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Human genetic population structure: patterns and underlying processes   Guido Barbujani Dipartimento di Biologia ed Evoluzione, Università di Ferrara [email_address]
[object Object],[object Object],[object Object],[object Object],Human genetic population structure:  patterns and underlying processes
There are clear morphological differences (“types”)
But each group harbours extensive diversity
Analyses of morphological traits led to inconsistent lists of races ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Skin colour Stature Variation is  continuous  and  discordant . It is possible to cluster people one the basis of any trait, but the resulting classification does not allow one to predict clustering for other traits The trouble with morphological traits
1. Estimating variances from sequence comparisons - TA C GAACATC A GGC - - TA T GAACATC A GGC - - TA T GAACATC G GGC -
Independent studies of genetic variances yield very similar results: 85, 5, 10 Lewontin (1972)    17 loci 85% 8%  6% Latter (1973)   18 86% 5%  9% Barbujani et al. (1997) 109 85% 5%  10% Jorde et al. (2000) 100 85% 2%  13% Romualdi et al. (2002)    32 83% 8%  9% Rosenberg et al. (2002)  377 93%   3%  4% Excoffier & Hamilton (2003) 377  88% 3%  9% Ramachandran et al. (2005)   17 90%   5%   5% Bastos-Rodriguez et al. (2006)  40 86%   2%   12% Li et al. (2008)  650 000   89%   2%   9% MEDIAN 85%   5%   10% within populations among populations among continents
What does it mean, in practice? 100% 100% 100% Members of our community are only slightly less different from us than members of distant populations 85% 85% 85%
Mind the numbers Humans and chimps share >98% of their genomes Among the 2% differences, 1.9% are fixed differences within species The remaining fraction, 0.1%, contains all human genomic variation 85% of that 0.1% represents differences among members of the same population The differences among the main continental groups represent 10% of 0.1% of the total, that is, 0.01% But 0.01% of <3 billion DNA sites means <300 000 variable sites
2. Clustering genotypes or haplotypes Rosenberg et al., 2002
Clustering genotypes by algorithms identifying structure K=3 K=4
SNPs Haplotypes CNV Jakobsson et al. 2008 Structure inferred from SNPs and haplotypes differs from that inferred from Copy Number Variation
Genes, as well as morphology, suggest inconsistent clusterings of genotypes Africa Asia, Europe, Australia, Americas Americas Africa, Asia,  Americas, Oceania Asia  Europe Africa, Asia, Europe Oceania Y chromosome: Romualdi et al. 2002 Alu insertions: Romualdi et al. 2002 X chromosome: Wilson et al. 2001 Europe, Ethiopia S. Africa   N. Guinea Asia
Genes, as well as morphology, suggest inconsistent clusterings of genotypes 377 STR loci: Rosenberg et al. 2005 Kalash W. Eurasia E. Asia Africa Americas Oceania Melanesia Eurasia N Africa N America Maya S. Africa 377 STR loci: Barbujani and Belle 2006 E Africa C Africa Piapoco Suruì Karitiana
Sampling has a large effect on the apparent structuring Serre and P ääbo 2004
Variation is  continuous  and  discordant . It is possible to cluster people one the basis of any trait, but the resulting classification does not allow one to predict clustering for other traits The trouble with genetic traits MCPH D-haplogroup NAT2 acetylator
Sampling points in the geographic space 3. Identifying genomic boundaries
The sampling points are connected by edges
d d d d d d d d d d d d d d d d d d d d d d d d d d d d d d d d d Genetic distances between neighbours are associated  to each edge of the reticulation
d d d d d d d d d d d d d d d d d d d d d d d d d d d d d d d d Boundaries are traced perpendicular to the edge showing the highest genetic distance and extended through the adjacent edges
d d d d d d d d d d d d d d d d d d d d d d d d d d d 1 1 A boundary is completed when it exits the reticulation or closes on a preexisting boundary
d d d d d d d d d d d d d d d d d d d 1 1 2 2 3 3 The number of boundaries one may detect is arbitrary, but there are methods to choose
1 1 2 2 3 3 Four genetic clusters are identified, each separated from the others by a boundary
8 6 2 4 5 9 1 7 Genomic boundaries inferred from diversity at 377 STR loci (Barbujani and Belle 2006) Eight significant boundaries, defining 9 groups of populations
81% of SNPs cosmopolitan. Alleles present in one continent only: 0.91% in Africa, 0.75% in Eurasia, practically 0 elsewhere. Hunting-gathering populations distinct from farmers in Africa Jakobsson et al. 2008 (525910 SNPs, 396 CNVs)
12.4% of haplotypes cosmopolitan, 29% continent-specific, 18% of which in Africa. More than 50% present in 1 or 2 continents Jakobsson et al. 2008
LD decreasing with physical distance between loci and with geographic distance from East Africa Jakobsson et al. 2008
Models with an African population replacing previous human continental groups explain the data better than any alternative models Fagundes et al. (2007)
Patterns of  morphological  and  genetic  variation are compatible with the effects of dispersal from Africa Manica et al. 2007
Fitting a model of isolation by distance to human genetic diversity Liu et al. (2006)
Average coalescence times and gene diversity decline as a function of distance from Africa Best fit of the model for an African exit 56,000 years ago
Fagundes et al. (2007) http://info.med.yale.edu/genetics/kkidd/point.html The best available estimates place our species’ origin and its exit from Africa in a not-so-remote past
Linguistic and genetic differences are often correlated
Genetic variances are significant among language groups   Correlations between distance measures   r  r 2 GEN-GEO 0.746***   0.557 GEN-LAN 0.311***   0.097 GEO-LAN 0.269***   0.072 GEN-GEO.LAN 0.723***   0.523 GEN-LAN.GEO 0.172***   0.030 Percentages of the total variance Genetic distance  Fst  Rst Among lang. phyla  2.9  6.7 Among pops. of  2.4  2.9 the same phylum Within populations  94.7  90.4 Belle and Barbujani 2007
Origins: Attempting a synthesis ,[object Object],[object Object],[object Object],[object Object]

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Sociology 101 Demonstration of Learning Exhibit
 

Human genetic diversity. ESHG Barcelona

  • 1. Human genetic population structure: patterns and underlying processes Guido Barbujani Dipartimento di Biologia ed Evoluzione, Università di Ferrara [email_address]
  • 2.
  • 3. There are clear morphological differences (“types”)
  • 4. But each group harbours extensive diversity
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  • 6. Skin colour Stature Variation is continuous and discordant . It is possible to cluster people one the basis of any trait, but the resulting classification does not allow one to predict clustering for other traits The trouble with morphological traits
  • 7. 1. Estimating variances from sequence comparisons - TA C GAACATC A GGC - - TA T GAACATC A GGC - - TA T GAACATC G GGC -
  • 8. Independent studies of genetic variances yield very similar results: 85, 5, 10 Lewontin (1972) 17 loci 85% 8% 6% Latter (1973) 18 86% 5% 9% Barbujani et al. (1997) 109 85% 5% 10% Jorde et al. (2000) 100 85% 2% 13% Romualdi et al. (2002) 32 83% 8% 9% Rosenberg et al. (2002) 377 93% 3% 4% Excoffier & Hamilton (2003) 377 88% 3% 9% Ramachandran et al. (2005) 17 90% 5% 5% Bastos-Rodriguez et al. (2006) 40 86% 2% 12% Li et al. (2008) 650 000 89% 2% 9% MEDIAN 85% 5% 10% within populations among populations among continents
  • 9. What does it mean, in practice? 100% 100% 100% Members of our community are only slightly less different from us than members of distant populations 85% 85% 85%
  • 10. Mind the numbers Humans and chimps share >98% of their genomes Among the 2% differences, 1.9% are fixed differences within species The remaining fraction, 0.1%, contains all human genomic variation 85% of that 0.1% represents differences among members of the same population The differences among the main continental groups represent 10% of 0.1% of the total, that is, 0.01% But 0.01% of <3 billion DNA sites means <300 000 variable sites
  • 11. 2. Clustering genotypes or haplotypes Rosenberg et al., 2002
  • 12. Clustering genotypes by algorithms identifying structure K=3 K=4
  • 13. SNPs Haplotypes CNV Jakobsson et al. 2008 Structure inferred from SNPs and haplotypes differs from that inferred from Copy Number Variation
  • 14. Genes, as well as morphology, suggest inconsistent clusterings of genotypes Africa Asia, Europe, Australia, Americas Americas Africa, Asia, Americas, Oceania Asia Europe Africa, Asia, Europe Oceania Y chromosome: Romualdi et al. 2002 Alu insertions: Romualdi et al. 2002 X chromosome: Wilson et al. 2001 Europe, Ethiopia S. Africa N. Guinea Asia
  • 15. Genes, as well as morphology, suggest inconsistent clusterings of genotypes 377 STR loci: Rosenberg et al. 2005 Kalash W. Eurasia E. Asia Africa Americas Oceania Melanesia Eurasia N Africa N America Maya S. Africa 377 STR loci: Barbujani and Belle 2006 E Africa C Africa Piapoco Suruì Karitiana
  • 16. Sampling has a large effect on the apparent structuring Serre and P ääbo 2004
  • 17. Variation is continuous and discordant . It is possible to cluster people one the basis of any trait, but the resulting classification does not allow one to predict clustering for other traits The trouble with genetic traits MCPH D-haplogroup NAT2 acetylator
  • 18. Sampling points in the geographic space 3. Identifying genomic boundaries
  • 19. The sampling points are connected by edges
  • 20. d d d d d d d d d d d d d d d d d d d d d d d d d d d d d d d d d Genetic distances between neighbours are associated to each edge of the reticulation
  • 21. d d d d d d d d d d d d d d d d d d d d d d d d d d d d d d d d Boundaries are traced perpendicular to the edge showing the highest genetic distance and extended through the adjacent edges
  • 22. d d d d d d d d d d d d d d d d d d d d d d d d d d d 1 1 A boundary is completed when it exits the reticulation or closes on a preexisting boundary
  • 23. d d d d d d d d d d d d d d d d d d d 1 1 2 2 3 3 The number of boundaries one may detect is arbitrary, but there are methods to choose
  • 24. 1 1 2 2 3 3 Four genetic clusters are identified, each separated from the others by a boundary
  • 25. 8 6 2 4 5 9 1 7 Genomic boundaries inferred from diversity at 377 STR loci (Barbujani and Belle 2006) Eight significant boundaries, defining 9 groups of populations
  • 26. 81% of SNPs cosmopolitan. Alleles present in one continent only: 0.91% in Africa, 0.75% in Eurasia, practically 0 elsewhere. Hunting-gathering populations distinct from farmers in Africa Jakobsson et al. 2008 (525910 SNPs, 396 CNVs)
  • 27. 12.4% of haplotypes cosmopolitan, 29% continent-specific, 18% of which in Africa. More than 50% present in 1 or 2 continents Jakobsson et al. 2008
  • 28. LD decreasing with physical distance between loci and with geographic distance from East Africa Jakobsson et al. 2008
  • 29. Models with an African population replacing previous human continental groups explain the data better than any alternative models Fagundes et al. (2007)
  • 30. Patterns of morphological and genetic variation are compatible with the effects of dispersal from Africa Manica et al. 2007
  • 31. Fitting a model of isolation by distance to human genetic diversity Liu et al. (2006)
  • 32. Average coalescence times and gene diversity decline as a function of distance from Africa Best fit of the model for an African exit 56,000 years ago
  • 33. Fagundes et al. (2007) http://info.med.yale.edu/genetics/kkidd/point.html The best available estimates place our species’ origin and its exit from Africa in a not-so-remote past
  • 34. Linguistic and genetic differences are often correlated
  • 35. Genetic variances are significant among language groups Correlations between distance measures r r 2 GEN-GEO 0.746*** 0.557 GEN-LAN 0.311*** 0.097 GEO-LAN 0.269*** 0.072 GEN-GEO.LAN 0.723*** 0.523 GEN-LAN.GEO 0.172*** 0.030 Percentages of the total variance Genetic distance Fst Rst Among lang. phyla 2.9 6.7 Among pops. of 2.4 2.9 the same phylum Within populations 94.7 90.4 Belle and Barbujani 2007
  • 36.