1. Population Structure and History in Sub-Saharan Africa
Manjinder Sandhu

2. Why study genetic diversity in Africa?
Africa has the greatest genetic and linguistic diversity in the world
Genetic diversity has implications for the design of studies
Can large scale genomic studies be carried out across Africa?
Can we utilise population differentiation for fine mapping?
Can admixture mapping be used for certain traits?

3. What is the African Genome Variation Project?
Study of 16 ethno-linguistic groups across SSA from populations relevant to medical genomics
100 individuals with dense (2.5M) genotype data in each
Largest diversity panel from Africa so far
Aim: to study genetic variation in Africa to inform large scale studies in African genomics

4. Population structure and admixture

5. Eurasian cline
YRI
CEU

6. Khoe-San cline
YRI
Juhoansi

7. European and Khoe-San admixture in SSA
South Africa
North Africa
East Africa
East Central Africa
West Central Africa
West Africa
Bantu
Khoe-San
K=2
K=3
K=4
K=5
K=6

8. Population differentiation

9. Confirming Eurasian and Khoe-San admixture
pop A
pop B
pop C
f3,C;A,B
Z score
CEU
YRI
Zulu
33.528
Fula
Jola
31.897
Mandinka
-4.5E-05
-0.338
Wolof
-5.376
Baganda
Banyarwanda
Barundi
LWK
Kalenjin
Kikuyu
SOMALI
AMHARA
-87.02
OROMO
Igbo
5.297
Ga-Adangbe
0.078
Sotho
31.282
pop A
pop B
pop C
f3,C;A,B
Z score
YRI
Juhoansi
Zulu
Sotho
Fula
38.379
Jola
51.022
Mandinka
29.069
Wolof
40.037
Baganda
-6.157
Barundi
Banyarwanda
-3.81
LWK
-1.394
Kikuyu
20.333
Kalenjin
49.396
Igbo
-5.39
Ga-Adangbe
-5.9E-05
-0.773
OROMO
92.355
AMHARA
99.369
SOMALI
94.98

10. Quantifying and dating admixture
European admixture
Khoe-San admixture

11. European and HG admixture in SSA
Khoe-San admixture
European admixture

12. Interpretation
Widespread European admixture in all SSA populations, dating to different time-points
Broadly consistent with demographic history
Hunter gatherer admixture seen in many SSA populations, particularly southern bantu populations
Dating consistent with the bantu expansion

13. How can this information be leveraged in the study of African genomics
Most populations used for epidemiological studies in Sub-Saharan Africa are not very differentiated
Differentiation seems to arise largely from admixture rather than divergence or drift
Implications for large-scale genomic studies and fine mapping

14. Next steps
Using admixture mapping of traits for phenotypically differentiated diseases between different populations
Understanding the relationship between admixture and LD structure in African populations in the context of fine mapping

15. Acknowledgements WTSI South Africa Deepti Gurdasani Eleftheria Zeggini
Tommy Carstensen
Savita Karthikeyan
Cristina Pomilla
Georgina Murphy
Elizabeth Young
Ioanna Tachmazidou
Konstantinos Hatzikotoulas
Wellcome Trust
MRC Uganda
Anatoli Kamali
Janet Seely
Pontiano Kaleebu
CRGGH
Charles Rotimi
Daniel Shriner
Fasil Ayele
Ayesha Motala
Fraser Pirie
Michele Ramsey
Ananyo Choudhury
Ghana/Kenya/Nigeria
Adebowale Adeyemo
Albert Amoah
Clement Adebamowo
Johnnie Oli
Ethiopia
Chris Tyler Smith
Luca Pagani
Malariagen
Dominic Kwiatkowski
Sanger pipelines