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From Gerbault, P., Thomas, M.G., 2015. Human Evolutionary Genetics. In: James D. Wright
(editor-in-chief), International Encyclopedia of the Social & Behavioral Sciences,
2nd edition, Vol 11. Oxford: Elsevier. pp. 289–296.
ISBN: 9780080970868
Copyright © 2015 Elsevier Ltd. unless otherwise stated. All rights reserved.
Elsevier
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Human Evolutionary Genetics
Pascale Gerbault and Mark G Thomas, Research Department of Genetics, Evolution and Environment, University College London,
London, UK
Ó 2015 Elsevier Ltd. All rights reserved.
This article is a revision of the previous edition article by J.L. Mountain, volume 10, pp. 6984–6991, Ó 2001, Elsevier Ltd.
Abstract
Traditionally, our knowledge of human evolution has come from the fossil and material culture records, and has been
studied by paleontologists, anthropologists, archaeologists, anatomists, and – to some extent – linguists. In the past 25 years
genetics has made substantial contributions to this field. While much of this has been driven by advances in molecular
biology techniques (i.e., the methods used to obtain genetic data), the principles underlying how genetic data can be used to
make inferences about our evolutionary past come from the field of population genetics. In this article we discuss how
population genetics has been used to address two sets of questions regarding human evolution: (1) when and where did
human populations originate (demographic history) and (2) to what extent and in what ways have humans adapted to
changes in our ecology by natural selection (adaptation history).
Introduction to Population Genetics
Genetic information is carried by the sequence of bases in
deoxyribonucleic acid (DNA). One of the most important
features of the DNA molecule is its ability to be replicated, and
so be passed on from a cell to its daughter cells, and from one
generation to the next, mostly unchanged. Much of human
DNA (referred to as the human genome) appears not to have
a direct function, and is sometimes referred to as junk DNA. In
the past it was generally thought that the only functionally
important parts of our genome were genes – regions of DNA
that provide information on how to make specific proteins –
and regions near those genes that control their expression.
However, in recent years other genomic regions have been
shown to be important, such as those coding for functionally
active ribonucleic acid (RNA) molecules.
During the replication process, some changes can occur in
the DNA sequence; any such change is termed a mutation.
Mutations can occur in reproductive and nonreproductive cells
(i.e., germ and somatic cells, respectively). However, only
mutations occurring in germ cells are heritable and therefore
a substrate for evolutionary processes. Mutations give rise to
new genetic variants, called alleles (Kimura, 1971), and the
overall constellation of alleles in an individual is known as its
genotype. It should be noted that mutations are relatively rare –
even on the evolutionary timescale – so most of the DNA
sequences in our genome are identical between individuals,
and indeed between humans and other primates. However, the
human genome is very large (around 3 billion base pairs) so
while comparatively rare, sites in the genome (loci) that are
variable between individuals are still numerous. In addition to
mutation, a process called recombination can shuffle the
distribution of alleles along DNA sequences into new combinations (see later).
The field of population genetics is chiefly concerned with
describing and understanding the distribution and fate of
genetic variation. Ultimately, an allele only has two fates, loss
(from the population) or fixation (i.e., loss of variation at that
locus). However, in the intervening time between mutation
International Encyclopedia of the Social & Behavioral Sciences, 2nd edition, Volume 11
and loss or fixation a locus will be variable or polymorphic. The
distribution and fate of genetic variation in a population is
shaped by three processes: mutation (including recombination), genetic drift, and natural selection. Mutation, as
explained above, gives rise to variation, natural selection favors
(and so increases/decreases in frequency) particular alleles, and
genetic drift is the random sampling of alleles from one
generation to the next through reproduction, and leads to
random changes in allele frequencies through time.
When a new mutation occurs in the protein-coding region
of a gene, or in other functionally important parts of the
genome, it may change the biological characteristics (phenotype) of an individual. While phenotypes are only partly
determined by genotype, they can affect the overall fitness of
individuals, and hence lead to differential survival and reproduction. This process is known as natural selection, and it can
act on phenotypes related to early development and survival to
reproductive age (viability), on success in attracting a mate
(sexual selection), on the consequent ability to fertilize
(gamete selection), or on the number of progeny produced
(fecundity). The sum of these various selection stages constitutes the fitness of a phenotype, which is partly dependent on
environmental variables. The selection coefficient is the relative
fitness of a genotype or genotypes in relation to others.
A new mutation might not affect the underlying phenotype. Such mutations give rise to alleles that are neither
advantageous nor disadvantageous, and are said to be neutral.
The fate of neutral mutations is determined by random
genetic drift – the chance passing on alleles to future generations. Genetic drift reduces genetic diversity through time
since it ultimately leads to the loss or fixation of alleles, but its
effect depends strongly on the effective size of the population
considered.
The effective population size (Ne) is the size of a randomly
mating population that would show the same degree of genetic
drift as the actual population (Wright, 1931). The relationship
between effective and census population sizes (Ne and N,
respectively) varies depending on certain properties of the
population considered (Rice, 2004). For example, if we assume
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that individuals mate randomly and generations are not overlapping, then when N changes through time it can be shown
that Ne is the harmonic mean of N over a given period of time.
In such conditions, Ne is disproportionately affected by small
values of the fluctuating population size, and Ne will be smaller
than the average value of N across generations. However, these
assumptions do not always hold (Rice, 2004).
The effect of genetic drift can be illustrated by looking at the
chance an allele has of being fixed in a population. For autosomes (chromosomes other than the sex chromosomes: X or Y)
the probability of fixation of a new allele equals its relative
frequency in the population and is therefore 1/(2Ne); 2Ne
because every individual carries two copies of each autosome.
For the Y chromosome (one copy, only carried by males) or the
mitochondrial DNA (mtDNA; only transmitted by females)
this probability is approximately 1/(0.5Ne), and for the X
chromosome (2 copies in females, 1 copy in males) this
probability becomes approximately 1/(1.5Ne). This highlights
that the larger the population the less is the effect of drift since
the smaller the chance a new allele has of being fixed in this
population.
An appreciation of the importance of genetic drift emerged
from a major branch of population genetics called Neutral
Theory (Kimura, 1971). Prior to this it was thought that natural
selection was the most important force shaping the fates of
alleles, and so evolution. Neutral Theory itself arose out of the
realization that there was far more genetic variation in natural
populations (including humans) than could be maintained by
natural selection alone. It postulates that most mutations give
rise to alleles that are either disadvantageous – so lost rapidly
from the population – or are selectively neutral, or sufficiently
nearly neutral that drift rather than selection is the main force
governing their fate. Neutral Theory does admit the possibility
of selectively advantageous alleles arising by mutation, but
assumes that such events are sufficiently rare that they
contribute little to overall patterns of genetic variation
(Kimura, 1968).
One interesting prediction of Neutral Theory relates to the
rate of change of DNA sequences through time. If selection
plays a major role in the determination of survival of new
mutations, then a constant mutation rate should not lead to
a constant rate of evolution (substitution rate, or the rate of
fixation of new alleles). Instead the substitution rate would be
expected to vary episodically through time, as selection intensity acting on different traits varies. Alternatively, if the Neutral
Theory holds then it is expected that the substitution rate is set
by the underlying mutation rate and the proportion of new
alleles that are selectively disadvantageous (the selective
constraint). Given that the mutation rate (m) and selective
constraint for any given gene is approximately constant
through time, this predicts that the rate of genetic change is also
constant over all evolutionary lineages. This is known as the
molecular clock hypothesis (Kimura, 1968; Zuckerkandl and
Pauling, 1965) and it permits the estimation of lineage/
species divergence times using genetic data.
Neutral Theory has had a profound effect on population
genetics; so much so that while there are undoubtedly examples where natural selection has shaped the fate of alleles,
neutrality is now widely accepted as the null hypothesis – the
default assumed status of alleles at polymorphic loci.
Given that patterns of genetic variation in populations are
shaped by mutation, drift (itself shaped by demographic
history) and natural selection, it stands to reason that those
patterns of variation in populations contain information
about mutation, demographic history, and natural selection.
However, extracting that information to make inferences about
our evolutionary past is not trivial, primarily because any
particular pattern of variation in one or a set of populations can
be the result of a very wide range of different evolutionary
scenarios (equifinality).
Population Genetics Inferences: Demographic History
and Natural Selection
One of the advantages of the neutral model of evolution is that
it makes predictions about the relationship between patterns of
genetic variation, the mutation rate, and demographic parameters, such as the effective population size (Kimura, 1968;
Zuckerkandl and Pauling, 1965). These predictions include: (1)
the expected level of diversity within a species at equilibrium is
a function of the mutation rate and the effective population
size; (2) as two species diverge, they accumulate differences at
the same rate (i.e., substitution rate) at which neutral mutations arise; and (3) the expected allele frequencies in a sample
of DNA sequences is a function of the effective population size
and of the sample size.
These predictions can then be compared to empirical data
(Zuckerkandl and Pauling, 1965). From there, any significant
deviations from neutral expectations would suggest the locus
or loci under study did not evolve neutrally but instead were
subjected to other forces, including natural selection. In other
words, the study of natural selection is mainly based upon tests
of significant deviation from the null hypothesis of neutral
evolution, rather than any direct measurements of selection.
However, an important complicating factor is that real populations typically have complicated demographic histories that
cannot be collapsed to a single population size parameter, and
some demographic histories can lead to patterns in genetic
variation that mimic those formed by natural selection (e.g.,
Currat et al., 2006).
A wide-range of methods have been developed to detect
genomic signature of natural selection. These methods differ
primarily in the nature of the genetic data they consider and the
evolutionary time frame they are sensitive to. Allele frequencies
and other measures of genetic variation can be used to estimate
the extent and nature of demographic changes, and population
structuring, or departures from neutrality.
The extent of Hardy–Weinberg disequilibrium can be used
as a crude test of neutral evolution. The Hardy–Weinberg
theorem assumes an idealized population with an infinite
number of randomly mating individuals, no selection, no
mutation, and no migration, and predicts that for a single locus
with two alleles, the three genotypes AA, Aa, and aa follow the
proportions p2, 2pq, and q2, where p and q are the initial relative
frequencies of the two alleles A and a, respectively. Different
types of natural selection will alter these proportions in distinct
ways (Nielsen, 2005). However, any deviation from Hardy–
Weinberg equilibrium can be due to various factors, such as
population structure, and should thus be treated with caution
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as evidence for selection. Importantly, natural selection affects
only those selected alleles, while demographic factors and
genetic drift act on the whole genome.
Classical methods for detecting selection are based on the
distribution of allele frequencies in single nucleotide polymorphisms (SNPs), also called the allele- or site-frequency
spectrum (SFS)-based methods. The allele-frequency spectrum
can be ‘unfolded’ or ‘folded’ depending on whether the derived
and the ancestral (i.e., fixed in other Apes) allele can be
distinguished or not, respectively. This basically involves
identifying the number of allele-frequency classes observed,
and counting the number of loci falling in each frequency class.
When considering the SFS negative selection tends to increase
the proportion of low-frequency variants compared to neutral
expectations (Nielsen, 2005).
Alternatively, a new mutation arising in a population may
be advantageous, in which case it will increase in frequency in
the population by positive selection. Much interest has focused
on positive selection due to its association with adaptation and
the evolution of new forms and functions. Over a prolonged
period, positive selection increases the fixation rate of beneficial function-altering alleles. It can be detected by comparison
of changes between species. The first test of selection based on
detecting regions where patterns of variation depart from those
expected under neutrality is the Hudson–Kreitman–Aguade
(HKA) test (Hudson et al., 1987). It compares levels of diversity
in different genes or genomic regions, calibrated by betweenspecies divergence rates for the same regions, to test whether
these levels are significantly increased or reduced in the region
of interest, compared to theoretical expectation or empirical
data for presumed neutral parts of the genome (Nielsen, 2005).
Balancing selection is a case of positive selection where the
new variant is advantageous in combination of other alleles.
Balancing selection thereby increases the proportion of intermediate-frequency variants with respect to neutral expectations, whereas positive selection increases the proportion of
high-frequency variants.
A selective sweep is an example of positive selection where the
new favored variant reaches a high frequency in the population, resulting in an overall decrease of the genetic variation
at the selected locus, as well as in the genomic region
surrounding it. A selective sweep tends to increase the
proportion of low-frequency variants in respect to neutral
expectations.
A widely-used type of a selection test based on the SFS is
Tajima’s D test (see Tajima, 1989). In cases of a selective sweep
or purifying selection, this test detects an excess of lowfrequency variants (indicated by negative values of the Tajima’s
D statistic) compared to what is expected under neutrality, or to
empirical data for presumed neutral parts of the genome.
Alternatively, in cases of balancing selection this test detects an
excess of alleles of intermediate frequency (indicated by positive values of D). Other extensions of this type of tests are based
on similar principles (e.g., Fay and Wu, 2000).
Selection can also differ between populations. This may
happen due to adaptations to local environment, in which
cases the level of population differentiation may increase for
loci under different selection in different populations. The
Lewontin–Krakauer (1973) test was one of the first neutrality
tests to use the level of genetic differentiation between
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populations. This test rejects neutral evolution when differentiation between populations at specific locus is larger than that
expected under a neutral model, or is outside the empirical
range observed in presumed neutral regions of the genome.
Other tests have been developed under the same principle
(reviewed in Nielsen (2005)). Notably, Akey et al. (2002) used
genome-wide data to look at the variation in FST (a traditional
measure of allele-frequency difference between populations) in
humans. Various statistical methods can use genome-wide
scans to detect selective sweeps without prior hypotheses on
candidate genes or regions under selection (Nielsen, 2005).
Another feature of selected genomic regions is an increase in
the level of linkage disequilibrium (LD). When a new allele
arises it is physically linked to alleles at other loci nearby on the
same chromosome. This generates a nonrandom association of
alleles known as LD, and initially, any new allele will be in
complete LD with other nearby alleles. These combinations of
alleles in a region of a chromosome are sometimes referred to
as haplotypes. Over time, recombination breaks down the
association between alleles at nearby loci, and the allele of
interest will become associated with an increasing number of
different haplotypes. LD also decreases with increasing genetic
distance from the selected site as recombination shuffles allele
combinations in proportion to distance away from it on the
chromosome. Thus, the number and lengths of haplotypes
associated with an allele of interest can act as a proxy for the age
of that allele. If an allele has been positively selected then it will
rise to high-frequency quicker than expected under genetic drift
alone. It therefore follows that high-frequency alleles that are
still in high LD with other nearby alleles (i.e., are recent in
origin) are good candidates for selection. These principles have
been used to develop a number of statistics for detecting recent
and strong natural selection. For example, the extended
haplotype test identifies tracts of homozygosity (identity in
randomly drawn pairs of sequences) associated with a core
haplotype using the ‘extended haplotype homozygosity’
(EHH) statistic (e.g., Voight et al., 2006). A haplotype containing an allele that has been positively selected is expected to
display high EHH values and high frequencies. In contrast,
haplotypes that reach high frequencies due to genetic drift are
likely to have taken longer to reach those high frequencies and
are therefore likely to have been subjected to more recombination and mutation events, and would consequently show
lower values of EHH.
A fundamental problem with some methods of detecting
genomic signatures of natural selection is the bias generated
when genetic data are obtained not through direct sequencing,
but by genotyping SNPs that have been discovered in other
samples. This is called ascertainment bias. Patterns observed in
the data, such as allele frequencies, population differentiation,
and LD, all depend on the procedure used to discover these
SNPs. For example, ascertained SNPs will almost invariably be
biased toward more common variants, thus skewing statistics
based in the SFS. Additionally, many SNPs were identified in
only a subset of populations, particularly Europeans. When the
SNP-discovery protocol is known, statistical methods can be
used to correct for ascertainment biases to some extent (e.g.,
Voight et al., 2006).
More importantly, most methods for detecting selection are
challenged by the confounding effects of demographic history
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(e.g., Przeworski, 2002; Currat et al., 2006). For example,
Tajima’s D test can reject a neutral model of evolution if the
population has undergone an expansion or was strongly
structured (e.g., Przeworski, 2002) since an expansion can lead
to an increase in the proportion of low-frequency variants,
mirroring the effect of a selective sweep, and the population
structure can lead to an increase in the proportion of intermediate-frequency variants, mirroring balancing selection.
Allele surfing, a process whereby a rare allele is driven to high
frequencies at the wave front of an expanding population, can
also mirror a selective sweep (e.g., Currat et al., 2006). Alternatively, a preferential loss of low-frequency variants is expected to occur during a population bottleneck, thereby
leading to an excess of intermediate-frequency variants, mirroring balancing selection.
In consequence, failure to reject a neutral model of evolution for a locus might be the result of a particular demographic
history. Methods to account for the confounding effects of
demography usually involve comparisons of distributions of
genomic patterns of diversity at a locus of interest – such as
selection–detection statistics – to those derived from the rest of
the genome (which is presumed to be mostly neutral), or
generation of distributions of statistics under specific demographic scenarios by simulation, and detection of outliers (e.g.,
Xue et al., 2009). These methods involve modeling specific
demographic histories by computer simulation. Alternatively,
a recently developed method for genome-wide data sets
accounts for shared population history and gene flow by
generating an empirical pattern of covariance in allele
frequencies between populations from a set of markers (Coop
et al., 2010). This is then used as a null model for identifying
loci involved in local adaptation by looking at unusual correlations between allele frequencies and ecological variables
(Coop et al., 2010).
An alternative approach to detecting selection makes use of
advances in ancient DNA analysis to directly assess the rate of
allele-frequency change through time (Wilde et al., 2014;
Sverrisdottir et al., 2014). While this approach currently
requires the assumption of population continuity between
temporally distinct DNA samples (Sverrisdottir et al., 2014), it
does have some key advantages over other methods. First, it is
a direct method to test for selection, unlike others based only
on contemporary DNA samples. Secondly, given sufficient data
it has the potential to detect episodic changes in selection
intensity through time. Thirdly, unlike some approaches it is
not dependent on estimates of mutation or recombination
rates (Wilde et al., 2014).
The Origins of Modern Humans: The Genetic Record
Population genetics is grounded in mathematics and probability theory (e.g., Kimura, 1968). The field has however
become fundamentally data driven since the discovery of the
ABO blood group system. Cavalli-Sforza and Edwards (1964)
estimated genetic distances between five populations based
on five different blood groups and inferred a tree in which
Europeans were separated from an Afro-Asian lineage. A larger
study analyzing 35 proteins linked Europeans and Asians to
the exclusion of Africans, estimating a Europeans/Asians
split 55 000 years ago and an older divergence from Africans
120 000 years ago (Nei and Roychoudry, 1974).
As genetic data increased, multivariate methods, such as
principal component analysis (PCA), started to be used to
summarize patterns of variation for multiple genetic markers
(including HLA and blood group protein markers) over space
(Cavalli-Sforza et al., 1994). Spatial gradients of allele
frequencies were often interpreted as corresponding to
hypothesized linguistic or cultural expansions. It has however
been shown that spatial patterns of genetic variation can arise
even when no major demographic events have occurred
(Novembre and Stephens, 2008) but simply because similarity
between locations tends to decay with geographic distance, as
predicted under an isolation by distance (IBD) model.
One of the earliest influential genetic studies of human
origins examined mtDNA variation from worldwide population sample of 147 individuals (Cann et al., 1987). The
inferred maximum parsimony tree indicated that the deepest
branches were in Africa and a molecular clock estimate placed
the deepest split, that is, the time to the most recent common
ancestor, at just less than 200 000 years ago. Despite the caveats
of this study, notably the ‘African sample’ was actually a sample
of African Americans, and the maximum parsimony analysis
used mid-point rooting, instead of the more reliable out-group
rooting, these results were corroborated some years later by
a more robust study (Ingman et al., 2000). Since then, estimates of the time to the most recent common ancestor
(TMRCA) for mtDNA and the Y chromosome have been highly
influential in studies of human evolution. Estimates for
mtDNA TMRCA currently range around 140–240 kya (e.g.,
Behar et al., 2012) while, until recently, estimates for the Y
chromosome TMRCA ranged between 60 and 140 kya (e.g.,
Wei et al., 2013).
These TMRCA estimates for mtDNA and the Y chromosome
differ considerably and might initially seem contradictory.
However, large differences are expected for two major reasons.
First, the variance in expected TMRCAs is large compared to its
mean, as predicted by coalescent theory (Kingman, 1982).
Second, the mode of inheritance of mtDNA and the Y chromosome (maternally and paternally inherited, respectively)
mean they may have different effective population sizes.
Coalescent theory is a retrospective mathematical model of
gene genealogies for a sample of DNA sequences under
a defined population history (Kingman, 1982). Demographic
processes shape the pattern in which those lineages (i.e.,
branches on a genealogical tree) connect to one another. We
say those lineages converge or ‘coalesce’ when this process is
looked at backward through time. Gene coalescence can be
thought of as drift run backwards through the genealogical tree.
However, while drift and coalescence are conceptually related,
simulating coalescence backward through time is considerably
more computationally efficient than simulating drift forward
through time, because it only considers the sample, not the
whole population. Coalescent theory shows that the expected
TMRCA is 2Ne in generations, and the final time interval, when
the remaining two lineages coalesce (i.e., join) into the MRCA,
represents more than half of the variance in the TMRCA. This is
because coalescences take longer when there are fewer lineages
and those times will tend to strongly affect the shape of the
genealogy.
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These high-expected variances in TMRCAs have been highlighted by the recent discovery of a Y chromosome lineage
branching at the basal portion of the Y chromosome genealogical tree (Mendez et al., 2013). This Y chromosome lineage
was identified in individuals of West Africa origin and recent
West African descent, and was labeled A00 (Mendez et al.,
2013). The updated Y chromosome genealogical tree
provided a TMRCA estimate of 338 kya (95% confidence
interval (237–581) kya) (Mendez et al., 2013). This estimate
does not only predate current mtDNA TMRCA but also the
earliest anatomically modern human fossils. This demonstrates
that interpretation of TMRCA date estimates for a single nonrecombining region of the genome should be treated with
caution – they do not necessarily represent the founding dates
of a population or species. Furthermore, because of the ubiquity of past migration and the stochastic process inherent to
gene genealogies, it is difficult to infer the geographic origin of
a genealogical lineage from its current distribution (Beaumont
et al., 2010).
Regardless of the TMRCA estimates, studies of mtDNA
variation (Cann et al., 1987; Ingman et al., 2000) and of
nonrecombining regions of the Y chromosome variation (e.g.,
Ke et al., 2001) in modern populations are consistent with, and
have been interpreted as strongly supporting a recent African
origin of our species. This relies on the observation that Africa is
the source of the deepest lineages and harbors the greatest
diversity. The genetic diversity of both systems is characteristic
of a rapidly expanding population or one that was subject to
positive selection, that is, long terminal branches and excess of
low-frequency polymorphisms. A similar signal was also
identified in various autosomal regions (e.g., Voight et al.,
2005). A demographic process where modern humans
expanded out of Africa between 50 000 and 100 000 years ago
and replaced other archaic humans has been shown to better
explain the general pattern of genetic diversity observed (e.g.,
Fagundes et al., 2007).
Although classification systems vary, archaic humans refer
to any Homo-related fossil remain that is not anatomically
modern humans (Homo sapiens sapiens). Traditionally, and
prior to the development of molecular genetics techniques,
information on the morphology and behavior of our ancestors
and the related species at different times came from fossil and
archaeological evidence. One of the most widely known
archaic human groups is Neanderthal (Homo neanderthalensis).
It represents a morphologically distinct group, with robust
morphology, large brains, and prominent brow ridges. These
fossils date to between 250 000 and 39 000 years ago (Higham
et al., 2014) and have been found in Europe and western and
central Asia. In contrast, the earliest widely accepted fully
modern human skull is Omo I, from Ethiopia, and dates to
195 000 years ago (McDougall et al., 2005).
Recently, the genomes of two archaic human forms,
Neanderthals (Green et al., 2010) and Denisovans (Reich et al.,
2010), have been partially sequenced, thereby providing some
estimates of the relationships of ancient and modern humans.
This was performed by first focusing on biallelic SNPs where
two present-day humans carry different alleles and the archaic
human genome carried the derived allele, that is, not matching
chimpanzee (Green et al., 2010; Reich et al., 2010). Then
a measure called D(H1,H2, A, chimpanzee) was computed to
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assess the difference in proportion of matching when the
derived allele in the archaic human (A) genome matched the
modern human genome H1 more often than the modern
human genome H2. D is positive if the archaic genome
matches H1 more often and negative if it matches H2 more
often. This measure led to an estimate of the proportion of
Neanderthal ancestry in genomes as 1–4% (Green et al., 2010)
and 4–6% of Denisovan ancestry in Melanesian genomes
(Reich et al., 2010). It thus appears that Neanderthals and
Denisovans did contribute some ancestry to non-African
modern humans.
Adaptations and Detection of Positive Selection
We now inhabit radically different ecological (e.g., hot/cold
and tundra/forest), cultural (e.g., variety of food resources and
of their uses and social and mating systems) and demographic
(high population densities and rates of gene flow) environments worldwide. Environmental changes have triggered
human adaptation, a process that involves physiological,
biochemical, and behavioral adjustments. Genetic adaptations
are additional responses of humans to changing environment.
Genome-wide scans have the potential to generate lists of
putatively selected genes that can be further studied from
a functional perspective. The most striking examples of signals
of selection include those identified in genomic regions
involved in human pigmentation (e.g., Wilde et al., 2014) and
for genetic polymorphisms conferring some resistance to
malaria (Kwiatkowski, 2005) and other infectious diseases
(e.g., Fumagalli et al., 2012). Both emphasize that the various
environments we have evolved in, either in the form of climate
factors (e.g., Hancock et al., 2010) – such as ultraviolet exposure – or of pathogens (e.g., Fumagalli et al., 2012) have driven
components of our evolution. In this respect, evidence for
correlations between patterns of genetic variation and environmental variables (e.g., Hancock et al., 2010), such as
temperature, precipitation, solar radiation, latitude, or elevation, have been identified. However, identifying the precise
selective pressure and assessing the functional advantage of
candidate genetic variants remains a challenge.
EDAR
This challenge is well exemplified by genes involved in hair
follicle development, which have shown higher levels of population differentiation than expected under neutrality. In
particular, the ectodysplasin-A receptor gene (EDAR; MIM
604095), located on chromosome 2q12.3, plays a central role
in generation of the primary hair follicle pattern. In humans
a derived allele in the EDAR gene, called EDAR370A, results in
a valine-to-alanine substitution at position 370 of the protein
sequence. This allele is associated with thicker hair in East Asian
populations (Kamberov et al., 2013) has shown a strong signal
of positive selection using tests based on the SFS (e.g., Kelley
et al., 2006), haplotype structure (Voight et al., 2006) and
population differentiation (e.g., Xue et al., 2009). Using
haplotype analysis, the EDAR370A allele has been dated to
between 1133 and 73 996 years ago (Bryk et al., 2008), but the
reasons for strong selection are not yet known.
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The only selection hypothesis explicitly formulated so far is
that this allele may have been beneficial under Asian climate
conditions some 25 000 to 10 000 years ago; a time when the
climate was significantly colder and dryer than present in
eastern and northern Asia. This hypothesis proposes that the
EDAR370A allele contributed to increase lubrication and
humidification of exposed surfaces (face and scalp) during the
dryer and colder Ice Age in East Asia (Chang et al., 2009).
This has however not been supported by a recent study
reporting an EDAR370A mouse model (Kamberov et al., 2013).
In this study EDAR370A mice showed (1) increased hair
thickness; (2) a higher mammary branch density and smaller
mammary fat pad area; and (3) an increase in eccrine sweat
gland density (Kamberov et al., 2013). This study also showed
that EDAR370A was associated with (4) tooth variation
(including single and double shoveling of the upper incisors);
and (5) a higher active eccrine sweat gland density in Han
Chinese (Kamberov et al., 2013). A spatially explicit forward in
time simulation model, integrating the origins and spread of
farming populations and fitting to observed modern allele
frequencies, suggested that EDAR370A arose in East Asia
around 30 000 years ago (95% credible interval 13 175–
39 575 years ago) with selection a coefficient among the
highest estimated for the human genome (0.122 with a 95%
credible interval 0.030–0.186).
This nonetheless does not explicitly tell us which of
EDAR370A pleiotropic effect/s was/were the actual target of
natural selection. For example, as EDAR affects the development of sweat glands and hair morphology, it may be
hypothesized that EDAR370A alters thermoregulation, and
possibly mate preference. Alternatively, mammary gland
branching and/or fat pad size may have been the adaptive
phenotype, either because of associated benefits on lactation
(not-yet assessed) or because of increased mate preferences
(Dixson et al., 2011). Identifying the selective pressure on
EDAR370A and the target phenotype of this selection are made
difficult by the various developmental pathways in which
EDAR is involved.
LCT
Pathways involved in nutrient intake, digestion, and metabolism are essential to energy production and growth, and
consequently to development and survival. Since the range
expansions out of Africa, our species has undergone several
dietary transitions, including a shift from a diet based on
animal hunting and a broad range of food gathering to a diet
based on farming domesticated plants and animals (Luca et al.,
2010). This Neolithic transition had profound effects on our
diet, particularly dietary breadth and carbohydrate content. The
Neolithic transition dates back to about 11 000 years ago in its
core regions and marks the transition from foraging
(i.e., hunter-gathering) to food-producing (i.e., farming) societies. It is associated with changes that include an increasingly
sedentary lifestyle; the development of alternative economies
that focus on animal and/or plant domesticates; and technical
innovations that include polished stone tools and pottery.
The dietary breadth of farming populations is known to be
narrower than that of hunter-gatherers (Luca et al., 2010).
Patterns of genetic variation in the gene coding for lactase
(LCT) provide what is probably the strongest evidence of
genetic adaptation to dietary specialization (Holden and Mace,
1997; Gerbault et al., 2011). Lactase is the enzyme that digests
the milk sugar lactose. It is expressed in young mammals but its
expression usually decreases after the weaning period is over;
a trait known as lactase nonpersistence. However, in certain
human populations many adults continue to express lactase
throughout adulthood, a trait termed lactase persistence.
Large differences in the frequency of lactase persistence in
different populations have been known for some time (Holden
and Mace, 1997). More recently, at least four independent
alleles associated with lactase persistence have been identified
indicating convergent evolution in different geographic locations (Enattah et al., 2007; Gerbault et al., 2011). In Europe
and southern and central Asia, a single allele (–13 910*T)
predominates, whereas in the Middle East and Africa three
additional alleles (13 915*G, 13 907*G, and 14 010*C) are
commonly found in lactase-persistent individuals. Furthermore, the correlation between the distribution of lactase
persistence and the distribution of pastoralism/dairying
(Holden and Mace, 1997) provide strong support for a geneculture coevolutionary process.
Selection coefficients on the -13 910*T allele have been
estimated to be between 1.4 and 19% using extended haplotype homozygosity (Bersaglieri et al., 2004), between 5.2 and
15.9% using spatially explicit simulation modeling (Itan et al.,
2009), and around 2.4% using ancient DNA (Sverrisdottir
et al., 2014), among the highest of the human genome over
the past 30 000 years. Furthermore, the age estimates for the
–13 910*T allele are relatively recent: 2188–20 650 years ago
using extended haplotype variation (Bersaglieri et al., 2004),
7475–10 250 years ago using closely linked microsatellite
variation (Mulcare, 2006) and 6256–8683 years ago using
simulation modeling (Itan et al., 2009). These age estimates
bracket dates for the domestication of milkable animals and
dairying.
Our knowledge of the evolution of lactase persistence in
Europe has benefitted considerably from archaeological data,
notably from the age of dairy animals at death (e.g., Vigne,
2008) and lipid residue analyses from potsherds (Evershed
et al., 2008). Both have shown that dairying was an early
feature of European Neolithic economies. By considering
ancient DNA data on the occurrence of the –13 910*T allele in
combination with archaeological data on milk use, it appears
that dairying was practiced before lactase persistence arose or
became common(Burger et al., 2007).
Conclusion
The propensity of our species to colonize a wide range of
environments illustrates our species plasticity and adaptability.
While much of that plasticity and adaptability is underwritten
by our capacity for cumulative culture (Powell et al., 2009), it
now seems certain that some biological adaptation has
occurred. Our ability to detect the genomic signatures of
natural selection has improved dramatically in the last 10 years.
This has been simultaneously driven by advances in the
molecular techniques used to generate genetic data, by
improvements in ancient DNA technologies, and critically, by
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Human Evolutionary Genetics
innovations in statistical inference and computer simulation
modeling. However, challenges remain in disentangling the
relative effects of mutation, recombination, migration, population structure, genetic drift, and natural selection on
patterns of genetic diversity. Strong evidence for the adaptive
status of a trait entails (1) evidence of differential fertility or
mortality dependent on particular genetic variation; (2)
evidence from in vitro and/or in vivo studies of functional
differences between genotypes that affect the reproductive
success of their carriers; and (3) evidence of concordance
between the distribution of a genetic traits and the environmental factors that drive selective pressures. Because providing
these three types of evidence remains a challenging task, few
strong examples of natural selection exist. We have presented
two of them, those involving EDAR and LCT genes.
As molecular techniques improve and become easier and
cheaper to perform, more genetic information accumulates,
bringing new challenges in handling and analyzing so much
data (Pool et al., 2010). With improvements in our understanding of metabolic networks and biological pathways, it
becomes increasingly evident that mutations leading to
changes in a gene product can affect phenotypes in multiple
and subtle ways. This makes the targeting of selective pressure
and their potential adaptive consequences difficult. Integrative
computational modeling conditioned on multiple data types
offers one solution to this problem, and provides a robust
means of testing evolutionary hypotheses.
See also: Adaptation, Fitness, and Evolution; Darwinism;
Evolution, History of; Genetics and Anthropology; Human
Behavioral Ecology; Microevolution.
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