1

5BBB0214

Multifactorial Inheritance

2016

Dr. Baljinder S. Mankoo

baljinder.mankoo@kcl.ac.uk

Discuss the concept that multifactorial influences lead to complex traits

• Many genes acting in an additive manner • Interaction of environment • Continuous variation • Discontinuous variation: threshold liability • Adult onset diseases • Congenital defects • Concordance

Explain approaches to mapping genes involved in complex disease and know the strengths and weaknesses of each approach (using relevant examples)

• Non-parametric methods: Empiric risk Heritability Association studies: Candidate gene studies, GWA, Twin Studies

Learning Outcomes

2

3

Multifactorial Inheritance Reading

Lewis: Human Genetics Chapter 7 Multifactorial traits Chapter 8 Genetics of behaviour Chapter 3 Twins biology Emery: Elements of Medical Genetics Chapter 9 Polygenic and multifactorial inheritance

Use the handout and read the full version on the course website

MCQ Quiz http://highered.mcgraw-hill.com/sites/007246268x/student_view0/chapter7/multiple_choice_quiz.html

4

Mendelian patterns of inheritance

5

Most Mendelian diseases are rare

Affecting about 1/10,000 to 1/100,000 live births

Common Mendelian diseases usually show high

frequency only in certain ethnic groups eg Tay-Sachs

in East European Jews, cystic fibrosis in west

Europeans, sickle cell anaemia in west Africans, or

where consanguinous marriages common

6

Multigenic disorders and genetic heterogeneity

Environmental effects

We shall not cover the following: Incomplete penetrance

Sex-limited expression

Parent-of-origin effects: imprinting

Pleiotropy

Anticipation

Mitochondrial inheritance

Causes of non-Mendelian inheritance

7

Contribution of Genes and/or Environment

Mendelian trait due to a single gene Polygenic trait due to multiple genes Multifactorial trait results from action of genes (often polygenic) and the environment, often show family clustering

8

Disorders showing multifactorial inheritance

Adult onset Diabetes mellitus Epilepsy Hypertension Ischaemic heart disease Mood disorders Obesity Psychotic disorders

9

Physical trait (disease)

Gene 1 Gene 2

Gene 3

Environment 3

Environment 2 Environment 1 Genetic factors:

Age Gender Lipoprotein polymorphisms Angiotensin I converting enzyme LDL receptor polymorphisms

Environmental factors:

Exercise level Dietary cholesterol Smoking Stress Hypertension

Coronary Artery Disease

GENETIC ENVIRONMENTAL

Duchenne muscular dystrophy

Haemophilia Osteogenesis imperfecta

Club foot Pyloric stenosis Dislocation of

hip

Peptic ulcer Diabetes

Tuberculosis

Phenylketonuria Galactosaemia

Spina bifida Ischaemic heart disease Ankylosing spondylitis

Scurvy

Contributions of genetic and environmental factors

Rare Genetics simple

Unifactorial High recurrence rate

Common Genetics complex Multifactorial Low recurrence rate

10

11

How common is common?

Type Incidence

at Birth (per 1,000)

Prevalence at Age 25

Years (per 1,000)

Population Prevalence (per 1,000)

Diseases due to genome/chromosome mutations

6 1.8 3.8

Diseases due to single gene mutations

10 3.6 20

Diseases with multifactorial inheritance

~50 ~50 ~600

12

Vocabulary

Discrete or qualitative trait a genetic disease trait that is either absent or present eg: cystic fibrosis, sickle cell anaemia Quantitative trait Traits that vary within the affected population Often measurable physiological or biochemical quantities Variation is continuous, not discrete Genes can have major or minor impacts Effect of genes is additive or synergistic also called quantitative trait loci (QTL) eg: height, high blood pressure, serum cholesterol

13

A quantatitive trait can be polygenic

Model: Alleles at two genes can produce five phenotypes Total number of human eye colour genes unknown, mice have >60

14

Many quantitative traits are influenced by combination of genes and the environment

Many genes + Trait environment Fingerprints prenatal touch Height nutrition Skin colour sun exposure

15

Height is influenced by genes

Normal distribution for each gender, and combined genders

Average height of males greater than females

Is this because males have a Y chromosome?

Is this because males have better nutrition than females?

90% of variation in adult height within a population explained by genetic variation

16

Effect of economic reforms on child growth in urban and rural areas of China

Shen et al. (1996) New Eng. J. Med. 335: 400-406

Improved nutrition (environment) can strongly impact genetic traits: average height has increased in successive generations

Environment can strongly impact on height

17

Causes of congenital abnormalities Genetic % Chromosomal 6 Single gene 7.5 Multigenic 20-30 Subtotal 30-40 Environmental Drugs and chemicals 2 Infections 2 Maternal Illness 2 Physical agents 1 Subtotal 5-10 Unknown 50

Multifactorial inheritance accounts for majority of

congenital abnormalities in which genetic factors have been

clearly implicated

Congenital Cleft lip/palate Congenital dislocation of the hip Congenital heart defects Neural tube defects Pyloric stenosis (5x more common in males 3 x more common in offspring of affected females)

18

More vocabulary

Recurrence risk: the likelihood that a trait or disorder present in one family member will occur again in other family members in the same or subsequent generations Concordance: two related individuals in a family have the same disease Discordance: one family member affected, other is not Concordance can occur even if the two affected individuals have different predisposing genotypes

PHENOCOPY: An environmentally caused trait that produces a phenotype that resembles a Mendelian disorder Eg thalidomide phenocopies phocomelia

19

1. Individual liability depends on combination of predisposing genes and exposure to environmental factors. Trait only appears once threshold is crossed

2. Disease can occur in isolation, with most affected children born to unaffected parents

3. Recurrence risk increases with the number of affected children in a family

Why Mendelian patterns of inheritance are not observed in multifactorial genetic diseases

20

4. The more severely affected an individual the more likely they will have an affected child

5. Disease occurs more frequently in one gender than in the other, but it is not a sex-limited trait 6. If the two sexes have a different probability of being affected, the least likely sex, if affected, is the most likely sex to produce an affected offspring

Why Mendelian patterns of inheritance are not observed in multifactorial genetic diseases

21

7. Risk of affected relatives falls off very quickly with the degree of relationship. Contrast this with autosomal dominant inheritance with incomplete penetrance, where the recurrence risk falls off proportionately with the degree of relationship 8. Concordance rates in monozygotic and dizygotic twins contradict Mendelian proportions 9. Environmental influences can increase or decrease the risk of the disease

Why Mendelian patterns of inheritance are not observed in multifactorial genetic diseases

22

Quantitative traits can produce discontinuous phenotypes

THRESHOLD MODEL : liability to develop disorder is normally distributed. Based on statistical analysis of clustering of traits in families Individual liability depends on combination of predisposing genes and exposure to environmental factors Trait only appears once threshold is crossed

Liability

Threshold

Some multifactorial conditions have an unequal sex ratio

Condition Sex ratio (males to females)

Pyloric stenosis 5 to 1 Hirschprung disease 3 to 1 Congenital dislocation of hip 1 to 6 Talipes 2 to 1 Rheumatoid arthritis 1 to 3 Peptic ulcer 2 to 1

Recurrence risk of pyloric stenosis higher in families where proband is female Females transmit a greater number of genes predisposing to disease This affects male offspring more than females 23

For some conditions there must be a different threshold for males and females

Threshold

Threshold

24

Some multifactorial conditions have an unequal sex ratio

25

How do we identify the genetic component?

Standard linkage (parametric method) depends upon assuming a specific mode of inheritance and then counting recombinant and nonrecombinant offspring in families

26

Why standard linkage is difficult for analyzing multifactorial traits

• Multiple genes with individually small risks often

implicated

• Environmental factors

• Variable age of onset

• Number of “informative meiosis” small

• Aetiological heterogeneity

26

27

Makes no assumption concerning number of loci or the role of the environment

Depends instead on assumption that two affected relatives will have disease-predisposing alleles in common

There is another way: Non-parametric methods

How evidence is gathered for genetic factors in complex diseases

Familial risks what is the incidence of a disorder in relatives compared with the

incidence in the general population? Association studies

how often is a particular region of the chromosome inherited from a common ancestor within the pedigree eg sib-pair analysis? OR how often is a particular haplotype associated with a trait in a large sample? - population studies

Twin studies what is the incidence in monozygotic compared with dizygotic twins? Adoption studies what is the incidence in adopted children of the disorders which their

parent had?

29

Non-parametric tools

1. Empiric risk - frequency in a specific population – predicts recurrence risks

2. Heritability - amount of phenotypic variation due to genetic variation

3. Association studies - case-control design searching for common genetic change in affected cases

30

1. Empiric risk

• A population statistic based on a large sample of affected patients and relatives

• Do not need to know mechanism of disease • Used to predict recurrence risk of a multifactorial trait

in a family • Increases with:

severity of disease number of family members affected closeness of relationship to affected individuals (the closer the relationship, the more genes they share in common) shared environmental influences

31

Relationship Degree Percent shared genes Identical twins 100% Siblings 1o 50% Parent and child 1o 50%

Uncle/aunt and niece/nephew 2o 25% Grandparent and grandchild 2o 25% First cousins 3o 12.5%

• Multifactorial traits: risk of affected relatives falls off very quickly with the degree of relationship. Contrast this with autosomal dominant inheritance with incomplete penetrance, where the recurrence risk falls off proportionately with the degree of relationship

32

33

1. Empiric risk: cleft palate

Empiric Risk of Recurrence

0.1%

0.3% 0.8% 3.5% 4.1% 40.0%

Relationship General population First cousin Niece or nephew Child Sibling Identical twin

Anomaly Risk to sibs %

Bilateral cleft lip and palate

5.7

Unilateral cleft lip and palate

4.2

Unilateral cleft lip alone

2.5

The more severe the manifestation of a multifactorial condition, the greater the probability of recurrence

34

35

For information only do not memorise these

36

For information only do not memorise these

37

For information only do not memorise these

38

Concept that summarizes how much of the variation in a

trait is due to variation in genetic factors It be difficult to get your head round as Heritability in a genetic sense is not the same as when used in everyday language (namely, genetic determination)

Estimated from empirical data on the observed and

expected resemblance between relatives.

Depends on assumptions regarding a trait's underlying

environmental and genetic causes

2. Heritability

39

Measures % of phenotype variation due to genes H2=1 phenotypic difference mostly due to genetic variation H2=0 phenotypic differences due to environmental variation 0.6

0.0

0.5-0.8

0.9 0.5 0.3 0.7 0.5 0.6 0.8 0.8 Heritability

Serum cholesterol *Eskimo eye colour

Intelligence

Fingerprint ridge count

Spelling aptitude Maths aptitude Verbal aptitude Body mass Blood pressure Height Clubfoot Trait

2. Heritability

40

Heritability in a genetic sense is not the same as when used in everyday language (namely, genetic determination) Environmental changes can cause dramatic changes in phenotype Height H2= 0.8 in developing countries increased by more than 1cm/decade IQ H2= 0.5 in industrialised countries increased 1-10 points/decade Eye colour in Eskimos H2= 0.0. Entirely genetic in origin, but no variation

2. Heritability

41

3. Association Studies

Advantage: Hypothesis-free Limitation: establish correlations, not causes

• Studies which compare a group of interest (individual

cases, not families) to a control group for the presence of a gene or SNP

• Controls are matched to cases for characteristics that may confound results: age, ethnicity, gender, environment

• If the SNP is present more often in cases than

controls, it is associated with the trait and implies that the SNP may be near a gene impacting the trait

• Can be candidate gene based or not

42

• Apolipoprotein E – APOE is a component of low-density lipoprotein (LDL). APOE is also a component of amyloid plaques and binds Ab peptide

• Rare familial form of Alzheimer’s showed linkage to genomic region including ApoE

• ApoE gene has three alleles E2, E3, E4 • SNP in ApoE distinguishes ApoE4 variant

3. Association study: candidate gene approach

43

• ApoE4 SNP is found in 14-17% of non-Alzheimer’s individuals, but 47-55% of Alzheimer’s patients – disequilibrium

• Not all ApoE4 individuals get Alzheimer’s, but their risk is increased relative to general population – typical of multigenic trait

• Other genetic and environmental factors? Chromosome 12 Diet Head injury (boxers)

Example 1: Alzheimer’s Disease

44

See handout and VC for details of further examples

2. Body weight: 75% heritability, Leptin deficiency causing familial morbid obesity

3. Diabetes Mellitus: Type 1 (insulin-dependent): Association study 95% patients heterozygous for HLA-DR3 or HLA-DR4. HLA-DR3;DR4 particularly susceptible 4. Neural tube defects NTDs: incidence of 1%: 5-15% population homozygous for a mutation in methyenetetrahydrofolate reductase gene, which is required in metabolism of folic acid

Genome-wide association (GWA) rapidly scanning markers across the complete sets of DNA, or genomes, of many people to find genetic variations associated with a particular disease Several hundred genetic loci in which common genetic variants occur that are reproducibly associated with polygenic/multifactorial traits

3. Association study: non-candidate gene approaches

45

Manhattan plots display GWA findings with respect to their genomic positions. Higher the dots, the stronger the genetic association GWAS studies identify risk-SNPs, but not risk-genes

-log(

P)

46

47

See handout and VC versions for details of further examples

1. Body weight: meta-analysis identified a neuronal influence

2. Diabetes Type II: meta-analysis, 16 loci associated with fasting glucose

3. Inflammatory bowel disease: 2 loci critical to autophagy . Mouse mutated for these genes have interesting phenotypes (Linkage studies had identified CARD15 as candidate)

4. Lung function: 5 loci

5. Schizophrenia meta-analysis: MHC, NRGN, TCF4 Increased power of meta-analyses. Why?

47

48

Example 1: Six new loci associated with body mass index highlight a neuronal influence on body weight regulation

Willer (2009) Nature Genetics

Meta-analysis of 15 genome-wide association studies for BMI (32,000) and followed up top signals in 14 additional cohorts (59,000) Several of the likely causal genes are highly expressed or known to act in the central nervous system

48

49

Example 2: Diabetes Mellitus

Two major types Type 1 (insulin-dependent) Type 2 (non-insulin-dependent Type 1 Caucasian population 1/200 incidence childhood, adolescence Probably autoimmune, destroys pancreatic beta-cells Cold weather? Virus? Breast-fed? Association study 95% patients heterozygous for HLA-DR3 or HLA-DR4 HLA-DR3;DR4 particularly susceptible MHC haplotype accounts for 13% of genetic contribution in siblings of affected proband 13 additional loci proposed eg glucose transporter, IGF1

49

GWA issues • Subject ascertainment and design • Case and Control subject selection • Sample size: the more the better • Population substructure eg ethnic mix • Family studies v Case-control • Replication • Meta-analysis obvious solution to overcome

restrictions of sample size, complicated by the lack of standardization that characterizes the measurement of many key biological traits and environmental exposures

• “Main goal of GWA studies is not prediction of individual risk but rather discovery of biologic pathways underlying polygenic diseases and traits” Hirschhorn (2009)NEJM 50

GWA studies good at identifying common variants contributing to the inherited component of common diseases. Most such variants have modest effect sizes. Even when combined, their impact on overall population variance and predictive power is limited. Few GWAs have resulted in identification of disease genes

Bush and Moore 2012 PLoS Comput Biol.

51

52

Estimates of heritability and other statistical techniques are useful in understanding the relative contribution of different types of influence and their relation to each other “Missing heritability” Useful for understanding why some types of behaviour often occur together Do not lead directly to predictive information regarding individuals, nor do they give reliable estimates of how strongly predictive a genetic test might be if it were developed

Limitations

53

Discovery of LMO1 at 11p15.4 as a neuroblastoma susceptibility locus Wang et al (2011) Nature doi:10.1038/nature09609

1. GWAs 2,251 patients and 6,097 control subjects of European ancestry from four case series. 2. LMO1 locus was aberrant in 12.4% of 70 tumours through a duplication event, and that this event was associated with more advanced disease 3. SNP risk alleles and somatic copy number gains were associated with increased LMO1 expression in neuroblastoma cell lines and primary tumours 4. shRNA-mediated depletion of LMO1 inhibited growth of neuroblastoma cells with high LMO1 expression, whereas forced expression of LMO1 in neuroblastoma cells with low LMO1 expression enhanced proliferation

GWAs + genomic analyses success: example 1

54

GWAs + genomic analyses success: example 2

Frayling TM, et al.. Science. 2007; 316:889–894. Scuteri A, et al. PLos Genetics. 2007; 3:e115. Dina C, et al. Nat Genet. 2007; 39:724–726.

SNPs in the FTO (fat mass and obesity associated) gene region on chromosome 16 strongly associated with type 2 diabetes and increased BMI cluster of 10 SNPs in the first intron of FTO

FTO is a nucleic acid demethylase, Ubiquitous expression, energy homeostasis?, links amino acid availability and mTORC1 signalling

55

Confirmed previous reports of common loci associated with prostate cancer at 8q24 and 17q. Three of the newly identified loci contain candidate susceptibility genes: MSMB,LMTK2 and KLK3 Eales et al. Nature Gen. 2008. doi:10.1038/ng.90

LMTK2 protein expression is significantly lower in prostate adenocarcinomas in comparison to normal prostate tissue. Loss of LMTK2 leads to an increase in cell proliferation and enhanced tumuor-forming capacity of LNCaP cells LMTK2 transcripts in prostate adenocarcinomas were only 32% of those in benign tissues - critical role in endosomal membrane trafficking Harries et al BMC Cancer 2010

GWAs + genomic analyses success: example 3

56

Chromosome 9p21 in amyotrophic lateral sclerosis in Finland: a GWA study Laaksovirta et al. (2010) Lancet Neurol. 9:978

Amyotrophic lateral sclerosis (ALS) Finland high incidence rate Genetically homogenous population Signal on chromosome 21q22 due to 27 familial cases and 13 apparently sporadic cases that were homozygous carriers of SOD1 D90A allele Chr 9p21 founder haplotype, 232 kb covering MOBKL2b, IFNK, C9ORF72 genes No amino acid mutations found

GWAs + Deep Sequencing success 1

57

Next-generation sequencing of 9p21 Large hexanucleotide (GGGGCC) repeat expansion (~250 copies)in the first intron of C9ORF72 on the affected haplotype Segregates perfectly with disease in the Finnish population, underlying 46.0% of familial ALS and 21.1% of sporadic ALS in that population RNA generated from such pathogenic repeat expansions are thought to disrupt transcription by sequestering normal RNA and proteins involved in transcription regulation

GWAs + Deep Sequencing success 1

A Hexanucleotide Repeat Expansion in C9ORF72 Is the cause of chromosome 9p21-Linked ALS-FTD Renton et al. (2011) Neuron 72: 257

Patient with rapidly metastatic CCCO refractory to taxane, platinum, pemetrexed, and bevacizumab NGS analysis of key oncogenes and tumour suppressor genes (exonic regions of 315 genes identified in GWAs): KRAS, PIK3CA, and TERT mutations Combined targeted therapy with trametinib (MEK inhibitor) and metformin (activates AMPK and inhibits AKT-mTOR pathway) resulted in a dramatic disease regression without toxicity Castro et al. Gynecologic Oncology Research and Practice (2015) 2:4 DOI 10.1186/s40661-015-0013-2 58

GWAs + Deep Sequencing success 2

59

GWAs + Deep Sequencing success 3 GWAS identified common variants in nine genomic regions associated with Atrial Fibrillation (KCNN3, PRRX1, PITX2, WNT8A, CAV1, C9orf3, SYNE2, HCN4, ZFHX3) NGS of nine atrial fibrillation candidate genes in patients with extreme trait of atrial fibrillation (at least one attack a day, not responding to therapy) and their unaffected parents Tsai C-T, et al. J Med Genet 2015;52:28–36. doi:10.1136/jmedgenet-2014-102618

De novo mutations Pitx2 5’ enahcer region KCNN3 one missense exon mutation ZFHX3 two missense exon mutations SYNE2 one missense exon mutation

60

To recap: Non-parametric tools

1. Empiric risk - frequency in a specific population – predicts recurrence risks 2. Heritability - amount of phenotypic variation due to genetic variation 3. Association studies - case-control design searching for common genetic change in affected cases

Separating Genetic and Environmental Factors

When genetics is important, the frequency of disease concordance increases as the degree of relatedness increases

The most extreme examples of two individuals having alleles in common are identical (monozygotic-MZ) twins. The next most closely related are first degree relatives, including fraternal (dizygotic-DZ) twins

61

Twin-studies: a special form of association study

Identical (monozygotic) twins arise from the splitting of a single fertilized egg:

clones, all alleles identical Fraternal (dizygotic) twins arise from two fertilized eggs:

half of alleles are same

Twin studies are often used to assess genetic vs. environmental effects on variation of a trait

62

Twins studies are very powerful genetic tool

Adopted individuals Dizygotic twins Monozygotic twins Twins raised apart

Share environment but not genes Share environment and 50% of genes Identical genotype, shared environment Share genotype but not environment 63

A trait that occurs more frequently in both members of MZ twin pairs than in both members of DZ twin pairs is at least partly controlled by heredity A trait mostly caused by environmental influences will have similar frequency of occurrence in both types of twins Comparing twin types assumes both types share similar environmental experiences (see Lewis p145 )

Compare MZ v DZ twins

64

Recurrence Risk Ratios for Relatives of Probands with Diseases Showing Familial Aggregation

Disease Relationship Risk Ratio Schizophrenia MZ twins

Siblings 48 12

Autism MZ twins Siblings

2000 150

Manic-depressive (bipolar) disorder

MZ twins Siblings

60 7

Type I diabetes mellitus

MZ twins Siblings

80 12

Crohn’s disease MZ twins Siblings

840 25

Multiple sclerosis MZ twins Siblings

800 24 65

Heritability in Twins

H2 near 1.0: identical twins will have the same value for the trait, even if they are reared apart

(except if environments are drastically different, e.g. starvation)

H2 near 0: identical twins reared apart from each other

are no more likely to have the same value for the trait than any two random people

H2 = proportion of the total variance of a trait that is caused by genes

66

Concordance

the expression of a trait in both twins

measured as a percentage of pairs in which both twins express the trait

Discordant - if both twins don’t share the trait

What do we measure in twin studies?

67

Concordance values in twins

68

69

Most behavioural traits are common

Prevalence/1000 Condition

1.8 Post-traumatic stress disorder

1.3 Schizophrenia 1.0 Bipolar disorder 6.0 Major depression 7.0 Mood disorders 3.0 Eating disorders 4.0 Drug addiction 0.1 Autism 2.0 Attention deficit hyperactivity disorder 1.0 Panic disorder 1.2 Obsessive compulsive disorder 1.5 Generalized anxiety disorder

2.5 Phobias 8.0 Anxiety 4.0 Alzheimer disease

70

Twin studies often used to investigate genetics of behavioural traits

Attention deficit disorder (ADD) or Attention deficit/Hyperactivity disorder (ADHD) Siblings of affected child show 3-5x greater risk than those without an affected sibling Twins studies indicate ~ 80% heritability Linkage studies indicated dopamine pathway may be involved in ADD/ADHD

Twin studies: MZ vs DZ twins

71

Concordance and interrelationship of atopic diseases and markers of allergic sensitization among adult female twins Strachan J. Allergy Clin. Immunol. 2001 Adult female twins St. Thomas Adult Twin Registry Recruitment by advertisement 344 MZ, 533 DZ pairs, aged 18-72 years

Example 1: asthma and allergy

72

73

MZ pairs DZ pairs MZ v DZ pairs

Asthma ever 1.86 1.16 1.60 Asthma past year 3.52 3.05 1.15 Hay fever ever 1.70 1.23 1.39 Seasonal rhinoconjunctivitis 2.65 1.60 1.66 Eczema ever 2.31 1.28 1.81 Flexural rash 2.91 0.33 8.84 Allergies Any 1.22 1.16 1.06 Pets 3.45 2.19 1.58 Pollen 1.97 1.33 1.48 Dust 2.09 1.67 1.25 Insect bites 2.08 1.49 1.39 Positive specific IgE Any 2.31 1.57 1.47

Genetic factors influence susceptibility to aeroallergen sensitization and clinical allergic disease However, genetically identical twins often discordant in their expression of atopy, suggesting a substantial modifying role for environmental factors

Conclusions

74

Example 2: Genetics of peanut allergy: A twin study Sicherer et al (2000) J. Allergy & Clinical Immunology

Affects 0.4% of the UK and US populations long-lived, often severe, and potentially fatal. Relative risk of peanut allergy among siblings (λs = 14) suggests an important genetic component Twin pairs with at least one member with peanut allergy were ascertained through the Food Allergy Network by advertisements. 75 twin pairs recruited. 17 pairs excluded because of unconvincing peanut allergy histories (9 pairs, including 4 of uncertain zygosity) or because one twin had reportedly never ingested peanut (8 pairs)! 75

Heritability of peanut allergy was estimated at 81.6% with model fitting using a population prevalence of peanut allergy of 0.4% Conclusion?

76

MZ pairs DZ pairs

Stroke death 10% 5%

Relative risk of stroke death 0.18 0.10

Heritability for liability of stroke death 0.32

Example 3: Danish Stroke Study Bak et al (2002) Stroke. 33:769-744

Conclusion?

77

Example 4: Scandinavian study using quantitative genetics

Genetic and environmental contributions to cancers at 28 anatomical sites Sample of almost 45,000 pairs of twins from the Swedish, Danish, and Finnish twin registries Some cohorts going back to the 19th century

New England Journal of Medicine (2000)

78

Some Scandinavian results Prostate cancer = 42% of risk explained by heredity (range 29-50 for 95% confidence band) Colorectal cancers = 35% due to heredity (range 10-48%) Breast cancer = 27% due to heredity (range 4-41%) For cancer at the common sites in identical twins, the rate of concordance is generally less than 15 percent Conclusion? more than half the risk of cancer is due to the environment Virtually all is due to “unshared” environment rather than common environments (e.g. family diet, in home smoke exposure)

79

80

Example 5: Retinal vascular twin study Risk marker of cardiovascular diseases. Combined STR and SNP Linkage analysis. Study population composed of 2000 twins and their siblings Example 6: Identification and Replication of Three Novel Myopia Common Susceptibility Gene Multi-locus tests of association using SNPs. 4273 UK twin subjects Three locations: MFN1, SOX2, PSARL Example 7: GWA twin study identifies variants associated with development of cutaneous nevi Strongly associated variants in MTAP, a gene adjacent to the familial melanoma susceptibility locus CDKN2A on 9p2, as well as in PLA2G6 on 22q13.1

Twin studies often try to use twins that have been separated from birth so they grow up in different environments Monozygous and dizygous twins are difficult to distinguish at birth – finger prints and blood proteins are used

The limitations of twin studies

81

Twins are not representative members of the population: They have a slower physical development They have a slower development of their IQ They have a high degree of lefthandedness (18% higher than the rest of the population)

82

• Large screening programmes required to collect large enough sample

• The sampling of twins is often biased, most research bodies use advertising: asthma study was mostly females collected for other studies

• The twins that respond do not represent the population this results in sampling error (remember peanut allergy study)

• Most useful data obtained from twins raised apart from birth – very difficult to collect a large sample

83

84

Future strategies

Haplotype mapping Requires genotyping of 500 000–1000 000 SNPs per individual to cover the entire genome, expensive but becoming cheaper Sequence-based mapping Focuses on gene regions, thus reducing the number of SNPs to 50 000–100 000, expensive but becoming cheaper Don’t forget the importance of trying to identify major environmental causal factors Demonstrated by the beneficial effect of folic acid supplementation in preventing neural tube defects