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The Identification of Human

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Title: The Identification of Human


1
The Identification of Human Quantitative Trait
Loci
Dr John Blangero Southwest Foundation for
Biomedical Research ChemGenex Pharmaceuticals
2
The Goals Genetic Analysis of Complex Phenotypes
QTL Localization Where in the genome is the QTL
located? QTL Identification What is (are) the
gene(s) involved? QTL Allelic
Architecture What are the specific QTNs? How many
QTNs? What are their frequencies and effect
sizes?
3
Quantitative Traits
  • Usually closer to gene action than disease
    itself.
  • Have superior statistical power.

4
Quantitative Endophenotypes
  • Heritable
  • Genetically correlated with disease or other
    focal phenotype
  • Closer to the action of the genes

5
Liability The Threshold Model
Affecteds
Normals
Disease-Related Trait
6
The process of finding and identifying
disease-related genes involves Objective
Prioritization.
7
Different Diseases
Different Designs
Different Methods
8
Family StudiesvsStudies of Unrelateds
9
Major Study Designs in Human Genetics Possible
Inferences

Inference Design
Heritability Linkage Association Unrelated
individuals No No
Yes Triads No
Yes Yes Sibling pairs
Yes Yes Yes Nuclear
families Yes Yes
Yes Extended pedigrees Yes
Yes Yes
10
You can exploit Linkage and Association
Information Jointly in Family Studies
11
Relative Per-Subject Power to Localize QTLs
Population Relative Ped.
Pedigree Study Efficiency
Size Type Jirel (Nepal) 1.00
2300 Extended (isolate) Vermont
0.91 331 Extended SAFHS
0.59 31 Extended
GAIT 0.35 19
Extended Framingham 0.24 5
Extended, nuclear Nuclear (4 sibs) 0.17
6 Nuclear Nuclear (3 sibs)
0.11 5 Nuclear Sib-pair
0.04 2 Relative pair
12
Linkage DesignsvsAssociation Designs
13
Power Linkage vs Association
14
Example 1 Positional Candidate Genes
  • QTL for serum leptin levels in the San Antonio
    Family Heart Study
  • Highly replicated QTL

15
Chromosome 2 Obesity QTL
16
Bioinformatic Prioritization GeneSniffer Results
2p22
POMC
GCKR
UCN
17
What Do You Do With A Good Positional Candidate
Gene?
The ALL or NOTHING principle Find all of the
variation in the gene. Preference Resequence
everyone (no bias against rare variants) Alternati
ve Resequence a subset of individuals
18
POMC Pattern of LD
19
POMC QTN Analysis Marginal Associations
20
How To Find the Most Likely Functional SNPs
  • Bayesian Quantitative Trait Nucleotide Analysis
    has the potential to aid the discovery of the DNA
    variants that influence risk of common disease.
    Objectively prioritizes SNPs for further
    functional work.

21
BQTN Analysis Bayesian Model Selection/Model
Averaging
Evaluate possible models of gene action. This may
be very large, 2n models of additive gene
action. Use Bayesian model selection to choose
best models and average parameters over models.
Eliminates problem of multiple testing. Yields
unbiased estimates of effect size. Allows
prioritization of polymorphisms for further lab
evaluation. Calculation of Posterior Probability
of Effect.
22
The Parallel Ranch 1,500 Processors
23
Sequential Oligogenic Linkage Analysis Routines
  • All analyses were performed using a parallel
    version of SOLAR on up to 1,500 processors.

For more information on SOLAR, follow the
software links at http//www.sfbr.org
24
BQTN analysis of POMC polymorphisms
  • Three variants account for 11 of variation in
    leptin levels.
  • The frequencies of these variants are 0.005,
    0.004 and 0.06.
  • LD with any other SNPs is very low 0.075, 0.248
    and 0.189.
  • It would be VERY HARD to find these by LD.

25
Linkage Conditional on POMC SNPs
Marginal LOD5.86 Conditional LOD3.05
26
What Do You Do With A Good Positional Candidate
Region?
The ALL or NOTHING principle Find all of the
variation in the region, say 5 10 Mb.
Preference Resequence everyone (no bias against
rare variants). This can be done NOW! It is the
wave of the future. Dont waste time with LD. It
is your ENEMY.
27
Example 2 Identifying Human QTLs Quickly
  • Expression phenotypes that are cis-regulated
    should be much easier to quickly identify
    functional variants and correlate them with
    disease risk.

28
Gene Expression Levels as Endophenotypes
  • Quantitative variation in gene expression levels
    explains some proportion of the variation in many
    phenotypes.
  • The amount of mRNA of a specific transcript in a
    tissue sample is about as close to gene action
    as possible hence, such phenotypes ought to be
    dissectible by statistical genetic approaches.
  • Array-based technologies make it feasible to
    quantify the expression levels of many
    transcripts simultaneously.

29
Project Description
  • San Antonio Family Heart Study (SAFHS) designed
    in 1991 to investigate the genetics of CVD in
    Mexican Americans
  • Includes 1,431 individuals from 42 families
  • 2 recalls since 1991
  • Extensive phenotypic data
  • anthropometry, blood pressure, lipids, obesity,
    diabetes, inflammation, oxidative stress,
    hormones, osteoporosis, brain structure/function
  • Genome scanned

30
Methodology
  • Blood samples collected from first SAFHS
    examination approx 15 years ago
  • Lymphocytes isolated from blood and stored in
    RPMI-C media in liquid nitrogen
  • RNA extracted and expression profiles generated
    on stored lymphocytes
  • 47,289 transcripts interrogated using the
    Illumina platform

31
Detection Statistics
  • 1,280 samples analyzed, good data from 1,240
    (97)
  • Of the 47,289 transcripts per array, we
    significantly detected 20,413 transcripts.

32
Heritabilities of Autosomal RefSeq Transcripts
33
Cis-Regulated Expression QTLs
34
Identifying Novel Candidate Genes for Disease Risk
  • After determining cis-regulated QTLs, look for
    correlations with phenotypes related to disease
    risk
  • Transcriptomic Epidemiologyusing high
    dimensional endophenotypic search
  • For example, 383 cis-regulated transcripts are
    significantly correlated with BMI (an index of
    obesity).
  • Many of these are novel genes of unknown function.

35
Expression QTLs LOD gt 3
Approximately, 34 of QTLs are Cis. Effect size
(QTL-specific heritability) is 64 larger for Cis
QTLs.
36
Cis Regulation UTS2 (urotensin 2 preprotein)
37
Cis and Trans Regulation HBG2 (G-gamma globin)
38
Trans Regulation LOC389472
39
Mitochondrial QTLs Influencing Expression
40
Identification of Human QTLs Example 3
QTL influencing inflammatory response A novel
positional candidate gene (SEPS1/SELS) found by
expression studies in an animal model
41
SEPS1 Gene Discovery
  • SEPS1 (formerly known as Tanis) was first
    identified by differential gene expression in
    liver of diabetic P. obesus
  • Putative functions related to ER stress response
    through processing and removal of misfolded
    proteins
  • (Ye et al (2004). Nature 429, 841-847)

42
SEPS1 Gene Discovery
  • Human SEPS1 gene is located on 15q26.3
  • Mammalian plasma membrane selenoprotein also a
    member of the GRP family
  • Consists of 6 exons, encodes a 204aa protein
  • 15q26 region shown to contain QTLs influencing
    inflammatory disorders
  • Zamani et al (1996). Hum Genet 98, 491-6.
  • Field et al (1994). Nat Genet 8, 189-94.
  • Blacker et al (2003).Hum Mol Genet 12, 23-32.
  • Susi et al (2001). Scand J Gastroenterol 36,
    372-4.
  • Mahaney et al (2005) Unpublished.

43
SEPS1 Variant Identification
  • Sequenced 9.3kb including putative promoter,
    exons, introns and conserved regions in 50
    individuals from three different ethnic
    populations
  • 16 variants genotyped in cohort of 522 Caucasian
    individuals from 92 families
  • Plasma levels of IL-1?, IL-6 and TNF-? measured
  • Results analyzed for association using SOLAR

44
Association Analysis
IL-1? IL-6 TNF-?
45
BQTN Analysis
  • BQTN analysis strongly supported a model in which
    the G-105A SNP was responsible for the observed
    associations with estimated posterior
    probabilities of gt0.999, 0.95, and 0.79 (for
    TNF-?, IL-1?, and IL-6 respectively)
  • Analysis indicates the G-105A SNP is of direct
    functional consequence (or is highly correlated
    with a functional variant)
  • Analysis performed to test the functionality of
    this G-105A variant

46
Effect of A or G variant on SEPS1 promoter
activity under Tunicamycin stress conditions
P 0.00006
2.5
2
1.5
Promoter activity (fold change in luc activity
over basal)
1
0.5
0
A variant
G variant
47
Physiological Role of SEPS1
48
Exploring the Effects of the SEPS1 G-105A QTN
  • Looked at the in vivo effects of SEPS1 G-105A QTN
    on expression levels of SEPS1 and genes in the
    following Gene Ontology categories
  • Endoplasmic Reticulum
  • Unfolded Protein Response
  • Golgi Stack and Protein Transportation
  • Oxidative Stress

49
SEPS1 Expression is Correlated With Disease In
Vivo
50
SEPS1 G-105A QTN Influences Expression In Vivo
  • SEPS1 transcript is cis-regulated (as defined by
    quantitative trait linkage analysis).
  • The rare A variant is associated with decreased
    expression in lymphocytes (p 0.032).

51
SEPS1 G-105A Associated Genes
52
Acknowledgements
ChemGenex Pharmaceuticals Jeremy Jowett Greg
Collier
  • Southwest Foundation for Biomedical Research
  • Joanne Curran Eric Moses
  • Matt Johnson Catherine Jett
  • Tom Dyer Shelley Cole
  • Harald Göring Jean MacCluer
  • Charles Peterson
  • Tony Comuzzie
  • Laura Almasy

Special thanks to the Azar family of San Antonio
for their financial support of our research
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