Tom Price

MRC SGDP Centre, Institute of Psychiatry

Linkage analysis and eQTL studies

Systems Biomedicine Graduate Programme 2008/9

Genetic Linkage Studies• Use the inheritance of markers within families to identify chromosomal regions where disease genes may lie

Disease susceptibility gene

Genetic markers

M7

M1

M2

M3

M4

M5M6

Linkage Pedigree

Random chance? Or linkage between marker and disease locus?

1 12 2

2 1 3 3

1 3 1 3 1 3 2 3 2 3 2 3

Disease cases

Genotype

The Possibilities

MendelianOne gene = one traitCystic fibrosis

Non-MendelianMultiple genes and environmentEpilepsy, liability to stroke

Quantitative traitsMultiple genes and environment

HeightS

IMP

LEC

OM

PLE

X

CONTINUOUS DISCRETE

Multiple alleles of a single geneDifferent alleles different effects

Trinucleotide repeat diseases

• Laws of heredity discovered by Mendel 1865

– Three laws of heredity

One Gene, One Trait?

Mendel’s Laws

1. Dominance• When two contrasting characters are crossed only one

appears in the next generation

2. Segregation• For each trait, a gamete carries only one of the two

parental alleles

3. Independent assortment• Alleles for different traits are inherited independently of

each other

Dominance for Hair Colour

Mendel’s Laws

1. Dominance• When two contrasting characters are crossed only one

appears in the next generation

2. Segregation• For each trait, a gamete carries only one of the two

parental alleles

3. Independent assortment• Alleles for different traits are inherited independently of

each other

Segregation

AB CD

A

C

DD

C

D C

C

Parental Genotypes

Mendel’s Laws

1. Dominance• When two contrasting characters are crossed only one

appears in the next generation

2. Segregation• For each trait, a gamete carries only one of the two

parental alleles

3. Independent assortment• Alleles for different traits are inherited independently of

each other

Independent Assortment

• Eye colour IS NOT predictable from hair colour– Blonde hair and brown or blue eyes

– Brown hair and blue or brown eyes

Mendel’s Laws

1. Dominance• When two contrasting characters are crossed only one

appears in the next generation

2. Segregation• For each trait, a gamete carries only one of the two

parental alleles

3. Independent assortment• Alleles for different traits are inherited independently of

each other

Independent Assortment

• Eye colour IS often predictable from hair colour– Blonde hair and blue eyes

– Brown hair and dark eyes

What is Linkage?

• A method to map the relative positions of two or more loci using genetic markers

– Occurs because loci do not obey Mendel’s third law

Breaking the Third Law

A, B, O = blood group genes affected, unaffected

Adapted from Phillip McLean http://www.ndsu.nodak.edu/instruct/mcclean/plsc431/linkage/

Breaking the Third Law

A, B, O = blood group alleles affected, unaffected

Adapted from Phillip McLean http://www.ndsu.nodak.edu/instruct/mcclean/plsc431/linkage/

Breaking the Third Law

A, B, O = blood group alleles affected, unaffected

ABO locus predictsD locus

Adapted from Phillip McLean http://www.ndsu.nodak.edu/instruct/mcclean/plsc431/linkage/

Genetics for Card Players

♠ We can think of genetic information as a deck of cards.

♥ The closer 2 cards are, the less likely it is that they will separate during shuffling.

♣ If not much shuffling has occurred, more distant cards can act as markers.

Linkage Groups

• If inheritance of two loci is independent– They are unlinked

• If inheritance of two loci is dependent– They are in the same linkage group– Linkage groups correspond to the physical

structures called chromosomes

Chromosomes

• Chromosomes are NOT inherited as a single block

• Recombination occurs at meiosis– Affects co-inheritance

of alleles

Recombination and Meiosis

• Nearby loci A and B are likely to co-segregate during meiosis.

• Distant loci B and C are less likely to co-segregate during meiosis.

Recombination

• For any pair of markers– Parental pattern = NR– Mixed pattern = R

AaBb

ccdd

AcBd

AcBd

Acbd

acBd

NR NR R R

AB

ab

Non-recombinant gametes

Recombination

• For any pair of markers– Parental pattern = NR– Mixed pattern = R

AaBb

ccdd

AcBd

AcBd

Acbd

acBd

NR NR R R

Ab

aB

Recombinant gametes

Recombination

• For any pair of markers– Parental pattern = NR– Mixed pattern = R

AaBb

ccdd

AcBd

AcBd

Acbd

acBd

NR NR R R

Recombination Fraction

= The proportion of offspring that are recombinant between two loci

• RF = 0.5 between unlinked loci (e.g. different chromosomes)

Parametric Linkage Analysis

• Uses pedigree information to estimate recombination fraction between markers and disease

• Assumes a particular model of inheritance (additive, dominant, recessive)

• Useful for Mendelian disorders (single gene)

Allele Sharing

• People with rare diseases are more highly related to each other near the disease-causing gene than you would typically expect.

• This is because nearby markers tend to be inherited together with the disease locus.

→We can look for excess allele sharing as a signal that a disease locus is nearby.

Identity By State

• When two individuals possess the same alleles at a locus, they are said to be identical by state (IBS).

• For example, these affected sibs share one allele IBS, the allele a.

adac

Identity By State

• But if the parental genotypes are unknown, we do not know whether the offspring have inherited the a allele from the same parent or from different parents.

• We can’t established shared inheritance, so IBS allele sharing is useless for linkage analysis.

adac

?? ??

Identity By Descent

• Individuals who share copies of a common ancestral allele are said to be identical by descent (IBD).

• For example, these affected sibs share one allele IBD. The paternal allele a has been transmitted to both offspring.

ab cd

adac

Allele Sharing in Affected Sib Pair

ab cd

??ac

Sibling genotypes

Alleles shared IBD

Expected Probability

ac ac 2 ¼

ac ad 1 ½

ac bc 1 ½

ac bd 0 ¼

Allele Sharing in Affected Sib Pair

ab cd

??ac

Sibling genotypes

Alleles shared IBD

Expected Probability

ac ac 2 ¼

ac ad 1 ½

ac bc 1 ½

ac bd 0 ¼

Probability under random transmission of marker alleles.

Allele Sharing in Affected Sib Pair

ab cd

??ac

Sibling genotypes

Alleles shared IBD

Expected Probability

ac ac 2 ¼

ac ad 1 ½

ac bc 1 ½

ac bd 0 ¼

Probability under random transmission of marker alleles. But what if the marker lies near a disease gene? Affected siblings are more likely to share marker alleles IBD.

Non-parametric Linkage Analysis

• Uses information on IBD allele sharing – Usually between affected sibs

• Do not need to specify the model of inheritance at any locus

• Useful for complex traits (multiple genes, different modes of inheritance)

Linkage Statistic for Affected Sib Pairs

Alleles IBD 0 1 2

Expect 0.25 0.50 0.25

Observed Z0 Z1 Z2

Under linkage

Linkage Statistic for Affected Sib Pairs

Alleles IBD 0 1 2

Expect 0.25 0.50 0.25

Observed Z0 Z1 Z2

Under linkage

Suppose x families share 0 alleles IBD,

y families share 1 allele IBD,

z families share 2 alleles IBD.

Under a multinomial model, the expected probability of the marker data Z0, Z1, Z2 assuming no linkage is

P( Z0, Z1, Z2 ) = x! y! z! 0.25 x 0.5 y 0.25 z

(x+y+z)!

Linkage Statistic for Affected Sib Pairs

Alleles IBD 0 1 2

Expect 0.25 0.50 0.25

Observed Z0 Z1 Z2

Under linkage

Suppose x families share 0 alleles IBD,

y families share 1 allele IBD,

z families share 2 alleles IBD.

LOD = log10 P(marker data given estimated sharing Z0, Z1, Z2 )

P(marker data given sharing 0.25, 0.5, 0.25)

= log10 Z0x Z1

y Z2z

0.25 x 0.5 y 0.25 z

Example: 200 ASPs

Sharing among 200 affected sibling pairs

0 1 2

Observed sharing 36 90 74

Expected sharing 50 100 50

• Z0 = 36/200 = 0.18

• Z1 = 90/200 = 0.45

• Z2 = 74/200 = 0.37

Recall: baseline values0.250.50.25

Example: 200 ASPs

Sharing among 200 affected sibling pairs

0 1 2

Observed sharing 36 90 74

Expected sharing 50 100 50

• Z0 = 36/200 = 0.18

• Z1 = 90/200 = 0.45

• Z2 = 74/200 = 0.37

• LOD = log10 0.1836 0.4590

0.3774

0.2536 0.590

0.2574

= 3.35 STRONG EVIDENCE FOR LINKAGE

Recall: baseline values0.250.50.25

Complications of Linkage Analysis

• With unknown parental genotypes, allele sharing must be estimated using population allele frequencies

• Families with less than four alleles may give unclear sharing

• Multipoint linkage analysis, using information from adjacent markers, will increase power to detect genes

• Computationally intensive: use computer programs to calculate LOD scores

• Other problems due to non-paternity, genotyping errors, sample mix-ups, poor phenotype definition

?? ??ab cd

Software• Several programs are available, including:

– Parametric:

LINKAGE

MLINK

– Non-parametric:

MERLIN

GENEHUNTER

Linkage Study Design

Candidate gene search: dense marker genotyping within a region of positional or functional interest

Genome search: - Aim to identify several susceptibility genes• Families are genotyped on polymorphic markers across all

chromosomes• 300-400 microsatellite markers across genome, separated

by 10cM(or, more recently, 10,000 SNP markers)– tighter marker spacing gives more information– few markers makes it difficult to reconstruct haplotypes,

particularly without parental genotypes

Significance Level

• Lander and Kruglyak (1994) suggested criteria for affected sibling pair studies in complex diseases

LOD score > 2.2 suggestive linkage

LOD score > 3.6 significant linkage

• These LOD scores are expected to occur by chance in 1 and 1/20 times in a genome search, respectively

• Many studies of complex disease do not reach these cut-offs

• Another approach is to report highest LOD scores even if they are below these thresholds and look for replication across studies

Does It Work?

• Very powerful for mapping single gene disorders, e.g. early-onset Alzheimer’s Disease, many forms of mental retardation…

Does It Work?

• Very powerful for mapping single gene disorders, e.g. early-onset Alzheimer’s Disease, many forms of mental retardation…

• …but many non-replications for complex traits

Linkage v Association

Linkage Association

Usual sample

Families Unrelated individuals (e.g. case control)

Good for finding

Rare variants with large effects

Common variants with small effects

Identifies Broad chromosomal region

Narrow region usually within a single gene

Break

• Next up: application of linkage analysis to gene expression phenotypes.

Central Dogma

DNA → mRNA → protein

Finding Disease Pathways1. Conduct linkage/association study to find candidate2. Determine candidate gene function experimentally

Problems:• Markers only give regional information, the identity

of the causal variations remains obscure• Many GWAS hits are nowhere near any genes• Reliance on animal and in vitro models to probe

function

Genetics of Gene Expression• Linkage study or GWAS using mRNA abundance as

the phenotype

Motivation:• mRNA abundance as ‘endophenotype’

– Lies on causal path between genetic variation and disease• Hits (‘eQTLs’) may have less complex inheritance

– Larger effect sizes, fewer causal variants?• We may already know which transcripts are

dysregulated in diseased tissues– eQTLs can provide a link to finding susceptibility genes

The First eQTL Study

Cis Regulation

• Genetic variation near the gene locus that influence its expression

• What we think of as “functional” polymorphisms fall into this category

Trans Regulation

• Genetic variation away from the gene locus that influence its expression

• e.g. polymorphisms in “hub” genes that act as master regulators

C

AB

ED

Microarray Experiment

Human eQTL Studies

1st Author Year Journal Population Sample Tissue Measure Genotyping

Morley 2004 Nature CEPH 14 pedigrees LCL 8K Affy

Linkage scan

Monks 2004 AJHG CEPH 15 pedigrees LCL 25K oligo

Linkage scan

Dixon 2007 Nat Gen MRC-A 206 families LCL 54K Affy

Linkage scan

Goring 2007 Nat Gen SAFHS 1240 individuals Lympho-cytes

47K Illumina

Illumina 100K

Stranger 2007 Science HapMap 270 individuals LCL 47K Illumina

2M SNPs + 7K CNVs

Emilsson 2008 Nature IFB/IFA 1002/673 individuals

Blood/ Adipose

25K oligo

Illumina 370K + Linkage scan

Selected list

• Largest human eQTL study to date • 1,240 subjects from extended pedigrees• Blood lymphocytes, not lymphocyte cell

lines• 47K Illumina WG-6 Series I microarray• Expression adjusted for age, sex

Heritability

85% of 19,648 transcripts detected were heritable (FDR 5%)

Cis Regulation

• Single LOD score calculated at gene locus to identify cis-regulated transcripts

• 1,345 (6.8%) cis-regulated transcripts detected (FDR 5%)

• eQTL effect size overall: median 1.8%, mean 5.0%

• eQTL effect size in significant loci:median 24.6%, mean 29.1%

Trans Regulation

• Much lower power• No evidence of master regulators:

only 58 transcripts had 2+ peaks with LOD > 3

Gene Discovery Using eQTLs

Promoter variants in VNN1 are associated with transcript abundance and HDL-C concentration

Consistency of Results

• Morley cis eQTLs confirmed by Göring, but not trans eQTLs.

• This is consistent with tissue specificity of trans regulation, but also with lower power to detect trans effects.

• Linkage & association study• Icelandic subjects• Blood and adipose tissue samples• Expression adjusted for age, sex, BMI

Tissue Specificity

• Linkage eQTLs (FDR 5%) for 20,877 expression traits, 10,364 of them heritable (FDR 5%)

Cis Trans

Blood 2,529 52

Adipose 1,489 25

Both 762 ?

Proximity of Cis Acting Variants

• Association eSNPs were within 100kb of the probe for 96% of expression traits with strong cis-acting effects

Potential Problem

• Microarray probes overlapping SNPs can give rise to spurious cis eQTLs

• Older studies did not have so much resequencing data available to identify probes containing SNPs

Further Directions

• Animal models (e.g. mouse F2 crosses)• Other tissues (e.g. mouse brain)• Evoked phenotypes

– genetics of expression response to e.g. ionizing radiation, drug/hormone treatment

• Causal modelling– Genotype data can establish whether expression

changes cause disease or are a consequence of itSchadt et al. (2005) Nature Genetics  37: 710-717.

Website

http://tomprice.net/

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