The Identification of Human Quantitative Trait Loci

Dr John Blangero

Southwest Foundation for Biomedical Research

ChemGenex Pharmaceuticals

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?

Quantitative Traits

Usually closer to gene action than disease itself.

Have superior statistical power.

Quantitative Endophenotypes

Heritable

Genetically correlated with disease or other focal phenotype

Closer to the action of the genes

Liability: The Threshold Model

The process of finding and identifying disease-related genes

involves

Objective Prioritization.

Different Diseases

Different Designs

Different Methods

Family Studiesvs

Studies of Unrelateds

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

You can exploit: Linkage and Association

Information Jointly in Family Studies

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

Linkage Designsvs

Association Designs

Power: Linkage vs Association

Example 1: Positional Candidate Genes

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

Highly replicated QTL

Chromosome 2 Obesity QTL

Bioinformatic Prioritization: GeneSniffer Results 2p22

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GCKR

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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?

POMC: Pattern of LD

POMC QTN Analysis: Marginal Associations

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.

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.

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

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

Linkage Conditional on POMC SNPs

MarginalLOD=5.86

Conditional LOD=3.05

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?

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.

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.

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

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

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.

Heritabilities of Autosomal RefSeq Transcripts

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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

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.

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.

Cis Regulation UTS2 (urotensin 2 preprotein)

Cis and Trans Regulation HBG2 (G-gamma globin)

Trans Regulation LOC389472

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

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

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)

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.

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

Association Analysis

IL-1

IL-6

TNF-

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

Effect of A or G variant on SEPS1 promoter activity under Tunicamycin stress conditions

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ER lumenER lumen

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Derlin-1

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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

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

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

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).

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

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