Open Access

The influence of habitats on female mobility in Central and Western Africa inferred from human mitochondrial variation

  • Valeria Montano1, 7Email author,
  • Veronica Marcari1,
  • Mariano Pavanello2, 3, 5,
  • Okorie Anyaele4,
  • David Comas5,
  • Giovanni Destro-Bisol1, 3, 5 and
  • Chiara Batini6Email author
Contributed equally
BMC Evolutionary Biology201313:24

DOI: 10.1186/1471-2148-13-24

Received: 3 August 2012

Accepted: 25 January 2013

Published: 29 January 2013

Abstract

Background

When studying the genetic structure of human populations, the role of cultural factors may be difficult to ascertain due to a lack of formal models. Linguistic diversity is a typical example of such a situation. Patrilocality, on the other hand, can be integrated into a biological framework, allowing the formulation of explicit working hypotheses. The present study is based on the assumption that patrilocal traditions make the hypervariable region I of the mtDNA a valuable tool for the exploration of migratory dynamics, offering the opportunity to explore the relationships between genetic and linguistic diversity. We studied 85 Niger-Congo-speaking patrilocal populations that cover regions from Senegal to Central African Republic. A total of 4175 individuals were included in the study.

Results

By combining a multivariate analysis aimed at investigating the population genetic structure, with a Bayesian approach used to test models and extent of migration, we were able to detect a stepping-stone migration model as the best descriptor of gene flow across the region, with the main discontinuities corresponding to forested areas.

Conclusions

Our analyses highlight an aspect of the influence of habitat variation on human genetic diversity that has yet to be understood. Rather than depending simply on geographic linear distances, patterns of female genetic variation vary substantially between savannah and rainforest environments. Our findings may be explained by the effects of recent gene flow constrained by environmental factors, which superimposes on a background shaped by pre-agricultural peopling.

Keywords

Mitochondrial DNA Migration Population genetic structure Bayesian inference Western Central Africa

Background

Understanding how human populations interact and admix is one of the primary aims of human evolutionary genetics. To date, three main factors have been studied in detail which could be possible determinants of gene flow within and among human groups: geography, language and social structure.

Geographical factors have been shown to play an important role in shaping genetic structure, at both inter and intra-continental levels (e.g. [17]). Along with the evidence which indicates a geographical continental structure of human populations that is, systematically revealed by the analysis of nuclear loci [25], natural barriers have also been indicated as one of the possible elements driving the distribution of human diversity at a local level [6, 7].

The relationship between linguistic and genetic diversity has been investigated in numerous studies aimed at understanding how cultural factors may shape gene pools (e.g. [810]). Their results highlight a variable degree of correlation, depending not only on the geographic location and scale adopted, but also on the genetic loci analysed when the same set of populations is considered [7, 1113].

Finally, following the seminal study by Seielstad et al. [14], there has been a surge of interest in the role of sex-biased matrimonial mobility, an important aspect of human social structure. In accordance with the prevalence of patrilocal habits, where women move to their husbands households after the marriage, higher female transgenerational migration rates have been inferred at both local and continental level in most populations studied [1418].

Even though the vast literature accumulated over twenty years (e.g. [8, 9]) has produced important insights into the structure of human genetic variation, there are two critical points in the current approaches which need to be adequately considered when planning new research work. Inferences based on extent and patterns of gene flow are usually indirect, being derived from analyses of genetic distances among populations, and assuming simplified migration schemes. This is, in fact, the case of the island model [14, 18]. Additionally, the relation between genetic variation and geography has been generally investigated simply by focusing on physical linear distances among populations [3, 13, 1921], an approach which might be misleading if we consider how human mobility can be influenced by geographical and environmental barriers or even facilitated by natural corridors on both local and global scales [7, 22, 23].

In this context, given their high cultural and linguistic diversity and their complex history, African populations probably represent one of the most interesting case studies. Recent studies on large-scale datasets regarding autosomal markers (both STRs and SNPs) support the role of both geography and language in explaining the distribution of genetic variation in Africa [24, 25]. Among the four linguistic groups found in the continent, the Niger-Congo includes populations with the widest geographical distribution, spanning from the west to the east and south, and yet the highest common autosomal genetic ancestry (see [24, 25], but also [2, 4]). This is particularly surprising when considering the complexity of this phylum and its history, for the most part deduced from linguistic data. Due to the uncertain position of Kordofanian languages in the NC tree, the initial centre of diffusion of the phylum is still matter of debate. Ehret (2000) proposed the Nuba Mountains in Sudan, whereas Blench (2006) suggested the Western regions of Africa. On the other hand, the later history of this phylum is generally agreed upon. In summary, around 10–8 thousand years ago (kya), NC languages moved through the savannah of Western Africa, reaching the rainforest 2 ky later. Subsequently, the Bantu languages expanded (5 kya) from Cameroon into the equatorial forest of the Congo, and southward. Finally, they spread to the east (the region of great lakes) and to the south of the rainforest (Angola) around 3kya and from there to the south [26]. However, genetic data indicate that the expansion of Bantu speaking individuals through the African continent could have been more complex than previously thought [2729] and as also previously pointed out by language and archaeology [30, 31].

In this work, we investigated the genetic structure and the patterns of gene flow in a broad dataset (85 populations, 5 typed ex novo and 80 collected from the literature) of individuals settled in an area spanning from Central to Western Africa. The populations under study inhabit both the savannah and the rainforest regions, and all speak languages belonging to the Niger-Congo phylum [32] and share traditional patrilocal behaviour, which is here assumed to have been constant through time [3336]. Therefore, the migration of male individuals should be culturally more limited than females and the analysis of maternal lineages, rather than male-specific and autosomal loci, should allow for the exploration of patterns related to geographical habitat differences and/or linguistic barriers. It is in fact reasonable to expect that female gene flow is the main contributor to gene exchange between populations. In a patrilocal context, if either linguistics or geography is playing a role in structuring genetic variation among the populations under study, this should have left a signature in the distribution of mtDNA variation. On the other hand, when the distribution of male lineages is found to be correlated with linguistic diversity [12, 13, 21], it is difficult to determine whether such a correlation is a cause or effect of genetic isolation, due to the lack of formal models relating linguistic to genetic evolution. Last but not least, the hypervariable region I of mitochondrial DNA (mtDNA) is at present the only source of information on human genetic variation which provides an adequate genetic coverage of populations settled in the region under study [1, 37]. We first explore the distribution of maternal lineages using a new multivariate statistical method (the discriminant analysis of principal components, DAPC; [38]). Thereafter, we compare the fit of three different migration models as descriptors of the relationships among the clusters previously identified, using a Bayesian approach [3941]. By combining these two methods, our study suggests that the genetic structure of Central and Western African populations may be explained by the effects of recent gene flow constrained by environmental factors, which superimposes on a background shaped by pre-agricultural peopling.

Results

Intra-population variation and genetic distances

Intra-population diversity parameters are shown in Table 1. HD ranges between 0.932 in Eviya and 1.000 in Akampka, and MNPD between 6.029 in Sefwi-Wiawso and 10.895 in Orungu. Fu's Fs neutrality test provided large significant negative values for the great majority of populations analysed. Only 7 out of 85 departed from this pattern, five of which were located between Gabon and Congo, the other two being settled in Western Africa (Table 1 and Additional file 1: Table S1).
Table 1

Intra-population summary statistics

Population

Abbreviation

N

K

S

HD

MNDP

Fs

Fs(p)

CENTRAL

        

Bakaka

Bak

50

36

59

0.983 +/− 0.008

9.821 +/− 4.571

−17.339

0.000

Bamileke

Bam

48

36

55

0.988 +/− 0.007

8.108 +/− 3.821

−22.157

0.000

BatekeN

Ban

53

43

59

0.988 +/− 0.008

8.782 +/− 4.116

−24.77

0.000

Bassa

Bas

47

40

61

0.993 +/− 0.006

9.433 +/− 4.408

−24.685

0.000

BatekeS

Bat

50

23

42

0.944 +/− 0.017

6.621 +/− 3.179

−5.416

0.062

Benga

Ben

50

26

55

0.952 +/− 0.015

9.922 +/− 4.616

−4.526

0.094

Beti

Bet

48

29

52

0.968 +/− 0.012

8.758 +/− 4.112

−9.449

0.006

Foumban

Caf

107

71

67

0.989 +/− 0.003

7.959 +/− 3.728

−24.73

0.000

Wum

Caw

115

63

57

0.983 +/− 0.004

7.519 +/− 3.537

−24.782

0.000

Bankim

Cbt

34

24

44

0.968 +/− 0.017

7.686 +/− 3.673

−9.603

0.001

Duma

Dum

47

29

55

0.973 +/− 0.010

9.258 +/− 4.332

−9.884

0.008

Eviya

Evi

38

16

45

0.932 +/− 0.018

9.135 +/− 4.297

−0.79

0.523

Ewondo

Ewd

25

12

37

0.933 +/− 0.023

9.933 +/− 4.701

0.954

0.676

Ewondo

Ewo

53

39

54

0.983 +/− 0.008

10.162 +/− 4.716

−20.307

0.000

Fang

Fac

39

27

45

0.965 +/− 0.015

9.501 +/− 4.454

−9.457

0.006

Fang

Fag

66

36

55

0.971 +/− 0.009

8.878 +/− 4.145

−12.994

0.005

Fali

Fal

42

27

43

0.978 +/− 0.009

8.197 +/− 3.878

−9.731

0.003

FulbeC

Fuc

34

26

36

0.975 +/− 0.016

6.674 +/− 3.228

−14.831

0.001

Galoa

Gal

51

27

56

0.965 +/− 0.011

9.001 +/− 4.214

−6.128

0.045

Eshira

Gis

40

25

53

0.970 +/− 0.012

10.077 +/− 4.703

−5.839

0.041

Akele

Kel

48

35

54

0.985 +/− 0.008

9.811 +/− 4.571

−16.756

0.000

Kota

Kot

56

32

59

0.967 +/− 0.010

10.562 +/− 4.885

−8.279

0.022

Makina

Mak

45

27

51

0.962 +/− 0.015

9.306 +/− 4.356

−7.284

0.020

Ndumu

Ndu

39

26

53

0.973 +/− 0.012

9.417 +/− 4.417

−8.013

0.010

Ngoumba

Ngo

44

36

52

0.990 +/− 0.007

8.973 +/− 4.213

−23.106

0.000

Ngumba

Ngu

88

43

57

0.969 +/− 0.007

10.081 +/− 4.655

−14.1

0.003

Nzebi

Nze

63

42

64

0.976 +/− 0.001

8.955 +/− 4.181

−22.917

0.000

Obamba

Oba

47

35

63

0.988 +/− 0.007

9.741 +/− 4.542

−17.487

0.000

Orungu

Oru

20

16

40

0.973 +/− 0.025

10.895 +/− 5.173

−3.53

0.086

Punu

Pun

52

35

64

0.982 +/− 0.007

9.123 +/− 4.265

−15.937

0.000

Sanga

San

30

21

36

0.970 +/− 0.016

8.970 +/− 4.250

−5.877

0.022

Shake

Sha

51

34

57

0.973 +/− 0.011

10.194 +/− 4.733

−13.011

0.000

Tali

Tal

20

15

34

0.974 +/− 0.022

6.695 +/− 3.296

−4.77

0.025

Ateke

Tek

54

39

53

0.985 +/− 0.007

9.088 +/− 4.248

−21.957

0.000

Tsogo

Tso

64

33

56

0.961 +/− 0.010

9.058 +/− 4.224

−9.5

0.010

Tupuri

Tup

26

24

53

0.994 +/− 0.013

7.917 +/− 3.804

−15.876

0.000

WEST-CENTRAL

       

Afaha Obong

Ana

37

31

45

0.989 +/− 0.009

7.137 +/− 3.424

−22.296

0.000

Ediene Abak

Ane

26

23

33

0.988 +/− 0.016

6.252 +/− 3.067

−16.121

0.000

Ikot Obioma

Ani

44

37

48

0.991 +/− 0.007

7.246 +/− 3.451

−25.019

0.000

Efut 1

Efe

49

44

58

0.996 +/− 0.005

8.550 +/− 4.021

−24.807

0.000

Efut 2

Efi

48

39

52

0.991 +/− 0.006

7.566 +/− 3.593

−24.958

0.000

Uwanse

Efo

48

40

55

0.988 +/− 0.009

7.779 +/− 3.686

−24.925

0.000

Akampka

Eka

17

17

33

1.000 +/− 0.020

7.698 +/− 3.775

−11.201

0.000

Calabar

Ekc

28

24

44

0.989 +/− 0.012

7.259 +/− 3.504

−14.509

0.000

Ikom

Eki

38

33

51

0.991 +/− 0.009

7.368 +/− 3.524

−24.653

0.000

Akampka

Ekn

50

47

53

0.997 +/− 0.005

7.169 +/− 3.418

−25.03

0.000

Enchi1

Ghe

20

19

35

0.995 +/− 0.018

7.400 +/− 3.612

−11.922

0.000

Enchi

Ghf

59

46

53

0.988 +/− 0.006

6.965 +/− 3.321

−25.054

0.000

Ho

Ghh

87

54

48

0.984 +/− 0.005

6.294 +/− 3.015

−25.138

0.000

Kibi

Ghk

51

42

53

0.989 +/− 0.007

6.452 +/− 3.104

−25.17

0.000

Afaha Eket

Iae

50

36

48

0.984 +/− 0.007

7.234 +/− 3.446

−23.108

0.000

Awa

Iba

28

24

38

0.987 +/− 0.014

7.241 +/− 3.496

−14.54

0.000

Itam

Ibi

48

42

51

0.994 +/− 0.006

7.113 +/− 3.396

−25.042

0.000

Oku

Ibo

48

39

50

0.988 +/− 0.008

7.662 +/− 3.635

−24.939

0.000

Idoma

Ido

37

28

49

0.979 +/− 0.012

7.096 +/− 3.407

−15.86

0.000

Edienne Ikono

Iei

49

43

55

0.995 +/− 0.005

7.985 +/− 3.774

−24.89

0.000

Igala

Iga

41

35

45

0.990 +/− 0.008

6.754 +/− 3.249

−24.98

0.000

Calabar

Igc

96

69

56

0.988 +/− 0.005

7.435 +/− 3.506

−24.865

0.000

Enugu

Ige

54

45

58

0.992 +/− 0.006

8.117 +/− 3.826

−24.863

0.000

Nenwe

Ign

50

38

50

0.981 +/− 0.011

7.739 +/− 3.666

−24.652

0.000

Ntan Ibiono

Ini

50

38

47

0.988 +/− 0.007

7.177 +/− 3.421

−24.965

0.000

Nnung Ndem

Inn

50

39

53

0.989 +/− 0.006

7.962 +/− 3.763

−24.832

0.000

Oku-Iboku

Ioi

50

36

41

0.985 +/− 0.007

7.225 +/− 3.442

−23.131

0.000

Obong Itam

Ita

50

44

45

0.994 +/− 0.005

7.329 +/− 3.488

−24.999

0.000

Ukpom Ette

Iue

50

42

52

0.993 +/− 0.005

7.701 +/− 3.650

−24.935

0.000

Western Nsit

Iwn

36

26

44

0.975 +/− 0.014

7.187 +/− 3.449

−12.604

0.000

Afaha Okpo

Oao

28

23

38

0.987 +/− 0.013

6.598 +/− 3.212

−13.445

0.000

Afaha Ukwong

Oau

70

47

48

0.987 +/− 0.005

7.409 +/− 3.505

−24.943

0.000

Tiv

Tiv

51

43

55

0.992 +/− 0.006

8.042 +/− 3.797

−24.88

0.000

Yoruba

Yor

34

31

42

0.995 +/− 0.009

6.371 +/− 3.099

−25.145

0.000

WEST

        

Gb1*

Gb1

50

37

47

0.989 +/− 0.006

6.693 +/− 3.211

−24.988

0.000

Gb2*

Gb2

22

15

35

0.957 +/− 0.028

8.216 +/− 3.961

−2.846

0.095

Gb3*

Gb3

62

50

51

0.992 +/− 0.005

8.703 +/− 4.072

−24.756

0.000

Gb4*

Gb4

77

49

56

0.978 +/− 0.007

7.289 +/− 3.450

−24.946

0.000

Gb5*

Gb5

77

49

57

0.976 +/− 0.008

7.378 +/− 3.488

−24.93

0.000

Gb6*

Gb6

58

47

61

0.987 +/− 0.008

7.685 +/−3.634

−24.924

0.000

Gb7*

Gb7

26

20

42

0.969 +/− 0.022

7.520 +/− 3.628

−7.982

0.000

Limba

Lim

67

48

56

0.984 +/− 0.007

6.728 +/− 3.211

−25.085

0.000

Loko

Lok

29

27

45

0.988 +/− 0.011

8.393 +/− 3.989

−15.409

0.010

Mandenka

Mad

78

25

44

0.935 +/− 0.012

6.226 +/− 2.989

−4.59

0.070

Mende

Men

55

49

59

0.996 +/− 0.004

8.475 +/− 3.980

−24.805

0.000

Serer

Ser

23

18

36

0.968 +/− 0.026

7.533 +/− 3.650

−6.678

0.000

Temne

Tem

122

77

71

0.989 +/− 0.003

7.787 +/− 3.651

−24.715

0.000

Woloff

Wol

48

39

44

0.991 +/− 0.006

7.622 +/− 3.618

−24.947

0.000

N, number of individuals for each population; K, number of haplotypes; S, number of segregating sites; HD, haplotype diversity; MNPD, mean number of pairwise differences; Fs, Fu's statistic; p, statistical significance (in italics, non-significant). In bold, populations typed in the present study; for additional information, refer to Additional file 1: Table S1. For populations labelled with * please refer to Additional file 1: Tables S1 and original publication for further details.

Pairwise genetic distances were calculated among all populations and the matrix represented in a MDS plot, shown in Figure 1. The two-dimensional plot presented a stress value of 0.122, which is lower than the 1% cut-off value of 0.390 ascertained in Sturrock and Rocha (2000) [42]. Populations from Western, Central-Western and Central African regions, are well recognizable in the MDS plot (Additional file 1: Table S1 and Figure 1a), with the latter showing higher average genetic distances. As expected, this geographic trend is no longer observed at single-country level, underlining the non-representativeness of African political boundaries in defining population units. In particular, North Cameroonian populations (Tali, Tupuri and FulbeC) group together with Western populations from Senegal and Sierra-Leone, while Western Cameroonians (Foumban, Wum, Bankim, and, to a lesser extent, Bamileke) are closer to Nigerians and the other Western-Central groups. Both Bateke populations from Congo appeared to be closer to Central Western groups than Central ones. Finally, Idoma from Nigeria present lower average genetic distances from Western African populations than from Western Central, despite their geographical proximity (Additional file 2: Table S2).
https://static-content.springer.com/image/art%3A10.1186%2F1471-2148-13-24/MediaObjects/12862_2012_Article_2289_Fig1_HTML.jpg
Figure 1

MDS plot representing a pairwise distance matrix for 85 populations from Central and Western Africa. Stress value = 0.122. a) geographical labels (yellow to orange circles: Central; green diamonds: Central-West; light to dark blue triangles: West) b) linguistic labels.

From a linguistic point of view (see Figure 1b), the different families of the Niger-Congo phylum already show a geographically structured distribution, but, at a more refined level of classification, linguistic genealogical relationships do not correlate with genetic distances (see Additional file 3: Figure S1b).

Population genetic structure

The Bayesian Information Criterion (BIC; Additional file 4: Figure S2a) established that 7 was the best number of clusters to describe the genetic structure of the dataset analysed: cluster assignations are presented in Table 2 and Additional file 4: Figure S2b. The a.score was 0.752, which means that the probability of re-assignment of populations to true clusters is three times higher than to randomly permuted clusters. Some ambiguity was observed in the population clustering but this mainly concerned pairs of close groups (mostly 3–1; to a much lower extent 2–7 and 5–6, see Additional file 4: Figure S2b).
Table 2

Assignation to DAPC clusters and habitat (s, savannah, and r, rainforest, based on reconstructed map of biomass from Baccini et al., (2008), [43]; see Methods) for each population with the relative Fu's statistic (Fs) values (in italics, non-significant) and the Fs mean value per cluster

Population

Country

DAPC cluster

Habitat

Fs

Mean Fs

Ghe

Ghana

1

s

−11.922

 

Ghf

Ghana

1

s

−25.054

 

Ghh

Ghana

1

s

−25.138

 

Ghk

Ghana

1

s

−25.170

 

Ghs

Ghana

1

s

−12.390

−21.832

Ibi

Nigeria

1

s

−25.042

 

Iga

Nigeria

1

s

−24.979

 

Ini

Nigeria

1

s

−24.965

 

Ben

Gabon

2

r

−4.526

 

Evi

Gabon

2

r

−0.790

 

Ewd

Cameroon

2

s

0.954

 

Fac

Cameroon

2

r

−9.457

 

Gis

Gabon

2

r

−5.839

 

Kel

Gabon

2

r

−16.756

−7.388

Kot

Gabon

2

r

−8.279

 

Mak

Gabon

2

r

−7.284

 

Ngu

Cameroon

2

r

−14.100

 

Oru

Gabon

2

r

−3.530

 

San

Central African Republic

2

s

−5.877

 

Sha

Gabon

2

r

−13.011

 

Ana

Nigeria

3

s

−22.296

 

Ane

Nigeria

3

s

−16.121

 

Ani

Nigeria

3

s

−25.019

 

Caf

Cameroon

3

s

−24.730

 

Caw

Cameroon

3

s

−24.782

 

Cbt

Cameroon

3

s

−9.603

 

Efe

Nigeria

3

s

−24.807

 

Efi

Nigeria

3

s

−24.958

 

Efo

Nigeria

3

s

−24.925

 

Eka

Nigeria

3

s

−11.201

 

Ekc

Nigeria

3

s

−14.509

−21.607

Eki

Nigeria

3

s

−24.653

 

Ekn

Nigeria

3

s

−25.030

 

Iae

Nigeria

3

s

−23.108

 

Iba

Nigeria

3

s

−14.540

 

Ibo

Nigeria

3

s

−24.939

 

Iei

Nigeria

3

s

−24.890

 

Igc

Nigeria

3

s

−24.865

 

Ige

Nigeria

3

s

−24.863

 

Ign

Nigeria

3

s

−24.652

 

Inn

Nigeria

3

s

−24.832

 

Ioi

Nigeria

3

s

−23.131

 

Ita

Nigeria

3

s

−24.999

 

Iue

Nigeria

3

s

−24.935

 

Iwn

Nigeria

3

s

−12.604

 

Oao

Nigeria

3

s

−13.445

 

Oau

Nigeria

3

s

−24.943

 

Bak

Cameroon

4

r

−17.339

 

Bam

Cameroon

4

s

−22.157

 

Ban

Congo

4

r

−24.766

 

Bas

Cameroon

4

r

−24.685

−19.009

Bat

Congo

4

r

−5.416

 

Fal

Cameroon

4

s

−9.731

 

Ngo

Cameroon

4

r

−23.106

 

Tiv

Nigeria

4

s

−24.877

 

Gb5

Guinea Bissau

5

s

−24.930

 

Lok

Sierra Leone

5

s

−15.409

 

Mad

Senegal

5

s

−4.590

−16.893

Men

Sierra Leone

5

s

−24.805

 

Ser

Senegal

5

s

−6.678

 

Wol

Senegal

5

s

−24.947

 

Fuc

Cameroon

6

s

−14.831

 

Gb1

Guinea Bissau

6

s

−24.988

 

Gb2

Guinea Bissau

6

s

−2.846

 

Gb3

Guinea Bissau

6

s

−24.756

 

Gb4

Guinea Bissau

6

s

−24.946

 

Gb6

Guinea Bissau

6

s

−24.924

−18.209

Gb7

Guinea Bissau

6

s

−7.982

 

Ido

Nigeria

6

s

−15.857

 

Lim

Sierra Leone

6

s

−25.085

 

Tal

Cameroon

6

s

−4.770

 

Tem

Sierra Leone

6

s

−24.715

 

Tup

Cameroon

6

s

−15.876

 

Yor

Nigeria

6

s

−25.145

 

Bet

Congo

7

r

−9.449

 

Dum

Gabon

7

r

−9.884

 

Ewo

Cameroon

7

r

−20.307

 

Fag

Gabon

7

r

−12.994

 

Gal

Gabon

7

r

−6.128

 

Ndu

Gabon

7

r

−8.013

−14.052

Nze

Gabon

7

r

−22.917

 

Oba

Gabon

7

r

−17.487

 

Pun

Gabon

7

r

−15.937

 

Tek

Gabon

7

r

−21.957

 

Tso

Gabon

7

r

−9.500

 

For abbreviations and additional information, refer to Additional file 1: Table S1.

As shown in the bi-dimensional plot, the 7 clusters were distributed according to a geographical pattern (Figure 2). In fact, the first discriminant function separated clusters 4, 7 and 2 (including most of the Central groups) from clusters 5, 6, 1 and 3. The second function separated these last four into two clearly distinguishable groups, a Western (clusters 5 and 6) and a Western-Central one (clusters 1 and 3). The third discriminant function slightly separated cluster 4 and 5 and presented very similar values for the rest (data not shown).
https://static-content.springer.com/image/art%3A10.1186%2F1471-2148-13-24/MediaObjects/12862_2012_Article_2289_Fig2_HTML.jpg
Figure 2

Scatterplot of the populations' coordinates onto the discriminant functions 1 and 2. Ellipses of dispersion are proportional to the internal variance of the clusters. In the right upper corner, the eigenvalues for discriminant functions 1 and 2 are reported. See Figure 4 for a map of the populations, labelled according to cluster assignation.

Most clusters were found to group populations that are geographically close together, with few exceptions (see Table 2). Clusters 2, 4 and 7 are composed mainly by populations inhabiting the rainforest areas, starting from Central Cameroon (Table 2, [43]). The less heterogeneous is cluster 4 presenting two populations living in Central-North Cameroon (Bam and Fal) and one population from Nigeria (Tiv). The variance of the geographic distances among clusters was 28 times higher than within cluster (F = 28.376, p = 0.000). Cluster 6 was the less geographically homogeneous, including two populations from Nigeria (Yoruba and Idoma) and the three nomadic groups from north Cameroon (Tali, Tupuri and FulbeC) along with Western Africans. On the other hand, the ellipses of dispersion indicated that clusters 3 and 7, even though they account for the highest number of populations, had lower internal variances. This is probably due to the fact that they include the geographical areas with the densest sampling coverage, which results in a higher number of genetically more closely related populations.

Summary statistics calculated for the seven clusters are reported in Additional file 5: Table S3. The MNPD was shown to increase (albeit not significantly) moving from clusters 4, 7 and 2 to the rest. The minimum evolution phylogenetic trees also presented much longer branches and consequently higher divergence for the sequences belonging to cluster 2 and 7 in comparison with the others (Additional file 6: Figure S3). An AMOVA was performed on the rainforest (populations in clusters 4, 7 and 2) vs savannah groups (populations in clusters 5, 6, 1 and 3). The percentage of molecular variance among populations within the two groups was lower than among groups (2.54% vs 5.24%, both p < 0.001).

In addition, the Mantel test showed a low but statistically significant correlation between geographic and genetic distances for the whole sample (r = 0.296; p < 0.001). When dividing the populations according to their habitat, geographic and genetic distances were highly correlated within the savannah region (r = 0.609; p < 0.001), while the rainforest area seemed characterized by a weaker but still significant correlation between the parameters (r = 0.251; p < 0.02). This trend was confirmed when plotting the linear regression for the genetic and geographic distances of the clusters in directions West to East (which implies cluster 5 as the starting point; Additional file 7: Figure S4a) and East to West (with cluster 2 as the point of origin; Additional file 7: Figure S4b). In the former case, the correlation between linear and genetic distances was significant at 0.05 level (p value = 0.015) and stronger than in the latter (R2 = 0.73 vs R2 = 0.53) which was non-significant (p value = 0.065).

Interestingly, cluster 2 included four populations with a non-significant value of Fu's statistics. When averaging this parameter among populations within each cluster, cluster 2 presented the least negative value (−7.388), while the others ranged from a mean value of −14.052 to −21.832 (Table 2). The Wilcoxon Mann–Whitney test indicated that the two sets of Fu's values for the savannah and rainforest populations are likely to be drawn from two differing distributions (p-value = 6.817e-06) the median values of the Fu's statistic being −24.794 and −9.499 respectively.

Migration models and migration rates

Three different migration patterns were tested through a Bayesian approach, including a full island (A), a linear stepping-stone (B) and an intermediate model (C; see Figure 3 for a schematic representation). The calculation of the LBF indicated model B as the best descriptor for the migration processes occurring in the region under study for all the five independent sub-samples (Table 3; see Material and Methods for details). The values of theta (Θ) and the migration rates (M) obtained with model B were averaged for the fifteen independent runs and are reported in Table 4. Most of the posterior distributions showed normal shapes (Additional file 8: Figure S5) and the runs converged to very close values for all the parameters across the three runs (see standard deviation values in Tables 4 and 5). However, posteriors for the M parameters between clusters 7 and 2 and 1 and 6 were found to have a mode which was close to zero (see Table 4) and a constantly decreasing distribution when moving towards positive values. In these cases, the contribution of migrants exchanged to the observed variation could be considered as null (in italic in Table 4). Therefore, the resulting model is a stepping-stone with two main discontinuities, as described above, across the whole region (Figure 4).
https://static-content.springer.com/image/art%3A10.1186%2F1471-2148-13-24/MediaObjects/12862_2012_Article_2289_Fig3_HTML.jpg
Figure 3

Schemes of the migration models tested in the present study. A) Full island. B) Linear stepping-stone. C) Intermediate (see Materials and Methods for further details).

Table 3

Log Bayes Factor (LBF) calculated to compare the three migration models

SUB1

         

LBF (MA | MB)

−714.0927

−868.8034

−959.4728

−821.0730

−761.8231

−826.2525

−873.9830

−847.3129

−954.2933

LBF (MB | MC)

327.0815

360.9551

391.0639

253.9748

434.0619

385.8844

366.1346

439.2414

366.1347

LBF (MA | MC)

−387.0111

−507.8483

−568.4088

−460.1179

−434.7416

−435.1886

−482.9190

−520.2314

−593.3381

SUB2

         

LBF (MA | MB)

−1018.3370

−931.4481

−1025.5500

−914.7968

−1034.9890

−967.9367

−984.5880

−1075.9510

−972.4104

LBF (MB | MC)

566.1677

578.1712

629.6136

681.7117

462.6272

680.0142

1388.8972

576.4737

631.3111

LBF (MA | MC)

−452.1696

−353.2769

−395.9367

−336.6256

−468.8209

−338.3231

−354.9744

−509.7832

−394.2400

SUB3

         

LBF (MA | MB)

−820.9318

−952.3143

−871.5584

−983.2491

−789.9969

−907.2188

−785.2714

−947.5887

−876.2840

LBF (MB | MC)

285.2062

567.2732

513.2826

404.9559

447.5236

426.9956

371.4934

589.3129

491.2430

LBF (MA | MC)

−535.7255

−385.0410

−358.2758

−415.9758

−504.7906

−393.9361

−500.0651

−380.3154

−363.0013

SUB4

         

LBF (MA | MB)

−729.1903

−921.9774

−873.2387

−843.5139

−807.6538

−870.2610

−948.7245

−732.1681

−846.4916

LBF (MB | MC)

130.8856

274.7434

287.9298

160.4199

245.2092

146.8591

261.1827

271.9562

301.4905

LBF (MA | MC)

−598.3048

−647.2339

−585.3090

−568.7705

−676.7682

−582.3313

−660.7947

−601.2825

−571.7482

SUB5

         

LBF (MA | MB)

−806.5704

−826.3470

−819.5543

−815.0049

−817.9125

−812.2241

−823.5663

−813.9005

−822.3351

LBF (MB | MC)

521.3028

616.3976

414.1053

607.9631

529.7374

416.8861

408.4515

526.9566

613.6168

LBF (MA | MC)

−285.2675

−209.9494

−405.4490

−198.6073

−296.6097

−398.1189

−409.4610

−292.5977

−205.9374

Each sub-sampling was run three times for each model allowing 27 pairs of model comparisons based on the thermodynamic integration value. MA is the full island model, MB the stepping-stone model and MC is the intermediate model. An LBF > 2 indicates a higher probability for the numerator model; values < 2 indicate the contrary.

Table 4

Theta and M values estimated for MB (stepping-stone)

 

Averaged values of theta and M

  

M incoming

     
 

θ

Clu 2

Clu 7

Clu 4

Clu 3

Clu 1

Clu 6

Clu 5

M outgoing

Clu 2

0.0198

0.2500

-

-

-

-

-

 

Clu 7

0.2500

0.0269

5.2500

-

-

-

-

 

Clu 4

-

10.6500

0.0129

7.3500

-

-

-

 

Clu 3

-

-

4.8500

0.0198

5.1500

-

-

 

Clu 1

-

-

-

8.0500

0.0127

0.2500

-

 

Clu 6

-

-

-

-

0.2500

0.0254

2.7500

 

Clu 5

-

-

-

-

-

11.050

0.0067

 

s. d. of theta and M values

 

θ

Clu 2

Clu 7

Clu 4

Clu 3

Clu 1

Clu 6

Clu 5

 

Clu 2

0.0003

0.0000

-

-

-

-

-

 

Clu 7

0.0000

0.0005

0.3535

-

-

-

-

 

Clu 4

-

0.2236

0.0003

0.2236

-

-

-

 

Clu 3

-

-

0.2236

0.0000

0.4183

-

-

 

Clu 1

-

-

-

0.2738

0.0004

0.0000

-

 

Clu 6

-

-

-

-

0.0000

0.0005

0.4183

 

Clu 5

-

-

-

-

-

0.2738

0.0002

Values of thetas are reported on the diagonal. Direction of migration is represented as outgoing from the clusters in row and incoming into the clusters in column (e.g. M is 5.2500 in the direction 7 - > 4, and 10.6500 in the direction 4 - > 7); "--" states for migration flows not allowed.

Table 5

Averaged values of first and last percentile of the distributions of Theta and M with standard deviations calculated combining all the runs for the stepping-stone model

 

2.5%

s.d.

mode

s.d.

97.5%

s.d.

θ clust2

0.0055

0.0003

0.0198

0.0003

0.0390

0.0012

θ clust7

0.0103

0.0004

0.0269

0.0005

0.0527

0.0025

θ clust4

0.0000

0.0000

0.0129

0.0003

0.0301

0.0011

θ clust3

0.0058

0.0002

0.0198

0.0000

0.0386

0.0019

θ clust1

0.0000

0.0000

0.0127

0.0004

0.0308

0.0013

θ clust6

0.0095

0.0005

0.0253

0.0005

0.0512

0.0023

θ clust5

0.0000

0.0000

0.0067

0.0002

0.0194

0.0017

M7- > 2

0.0000

0.0000

0.2500

0.0000

10.0000

0.0000

M2- > 7

0.0000

0.0000

0.2500

0.0000

10.5000

0.3535

M4- > 7

0.0000

0.0000

10.6500

0.2236

22.2000

0.5700

M7- > 4

0.0000

0.0000

5.2500

0.3535

15.1000

0.2236

M3- > 4

0.0000

0.0000

4.8500

0.2236

14.5000

0.0000

M4- > 3

0.0000

0.0000

7.3500

0.2236

17.2000

0.2738

M1- > 3

0.0000

0.0000

8.0500

0.2739

17.8000

0.4472

M3- > 1

0.0000

0.0000

5.1500

0.4183

15.2000

0.2738

M6- > 1

0.0000

0.0000

0.2500

0.0000

10.4000

0.4183

M1- > 6

0.0000

0.0000

0.2500

0.0000

9.7000

0.2738

M5- > 6

0.0000

0.0000

11.0500

0.2739

22.4000

0.8944

M6- > 5

0.0000

0.0000

2.7500

0.4183

11.8000

0.2738

https://static-content.springer.com/image/art%3A10.1186%2F1471-2148-13-24/MediaObjects/12862_2012_Article_2289_Fig4_HTML.jpg
Figure 4

a) Results of the best migration model among DAPC-clustered populations. Arrows represent the migration rates > 0.01 and their thickness is proportional to the original value. b) Map of the populations labelled according to the cluster analysis with the white lines representing discontinuities in gene flow (see Table 4).

Cluster 5 shows the lowest value of effective population size, having Θ = 0.007, while, for the remaining clusters, Θ values range between 0.013 and 0.027 (Table 4). Clusters 7, 3 and 6, which have the highest Θ values, presented the highest rates of immigrants ranging from 8 to 11%. Cluster 4 is characterized by high flows both incoming and outgoing, while cluster 1 exchanges high rates of migrants with cluster 3 but no flow is retrieved with cluster 6. Finally, cluster 5 is connected to cluster 6 through a high outgoing but low incoming migrant rate. This is to be expected considering the lower Θ value compared to the other clusters (Table 4).

Discussion

Populations speaking languages belonging to the Niger-Congo phylum have been the object of several studies, some of which aimed to assess the patterns associated with the diffusion of Bantu languages [13, 21, 28, 29, 44, 45]. This is the phylum containing the highest number of languages worldwide and genealogical classification of its families is still under debate [46]. However, there is a consensus on the fact that western Atlantic and Mande are more ancient than central Benue-Congo and Bantu branches, while the emergence of Kordofanian remains unclear [26, 46, 47]. When autosomal variation is analysed, only a slight substructure among the populations belonging to the entire phylum is observed [25]. By increasing both the number of populations and the geographical coverage, we were able to obtain new insights into the relations among Niger-Congo speakers.

The populations included in our dataset speak languages belonging to several sub-branches of the NC family (see Additional file 3: Figure S1b) and are scattered through a vast area of sub-Saharan Africa, which mainly includes two habitats: the savannah and the rainforest. Roughly speaking, the first prevails in the region from Senegal to Northern Cameroon while the second characterizes most of the areas corresponding to Southern Cameroon, Gabon and Congo. Climatic studies have shown that after the phenomenon known as the Younger Dryas (11.5 ± 0.25 ka B.P; [48, 49]), the climatic conditions in the sub-Saharan region became less arid and the distribution and density of the rainforest have remained stable for the last 9.5 ky [50]. The peopling of the sub-Saharan region is likely to have increased since then and the populations here considered have probably been in contact within the same time frame.

Given the shared traditional patrilocal habit of the populations under study, we were able to focus on mtDNA variation as the source of genetic information for microevolutionary inference. By combining a multivariate approach with the test of specific migration patterns, we were able to detect a complex structure among the populations under study, which seems to be better explained by the effect of local environmental factors rather than the internal linguistic complexity of the NC phylum.

After testing three migratory models (Figure 3), we observed that the stepping-stone model better describes the distribution of mtDNA variation throughout the whole region. This may indicate a general tendency of women to spread out from their villages with the intensity of the migration decreasing with distance, so that only neighbouring groups share common genetic variation. The isolation by distance (IBD) pattern observed in our sample is in agreement with previous studies which showed that geographic distances better explain genetic differences among human populations than ethnic affiliations [19, 51].

Apart from this general indication, the analysis of mtDNA variation allowed us to identify two main groups quite clearly, with the rainforest populations being more structured and diverse than the savannah groups. In fact, the former populations are characterized by higher values of molecular measures of within-population diversity (see for example the MNPD in Table 1), larger genetic distances and phylogenetic trees with longer branches, and a lower proportion of different haplotypes (corresponding to Central in Table 1, and to clusters 2,7 and 4 in Figure 2). The analysis of genetic structure detected the main signal of differentiation in this group, separating clusters 4, 2 and 7 from the others. The two groups also show a significant difference in the distribution of their Fs values, with rainforest populations showing a less negative average (one tailed t-test for mean comparison, p-value = 2.3e-10) as well as including 5 out of the 7 populations with non-significant Fs values (Table 1), suggesting a less important role of demographic expansions in their evolutionary history. The Fu's test, and other statistics relying on haplotype frequencies, were found to be more sensitive for detecting expansions on nonrecombining genomic regions than Tajima's D and other tests [52]. This signature of genetic drift could have been enhanced by the reduced effective population size of the mtDNA compared to autosomal loci, but it is unlikely to have generated the non random genetic structure observed here.

The signature of IBD detected within the savannah region is higher than the one in the rainforest, and indicates, together with the observations of a lower degree of isolation among the former, that the migratory patterns are more straightforward to interpret in the savannah than in the forest. Therefore, we may conclude that although geographic factors have a role in both areas, for the savannah this can be simply described as a linear correlation between physical and genetic distances, while for the rainforest the role played by environmental factors is probably more complex. This conclusion highlights the usefulness of explicit geographic models in trying to understand human genetic diversity, which has been previously suggested by Ray and Excoffier (2009) [53].

As an important evolutionary consideration, we should take into account the possibility that differences in Fu's statistical values between savannah and rainforest could be also explained by the role of selection. However, although the worldwide distribution of mtDNA lineages has been proposed to be driven by selective processes related to temperature changes, the geographic region here analysed appears to be quite homogeneous for this putative temperature effect [54]. In future studies, researchers should consider that other climatic parameters which are different in the savannah and rainforest environments have yet to be explored.

Another caveat of the present study may be the a priori definition of population units, based on the sampling location and the languages spoken by the individuals. We are aware that such a definition may lead to an approximation in the estimate of the spatial distribution of allele frequencies, since each population is considered as a sampling point. In the present case, we believe that, despite the vast geographical area covered by our dataset, the homogeneous nature of sampling helps overcoming this limitation and is allowing a reliable representation of the distribution of maternal lineages.

The complexity of the migratory patterns observed here is further emphasized by a discontinuity detected between clusters 7 and 2, which overlaps with a broad area of the rainforest region (encompassing Cameroon, Gabon, Congo and Central African Republic) where the sampling coverage is fairly homogeneous. Cultural factors do not seem to offer an explanation for this separation. In fact, the populations composing the two clusters speak languages that are closely related, within the Narrow Bantu family and show no major differences in their subsistence economy. On the other hand, environmental factors could have played a role if one considers that the rainforest habitat may decrease the intensity of gene flow among populations after their initial settlement in deforested areas, making migration more difficult. Another discontinuity in the pattern (between clusters 1 and 6) overlapped with a gap in the sampling coverage of the dataset under study, corresponding to the area of Guinea, the Ivory Cost and Liberia, where tropical rainforest vegetation generally prevails. In the absence of these samples, any further inference on the validity of the observed discontinuity would be very speculative. However, their analysis could contribute to a more exhaustive testing of the influence of different environments on the intensity of migrations among human populations.

Considering all the previous observations, we suggest that farming rainforest populations have probably undergone a local, more recent, and less intense demographic expansion than other food producer populations of the Niger-Congo phylum, which has been previously observed in Gabon through the analysis of Y chromosome lineages [29]. Evidence of ancient peopling should also be taken into account when interpreting genetic data. In fact, central Africa is characterized by a well-defined succession of Middle Stone Age industries while western Africa seems to have been populated at very low densities until 10–12 kya [47, 55]. Rainforest farmers have also been shown to share both recent and ancient genetic backgrounds with hunter-gatherer populations [5660].

It is interesting to note the unexpected association observed in cluster 6 where populations of nomadic shepherds from Northern Cameroon (Tali, Tupuri and FulbeC; see also MDS plot in Figure 1) were grouped together with Western groups. Complex relationships among Cameroon ethnic groups have already been reported in previous studies [21, 24, 29, 61]. Although the intermediate model we tested was not the best supported by the analysis, it actually detected high migration rates from cluster 6 to clusters 3 and 4 (data not shown). Mixed hierarchical models of migration combined with a better knowledge of the nomadic routes followed by these populations would be worth investigating in order to clarify our findings.

Focusing on the genetic variation of Niger-Congo-speaking populations, our results highlighted a stronger structure among the populations settled in the Central area, which correspond to the Bantu-speaking groups. In fact, populations settled in Nigeria and Ghana (clusters 3 and 1) and Guinea Bissau and Senegal (clusters 6 and 5), which present a high linguistic diversity, seem to be characterized by a rather continuous gene flow and show smaller inter-population differences. This contradicts the expectations described above, based on linguistic data, of a recent demic expansion from the area of Nigeria-Cameroon towards Central Southern and Eastern Africa, and an earlier diffusion from Western to Central Africa [26].

As a general conclusion, language does not seem to be the main predictor for the distribution of genetic variation among Niger-Congo-speaking populations. Despite the general belief that language is transmitted by migrating women, genetic analyses have repeatedly shown its preferential correlation with paternal rather than maternal genetic variation [12, 13, 21].

Unfortunately, we were unable to find a reliable approach for the definition of linguistic distances. Comprehensive classifications based on a quantitative measure of lexical similarities are only available for the Narrow Bantu languages, and not systematically for other Niger-Congo branches (Koen Bostoen, personal communication). Since in this dataset only 28/85 populations belong to the Narrow Bantu family, we decided to avoid this approach in order not to introduce interpretation biases due to inaccurate or questionable linguistic classification.

Even though the genetic clusters here reported cannot be considered as random mating units, the picture presented in our study suggests that, in particular thanks to female-biased movements, gene flow occurs among human populations speaking very different languages.

The analysis of paternal patterns of migration would be useful to shed light on the substructure and the random mating areas among patrilocal populations, while autosomal and X-chromosomal data could be productively investigated to explore whether sex-biased movements are detectable in the distribution of genome variation.

Conclusions

In this paper, we present a genetic study on female patterns of migration in populations from Central and Western Africa which share a patrilocal tradition and belong to the same linguistic phylum. Our results show how macro habitats seem to play a major role in determining population genetic structure. Population samples from Guinea, the Ivory Coast and Liberia could allow us to test whether this working hypothesis applies to an even larger area of the continent. However, we highlight here how fundamental the knowledge of cultural factors is when planning a population genetic study. In fact, having reliable information about matrimonial behaviour, even the resolution provided by a relatively small region of mtDNA, proved useful in inferring complex patterns of migration and isolation.

Methods

Sampling and database

Our dataset contains 4175 individuals from 85 Niger-Congo speaking populations from Western-Central sub-Saharan Africa (15 Cameroon, 1 Central African Republic, 1 Congo, 17 Gabon, 5 Ghana, 7 Guinea Bissau, 27 Nigeria, 4 Sierra Leone, 3 Senegal; see Additional file 1: Table S1 for further details and Additional file 3: Figure S1a for exact geographical locations). Eighty were obtained from a systematic mining of mtDNA online databases [62] and from current literature, while the remaining 5 were analysed for this study. A total of 230 samples were collected from 3 Nigerian populations (37 Idoma, 41 Igala and 51 Tiv) and 2 Congolese populations (53 North Bateke and 48 Beti). The map of biomass reconstructed by Baccini et al., (2008) [43] was used to assign each population to the savannah or the rainforest group (see Table 2). The threshold for an area to be defined forest is 112 or more of biomass index [43]. Linguistic affiliation, which was defined according to Ethnologue's classification, is reported in Additional file 1: Table S1 ([63] Ethnologue: SIL International. Online version: http://​www.​ethnologue.​com/​), while a tree representing structure within Niger-Congo and relations among languages spoken in the populations analysed is presented in Additional file 3: Figure S1b. Sample collection methodology and the aims of the study have been approved by the ethical committees of the University of Ibadan and Sapienza University of Rome. The sampling took place in hospitals under the supervision of the local medical staff in compliance with the Helsinki Declaration. Each participant signed an informed consent which was drafted in English. The forms included the following information: 1) aims, procedure and scientific benefits, absence of economical benefits; 2) the fact that potential injuries related to withdrawal of the check swabs would be treated by the medical staff; 3) personal information about the volunteer is not transferred in digital format and stored as physical brochure; 4) participants can withdraw at any moment; 5) no material is stored in biobanks.

The HVR1 of mtDNA, from position 16024 to 16383, was sequenced in all individuals and used for all further analyses. Sequencing was carried out according to Vigilant et al. (1989) [64], with minor modifications. HVR1 was amplified using primers L15996 and H16401, and then sequenced on both strands using the BigDye Terminator v3.1 Cycle Sequencing Kit (Applied Biosystems). The quality control of the final data was performed through a phylogenetic approach and each missing diagnostic mutation or private change was confirmed through resequencing. Haplogroup assignment was carried out manually and labelling was performed in agreement with PhyloTree [65]. The haplotypes and haplogroups for the newly typed populations are provided in Additional file 9: Table S4. Haplogroup frequencies for the 85 populations included in the study are reported in Additional file 10: Table S5.

Statistical analyses

Intra-population diversity parameters, Fu's neutrality test, pairwise genetic distances, AMOVA and Mantel test statistics were calculated using Arlequin 3.5 software [66]. The distance matrix was represented in a non-metric multidimensional scaling (MDS) plot using the SPSS 15.0 software (SPSS for Windows, Rel. 11.2006. Chicago: SPSS Inc). A Wilcoxon Mann–Whitney test was used to compare two sets of Fu's statistical values and was performed with an R base package (r-base-core; R Core Development Team 2011; [67]).

Genetic structure was inferred through the Discriminant Analysis of Principal Components (DAPC; [38]). To analyse population structure with mtDNA, we used the matrix of mtDNA mutation frequencies calculated at population level. In this way, all the variation in the individual sequences is included, and the principal components (PC) naturally retrieve the correlation among the variables. Applying the PC analysis directly to individual mtDNA sequences would otherwise have detected the pattern of phylogenetic relationships among the haplogroups [68].

The first step of the structure analysis consisted in assigning populations or individuals to clusters through the k-means approach, which relies on classical ANOVA. This method maximizes the variance among groups and minimizes the variance within groups. The Bayesian Information Criterion (BIC) was used to detect the best number of groups comparing the decrease of the residual variance among different numbers of clusters, with the best number corresponding to the minimum BIC value [38].

DAPC was performed on the clusters inferred with the k-means in order to investigate their separation which is summarized by the discriminant components [38]. This analysis is composed by a first step, a classical PC analysis, and a second step, which is the actual discriminant analysis applied to the matrix of principal components. The components, or discriminant functions, thus maximize the ratio of the variance among groups and the variance within groups. Group positions, defined by the discriminant functions, are presented in a scatterplot. The residual of the probability of population assignment to true clusters versus randomly permuted clusters (a.score) was calculated to test the goodness-of-fit of the discriminant analysis [67, 69, 70].

A simple linear regression analysis was performed to evaluate the correlation between genetic and geographic distances among the clusters using the geographic coordinates of their centroids (calculated as mean(lat) and mean(long) of the populations in the cluster). This was then plotted for both East to West and West to East directions [67].

Mega 5.05 software was used to calculate the alpha value of the gamma distribution for the mutation rate of the whole dataset and to obtain trees of Minimum Evolution for the sequences included in each cluster (see Supplementary Materials for further details; [71]).

Once the unbiased structure of the populations under study was determined, the migration pattern among the clusters identified was tested through a Bayesian approach, which is implemented in migrate-n software version 3.2.9 [39, 41]. The software also allows maximum likelihood inference to be drawn, but Bayesian estimation was seen to be more efficient when using data from a single locus [40]. Three migration schemes were modelled and compared, with the aim of explaining the distribution of the clusters in the DAPC plot integrated with their geographical relative locations. The first (model A) is a full island model where all the clusters are allowed to interchange migrants and can be considered as a null model without prior assumptions. The second (model B) is a linear stepping-stone model where cluster 2 and 5 are at the extremes. This is the most parsimonious model allowable, where the connections among the clusters are assigned taking into account both their positions on the discriminant axes and the geographical region most represented in each cluster. The last one (model C) is intermediate between a stepping-stone (Central clusters: 2, 7 and 4) and a full island model (Central-Western and Western clusters: 4, 3, 1, 6 and 5), where cluster 4 represents the link between the two schemes. In model C, we excluded the connection between clusters 4 and 5, since they do not share any population from a common region and they are also separated by the third discriminant component (data not shown). The rationale for the intermediate model is based exclusively on the pattern highlighted in the the DAPC plot. Here, cluster 2 is very well separated from cluster 4, suggesting no close migratory relation and an overall stepping stone model for cluster 4, 7 and 2. The best model was chosen through the Log Bayes Factor (LBF) calculation, which was carried out using the value of thermodynamic integration instead of the harmonic mean, since the latter has been shown to be less reliable [40, 72]. The parameters estimated are theta (Θ) and migration rates (M) expressed as the number of migrants. Model details and specific run conditions are provided in a supplementary text.

In order to reduce the prohibitive computational time, migration estimates were carried out on a proportional sub-sampling of each cluster. A random sub-set accounting for 30% of each cluster, for a total of 1024 individuals, was pooled five times. Considering the high amount of samples included in cluster 3 and the fact that they belong to a very small geographical area, which is overrepresented in comparison to the rest of the region, the cluster 3 sampling was reduced to 15% in order to obtain a comparable sample size for all clusters. Each model was then run 3 times for each different sub-dataset for a total of 45 runs. Log Bayes Factors were calculated as follows for a total of 45 crossed comparisons among pairs of models:

Log BayesFactor = 2ln (Prob(D|Model1)-Prob(D|Model2)

Sub-samples were compared with the original sample through basic summary statistics using Arlequin 3.5 software [65]. Comparisons among original clusters and relative sub-samplings for gene diversity and mean number of pairwise differences were found to be non-significant, as well as the FST values among each cluster and its subsets (Additional file 5: Table S3 and data not shown). The number of polymorphic sites showed a decrease in 10-20% of the original value, which is to be expected given that this statistic is directly dependent on the sample size. Although this does not influence the estimates of theta (Θ) values, the loss of rare haplotypes in the sub-samples may lead to underestimated migration rates. For this reason, instead of calculating the number of immigrants (2 Nm), we discuss the M value which represents the immigration rates scaled for the mutation rate per site per generation (m/μ) and which indicates the relative contribution of migration over mutation processes to the variation observed.

Declarations

Acknowledgements

This study was made possible thanks to the contribution of all the DNA donors from sub-Saharan Africa. The laboratory of Molecular Anthropology of Sapienza University of Rome (Italy) and the University of Ibadan (Nigeria) collaborated for the sampling in the Benue River Valley. We are grateful to Thibaut Jombart (Imperial College London, UK) and Peter Beerli (Florida States University, USA) for developing the elegant methods which made this work possible and for their patient willingness in answering our queries. We would also like to thank Mark Jobling (University of Leicester, UK) and Richard Nichols (Queen Mary University of London, UK) for their useful comments and revisions; and Roger Anglada, Stephanie Plaza and Mònica Vallés (Universitat Pompeu Fabra, Barcelona, Spain) for their technical support. Finally, we would like to thank anonymous reviewers which helped invaluably to the final quality of the manuscript. This research was partially supported by the Istituto Italiano di Antropologia and the University of Rome “La Sapienza” (funds to GDB).

Data archiving

Data are available either from the AnthroDigitdata repository (http://​www.​isita-org.​com/​Anthro-Digit/​data.​htm) or through Genbank (accession numbers KC544024-KC544253).

Authors’ Affiliations

(1)
Dipartimento di Biologia Ambientale, Sapienza Università di Roma
(2)
Dipartimento di Storia, Culture, Religioni, Sapienza Università di Roma
(3)
Istituto Italiano di Antropologia
(4)
Department of Zoology, University of Ibadan
(5)
Institut de Biologia Evolutiva (CSIC-UPF), Departament de Ciències Experimentals i de la Salut, Universitat Pompeu Fabra
(6)
Department of Genetics, University of Leicester
(7)
Department for Integrative Biology and Evolution, University of Veterinary Medicine

References

  1. Salas A, Richards M, De la Fe T, Lareu MV, Sobrino B, Sanchez-Diz P, Macaulay V, Carracedo A: The making of the African mtDNA landscape. Am J Hum Genet. 2002, 71 (5): 1082-1111. 10.1086/344348.PubMed CentralPubMedView Article
  2. Rosenberg NA, Pritchard JK, Weber JL, Cann HM, Kidd KK, Zhivotovsky LA, Feldman MW: Genetic structure of human populations. Science. 2002, 298 (5602): 2381-2385. 10.1126/science.1078311.PubMedView Article
  3. Prugnolle F, Manica A, Balloux F: Geography predicts neutral genetic diversity of human populations. Curr Biol. 2005, 15 (5): R159-R160. 10.1016/j.cub.2005.02.038.PubMed CentralPubMedView Article
  4. Li JZ, Absher DM, Tang H, Southwick AM, Casto AM, Ramachandran S, Cann HM, Barsh GS, Feldman M, Cavalli-Sforza LL, Myers RM: Worldwide human relationships inferred from genome-wide patterns of variation. Science. 2008, 319 (5866): 1100-1104. 10.1126/science.1153717.PubMedView Article
  5. Novembre J, Johnson T, Bryc K, Kutalik Z, Boyko AR, Auton A, Indap A, King KS, Bergmann S, Nelson MR, Stephens M, Bustamante CD: Genes mirror geography within Europe. Nature. 2008, 456 (7218): 98-101. 10.1038/nature07331.PubMed CentralPubMedView Article
  6. Thangaraj K, Naidu BP, Crivellaro F, Tamang R, Upadhyay S, Sharma VK, Reddy AG, Walimbe SR, Chaubey G, Kivisild T, Singh L: The influence of natural barriers in shaping the genetic structure of Maharashtra populations. PLoS One. 2010, 5 (12): e15283-10.1371/journal.pone.0015283.PubMed CentralPubMedView Article
  7. Chaubey G, Metspalu M, Choi Y, Magi R, Romero IG, Soares P, van Oven M, Behar DM, Rootsi S, Hudjashov G, Mallick CB, Karmin M, Nelis M, Parik J, Reddy AG, Metspalu E, van Driem G, Xue Y, Tyler-Smith C, Thangaraj K, Singh L, Remm M, Richards MB, Lahr MM, Kayser M, Villems R, Kivisild T: Population genetic structure in Indian Austroasiatic speakers: the role of landscape barriers and sex-specific admixture. Mol Biol Evol. 2011, 28 (2): 1013-1024. 10.1093/molbev/msq288.PubMed CentralPubMedView Article
  8. Cavalli-Sforza LL, Menozzi P, Piazza A: The history and geography of human genes. 1993, Princeton University Press, Princeton
  9. Jobling MA, Hurles ME, Tyler-Smith C: Human evolutionary genetics. 2004, Garland Science, New York and Abingdon
  10. Scheinfeldt LB, Soi S, Tishkoff SA: Colloquium paper: working toward a synthesis of archaeological, linguistic, and genetic data for inferring African population history. Proc Natl Acad Sci USA. 2010, 107 (Suppl 2): 8931-8938.PubMed CentralPubMedView Article
  11. Quintana-Murci L, Krausz C, Zerjal T, Sayar SH, Hammer MF, Mehdi SQ, Ayub Q, Qamar R, Mohyuddin A, Radhakrishna U, Jobling MA, Tyler-Smith C, McElreavey K: Y-chromosome lineages trace diffusion of people and languages in southwestern Asia. Am J Hum Genet. 2001, 68 (2): 537-542. 10.1086/318200.PubMed CentralPubMedView Article
  12. Lane AB, Soodyall H, Arndt S, Ratshikhopha ME, Jonker E, Freeman C, Young L, Morar B, Toffie L: Genetic substructure in South African Bantu-speakers: evidence from autosomal DNA and Y-chromosome studies. Am J Phys Anthropol. 2002, 119 (2): 175-185. 10.1002/ajpa.10097.PubMedView Article
  13. Wood ET, Stover DA, Ehret C, Destro-Bisol G, Spedini G, McLeod H, Louie L, Bamshad M, Strassmann BI, Soodyall H, Hammer MF: Contrasting patterns of Y chromosome and mtDNA variation in Africa: evidence for sex-biased demographic processes. Eur J Hum Genet. 2005, 13 (7): 867-876. 10.1038/sj.ejhg.5201408.PubMedView Article
  14. Seielstad MT, Minch E, Cavalli-Sforza LL: Genetic evidence for a higher female migration rate in humans. Nat Genet. 1998, 20 (3): 278-280. 10.1038/3088.PubMedView Article
  15. Oota H, Settheetham-Ishida W, Tiwawech D, Ishida T, Stoneking M: Human mtDNA and Y-chromosome variation is correlated with matrilocal versus patrilocal residence. Nat Genet. 2001, 29 (1): 20-21. 10.1038/ng711.PubMedView Article
  16. Wilder JA, Kingan SB, Mobasher Z, Pilkington MM, Hammer MF: Global patterns of human mitochondrial DNA and Y-chromosome structure are not influenced by higher migration rates of females versus males. Nat Genet. 2004, 36 (10): 1122-1125. 10.1038/ng1428.PubMedView Article
  17. Hamilton G, Stoneking M, Excoffier L: Molecular analysis reveals tighter social regulation of immigration in patrilocal populations than in matrilocal populations. Proc Natl Acad Sci USA. 2005, 102 (21): 7476-7480. 10.1073/pnas.0409253102.PubMed CentralPubMedView Article
  18. Wilkins JF, Marlowe FW: Sex-biased migration in humans: what should we expect from genetic data?. Bioessays. 2006, 28 (3): 290-300. 10.1002/bies.20378.PubMedView Article
  19. Ramachandran S, Deshpande O, Roseman CC, Rosenberg NA, Feldman MW, Cavalli-Sforza LL: Support from the relationship of genetic and geographic distance in human populations for a serial founder effect originating in Africa. Proc Natl Acad Sci USA. 2005, 102 (44): 15942-15947. 10.1073/pnas.0507611102.PubMed CentralPubMedView Article
  20. Rosenberg NA, Mahajan S, Gonzalez-Quevedo C, Blum MG, Nino-Rosales L, Ninis V, Das P, Hegde M, Molinari L, Zapata G, Weber JL, Belmont JW, Patel PI: Low levels of genetic divergence across geographically and linguistically diverse populations from India. PLoS Genet. 2006, 2 (12): e215-10.1371/journal.pgen.0020215.PubMed CentralPubMedView Article
  21. Coia V, Brisighelli F, Donati F, Pascali V, Boschi I, Luiselli D, Battaggia C, Batini C, Taglioli L, Cruciani F, Paoli G, Capelli C, Spedini G, Destro-Bisol G: A multi-perspective view of genetic variation in Cameroon. Am J Phys Anthropol. 2009, 140 (3): 454-464. 10.1002/ajpa.21088.PubMedView Article
  22. Krings M, Salem AE, Bauer K, Geisert H, Malek AK, Chaix L, Simon C, Welsby D, Di Rienzo A, Utermann G, Sajantila A, Paabo S: Stoneking M: mtDNA analysis of Nile River Valley populations: A genetic corridor or a barrier to migration?. Am J Hum Genet. 1999, 64 (4): 1166-1176. 10.1086/302314.PubMed CentralPubMedView Article
  23. Quintana-Murci L, Chaix R, Wells RS, Behar DM, Sayar H, Scozzari R, Rengo C, Al-Zahery N, Semino O, Santachiara-Benerecetti AS, Coppa A, Ayub Q, Mohyuddin A, Tyler-Smith C, Qasim Mehdi S, Torroni A, McElreavey K: Where west meets east: the complex mtDNA landscape of the southwest and Central Asian corridor. Am J Hum Genet. 2004, 74 (5): 827-845. 10.1086/383236.PubMed CentralPubMedView Article
  24. Tishkoff SA, Reed FA, Friedlaender FR, Ehret C, Ranciaro A, Froment A, Hirbo JB, Awomoyi AA, Bodo JM, Doumbo O, Ibrahim M, Juma AT, Kotze MJ, Lema G, Moore JH, Mortensen H, Nyambo TB, Omar SA, Powell K, Pretorius GS, Smith MW, Thera MA, Wambebe C, Weber JL, Williams SM: The genetic structure and history of Africans and African Americans. Science. 2009, 324 (5930): 1035-1044. 10.1126/science.1172257.PubMed CentralPubMedView Article
  25. Henn BM, Gignoux CR, Jobin M, Granka JM, Macpherson JM, Kidd JM, Rodriguez-Botigue L, Ramachandran S, Hon L, Brisbin A, Lin AA, Underhill PA, Comas D, Kidd KK, Norman PJ, Parham P, Bustamante CD, Mountain JL, Feldman MW: Hunter-gatherer genomic diversity suggests a southern African origin for modern humans. Proc Natl Acad Sci USA. 2011, 108 (13): 5154-5162. 10.1073/pnas.1017511108.PubMed CentralPubMedView Article
  26. Ehret C: Language and history. African languages: an introduction. Edited by: Heine B, Nurse D. 2000, Cambridge University Press, Cambridge, 272-297.
  27. Sikora M, Laayouni H, Calafell F, Comas D, Bertranpetit J: A genomic analysis identifies a novel component in the genetic structure of sub-Saharan African populations. Eur J Hum Genet. 2011, 19 (1): 84-88. 10.1038/ejhg.2010.141.PubMed CentralPubMedView Article
  28. de Filippo C, Barbieri C, Whitten M, Mpoloka SW, Gunnarsdottir ED, Bostoen K, Nyambe T, Beyer K, Schreiber H, de Knijff P, Luiselli D, Stoneking M, Pakendorf B: Y-chromosomal variation in sub-Saharan Africa: insights into the history of Niger-Congo groups. Mol Biol Evol. 2011, 28 (3): 1255-1269. 10.1093/molbev/msq312.PubMed CentralPubMedView Article
  29. Montano V, Ferri G, Marcari V, Batini C, Anyaele O, Destro-Bisol G, Comas D: The Bantu expansion revisited: a new analysis of Y chromosome variation in Central Western Africa. Mol Ecol. 2011, 20 (13): 2693-2708. 10.1111/j.1365-294X.2011.05130.x.PubMedView Article
  30. Pakendorf B, Bostoen K, de Filippo C: Molecular perspectives on the Bantu expansion: a synthesis. Lang Dynam Change. 2011, 1: 50-88. 10.1163/221058211X570349.View Article
  31. Robertshaw P: African Archaeology, Multidisciplinary Reconstructions of Africa’s Recent Past, and Archaeology’s Role in Future Collaborative Research. Afr Archaeol Rev. 2012, 29 (2): 95-108. 10.1007/s10437-012-9113-0.View Article
  32. Greenberg JH: Studies in African Linguistic Classification: I The Niger-Congo Family. Southwestern. J Anthropol. 1949, 5: 79-100.
  33. Baumann H, Westermann D: Les peuples et les civilisations de l'Afrique. 1970, Payot, Paris
  34. Biasutti R: Le razze e i popoli della terra. Vol 3: Africa. 1967, UTET, Torino
  35. Murdock GP: Social Structure. 1949, Macmillan, New York
  36. Murdock GP: Africa. Its Peoples and Their Culture History. 1959, McGraw-Hill, New York
  37. Rosa A, Brehem A: African human mtDNA phylogeography at-a-glance. J Anthropol Sci. 2011, 89: 25-58.PubMed
  38. Jombart T, Devillard S, Balloux F: Discriminant analysis of principal components: a new method for the analysis of genetically structured populations. BMC Genet. 2010, 11: 94-PubMed CentralPubMedView Article
  39. Beerli P, Felsenstein J: Maximum-likelihood estimation of migration rates and effective population numbers in two populations using a coalescent approach. Genetics. 1999, 152 (2): 763-773.PubMed CentralPubMed
  40. Beerli P: Comparison of Bayesian and maximum-likelihood inference of population genetic parameters. Bioinformatics. 2006, 22 (3): 341-345. 10.1093/bioinformatics/bti803.PubMedView Article
  41. Beerli P, Palczewski M: Unified framework to evaluate panmixia and migration direction among multiple sampling locations. Genetics. 2010, 185 (1): 313-326. 10.1534/genetics.109.112532.PubMed CentralPubMedView Article
  42. Sturrock K, Rocha J: A multidimensional scaling stress evaluation table. Field Methods. 2000, 12: 49-60. 10.1177/1525822X0001200104.View Article
  43. Baccini A, Laporte N, Goetz SJ, Sun M, Dong H: A first map of tropical Africa's above-ground biomass derived from satellite imagery. Environ Res Lett. 2008, 3 (4): 10.1088/1748-9326/3/4/045011.
  44. Plaza S, Salas A, Calafell F, Corte-Real F, Bertranpetit J, Carracedo A, Comas D: Insights into the western Bantu dispersal: mtDNA lineage analysis in Angola. Hum Genet. 2004, 115 (5): 439-447.PubMedView Article
  45. Beleza S, Gusmao L, Amorim A, Carracedo A, Salas A: The genetic legacy of western Bantu migrations. Hum Genet. 2005, 117 (4): 366-375. 10.1007/s00439-005-1290-3.PubMedView Article
  46. Williamson K, Blench R: Niger–Congo. African Languages — An Introduction. Edited by: Heine B, Nurse D. 2000, 42, Cambridge, 11-
  47. Blench R: Archaeology, language, and the African past. 2006, Oxford: AltaMira Press, Lanham, MD
  48. Jansen F, Ufkes E, Bey Khelifa L: The younger Dryas in equatorial and southern Africa and in the southeast Atlantic Ocean. The younger Dryas. Edited by: Troelstra SR, Hinte JE, Ganssen GM. 1995, 141-147. 44
  49. Talbot MR, Jensen NB, Tiercelin J: An abrupt change in the African monsoon at the end of the Younger Dryas. Geochem Geophys Geosyst. 2007, 8 (3): 1-16.View Article
  50. Hamilton AC, Taylor D: History of climate and forests in tropical Africa during the last 8 million years. Clim Chang. 1991, 19 (1–2): 65-78.View Article
  51. Manica A, Prugnolle F, Balloux F: Geography is a better determinant of human genetic differentiation than ethnicity. Hum Genet. 2005, 118 (3–4): 366-371.PubMed CentralPubMedView Article
  52. Ramirez-Soriano A, Ramos-Onsins SE, Rozas J, Calafell F, Navarro A: Statistical power analysis of neutrality tests under demographic expansions, contractions and bottlenecks with recombination. Genetics. 2008, 179 (1): 555-567. 10.1534/genetics.107.083006.PubMed CentralPubMedView Article
  53. Ray N, Excoffier L: Inferring past demography using spatially explicit population genetic models. Hum Biol. 2009, 81 (2–3): 141-157.PubMedView Article
  54. Balloux F, Handley LJ, Jombart T, Liu H, Manica A: Climate shaped the worldwide distribution of human mitochondrial DNA sequence variation. Proc Biol Sci. 2009, 276 (1672): 3447-3455. 10.1098/rspb.2009.0752.PubMed CentralPubMedView Article
  55. Cornelissen E: Human responses to changing environments in Central Africa between 40,000 and 12,000 BP. J World Prehist. 2002, 16 (3): 197-235. 10.1023/A:1020949501304.View Article
  56. Batini C, Coia V, Battaggia C, Rocha J, Pilkington MM, Spedini G, Comas D, Destro-Bisol G, Calafell F: Phylogeography of the human mitochondrial L1c haplogroup: genetic signatures of the prehistory of Central Africa. Mol Phylogenet Evol. 2007, 43 (2): 635-644. 10.1016/j.ympev.2006.09.014.PubMedView Article
  57. Quintana-Murci L, Quach H, Harmant C, Luca F, Massonnet B, Patin E, Sica L, Mouguiama-Daouda P, Comas D, Tzur S, Balanovsky O, Kidd KK, Kidd JR, van der Veen L, Hombert JM, Gessain A, Verdu P, Froment A, Bahuchet S, Heyer E, Dausset J, Salas A, Behar DM: Maternal traces of deep common ancestry and asymmetric gene flow between Pygmy hunter-gatherers and Bantu-speaking farmers. Proc Natl Acad Sci USA. 2008, 105 (5): 1596-1601. 10.1073/pnas.0711467105.PubMed CentralPubMedView Article
  58. Berniell-Lee G, Calafell F, Bosch E, Heyer E, Sica L, Mouguiama-Daouda P, van der Veen L, Hombert JM, Quintana-Murci L, Comas D: Genetic and demographic implications of the Bantu expansion: insights from human paternal lineages. Mol Biol Evol. 2009, 26 (7): 1581-1589. 10.1093/molbev/msp069.PubMedView Article
  59. Verdu P, Austerlitz F, Estoup A, Vitalis R, Georges M, Thery S, Froment A, Le Bomin S, Gessain A, Hombert JM, Van der Veen L, Quintana-Murci L, Bahuchet S, Heyer E: Origins and genetic diversity of pygmy hunter-gatherers from Western Central Africa. Curr Biol. 2009, 19 (4): 312-318. 10.1016/j.cub.2008.12.049.PubMedView Article
  60. Batini C, Lopes J, Behar DM, Calafell F, Jorde LB, van der Veen L, Quintana-Murci L, Spedini G, Destro-Bisol G, Comas D: Insights into the demographic history of African Pygmies from complete mitochondrial genomes. Mol Biol Evol. 2011, 28 (2): 1099-1110. 10.1093/molbev/msq294.PubMedView Article
  61. Spinola H, Couto AR, Peixoto MJ, Anagnostou P, Destro-Bisol G, Spedini G, Lopez-Larrea C, Bruges-Armas J: HLA Class-I Diversity in Cameroon: Evidence for a North–south Structure of Genetic Variation and Relationships with African Populations. Ann Hum Genet. 2011, 75 (6): 665-677. 10.1111/j.1469-1809.2011.00672.x.PubMedView Article
  62. Congiu A, Anagnostou P, Milia N, Capocasa M, Montinaro F, Destro Bisol G: Online databases for mtDNA and Y chromosome polymorphisms in human populations. J Anthropol Sci. 2012, 90: 201-215.PubMed
  63. Ethnologue: Languages of the World, Sixteenth edition. Edited by: Lewis MP. 2009, SIL International, Dallas, Tex, Online version: http://​www.​ethnologue.​com/​
  64. Vigilant L, Pennington R, Harpending H, Kocher TD, Wilson AC: Mitochondrial DNA sequences in single hairs from a southern African population. Proc Natl Acad Sci USA. 1989, 86 (23): 9350-9354. 10.1073/pnas.86.23.9350.PubMed CentralPubMedView Article
  65. van Oven M, Kayser M: Updated comprehensive phylogenetic tree of global human mitochondrial DNA variation. Hum Mutat. 2009, 30 (2): E386-94. 10.1002/humu.20921.PubMedView Article
  66. Excoffier L, Lischer HE: Arlequin suite ver 3.5: a new series of programs to perform population genetics analyses under Linux and Windows. Mol Ecol Resour. 2010, 10 (3): 564-567. 10.1111/j.1755-0998.2010.02847.x.PubMedView Article
  67. R Core Development Team: R: A Language and Environment for Statistical Computing. 2009, R Foundation for Statistical Computing, Vienna
  68. Alexe G, Satya RV, Seiler M, Platt D, Bhanot T, Hui S, Tanaka M, Levine AJ, Bhanot G: PCA and clustering reveal alternate mtDNA phylogeny of N and M clades. J Mol Evol. 2008, 67 (5): 465-487. 10.1007/s00239-008-9148-7.PubMedView Article
  69. Jombart T: adegenet: a R package for the multivariate analysis of genetic markers. Bioinformatics. 2008, 24 (11): 1403-1405. 10.1093/bioinformatics/btn129.PubMedView Article
  70. Jombart T, Ahmed I: adegenet 1.3-1: new tools for the analysis of genome-wide SNP data. Bioinformatics. 2011, 27 (21): 3070-3071. 10.1093/bioinformatics/btr521.PubMed CentralPubMedView Article
  71. Tamura K, Peterson D, Peterson N, Stecher G, Nei M, Kumar S: MEGA5: Molecular Evolutionary Genetics Analysis Using Maximum Likelihood, Evolutionary Distance, and Maximum Parsimony Methods. Mol Biol Evol. 2011, 28 (10): 2371-2379.View Article
  72. Kass RRA: Bayes factors and model uncertainty. J Am Stat Assoc. 1995, 90: 773-795. 10.1080/01621459.1995.10476572.View Article

Copyright

© Montano et al.; licensee BioMed Central Ltd. 2013

This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://​creativecommons.​org/​licenses/​by/​2.​0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.