Statistical methods for quantitative trait loci qtl mapping ii
Download
1 / 33

Statistical Methods for Quantitative Trait Loci (QTL) Mapping II - PowerPoint PPT Presentation


  • 113 Views
  • Uploaded on

Statistical Methods for Quantitative Trait Loci (QTL) Mapping II. Lectures 5 – Oct 12, 2011 CSE 527 Computational Biology, Fall 2011 Instructor: Su-In Lee TA: Christopher Miles Monday & Wednesday 12:00-1:20 Johnson Hall (JHN) 022. Course Announcements. HW #1 is out Project proposal

loader
I am the owner, or an agent authorized to act on behalf of the owner, of the copyrighted work described.
capcha
Download Presentation

PowerPoint Slideshow about ' Statistical Methods for Quantitative Trait Loci (QTL) Mapping II' - quasar


An Image/Link below is provided (as is) to download presentation

Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author.While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server.


- - - - - - - - - - - - - - - - - - - - - - - - - - E N D - - - - - - - - - - - - - - - - - - - - - - - - - -
Presentation Transcript
Statistical methods for quantitative trait loci qtl mapping ii

Statistical Methods for Quantitative Trait Loci (QTL) Mapping II

Lectures 5 – Oct 12, 2011

CSE 527 Computational Biology, Fall 2011

Instructor: Su-In Lee

TA: Christopher Miles

Monday & Wednesday 12:00-1:20

Johnson Hall (JHN) 022


Course announcements
Course Announcements Mapping II

  • HW #1 is out

  • Project proposal

    • Due next Wed

    • 1 paragraph describing what you’d like to work on for the class project.


Why are we so different
Why are we so different? Mapping II

Any observable characteristic or trait

  • Human genetic diversity

  • Different “phenotype”

    • Appearance

    • Disease susceptibility

    • Drug responses

      :

  • Different “genotype”

    • Individual-specific DNA

    • 3 billion-long string

TGATCGAAGCTAAATGCATCAGCTGATGATCCTAGC…

TGATCGTAGCTAAATGCATCAGCTGATGATCGTAGC…

……ACTGTTAGGCTGAGCTAGCCCAAAATTTATAGCGTCGACTGCAGGGTCCACCAAAGCTCGACTGCAGTCGACGACCTAAAATTTAACCGACTACGAGATGGGCACGTCACTTTTACGCAGCTTGATGATGCTAGCTGATCGTAGCTAAATGCATCAGCTGATGATCGTAGCTAAATGCATCAGCTGATGATCGTAGCTAAATGCATCAGCTGATGATCGTAGCTAAATGCATCAGCTGATTCACTTTTACGCAGCTTGATGACGACTACGAGATGGGCACGTTCACCATCTACTACTACTCATCTACTCATCAACCAAAAACACTACTCATCATCATCATCTACATCTATCATCATCACATCTACTGGGGGTGGGATAGATAGTGTGCTCGATCGATCGATCGTCAGCTGATCGACGGCAG……

TGATCGCAGCTAAATGCAGCAGCTGATGATCGTAGC…


Motivation

AG Mapping II

GTC

Instruction

Different instruction

XX

XXX

DNA – 3 billion long!

ACTTCGGAACATATCAAATCCAACGC

cell

Motivation

  • Appearance, Personality, Disease susceptibility, Drug responses, …

  • Which sequence variation affects a trait?

    • Better understanding disease mechanisms

    • Personalized medicine

Sequence variations

Obese? 15%

Bold? 30%

Diabetes? 6.2%

Parkinson’s disease? 0.3%

Heart disease? 20.1%

Colon cancer? 6.5%

:

cell

A different person

A person


Qtl mapping

3000 markers Mapping II

mouse

individuals

1

1

0

:

0

0

1

0

:

0

1

0

0

:

0

QTL mapping

Genotype data

Phenotype data

1 2 3 4 5 … 3,000

0101100100…011

1011110100…001

0010110000…010

:

0000010100…101

0010000000…100

:

  • Data

    • Phenotypes: yi = trait value for mouse i

    • Genotypes: xik = 1/0 (i.e. AB/AA) of mouse i at marker k

    • Genetic map: Locations of genetic markers

  • Goals: Identify the genomic regions (QTLs) contributing to variation in the phenotype.


Outline

mouse Mapping II

individuals

1

1

0

:

0

0

1

0

:

0

1

0

0

:

0

Outline

  • Statistical methods for mapping QTL

    • What is QTL?

    • Experimental animals

    • Analysis of variance (marker regression)

    • Interval mapping (EM)

QTL?

1 2 3 4 5 … 3,000

:


Interval mapping lander and botstein 1989
Interval mapping Mapping II[Lander and Botstein, 1989]

  • Consider any one position in the genome as the location for a putative QTL.

  • For a particular mouse, let z = 1/0 if (unobserved) genotype at QTL is AB/AA.

  • Calculate P(z = 1 | marker data).

    • Need only consider nearby genotyped markers.

    • May allow for the presence of genotypic errors.

  • Given genotype at the QTL, phenotype is distributed as N(µ+∆z, σ2).

  • Given marker data, phenotype follows a mixture of normal distributions.


Im the mixture model
IM: the mixture model Mapping II

Nearest flanking markers

99% AB

M1/M2

65% AB

35% AA

M1 QTL M2

35% AB

65% AA

0 7 20

99% AA

  • Let’s say that the mice with QTL genotype AA have average phenotype µA while the mice with QTL genotype AB have average phenotype µB.

  • The QTL has effect ∆ = µB - µA.

  • What are unknowns?

    • µA and µB

    • Genotype of QTL


Im estimation and lod scores
IM: estimation and LOD scores Mapping II

Use a version of the EM algorithm to obtain estimates of µA, µB, σ and expectation on z (an iterative algorithm).

Calculate the LOD score

Repeat for all other genomic positions (in practice, at 0.5 cM steps along genome).


A simulated example
A simulated example Mapping II

Genetic markers

LOD score curves


Interval mapping
Interval mapping Mapping II

  • Advantages

    • Make proper account of missing data

    • Can allow for the presence of genotypic errors

    • Pretty pictures

    • High power in low-density scans

    • Improved estimate of QTL location

  • Disadvantages

    • Greater computational effort (doing EM for each position)

    • Requires specialized software

    • More difficult to include covariates

    • Only considers one QTL at a time


Statistical significance
Statistical significance Mapping II

Null hypothesis – assuming that there are no QTLs segregating in the population.

Null distribution of the LOD scores at a particular genomic position (solid curve)

Null distribution of the LOD scores at a particular genomic position (solid curve) and of the maximum LOD score from a genome scan (dashed curve).

Large LOD score → evidence for QTL

Question: How large is large?

Answer 1: Consider distribution of LOD score if there were no QTL.

Answer 2: Consider distribution of maximum LOD score.

Only ~3% of chance that the genomic position gets LOD score≥1.


Lod thresholds
LOD thresholds Mapping II

  • To account for the genome-wide search, compare the observed LOD scores to the null distribution of the maximum LOD score, genome-wide, that would be obtained if there were no QTL anywhere.

  • LOD threshold = 95th percentile of the distribution of genome-wide max LOD, when there are no QTL anywhere.

  • Methods for obtaining thresholds

    • Analytical calculations (assuming dense map of markers) (Lander & Botstein, 1989)

    • Computer simulations

    • Permutation/ randomized test (Churchill & Doerge, 1994)


More on lod thresholds
More on LOD thresholds Mapping II

  • Appropriate threshold depends on:

    • Size of genome

    • Number of typed markers

    • Pattern of missing data

    • Stringency of significance threshold

    • Type of cross (e.g. F2 intercross vs backcross)

    • Etc


An example
An example Mapping II

Permutation distribution for a trait


Modeling multiple qtls
Modeling multiple QTLs Mapping II

Trait variation that is not explained by a detected putative QTL.

The effect of QTL1 is the same, irrespective of the genotype of QTL 2, and vice versa

The effect of QTL1 depends on the genotype of QTL 2, and vice versa

  • Advantages

    • Reduce the residual variation and obtain greater power to detect additional QTLs.

    • Identification of (epistatic) interactions between QTLs requires the joint modeling of multiple QTLs.

  • Interactions between two loci


Multiple marker model
Multiple marker model Mapping II

  • Let y = phenotype,

    x = genotype data.

  • Imagine a small number of QTL with genotypes x1,…,xp

    • 2p or 3p distinct genotypes for backcross and intercross, respectively

  • We assume that

    E(y|x) = µ(x1,…,xp), var(y|x) = σ2(x1,…,xp)


Multiple marker model1
Multiple marker model Mapping II

  • Constant variance

    • σ2(x1,…,xp)=σ2

  • Assuming normality

    • y|x ~ N(µg, σ2)

  • Additivity

    • µ(x1,…,xp) = µ + ∑j ∆jxj

  • Epistasis

    • µ(x1,…,xp) = µ + ∑j ∆jxj + ∑j,k wj,kxjxk


Computational problem
Computational problem Mapping II

  • N backcross individuals, M markers in all with at most a handful expected to be near QTL

  • xij = genotype (0/1) of mouse i at marker j

  • yi= phenotype (trait value) of mouse i

  • Assuming addivitity,

    yi = µ + ∑j ∆jxij + e which ∆j ≠ 0?

    Variable selection in linear regression models


Mapping qtl as model selection

x Mapping II1

x2

Mapping QTL as model selection

xN

w2

w1

wN

Phenotype (y)

y = w1 x1+…+wN xN+ε

minimizew (w1x1 + … wNxN - y)2?

  • Select the class of models

    • Additive models

    • Additive with pairwise interactions

    • Regression trees


Linear regression
Linear Regression Mapping II

x1

x2

w2

w1

wN

xN

w2

w1

wN

parameters

Phenotype (y)

Y = w1 x1+…+wN xN+ε

minimizew (w1x1 + … wNxN - y)2+model complexity

  • Search model space

    • Forward selection (FS)

    • Backward deletion (BE)

    • FS followed by BE


Lasso l 1 regression
Lasso* (L Mapping II1) Regression

x1

x2

x1

x2

w2

w1

L1 term

xN

L2

L1

w2

w1

wN

parameters

Phenotype (y)

minimizew (w1x1 + … wNxN - y)2+  C |wi|

  • Induces sparsity in the solution w (many wi‘s set to zero)

    • Provably selects “right” features when many features are irrelevant

  • Convex optimization problem

    • No combinatorial search

    • Unique global optimum

    • Efficient optimization

* Tibshirani, 1996


Model selection
Model selection Mapping II

  • Compare models

    • Likelihood function + model complexity (eg # QTLs)

    • Cross validation test

    • Sequential permutation tests

  • Assess performance

    • Maximize the number of QTL found

    • Control the false positive rate


Outline1
Outline Mapping II

  • Basic concepts

    • Haplotype, haplotype frequency

    • Recombination rate

    • Linkage disequilibrium

  • Haplotype reconstruction

    • Parsimony-based approach

    • EM-based approach


Review genetic variation
Review: genetic variation Mapping II

  • Single nucleotide polymorphism (SNP)

    • Each variant is called an allele; each allele has a frequency

  • Hardy Weinberg equilibrium (HWE)

    • Relationship between allele and genotype frequencies

  • How about the relationship between alleles of neighboring SNPs?

    • We need to know about linkage (dis)equilibrium



History of two neighboring alleles
History of two neighboring alleles Mapping II

Before mutation

A

After mutation

A

Mutation

C

Alleles that exist today arose through ancient mutation events…


History of two neighboring alleles1
History of two neighboring alleles Mapping II

  • One allele arose first, and then the other…

Before mutation

A

G

C

G

After mutation

G

A

C

G

Mutation

C

C

Haplotype: combination of alleles present in a chromosome


Recombination can create more haplotypes
Recombination can create more haplotypes Mapping II

G

A

C

C

G

A

C

C

A

C

C

G

No recombination (or 2n recombination events)

Recombination


Without recombination Mapping II

A

G

G

C

C

C

With recombination

A

G

G

C

C

C

C

A

Recombinant haplotype


Haplotype
Haplotype Mapping II

  • Consider N binary SNPs in a genomic region

  • There are 2N possible haplotypes

    • But in fact, far fewer are seen in human population

A combination of alleles present in a chromosome

Each haplotype has a frequency, which is the proportion of chromosomes of that type in the population


More on haplotype
More on haplotype Mapping II

  • What determines haplotype frequencies?

    • Recombination rate (r) between neighboring alleles

    • Depends on the population

    • r is different for different regions in genome

  • Linkage disequilibrium (LD)

    • Non-random association of alleles at two or more loci, not necessarily on the same chromosome.

  • Why do we care about haplotypes or LD?


References
References Mapping II

  • Prof Goncalo Abecasis (Univ of Michigan)’s lecture note

  • Broman, K.W., Review of statistical methods for QTL mapping in experimental crosses

  • Doerge, R.W., et al. Statistical issues in the search for genes affecting quantitative traits in experimental populations. Stat. Sci.; 12:195-219, 1997.

  • Lynch, M. and Walsh, B. Genetics and analysis of quantitative traits. Sinauer Associates, Sunderland, MA, pp. 431-89, 1998.

  • Broman, K.W., Speed, T.P. A review of methods for identifying QTLs in experimental crosses, 1999.


ad