Transcript The New Biology: From Science in the Modern World to the Genetics of Diabetes Gilbert S.

The New Biology: From Science in
the Modern World to the
Genetics of Diabetes
Gilbert S. Omenn, M.D., Ph.D.
University of Michigan, Ann Arbor, MI, USA
SuperCourse of Science Conference
6 January 2009
Bibliotheca Alexandrina, Egypt
A Call for Renewal of Science in
Muslim Countries
Our Muslim forefathers first held up the torch of
rationality, tolerance, and advancement of
knowledge throughout the Dark Ages of
medieval Europe. [astronomy, math, chemistry]
Ibn Al-Haytham (10th C) laid down rules for the
scientific method of observation, experiment,
and search for truth. Ibn Al-Nafis (13th C)
emphasized respect for contrarian views to be
tested with evidence. Then came Taqlid.
Science requires freedom to enquire, challenge,
think, and envision the unimagined.
--Ismail Serageldin, SCIENCE 8-08-08
Education is the most
powerful weapon which
you can use to change
the world.
Nelson Mandela
The Bibliotheca Alexandrina
A beacon and compass for science, education,
and peace in the Muslim world and the broader
developing world
An institution with a stunning legacy, magnificent
architecture, a splendid leader, fully digitalized
resources, and remarkable, diverse initiatives,
including—among many others---the
SuperCourse of Science.
A leading force for cooperation and collaboration
among equals between North and South.
Europe: Investing in Intelligence
“Research and innovation are the main
keys to Europe’s development. They are
also the most efficient way to respond to
the challenges set by Asia’s large
emerging economies and to lay the
foundation for sustainable development for
the entire planet.”
---Nicolas Sarkozy
14 May, 2008
FRONTIER SCIENCE AND GRAND
CHALLENGES: INVESTING IN HIGHPOTENTIAL INDIVIDUALS AND HIGHPAYOFF SCIENTIFIC FIELDS
Gilbert S. Omenn
University of Michigan
French Presidency of the EU
Symposium Celebrating Frontier Science
Paris, 7 October, 2008
Kudos to the EU on the Launch of the
Frontiers of Science Program
Investments in young scientists and their
individual investigator-initiated projects
Sufficient funding to make a difference
High standards
The “Ideas Program”, complementary to
the 7th Framework cooperative networks
Congratulations to those honored today
The rest of the world has noticed!
Grand Challenges for S&T and Society
1. Pursue the unknowns in each scientific
discipline from math to biology to
education.
2. Mobilize multidisciplinary research and
development for food security, energy,
health, green chemistry.
3. Combine S&T with political will and social
purpose to overcome poverty and hunger,
scarcity of water, and climate change, for
sustainable economic development.
--G.S. Omenn, SCIENCE 15 Dec 2006
Obama Statement on Science
Saturday December 13 announcement of
Presidential Science and Technology Adviser
John Holdren, Co-Chairs of President’s
Committee of Advisers on Science and
Technology (PCAST) genetics pioneers Harold
Varmus and Eric Lander, and ecologist Jane
Lubchenco
Affirmation of the importance of science
Commitment to integrity of review of scientific
issues—expect support for stem cell research,
teaching of evolution, and control of greenhouse
gases/climate change.
U.N. MILLENIUM DEVELOPMENT GOALS
These goals for peace, security, development, human rights
and fundamental freedoms (1990 to 2015) are peoplecentered, time-bound, and measurable.
1.
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3.
4.
5.
6.
7.
8.
Eradicate extreme poverty (<$1/day; 1 billion people)
and hunger--by 50%
Achieve universal primary education for boys and girls
Promote gender equality and empower women
Reduce child mortality rate before age 5 by 67%
Improve maternal health--reduce mortality ratio by 75%
Combat HIV/AIDS, malaria and other diseases---begin to
reverse incidence and spread
Ensure environmental sustainabiity--50% reduction in
those without safe drinking water
Develop a global partnership for development
GRAND CHALLENGES IN GLOBAL INFECTIOUS
DISEASES (7 Goals, 14 Challenges)—Gates Foundation
Improve childhood vaccines (3)
Create new vaccines (3)
Control insects that transmit agents of
disease (2)
Improve nutrition to promote health (1)
Improve drug treatment of infectious
diseases (1)
Cure latent and chronic infection (2)
Measure health status accurately and
economically (2)
It’s a New World in Life Sciences
New Biology---New Technology
Genome Expression Microarrays
Comparative Genomics, Epigenetics,
miRNA Gene Regulation
Proteomics, incl alternative splice isoforms
Bioinformatics
Systems Biology
Path to predictive, personalized,
preventive (P3) healthcare
Biology as an Information Science:
Historical Milestones
The molecule of inheritance is DNA, not protein: 1944
The Watson-Crick double-helix model of DNA permits
transcription and replication and mutations: 1953
46, not 48, human chromosomes: 1956
The triplet code for proteins demonstrated: 1960
The principle of “unity in diversity” applies to all living
things---at all levels from molecules to cells to organ
functions to ecosystems
Systems biology combines the digital code of genetics
with environmental and behavioral inputs and
perturbations (Leroy Hood)
Latest: Synthetic Biology (George Church)
The DNA Pioneers
The Historic Weekend of Feb 15-16, 2001
U.S. Leaders of the Human Genome Project
Eric Lander
J. Craig Venter and Francis Collins
Ari Patrinos
Protein
DNA
Avalanche of Genomic Information
The International HapMap Consortium aims to
genotype 1 million SNPs from 270 individuals.
Direct associations of individual SNP alleles with
disease phenotypes (including linkage
disequilibrium, LD) are more powerful than
linkage-based indirect association analyses.
dbSNP has >10 million validated SNPs.
Haplotype structures can be obtained via
genome-wide LD, haplotype blocks (1 KB to 1
MB), and haplotype-tagging SNPs, respecting
recombination hotspots and variable LD.
ESTIMATED COSTS OF GENOTYPING
When Human Genome sequence published in
2001, along with 10M common SNPs identified,
proposed case/control studies of 1000 + 1000
participants with 20B genotypes @ $0.50 had cost
estimate of $10B.
HapMap brought cost of 300,000 tagging SNPs @
$0.003 to $2M per common disease (5000x
decrease in 4 years).
Now we have even more powerful analyses with
“next-generation sequencing of the genome”
Computational muscle: “Skate where the puck is
gonna be” (Gretzky) in planning big studies
A Golden Age for the
Public Health Sciences
Sequencing and analyzing the human genome is
generating genetic information that must be linked
with information about:
• Nutrition and metabolism
• Lifestyle behaviors
• Diseases and medications
• Microbial, chemical, physical exposures
Every discipline of public health sciences needed.
NIH National Centers for Biomedical Computing
Physics-Based Simulation of
Biological Structures (SIMBIOS)
Russ Altman, PI
National Center for Integrative
Biomedical Informatics (NCIBI)
Brian D. Athey, PI
Informatics for Integrating
Biology and the Bedside (i2b2)
Isaac Kohane, PI
National Alliance for Medical
Imaging Computing (NA-MIC)
Ron Kikinis, PI
The National Center For
Biomedical Ontology (NCBO)
Mark Musen, PI
Multiscale Analysis of Genomic
and Cellular Networks (MAGNet)
Andrea Califano, PI
Center for Computational Biology
(CCB) Arthur Toga, PI
Multi- and Interdisciplinary Research will be
Required to Solve the “Puzzle” of Complex
Diseases and Conditions—such as Diabetes
Genes
Behavior
Diet/Nutrition
Infectious agents
Environment
Society
???
44,000 Faculty
3500 Universities
174 Countries
Supercourse Mirror Sites
42 Mirrored Sites,
MOH Egypt, Sudan, China, Mongolia, Russia
East-West Collaboration
A.Husseini (Birzeit University, West
Bank): “Diabetes in the Arab World”,
from the SuperCourse
P
Prevalence Estimates
of Diabetes in selected
r
Arab Countriese > 20 Years old in the Year 2025
Dev Countries/World/Tunisia/Oman/Saudi
Arabia/Egypt
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Genetics of Diabetes and Its
Complications: Layers of Complexity
Craig L. Hanis, Ph.D., University of Texas
at Houston; delivered at Univ Pittsburgh,
23 October, 2001
#1 ranked “Genetics and Diabetes” lecture
at www.pitt.edu/~super1/
Rising Interest in the Genetics of
Diabetes and Its Complications
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1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000
A Brief History of the Genetics
of Diabetes
Nightmare
Disequilibrium
Headache
Linkage
Heterogeneity
Complexity
Interactions
Complex Inheritance
Model Free Linkage Approaches
– Affected Pairs
Concordant Sib Pairs
Discordant Sib Pairs
Association Based Mapping
– Transmission Disequilibrium Testing
Parent - Offspring Trios (pairs)
– Traditional Associations
SNP-based mapping
Fine Mapping
Ultimately a search for association
of disease with single-nucleotide
polymorphisms (SNP)
Criteria for selecting samples
– Affected/Unaffected
– Segregating/Non-segregating
– Haplotype Determination
enhanced by pedigrees?
Type 2 Diabetes in 3 Ethnic Groups
P
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70%
60%
50%
40%
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20%
10%
0%
35-44
45-54
55-64
65-74
75+
Age Category
Pima Indians
Starr County
USPHS
Genome-Wide Association (GWA) Studies
GWA studies represent a systematic search with nucleic
acid probes (chips) for variants in the genome
statistically associated with particular diseases or traits.
“Next-generation sequencing” is replacing chip arrays.
Only 2% of the DNA codes for protein products, so few
of these variants actually occur in such coding genes,
but they may still influence regulation of gene function.
Tremendous investment and output past several years
has transformed the genetic side of molecular
epidemiology, but neglected non-genetic variables
Variants give clues to unsuspected genes and pathways
potentially involved in diseases like diabetes mellitus.
I focus rest of the lecture on genomics and diabetes, as a
bridge to the WHO course starting today on
Epidemiology of Diabetes.
First GWA Studies for T2DM
In 2007, five GWA studies were reported:
They replicated earlier evidence for three genome
variants: TCF7L2, PPARG, and KCNJ11.
They identified at least six additional variants in or
near these loci: SLC30A8, IGF2BP2, FTO,
HHEX-IDE, CDKAL1, CDKN2A-CDKN2B.
Only one (SLC30A8) is a likely functional variant at
the protein level.
Variants in FTO are associated also with body
mass index.
Interpretation of GWA Studies of
Type 2 Diabetes
These studies are unbiased by previous
hypotheses of predisposing genes
The results are limited by modest effects and
need for stringent statistical thresholds and very
large sample sizes.
The largest allelic OR for any established variant
is <= 1.35 for TCF7L2; at least nine others (now
about 20) have OR 1.1-1.2.
The aggregate attributable risk is <10 percent.
Meta-Analysis of GWA Data for
Susceptibility Loci for Type 2 Diabetes
[Zeggini et al, Nature Genetics 2008]
Common variants at multiple loci have modest
but reproducible association with risk of T2DM.
Three studies combined (DGI, FUSION,
WTCCC): 10,128 individuals of European
descent; 2.2 million SNPs genotyped/extended
with imputed SNPs from haplotype variation
Used both Affy 500K and Illumina 317K chips
Tried to replicate findings analysis for 11
variants with p<10-5 with 53,975 samples
Found at least six more previously unknown loci:
JAZF1, CDC123-CAMK1D, TSPAN8-LGR5,
THADA, ADAMTS9, NOTCH2. The first three
are probably associated with insulin release.
Complementary Strategy: GWA Studies of
Risk Factors for T2 Diabetes
[Mohlke et al, Hum Mol Genetics 2008]
Classic genetic epidemiology studies estimate
genetic effects explain 25% of variance for 20
measures of cardiovascular function, 51% for
five anthropologic measures, and 40%s for 38
blood tests, including cholesterol and
metabolism.
They reviewed GWA studies of >200,000 SNPs
that reported at least one SNP exceeding
statistical significance threshold of p<5x10-8 for
cholesterol and lipid levels, obesity, myocardial
infarction, or coronary heart disease.
Cholesterol, Lipoproteins, Lipids
and CRP [Mohlke et al, Hum Mol Genetics 2008]
Glucokinase regulator (GCKR) initially
associated with triglycerides
Then with HDL-C, LDL-C, TG and 11 additional
previously reported SNP variants and 7 new loci
SNPs near SORT1-PSRC1-CELSR2 loci were
associated with LDL-C; a SNP explained 5886% of the inter-individual variability in transcript
levels for these three neighboring genes.
7 variants are associated with C-reactive protein
levels, including CRP itself, APOE, leptin
receptor, and HNF1 homeobox A (HNF1A).
Fat Mass and Obesity Genes
A 2005 review cited 127 gene candidates and 253
quantitative trait loci reported from linkage studies
of obesity. Hardly any were confirmed.
In 2007 two independent GWA studies identified
obesity-associated variants in the first intron of the
FTO gene; now replicated many times. FTO
encodes a 2-oxoglutarate-dependent nucleic acid
demethylase whose relation to obesity or BMI is
not yet understood.
Informative Heterogeneity
The initial association of FTO with diabetes was
not replicated in several well-powered GWA
studies.
Whether or not FTO turns up in T2DM GWA
studies depends entirely on the inclusion criteria
for cases—if obese individuals are excluded, as
in the GWA studies above, FTO is not
associated; if they are included, FTO is
associated (indirectly) with T2DM.
Obesity and MC4R (chromosome 18q21)
Two recent large GWA studies for obesityrelated traits identified associated SNPs near the
melanocortin-4 receptor (MC4R) gene. This
receptor is a major target in drug development
for obesity. Mutations in MC4R can produce a
rare extreme form of childhood obesity.
BMI, insulin resistance, and waist circumference were associated with these variants 188
kb downstream of MC4R. What is actually
happening with these allelic substitutions is
unknown, but under investigation.
Together FTO and MC4R account for only 1.2
kg/m2 variation in BMI in adults.
Other Quantitative Metabolic Variables
For fasting glucose level, there are
common sequence variants in glucokinase
(GCK) promoter and in islet-specific
glucose-6-phosphatase, catalytic 2
(G6PC2).
Uric acid levels are associated with
variants at solute carrier/glucose
transporter SLC2A9.
Surprisingly, none for high blood pressure
or systolic or diastolic blood pressures.
Evidence for Association of T2DM with Several Traits on
Chromosome 9p21: SNPs in 10,128 GWA samples. Arrows =
locations of SNPs. Black bars = recombination hotspots.
Genes and transcripts at the bottom.
Stature/Height—Heritability >0.8
[Sanna et al and Lettre et al, Nature Genetics 2008]
Body mass index comprises height and weight
measures.
Several rare mutations are definitely associated with
height in Mendelian syndromes
Common variants in transcription factor HMGA2 are
associated with height in the general population.
GWA studies from Finland and Sardinia reveal an
association of osteoarthritis-associated locus GDF5UQCC---perhaps through bone growth [Sanna et al]
With six populations, 10 additional loci have now been
associated [Lettre et al], and the two above confirmed;
however, together they (and others) account for just 2
percent of population variation in height. They do expand
our ideas of biological regulation of height.
Classic Approach of Detecting
Large-Effect Rare Mutations
Three of the T2DM-associated variant loci were
actually discovered through analysis of the
heterogeneity of the disorder
Rare Mendelian mutants of KCNJ11, WFS1, and
HNF1B can cause diabetes, including MaturityOnset Diabetes of the Young. These variants
have been confirmed repeatedly by GWA.
Their potential pathways relevant to diabetes
biology are shown in next slide.
Rare or small-effect loci may still be clues to
underlying pathophysiology and targets to treat.
Copy-number variants are also missed in GWA
studies.
Processes involved in genetic predisposition to type 2 diabetes,
based on the best candidates within each signal and human
physiological studies. Most genes implicated in diabetes
susceptibility act through effects on beta-cell function or mass.
[McCarthy and Hattersly, 2008]
Resources to Keep up with Field
U.S. NIH (NCI-NHGRI) maintain an ongoing
catalog of published genome-wide association
studies
There are many databases of gene sequences
and variants, and protein variants to assist in
annotation of the potential biological roles of
variants in or near mapped genes.
Statistical compendia for tests and adjustments
for bias due to selection, misclassification, and
population stratification are established; see
McCarthy et al, Nature Reviews/Genetics 2008.
GWAS Graphical User Interface: graphical
browser [Chen et al, Bioinformatics 2008]
Special Challenges & Opportunities in
Muslim Countries
Nearly all GWA studies have been
performed on Causasians of European
origins.
It is very likely that different variants will be
important in African and Asian
populations, so population-based studies
of the kind recently initiated here for
cardiomyopathy would be expected to
yield interesting and useful findings.
General Challenges and Opportunities for
Diabetes Epidemiologists
This explosion of new findings about potential
genetic predispositions to Type 2 Diabetes, and
analogous findings for T1 Diabetes, explains
only a modest aggregate proportion of risk
explained by the genetic variants (<10%).
More and larger GWA and re-sequencing
studies will find more variants, probably of
smaller and smaller effect.
The big effects are almost surely to be found
among non-genetic variables (environmental,
behavioral, dietary), as in our early diagram--and in gene-environment interactions.
KEY COMPONENTS OF THE
VISION
An avalanche of genomic information: validated
SNPs, haplotype blocks, candidate genes/alleles,
proteins, & metabolites--associated with disease risk
Powerful computational methods
Effective linkages with better environmental and
behavioral datasets for eco-genetic analyses
Credible privacy and confidentiality protections
Breakthrough tests, vaccines, drugs, behaviors,
and regulatory actions to reduce health risks and
cost-effectively treat patients in the US and globally.
Getting Ahead of the Science:
Personalized Genomics
23andme.com is a company in California,
offering:
Disease Risks: premature—genome
variants associated with various diseases,
but very little of the attributable risk known
Ancestry testing: Good—Haplotypes tied
to population origins (Africa, Europe, Asia)
Geneology/family roots: Good, using Y
chromosome and mitochondrial DNA
Synthetic Biology, an Emerging Field
Interdisciplinary science and engineering to design and build
novel biological functions and systems to:
Gain insights into what makes life tick, constructing genetic
circuits to achieve what nature evolved over eons
Develop powerful biotechnologies by integrating biological
components, circuits and replicating organisms
Applications:
Engineered microorganisms that produce drugs
Biosensors for detecting abnormalities and diseases
Microorganisms that convert renewable resources into
energy carriers
Microorganisms to remediate hazardous material
contaminated sites—”environmental biotechnology”
Safety regimens will be critical.
Engineering Life: Building a FAB for Biology
The BIO FAB Group: David Baker, George
Church, Jim Collins, Drew Endy, Joseph
Jacobson, Jay Keasling, Paul Modrich, Christina
Smolke and Ron Weiss (Scientific American
2006)
BIOLOGICAL COMPONENTS are the basis of an
approach to biotechnology modeled on
electronics engineering.
Principles and practices learned from engineering
successes, especially standardization of parts
and automation of processes can help transform
biotechnology and “genetic engineering” from a
specialized craft into a mature industry.
Pierre Teilhard de Chardin
1881-1955
The future belongs to those who give the
next generation hope.
“There are those
who look at
things the way
they are, and
ask, why?...
I dream of
things that never
were, and ask,
why not?”
--Robert F.
Kennedy (1968)