3. What is eugenics?
“…the science which deals with the influences that improve
the inborn qualities of a race…and encourage action in the
direction of perpetuating a higher racial standard.”
Encyclopaedia Brittanica (1910)
4. What is eugenics?
“…the science which deals with the influences that improve
the inborn qualities of a race…and encourage action in the
direction of perpetuating a higher racial standard.”
Encyclopaedia Brittanica (1910)
“…a social philosophy advocating the improvement of
human genetic traits…” Wikipedia (2014)
5. Early genetics ~= eugenics
• Francis Galton
• Karl Pearson
• Charles Darwin
• Theodore Roosevelt
• Margaret Sanger
• Cold Spring Harbor
Laboratory
• Alexander G. Bell
• Rockefeller Foundation
wikipedia
• Ronald A. Fisher
• Linus Pauling
• John M. Keynes
• Winston Churchill
International
Eugenics
Congress, 1921
6. Early genetics ~= eugenics
NOT Thomas H. Morgan
• Francis Galton
• Karl Pearson
• Charles Darwin
• Theodore Roosevelt
• Margaret Sanger
• Cold Spring Harbor
Laboratory
• Alexander G. Bell
• Rockefeller Foundation
wikipedia
• Ronald A. Fisher
• Linus Pauling
• John M. Keynes
• Winston Churchill
10. The Bell Curve: IQ and race
• Often taken to represent
“intelligence” (controversial)
• Heritability: 40-80%
(twin studies)
• Affected by
socioeconomic/cultural
factors
• Correlated with economic
success
11. The Bell Curve: IQ and race
IQ is an “objective” measure that can
be used to evaluate and rank people.
12. The Bell Curve: IQ and race
“…on the whole, America had
already achieved enough
objective equalization in its
schools by 1964 so that it was
hard to pick up any effects of
unequal school quality.”
13. “It is sometimes suggested that the Black/White
differential in psychometric intelligence is partly due to
genetic differences (Jensen, 1972). There is not much
direct evidence on this point, but what little there is fails
to support the genetic hypothesis.”
-American Psychological Association task force (1996)
14. “It is sometimes suggested that the Black/White
differential in psychometric intelligence is partly due to
genetic differences (Jensen, 1972). There is not much
direct evidence on this point, but what little there is fails
to support the genetic hypothesis.”
-American Psychological Association task force (1996)
(3 variants explain ~3 IQ points)
16. Bell Curve spinoffs
• Nicholas Wade, A Troublesome Inheritance (2014)
• Richard Lynn, The Global Bell Curve (2008)
– Many societies are racially diverse, not just USA
– “consistent evidence of race-based social hierarchies”
(wikipedia)
17. The papers today
Author Year Journal Reviewed?
D. Piffer 2013 Mankind Quarterly +
D. Piffer 2014 IBC +/-
D. Piffer 2014 BioRxiv -
18. The papers today
Author Year Journal Reviewed? data
D. Piffer 2013 Mankind Quarterly + 1KG, HapMap
D. Piffer 2014 IBC +/- 1KG
D. Piffer 2014 BioRxiv - 1KG
19. The papers today
Author Year Journal Reviewed? data
D. Piffer 2013 Mankind Quarterly + 1KG, HapMap
D. Piffer 2014 IBC +/- 1KG
D. Piffer 2014 BioRxiv - 1KG
20. The papers today
Author Year Journal Reviewed? data
D. Piffer 2013 Mankind Quarterly + 1KG, HapMap
D. Piffer 2014 IBC +/- 1KG
D. Piffer 2014 BioRxiv - 1KG
Reviewer 2: Richard Lynn, University of Ulster
23. Reviewer 2: Richard Lynn, University of Ulster
“This is a highly innovative paper that presents novel
statistical tools to detect recent polygenic selection, by
using open access data sets available to everyone. I foresee
fruitful developments based on the ideas presented in this
paper and a cascade of publications centered on this
neglected but extremely important topic.”
24. The papers today
Author Year Journal Reviewed? phenotype
D. Piffer 2013 Mankind Quarterly + IQ
D. Piffer 2014 IBC +/- height
D. Piffer 2014 BioRxiv - IQ
25. The papers today
Author Year Journal Reviewed? phenotype
D. Piffer 2013 Mankind Quarterly + IQ
D. Piffer 2014 IBC +/- height
D. Piffer 2014 BioRxiv - IQ
28. Polygenic selection
Variants under a polygenic selection should be:
• Correlated with each other in their allelic distribution
• Correlated with the phenotype in question
29. Data:
1000 genomes data for 89 SNPs associated with height (Lango Allen et al.
2009) average height measurements for 14 1000 genomes populations
GWAS SNPs
Pop rs1 rs2 rs3 rs4 rs5 rs6 rs7 rs8 … rsN
CEU
YRI
CHB
1KG populations
30. Data:
1000 genomes data for 89 SNPs associated with height (Lango Allen et al.
2009) average height measurements for 14 1000 genomes populations
Divided into 9 bins, allele frequencies
GWAS SNP frequencies
Pop rs1 rs2 rs3 rs4 rs5 rs6 rs7 rs8 … rsN
CEU .32 …
YRI .49
CHB .25
1KG populations
31. Data:
1000 genomes data for 89 SNPs associated with height (Lango Allen et al.
2009) average height measurements for 14 1000 genomes populations
Divided into 9 bins, allele frequencies averaged within bins for each
population
GWAS SNP average frequencies
Pop rs1+2 rs3+4 rs5+6 rs7+8 rsN-1+N
CEU .32
YRI .49
CHB .25
1KG populations
32. Data:
1000 genomes data for 89 SNPs associated with height (Lango Allen et al.
2009) average height measurements for 14 1000 genomes populations
Divided into 9 bins, allele frequencies averaged within bins for each
population PCA on 14 x 9 matrix of meta-allele frequencies
GWAS SNP average frequencies
Pop rs1+2 rs3+4 rs5+6 rs7+8 rsN-1+N
CEU .32
YRI .49
CHB .25
1KG populations
35. PCA for “factor” extraction
SALARY
PC1: “CAPITALIST SCORE”
PC2: “LUCK”
HOURS WORKED / WEEK
36. PCA for “factor” extraction
PC1: “CAPITALIST SCORE”
PC2: “LUCK”
Person Capitalism Luck
Lisa 10 -5
Frank 0 5
Erin 5 0
37. PCA for “factor” extraction
PC1: “CAPITALIST SCORE”
PC2: “LUCK”
Person Capitalism Luck Happiness
Lisa 10 -5 3
Frank 0 5 -5
Erin 5 0 1
38. PCA for “factor” extraction
PC1: “CAPITALIST SCORE”
PC2: “LUCK”
Person Capitalism Luck Happiness
Lisa 10 -5 3
Frank 0 5 -5
Erin 5 0 1
Being a capitalist (PC1) makes you happier than being lucky (PC2).
39. PCA to get a “polygenic score”
Allele 2 frequency
Allele 1 frequency
PC1
PC2
40. PCA to get a “polygenic score”
Allele 2 frequency
Population PC
Allele 1 frequency
1
PC1
PC
2
CEU 10 7
YRI 0 5
CHB 5 0
PC2
41. Go fishing for a PC correlated to height
Allele 2 frequency
Population PC
Allele 1 frequency
1
PC1
PC
2
height
CEU 10 7 180
YRI 0 5 178
CHB 5 0 170
PC2
44. Fishing for a ‘height’ factor
r = 0.98 (? 0.84)
P = 0.02 (? 6e-5)
45. Fishing for a ‘height’ factor
r = 0.98 (? 0.84)
P = 0.02 (? 6e-5)
Populations of European
And African descent
East Asian populations
46. The papers today
Author Year Journal Reviewed? phenotype
D. Piffer 2013 Mankind Quarterly + IQ
D. Piffer 2014 IBC +/- height
D. Piffer 2014 BioRxiv - IQ
50. Mankind Quarterly paper:
same, but for IQ
r = 0.9
P < 0.001
East Asian
populations
Populations of
European descent
Admixed Latin
American populations
Populations of
African descent
51. Mankind Quarterly paper:
same, but for IQ
r = 0.9
P < 0.001
East Asian
populations
Populations of
European descent
Admixed Latin
American populations
Populations of
African descent
???
Explanation?
52. What do you see (PC1 and PC2)
when you do PCA on 1KG data?
53. What do you see (PC1 and PC2)
when you do PCA on 1KG data?
1000 Genomes,
Nature (2012)
54. What do you see (PC1 and PC2)
when you do PCA on 1KG data?
1000 Genomes,
Nature (2012)
(African descent vs. everyone else)
(East Asian descent
vs. everyone else)
55. What do you see (PC1 and PC2)
when you do PCA on 1KG data?
1000 Genomes,
Nature (2012) “IQ” PC1?
56. What do you see (PC1 and PC2)
when you do PCA on 1KG data?
1000 Genomes,
Nature (2012)
“Height” PC2?
67. Conclusions
• Eugenics is alive and well
• Ideologically motivated researchers:
– read genetics literature
– use openly available data
– exploit modern research dissemination venues
– act unethically to get papers published
68. Conclusions
• Eugenics is alive and well
• Ideologically motivated researchers:
– read genetics literature
– use openly available data
– exploit modern research dissemination venues
– act unethically to get papers published
• Mainstream researchers do weird things with
genetic data too, they just get challenged on it
69. Some questions
• How do we feel about unethical researchers
having access to data?
• Should genomicists do more to counter flawed
analyses of genomic data?
• Is it worth paying attention
to marginal research
communication venues?
70. Some questions
• How do we feel about unethical researchers
having access to data?
• Should genomicists do more to counter flawed
analyses of genomic data?
• Is it worth paying attention
to marginal research
communication venues?
71. Some questions
• How do we feel about unethical researchers
having access to data?
• Should genomicists do more to counter flawed
analyses of genomic data?
• Is it worth paying attention
to marginal research
communication venues?
