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Gil McVean. What makes us different?. Image: Wikimedia commons. The genetic axes. Strong. Genetic disorders. Cancer. Inherited. Somatic. Complex disease. Aging. Weak. Images:Wikimedia commons. Characterising individual genomes. Image: Wikimedia commons. Image: Wikimedia commons.

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Gil McVean

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Gil mcvean

Gil McVean


What makes us different

What makes us different?

Image: Wikimedia commons


The genetic axes

The genetic axes

Strong

Genetic disorders

Cancer

Inherited

Somatic

Complex disease

Aging

Weak

Images:Wikimedia commons


Characterising individual genomes

Characterising individual genomes

Image: Wikimedia commons

Image: Wikimedia commons

Image: Illumina Cambridge Ltd


Why 1000 genomes

Why 1000 genomes?

  • To find all common (>5%) variants in the accessible human genome

  • To find at least 95% of variants at 1% in populations of medical genetics interest

    • 95% of variants at 0.1% in genes

  • To provide a fully public framework for interpreting rare genetic variation in the context of disease

    • Screening

    • Imputation


The 1000 genomes project

The 1000 Genomes Project


1000 genomes project design

1000 Genomes Project design


Gil mcvean

Population sequencing

Haplotypes

2x

10x


A map of shared variation

A map of shared variation


Gil mcvean

www.1000genomes.org

http://browser.1000genomes.org


Good but not perfect

Good, but not perfect

Post-hoc filtering

Not genotyped


Gil mcvean

4 million sites that differ from the human reference genome

12,000 changes to proteins

100 changes that knockout gene function

5 rare variants that are known to cause disease


Most variation is common most common variation is cosmopolitan

Most variation is common – Most common variation is cosmopolitan

Number of variants in typical genome

Found in all continents

92%

Found only in Europe

0.3%

Found only in the UK

0.1%

Found only in you

0.002%


Imputation from 1000 genomes

Imputation from 1000 Genomes

  • Imputation similar for all variant types across populations

  • Comparable to imputation from high quality SNP haplotypes


But it can work for common variants

…but it can work for common variants


The 1000 genomes sampling design

The 1000 Genomes Sampling design


The 1000 genomes sampling design1

The 1000 Genomes Sampling design


What have we learned about low frequency genetic variation from the 1000 genomes project

What have we learned about low-frequency genetic variation from the 1000 Genomes Project?

  • How many rare (<0.5%) and low-frequency (0.5-5%) variants are there, how does it vary between populations and what does it tell use about demography?

  • To what extent has natural selection shaped the distribution of rare variants within and between populations?

  • What are the implications of these findings for the interpretation of genetic variation in individual genomes?


Populations differ in load of rare and common variants

Populations differ in load of rare and common variants


Most rare variation is private

Most rare variation is private


Rare variant differentiation within ancestry groupings increases as variant frequency decreases

Rare variant differentiation within ancestry groupings increases as variant frequency decreases


Not all populations are equal

Not all populations are equal


Rare variants identify recent historical links between populations

Rare variants identify recent historical links between populations

48% of IBS variants shared with American populations

ASW shows stronger sharing with YRI than LWK


What about variants that affect gene function

What about variants that affect gene function?


Conserved variant load per individual

Conserved variant load per individual


Gil mcvean

The proportion of rare variants is predicted by conservation, with the exception of splice-disrupting and STOP+ variants


Kegg pathways show variation in excess rare variant load

KEGG ‘pathways’ show variation in excess rare-variant load


Patterns of variation inform about selective constraint

Patterns of variation inform about selective constraint

CTCF-binding motif


Variants under selection showed elevated levels of population differentiation

Variants under selection showed elevated levels of population differentiation

Proportion of pairwise comparisons where nonsynonymous variants are more differentiated than synonymous ones


Rare variant differentiation can confound the genetic study of disease

Rare variant differentiation can confound the genetic study of disease

Mathieson and McVean (2012)


Implications

Implications

  • Rare variants have spatial and ancestry-related distributions that reflect recent demographic events and selection.

  • Purifying selection elevates local differentiation of rare variants.

  • The functional and aetiological interpretation of rare variants in the context of disease needs to be aware of the local genetic background.


Gil mcvean

The final resource – mid 2013

AFRICA

Gambian in Western Division, The Gambia (GWD)

Malawian in Blantyre, Malawi (MAB)

Mende in Sierra Leone (MSL)

Esan in Nigeria (ESN)

SOUTH ASIAN

Punjabi in Lahore, Pakistan (PJL)

Bengali in Bangladesh (BEB)

Sri Lankan Tamil in the UK (STU)

Indian Telugu in the UK (ITU)

AMERICAS

African American in Jackson, MS (AJM)

100

200

100

100

100

100

80


What more could we learn about human population genetics

What more could we learn about human population genetics?

  • There is a need for continuing the programme of developing public resources describing genetic variation across new populations, with high resolution spatial information.

    • This will not just shed light on population history and selection, but be important for interpreting (rare) genetic variation in individual genomes.

  • The Phase 1 1000 Genomes data has made clear the extent of variation in conserved regulatory sequence within genomes

    • How does this relate to variation in function in different cell types?

  • Many of the most interesting parts of the genome (for the study of selection) are still poorly-covered by HTS data

    • Need to collect ‘bespoke’ data types for some genomic regions


Gil mcvean

The 1000 Genomes Project Consortium

http://www.1000genomes.org/


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