5 PCSK9 CFH 2005 2006 2007 Slide courtesy of David Altshuler

  18. FUSION: Finland-United States Investigation of NIDDM Genetics NHGRI, Bethesda, Francis Collins Cedars Sinai, Los Angeles, Richard Bergman KTL, Helsinki, Jaakko Tuomilehto U Michigan, Michael Boehnke Soumitra Ghosh (postdoc), Beth Hauser (student) 19

  19. FUSION: Finland-United States Investigation of NIDDM Genetics NHGRI, Bethesda, Francis Collins Cedars Sinai, Los Angeles, Richard Bergman KTL, Helsinki, Jaakko Tuomilehto U North Carolina, Karen Mohlke U Eastern Finland, Markku Laakso U Michigan, Michael Boehnke and Laura Scott 20

  20. FUSION study goals Identify genetic variants that predispose to type 2 diabetes (T2D) or are responsible for variability in T2D-related traits

  21. FUSION ASP families for linkage analysis FUSION started in 1990s as a T2D family study Sampled >5000 individuals from >800 families with ≥2 affected siblings Obtained extensive phenotype information Genotyped participants at ~400 microsatellite markers at CIDR

  22. FUSION T2D linkage studies S Ghosh Chromosome 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 X FUSION 1 LOD K Silander FUSION 2 LOD FUSION 1+2 LOD 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 X Ghosh et al. Am J Hum Genet 67:1174, 2000; Silander et al. Diabetes 53:821, 2004

  23. International T2D Linkage Analysis Consortium Approach: combine linkage data across studies >6000 families from ~20 studies Guan et al. Human Heredity 2008 N Cox P Froguel B Mitchell W Guan D Burns A Pluzhnikov S Elbein LOD 24 24 24

  24. “If you’re going through hell, keep going.” Winston Churchill

  25. The Future of Genetic Studies of Complex Human Diseases Neil Risch and Kathleen Merikangas SCIENCE  VOL. 273  13 SEPTEMBER 1996

  26. Genome-Wide Association Study (GWAS) • Risch and MerikangasScience 1996 • Sample individuals with and without disease (e.g. “cases” with T2D, “controls” without T2D) • Genotype individuals for 100,000s of single nucleotide polymorphisms (SNPs) across the genome • Test for disease-variant association • Identify variants statistically associated with disease, suggesting disease gene nearby • Anonymous reviewer: “science fiction”

  27. Drop in genotypecosts Cost per genotype $1.00 ABI TaqMan Sequenom ABI SNPlex $0.10 Illumina Golden Gate Affymetrix MegAllele Affymetrix 10K Perlegen $0.01 Affymetrix 100K/500K-1M Illumina Infinium-1-5M # of SNPs 102 105 1 10 103 104 106 2010 2001 Slide courtesy of Stephen Chanock

  28. Drop in genotypecosts Cost per genotype Now: $50 for 1 million SNPs, < $10-4 per genotype $1.00 ABI TaqMan Sequenom ABI SNPlex $0.10 Illumina Golden Gate Affymetrix MegAllele Affymetrix 10K Perlegen $0.01 Affymetrix 100K/500K-1M Illumina Infinium-1-5M # of SNPs 102 105 1 10 103 104 106 2010 2001 Slide courtesy of Stephen Chanock

  29. When manna falls from heaven, say thank you. And take advantage of it! Be opportunistic

  30. FUSION a grant, not a contract • Grant renewal obtained in 2003 • Specific aims: fine map several T2D linkage peaks

  31. FUSION is a grant, not a contract • Grant renewal obtained in 2003 • Specific aims: fine map several T2D linkage peaks • Ignored aims, instead carried out one of the first GWAS (for T2D) • First GWAS to use genotype imputation • First GWAS meta-analysis • Developed methods for efficient two-stage designs Laura Scott Yun Li Andrew SkolGonçaloAbecasis

  32. Joint analysis is more powerful than replication-based analysis Skol, Scott, Abecasis, Boehnke (2006) Nature Genetics 38:209-213 One-stage power M = 300,000 SNPs genotyped on 1000 cases, 1000 controls Multiplicative model, prevalence 10%, GRR = 1.4

  33. FUSION stage 1 GWAS 1161 Finnish T2D cases + 1174 Finnish NGT controls -log10(p-value) -log10(p-value) Logistic regression, additive genetic model

  34. TCF7L2 PPARG KCNJ11 FUSION stage 1 GWAS: known positives 1161 Finnish T2D cases + 1174 Finnish NGT controls -log10(p-value) Logistic regression, additive genetic model

  35. Three-study collaboration D Altshuler • FUSION: Finnish cases and controls • Diabetes Genetic Initiative (DGI): Finnish, Swedish cases and controls • UKT2D: UK cases, controls • Problem: FUSION genotyped Illumina 317K chip; DGI, UK Affymetrix 500K chip • Combine results across studies with different SNP sets by genotype imputation (Li, Abecasis et al.) M McCarthy Y Li G Abecasis

  36. FUSION, DGI, UK cases + controls FUSION 1: 1161 + 1174 2: 1215 + 1258 DGI 1: 1464 + 1467 2: 5065 + 5785 WTCCC/UKT2DGC 1: 1924 + 2938 2: 3757 + 5346 TOTAL 1: 4549 + 5579 2: 10037+12389 Sweden United States (off map) Poland 37

  37. E Zeggini R Saxena Science April2007 38 38 L Scott >2800 citations to date

  38. #5 – New Diabetes Genes Having a parent with type 2 diabetes ups your odds of developing the disease, but why do some sibs get it and others don’t? The answer lies somewhere in your genetic code, and this year brought scientific sleuths closer to cracking it. Research teams from the United States and Finland uncovered four new genetic variants linked to an increased risk of diabetes, which afflicts about 170 million people worldwide. Combined with the six variants scientists had discovered previously, it brings the total to ten. Eventually, these discoveries will aid experts in pinpointing those at greatest risk for developing type 2 diabetes.

  39. http://www.sciencemag.org/cgi/ content/full/318/5858/1842

  40. Larger n, more accurate imputation, multiple ancestries → more T2D loci ANKRD55 ANK1 TLE1 ZMIZ1 KLHDC5 BCAR1 MC4R CILP2 GIPR CCND2 LAMA1 BCL2 GATAD2A TMEM163 RBM43-RND3 FAF1 LPP TMEM154 ARL15 SSR1-RREB1 POU5F1-TCF19 MPHOSPH9 PAM PDX1 MACF1 COBLL1 DNER MIR129-LEP GPSM1 GRK5 SGCG RASGRP1 SLC16A13 FAM58A BCL11A ZBED3 KLF14 TP53INP1 TLE4 CENTD2 HMGA2 HNF1A ZFAND6 PRC1 DUSP9 SRR UBE2E2 RBMS1 PTPRD SPRY2 C2CD4/B MTNR1B GCK DGKB GCKR ADCY5 PROX1 GRB14 ST6GAL1 VPS26A HMG20A AP3S2 HNF4A MAEA GLIS3 GCC1-PAX4 PSMD6 ZFAND3 PEPD KCNK16 JAZF1 CDC123/CAMK1D TSPAN8/LGR5 THADA ADAMTS9 NOTCH2 KCNQ1 IGF2BP2 CDKAL1 CKDN2A/B FTO HNF1B WFS1 PPARG SLC30A8 HHEX TCF7L2 KCNJ11 DUSP8 IRS1

  41. Larger n, more accurate imputation, multiple ancestries → more T2D loci Now >450 ANKRD55 ANK1 TLE1 ZMIZ1 KLHDC5 BCAR1 MC4R CILP2 GIPR CCND2 LAMA1 BCL2 GATAD2A TMEM163 RBM43-RND3 Now 147K cases, 948K controls, 5 ancestries FAF1 LPP TMEM154 ARL15 SSR1-RREB1 POU5F1-TCF19 MPHOSPH9 PAM PDX1 MACF1 COBLL1 DNER MIR129-LEP GPSM1 GRK5 SGCG RASGRP1 SLC16A13 FAM58A BCL11A ZBED3 KLF14 TP53INP1 TLE4 CENTD2 HMGA2 HNF1A ZFAND6 PRC1 DUSP9 SRR UBE2E2 RBMS1 PTPRD SPRY2 C2CD4/B MTNR1B GCK DGKB GCKR ADCY5 PROX1 GRB14 ST6GAL1 VPS26A HMG20A AP3S2 HNF4A MAEA GLIS3 GCC1-PAX4 PSMD6 ZFAND3 PEPD KCNK16 JAZF1 CDC123/CAMK1D TSPAN8/LGR5 THADA ADAMTS9 NOTCH2 KCNQ1 IGF2BP2 CDKAL1 CKDN2A/B FTO HNF1B WFS1 PPARG SLC30A8 HHEX TCF7L2 KCNJ11 DUSP8 IRS1

  42. Imputation accuracy: EuropeansComplete Genomics sequence data as truth Expect similar benefits for African Americans, Hispanics.

  43. Testing for association with T2D-related traits • Routinely test for association with T2D-associated traits: glucose, insulin; BMI, WHR, height; lipids; … • Given phenotypes/genotypes, no additional experiments required • Our GWAS meta-analysis consortia (MAGIC, GIANT, Global Lipids, …) have identified >1000 loci for these traits; many papers in Nature, Nature Genetics, …

  44. Next step: sequencing • Impressive successes with array-based GWAS • Now also genome and exome sequencing • Allows near-complete assay of • full allele-frequency spectrum • all classes of genetic variation • Why haven’t we been doing this all along?

  45. T2D-GENES/AMP-T2D 45K exomes • Published association results for 13K exomes from 5 ancestry groups (Fuchsberger et al. Nature 2016) • Now expanded analysis to >45K exomes as C Fuchsberger part of Accelerating Medicines Partnership project • Testing for T2D association: single-variant, gene-based, gene sets (Flannick et al. Nature 2019) J Flannick

  46. Finnish exome sequencing FinMetSeq: N~20KLocke et al. Nature, in press • METSIM • Population based study of 10,197 Kuopio men • Ages 45-70 years at baseline • Baseline study in 2005-2010, follow-up ongoing • FINRISK • Health surveys every five years since 1972 • Sample sizes 6,000-12,000 individuals • Ages 25-74 • Follow-up in 25K Finns with GWAS data • Results • 478 novel associations at 126 loci for 64 traits • Many associated variants enriched in Finns • Some effect sizes large • E.g. AF=.004 variant in THBSP4 associated with 6kg decrease in weight, 35x enriched in Finns Kuopio K Meltz-Steinberg A Locke

More Related