# Presentation: 133001

## Genetics for EpidemiologistsLecture 2: Measurement of Genetic Exposures

- National Human Genome Research Institute

- National  Institutes of Health

- U.S. Department of Health and Human Services

**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 

*Am J Med Genet* 1984; 19:525-32.

- 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)

## Restriction Fragment Length Polymorphisms (RFLPs) 

*Am J Hum Genet* 1980; 32:314-331.

- 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

## Restriction Fragment Length Polymorphisms (RFLPs) 

*Am J Hum Genet* 1980; 32:314-331.

- 	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.

## RFLPs Used to Map Neurofibromatosis 

*Science *1987; 236:1100-1102.

- 	Linkage analysis of 15 Utah kindreds showed  that a gene responsible for von Recklinghausen neurofibromatosis (NF) is located near the centromere on chromosome 17

**   **

**   **

## RFLPs Used to Map Neurofibromatosis 

*Science *1987; 236:1100-1102.

**   **

**   **

- 	Cosegration of NF with the A2 (1.9 kb) allele and not A1 (2.4kb) in each of four affected offspring.

## 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 

- 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

- Repetition in tandem of a short (2- to 6-bp) motif from 5-5,000 times

## 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.

## SNPs 1 / 300 bases

**GAAATAATTAATGTTTTCCTTCCTTCTCCTATTTTGTCCTTTACTTCAATTTATTTATTTATTATTAATATTATTATTTTTTGAGACGGAGTTT****C/A****CTCTTGTTGCCAACCTGGAGTGCAGTGGCGTGATCTCAGCTCACTGCACACTCCGCTTTCCTGGTTTCAAGCGATTCTCCTGCCTCAGCCTCCTGAGTAGCTGGGACTACAGTCACACACCACCACGCCCGGCTAATTTTTGTATTTTTAGTAGAGTTGGGGTTTCACCATGTTGGCCAGACTGGTCTCGAACTCCTGACCTTGTGATCCGCCAGCCTCTGCCTCCCAAAGAGCTGGGATTACAGGCGTGAGCCACCGCGCTCGGCCCTTTGCATCAATTTCTACAGCTTGTTTTCTTTGCCTGGACTTTACAAGTCTTACCTTGTTCTGC****C/T****TCAGATATTTGTGTGGTCTCATTCTGGTGTGCCAGTAGCTAAAAATCCATGATTTGCTCTCATCCCACTCCTGTTGTTCATCTCCTCTTATCTGGGGTCAC****A/C****TATCTCTTCGTGATTGCATTCTGATCCCCAGTACTTAGCATGTGCGTAACAACTCTGCCTCTGCTTTCCCAGGCTGTTGATGGGGTGCTGTTCATGCCTCAGAAAAATGCATTGTAAGTTAAATTATTAAAGATTTTAAATATAGGAAAAAAGTAAGCAAACATAAGGAACAAAAAGGAAAGAACATGTATTCTAATCCATTATTTATTATACAATTAAGAAATTTGGAAACTTTAGATTACACTGCTTTTAGAGATGGAGATGTAGTAAGTCTTTTACTCTTTACAAAATACATGTGTTAGCAATTTTGGGAAGAATAGTAACTCACCCGAACAGT****G/T****AATGTGAATATGTCACTTACTAGAGGAAAGAAGGCACTTGAAAAACATCTCTAAACCGTATAAAAACAATTACATCATAATGATGAAAACCCAAGGAATTTTTTTAGAAAACATTACCAGGGCTAATAACAAAGTAGAGCCACATGTCATTTATCTTCCCTTTGTGTCTGTGTGAGAATTCTAGAGTTATATTTGTACATAGCATGGAAAAATGAGAGGCTAGTTTATCAACTAGTTCATTTTTAAAAGTCTAACACATCCTAGGTATAGGTGAACTGTCCTCCTGCCAATGTATTGCACATTTGTGCCCAGATCCAGCATAGGGTATGTTTGCCATTTACAAACGTTTATGTCTTAAGAGAGGAAATATGAAGAGCAAAACAGTGCATGCTGGAGAGAGAAAGCTGATACAAATATAAA****T/G****AAACAATAATTGGAAAAATTGAGAAACTACTCATTTTCTAAATTACTCATGTATTTTCCTAGAATTTAAGTCTTTTAATTTTTGATAAATCCCAATGTGAGACAAGATAAGTATTAGTGATGGTATGAGTAATTAATATCTGTTATATAATATTCATTTTCATAGTGGAAGAAATAAAATAAAGGTTGTGATGATTGTTGATTATTTTTTCTAGAGGGGTTGTCAGGGAAAGAAATTGCTTTTT**
**SNPs 1 / 300 bases**
**~ 10 million across genome**

**Single Nucleotide Polymorphisms (SNPs)**

**Notes:**
2110 SNPs 

## Mapping the Relationships Among SNPs

**Mapping the Relationships Among SNPs**

## Chromosome 9p21 Region Associated with MI

- Samani N et al, *N Engl J Med*  2007; 357:443-453.

## Distances Among East Coast Cities

|  |  | Boston | Provi-dence | New York | Phila-delphia | Balti-more |
| --- | --- | --- | --- | --- | --- | --- |
| Providence |  | 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**

## Distances Among East Coast Cities

|  |  | Boston | Provi-dence | New York | Phila-delphia | Balti-more |
| --- | --- | --- | --- | --- | --- | --- |
| Providence |  | 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**

| < 100 |  | 101-200 |  | 201-300 |  | 301-400 |  | > 400 |  |
| --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |

## Distances Among East Coast Cities

|  |  | Boston | Provi-dence | New York | Phila-delphia | Balti-more |
| --- | --- | --- | --- | --- | --- | --- |
| Providence |  | 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**

| < 100 |  | 101-200 |  | 201-300 |  | 301-400 |  | > 400 |  |
| --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |

## Distances Among East Coast Cities

## Distances Among East Coast Cities

| Boston | Provi-dence | New York | Phila-delphia | Balti-more | Wash-ington |
| --- | --- | --- | --- | --- | --- |

## One Tag SNP May Serve as Proxy for Many

** **
_**C**__**A**__**GAT**__**C**__**GCTG**__**G**__**ATG**__**A**__**ATC**__**G**__**CATC**__**T**__**GTA**__**A**__**GCA**__**T**_
_**C**__**G**__**GAT**__**T**__**GCTG**__**C**__**ATG**__**G**__**ATC**__**G**__**CATC**__**T**__**GTA**__**A**__**GCA**__**C**_
_**C**__**A**__**GAT**__**C**__**GCTG**__**G**__**ATG**__**A**__**ATC**__**G**__**CATC**__**T**__**GTA**__**A**__**GCA**__**T**_
_**C**__**A**__**GAT**__**C**__**GCTG**__**G**__**ATG**__**A**__**ATC**__**C**__**CATC**__**A**__**GTA**__**C**__**GCA**__**T**_
_**C**__**G**__**GAT**__**T**__**GCTG**__**C**__**ATG**__**G**__**ATC**__**C**__**CATC**__**A**__**GTA**__**C**__**GCA**__**T**_
_**C**__**G**__**GAT**__**T**__**GCTG**__**C**__**ATG**__**G**__**ATC**__**C**__**CATC**__**A**__**GTA**__**C**__**GCA**__**C**_**   **** **
**	****	****	**

**SNP2**
**↓**

**SNP3**
**↓**

**SNP4**
**↓**

**SNP5**
**↓**

**SNP6**
**↓**

**SNP1**
**↓**

- }

- }

- Block 1

- Block 2

**SNP7**
**↓**

**SNP8**
**↓**

## One Tag SNP May Serve as Proxy for Many

** **
_**C**__**A**__**GAT**__**C**__**GCTG**__**G**__**ATG**__**A**__**ATC**__**G**__**CATC**__**T**__**GTA**__**A**__**GCA**__**T**_
_**C**__**G**__**GAT**__**T**__**GCTG**__**C**__**ATG**__**G**__**ATC**__**G**__**CATC**__**T**__**GTA**__**A**__**GCA**__**C**_
_**C**__**A**__**GAT**__**C**__**GCTG**__**G**__**ATG**__**A**__**ATC**__**G**__**CATC**__**T**__**GTA**__**A**__**GCA**__**T**_
_**C**__**A**__**GAT**__**C**__**GCTG**__**G**__**ATG**__**A**__**ATC**__**C**__**CATC**__**A**__**GTA**__**C**__**GCA**__**T**_
_**C**__**G**__**GAT**__**T**__**GCTG**__**C**__**ATG**__**G**__**ATC**__**C**__**CATC**__**A**__**GTA**__**C**__**GCA**__**T**_
_**C**__**G**__**GAT**__**T**__**GCTG**__**C**__**ATG**__**G**__**ATC**__**C**__**CATC**__**A**__**GTA**__**C**__**GCA**__**C**_
**%**

**SNP2**
**↓**

**SNP3**
**↓**

**SNP4**
**↓**

**SNP5**
**↓**

**SNP6**
**↓**

**SNP1**
**↓**

- Block 1

- Block 2

**SNP7**
**↓**

**SNP8**
**↓**

- }

- }

## One Tag SNP May Serve as Proxy for Many

** **
_**CAGATCGCTG**__**G**__**ATGAATC**__**G**__**CATC**__**T**__**GTA**__**A**__**GCA**__**T**_
_**CGGATTGCTG**__**C**__**ATGGATC**__**G**__**CATC**__**T**__**GTA**__**A**__**GCA**__**C**_
_**CAGATCGCTG**__**G**__**ATGAATC**__**G**__**CATC**__**T**__**GTA**__**A**__**GCA**__**T**_
_**CAGATCGCTG**__**G**__**ATGAATC**__**C**__**CATC**__**A**__**GTA**__**C**__**GCA**__**T**_
_**CGGATTGCTG**__**C**__**ATGGATC**__**C**__**CATC**__**A**__**GTA**__**C**__**GCA**__**T**_
_**CGGATTGCTG**__**C**__**ATGGATC**__**C**__**CATC**__**A**__**GTA**__**C**__**GCA**__**C**_
**%**

**SNP3**
**↓**

**SNP5**
**↓**

**SNP6**
**↓**

- Block 1

- Block 2

**SNP7**
**↓**

**SNP8**
**↓**

- }

- }

## One Tag SNP May Serve as Proxy for Many

** **
_**CAGATCGCTG**__**G**__**ATGAATCGCATC**__**T**__**GTAAGCA**__**T**_
_**CGGATTGCTG**__**C**__**ATGGATCGCATC**__**T**__**GTAAGCA**__**C**_
_**CAGATCGCTG**__**G**__**ATGAATCGCATC**__**T**__**GTAAGCA**__**T**_
_**CAGATCGCTG**__**G**__**ATGAATCCCATC**__**A**__**GTACGCA**__**T**_
_**CGGATTGCTG**__**C**__**ATGGATCCCATC**__**A**__**GTACGCA**__**T**_
_**CGGATTGCTG**__**C**__**ATGGATCCCATC**__**A**__**GTACGCA**__**C**_
**%**

**SNP3**
**↓**

**SNP6**
**↓**

- Block 1

- Block 2

**SNP8**
**↓**

- }

- }

## One Tag SNP May Serve as Proxy for Many

** **
**	****	****	****	**_**G**__**T**__**T**_** ****	****	****    ****	****	****35****%**
**	****	****	****	**_**C**__**T**__**C**_** ****	****	****	****	****30****%**
**	****	****	****	**_**G**__**T**__**T**_** ****	****	****	****	****10****%**
**	****	****	****	**_**G**__**A**__**T**_** ****	****	****	****	****  8****%**
**	****	****	****	**_**C**__**A**__**T**_** ****	****	****	****	****  7****%**
**	****	****	****	**_**C**__**A**__**C**_** ****	****	****	****	****  6%**** **
**	****   ****	****	****other haplotypes****   ****	****	****  4%**

- Block 1

- Block 2

- Frequency

- Singleton

## Pair-Wise Linkage Disequilibrium (LD) Measures  

- For a discussion and comparison of these LD measures, see Devlin B, Risch N, *Genomics* 1995; 29:311-22.

| Name | Symbol | Definition |
| --- | --- | --- |
| "Lewontin's D" | D | pABpab – pAbpaB  |
| "D prime" | D' | D / max (D) |
| Correlation
("r-squared") | r2 | D2 / pApapBpb |

- 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

**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

**LD Patterns in *****TCF7L2***** Association Region**

## LD in Three HapMap Populations

## 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

## www.hapmap.org

## Progress in Genotyping Technology

**1**

**10**

**10****2**

**10****3**

**10****4**

**10****5**

**10****6**

**Nb of **
**SNPs**

**Cost per genotype (Cents, USD)**

**10**

**1**

**10****2**

**ABI**
**TaqMan**

**ABI**
**SNPlex**

**Illumina**
**Golden Gate**** **

**Illumina**
**Infinium/Sentrix**** **

**Affymetrix**
**100K/500K**** **

**Perlegen **

**Affymetrix**
**MegAllele**** **

**2001**

**2005**

**Affymetrix**
**10K**

- Courtesy S. Chanock, NCI

## Continued Progress in Genotyping Technology

**Affymetrix**
** ****500K**

**Illumina**
** ****317K**

**Illumina**
** ****550K**

**Illumina**
** ****650Y**

**Continued Progress in Genotyping Technology**

- Courtesy S. Gabriel, Broad/MIT

**July 2005**

**Oct 2006**

**Cost per person (USD)**

## 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**

## Cost of a Genome-Wide Association Study in 2,000 People

| Year | Number of SNPs | Cost/SNP | Cost/Study |
| --- | --- | --- | --- |
| 2001 |  |  |  |
|  |  |  |  |

**Cost of a Genome-Wide Association Study in 2,000 People**

## Cost of a Genome-Wide Association Study in 2,000 People

| Year | Number of SNPs | Cost/SNP | Cost/Study |
| --- | --- | --- | --- |
| 2001 | 10,000,000 |  |  |
|  |  |  |  |

**Cost of a Genome-Wide Association Study in 2,000 People**

## 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 |  |
|  |  |  |  |

**Cost of a Genome-Wide Association Study in 2,000 People**

## 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**

## 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**

## 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**

## 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**

## 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 |

**Cost of a Genome-Wide Association Study in 2,000 People**

## Coverage (% SNPs tagged at r2 > 0.8) of Commercial Genotyping Platforms

- Manolio et al, *J Clin Invest  *2008; 118:1590-605.

|  |     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 |

**Notes:**
Equal weight to pedigrees
Expect all to be zero if no genetic contribution

## Following the Polymorphism Literature

- Sometimes named for:
- amino acid change (*AGT*  M235T)
- nucleotide sequence (*AGTR1* A1166C)
- promoter (*AGT* -6 G/A)
- restriction enzyme site (*Xba*I, *Pvu*II, *Hind*III)
- 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.

**Notes:**
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). 

## 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 | 1.77 [1.67,1.88]  | < 0.001 |
| 2 | 1.36 [1.27,1.44]  | < 0.001 |
| 3 | 1.18 [1.14,1.23]  | < 0.001 |
| 4 | 1.10 [1.06,1.13]  | < 0.001 |
| 5 | 1.05 [1.02,1.07]  | < 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

**Notes:**
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

## 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 |
| Sibling |  1,486 | 0.16 |     0.5 |
| Avuncular |     369   | 0.06 |     0.25 |

- after Post W et al, *Hypertension* 1997; 30:1025-1028.

**Notes:**
Equal weight to pedigrees
Expect all to be zero if no genetic contribution

## 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

| Relative | N | Mean (u/L) | Major Gene Effect |  |
| --- | --- | --- | --- | --- |
|  |  |  | Mean (u/L) | % Variance |
| 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.

**Notes:**
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

## 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 | 9 @ ~1.3 OR | λs = 4-11 | Liu 
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:

- After G. Thomas, NCI

- Freq AA = p2

- 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.

- Larson, G.  *The Complete Far Side*. 2003.

## Slide 68

## 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