1. Genetics for Epidemiologists Lecture 2: Measurement of Genetic Exposures
National Human Genome Research Institute
U.S. Department of Health and Human Services
National Institutes of Health
National Human Genome Research Institute
National Institutes of Health
Teri A. Manolio, M.D., Ph.D. Director, Office of Population Genomics and
Senior Advisor to the Director, NHGRI,
for Population Genomics
U.S. Department of Health and Human Services

2. 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

3. Larson, G. The Complete Far Side. 2003.

4. 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:

5. Restriction Fragment Length Polymorphisms (RFLPs)
Define polymorphic marker loci that can be detected as differences in length of DNA fragments after digestion with DNA sequence-specific endonucleases
Establish linkage relationships using pedigree analysis
Am J Hum Genet 1980; 32:

6. 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:

7. RFLPs Used to Map Neurofibromatosis     
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:

8. RFLPs Used to Map Neurofibromatosis     
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:

9. 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:

10. Kringle-IV Encoding Sequences of Human apo(a) cDNA ApoA1 Alleles
Lackner et al, Hum Mol Genet 1993; 2:

11. Correlations of ApoA Molecular Weight with Lp(a) Levels and Number of Kringle-IV Repeats
Gavish et al, J Clin Invest 1989; 84:

12. 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 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

13. Multipoint LOD Scores for Long-term SBP and DBP on Chromosome 17
Levy et al, Hypertension 2000;36:

14. Larson, G. The Complete Far Side. 2003.

15. ~ 10 million across genome
Single Nucleotide Polymorphisms (SNPs)
GAAATAATTAATGTTTTCCTTCCTTCTCCTATTTTGTCCTTTACTTCAATTTATTTATTTATTATTAATATTATTATTTTTTGAGACGGAGTTTC/ACTCTTGTTGCCAACCTGGAGTGCAGTGGCGTGATCTCAGCTCACTGCACACTCCGCTTTCCTGGTTTCAAGCGATTCTCCTGCCTCAGCCTCCTGAGTAGCTGGGACTACAGTCACACACCACCACGCCCGGCTAATTTTTGTATTTTTAGTAGAGTTGGGGTTTCACCATGTTGGCCAGACTGGTCTCGAACTCCTGACCTTGTGATCCGCCAGCCTCTGCCTCCCAAAGAGCTGGGATTACAGGCGTGAGCCACCGCGCTCGGCCCTTTGCATCAATTTCTACAGCTTGTTTTCTTTGCCTGGACTTTACAAGTCTTACCTTGTTCTGCC/TTCAGATATTTGTGTGGTCTCATTCTGGTGTGCCAGTAGCTAAAAATCCATGATTTGCTCTCATCCCACTCCTGTTGTTCATCTCCTCTTATCTGGGGTCACA/CTATCTCTTCGTGATTGCATTCTGATCCCCAGTACTTAGCATGTGCGTAACAACTCTGCCTCTGCTTTCCCAGGCTGTTGATGGGGTGCTGTTCATGCCTCAGAAAAATGCATTGTAAGTTAAATTATTAAAGATTTTAAATATAGGAAAAAAGTAAGCAAACATAAGGAACAAAAAGGAAAGAACATGTATTCTAATCCATTATTTATTATACAATTAAGAAATTTGGAAACTTTAGATTACACTGCTTTTAGAGATGGAGATGTAGTAAGTCTTTTACTCTTTACAAAATACATGTGTTAGCAATTTTGGGAAGAATAGTAACTCACCCGAACAGTG/TAATGTGAATATGTCACTTACTAGAGGAAAGAAGGCACTTGAAAAACATCTCTAAACCGTATAAAAACAATTACATCATAATGATGAAAACCCAAGGAATTTTTTTAGAAAACATTACCAGGGCTAATAACAAAGTAGAGCCACATGTCATTTATCTTCCCTTTGTGTCTGTGTGAGAATTCTAGAGTTATATTTGTACATAGCATGGAAAAATGAGAGGCTAGTTTATCAACTAGTTCATTTTTAAAAGTCTAACACATCCTAGGTATAGGTGAACTGTCCTCCTGCCAATGTATTGCACATTTGTGCCCAGATCCAGCATAGGGTATGTTTGCCATTTACAAACGTTTATGTCTTAAGAGAGGAAATATGAAGAGCAAAACAGTGCATGCTGGAGAGAGAAAGCTGATACAAATATAAAT/GAAACAATAATTGGAAAAATTGAGAAACTACTCATTTTCTAAATTACTCATGTATTTTCCTAGAATTTAAGTCTTTTAATTTTTGATAAATCCCAATGTGAGACAAGATAAGTATTAGTGATGGTATGAGTAATTAATATCTGTTATATAATATTCATTTTCATAGTGGAAGAAATAAAATAAAGGTTGTGATGATTGTTGATTATTTTTTCTAGAGGGGTTGTCAGGGAAAGAAATTGCTTTTT
SNPs 1 / 300 bases
~ 10 million across genome
2110 SNPs

16. Mapping the Relationships Among SNPs
Christensen and Murray, N Engl J Med 2007; 356:

17. Chromosome 9p21 Region Associated with MI
Samani N et al, N Engl J Med 2007; 357:

18. Distances Among East Coast Cities
Boston
Provi-dence
New York
Phila-delphia
Balti-more
Providence 59
210
152
Philadelphia
320
237 86
Baltimore
430
325
173 87
Washington
450
358
206
120 34

19. Distances Among East Coast Cities
Boston
Provi-dence
New York
Phila-delphia
Balti-more
Providence 59
210
152
Philadelphia
320
237 86
Baltimore
430
325
173 87
Washington
450
358
206
120 34
< 100
> 400

20. Distances Among East Coast Cities
Boston
Provi-dence
New York
Phila-delphia
Balti-more
Providence 59
210
152
Philadelphia
320
237 86
Baltimore
430
325
173 87
Washington
450
358
206
120 34
< 100
> 400

21. Distances Among East Coast Cities
Boston
Provi-
dence
New
York
Phila-
delphia
Balti-
more
Wash-
ington

22. Distances Among East Coast Cities
Boston
Provi-dence
New York
Phila-delphia
Balti-more
Wash-ington

23. One Tag SNP May Serve as Proxy for Many}
One Tag SNP May Serve as Proxy for Many }
Block 1
Block 2
SNP1 ↓
CAGATCGCTGGATGAATCGCATCTGTAAGCAT
CGGATTGCTGCATGGATCGCATCTGTAAGCAC
CAGATCGCTGGATGAATCCCATCAGTACGCAT
CGGATTGCTGCATGGATCCCATCAGTACGCAT
CGGATTGCTGCATGGATCCCATCAGTACGCAC  
SNP2 ↓
SNP3 ↓
SNP4 ↓
SNP5 ↓
SNP6 ↓
SNP7 ↓
SNP8 ↓

24. One Tag SNP May Serve as Proxy for Many}
One Tag SNP May Serve as Proxy for Many }
Block 1
Block 2
SNP1 ↓
CAGATCGCTGGATGAATCGCATCTGTAAGCAT
CGGATTGCTGCATGGATCGCATCTGTAAGCAC
CAGATCGCTGGATGAATCCCATCAGTACGCAT
CGGATTGCTGCATGGATCCCATCAGTACGCAT
CGGATTGCTGCATGGATCCCATCAGTACGCAC %
SNP2 ↓
SNP3 ↓
SNP4 ↓
SNP5 ↓
SNP6 ↓
SNP7 ↓
SNP8 ↓

25. One Tag SNP May Serve as Proxy for Many}
One Tag SNP May Serve as Proxy for Many }
Block 1
Block 2
CAGATCGCTGGATGAATCGCATCTGTAAGCAT
CGGATTGCTGCATGGATCGCATCTGTAAGCAC
CAGATCGCTGGATGAATCCCATCAGTACGCAT
CGGATTGCTGCATGGATCCCATCAGTACGCAT
CGGATTGCTGCATGGATCCCATCAGTACGCAC %
SNP3 ↓
SNP5 ↓
SNP6 ↓
SNP7 ↓
SNP8 ↓

26. One Tag SNP May Serve as Proxy for Many}
One Tag SNP May Serve as Proxy for Many }
Block 1
Block 2
CAGATCGCTGGATGAATCGCATCTGTAAGCAT
CGGATTGCTGCATGGATCGCATCTGTAAGCAC
CAGATCGCTGGATGAATCCCATCAGTACGCAT
CGGATTGCTGCATGGATCCCATCAGTACGCAT
CGGATTGCTGCATGGATCCCATCAGTACGCAC %
SNP3 ↓
SNP6 ↓
SNP8 ↓

27. One Tag SNP May Serve as Proxy for Many
Block 1
Block 2
Singleton
Frequency
GTT %
CTC %
GTT %
GAT %
CAT %
CAC % 
other haplotypes %

28. 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:
Courtesy K. Jacobs, NCI

29. 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

30. 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

31. Association Signal for Coronary Artery Disease on Chromosome 9
Samani N et al, N Engl J Med 2007; 357:

32. Region of Chromosome 1 Showing Strong Association with Inflammatory Bowel Disease
Duerr R et al. Science 2006; 314:

33. LD Patterns in TCF7L2 Association Region
Grant et al, Nat Genet 2006; 38:

34. LD in Three HapMap Populations
International HapMap Consortium, Nature 2005; 437:

35. 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

36. 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)

37.
International HapMap Consortium, Nature 2005; 437:

38.
International HapMap Consortium, Nature 2007; 449:

39. Progress in Genotyping Technology
102
ABI
TaqMan
ABI
SNPlex 10
Cost per genotype (Cents, USD)
Illumina
Golden Gate
Affymetrix
MegAllele
Affymetrix
10K
Illumina
Infinium/Sentrix
Perlegen 1
Affymetrix
100K/500K
Nb of
SNPs 1 10
102
103
104
105
106
2001
2005
Courtesy S. Chanock, NCI

40. Continued Progress in Genotyping Technology
Affymetrix
500K
Illumina
550K
Illumina
650Y
Illumina
317K
Cost per person (USD)
July 2005
Oct 2006
Courtesy S. Gabriel, Broad/MIT

41. Cost of a Genome-Wide Association Study in 2,000 People
Year
Number of SNPs
Cost/SNP
Cost/Study

42. Cost of a Genome-Wide Association Study in 2,000 People
Year
Number of SNPs
Cost/SNP
Cost/Study
2001

43. Cost of a Genome-Wide Association Study in 2,000 People
Year
Number of SNPs
Cost/SNP
Cost/Study
2001
10,000,000

44. 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

45. 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

46. 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

47. 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

48. 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¢

49. 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

50. HapMap population sample
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 89 84
Perlegen 600K 47 92
Equal weight to pedigrees
Expect all to be zero if no genetic contribution
Manolio et al, J Clin Invest 2008; 118:

51. 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 (ss )
Good sources for information: OMIM, HUGO, dbSNP, UCSC Genome Browser
Courtesy S. Chanock, NCI

52. 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

53. Sidney Harris, http://www.sciencecartoonsplus.com/gallery.htm.

54. 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

55. Familial Resemblance?

56. 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

57. Sibling Relative Risk of Living to Age 90 Centenarians vs
Sibling Relative Risk of Living to Age 90 Centenarians vs. Those Dying at Age 73
102 centenarians (10 men, 92 women) in eastern Massachusetts, 77 controls (28 men, 49 women) born in 1896 but died age 73
centenarians had 456 sibs (233 men, 223 women), controls had 240 sibs (121 men, 119 women)
RR higher after age 70
For any age after 65, siblings of centenarians had a 42·4% lower hazard of death (95% CI 0·334–0·538, p<0·0005).
Perls TT et al, Lancet 1998; 351:1560.

58. Large Representative Pedigree Showing 69 Patients with Atrial Fibrillation
Arnar et al, Europ Heart J 2006; 27:

59. 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
1.77 [1.67,1.88]
< 0.001 2
1.36 [1.27,1.44] 3
1.18 [1.14,1.23] 4
1.10 [1.06,1.13] 5
1.05 [1.02,1.07]
Arnar et al, Europ Heart J 2006; 27:

60. 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
Spouse correlations are often used to “control” for the effects of shared environment, though they may be increased by assortative mating or decreased by gender differences

61. Familial Correlations of Sex-Specific LV Mass, Multiply-Adjusted
Relative Pair
Pairs (n)
Correlation
Expected
Spouse
855
0.05
Parent-offspring
662
0.15
0.5
Sibling
1,486
0.16
Avuncular
369
0.06
0.25
Equal weight to pedigrees
Expect all to be zero if no genetic contribution
after Post W et al, Hypertension 1997; 30:

62. 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

63. Genetic Basis of Familial Clustering of Plasma ACE Activity
Relative N
Mean (u/L)
Major Gene Effect
% Variance
Fathers 87
34.1
4.8 29
Mothers
30.7
4.0
Siblings
169
43.1
10.8 75
codominant major gene effect, twice as high in homozygotes as heterozygotes and in offspring as parents
minor allele frequency 0.26; in HWE 7% homozygotes for minor, 38% heterozygotes, 55% homozygote wild
Cambien F, et. al., Am J Hum Genet 1988; 43:

64. Estimated Heritability Explained by GWA Findings to Date
Estimated GWA σ2G
Estimated Total σ2G
Reference
Height 3%
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
Psoriasis
~1.3 OR
λs = 4-11
Liu
PLoS Genet 2008
NHGRI GWA Catalog,

65. 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
Freq Aa = 2p(1-p)
Freq aa = (1-p)2
After G. Thomas, NCI

66. 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

67. Larson, G. The Complete Far Side. 2003.

69. 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

70. 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

71. 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

72. 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

73. 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

74. 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