The Identification of Human

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The Identification of Human Quantitative Trait
Loci
Dr John Blangero Southwest Foundation for
Biomedical Research ChemGenex Pharmaceuticals
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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?
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Quantitative Traits

• Usually closer to gene action than disease
  itself.
• Have superior statistical power.

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Quantitative Endophenotypes

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

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Liability The Threshold Model
Affecteds
Normals
Disease-Related Trait
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The process of finding and identifying
disease-related genes involves Objective
Prioritization.
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Different Diseases
Different Designs
Different Methods
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Family StudiesvsStudies of Unrelateds
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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
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You can exploit Linkage and Association
Information Jointly in Family Studies
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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
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Linkage DesignsvsAssociation Designs
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Power Linkage vs Association
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Example 1 Positional Candidate Genes

• QTL for serum leptin levels in the San Antonio
  Family Heart Study
• Highly replicated QTL

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Chromosome 2 Obesity QTL
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Bioinformatic Prioritization GeneSniffer Results
2p22
POMC
GCKR
UCN
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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
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POMC Pattern of LD
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POMC QTN Analysis Marginal Associations
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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.

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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.
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The Parallel Ranch 1,500 Processors
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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
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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.

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Linkage Conditional on POMC SNPs
Marginal LOD5.86 Conditional LOD3.05
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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.
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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.

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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.

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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

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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

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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.

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Heritabilities of Autosomal RefSeq Transcripts
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Cis-Regulated Expression QTLs
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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.

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Expression QTLs LOD gt 3
Approximately, 34 of QTLs are Cis. Effect size
(QTL-specific heritability) is 64 larger for Cis
QTLs.
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Cis Regulation UTS2 (urotensin 2 preprotein)
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Cis and Trans Regulation HBG2 (G-gamma globin)
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Trans Regulation LOC389472
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Mitochondrial QTLs Influencing Expression
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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
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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)

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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.

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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

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Association Analysis
IL-1? IL-6 TNF-?
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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

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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
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Physiological Role of SEPS1
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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

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SEPS1 Expression is Correlated With Disease In
Vivo
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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).

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SEPS1 G-105A Associated Genes
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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