Statistical Methods for Quantitative Trait Loci (QTL) Mapping II
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![Page 1: The Identification of Human Quantitative Trait Loci Dr John Blangero Southwest Foundation for Biomedical Research ChemGenex Pharmaceuticals.](https://reader035.fdocuments.in/reader035/viewer/2022062718/56649e955503460f94b99848/html5/thumbnails/1.jpg)
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 LocalizationWhere in the genome is the QTL located?
QTL IdentificationWhat is (are) the gene(s) involved?
QTL Allelic ArchitectureWhat 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
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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 Studiesvs
Studies 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 YesSibling pairs Yes Yes YesNuclear families Yes Yes YesExtended pedigrees Yes Yes Yes
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You can exploit: Linkage and Association
Information Jointly in Family Studies
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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
Relative Per-Subject Power to Localize QTLs
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Linkage Designsvs
Association 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
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Mb Coordinate
Hit
sc
ore
POMC
GCKR
UCN
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The ALL or NOTHING principle
Find all of the variation in the gene.
Preference: Resequence everyone (no bias against rare variants)
Alternative: Resequence a subset of individuals
What Do You Do With A Good Positional Candidate Gene?
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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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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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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.
BQTN analysis of POMC polymorphisms
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Linkage Conditional on POMC SNPs
MarginalLOD=5.86
Conditional LOD=3.05
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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. Don’t waste time with LD. It is your ENEMY.
What Do You Do With A Good Positional Candidate Region?
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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
0
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heritability estimate
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Cis-Regulated Expression QTLs
FDR Significant Transcripts
Expected Number
P-value cutoff
0.01 859 850 0.0004
0.05 1238 1177 0.0031
0.10 1569 1412 0.0080
0.25 2620 1966 0.0333
0.30 3000 2102 0.0480
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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 Epidemiology—using 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 > 3
Type of QTL Number of QTLs Mean LOD
Cis 693 9.46
Trans 1,325 3.53
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
FDR Significant Transcripts
Expected Number True
P-value cutoff
0.01 16 16 0.000008
0.05 35 33 0.000076
0.10 73 66 0.00035
0.25 159 127 0.00146
0.30 251 176 0.0037
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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 >0.999, 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
Pro
mot
er a
ctiv
ity(f
old
chan
ge in
luc
activ
ity o
ver
basa
l)
0
0.5
1
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2
2.5
A variant
Basal
Tunicamycin
G variant
P = 0.00006
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PM
Cytoplasm
ER lumenER lumen
Nucleus
Derlin-1
SelS
p97
Physiological Role of SEPS1
Misfoldedprotein
26S proteasome
Poly-Ub
Golgi
Cell survival
Secretion
PM
Foldedprotein
chaperone
chaperone
mRNA
Proteins
Activation of JNK, caspase12, NFkB
Cytokine production, Apoptosis
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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
Phenotype Direction of Correlation
P-value
2 Hr Glucose 0.027
Diabetes Risk 0.050
BMI 0.0006
Relative Fat 0.032
Triglycerides 0.0023
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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
Gene Correlation p-value Function
CSX2001 0.00096 Transcript. repressor
CSX2002 0.0022 Golgi traffic w/ER
CSX2003 0.0022 Golgi traffic w/ER
SEPN1 0.0025 ER stress
GRP94 0.0034 ER unfolded protein resp
GLUT3 0.0055 Oxidative stress
STX6 0.0068 TFNalpha secretion
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Acknowledgements
Southwest Foundation for Biomedical Research
Joanne Curran Eric MosesMatt Johnson Catherine JettTom Dyer Shelley ColeHarald Göring Jean MacCluerCharles PetersonTony ComuzzieLaura Almasy
ChemGenex Pharmaceuticals
Jeremy Jowett
Greg Collier
Special thanks to the Azar family of San Antonio for their financial support of our research