Human Evolution Talk

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H%'() $p *f "#$ %+r!!:Humans are no longer evolving.

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Present

Future

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Distribution of LP in the ‘Old World’

Gerbault P et al. Phil. Trans. R. Soc. B 2011;366:863-877

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C$/.$r!acceleratesevolution

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SHRINKING BRAIN1! *',r!(*b/!

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20,000 years ago Present day1500 cc 1350 cc

Microcephaly

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morphism and an excess of rare alleles (46).To confirm this, we calculated nucleotidediversity (p) and Tajima_s D for the 47 indi-viduals who are homozygous for haplogroupD chromosomes, and we compared these valuesto those of the non-D chromosomes. The pvalue of the D chromosomes is lower, by afactor of 12, than that of the non-D chromo-somes (0.000077 and 0.00092, respectively),even though the D chromosomes representabout 70% of the chromosomes in the panel.Tajima_s D, which is a summary statistic forthe frequency spectrum of alleles, is –2.3 forhaplogroup D (whereas it is –1.2 for thenon-D chromosomes). This strongly negativeTajima_s D indicates a starlike genealogy forhaplogroup D chromosomes (47). Thus, bothsummary statistics contrast sharply between Dand non-D chromosomes and are consistentwith the recent age and rapid expansion ofhaplogroup D. We note that these calculationsdo not provide a statistically stringent test ofpositive selection, because they are done onsubsets of the genealogy. Nevertheless, they do

reveal qualitative signatures of positive selec-tion that further corroborate the more stringentstatistical tests described earlier.

Another sign of a positive selective sweepis extended LD around the selected allele. Thisis apparent in the region of Microcephalininvestigated here, where haplogroup D chro-mosomes show near-complete LD across theentire region. The only exceptions are hap-lotypes 1, 68, and 84 (each found in a singlecopy in the Coriell panel), which are recombi-nants between D and non-D chromosomes asevidenced by recombination tracts (table S2).The remaining 121 copies of haplogroup Dchromosomes show no evidence of recom-bination. By comparison, the non-D chromo-somes do not display any significant LD acrossthe region.

To probe the extent of LD beyond the29-kb core region, we sequenced the Coriellpanel for two segments of about 3 kb each,situated at the beginning and end of the geneseparated from each other by about 235 kb. Inthese flanking regions, there is clear evidence

of LD decay from the core region, whichsupports the idea that selection has most likelyoperated on a site (or sites) around the coreregion. Our present data cannot resolve theexact site(s) of selection, and the G37995Cnonsynonymous SNP used to define hap-logroup D is just a candidate.

To obtain a more detailed frequency dis-tribution of haplogroup D across the globe, weanalyzed a much larger human population panelcontaining 1184 globally diverse individuals.We genotyped the diagnostic G37995C SNP inthis panel to infer the frequency of haplogroup Dchromosomes (Fig. 3). Geographic variationwas observed, with sub-Saharan populationsgenerally having lower frequencies than others.The statistic for genetic differentiation, FST, is0.48 between sub-Saharans and others, whichindicates strong differentiation (48) and is sig-nificantly higher than the genome average of0.12 (P G 0.03 based on previously establishedgenomewide FST distribution) (49). Such pop-ulation differentiation may reflect a Eurasianorigin of haplogroup D, local adaptation, and/or

Fig. 3. Global frequencies of Microcephalin haplogroup D chromosomes(defined as having the derived C allele at the G37995C diagnostic SNP) in apanel of 1184 individuals. For each population, the country of origin,number of individuals sampled, and frequency of haplogroup D chro-mosomes are given (in parentheses) as follows: 1, Southeastern andSouthwestern Bantu (South Africa, 8, 31.3%); 2, San (Namibia, 7, 7.1%);3, Mbuti Pygmy (Democratic Republic of Congo, 15, 3.3%); 4, Masai(Tanzania, 27, 29.6%); 5, Sandawe (Tanzania, 32, 39.1%); 6, Burunge (Tan-zania, 28, 30.4%); 7, Turu (Tanzania, 23, 15.2%); 8, Northeastern Bantu(Kenya, 12, 25%); 9, Biaka Pygmy (Central African Republic, 32, 26.6%); 10,Zime (Cameroon, 23, 8.7%); 11, Bakola Pygmy (Cameroon, 24, 10.4%); 12,Bamoun (Cameroon, 28, 17.9%); 13, Yoruba (Nigeria, 25, 24%); 14, Man-denka (Senegal, 24, 16.7%); 15, Mozabite [Algeria (Mzab region), 29, 53.5%];16, Druze [Israel (Carmel region), 44, 60.2%]; 17, Palestinian [Israel (Central),40, 63.8%]; 18, Bedouin [Israel (Negev region), 44, 54.6%]; 19, Hazara(Pakistan, 20, 85%); 20, Balochi (Pakistan, 23, 78.3%); 21, Pathan (Pakistan,23, 76.1%); 22, Burusho (Pakistan, 25, 66%); 23, Makrani (Pakistan, 24,

62.5%); 24, Brahui (Pakistan, 25, 78%); 25, Kalash (Pakistan, 24, 62.5%); 26,Sindhi (Pakistan, 25, 78%); 27, Hezhen (China, 9, 77.8%); 28, Mongola(China, 10, 100%); 29, Daur (China, 10, 85%); 30, Orogen (China, 10,100%); 31, Miaozu (China, 9, 77.8%); 32, Yizu (China, 10, 85%); 33, Tujia(China, 10, 75%); 34, Han (China, 41, 82.9%); 35, Xibo (China, 9, 83.3%);36, Uygur (China, 10, 90%); 37, Dai (China, 9, 55.6%); 38, Lahu (China,10, 85%); 39, She (China, 9, 88.9%); 40, Naxi (China, 10, 95%); 41, Tu(China, 10, 75%); 42, Cambodian (Cambodia, 11, 72.7%); 43, Japanese(Japan, 27, 77.8%); 44, Yakut [Russia (Siberia region), 25, 98%]; 45, Papuan(New Guinea, 17, 91.2%); 46, NAN Melanesian (Bougainville, 18, 72.2%);47, French Basque (France, 24, 83.3%); 48, French (France, 28, 78.6%); 49,Sardinian (Italy, 26, 90.4%); 50, North Italian [Italy (Bergamo region), 13,76.9%]; 51, Tuscan (Italy, 8, 87.5%); 52, Orcadian (Orkney Islands, 16, 81.3%);53, Russian (Russia, 24, 79.2%); 54, Adygei [Russia (Caucasus region), 15,63.3%]; 55, Karitiana (Brazil, 21, 100%); 56, Surui (Brazil, 20, 100%); 57,Colombian (Colombia, 11, 100%); 58, Pima (Mexico, 25, 92%); 59, Maya(Mexico, 25, 92%).

R E P O R T S

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Table 2. Candidate genes, the tests used to identify selection and GWSSs that found them. (The candidate genes with any evidence of selection found by genome scan are in bold. n.a.,not applicable.)

chromo-some gene location author discovered by test(s) population(s)

found by scan (samelocus)

found by scan(nearby locus)

1 FY 1q21–q22 Hamblin & DiRienzo (2000)

frequency spectrum,populationdifferentiation

Fay and Wu’s H, FST Africans by 50 kb (Frazeret al. 2007)

1 AGT 1q42–q43 Nakajima et al.(2004)

unusual LD tight LD Africans

1 ASPM 1q31 Mekel-Bobrov et al.(2005)

comparative methods Ka/Ks World

2 LCT 2q21 Bersaglieri et al.(2004)

unusual LD iHs, EHH Europeans,World

Frazer et al. (2007);Nielsen et al. (2005);Voight et al. (2006)

2 CAPN10 2q37.3 Fullerton et al.(2002)

population differences FST Africans versusnon-African

3 CCR5 3p21.31 Stephens et al.(1998)

population differences FST and lowheterozygosity

Europeans Oleksyk et al. (2008)

4 ADH1B 4q21–q23 Osier et al. (2004,2002)

unusual LD, andpopulationdifferences

LD, FST Asians Frazer et al. (2007) by 100 kb (Voightet al. 2006)

5 IL13 5q31 Sakagami et al.(2004)

population differences FST World by 200 kb (Oleksyket al. 2008)

5 IL4 5q31.1 Rockman et al.(2003)

population differences FST World by 200 kb (Oleksyket al. 2008)

6 HFE 6p21.3 Toomajian &Kreitman (2002)

multiple Ka/Ks, LD, FST Asians by 100 kb (Voightet al. 2006)

6 TRPV6 7q33–q34 Akey et al. (2006) low diversity andfrequency spectrum

Tajima’s D, and lowdiversity

Africans at differentcoordinates(Carlson et al.2005)

7 CYP3A5 7q21.1 Thompson et al.(2004, 2006)

frequency spectrum Tajima’s D Europeans,Asians

Carlson et al. (2005);Oleksyk et al. (2008)

by 200 kb (Voightet al. 2006)

7 FOXP2 7q31 Enard et al. (2002) comparative methods Ka/Ks World Oleksyk et al. (2008)8 MCPH1 8p23.1 Evans et al. (2006a) comparative methods Ka/Ks World Bustamante et al. (2005)

(negative)8 NAT2 8p22 Patin et al. (2006) unusual LD REHH Europeans9 CDK5RAP2 9q33.2 Evans et al. (2006b) comparative methods Ka/Ks World10 FGFR2 10q26 Goriely et al. (2003);

Goriely et al.(2005)

n.a. distribution ofmutations in spermskewed

World

11 DRD4 11p15.5 Ding et al. (2002);Wang et al. (2004)

unusual LD LD World

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P/%)-#(*$- f%/,*p%r$-

Malaria

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660,000 deaths in 2010

Hemoglobin A/S

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)*,2/! ,!//anemia

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Aidoo et al. (2002)

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Balancing selection

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http://edition.cnn.com/2012/11/14/opinion/china-challenges-one-child-brooks

That’s 52 million.



Past Present futureAsia’s missing women

Evolutionary Psychology – ISSN 1474-7049 – Volume 12(5). 2012. -912-

Figure 1. Childhood sex ratios in eight countries responsible for the largest number of missing women and girls, compared with the childhood sex ratio for the rest of the world (dashed line)

Note: Data from CIA World Factbook.

Some diseases alter the probability of a woman giving birth to a boy or girl. Infection with the protozoan parasite Toxoplasma gondii, for example, can cause sex ratios as dramatic as 260 boys born for every 100 girls (Kankova et al., 2007). And, similarly, 150 boys are born for every 100 girls to mothers with Hepatitis B. The economist Emily Oster (2005) suggested that Hepatitis B infection rates might explain as much as half of the “missing women”, but she has since admitted that she was wrong about the potential for Hepatitis B infection rates to explain a large proportion of Asia’s missing women, based on better estimates of the effects of Hepatitis B infection on sex ratios (Oster, Chen, Yu, and Lin, 2010).

Throughout the region where population sex ratios are so strikingly male biased, parents express very strong son preferences (Hesketh and Xing, 2006), and the lives of women and girls are often less valued by both families and communities as a whole than they are in other parts of the world (Bandyopadhyay, 2003; Hesketh and Xing, 2006).

Son preferences can bias the sex ratio toward males in a number of ways. Most

Br##2) (2012)

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SeX R

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Infanticide

Male conflict

Reproductive skew

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Future

I! "#$%& 'v()#*+on

finished?

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influenza norovirusmulti-drug resistant TBMRSASARSpertussis (whooping cough)Haiti cholera outbreak

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H$-%' +!'!.*, (*v!r)*."has never been greater

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We then extracted the number of recombinations perpopulation with the IRiS method run on 100 permuted da-tasets with 18 chromosomes per population. The averagenumber of recombinations detected per population wasmultiplied by the corresponding sensitivity of the method(7.7%) assessed by simulations (see supplementary mate-rial, Supplementary Material online) at a recombinationrate of 1.8 ! 10"8. Sensitivity was not affected by SNP di-versity (Spearman’s r5"0.161; P50.111) and therefore, thesame value could be applied for all populations.

Estimates of Ne for each of the populations based on the100 permutations and the corresponding standard devia-tions are given in supplementary table S4, SupplementaryMaterial online and plotted in figure 2. As expected, resultsconsistently show that Sub-Saharan Africans have muchhigher Ne than all other populations; values are roughly4-fold larger or, in absolute terms, of ;4,000 for Africanpopulations and of;1,000 for the rest. This result is in linewith the low values obtained with LD-based estimates(Tenesa et al. 2007; Laval et al. 2010) and the ;2.5 timeshigher African effective sizes found from genetic diversityestimates (Laval et al. 2010) and from LD (Hayes et al. 2003;Tenesa et al. 2007). On the other hand, the estimation of Ne

is subject to uncertainties that may carry into the inferenceof Ne as observed in the simulation analysis, but if signif-icant differences in Ne exist between populations, they willbe recovered by our method.

For the first time, we provide specific effective sizes fora wide range of Old World populations in relative and ab-solute values (supplementary table S4, Supplementary Ma-terial online) and a number of interesting patterns arerevealed. The populations with the largest sizes other thanSub-Saharan Africans are North Africans (Moroccans andEgyptians) due to their known Sub-Saharan admixture(Krings et al. 1999; Bosch et al. 2001; Brakez et al. 2001).Outside of Africa, the largest Ne is found in South Asia; onlyrecently, the high internal diversity of Indian populations isbeing appreciated (Xing et al. 2010). Europeans and East

Asians have similar Ne. Tibetans and Basques showedthe lowest values, a direct measure of small population sizeand isolation.

We further investigated the geographic variation of bothSNPs and recombinations to understand the general pat-tern of genetic variation and population history. In order tocompare patterns of diversity across populations, we usedNei’s nucleotide diversity statistic (Nei 1987) to calculatediversity using either SNP allele frequencies (SNP diversity)or population frequencies of each recombination event(recombination diversity). We provide a geographic frame-work to these values by plotting them against the geo-graphic distance of each population to Eastern Africa,the presumed place of origin of modern humans(Quintana-Murci et al. 1999; Tishkoff et al. 2009) if leavingAfrica through the north of Egypt. Note that the relativedifferences in the distance between non sub-Saharan pop-ulations to a putative origin would not change even if anorigin in more Central or Southern in Africa was considered(Liu et al. 2006; Betti et al. 2009; Henn et al. 2011) (see fig. 1).Geographic distances were calculated as in Prugnolle et al.(2005) in which the shortest landmass path between a spe-cific point of origin and our 30 populations can becalculated based on graph theory.

As expected, SNP diversity was found to be highly cor-related with geographical distance with East Africa (Spear-man’s r5"0.596; P50.00064) (supplementary fig. S2,Supplementary Material online) (Prugnolle et al. 2005;Ramachandran et al. 2005; Li et al. 2008), even if sub-Saharan African samples were removed (r5"0.441,P50.024). With recombination diversity, however,the correlation that is found is lower (r5"0.391;P50.033) and vanishes if African samples are removed(r5"0.077; P50.71) (fig. 3A). African populations showsignificantly higher recombination diversity than anyother population (Mann–Whitney U test; P50.0015), ina proportion that goes to a 4- or 5-fold higher diversitythan the mean for non-Africans; European populations

FIG. 2. Inferred effective population sizes from the number of recombinations detected and the corresponding sampling standard deviationscalculated based on the 100 permuted datasets. Population abbreviations as in supplementary table S4, Supplementary Material online.

Recombination estimates of human effective population size · doi:10.1093/molbev/msr213 MBE

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Africa Europe / Asia

Méle et al. (2012)

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http://www.nature.com/scitable/topicpage/ribosomes-transcription-and-translation-14120660



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3.2 billion base pairs.

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Mutation rate of 1.0⇥ 10

�9(2.3⇥ 10

�8).

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�9(2.3⇥ 10

�8).

1.0⇥ 10

�9 · 3.2⇥ 10

9 · 2 = 6.4 mutations per person.

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�9(2.3⇥ 10

�8).

1.0⇥ 10

�9 · 3.2⇥ 10


2.3⇥ 10

�8 · 3.2⇥ 10


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�9(2.3⇥ 10

�8).

1.0⇥ 10

�9 · 3.2⇥ 10


2.3⇥ 10

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⇡ 6.9 billion people as of mid-2010.

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�9(2.3⇥ 10

�8).

1.0⇥ 10

�9 · 3.2⇥ 10


2.3⇥ 10

�8 · 3.2⇥ 10


⇡ 6.9 billion people as of mid-2010.

About 3.6 billion of reproductive age.

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22 billion to 529 billion

mutations per generation!

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M#*%*+on +! *"' ,') (f 'v()#*+onAnd the tank has never been fuller.

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NATURAL selection

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Thanks!

Human Evolution Talk

Education

Transcript of Human Evolution Talk