Authentic Biology Human Genetics - €¦ · Authentic Biology Human Genetics ... 2007/9 WEIGHT & WAIST Lindgren et al 2009 NOVEL BMI Speliotes et al 2010 ... SBP DBP ALT GGT

Authentic Biology Human Genetics

Debbie A Lawlor ([email protected])

Nic J Timpson ([email protected])

• What is a genome-wide association study?

• Why are we interested in gene-disease association?

2000 2007

DNA tests to revolutionise fight against

cancer and help 100,000 NHS patients Monday 10 December 2012

Prime Minister David Cameron ...to transform cancer

treatment in England with new proposals to introduce

high-tech DNA mapping for cancer patients and those

with rare diseases, within the NHS. The UK will be the

first country in the world to introduce the technology

within a mainstream health system.... The genome

profile will give doctors a new, advanced

understanding ... ensuring they have access to the

right drugs and personalised care far quicker than

ever before.

Plans for NHS database of patients' DNA angers privacy campaigners Critics of initiative to be unveiled by David Cameron talk of 'Big Brother' system that could identify individuals The Guardian | TheObserver

NHS faces privacy storm over

plan to store thousands of

patients' DNA to help develop

life-saving treatments.

Daily Mail

Familial analyses

Montague et al, Nature 387:903–908

Monogenic Obesity

Michael et al Nature (2000) 404, 661-671

0500

1000

1500

Frequency

20 40 60

BMI

- Severely obese cousins from a highly consanguineous family. - Both children had undetectable levels of serum leptin.

- Individuals were found to be homozygous for a frameshift mutation in the “ob” gene, which resulted in a truncated protein that was not secreted.

GWAS

Now we have the ability to ask all available hypotheses at the genetic level simultaneously and to avoid the biases of candidacy.

Technological advance

0.05

.1.15

Density

20 30 40 50 60

BMI

BUT, are these “genes for obesity”?

FTO effect ~0.1SD

across the distribution

Equivalent to <1kg per

change at this gene

0100

200

300

400

500

Frequency

20 30 40 50 60

BMI

0500

1000

1500

Frequency

20 40 60

BMI

Uses of GWAS

• Better understanding of disease

• New drug treatments

• Whether none genetic modifiable risk factors cause disease

Uses of GWAS

• Better understanding of disease

• New targets/mechanisms

• Whether none genetic modifiable risk factors cause disease

“We anticipate that our data, results and software, which will be widely available to other investigators, will provide a

powerful resource for human genetics research.”

TCF7L2

FTO

Genome-wide association study of 14,000 cases of seven common diseases and 3,000 shared controls. Nature 2007;447:661-678.

TCF7L2

Uses of GWAS

• Better understanding of disease

• New targets/mechanisms

• Whether none genetic modifiable risk factors cause disease

Example: Definition of FTO effect

Science 2007;316:889-894.

Pauling Centre for Human Sciences – Feb 2012

FTO

TMEM18

MC4R

GNPDA2 BDNF

NEGR1

SH2B1

ETV5

MTCH2

KCTD15

SEC16B

TFAP2B FAIM2

NRXN3

GPRC5B RBJ

MAP2K5

QPCTL

FANCL

TNNI3K

LRRN6C

FLJ35779

SLC39A8

TMEM160

CADM2

LRP1B

PRKD1

MTIF3

ZNF608

PTBP2

RPL27A

NUDT3

ORIGINAL FINDINGS Frayling et al Loos et al Willer et al 2007/9

WEIGHT & WAIST Lindgren et al 2009

NOVEL BMI Speliotes et al 2010

Pauling Centre for Human Sciences – Feb 2012

FTO – An effect through appetite?

American Journal of Clinical Nutrition 2008;88:971-978.

N Engl J Med 2008; 359:2558-2566

Science 2007;318:1469-1472

TT AT AA

1732.12 (1730.15, 1734.08)

1750.21 (1748.25, 1752.18)

1785.31 (1783.34, 1787.29)

Kcal/dat p<0.05

Persons carrying minor variants at rs9939609 were consuming more fat and total energy than were those not carrying such variants This difference was not simply dependent on having higher average BMIs

Dietary record data (3-4 day records) *Corrected for mis-reporting *Adjusted for BMI

Energy intake

American Journal of Clinical Nutrition 2008;88:971-978.

Energy intake Kcal/day/BW

Church et al. Nature Genetics 2010;42:1086–1092

Uses of GWAS

• Better understanding of disease

• New targets/mechanisms

• Whether none genetic modifiable risk factors cause disease

• BMI is associated with a wide range of health outcomes, including associations with greater glucose, insulin and adverse lipid profile

• Is the association exaggerated due to confounding by e.g. SEP, physical activity?

• Is the association an underestimate because of masking (confounding) by smoking and reverse causality?

Example: Is greater BMI causally associated with adverse metabolic & vascular traits?

Body mass Heart Disease

Diabetes

CONFOUNDED

Confound it! The batteries are dead

Body mass genes

0.05

.1.15

Density

20 30 40 50 60

BMI

Nature’s randomised controlled trials Random allocation of alleles e.g. coding for BMI

TT

Higher BMI

AT or AA

Lower BMI

TT AT/AA p

Manual Social Class 56% 57% 0.8

Smoking 44% 44% 0.9

Sedentary 65% 64% 0.3

CHD Outcomes CHD Outcomes

Triangulation of associations

BMI

FTO genotype

Metabolic traits

a c

b

Triangulation: Insulin

FTO genotype

BMI Fasting Insulin

a = 0.088SD greater BMI per A allele FTO

b = 0.038SD greater insulin per 0.088 SD BMI

c = ??

Triangulation: Insulin

FTO genotype

BMI Fasting Insulin

a = 0.088SD greater BMI per A allele FTO

b = 0.038SD greater insulin per 0.088 SD BMI

c = 0.039SD per A allele FTO

-0.06

-0.04

-0.02

0

0.02

0.04

0.06

0.08

-0.04 -0.02 0 0.02 0.04 0.06 0.08

Insulin

Glucose

HDL

LDL

Triglycerides

SBP

DBP

ALT

GGT

HbA1c

Expected SD change in trait per 0.1 SD BMI

Observed SD change in trait per FTO A allele

Observed effect sizes were exactly as expected for all traits

Error bars show 95% CIs

Points to take away from this introduction:

**What is known about the genetics of complex traits like adiposity? **How did we make those discoveries and how has technology drive change? **How should we interpret genetic associations with complex traits? **What can be done with associations of common genetic variation with obesity/BMI – function/phenotype definition/causality/?

Then we figure out the function of genes with cell biology studies in zebrafish

Date What Who

12th

Dec.

Introduction to human genetics/

GWAS

Debbie Lawlor, Nic Timpson,

Rebecca Richmond, Kaitlin

Ward ....

17th

Dec.

Authentic biology symposium Queen

Mary’s London

9th Jan. Human GWAS practical Nic Timpson, Rebecca

Richmond, Kaitlin Ward,

Debbie Lawlor ....

Xth Jan. Zebra Fish husbandary and genetic

tools

Chrissy Hammond

Yth Jan. ZFIN identification of disease gene

orthologues

Going forward – time-table