15.18: Evolution of Humans - Biology

15.18: Evolution of Humans - Biology

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The family Hominidae of order Primates includes the hominoids: the great apes (Figure 1). The term hominin is used to refer to those species that evolved after this split of the primate line, thereby designating species that are more closely related to humans than to chimpanzees. Hominins were predominantly bipedal and include those groups that likely gave rise to our species—including Australopithecus, Homo habilis, and Homo erectus—and those non-ancestral groups that can be considered “cousins” of modern humans, such as Neanderthals. In the past several years, however, many new fossils have been found, and it is clear that there was often more than one species alive at any one time and that many of the fossils found (and species named) represent hominin species that died out and are not ancestral to modern humans.

Very Early Hominins

Three species of very early hominids have made news in the past few years. The oldest of these, Sahelanthropus tchadensis, has been dated to nearly 7 million years ago. There is a single specimen of this genus, a skull that was a surface find in Chad. The fossil, informally called “Toumai,” is a mosaic of primitive and evolved characteristics, and it is unclear how this fossil fits with the picture given by molecular data, namely that the line leading to modern humans and modern chimpanzees apparently bifurcated about 6 million years ago. It is not thought at this time that this species was an ancestor of modern humans.

A second, younger species, Orrorin tugenensis, is also a relatively recent discovery, found in 2000. There are several specimens ofOrrorin. It is not known whether Orrorin was a human ancestor, but this possibility has not been ruled out. Some features of Orrorinare more similar to those of modern humans than are the australopiths, although Orrorin is much older.

A third genus, Ardipithecus, was discovered in the 1990s, and the scientists who discovered the first fossil found that some other scientists did not believe the organism to be a biped (thus, it would not be considered a hominid). In the intervening years, several more specimens of Ardipithecus, classified as two different species, demonstrated that the organism was bipedal. Again, the status of this genus as a human ancestor is uncertain.

Early Hominins: Genus Australopithecus

Australopithecus (“southern ape”) is a genus of hominin that evolved in eastern Africa approximately 4 million years ago and went extinct about 2 million years ago. This genus is of particular interest to us as it is thought that our genus, genus Homo, evolved from Australopithecus about 2 million years ago (after likely passing through some transitional states). Australopithecus had a number of characteristics that were more similar to the great apes than to modern humans. For example, sexual dimorphism was more exaggerated than in modern humans. Males were up to 50 percent larger than females, a ratio that is similar to that seen in modern gorillas and orangutans. In contrast, modern human males are approximately 15 to 20 percent larger than females. The brain size of Australopithecus relative to its body mass was also smaller than modern humans and more similar to that seen in the great apes. A key feature that Australopithecus had in common with modern humans was bipedalism, although it is likely that Australopithecus also spent time in trees. Hominin footprints, similar to those of modern humans, were found in Laetoli, Tanzania and dated to 3.6 million years ago. They showed that hominins at the time of Australopithecus were walking upright.

There were a number of Australopithecus species, which are often referred to as australopiths. Australopithecus anamensis lived about 4.2 million years ago. More is known about another early species, Australopithecus afarensis, which lived between 3.9 and 2.9 million years ago. This species demonstrates a trend in human evolution: the reduction of the dentition and jaw in size. A. afarensis (Figure 2) had smaller canines and molars compared to apes, but these were larger than those of modern humans.

Its brain size was 380–450 cubic centimeters, approximately the size of a modern chimpanzee brain. It also had prognathic jaws, which is a relatively longer jaw than that of modern humans. In the mid-1970s, the fossil of an adult female A. afarensis was found in the Afar region of Ethiopia and dated to 3.24 million years ago (Figure 3). The fossil, which is informally called “Lucy,” is significant because it was the most complete australopith fossil found, with 40 percent of the skeleton recovered.

Australopithecus africanus lived between 2 and 3 million years ago. It had a slender build and was bipedal, but had robust arm bones and, like other early hominids, may have spent significant time in trees. Its brain was larger than that of A. afarensis at 500 cubic centimeters, which is slightly less than one-third the size of modern human brains. Two other species, Australopithecus bahrelghazaliand Australopithecus garhi, have been added to the roster of australopiths in recent years.

A Dead End: Genus Paranthropus

The australopiths had a relatively slender build and teeth that were suited for soft food. In the past several years, fossils of hominids of a different body type have been found and dated to approximately 2.5 million years ago. These hominids, of the genus Paranthropus, were relatively large and had large grinding teeth. Their molars showed heavy wear, suggesting that they had a coarse and fibrous vegetarian diet as opposed to the partially carnivorous diet of the australopiths. Paranthropus includes Paranthropusrobustus of South Africa, and Paranthropusaethiopicus and Paranthropusboisei of East Africa. The hominids in this genus went extinct more than 1 million years ago and are not thought to be ancestral to modern humans, but rather members of an evolutionary branch on the hominin tree that left no descendants.

Early Hominins: Genus Homo

The human genus, Homo, first appeared between 2.5 and 3 million years ago. For many years, fossils of a species called H. habiliswere the oldest examples in the genus Homo, but in 2010, a new species called Homo gautengensis was discovered and may be older. Compared to A. africanus, H. habilis had a number of features more similar to modern humans. H. habilis had a jaw that was less prognathic than the australopiths and a larger brain, at 600–750 cubic centimeters. However, H. habilis retained some features of older hominin species, such as long arms. The name H. habilis means “handy man,” which is a reference to the stone tools that have been found with its remains.

Watch this video about Smithsonian paleontologist Briana Pobiner explaining the link between hominin eating of meat and evolutionary trends.

A YouTube element has been excluded from this version of the text. You can view it online here:

H. erectus appeared approximately 1.8 million years ago (Figure 4). It is believed to have originated in East Africa and was the first hominin species to migrate out of Africa. Fossils of H. erectus have been found in India, China, Java, and Europe, and were known in the past as “Java Man” or “Peking Man.” H. erectus had a number of features that were more similar to modern humans than those of H. habilis. erectus was larger in size than earlier hominins, reaching heights up to 1.85 meters and weighing up to 65 kilograms, which are sizes similar to those of modern humans. Its degree of sexual dimorphism was less than earlier species, with males being 20 to 30 percent larger than females, which is close to the size difference seen in our species. erectus had a larger brain than earlier species at 775–1,100 cubic centimeters, which compares to the 1,130–1,260 cubic centimeters seen in modern human brains. erectus also had a nose with downward-facing nostrils similar to modern humans, rather than the forward facing nostrils found in other primates. Longer, downward-facing nostrils allow for the warming of cold air before it enters the lungs and may have been an adaptation to colder climates. Artifacts found with fossils of H. erectus suggest that it was the first hominin to use fire, hunt, and have a home base. erectus is generally thought to have lived until about 50,000 years ago.

Humans: Homo sapiens

A number of species, sometimes called archaic Homo sapiens, apparently evolved from H. erectus starting about 500,000 years ago. These species include Homo heidelbergensis, Homo rhodesiensis, and Homo neanderthalensis. These archaic H. sapiens had a brain size similar to that of modern humans, averaging 1,200–1,400 cubic centimeters. They differed from modern humans by having a thick skull, a prominent brow ridge, and a receding chin. Some of these species survived until 30,000–10,000 years ago, overlapping with modern humans (Figure 5).

There is considerable debate about the origins of anatomically modern humans or Homo sapiens sapiens. As discussed earlier, H. erectus migrated out of Africa and into Asia and Europe in the first major wave of migration about 1.5 million years ago. It is thought that modern humans arose in Africa from H. erectus and migrated out of Africa about 100,000 years ago in a second major migration wave. Then, modern humans replaced H. erectus species that had migrated into Asia and Europe in the first wave.

This evolutionary timeline is supported by molecular evidence. One approach to studying the origins of modern humans is to examine mitochondrial DNA (mtDNA) from populations around the world. Because a fetus develops from an egg containing its mother’s mitochondria (which have their own, non-nuclear DNA), mtDNA is passed entirely through the maternal line. Mutations in mtDNA can now be used to estimate the timeline of genetic divergence. The resulting evidence suggests that all modern humans have mtDNA inherited from a common ancestor that lived in Africa about 160,000 years ago. Another approach to the molecular understanding of human evolution is to examine the Y chromosome, which is passed from father to son. This evidence suggests that all men today inherited a Y chromosome from a male that lived in Africa about 140,000 years ago.

Evolutionary Biology for the 21st Century

Affiliations Museum of Comparative Zoology and Department of Organismic and Evolutionary Biology, Harvard University, Cambridge, Massachusetts, United States of America, Department of Molecular and Cellular Biology, Harvard University, Cambridge, Massachusetts, United States of America

Affiliation Department of Biochemistry and Biophysics, University of California, San Francisco, California, United States of America

Affiliation Department of Ecology and Evolutionary Biology, Yale University, New Haven, Connecticut, United States of America

Affiliations Museum of Vertebrate Zoology, University of California, Berkeley, California, United States of America, The Australian National University, Canberra, Australia

Affiliation Department of Biology, University of Rochester, Rochester, New York, United States of America

Affiliation Department of Biology, Stanford University, Stanford, California, United States of America

Affiliation Department of Biology, University of Munich, Munich, Germany

Affiliation Department of Biology, University of Missouri, St. Louis, Missouri, United States of America

Affiliation Florida Museum of Natural History, University of Florida, Gainesville, Florida, United States of America

Affiliation Department of Ecology, Evolution and Marine Biology, University of California, Santa Barbara, California, United States of America


I wish to thank Susan Anton, Melinda Zeder, Tim Lewens, Polly Wiessner, Tim Ingold, Robert Sussman, Kim Sterelny, Jeffery Peterson, Celia Deane-Drummond and Marc Kissel for their influence on the themes and content in this article and the organizers of the ‘New trends in evolutionary biology: biological, philosophical and social science perspectives’, co-sponsored by the Royal Society and the British Academy, for their kind invitation to participate. I also thank the editor of Interface Focus and two anonymous reviewers for substantial and efficient critiques and commentary on earlier versions of this article. Agustin Fuentes is responsible for 100% of the development and writing of this article.

The Model

The basic model, due to BR, considers a population in which each individual has one of two possible cultural variants: A or B. A set of n ( n = 3,4 , … ) individuals from the parental generation affects the cultural trait of the offspring. The probability that an offspring is A when there are j role models of type A is Pr offspring A | j role models A = j n + D ( j ) n . [2] The function D ( j ) , where j = 0,1,2 , … , n , determines the strength of frequency-dependent bias in a set of n role models, among whom j are of type A. As pointed out by BR, the conformity coefficients D ( j ) have the following properties: D ( 0 ) = D ( n ) = 0 , [3a] D ( n − j ) = − D ( j ) f o r j = 0,1 , … , n . [3b]

Eq. 3a simply means that the cultural type of the offspring coincides with that of the role models when all of the latter have the same cultural type in these cases, there is no transmission error, and transmission is only from role models. Eq. 3b asserts transmission symmetry between the two cultural types, A and B.

In the parental population, let p be the frequency of type A. Then, since the n role models are chosen at random, the number of A role models has a binomial distribution with parameters n and p. Since D ( 0 ) = D ( n ) = 0 and 0 < j n + D ( j ) n < 1 , j = 1,2 , … , n − 1 , [4a] we must have − j < D ( j ) < n − j , j = 1,2 , … , n − 1 , [4b] and if n is even, D ( n 2 ) = D ( n − n 2 ) = − D ( n 2 ) = 0 . Then, p ′ , the frequency of A in the next generation, is p ′ = ∑ j = 0 n j n + D ( j ) n n j p j ( 1 − p ) n − j , [5] and p ′ = p + ∑ j = 0 n D ( j ) n n j p j ( 1 − p ) n − j . [6] BR (box 7.4) point out that Eq. 6 can also be written in the form p ′ = p + ∑ j = k n D ( j ) n n j p j ( 1 − p ) n − j − p n − j ( 1 − p ) j . [7] Throughout what follows, we take k = n 2 + 1 when n is even and k = n + 1 2 when n is odd.

Let us write Eq. 7 in the form p ′ = p + F n ( p ) , [8] then we have the following result.

Result 1.

F n ( p ) = ( 2 p − 1 ) G n ( z ) where G n ( z ) is a polynomial in terms of z = p ( 1 − p ) and G n ( 0 ) = 0 .

Equilibria and Stability without Selection.

The above model corresponds to the case where there is no selection on the cultural traits. We now explore the possible equilibria of recursion Eq. 8 and when they are stable. From Result 1, as p ′ = p + F n ( p ) , where F n ( p ) = ( 2 p − 1 ) G n ( z ) and G n ( z ) is a polynomial in z = p ( 1 − p ) with G n ( 0 ) = 0 , it is clear that p * = 0 , p * = 1 , and p * = 1 2 are equilibrium points. Their stability conditions are specified in Result 2, whose proof is in SI Appendix, section B.

Result 2.

1) If D ( 1 ) < 0 , then both p * = 0 and p * = 1 are locally stable. If D ( 1 ) > 0 , both are locally unstable.

2) If − 2 n − 1 < ∑ j = k n − 1 D ( j ) n n j ( 2 j − n ) < 0 , then p * = 1 2 is locally stable otherwise, it is not locally stable.

Hence, if the transmission probability of a single distinct role model ( 1 / n + D ( 1 ) / n ) is smaller than expected from its frequency ( < 1 / n ) , then fixations of both types are stable, whereas if this probability is greater than expected from its frequency ( > 1 / n ) , then fixations of both types are unstable and a polymorphism may exist.

Remark 1.

1) The stability conditions in Result 2 should be coupled with the general constraints in Eq. 4. Hence, for example, for p * = 0 and p * = 1 to be stable, we need − 1 < D ( 1 ) < 0 , and for them to be unstable, we need 0 < D ( 1 ) < n − 1 .

2) The conditions can be written in terms of D ( n − 1 ) = − D ( 1 ) . For example, p * = 0 and p * = 1 are locally stable if D ( n − 1 ) > 0 and unstable if D ( n − 1 ) < 0 .

Comparing the stability conditions for p * = 0 , p * = 1 to those of p * = 1 2 , we see that they are not complementary. It is possible, for example, that both p * = 0 and p * = 1 are not locally stable, which entails that there is a protected polymorphism, but p * = 1 2 is not stable. This would suggest the existence of stable polymorphic equilibria other than p * = 1 2 or some kind of stable cycle or chaos. We can then explore when p * = 1 2 is the unique stable polymorphism and if other stable polymorphic equilibria exist. This depends on n, the number of role models, and the values of D ( j ) . Since G n ( z ) is a polynomial in z = p ( 1 − p ) , equilibria other than p * = 1 2 satisfying G n ( z * ) = G n p * ( 1 − p * ) = 0 must occur as complementary pairs whose sum is 1.

From the equilibrium equations for n = 3,4,5 , for example, we see that for n = 3 and 4, the equilibrium equations are identical (recall that if n is even, D ( n 2 ) = 0 ): p ( 1 − p ) ( 2 p − 1 ) = 0 , [9] giving rise only to p * = 0 , p * = 1 , p * = 1 2 . In this case there is only one D ( j ) involved, D ( 1 ) = − D ( 2 ) for n = 3 and D ( 1 ) = − D ( 3 ) (and D ( 2 ) = 0 ) for n = 4 , and the stability conditions for p * = 0 , p * = 1 , and p * = 1 2 complement each other: When p * = 0 and p * = 1 are stable, p * = 1 2 is not stable, and when p * = 0 and p * = 1 are not stable, p * = 1 2 is stable. Moreover, there is global convergence to the stable equilibria.

When n = 5 , there are two D ( j ) s involved, D ( 1 ) = − D ( 4 ) and D ( 2 ) = − D ( 3 ) , and the equilibrium equation is p ( 1 − p ) ( 2 p − 1 ) D ( 4 ) − p ( 1 − p ) D ( 4 ) − 2 D ( 3 ) = 0 . [10] When D ( 3 ) and D ( 4 ) are of different signs, it is possible to have more than three equilibria. For example, when D ( 4 ) = − 0.7 and D ( 3 ) = 1.9 , stable equilibria occur at 0.1927 and 0.8073, while 0, 1 2 , and 1 are unstable (blue line in Fig. 1).

Three polymorphic equilibria (two stable, one unstable) can exist with n = 5 role models. p ′ − p is plotted as a function of p (Eq. 7) for n = 5 , D ( 3 ) = 1.9 , D ( 4 ) = − 0.7 , and s = 0 , shown in blue. The same plot, but with s = 0.05 (Eq. 13), is shown in red. The black horizontal line illustrates p = p ′ , and the equilibria, i.e., solutions to Eq. 10, occur when the colored lines cross the black line, shown with circles. Open circles mark unstable equilibria, and filled circles mark stable equilibria. Arrows point away from unstable equilibria and toward stable equilibria. In the s = 0 case, there are five equilibria total, with the stable ones at p ^ = 0.1927 and 1 − p ^ = 0.8073 . In the s = 0.05 case, there are also five equilibria, with the stable ones at p ^ = 0.2263 and 0.8331. Type A is favored if s > 0 , and, in this case, the stable frequency of A is higher than with s = 0 .

It is interesting to find general conditions under which p * = 1 2 is the only polymorphic equilibrium. This is the content of Result 3.

Result 3.

Suppose there are n role models ( n ≥ 3 ). Then, p * = 1 2 is the unique polymorphic equilibrium if D ( j ) has the same positive or negative sign for all k ≤ j < n .

Proof. From Eqs. 6 and 7, we see that F n ( 0 ) = F n ( 1 ) = F n ( 1 2 ) = 0 . But as 2 j − n ≥ 1 and 0 < p < 1 2 → p 2 j − n − ( 1 − p ) 2 j − n < 0 , [11] 1 2 < p < 1 → p 2 j − n − ( 1 − p ) 2 j − n > 0 . [12]

Under the assumptions of Result 3, F n ( p ) changes sign in ( 0,1 ) only at p * = 1 2 , implying that p * = 1 2 is the only polymorphic equilibrium. (A special case of this is D ( j ) = D for all k ≤ j < n .)

It might be expected that if p * = 1 2 is the unique polymorphic equilibrium, then either p * = 0 and p * = 1 are both stable and p * = 1 2 is not stable, or both p * = 0 and p * = 1 are not stable and p * = 1 2 is stable, since it is a protected polymorphism. In fact, when p * = 0 and p * = 1 are both stable, there is global convergence to one of them p * = 1 2 is not stable, such that 0 , 1 2 is the domain of attraction of p * = 0 and 1 2 , 1 that of p * = 1 . However, when both p * = 0 and p * = 1 are not stable, then even when p * = 1 2 is the unique polymorphic equilibrium, it is possible that p * = 1 2 is not stable. For example, following Eq. B8 in SI Appendix, section B, let ϕ n = 1 + 1 2 n − 2 ∑ j = k n − 1 D ( j ) n n j ( 2 j − n ) . From Eq. 4, the lower bound of ϕ n occurs when D ( j ) = − j for all k ≤ j ≤ n − 1 , in which case all of the D ( j ) s are negative and p * = 1 2 is unique by Result 3. SI Appendix, Table S1 presents the lower bounds on ϕ n for n = 3,4 , … , 20 . The bounds on D, namely, − j < D ( j ) < n − j , do not provide a predictable relationship between j and D. Changing n can change the bounds on ϕ ( n ) , and if D ( j ) is at its lower bound, then the dynamics are affected by n. This is shown in the third column of SI Appendix, Table S1.

We note that unless n = 3,4 , the lower bound of ϕ n is less than − 1 , in which case Result 2 asserts that p * = 1 2 is not locally stable for these values of ϕ n . Indeed, none of the equilibria may be stable, and in this case, there can be a stable cycle, an example of which for n = 5 is shown in Fig. 2.

Stable cycles can occur when n = 5 role models. (A and B) p ′ (A) and p ″ (B) are plotted against p—where p is the frequency of phenotype A in the current generation, p ′ is its frequency in the next generation, and p ″ is its frequency in the generation after that—for n = 5 , D ( 4 ) = − 3.9 , D ( 3 ) = − 2.9 , and s = 0 , shown in blue. Open circles mark unstable equilibria, and filled circles mark stable equilibria with period 2 (i.e., the frequency returns to this point every two generations). Arrows point away from unstable equilibria and/or toward stable equilibria with period 2. (C) p over time. (D) p over time when the parameters are kept the same, except that s, the selection coefficient in favor of A, is changed to 1.

Equilibria and Stability with Selection.

Suppose that selection operates on A and B with w A = 1 + s and w B = 1 the associated fitness parameters. Thus, if s > 0 , which we shall assume throughout, type A has a selective advantage. The evolution is then determined by the transformation W p ′ = ( 1 + s ) p + F n ( p ) W ( 1 − p ) ′ = ( 1 − p ) − F n ( p ) , [13] where p and p ′ are the frequencies of type A individuals in the present and the next generation, respectively. F n ( p ) is specified in Eqs. 6 and 7, and W, the normalizing factor, is W = 1 + s p + F n ( p ) . [14] As F n ( 0 ) = F n ( 1 ) = 0 , it is clear from Eq. 13 that both p * = 0 and p * = 1 are equilibrium points. In order to find polymorphic equilibria, we solve the equilibrium equation W p = ( 1 + s ) p + F n ( p ) . [15] From Eqs. 6 and 7, we have F n ( p ) = p ( 1 − p ) ( 2 p − 1 ) H n ( p ) , where H n ( p ) = ∑ j = k n − 1 D ( j ) n n j p ( 1 − p ) n − 1 − j × ∑ i = 0 2 j − n − 1 ( 1 − p ) i p 2 j − n − 1 − i . [16] Observe that H n ( 0 ) = H n ( 1 ) = D ( n − 1 ) = − D ( 1 ) . [17] We can rewrite Eq. 15 as W p = ( 1 + s ) p + p ( 1 − p ) ( 2 p − 1 ) H n ( p ) , [18] so either p = 0 or W = ( 1 + s ) 1 + ( 1 − p ) ( 2 p − 1 ) H n ( p ) . [19] Also, from Eq. 14, W = 1 + s [ p + p ( 1 − p ) ( 2 p − 1 ) H n ( p ) ] . [20] Combining Eqs. 19 and 20, we deduce that either p = 1 or p satisfies the equation Q n ( p ) = ( 2 p − 1 ) 1 + s ( 1 − p ) H n ( p ) + s = 0 . [21] Q n ( p ) is a polynomial in p, and from Eq. 17, Q n ( 0 ) = s − ( 1 + s ) D ( n − 1 ) , Q n ( 1 ) = s + D ( n − 1 ) , Q n ( 1 2 ) = s . [22] To sum up, the equilibria are p * = 0 , p * = 1 , and possible polymorphic equilibria p * that lie in ( 0,1 ) and satisfy Q n ( p * ) = 0 . Before analyzing the general case with respect to stability of p * = 0 , p * = 1 , and the existence of polymorphic equilibria, we treat the case n = 3 , which has received a great deal of attention (3, 21, 29).

When n = 3 , we have D ( 0 ) = D ( 3 ) = 0 and D ( 2 ) = − D ( 1 ) . Let D ( 2 ) = v then, the transformation Eq. 13 becomes W p ′ = ( 1 + s ) p + v p ( 1 − p ) ( 2 p − 1 ) , [23] with W = 1 + s [ p + v p ( 1 − p ) ( 2 p − 1 ) ] . [24] At equilibrium p * = 0 , p * = 1 , or there is a polymorphic equilibrium satisfying the equation Q 3 ( p ) = − 2 s v p 2 + ( 3 s + 2 ) v p − v ( 1 + s ) + s = 0 . (25) Here, Q 3 ( 0 ) = s − v ( 1 + s ) , Q 3 ( 1 ) = s + v , Q 3 1 2 = s . [26] First, we find the local stability conditions for p * = 0 and p * = 1 . The linear approximation to Eq. 23 near p * = 0 is p ′ = ( 1 + s ) ( 1 − v ) p , [27] and near p * = 1 ( 1 + s ) ( 1 − p ) ′ = ( 1 − p ) ( 1 − v ) . [28] Hence, p * = 0 is locally stable when ( 1 + s ) ( 1 − v ) < 1 or v ( 1 + s ) − s > 0 and unstable if v ( 1 + s ) − s < 0 . Similarly, p * = 1 is locally stable when 1 − v 1 + s < 1 or when ( s + v ) > 0 , and unstable if ( s + v ) < 0 . Comparing these stability conditions to Eq. 26, we conclude the following.

1) With s > 0 , if neither p * = 0 nor p * = 1 is stable, v ( 1 + s ) − s < 0 and ( s + v ) < 0 , and then Q 3 ( 0 ) > 0 and Q 3 ( 1 ) < 0 . Therefore, a polymorphic equilibrium p * exists such that Q 3 ( p * ) = 0 , it is unique since Q 3 ( + ∞ ) < 0 , and since Q 3 ( 1 2 ) = s > 0 , we have 1 2 < p * < 1 .

2) If both p * = 0 and p * = 1 are stable with v ( 1 + s ) − s > 0 and ( s + v ) > 0 , then Q 3 ( 0 ) < 0 , Q 3 ( 1 ) > 0 , and as Q 3 ( 1 2 ) > 0 a unique polymorphism p * exists with 0 < p * < 1 2 .

If the two fixations p * = 0 and p * = 1 are not stable, then it can be shown that the unique polymorphism p * is globally stable. If both p * = 0 and p * = 1 are stable, then the unique polymorphism p * is not stable and separates the domains of attraction to p * = 0 and p * = 1 into [ 0 , p * ) and ( p * , 1 ] , respectively. If s > 0 and − s < v < s 1 + s , then p * = 1 is stable and p * = 0 is not stable, in which case no polymorphic equilibrium exists and p * = 1 is globally stable. SI Appendix, Fig. S1 shows examples of all three cases.

In the case n = 3 , the stability conditions of p * = 0 and p * = 1 are related to the possible existence of polymorphic equilibria. In fact, this is the case in general. We start with the stability conditions for p * = 0 and p * = 1 .

Result 4.

1) p * = 0 (fixation of the disfavored type) is locally stable if − D ( 1 ) = D ( n − 1 ) > s / ( 1 + s ) and unstable if − D ( 1 ) = D ( n − 1 ) < s / ( 1 + s ) .

2) p * = 1 (fixation of the favored type) is locally stable if − D ( 1 ) = D ( n − 1 ) > − s and unstable if D ( n − 1 ) < − s .

3) It is possible that p * = 0 and p * = 1 are both stable or both unstable. Also, p * = 1 can be stable while p * = 0 is unstable, but the opposite cannot occur. That is, if fixation of the disfavored type is unstable, then fixation of the favored type may be stable, but the opposite is not true.

Proof. Following Eqs. 1317, the linear approximation of the frequency transformation near p * = 0 is p ′ = ( 1 + s ) p 1 − H n ( 0 ) = ( 1 + s ) p 1 − D ( n − 1 ) . [29] Therefore, p * = 0 is locally stable if ( 1 + s ) 1 − D ( n − 1 ) < 1 or if s − ( 1 + s ) D ( n − 1 ) < 0 . The linear approximation near p * = 1 is ( 1 + s ) ( 1 − p ) ′ = ( 1 − p ) 1 − H n ( 0 ) = ( 1 − p ) 1 − D ( n − 1 ) , [30] and p * = 1 is locally stable if 1 − D ( n − 1 ) 1 + s < 1 or s + D ( n − 1 ) > 0 . Hence, p * = 0 and p * = 1 are both locally stable if D ( n − 1 ) > s 1 + s , and both are unstable if D ( n − 1 ) < − s . Further, p * = 0 is not stable, while p * = 1 is locally stable if − s < D ( n − 1 ) < s 1 + s . It is impossible that p * = 0 is locally stable and p * = 1 is not.

Comparing Result 4 with the values of Q n ( p ) at 0,1 , 1 2 given in Eq. 22, we have:

Result 5.

If both p * = 0 and p * = 1 are locally stable or unstable, then there exists at least one polymorphic equilibrium p * with 0 < p * < 1 .

Proof. When both p * = 0 and p * = 1 are stable or unstable, the stability conditions for p * = 0 and p * = 1 given in Result 4 imply that Q n ( 0 ) Q n ( 1 ) < 0 and Q n ( p ) changes signs at least once for 0 < p < 1 . The continuity of Q n ( p ) implies that at least one 0 < p * < 1 exists such that Q n ( p * ) = 0 , so at least one polymorphic equilibrium exists.

Remark 2:

1) If only one of p * = 0 and p * = 1 is stable, then, by Result 4, it is p * = 1 that is stable and p * = 0 is not stable. In this case, Q n ( p ) > 0 for p = 0 , p = 1 2 , p = 1 , and it is possible that Q n ( p ) > 0 for all 0 < p < 1 , and no polymorphic equilibrium exists.

2) If p * = 0 and p * = 1 are stable, then s − ( 1 + s ) D ( n − 1 ) < 0 and Q n ( 0 ) < 0 . As Q n ( 1 2 ) = s > 0 , in this case, there exists a polymorphic equilibrium p * with 0 < p * < 1 2 .

3) The value of the mean fitness W in Eq. 24 evaluated at the polymorphic equilibrium p * is increasing as conformity increases (positive v) or as anticonformity decreases (v becomes less negative). An example is shown in SI Appendix, Fig. S3.

4) If p * = 1 and p * = 0 are unstable, then s + D ( n − 1 ) < 0 and Q n ( 1 ) < 0 . Again, as Q n ( 1 2 ) = s > 0 , at least one polymorphic equilibrium p * exists with 1 2 < p * < 1 .

5) BR stress the importance of p = 1 2 in dividing the domains of attraction, even in the case where the selection coefficient s ≠ 0 (i.e., with selection). We have shown in Results 4 and 5 that it is not p = 1 2 , but a more complicated equilibrium in terms of s and the conformity parameters that may be stable. Under some cases, there may be multiple polymorphic equilibria (red line in Fig. 1). Under some cases, there may be no stable polymorphic equilibria, and cycles may occur (Fig. 2D) or chaos. Both cycles and chaos can occur under anticonformity ( D ( j ) < 0 f o r j ≥ k ) or a combination of conformity and anticonformity (some D ( j ) > 0 and some D ( j ) < 0 for j ≥ k ), but not in the case of pure conformity.

These results are important for the evolution of conformity, as we now show.

Culture drives human evolution more than genetics

In a new study, University of Maine researchers found that culture helps humans adapt to their environment and overcome challenges better and faster than genetics.

After conducting an extensive review of the literature and evidence of long-term human evolution, scientists Tim Waring and Zach Wood concluded that humans are experiencing a "special evolutionary transition" in which the importance of culture, such as learned knowledge, practices and skills, is surpassing the value of genes as the primary driver of human evolution.

Culture is an under-appreciated factor in human evolution, Waring says. Like genes, culture helps people adjust to their environment and meet the challenges of survival and reproduction. Culture, however, does so more effectively than genes because the transfer of knowledge is faster and more flexible than the inheritance of genes, according to Waring and Wood.

Culture is a stronger mechanism of adaptation for a couple of reasons, Waring says. It's faster: gene transfer occurs only once a generation, while cultural practices can be rapidly learned and frequently updated. Culture is also more flexible than genes: gene transfer is rigid and limited to the genetic information of two parents, while cultural transmission is based on flexible human learning and effectively unlimited with the ability to make use of information from peers and experts far beyond parents. As a result, cultural evolution is a stronger type of adaptation than old genetics.

Waring, an associate professor of social-ecological systems modeling, and Wood, a postdoctoral research associate with the School of Biology and Ecology, have just published their findings in a literature review in the Proceedings of the Royal Society B, the flagship biological research journal of The Royal Society in London.

"This research explains why humans are such a unique species. We evolve both genetically and culturally over time, but we are slowly becoming ever more cultural and ever less genetic," Waring says.

Culture has influenced how humans survive and evolve for millenia. According to Waring and Wood, the combination of both culture and genes has fueled several key adaptations in humans such as reduced aggression, cooperative inclinations, collaborative abilities and the capacity for social learning. Increasingly, the researchers suggest, human adaptations are steered by culture, and require genes to accommodate.

Waring and Wood say culture is also special in one important way: it is strongly group-oriented. Factors like conformity, social identity and shared norms and institutions -- factors that have no genetic equivalent -- make cultural evolution very group-oriented, according to researchers. Therefore, competition between culturally organized groups propels adaptations such as new cooperative norms and social systems that help groups survive better together.

According to researchers, "culturally organized groups appear to solve adaptive problems more readily than individuals, through the compounding value of social learning and cultural transmission in groups." Cultural adaptations may also occur faster in larger groups than in small ones.

With groups primarily driving culture and culture now fueling human evolution more than genetics, Waring and Wood found that evolution itself has become more group-oriented.

"In the very long term, we suggest that humans are evolving from individual genetic organisms to cultural groups which function as superorganisms, similar to ant colonies and beehives," Waring says. "The 'society as organism' metaphor is not so metaphorical after all. This insight can help society better understand how individuals can fit into a well-organized and mutually beneficial system. Take the coronavirus pandemic, for example. An effective national epidemic response program is truly a national immune system, and we can therefore learn directly from how immune systems work to improve our COVID response."

The Impact of Evolutionary Driving Forces on Human Complex Diseases: A Population Genetics Approach

Investigating the molecular evolution of human genome has paved the way to understand genetic adaptation of humans to the environmental changes and corresponding complex diseases. In this review, we discussed the historical origin of genetic diversity among human populations, the evolutionary driving forces that can affect genetic diversity among populations, and the effects of human movement into new environments and gene flow on population genetic diversity. Furthermore, we presented the role of natural selection on genetic diversity and complex diseases. Then we reviewed the disadvantageous consequences of historical selection events in modern time and their relation to the development of complex diseases. In addition, we discussed the effect of consanguinity on the incidence of complex diseases in human populations. Finally, we presented the latest information about the role of ancient genes acquired from interbreeding with ancient hominids in the development of complex diseases.

1. Introduction

Geneticists have made significant progress in understanding the genetics behind many human diseases. These accomplishments include monogenic disease such as Huntington’s disease. On the other hand, the discovery of genetic determinants for complex diseases such as diabetes, Crohn’s disease, ischemic heart disease, stroke and some types of cancer (e.g., lung, colon, prostate, and breast), schizophrenia, and bipolar disorder is still poorly understood [1, 2]. However, release of the complete human genome sequence in 2001 has improved our understanding of the patterns of human genome diversity and its linkage to human complex diseases in the last decade [3, 4]. In order to study genetic diversity of the human genome at population level, the HapMap project was initiated to investigate the genetic differences on both inter- and intrapopulation levels. This was made possible by the introduction of advanced technologies such as Chip-based genotyping and next-generation sequencing techniques [5–7]. All these efforts have led to a vast amount of population genetic information. For instance, allele frequencies and levels of genetic association information for 3.5 million single nucleotide polymorphisms (SNPs), allele frequencies of approximately 15 million SNPs, 1 million short insertions and deletions, and 20000 structural variants are now available [5–8]. This huge amount of genetic variation data has been used in many Genome Wide Association Studies (GWAS) on various human diseases. According to National Human Genome Research Institute, the number of published GWAS studies till May 28, 2014, is 1921 [9] focusing on different human traits, such as height (522), and diseases, such as diabetes (251), breast cancer (191), lung cancer (35), coronary heart disease (150), and hypertension (39). GWAS have generated vast amount of information that increased our understanding of the genetic basis of many complex diseases by identifying genetic variants associated with the disease and its distribution in different populations. The availability of this information facilitates deeper understanding of complex diseases in both population genetics and evolutionary context.

2. Origin of Genetic Diversity in Human Populations

There are several factors that determine the amount of inter- and intragenetic diversity in human populations, which in turn is reflected in different phenotypes, including healthy and diseased phenotypes. These include mutation rates and recombination events that create and reorganize genetic diversity on the molecular level. Moreover, other factors are capable of changing the population size such as migration rates in or out of the population and birth and death rates. In addition, cultural behavior of human populations, such as selective or directed marriages or consanguinity, is also capable of effecting allelic frequencies within populations [10–14].

Generally, genetic, historical, and archeological evidences supported the Out-of-Africa hypothesis, which emphasized the elevated diversity of the original African population [15–18]. On the other hand, other evidences suggest much more multifaceted scenario in which early human populations have interbred with ancient hominids such as Neanderthals and Denisovans that lead to 1–6% contribution in modern Eurasian genomes and Melanesian genomes [19–21].

3. Evolutionary Driving Forces Effecting Genetic Diversity

It is well known that the main driving forces of evolution in any population are mutation, natural selection, genetic drift, and gene flow. The ability of these driving forces to perform their role is dependent on the amount of genetic diversity within and among populations. Genetic diversity among populations rises from mutations in genetic material, reshuffling of genes through sexual reproduction, and migration of individuals among populations (gene flow) [22]. The effect of the evolutionary driving forces on genetic diversity and evolution depends on the amount of genetic variations that already exist in a population. The amount of genetic variation within a given population remains constant in the absence of selection, mutation, migration, and genetic drift [23].

4. New Environment Effect of Genetic Diversity

The migration of human populations to new and different geographical habitats with different environmental challenges such as new climate, food varieties, and exotic pathogens acted as selective pressure on human populations that lead to adaptive changes in population genetic makeup to cope with these new challenges in order to achieve the golden goal of survival [24]. This selective pressure “natural selection” leads to the increase of frequency of favored genetic makeups and the elimination of deleterious genetic makeups that fail to adapt with the new environmental challenges [25]. This in turn may lead to the reduction of genetic diversity. Thus, natural selective events have shaped the present genetic diversity of the existing populations and consequently genetic variants involved in many diseases in both direct and indirect fashion [26–30].

5. Genetic Differentiation among Human Populations and the Role of Gene Flow

Genetic differentiation among human populations is significantly influenced by geographical isolation due to the accumulation of local allele frequency differences [31]. It was Wright in 1943 that first introduced the theory of Isolation By Distance (IBD) which describes the accumulation of local genetic differences under the assumption of local spatial dispersal [32]. According to IBD theory, pairwise measures of genetic differentiation are expected to increase with increasing geographical separation. This was proven in human populations on global, continental, and regional scales [33–35]. Physical barriers such as mountain chains, deserts, and large water bodies can limit gene flow among populations. Limited migration of individuals or groups among population can have an effect on genetic diversity leading to genetic differentiation among these populations and leads to the adaptive evolution in isolation. For example, the Sahara barrier causes the north to south (N–S) major orientation of genetic differentiation among the inhabitants of Africa [31]. Another significant geographic barrier, which has been suggested as an obstacle for gene flow, is the Himalaya mountain range resulting in the east to west (E–W) pattern of Asiatic genetic differentiation despite the fact that many problems with human populations sampling around the mountain were documented [31, 36–38]. It is well known that the rate of genetic differentiation differs according to orientations in Africa, Asia, and Europe, but not in the Americas [31] which can partially be justified by the presence of physical barriers that limited gene flow in certain directions in these continents. Thus, lack of significant physical barriers justifies that lack of directional genetic differentiation in the two Americas.

It was found that when comparing two nearby populations, Europe was found to be the continent with the smallest genetic differentiation, in relation to geographic distances measured using

-statistics (FST) (FST = 5 × 10 −4 ) followed by Asia (FST = 9 × 10 −3 ), Africa (FST = 1.7 × 10 −2 ), and America (FST = 2.6 × 10 −2 ). Generally, the genetic differentiation among two European populations separated by a thousand km is at least one order of magnitude lesser than in African, American, or Asiatic populations [31].

6. Natural Selection: The Most Significant Evolutionary Driving Force

Negative selection, also called purifying selection, is the most well-known form of natural selection [39]. Negative selection removes disadvantageous alleles or mutations from the population gene pool and reduces their frequencies in the population with a reduction rate corresponding to their biological effect. Thus, we should expect that lethal, nonsynonymous, or nonsense mutations will be eliminated from the population gene pool faster than synonymous mutations. On the other hand, less deleterious mutations that have milder effect on the correct expression of a gene can be found in a lower frequency in the population. The resulting change of genetic diversity in the population gene pool is low since negative selection effect on these mutations is mild. Another form of natural selection is positive selection, also called Darwinian selection, in which natural selection favors genetic mutations that are advantageous for the fitness or the survival of individuals. Positive selection will increase the frequencies of such variants in the population gene pool [25, 40]. The increase of the frequencies of variants will affect the genetic diversity in the population directly and indirectly by increasing the frequencies of genetically linked variants through genetic draft or genetic hitchhiking process [41, 42]. For example, several data indicate that the 503F variant of OCTN1 gene has increased in frequency due to recent positive selection and that disease-causing variants in linkage disequilibrium with 503F have hitchhiked to relatively high frequency, thus forming the inflammatory bowel disease 5 (IBD5) risk haplotype. Moreover, association results and expression data support IRF1, which is nearby of 503F hitchhiking variants, as a strong candidate for Crohn’s disease causation [43]. This may justify the observation that IBD5, which is a 250 kb haplotype on chromosome 5, is associated with an increased risk of Crohn’s disease in European population [44–46]. On the other hand, other genetic variants that are not linked with the positively selected variants will be eliminated resulting in reduction of genetic diversity in a process called selective sweep. For instance, evidences for positive selection at the GPX1 locus (3p21) and recent selective sweep in the vicinity of the locus were observed in Asian populations [47]. GPX1 locus is a selenoprotein gene characterized by the integration of selenium into the primary sequence as the amino acid selenocysteine. Selenoproteins have antioxidant properties, and thus interindividual differences in selenoprotein expression or activity could encompass an effect on risk for a range of complex diseases, cancers, neurodegenerative disorders, and diabetes complications [48–51]. Information about selective sweep of GPX1 gene can illustrate the role of selenoprotein genetic variants in the etiology of various human complex diseases [52–55]. An additional form of natural selection is the balancing selection, in which several alleles may coexist at a given locus if they are advantageous either individually or together [56, 57]. Balancing selection is favored when heterozygote genotype has a higher relative fitness than homozygote genotype. Crohn’s disease and ulcerative colitis are examples of balancing selection mediated evolution, which have been shown to be evolved in response to pathogen-driven balancing selection [58]. Based on “hygiene hypothesis,” the lack of exposure to parasites in modern settings resulted in immune imbalances, augmenting susceptibility to the development of autoimmune and allergic conditions. Population genetics analysis showed that five interleukin (IL) genes, including IL7R and IL18RAP, have been a target of balancing selection, a selection process that maintains genetic variability within a population. Fumagalli et al. showed that six risk alleles for inflammatory bowel disease (IBD) or celiac disease are significantly correlated with micropathogen richness validating the hygiene hypothesis for IBD and provide a large set of putative targets for susceptibility to helminthes infections [58].

7. Detecting the Effects of Natural Selection

All mentioned above forms of selection create characteristic molecular fingerprint also called selection signature. These selection signatures could be in the form of differences in rate of nucleotide diversity, allele frequency spectra, haplotype diversity, or genetic differentiation within or among population genomes [59]. As mentioned above, the most famous method of detecting natural selection signature is FST which is depending on the level of genetic differentiation among populations who experienced diverse forms of selection pressures because of many reasons, such as geographical isolation and environmental or nutritional conditions [60, 61]. Thus geographical isolation along with varying selection forces should increase the degree of differentiation among human populations resulting in an increase in FST value at the locus under selection [62].

8. Natural Selection Signature on Complex Diseases in Human Populations

Natural selection signatures have been detected on many complex diseases (Table 1). Among the complex diseases showing clear signatures of natural selection among human populations is blood pressure. Genetic differentiation analysis (FST) of blood pressure associated single nucleotide polymorphism (SNP) analysis showed accelerated differentiation among the four studied European subpopulations, namely, Utah Residents with Northern and Western European ancestry (CEU), British in England and Scotland (GBR), Toscani in Italia (TSI), and Finnish in Finland (FIN), with FST (EUR)

value = 0.0022 and 0.0054, respectively, for systolic blood pressure (SBP) and diastolic blood pressure (DBP).

At the individual SNP level, a nonsynonymous SNP (rs3184504) in SH2B3 gene that is associated with blood pressure showed significant differentiation between European and non-European populations with FST value = 0.0042 and branch length value = 0.0088. It was found that the allele (T) was rare in African and Asian populations with

and 0.01, respectively, while it has a high minor allele frequency of

in the European population [63]. Moreover, genome wide association (GWA) SNPs associated with systemic lupus erythematosus (SLE) showed the most significant collective molecular selection signatures among all studied inflammatory and autoimmune disorders. The 29 SLE SNPs were significant for global genetic differentiation among human populations with FST value of 0.008 and branch length analyses value of 0.0072. Most of the observed genetic differentiation in SLE associated SNPs allele frequencies differences was driven by differences between African and European populations with FST AFR-EUR value of 0.0028 or the Eurasia split in the branch length analysis value of 0.001. For instance, a risk SNP (rs6705628) identified in Asian samples had a low allele frequency in Europeans of 0.01 but high allele frequency in Africans of 0.36 and Asians of 0.19 [63, 64]. In addition, the population genetics analysis of type 2 diabetes (T2D) suggested marginally increased differentiation of T2D SNPs among global populations with FST (ALL) value of 0.0354, which was likely attributed to the Eurasia split from Africa. At the individual T2D SNP level, the rs8042680 in PRC1 gene showed the most significant selection signal. This SNP has a high derived protective allele frequency in European but is rare in African and absent in Asian populations [63, 64]. An additional complex disease that showed selection signature is coronary heart disease (CHD). The population genetics analysis of CHD associated SNPs showed a marginal increase of genetic differentiation between African and European populations with FST (African-European (AFR-EUR)) value of 0.034. The individual CHD SNP showing the most significant selection signal was rs599839 in PSRC1 gene, which was also significantly associated with low-density lipoprotein (LDL) [63, 65, 66].

Furthermore, several genetic differentiation analyses of GWA studies of SNPs associated with different types of cancers, such as breast, prostate, and colorectal cancers were performed. The most significant collective evidence of global population differentiations was observed in the 34 SNPs associated with prostate cancer with a global FST value of 0.017 or total branch length value of 0.01. Majority of the observed differentiation was mapped to the African lineage in the maximum likelihood (ML) branch length analysis value (AFR) of 0.0002. The most two significant SNPs (rs1465618 and rs103294) are located in THADA gene and near LILRA3 gene, respectively. Moreover, multiple SNPs (rs7590268, rs6732426, rs13429458, rs17030845, rs12478601, rs7578597, and rs10495903) in the THADA gene have been reported to be associated with a variety of complex traits or diseases such as cleft palate [67, 68], hair morphology [69], polycystic ovary syndrome [70, 71], platelet counts [72], type 2 diabetes [73], IBD, and Crohn’s disease [74, 75]. This gene has also been reported as a gene under selection [30, 63, 76, 77]. In addition, a sign of high differentiation of colorectal cancer SNPs was detected among the three Asian populations, namely, Han Chinese in Beijing (CHB), Southern Han Chinese (CHS), and Japanese in Tokyo (JPT) with FST (ASN) value of 0.0006. In addition, the significant colorectal cancer SNP rs4925386 in LAMA5 gene has higher derived allele frequency in Africans, but relatively low frequencies in Asians and Europeans.

9. Natural Selection and Cancer

Even though [78] Peto et al. in 1975 suggested a paradox that advocated that large animals might have developed some mechanisms to resist cancer in a counterselection process [79], very few studies have investigated the effect of selection on the evolution of cancer-related genes. An example for cancer-related genes under negative selection is breast cancer 1, early onset gene (BRCA1) [39, 80]. Not only is this gene strongly associated with female breast cancer but its mutations have been reported as risk factor for several other types of cancers including male breast cancer, fallopian tube cancer, and pancreatic cancer [81–86]. On the other hand, signature of positive selection was identified on the TRPV6 gene, which is aggressiveness of prostate cancer among European-Americans. Additionally, TRPV6 gene has experienced positive selection in non-African populations, resulting in several nonsynonymous codon differences among individuals of different genetic backgrounds [87, 88]. Moreover UGT2B4 gene, associated with increased risk of breast cancer in Nigerians and African Americans, shows molecular signatures of recent positive selection or balancing selection [89]. Furthermore, signature of positive selection was identified on the PPP2R5E gene, which is involved in the negative regulation of cell growth and division. PPP2R5E gene encodes a regulatory subunit of the tumor suppressing protein phosphatase 2A and resides in a naturally selected genomic region in the Caucasian population of the HapMap [90]. This observed positive selection favors the Caucasian population making them less susceptible for soft tissue sarcoma. Scrutinizing molecular signatures of selection of this gene can lead to the identification of disease susceptibility variants. This information shows that cancer disease and its related genes were under the forces of evolution and natural selection throughout the evolutionary history and these evolutionary forces worked differently in different human populations.

10. Detrimental Consequences of Historical Selection Events in Modern Time

It was suggested that prehistoric selection events that may favored some genetic variants in ancient lifestyles, such as hunter-gatherers lifestyle, are not advantageous any longer. On the contrary, these positively selected genetic variants have become disadvantageous in modern societies with modern lifestyles. Many complex diseases, such as diabetes, obesity, hypertension, inflammatory or autoimmune diseases, allergies, and cancers, may have been by-products of these disadvantageous prehistoric selection events that are not fit with modern and more sedentary lifestyles. An excellent example is the thrifty gene hypothesis and the evolution and increased incidence of diabetes in modern populations. The thrifty gene hypothesis was first suggested by Neel, who suggested that diabetes predisposition genotypes in modern times were advantageous genotypes historically [91]. These positively selected genotypes that favored the storage of large quantities of body fat and slower metabolic rates were advantageous in the nomadic hunter-gatherers lifestyle and expected famine incidences. However, the change in the lifestyle to more sedentary type and the increase of available food resources lead to high rates of obesity and increased the risk of developing type II diabetes in individuals carrying these genotypes at present. Several studies supporting thrifty genotype hypothesis showed that the rapid change to modern lifestyle has led to high risk of diabetes and high levels of obesity in studied populations such as Native Americans of the United States and Tongans of the Pacific populations [92, 93]. Nominal evidence for positive selection at 14 loci of the diabetes susceptibility in samples of African, European, and East Asian ancestry was found only when using locus-by-locus analysis [94]. However, the debate about the validity of thrifty gene hypothesis is still ongoing.

Additional examples of detrimental health costs of historical natural selection leading to nowadays complex diseases are inflammatory and autoimmune diseases, such as type 1 diabetes, inflammatory bowel disease, Crohn’s disease, celiac disease, and rheumatoid arthritis. This can be justified by the hygiene hypothesis especially in North European populations [95]. The “hygiene hypothesis” was first proposed by Strachan [96]. The major concept of hygiene hypothesis is that coevolution with some pathogenic agents is protecting humans from a large spectrum of immune-related disorders. Historically, a strong and intensified immune response was the best way to survive in pathogen-rich environments thus, it was under strong positive selection, despite the fact that the same pathogens are still present but advancement in hygienic care and the use of antibiotics and vaccination, in the modern societies, lead to the reduction of pathogen-driven selection pressures. This reduction of selection pressures led to the conversion of the intensified immune response from being advantageous for human survival to be a health burden through inflammatory and autoimmune diseases [95, 97]. There is an increase of prevalence of autoimmune diseases in both developed and developing countries compared to third world countries. For example, type 1 diabetes has become a serious public health problem in some European countries, especially Finland [98]. In addition, incidences of inflammatory bowel diseases, Crohn’s disease or ulcerative colitis, and primary biliary cirrhosis are also rising. Similarly, Africans living in the United States and Asians living in the United Kingdom in these days exhibited a higher risk of developing allergic inflammatory diseases and asthma compared with the general population in these countries [99–101]. Genetic and ethnic backgrounds of these populations were found to have higher impact on the prevalence of asthma compared to environmental effects [102, 103]. Evolutionary justification of the above-mentioned examples is that, in these populations, alleles conferring high risk for inflammatory and autoimmune diseases were under strong selective pressure in the past and in different environmental conditions [104] and that inflammatory and autoimmune disorders observed nowadays are the by-products of past selection against infectious diseases [97].

11. Consanguinity and Complex Diseases

As we mentioned above, cultural behavior of human populations, such as directed marriages or consanguinity, is capable of effecting allelic frequencies and genetic diversity within populations. Complex diseases can be affected by consanguinity when they are controlled by multiple rare genes and transmitted in an autosomal recessive manner [105]. Unfortunately, little is known about the effects of consanguinity on the complex diseases despite its great importance to global health. It is worth mentioning that consanguineous marriage is a common tradition in many populations in North Africa, Middle East, West Asia, and South India [105, 106]. Highly consanguineous populations, especially those with relatively small effective population sizes, provide an uncomplicated route for identifying recessively inherited genes for complex diseases such as identifying multiple loci for Alzheimer disease in an Arab population [107]. Moreover, some studies showed increased incidence of complex diseases among consanguineous marriage offspring. For example, minimal but significant increase of schizophrenia incidence among progeny of cousin marriages among Bedouin Arabs was observed [108]. In addition, higher rate of ischemic stroke was observed among religiously isolated inbreeding population in Netherlands compared to the general population [109]. In addition, global high rate of consanguinity may have a special impact on a polygenic disease like diabetes mellitus, especially type 2 diabetes. Anokute, in a study of 210 cases of diabetes in the central region of Saudi Arabia, found that familial aggregation compared to nonaggregation yielded an odds ratio of 6 : 2, respectively, which suggests a casual association with diabetes that needs to be further explored in future studies [110]. These findings do not extend to other populations in the same region such as Palestinians and Bahrainis where there is no increase in prevalence of type 2 diabetes in consanguine marriages [111, 112]. A study by Bener et al., 2005, which was done in Qatar showed that diabetes was significantly common among the consanguineous marriages of the first-degree relatives compared with the control group (33.1% versus 24.6%) (OR = 1.59 95% CI = 1.11–2.29

) [113]. In another study done in Qatar by Bener et al., 2007, to determine the extent and nature of consanguinity in the Qatari population and its effects on common adult diseases, the rate of consanguinity in the present generation was 51% with a coefficient of inbreeding of 0.023724 [114]. The consanguinity rate and coefficient of inbreeding in the current generation were significantly higher than the maternal rate (51% versus 40.3% and 0.023724 versus 0.016410), respectively. All types of consanguineous marriages were higher in this generation, particularly first cousins (26.7 versus 21.4% paternal and 23.1% maternal) and double first cousins (4.3 versus 2.9% paternal and 0.8% maternal). The current generation of consanguineous parents had a slightly higher risk for most diseases such as cancer, mental disorders, heart diseases, gastrointestinal disorders, hypertension, hearing deficiency, and diabetes mellitus. All the reported diseases were more frequent in consanguineous marriages. Gosadi investigated the potential effect of consanguinity on type 2 diabetes susceptibility in Saudi population [115]. He suggested that consanguinity might increase the risk of type 2 diabetes by earlier development of the disease and by strengthening possible genetic effect on fasting blood glucose (FBG). Contradictory results have been obtained from association studies on breast cancer in consanguineous populations for BRCA1 and BRCA2 genes [116, 117]. Though, valuable information about the genetic background of complex diseases can be obtained from consanguineous populations if cultural, religious, and political bias concerning consanguineous marriage are circumvented.

12. Ancient Genes and Complex Diseases

Neanderthals, ancient hominids, and modern humans have coexisted for thousands of years and interbred outside of Africa especially in Europe and Asia [17]. This leads to the presence of several Neanderthals ancient genes in current European and Asian genomes (approximately 1–4%), while no Neanderthals ancient genes were observed among current African populations [19, 118]. Moreover, it was found that Neanderthal component in non-African modern human was more related to the Mezmaiskaya Neanderthal (Caucasus) than to the Altai Neanderthal (Siberia) or the Vindija Neanderthals [118]. In addition, several studies showed a higher Neanderthal admixture in East Asians when compared to Europeans [12, 119–121]. It was found that genes affecting keratin were found to have been introgressed from Neanderthals into East Asian and European humans, suggesting Neanderthals donated both morphological adaptation genes modern humans to cope with the new environments outside of Africa [120, 121].

Moreover, recent studies showed that the increased rates of type 2 diabetes in Europeans and Asians compared to Africans are due to interbreeding with ancient Neanderthals. It was found that many genes associated with complex diseases such as systemic lupus erythematosus, primary biliary cirrhosis, Crohn’s disease, and diabetes mellitus type 2 have been introgressed from Neanderthals into non-African modern humans [121]. Though some beneficial genes such as immune-related genes are donated from Neanderthal to non-African modern humans. For example, HLA-C

0702, found in Neanderthals, is common in modern Europeans and Asians but is rarely seen in Africans [122].

13. Conclusion

Population genetics and molecular evolution studies have paved the way to gain better understanding of genetic adaptation of human in order to cope with environmental and lifestyle changes. Understanding the effect of evolutionary driving forces on human complex traits, such as natural selection, facilitated our ability to understand the relationship between genetic diversity, adaptive phenotypes, and complex disease. Huge amount of population genetics data for different human populations is available and waiting to be investigated deeply integrating both population genetics and molecular evolution contexts. Molecular signatures of genetic variations such as single nucleotide polymorphism, copy number variation, and genomic structural variations should be investigated and linked with human adaptation, the changing environment, and complex diseases. In addition large scale investigations about changes in lifestyles and the development of complex diseases are needed, especially in the Arabian Gulf area where drastic lifestyle changes accrued after the petroleum discovery. Integrating information about population genetics, molecular evolution, environmental changes, epidemiology, and social and cultural studies is an immediate need. These multidisciplinary efforts can elucidate the relationship between molecular evolution concept and complex diseases and improve our understanding of the evolutionary mechanisms in disease susceptibility, resistance, or progression, in turn facilitating disease prevention, diagnosis, and treatment.

Competing Interests

The authors declare that they have no competing interests.


The authors would like to thank the Scientific Publishing Department in Diabetes Strategic Research Center for their help in preparing this work. This study was supported by the Diabetes Strategic Research Center, King Saudi University, Kingdom of Saudi Arabia.


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Human evolution

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Human evolution, the process by which human beings developed on Earth from now-extinct primates. Viewed zoologically, we humans are Homo sapiens, a culture-bearing upright-walking species that lives on the ground and very likely first evolved in Africa about 315,000 years ago. We are now the only living members of what many zoologists refer to as the human tribe, Hominini, but there is abundant fossil evidence to indicate that we were preceded for millions of years by other hominins, such as Ardipithecus, Australopithecus, and other species of Homo, and that our species also lived for a time contemporaneously with at least one other member of our genus, H. neanderthalensis (the Neanderthals). In addition, we and our predecessors have always shared Earth with other apelike primates, from the modern-day gorilla to the long-extinct Dryopithecus. That we and the extinct hominins are somehow related and that we and the apes, both living and extinct, are also somehow related is accepted by anthropologists and biologists everywhere. Yet the exact nature of our evolutionary relationships has been the subject of debate and investigation since the great British naturalist Charles Darwin published his monumental books On the Origin of Species (1859) and The Descent of Man (1871). Darwin never claimed, as some of his Victorian contemporaries insisted he had, that “man was descended from the apes,” and modern scientists would view such a statement as a useless simplification—just as they would dismiss any popular notions that a certain extinct species is the “ missing link” between humans and the apes. There is theoretically, however, a common ancestor that existed millions of years ago. This ancestral species does not constitute a “missing link” along a lineage but rather a node for divergence into separate lineages. This ancient primate has not been identified and may never be known with certainty, because fossil relationships are unclear even within the human lineage, which is more recent. In fact, the human “family tree” may be better described as a “family bush,” within which it is impossible to connect a full chronological series of species, leading to Homo sapiens, that experts can agree upon.

What is a human being?

Humans are culture-bearing primates classified in the genus Homo, especially the species Homo sapiens. They are anatomically similar and related to the great apes (orangutans, chimpanzees, bonobos, and gorillas) but are distinguished by a more highly developed brain that allows for the capacity for articulate speech and abstract reasoning. Humans display a marked erectness of body carriage that frees the hands for use as manipulative members.

When did humans evolve?

The answer to this question is challenging, since paleontologists have only partial information on what happened when. So far, scientists have been unable to detect the sudden “moment” of evolution for any species, but they are able to infer evolutionary signposts that help to frame our understanding of the emergence of humans. Strong evidence supports the branching of the human lineage from the one that produced great apes (orangutans, chimpanzees, bonobos, and gorillas) in Africa sometime between 6 and 7 million years ago. Evidence of toolmaking dates to about 3.3 million years ago in Kenya. However, the age of the oldest remains of the genus Homo is younger than this technological milestone, dating to some 2.8–2.75 million years ago in Ethiopia. The oldest known remains of Homo sapiens—a collection of skull fragments, a complete jawbone, and stone tools—date to about 315,000 years ago.

Did humans evolve from apes?

No. Humans are one type of several living species of great apes. Humans evolved alongside orangutans, chimpanzees, bonobos, and gorillas. All of these share a common ancestor before about 7 million years ago.

Are Neanderthals classified as humans?

Yes. Neanderthals (Homo neanderthalensis) were archaic humans who emerged at least 200,000 years ago and died out perhaps between 35,000 and 24,000 years ago. They manufactured and used tools (including blades, awls, and sharpening instruments), developed a spoken language, and developed a rich culture that involved hearth construction, traditional medicine, and the burial of their dead. Neanderthals also created art evidence shows that some painted with naturally occurring pigments. In the end, Neanderthals were likely replaced by modern humans (H. sapiens), but not before some members of these species bred with one another where their ranges overlapped.

The primary resource for detailing the path of human evolution will always be fossil specimens. Certainly, the trove of fossils from Africa and Eurasia indicates that, unlike today, more than one species of our family has lived at the same time for most of human history. The nature of specific fossil specimens and species can be accurately described, as can the location where they were found and the period of time when they lived but questions of how species lived and why they might have either died out or evolved into other species can only be addressed by formulating scenarios, albeit scientifically informed ones. These scenarios are based on contextual information gleaned from localities where the fossils were collected. In devising such scenarios and filling in the human family bush, researchers must consult a large and diverse array of fossils, and they must also employ refined excavation methods and records, geochemical dating techniques, and data from other specialized fields such as genetics, ecology and paleoecology, and ethology (animal behaviour)—in short, all the tools of the multidisciplinary science of paleoanthropology.

This article is a discussion of the broad career of the human tribe from its probable beginnings millions of years ago in the Miocene Epoch (23 million to 5.3 million years ago [mya]) to the development of tool-based and symbolically structured modern human culture only tens of thousands of years ago, during the geologically recent Pleistocene Epoch (about 2.6 million to 11,700 years ago). Particular attention is paid to the fossil evidence for this history and to the principal models of evolution that have gained the most credence in the scientific community.See the article evolution for a full explanation of evolutionary theory, including its main proponents both before and after Darwin, its arousal of both resistance and acceptance in society, and the scientific tools used to investigate the theory and prove its validity.


Biology of Humans: Concepts, Applications, and Issues, 6th Edition is also available via Pearson eText, a simple-to-use, mobile, personalized reading experience that lets instructors connect with and motivate students – right in their eTextbook. Learn more.

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New to This Edition

Biology of Humans: Concepts, Applications, and Issues, 6th Edition is also available via Pearson eText, a simple-to-use, mobile, personalized reading experience that lets instructors connect with and motivate students – right in their eTextbook. Learn more.

Personalize Learning with Mastering Biology

Mastering™ Biology is an online homework, tutorial, and assessment product designed to improve results by helping students quickly master concepts. Students benefit from self-paced tutorials that feature immediate wrong-answer feedback and hints that emulate the office-hour experience to help keep students on track. With a wide range of interactive, engaging, and assignable activities, students are encouraged to actively learn and retain tough course concepts.

De novo mutations in clinical practice

The recent recognition of the importance of de novo mutations in human disease has many implications for routine genetic testing and clinical practice. De novo mutations are now established as the cause of disease in a large fraction of patients with severe early-onset disorders, ranging from rare congenital malformation syndromes [185, 186] to more-common neurodevelopmental disorders, such as severe forms of intellectual disability [33], epilepsy [31], and autism [29]. Together, these disorders represent a substantial proportion of all patients seen at neuropediatric and clinical genetics departments around the world.

Pinpointing the genetic cause of a disorder caused by a de novo mutation in an individual can be challenging from the clinical point of view because of pleiotropy as well as genetic heterogeneity underlying a single phenotype. For instance, intellectual disability can be caused by de novo point mutations, indels, or CNVs in any of hundreds of genes [117]. This obstacle to providing a clinical diagnosis strongly argues for a reliable and affordable genomics approach that can be used to detect these de novo mutations in large groups of patients. Exome and genome sequencing (which additionally offers the possibility of accurate detection of structural variation) of patient–parent trios is ideal for this and will soon become the first-tier diagnostic approach for these disorders. A key advantage of this trio-based sequencing approach is that it helps prioritize candidates by de novo occurrence, allowing clinical laboratories to focus on the most likely candidate mutations for follow-up and interpretation (Box 3) [187]. The interpretation of candidate de novo mutations can be guided by the use of different scores, such as the “residual variation intolerance score” (RVIS), based on the comparison of rare versus common missense human variation per gene [188]. Alternatively, “selective constraint scores” can be used, based on the observed versus expected rare functional variation per gene within humans [126].

The identification of a de novo mutation as the cause of disease in a patient has several implications for the patient and his or her family. First, the detection of the genetic defect underlying the phenotype establishes a genetic diagnosis that can be used to provide a prognosis based on data from other patients with similar mutations [189] and information about current treatment options [190] and, in the future, for the development and application of personalized therapeutic interventions [191]. Furthermore, the identification of a de novo mutation offers the parents of the affected patient an explanation as to why the disorder occurred and might help deal with feelings of guilt [192, 193]. In terms of family planning, the identification of a de novo mutation as the cause of disease in a child can be positive news with regard to recurrence risk, as it is much lower than for recessive or dominant inherited disorders (slightly above 1% versus 25 and 50%, respectively) [11, 158]. However, the recurrence risk is strongly dependent on the timing of the mutation as parental mosaicism for the mutation increases the risk of recurrence [158]. Approximately 4% of seemingly de novo mutations originate from parental mosaicism detectable in blood [11], and recent work suggests that transmission of parental mosaicism could explain up to 10% of de novo mutations in autism spectrum disorder [194]. This entails that a fraction of de novo mutations have an estimated recurrence risk above 5% [158]. Furthermore, close to 7% of seemingly de novo mutations arise as postzygotic events in the offspring [88, 89, 91]. Parents of an individual with a postzygotic mutation have a low risk for recurrence of the mutation in an additional child, estimated as being the same as the population risk [90]. Targeted deep sequencing of a disease-causing mutation can be performed to test for its presence in parental blood and detect mosaicism in the offspring. Although it is not yet offered on a routine basis, this kind of testing can provide a personalized and stratified estimate of the recurrence risk based on the presence or absence of mosaicism in the parents or in the offspring.

Finally, it is impossible to prevent de novo mutations from arising in the germline of each new generation, but attention must be brought to the factors that increase the number of de novo mutations in the offspring. The single most important risk factor is advanced paternal age at conception [15], which is of great importance from an epidemiological perspective since most couples in Western countries are having children at later ages. In fact, this increase in de novo mutations with paternal age at conception might explain epidemiological studies that link increased paternal age to increased risk of neurodevelopmental disorders in offspring [195]. A recent population-genetic modeling study, however, indicated that de novo mutations might not explain much of the increased risk of psychiatric disorders in children born to older fathers [122]. While this might be the case for relatively mild and later-onset phenotypes such as schizophrenia, de novo mutations are responsible for the majority of the most severe pediatric disorders arising in outbred populations [10, 196]. At present, most attention, advice, and guidelines are focused on advanced maternal age as a public health issue. It is evident from current work on de novo mutations that advising the public, including policy makers, on potential risks of advanced paternal age and the burden it might bring on society is crucial. An extreme “solution” if reproduction is to be postponed might be to promote cryopreservation of oocytes and sperm [197], a measure under much debate that has been termed “social freezing”.

Where did all this happen, and why does it matter where?

Both genetic and fossil evidence show that until relatively recently, human evolution happened in Africa. Whether the genus Homo first emerged in southern or in eastern Africa remains unclear. Knowing where our species evolved matters because the environment it adapted to helped shape the genetic makeup we still carry with us today. Where we came from is the first chapter in the long story of how we got to where we are now.

Around 60,000 years ago—again according to both genetic and fossil evidence—modern humans migrated out of Africa and began colonizing the world. Genetic evidence suggests that soon after leaving Africa, they interbred to some extent with the Neanderthals and a mysterious population in Asia called the Denisovans. Homo sapiens is now the only species of human on Earth. But that’s been true for less than 30,000 years.

Watch the video: 1. Introduction to Human Behavioral Biology (September 2022).


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  2. Tannis

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  3. Gerred

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