Diabetes research in the era of complete genomes
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Diabetes Research in the Era of Complete Genomes. American Diabetes Association June 17, 2002 Francis S. Collins, M.D., Ph.D. Fulfilling the Promise of Genomics for Better Health. Medical Genomics. Functional Genomics. Proteomics. Comparative Genomics.

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Diabetes research in the era of complete genomes

Diabetes Research in the Era of Complete Genomes

American Diabetes Association

June 17, 2002

Francis S. Collins, M.D., Ph.D.


Diabetes research in the era of complete genomes

Fulfilling the Promise of Genomics for Better Health

Medical Genomics

Functional Genomics

Proteomics

Comparative Genomics


Diabetes research in the era of complete genomes

Positional cloning of a gene for a highly penetrant Mendeliandisorder is now straightforward –but tracking genetic susceptibility factors for non-Mendelian disorders continues to be vexing


F inland u nited s tates i nvestigation o f n iddm

FinlandUnitedStatesInvestigation OfNIDDM


Study design

Study design

  • Linkage study using affected sibling pairs (ASPs)

  • In the presence of linkage, siblings show excess sharing

  • Additional relatives collected to determine haplotypes


Study sample

Study sample

  • Collected from Finland

    • Founder population

    • High participation rate

FUSION 1: 580 familiesn = 3584

FUSION 2: 275 familiesn = 877

Elderly controlsn = 231


Diabetes research in the era of complete genomes

Linkage analysis

Positive LOD score identified

Fine mapping with SNPs

Association identified

Causative variant identified


Genome scan linkage results

Genome scan linkage results

Ghosh et al (2000) AJHG 67:1174


Diabetes research in the era of complete genomes

HNF4A

SLC2A10

PTPN1

Microsatellites


Diabetes research in the era of complete genomes

Linkage analysis

Positive LOD score identified

Fine mapping with SNPs

Association identified

Causative variant identified


Primer extension mass spectrometry

Mass in Daltons

GACCTGGAGCCCCCACC

5430.5

GACCTGGAGCCCCCACCC

5703.7

GACCTGGAGCCCCCACCTG

6047.9

Primer extension massspectrometry

Primer extension reactions designed to generate different sized products

C

T

primer


Diabetes research in the era of complete genomes

Allele Frequency Difference of Cases vs. Elderly Controls


Association is much more powerful than linkage to identify common susceptibility variants

Association is much more powerful than linkage to identify common susceptibility variants

N. Risch and S. Merikangas

Science 273: 1516-1517, 1996


Diabetes research in the era of complete genomes

Linkage analysis

Positive LOD score identified

Fine mapping with SNPs

Association identified

Causative variant identified


Diabetes research in the era of complete genomes

Fine mapping with SNPs

Association identified

Causative variant identified


Genome scan association results

-log10(p-value)

-log10(p-value)

Genome scan association results

Ghosh et al (2000) AJHG 67:1174


Diabetes research in the era of complete genomes

A cluster of markers on chromosome 22 shows association with T2DM

-log10(p-value)

kilobases


Diabetes research in the era of complete genomes

Fine mapping with SNPs

Association identified

Causative variant identified


Whole genome association approach to common disease

Whole Genome Association Approach to Common Disease

  • Identify all 10 million common SNPs

  • Collect 1000 cases and 1000 controls

  • Genotype all DNAs for all SNPs


2000 dnas x 10 000 000 snps 20 000 000 000 genotypes

2000 DNAs x 10,000,000 SNPs = 20,000,000,000 genotypes


Current high throughput snp genotyping methods

Current high throughput SNP genotyping methods

  • DNA chips

  • Beads/fiberoptics

  • Fluorescent single base extension

  • Pyrosequencing

  • Mass spectrometry

  • TaqMan

  • Invader

  • Etc., etc.


Shortcut 1

Shortcut #1:

Use haplotypes to reduce number of SNPs that have to be genotyped


Diabetes research in the era of complete genomes

Sequence from chromosome 7

GAAATAATTAATGTTTTCCTTCCTTCTCCTATTTTGTCCTTTACTTCAATTTATTTATTTATTATTAATATTATTATTTTTTGAGACGGAGTTTCACTCTTGTTGCCAACCTGGAGTGCAGTGGCGTGATCTCAGCTCACTGCACACTCCGCTTTCC/TGGTTTCAAGCGATTCTCCTGCCTCAGCCTCCTGAGTAGCTGGGACTACAGTCACACACCACCACGCCCGGCTAATTTTTGTATTTTTAGTAGAGTTGGGGTTTCACCATGTTGGCCAGACTGGTCTCGAACTCCTGACCTTGTGATCCGCCAGCCTCTGCCTCCCAAAGAGCTGGGATTACAGGCGTGAGCCACCGCGCTCGGCCCTTTGCATCAATTTCTACAGCTTGTTTTCTTTGCCTGGACTTTACAAGTCTTACCTTGTTCTGCCTTCAGATATTTGTGTGGTCTCATTCTGGTGTGCCAGTAGCTAAAAATCCATGATTTGCTCTCATCCCACTCCTGTTGTTCATCTCCTCTTATCTGGGGTCACA/CTATCTCTTCGTGATTGCATTCTGATCCCCAGTACTTAGCATGTGCGTAACAACTCTGCCTCTGCTTTCCCAGGCTGTTGATGGGGTGCTGTTCATGCCTCAGAAAAATGCATTGTAAGTTAAATTATTAAAGATTTTAAATATAGGAAAAAAGTAAGCAAACATAAGGAACAAAAAGGAAAGAACATGTATTCTAATCCATTATTTATTATACAATTAAGAAATTTGGAAACTTTAGATTACACTGCTTTTAGAGATGGAGATGTAGTAAGTCTTTTACTCTTTACAAAATACATGTGTTAGCAATTTTGGGAAGAATAGTAACTCACCCGAACAGTGTAATGTGAATATGTCACTTACTAGAGGAAAGAAGGCACTTGAAAAACATCTCTAAACCGTATAAAAACAATTACATCATAATGATGAAAACCCAAGGAATTTTTTTAGAAAACATTACCAGGGCTAATAACAAAGTAGAGCCACATGTCATTTATCTTCCCTTTGTGTCTGTGTGAGAATTCTAGAGTTATATTTGTACATAGCATGGAAAAATGAGAGGCTAGTTTATCAACTAGTTCATTTTTAAAAGTCTAACACATCCTAGGTATAGGTGAACTGTCCTCCTGCCAATGTATTGCACATTTGTGCCCAGATCCAGCATAGGGTATGTTTGCCATTTACAAACGTTTATGTCTTAAGAGAGGAAATATGAAGAGCAAAACAGTGCATGCTGGAGAGAGAAAGCTGATACAAATATAAATGAAACAATAATTGGAAAAATTGAGAAACTACTCATTTTCTAAATTACTCATGTATTTTCCTAGAATTTAAGTCTTTTAATTTTTGATAAATCCCAATGTGAGACAAGATAAGTATTAGTGATGGTATGAGTAATTAATATCTGTTATATAATATTCATTTTCATAGTGGAAGAAATAAAATAAAGGTTGTGATGATTGTTGATTATTTTTTCTAGAGGGGTTGTCAGGGAAAGAAATTGCTTTTTTTCATTCTCTCTTTCCACTAAGAAAGTTCAACTATTAATTTAGGCACATACAATAATTACTCCATTCTAAAATGCCAAAAAGGTAATTTAAGAGACTTAAAACTGAAAAGTTTAAGATAGTCACACTGAACTATATTAAAAAATCCACAGGGTGGTTGGAACTAGGCCTTATATTAAAGAGGCTAAAAATTGCAATAAGACCACAGGCTTTAAATATGGCTTTAAACTGTGAAAGGTGAAACTAGAATGAATAAAATCCTATAAATTTAAATCAAAAGAAAGAAACAAACTA/GAAATTAAAGTTAATATACAAGAATATGGTGGCCTGGATCTAGTGAACATATAGTAAAGATAAAACAGAATATTTCTGAAAAATCCTGGAAAATCTTTTGGGCTAACCTGAAAACAGTATATTTGAAACTATTTTTAAA

Three variants are present


These three variants could theoretically occur in 8 different haplotypes

These three variants could theoretically occur in 8 different haplotypes

…C…A…A…

…C…A…G…

…C…C…A…

…C…C…G…

…T…A…A…

…T…A…G…

…T…C…A…

…T…C…G…


But in practice only two are observed

But in practice, only two are observed

…C…A…A…

…C…A…G…

…C…C…A…

…C…C…G…

…T…A…A…

…T…A…G…

…T…C…A…

…T…C…G…


Diabetes research in the era of complete genomes

~ 25 kb


A haplotype map of human variation

A Haplotype Map of Human Variation

  • Goal is to define all common haplotypes in the human genome

  • Genome-wide association studies can then be done with a haplotype tag set of about 250,000 SNPs

  • Pilot studies underway to determine how many populations to sample, and best strategy for defining haplotype blocks


2000 dnas x 250 000 snps 500 000 000 genotypes

2000 DNAs x 250,000 SNPs = 500,000,000 genotypes


Shortcut 2

Shortcut #2:

Use DNA pooling to greatly reduce amount of genotyping


Diabetes research in the era of complete genomes

Creating DNA Pools

Case DNA Samples

Control DNA Samples

“Case” Pool

“Control” Pool


Testing performance of methods for determining allele frequency differences in pools

Testing performance of methods for determining allele frequency differences in pools

  • Tested 16 non-optimized SNPs with:

    • MALDI-TOF mass spec

    • Pyrosequencing

    • Fluorescent primer extension

  • Each assay carried out 8 – 16 times

  • Each method tested on three pools with n > 100

    • Diabetics

    • Spouses

    • Elderly non-diabetics


Diabetes research in the era of complete genomes

G

A

Diabetic Probands

38% A

G

Spouse Controls

34% A

A

G

Elderly

Non-diabetic Controls

30% A

A

MALDI-TOF Mass Spectrometric Detection

of Allele Frequencies in Pooled DNA Samples


Diabetes research in the era of complete genomes

Frequency

Difference from

Pooled

Genotypes

(MALDI-TOF)


2 dnas x 250 000 snps 500 000 genotypes

2 DNAs x 250,000 SNPs = 500,000 genotypes


Diabetes research in the era of complete genomes

Fine mapping with SNPs

Association identified

Causative variant identified


Diabetes research in the era of complete genomes

http://genome.ucsc.edu


Diabetes research in the era of complete genomes

Fulfilling the Promise of Genomics for Better Health

Medical Genomics

Functional Genomics

Proteomics

Comparative Genomics


A sample of ongoing public large scale animal genome sequencing projects

A sample of ongoing public large scale animal genome sequencing projects

Mouse – now at 7X

Rat – now at 4X

Tetraodon – now at 6X

Fugu – now at 5X

Zebrafish – now at 2X

Ciona intestinalis – now at 10X

Ciona savignyi – now at 12X

C. briggsae – now at 10X


Diabetes research in the era of complete genomes

The Mouse Genome Sequencing Consortium

The Sanger Institute

Washington University

Whitehead Institute

NHGRI

Wellcome Trust

www.ensembl.org


Diabetes research in the era of complete genomes

100%

50%

Reference = HUMAN

CAV2,1

MET

CAPZA2

ST7

WNT2

GASZ

CFTR

CORTBP2

PipMaker: Human-Mouse Alignment


Diabetes research in the era of complete genomes

MultiPIP Alignment: 12 Vertebrate Species


Diabetes research in the era of complete genomes1

Diabetes Research in the Era of Complete Genomes

The major predisposing genes for type 1 and type 2 diabetes should be identified in the next 5 – 7 years


The fusion research team

The FUSION Research Team


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