Transcript Document 7214863

Genetics for Epidemiologists
National Human
Genome Research
Institute
National
Institutes of
Health
U.S. Department
of Health and
Human Services
Lecture 2: Measurement of Genetic
Exposures
U.S. Department of Health and Human Services
National Institutes of Health
National Human Genome Research Institute
Teri A. Manolio, M.D., Ph.D.
Director, Office of Population Genomics and
Senior Advisor to the Director, NHGRI,
for Population Genomics
Topics to be Covered
• Measuring genetic variation
– Blood group markers
– Restriction-fragment length polymorphisms
– Variable number of tandem repeats (VNTRs,
minisatellites and microsatellites)
– Single nucleotide polymorphisms (SNPs)
• Linkage disequilibrium (LD)
• Familial resemblance and family history
Larson, G. The Complete Far Side. 2003.
Measuring Genetic Variation:
Blood Group and Enzymatic Markers
• RBC COMT activity measured in 5 large families with
hypertension (total 518 individuals)
• Associations tested with 25 genetic markers: ABO, Rh, K,
MNS, P, Fy, Jk, PGD, ADA, ACP1, PGM1, HBB, GPT,
C3, HPA, TF, GC, OR, GM, KM, BF, ESD, GLO1, Le
• Lod score of 1.27 and estimated recombination fraction of
0.1 found for phosphogluconate dehydrogenase (PGD)
Am J Med Genet 1984; 19:525-32.
Restriction Fragment Length Polymorphisms
(RFLPs)
• Define polymorphic marker loci that can be
detected as differences in length of DNA
fragments after digestion with DNA sequencespecific endonucleases
• Establish linkage relationships using pedigree
analysis
Am J Hum Genet 1980; 32:314-331.
Restriction Fragment Length Polymorphisms
(RFLPs)
Since the RFLPs are being used simply as genetic
markers, any trait… segregating in a pedigree can be
mapped. Such a procedure would not require any
knowledge of the biochemical nature of the trait or of
the nature of the alterations in the DNA responsible for
the trait.
Am J Hum Genet 1980; 32:314-331.
RFLPs Used to Map Neurofibromatosis
Linkage analysis of 15 Utah kindreds showed
that a gene responsible for von Recklinghausen
neurofibromatosis (NF) is located near the
centromere on chromosome 17
Science 1987; 236:1100-1102.
RFLPs Used to Map Neurofibromatosis
Cosegration of NF with the A2 (1.9 kb) allele and
not A1 (2.4kb) in each of four affected offspring.
Science 1987; 236:1100-1102.
Variable Numbers of Tandem Repeats
(VNTRs): Minisatellites
• Repetition in tandem of a short (6- to 100-bp)
motif spanning 0.5 kb to several kb
– Opened the way to DNA fingerprinting for
individual identification
– Provided the first highly polymorphic,
multiallelic markers for linkage studies
– Associated with many interesting features of
human genome biology and evolution
• Well-known minisatellite is 5.5kb, kringle IV
repeat in apolipoprotein(a) and plasminogen
Vernaud G and Denoued F, Genome Res 2000; 10:899-907.
Kringle-IV Encoding Sequences of Human
apo(a) cDNA ApoA1 Alleles
Lackner et al, Hum Mol Genet 1993; 2:933-40.
Correlations of ApoA Molecular Weight with Lp(a)
Levels and Number of Kringle-IV Repeats
Gavish et al, J Clin Invest 1989; 84:2021-27.
Simple Sequence Repeats (also “VNTRs”):
Microsatellites
Repetition in tandem of a short (2- to 6-bp) motif
from 5-5,000 times
• Most are di-, tri-, and tetra-nucleotide repeats
repeated 20-50 times
• Most are highly polymorphic making them
enormously useful for mapping and linkage
• Marshfield and similar maps placed ~400
microsatellites across genome, provided
primers for analysis
• Could be highly automated: NHLBI and CIDR
large-scale genotyping services
Multipoint LOD Scores for Long-term SBP
and DBP on Chromosome 17
Levy et al, Hypertension 2000;36:477-483.
Larson, G. The Complete Far Side. 2003.
Single Nucleotide Polymorphisms (SNPs)
GAAATAATTAATGTTTTCCTTCCTTCTCCTATTTTGTCCTTTACTTCAATTTATTTATTTATTATTAATATTATTATTTTTTGAG
ACGGAGTTTC/ACTCTTGTTGCCAACCTGGAGTGCAGTGGCGTGATCTCAGCTCACTGCACACTCCGCTTTCCTGGTT
TCAAGCGATTCTCCTGCCTCAGCCTCCTGAGTAGCTGGGACTACAGTCACACACCACCACGCCCGGCTAATTTTTGTA
TTTTTAGTAGAGTTGGGGTTTCACCATGTTGGCCAGACTGGTCTCGAACTCCTGACCTTGTGATCCGCCAGCCTCTGC
CTCCCAAAGAGCTGGGATTACAGGCGTGAGCCACCGCGCTCGGCCCTTTGCATCAATTTCTACAGCTTGTTTTCTTTG
CCTGGACTTTACAAGTCTTACCTTGTTCTGCC/TTCAGATATTTGTGTGGTCTCATTCTGGTGTGCCAGTAGCTAAAAAT
CCATGATTTGCTCTCATCCCACTCCTGTTGTTCATCTCCTCTTATCTGGGGTCACA/CTATCTCTTCGTGATTGCATTCT
GATCCCCAGTACTTAGCATGTGCGTAACAACTCTGCCTCTGCTTTCCCAGGCTGTTGATGGGGTGCTGTTCATGCCTCA
GAAAAATGCATTGTAAGTTAAATTATTAAAGATTTTAAATATAGGAAAAAAGTAAGCAAACATAAGGAACAAAAAGGAA
AGAACATGTATTCTAATCCATTATTTATTATACAATTAAGAAATTTGGAAACTTTAGATTACACTGCTTTTAGAGATGGAGA
TGTAGTAAGTCTTTTACTCTTTACAAAATACATGTGTTAGCAATTTTGGGAAGAATAGTAACTCACCCGAACAGT G/TAA
TGTGAATATGTCACTTACTAGAGGAAAGAAGGCACTTGAAAAACATCTCTAAACCGTATAAAAACAATTACATCATAATG
ATGAAAACCCAAGGAATTTTTTTAGAAAACATTACCAGGGCTAATAACAAAGTAGAGCCACATGTCATTTATCTTCCCTT
TGTGTCTGTGTGAGAATTCTAGAGTTATATTTGTACATAGCATGGAAAAATGAGAGGCTAGTTTATCAACTAGTTCATTTT
TAAAAGTCTAACACATCCTAGGTATAGGTGAACTGTCCTCCTGCCAATGTATTGCACATTTGTGCCCAGATCCAGCATA
GGGTATGTTTGCCATTTACAAACGTTTATGTCTTAAGAGAGGAAATATGAAGAGCAAAACAGTGCATGCTGGAGAGAG
AAAGCTGATACAAATATAAAT/GAAACAATAATTGGAAAAATTGAGAAACTACTCATTTTCTAAATTACTCATGTATTTTC
CTAGAATTTAAGTCTTTTAATTTTTGATAAATCCCAATGTGAGACAAGATAAGTATTAGTGATGGTATGAGTAATTAATATC
TGTTATATAATATTCATTTTCATAGTGGAAGAAATAAAATAAAGGTTGTGATGATTGTTGATTATTTTTTCTAGAGGGGTTG
TCAGGGAAAGAAATTGCTTTTT
SNPs 1 / 300 bases
~ 10 million across genome
Mapping the Relationships Among SNPs
Christensen and Murray, N Engl J Med 2007; 356:1094-1097.
Chromosome 9p21 Region Associated with MI
Samani N et al, N Engl J Med 2007; 357:443-453.
Distances Among East Coast Cities
Boston
Providence
Providence
New
York
Philadelphia
Baltimore
59
New York
210
152
Philadelphia
320
237
86
Baltimore
430
325
173
87
Washington
450
358
206
120
34
Distances Among East Coast Cities
Boston
Providence
Providence
New
York
Philadelphia
59
New York
210
152
Philadelphia
320
237
86
Baltimore
430
325
173
87
Washington
450
358
206
120
< 100
Baltimore
101-200
201-300
301-400
> 400
34
Distances Among East Coast Cities
Boston
Providence
Providence
New
York
Philadelphia
59
New York
210
152
Philadelphia
320
237
86
Baltimore
430
325
173
87
Washington
450
358
206
120
< 100
Baltimore
101-200
201-300
301-400
> 400
34
Distances Among East Coast Cities
Distances Among East Coast Cities
Boston
Providence
New
York
Philadelphia
Baltimore
Washington
One Tag SNP May Serve as Proxy for Many
Block 1
SNP1 SNP2
↓
↓
Block 2
SNP3 SNP4 SNP5
↓
↓
↓
SNP6 SNP7 SNP8
↓
↓
↓
CAGATCGCTGGATGAATCGCATCTGTAAGCAT
CGGATTGCTGCATGGATCGCATCTGTAAGCAC
CAGATCGCTGGATGAATCGCATCTGTAAGCAT
CAGATCGCTGGATGAATCCCATCAGTACGCAT
CGGATTGCTGCATGGATCCCATCAGTACGCAT
CGGATTGCTGCATGGATCCCATCAGTACGCAC
One Tag SNP May Serve as Proxy for Many
Block 1
SNP1 SNP2
↓
↓
Block 2
SNP3 SNP4 SNP5
↓
↓
↓
SNP6 SNP7 SNP8
↓
↓
↓
CAGATCGCTGGATGAATCGCATCTGTAAGCAT
CGGATTGCTGCATGGATCGCATCTGTAAGCAC
CAGATCGCTGGATGAATCGCATCTGTAAGCAT
CAGATCGCTGGATGAATCCCATCAGTACGCAT
CGGATTGCTGCATGGATCCCATCAGTACGCAT
CGGATTGCTGCATGGATCCCATCAGTACGCAC
%
One Tag SNP May Serve as Proxy for Many
Block 1
Block 2
SNP3
↓
SNP5
↓
SNP6 SNP7 SNP8
↓
↓
↓
CAGATCGCTGGATGAATCGCATCTGTAAGCAT
CGGATTGCTGCATGGATCGCATCTGTAAGCAC
CAGATCGCTGGATGAATCGCATCTGTAAGCAT
CAGATCGCTGGATGAATCCCATCAGTACGCAT
CGGATTGCTGCATGGATCCCATCAGTACGCAT
CGGATTGCTGCATGGATCCCATCAGTACGCAC
%
One Tag SNP May Serve as Proxy for Many
Block 1
Block 2
SNP3
↓
SNP6
↓
SNP8
↓
CAGATCGCTGGATGAATCGCATCTGTAAGCAT
CGGATTGCTGCATGGATCGCATCTGTAAGCAC
CAGATCGCTGGATGAATCGCATCTGTAAGCAT
CAGATCGCTGGATGAATCCCATCAGTACGCAT
CGGATTGCTGCATGGATCCCATCAGTACGCAT
CGGATTGCTGCATGGATCCCATCAGTACGCAC
%
One Tag SNP May Serve as Proxy for Many
Block 1
Block 2
Singleton
Frequency
GTT
35%
CTC
30%
GTT
10%
GAT
8%
CAT
7%
CAC
6%
other haplotypes
4%
Pair-Wise Linkage Disequilibrium (LD)
Measures
Name
Symbol
Definition
"Lewontin's D"
D
pABpab – pAbpaB
"D prime"
D'
D / max (D)
Correlation
("r-squared")
r2
D2 / pApapBpb
For a discussion and comparison of these LD measures, see
Devlin B, Risch N, Genomics 1995; 29:311-22.
Courtesy K. Jacobs, NCI
Two Measures of LD: D' and r2
• D' varies from 0 (complete equilibrium) to 1
(complete disequilibrium)
• When D' = 0, typing one SNP provides no
information on the other SNP
• D' does not adequately account for allele
frequencies; r2 is correlation between SNPs, is
preferred measure
• When r2 = 1, two SNPs are in perfect LD; allele
frequencies are identical for both SNPs, and
typing one SNP provides complete information
on the other
What can LD do for me?
• Knowledge of patterns of LD can be quite useful in
the design and analysis of genetic data
• Design:
– Estimation of theoretical power to detect
associations
– Evaluation of degree of completeness of
sampling of genetic variants
– Choice of most informative genetic variants to
genotype
• Sample size increases by ~1/r2 to achieve same
power to detect association with SNP2 as SNP1
Courtesy K. Jacobs, NCI
Association Signal for Coronary Artery
Disease on Chromosome 9
Samani N et al, N Engl J Med 2007; 357:443-453.
Region of Chromosome 1 Showing Strong
Association with Inflammatory Bowel Disease
Duerr R et al. Science 2006; 314:1461-63.
LD Patterns in TCF7L2 Association Region
Grant et al, Nat Genet 2006; 38:320-23.
LD in Three HapMap Populations
International HapMap Consortium, Nature 2005; 437:1299-1320.
A HapMap for More Efficient Association
Studies: Goals
• Use just the density of SNPs needed to find
associations between SNPs and diseases
• Do not miss chromosomal regions with
disease association
• Produce a tool to assist in finding genes
affecting health and disease
• Ancestral populations differ in their degree of
LD; recent African ancestry populations are
older and have shorter stretches of LD, need
more SNPs for complete genome coverage
SNPs as Gateway to Genome-Wide
Association (GWA) Studies
• SNPs much more numerous than other
markers and easier to assay
• Genome-wide studies attempt to capture
majority of genomic variation (10M SNPs!)
• Variation inherited in groups, or blocks, so not
all 10 million points have to be tested
• Blocks are shorter (so need to test more
points) the less closely people are related
• SNP technology allows studies in unrelated
persons, assuming 5kb – 10kb lengths in
common (300,000 – 1,000,000 markers)
www.hapmap.org
International HapMap Consortium, Nature 2005; 437:1299-1320.
www.hapmap.org
International HapMap Consortium, Nature 2007; 449:851-861.
Cost per genotype (Cents, USD)
Progress in Genotyping Technology
102
ABI
TaqMan
ABI
SNPlex
10
Illumina
Golden Gate
Affymetrix
Affymetrix
10K
MegAllele
Illumina
Perlegen
Affymetrix
Infinium/Sentrix
1
100K/500K
1
10
2001
102
Courtesy S. Chanock, NCI
103
104
105
2005
106
Nb of
SNPs
Continued Progress in Genotyping
Technology
1800
Affymetrix
500K
Cost per person (USD)
1500
Illumina
Illumina
1200
550K
Illumina
650Y
317K
900
600
300
0
July
2005Oct-05
Jul-05
Jan-06
Courtesy S. Gabriel, Broad/MIT
Apr-06
2006
Jul-06 OctOct-06
Cost of a Genome-Wide Association Study
in 2,000 People
Year
Number of
SNPs
Cost/SNP
Cost/Study
Cost of a Genome-Wide Association Study
in 2,000 People
Year
2001
Number of
SNPs
Cost/SNP
Cost/Study
Cost of a Genome-Wide Association Study
in 2,000 People
Year
Number of
SNPs
2001
10,000,000
Cost/SNP
Cost/Study
Cost of a Genome-Wide Association Study
in 2,000 People
Year
Number of
SNPs
Cost/SNP
2001
10,000,000
$1.00
Cost/Study
Cost of a Genome-Wide Association Study
in 2,000 People
Year
Number of
SNPs
Cost/SNP
Cost/Study
2001
10,000,000
$1.00
$20 billion
Cost of a Genome-Wide Association Study
in 2,000 People
Year
Number of
SNPs
Cost/SNP
Cost/Study
2001
10,000,000
$1.00
$20 billion
2008
Cost of a Genome-Wide Association Study
in 2,000 People
Year
Number of
SNPs
Cost/SNP
Cost/Study
2001
10,000,000
$1.00
$20 billion
2008
1,000,000
Cost of a Genome-Wide Association Study
in 2,000 People
Year
Number of
SNPs
Cost/SNP
Cost/Study
2001
10,000,000
$1.00
$20 billion
2008
1,000,000
0.05¢
Cost of a Genome-Wide Association Study
in 2,000 People
Year
Number of
SNPs
Cost/SNP
Cost/Study
2001
10,000,000
$1.00
$20 billion
2008
1,000,000
0.05¢
$1 million
Coverage (% SNPs tagged at r2 > 0.8) of
Commercial Genotyping Platforms
HapMap population sample
Platform
YRI
CEU
CHB+JPT
Affymetrix GeneChip 500K
46
68
67
Affymetrix SNP Array 6.0
66
82
81
Illumina HumanHap300
33
77
63
Illumina HumanHap550
55
88
83
Illumina HumanHap650Y
66
89
84
Perlegen 600K
47
92
84
Manolio et al, J Clin Invest 2008; 118:1590-605.
Following the Polymorphism Literature
• Sometimes named for:
– amino acid change (AGT M235T)
– nucleotide sequence (AGTR1 A1166C)
– promoter (AGT -6 G/A)
– restriction enzyme site (XbaI, PvuII, HindIII)
– gene product (APOE*e2)
– legacy system (DRB1*0104)
– reference SNP (rs709932) or submitted SNP
(ss1487247)
• Good sources for information: OMIM, HUGO,
dbSNP, UCSC Genome Browser
Courtesy S. Chanock, NCI
Other Genomic Technologies
• Sequencing: measure variation at every point in
gene or candidate region in dozens to hundreds
of people to find functional variants
• Gene expression: measure changes in mRNA
(transcribed) in cases and controls or in response
to stimulation
• Epigenetics: measure DNA methylation or
histone deacetylation that turns genes on and off
Sidney Harris, http://www.sciencecartoonsplus.com/gallery.htm.
Summary Points: Genotyping Methods
• Unbelievably rapid progress from small number
of blood group markers to >10M SNPs, CNVs,
structural variants, sequence variants
• Technology will continue to change and will be
challenge to keep up with; difficult to know
when ready to apply to population studies
• SNPs are currently the dominant technology
(more to come in Lecture 4)
• Quality control is a major issue
Familial Resemblance?
http://en.wikipedia.org/wiki/Image:Kennedy_bros.jpg#file
Evidence for Genetic Influence on Disease or
Trait from Family Data
• Familial resemblance: trait more similar among
related than unrelated persons
• Familial clustering: risk of disease in relative of
case > risk in relative of non-case or of general
population; (sibling relative risk, Risch's λS)
• Distributions of continuous trait: mixtures of
distributions or commingling analysis
Sibling Relative Risk of Living to Age 90
Centenarians vs. Those Dying at Age 73
Perls TT et al, Lancet 1998; 351:1560.
Large Representative Pedigree Showing 69
Patients with Atrial Fibrillation
Arnar et al, Europ Heart J 2006; 27:708-12.
Strength of Extensive Genealogies
• Common diseases do not show Mendelian
inheritance patterns
• Affected siblings infrequent in common diseases, but
many patients may have more distant relatives with
same disease
Degree of
Relatives
Risk Ratio [95% CI]
P-Value
1
2
3
1.77 [1.67,1.88]
1.36 [1.27,1.44]
1.18 [1.14,1.23]
< 0.001
< 0.001
< 0.001
4
5
1.10 [1.06,1.13]
1.05 [1.02,1.07]
< 0.001
< 0.001
Arnar et al, Europ Heart J 2006; 27:708-12.
Familial Correlations
• Phenotypic resemblance among relatives
estimated by regression of one relative’s value
(offspring), on that of another (parent):
Yo = μ + β • [(Ym + Yf )/2] + ε
• Twice parent-offspring correlation is estimate
of heritability
• If trait under genetic control, expect trait
correlations among closer relatives to be
greater than those among more distant
relatives
Familial Correlations of Sex-Specific LV
Mass, Multiply-Adjusted
Relative Pair
Pairs (n) Correlation Expected
Spouse
855
0.05
0
Parent-offspring
662
0.15
0.5
1,486
0.16
0.5
369
0.06
0.25
Sibling
Avuncular
after Post W et al, Hypertension 1997; 30:1025-1028.
Assessing Familial and Genetic Nature of
a Phenotypic Trait: Heritability
• Often designated as H, h2, or σ2G /σ2P
• Proportion of total inter-individual variation in the
trait (σ2P) or phenotypic variation, attributable to
genetic variation (σ2G)
• Population- and environment-specific parameter
• Its value, high or low, does not indicate role of
genes in any specific individual
• Does allow one to predict expected degree of
familial aggregation of a trait
• Traits with high heritability should prove fruitful
in identifying trait-related genes
Genetic Basis of Familial Clustering of
Plasma ACE Activity
Major Gene Effect
Mean (u/L) % Variance
Relative
N
Mean (u/L)
Fathers
87
34.1
4.8
29
Mothers
87
30.7
4.0
29
Siblings
169
43.1
10.8
75
Cambien F, et. al., Am J Hum Genet 1988; 43:774-780.
Estimated Heritability Explained by GWA
Findings to Date
Estimated
GWA σ2G
Height
3%
Estimated
Total σ2G
Reference
90%
Weedon
Nat Genet 2008
T2DM
λs = 1.07
λs = 3.5
Zeggini/Scott
Science 2007
CRP
? 10.5%
30-50%
Reiner/Ridker
Nat Genet 2008
λs = 4-11
Liu
Psoriasis 9 @ ~1.3 OR
PLoS Genet 2008
NHGRI GWA Catalog, www.genome.gov/GWAstudies
Hardy-Weinberg Equilibrium
• Occurrence of two alleles of a SNP in the same
individual are two independent events
• Ideal conditions:
– random mating
- no selection (equal survival)
– no migration
- no mutation
– no inbreeding
- large population sizes
– gene frequencies equal in males and females)…
• If alleles A and a of SNP rs1234 have frequencies p
and 1-p, expected frequencies of the three
genotypes are:
Freq AA = p2
After G. Thomas, NCI
Freq Aa = 2p(1-p)
Freq aa = (1-p)2
Summary Points: Familial Clustering
• Indicator of possible genetic influence
• May over-estimate genetic component due to
poor assessment and adjustment for shared
environment
• Methods include twin studies, parent-offspring
correlation, “relative” relative risk, % variance
explained
• Current genes for complex disease explain only
tiny fraction of total heritability
Larson, G. The Complete Far Side. 2003.
Basic Definitions: Loci, Genes, Alleles
Locus: Place on a chromosome where a specific
gene or set of markers resides
Quantitative trait locus (QTL): a genetic factor
believed to influence a quantitative trait such as
blood pressure, lipoprotein levels, etc.
Gene: Contiguous piece of DNA that can contain
information to make or modify ‘expression’ of
specific protein(s)
Allele: A variant form of a DNA sequence at a
particular locus on a chromosome
Candidate gene: Gene believed to influence
expression of complex phenotypes due to known
biologic properties of their products
After S. Chanock, NCI
Basic Definitions: Parts of a Gene
Exon: a DNA sequence that usually specifies the
sequence of amino acids in translation
Intron: an intervening DNA sequence removed from
mRNA after transcription and thus does not
encode protein in translation
Splice site: Junction of intron and exon
Promoter: region of DNA to which an RNA
polymerase binds and initiates transcription - the
promoter regulates gene expression by controlling
the amount of mRNA transcribed
Polymorphism: Variation in the sequence of DNA
among individuals
After S. Chanock, NCI
SNPs and Function:
We know so little…
• Majority are “silent”
– No known functional change
• Some alter gene expression/regulation
– Promoter/enhancer/silencer
– mRNA stability
– Small RNAs
• Some alter function of gene product
– Change sequence of protein
Courtesy S. Chanock, NCI
SNPs within Genes
Coding SNPs (cSNPs)
• Synonymous: no change in amino acid
previously termed “silent” but…..
Can alter mRNA stability
DRD2 (Duan et al 2002)
Can alter speed of translation and protein folding
MDR1 (Gottesman et al 2007)
• Nonsynonymous: changes amino acid (codon)
conservative and radical
• Nonsense: insertion of stop codon
Frameshift (insertion/deletion): Disrupts codon
sequence, rare but disruptive
After S. Chanock, NCI
SNPs Outside Genes
• Majority distributed throughout genome are
“silent” (excellent as markers)
• Alter transcription
– Promoter, enhancer, silencer
• Regulate expression
– Locus control region, mRNA stability
• Most are assumed to be ‘silent hitchhikers’
– No function by predictive models or analysis
Courtesy S. Chanock, NCI
Sample Collection and Processing
• Obtaining samples for DNA preparation
– whole blood, buffy coat
– sputum
– buccal cells
– serum, urine
– pathology specimens
– placenta, excreta, other
• Purifying and quantifying DNA
• Transformed lymphocytes
• Whole genome amplification (WGA)
• ‘Barcode’ individual DNAs (QC)
After S. Chanock, NCI