1. Tom Price MRC SGDP Centre, Institute of Psychiatry
Linkage analysis and eQTL studies
Tom Price
MRC SGDP Centre, Institute of Psychiatry
Systems Biomedicine Graduate Programme 2008/9

2. Genetic Linkage Studies
Use the inheritance of markers within families to identify chromosomal regions where disease genes may lie
Genetic markers M7 M1 M2 M3 M4 M5 M6
Disease
susceptibility gene

3. Or linkage between marker and disease locus?
Linkage Pedigree
2 2
1 1
Disease cases
Genotype
2 1
3 3
1 3
1 3
1 3
2 3
2 3
2 3
Random chance?
Or linkage between marker and disease locus?

4. The Possibilities SIMPLE Multiple alleles of a single gene
Different alleles different effects
Trinucleotide repeat diseases
Mendelian
One gene = one trait
Cystic fibrosis
CONTINUOUS DISCRETE
Quantitative traits
Multiple genes and environment
Height
Non-Mendelian
Multiple genes and environment
Epilepsy, liability to stroke
COMPLEX

5. One Gene, One Trait? Three laws of heredity
Laws of heredity discovered by Mendel 1865
Three laws of heredity

6. Mendel’s Laws Dominance Segregation Independent assortment
When two contrasting characters are crossed only one appears in the next generation
Segregation
For each trait, a gamete carries only one of the two parental alleles
Independent assortment
Alleles for different traits are inherited independently of each other

7. Dominance for Hair Colour

8. Mendel’s Laws Dominance Segregation Independent assortment
When two contrasting characters are crossed only one appears in the next generation
Segregation
For each trait, a gamete carries only one of the two parental alleles
Independent assortment
Alleles for different traits are inherited independently of each other

9. Segregation AB CD
Parental Genotypes D C A C C D D C

10. Mendel’s Laws Dominance Segregation Independent assortment
When two contrasting characters are crossed only one appears in the next generation
Segregation
For each trait, a gamete carries only one of the two parental alleles
Independent assortment
Alleles for different traits are inherited independently of each other

11. Independent Assortment
Eye colour IS NOT predictable from hair colour
Blonde hair and brown or blue eyes
Brown hair and blue or brown eyes

12. X Mendel’s Laws Dominance Segregation Independent assortment
When two contrasting characters are crossed only one appears in the next generation
Segregation
For each trait, a gamete carries only one of the two parental alleles
Independent assortment
Alleles for different traits are inherited independently of each other
Failure of the third law makes gene-finding possible. X

13. Independent Assortment
Eye colour IS often predictable from hair colour
Blonde hair and blue eyes
Brown hair and dark eyes

14. 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

15. Breaking the Third Law A, B, O = blood group genes
affected, unaffected
Adapted from Phillip McLean

16. Breaking the Third Law A, B, O = blood group alleles
affected, unaffected
Adapted from Phillip McLean

17. Breaking the Third Law ABO locus predicts D locus
A, B, O = blood group alleles
affected, unaffected
Adapted from Phillip McLean

18. 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.

19. 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

20. Chromosomes Chromosomes are NOT inherited as a single block
Recombination occurs at meiosis
Affects co-inheritance of alleles

21. 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.

22. Recombination For any pair of markers Parental pattern = NR
Mixed pattern = R Aa Bb cc dd A B a b
Non-recombinant gametes Ac Bd Ac Bd Ac bd ac Bd
NR NR R R

23. Recombination For any pair of markers Parental pattern = NR
Mixed pattern = R Aa Bb cc dd A b a B
Recombinant gametes Ac Bd Ac Bd Ac bd ac Bd
NR NR R R

24. Recombination For any pair of markers Parental pattern = NR
Mixed pattern = R Aa Bb cc dd Ac Bd Ac Bd Ac bd ac Bd
NR NR R R

25. Recombination Fraction
= The proportion of offspring that are recombinant between two loci
RF = 0.5 between unlinked loci (e.g. different chromosomes)

26. 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)

27. 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.

28. 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. ac ad

29. 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. ?? ?? ac ad

30. 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 ac ad

31. Allele Sharing in Affected Sib Pair
Sibling genotypes
Alleles shared IBD
Expected Probability
ac ac 2
ac ad 1
ac bc
ac bd ab cd ac ??

32. Allele Sharing in Affected Sib Pair
Sibling genotypes
Alleles shared IBD
Expected Probability
ac ac 2
ac ad 1
ac bc
ac bd ab cd ac ??
Probability under random transmission of marker alleles.

33. Allele Sharing in Affected Sib Pair
Sibling genotypes
Alleles shared IBD
Expected Probability
ac ac 2
ac ad 1
ac bc
ac bd ab cd ac ??
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.

34. 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)

35. Linkage Statistic for Affected Sib Pairs
Alleles IBD 0 1 2
Expect
Observed Z0 Z1 Z2
Under linkage   

36. Linkage Statistic for Affected Sib Pairs
Alleles IBD
Expect
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! x 0.5 y z
(x+y+z)!

37. Linkage Statistic for Affected Sib Pairs
Alleles IBD 0 1 2
Expect
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)
= log Z0x Z1y Z2z
0.25 x 0.5 y z

38. Example: 200 ASPs Sharing among 200 affected sibling pairs 0 1 2
0 1 2
Observed sharing
Expected sharing
Z0 = 36/200 = 0.18
Z1 = 90/200 = 0.45
Z2 = 74/200 = 0.37
Recall: baseline values
0.25
0.5

39. Example: 200 ASPs Sharing among 200 affected sibling pairs 0 1 2
0 1 2
Observed sharing
Expected sharing
Z0 = 36/200 = 0.18
Z1 = 90/200 = 0.45
Z2 = 74/200 = 0.37
LOD = log
= STRONG EVIDENCE FOR LINKAGE
Recall: baseline values
0.25
0.5

40. Complications of Linkage Analysisab cd ?? ??
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

41. Software Several programs are available, including: LINKAGE MLINK
Parametric:
LINKAGE
MLINK
Non-parametric:
MERLIN
GENEHUNTER

42. 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
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

43. 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

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

45. 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

46. 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

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

48. Central Dogma
DNA → mRNA → protein

49. Finding Disease Pathways
Conduct linkage/association study to find candidate
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

50. 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

51. The First eQTL Study

52. Cis Regulation
Genetic variation near the gene locus that influence its expression
What we think of as “functional” polymorphisms fall into this category

53. 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 A B E D

54. Microarray Experiment

55. Human eQTL Studies Selected list 1st Author Year Journal Population
Sample
Tissue
Measure
Genotyping
Morley
2004
Nature
CEPH
14 pedigrees
LCL
8K Affy
Linkage scan
Monks
AJHG
15 pedigrees
25K oligo
Dixon
2007
Nat Gen
MRC-A
206 families
54K Affy
Goring
SAFHS
1240 individuals
Lympho-cytes
47K Illumina
Illumina 100K
Stranger
Science
HapMap
270 individuals
2M SNPs + 7K CNVs
Emilsson
2008
IFB/IFA
1002/673 individuals
Blood/ Adipose
Illumina 370K + Linkage scan
Selected list

56. 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

57. 85% of 19,648 transcripts detected
Heritability
85% of 19,648 transcripts detected
were heritable (FDR 5%)

58. 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%

59. Trans Regulation Much lower power No evidence of master regulators:
only 58 transcripts had 2+ peaks with LOD > 3

60. Gene Discovery Using eQTLs
Promoter variants in VNN1 are associated with transcript abundance and HDL-C concentration

61. 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.

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

63. Tissue Specificity Cis Trans Blood 2,529 52 Adipose 1,489 25 Both 762?
Linkage eQTLs (FDR 5%) for 20,877 expression traits, 10,364 of them heritable (FDR 5%)

64. Proximity of Cis Acting Variants
Association eSNPs were within 100kb of the probe for 96% of expression traits with strong cis-acting effects

65. 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

66. 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 it
Schadt et al. (2005) Nature Genetics  37:

67. Website
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