john hawks weblog

paleoanthropology, genetics and evolution

founder effect

  • Denisovan DNA in the islands, and an Australian genome

    Thu, 2011-09-22 18:09 -- John Hawks

    David Reich and colleagues today report on the persistence of Denisova-like ancestry in island Southeast Asia and Australia (citation not yet available). Meanwhile, Morten Rasmussen and colleagues (citation not yet available) report on the whole-genome sequencing of hair from an Aboriginal Australian who lived some 100 years ago.

    The most obvious story: These data utterly destroy the hypothesis of a single out-of-Africa colonization of Southeast Asia by modern humans. Many human geneticists have argued our present pattern of diversity originated in a wave of successive founder effects coming from a single recent African origin. They were wrong.

    Instead, we can turn to a complex model with successive dispersals and episodes of population mixture. This is not a static model of isolation-by-distance; it is a dynamic model in which populations grow and spread across large spans of the Old World, again and again and again. By my count, at least three massive episodes of population dispersal and mixture are necessary in Reich and colleagues' model. A picture of their admixture hypothesis:

    Denisova admixture model from Reich et al. 2011

    This model depicts (a) an early divergence of an African (represented by Yoruba) and Asian/Australasian populations. These mix with first Neandertals and then (for the Australian/New Guinea/Mamanwa populations) with Denisova-like people. Later (b), after the initial habitation of the Philippines by the ancestors of Mamanwa, a population like Andamanese Onge pushes into the islands, mixing with the ancestors of New Guinea and Australian populations. Later still (c), a population ancestral to today's Chinese people mixes with Philippines and other Southeast Asian people.

    As complicated as it looks, even this model must be a vast oversimplification. I don't like or attribute much belief to mixture models like this, as they assume too much about relative population sizes and the timing of mixture. Many recent hunting and gathering populations of Southeast Asia are not included in the current samples, and the Chinese sample is itself the result of very recent demographic events, covering what once may have been a wider diversity of peoples. Depicting Australian and New Guinean populations as monolithic is an artifact of the small sample; these places themselves housed a tremendous diversity of peoples. Nevertheless, the true model won't be simpler than this one; it will involve many more events that the data cannot yet resolve.

    Hints of that complexity emerge from the Aboriginal Australian whole genome. Rasmussen and colleagues show that this individual shares some ancestry with East Asian peoples, but on the whole populations in Europe and East Asia are much more genetically similar to each other than to this genome. The picture from the whole genome is essentially the same as that drawn by the SNP comparisons by Reich and colleagues, but with the potential (in the long run) to actually trace the histories of individual genes. And I think the gene-by-gene account of history will be important, because we already have some evidence that a few Denisovan genes do persist in mainland Asia, even though most are gone.

    To explain why, we can look at the proportion of Denisovan ancestry in different populations as depicted in a map by Reich and colleagues. The pie charts are confusing here, because they report the fraction of ancestry from Denisovans in each population relative to the 5% estimate for New Guinea. So Australians also have 5% in this figure, Timorese have around 2.5%, and Bougainville has more than 4%.

    Notice the apparent lack of Denisovan ancestry in anyone who lives anywhere that was once connected by land with mainland Asia. I say "apparent" deliberately: Abi-Rached and colleagues reported last month on the widespread distribution of Denisovan HLA types among today's Asian populations, and those may well be products of Denisovan genes that were later selected. I've already identified a handful of other loci that seem to reflect Denisovan ancestry in mainland Asian people. According to the comparisons by Reich and colleagues, such loci must be exceptions.

    At the same time, the mixture model presents an important idea: Once there were people in Southeast Asia who had much more Denisovan ancestry than any populations still remaining today. Both Australian/New Guinea populations and Philippine populations like the Mamanwa have subsequently mixed with new immigrants who lacked any sign of Denisovan ancestry. Prior to this later mixture, the ancestors of those populations must have been more Denisovan -- Reich and colleagues estimate 7%. This is the first evidence that ancestry from archaic people of Eurasia was diluted to a lower value by later population movements. If the population mixture originally happened somewhere in mainland Asia, any traces of Denisovan ancestry in those areas has been diluted almost to nonexistence. But the persistence of some genes would be predicted if natural selection were maintaining them in the face of demographic pressure from elsewhere.

    About the Australian genome, there will be much more interesting analyses to come, I expect. As whole-genome data come to represent more of the variation within human populations, we get a larger store of information about how we came to be variable. Variation traces not only to population movements and demography, but also to natural selection. Australia's population history has been very different from many populations of the Old World, and this genome should give us new perspective on the effects of that demographic history.

    Tags: 
    Australia
    Denisova
    New Guinea
    Philippines
    Asia
    East Asia
    China
    gene flow
    population expansion
    founder effect
    introgression
    Neolithic
    migrations
    Synopsis: 
    The hypothesis of a single out-of-Africa dispersal is rejected by new data about Denisovan mixture and whole-genome sequencing of an Aboriginal Australian.

  • Founder effect

    Sat, 2011-08-06 13:34 -- John Hawks
    Synopsis: 
    The founder effect is a special case of genetic drift that can happen when a small number of individuals found a new population
    The founder effect is caused by genetic drift in a small number of initial founders of a new population.

    One of the most important manifestations of genetic drift is in the founding of new populations by a small number of colonists. For example, the Afrikaner population of the country of South Africa today descends from Dutch colonists who arrived during the seventeenth century. Some of the earliest colonists to arrive had a large genetic contribution to the later Afrikaner population, because they had a chance to have lots of offspring who intermarried with later arrivals. The first Dutch colonists landed in 1652, and one of these colonists was a man who carried an allele causing Huntington's disease, a rare genetic disorder of the nervous system. Huntington's is a dominant genetic disorder, affecting all individuals who carry the allele, but it exerts most of its effect late in life --- after people generally reproduce. Although this harmful allele was carried by only one individual, it was a relatively large proportion of the new founder population --- much higher in frequency than it had been in Holland. After strong population growth, today's Afrikaners have a high frequency of the Huntington's allele, mainly from this single founder (Ridley 2002). This phenomenon of genetic drift is often called the \term{founder effect}.

    \subsection{Population structure and genetic drift}

    Genetic drift is stronger when there is more variability in reproduction.

    A simple reason for variability in reproduction is the different reproductive efforts of males and females. Female mammals face a high cost of reproduction. Mothers provide space and nutrients to their developing young while they still in the womb, and mothers provide high-energy milk and protection to their young after they are born. Although female fish and frogs may lay hundreds --- or even thousands --- of eggs, female mammals are limited to many fewer offspring over the course of their lifetimes. Males, on the other hand, do not face the same reproductive costs. If a male can mate with many females, he can potentially have many times the number of offspring of any single female. But males face a different cost: if they want to mate at all, they must first face competition from other males. In many species, a lucky few males may mate with many females, while most males do not mate at all. Thus, males are often much more variable in their reproductive success than females. Each generation of offspring in such a population includes the genes of many different females but only a few males. Only a few genes may be responsible for the and all the genes of these few males are boosted by genetic drift.

    Human history appears to have included some cases where single male lineages had exceptionally high mating success. Geneticists can trace male reproduction through the Y chromosome, which is passed from only from father to son. Because of this unique pattern of inheritance, the Y chromosome marks \term{patrilines}, lineages of males. In many human societies, social status or power may also be passed along patrilines, as kings and chiefs pass power to their sons. This cultural pattern of inheritance generally lasts only for a few generations, as some member of the male lineage ultimately fails to have a son as an heir, or the patriline simply loses power. But the history of some cultures gave a few patrilines exceptional mating opportunities, as kings and other high-ranking men sometimes kept harems of dozens or more women for their own exclusive mating.

    \begin{figure}
    \includegraphics[width=\textwidth]{genghis.png}
    \caption[Frequency of ``Genghis Khan'' Y chromosome haplotype in Asia]{Frequency of the ``Genghis Khan'' Y chromosome haplotype in samples of Asian populations. The ``star cluster'' refers to the rapid expansion in numbers of the haplotype in different populations since its origin around 1000 years ago. Reprinted from Zerjal \emph{et al.} (2003).}
    \label{fig:genghis}
    \end{figure}

    Two Y chromosome haplotypes in Asia are shared by many millions of men, even though they emerged within the past thousand years. One of these, carried by 8 percent of men in Central and Northeast Asia, appears to have originated in Mongolia around a thousand years ago [1]. At this frequency, the haplotype would occur in as many as 16 million men, all descendants of a single man within the past 1000 years. The large current population implies that these men descend from an exceptionally widespread and productive patriline. During the past 1000 years in Asia, the best candidate for such a patriline is that of the Mongol emperor Genghis Khan, who lived from around A.D. 1162--1227. After conquering history's largest land empire, Genghis and his descendants installed their male relatives as rulers of much of Asia. These descendants themselves must often have had extraordinary reproductive opportunities, so that their Y chromosomes became more and more common in Asian populations. A second Y chromosome haplotype is carried by around 3 percent of people in China and Mongolia, and may derive from the Manchu dynasty, which dates to the year 1644 [2]. Together, these haplotypes illustrate the chance for some rare alleles to increase greatly in frequency due to genetic drift in human history.

    References

    1. Zerjal T, Xue Y, Bertorelle G, Wells RS, Bao W, Zhu S, Qamar R, Ayub Q, Mohyuddin A, Fu S, et al. 2003. The Genetic Legacy of the {Mongols}. American Journal of Human Genetics 72:717–721.
    2. Xue Y, Zerjal T, Bao W, Zhu S, Lim S-K, Shu Q, Xu J, Du R, Fu S, Li P, et al. 2005. Recent Spread of a Y-Chromosomal Lineage in {Northern China} and {Mongolia}. American Journal of Human Genetics 77:1112–1116.
    Study questions: 
    1. Can you think of other populations in human history that might have undergone a founder effect?
    2. What evidence can we use to test whether a founder effect can explain the high frequency of an allele?
    Study terms: 
    founder effect
    genetic drift
    allele frequency
    deleterious
    patriline
    matriline
    mtDNA
    Y chromosome
    Tags: 
    founder effect
    genetic drift
    explainer
    population genetics

  • Genetic drift

    Fri, 2011-08-05 01:24 -- John Hawks
    Synopsis: 
    Many changes in gene frequencies are caused by random chance differences in reproduction.

    If everyone in a population lived a long life, mated, and reproduced absolutely equally (two offspring per person), then the population size would never change. There would always be approximately the same number of individuals, allowing for variations in when people are born or die. In this population, every gene has an equal chance of being passed into the next generation. Natural selection depends on differences in the chance that genes will survive and reproduce, so this population would not evolve by natural selection.

    But the population would still evolve by random chance. A single chromosome can illustrate this potential for evolution. The Y chromosome determines whether humans will be male or female: males have one X chromosome and one Y, females have no Y and two X chromosomes. Mendelian genetics predicts that if a father has two offspring, each of these children has a 50 percent chance of inheriting his Y chromosome and thereby being a son. But these odds mean that the man has a substantial chance of having no sons at all --- 25 percent of the time, both children will be daughters. If the man has no sons, then his Y chromosome is simply lost from the next generation. Genes disappear due to chance, even if everyone mates and reproduces equally.

    Genetic drift is a random change in allele frequencies.

    These random changes in allele frequency can accumulate over time. Across many generations, the frequency of an allele can gradually increase, gradually decrease, or fluctuate back and forth. In other words, the frequencies of different alleles seem to ``drift'' up and down, without any direction. This is why the random change in allele frequencies is called \term{genetic drift}. Over time, genetic drift can make once rare alleles common, or eliminate alleles altogether.

    Genetic drift is stronger in small populations.

    \begin{figure}
    \centering
    \includegraphics[width=4in]{genetic_variation_drift.png}
    \label{fig:genetic_drift}
    \caption[Genetic variation under genetic drift]{Genetic variation under genetic drift as a function of population size. The expected amount of genetic variation increases as a linear function of the size of the population, when genetic drift and mutation are the only causes of evolution. Larger populations are more variable; smaller populations are less variable. }
    \end{figure}

    The most obvious factor affecting the rate of genetic drift is the size of the population. If the population is small, then a small sample is taken of the gametic population in every generation. Small samples can vary more markedly from the larger sets from which they are selected than larger samples, so genetic drift is more powerful in smaller populations. For example, in a population of five individuals, an allele that exists in a single copy in one individual has a frequency of ten percent. Nevertheless, this allele is in constant jeopardy of being eliminated from the population, requiring only the chance of not being passed on once to never again be found. Likewise, it is very possible that in a very few generations this allele might increase from one copy to ten, eliminating all other alleles. In contrast, in a population of a thousand individuals, an allele with a frequency of ten percent exists in 200 copies. While random sampling of gametes will cause this number to fluctuate over time, it is extremely unlikely that chance alone would allow no copy of this allele to be passed on in any given generation. Indeed, it would likely take many hundreds of generations for random events to either eliminate this allele or all the others.

    Study questions: 
    1. Can you think of other human populations that have undergone founder effects?
    Study terms: 
    founder effect
    genetic drift
    population size
    allele frequency
    Tags: 
    genetic drift
    genetics
    explainer
    founder effect

  • The Finnish line

    Sat, 2009-09-26 09:30 -- John Hawks

    A new paper by Jukka Palo and colleagues investigates the population history of Finland:

    The Finnish population in Northern Europe has been a target of extensive genetic studies during the last decades. The population is considered as a homogeneous isolate, well suited for gene mapping studies because of its reduced diversity and homogeneity. However, several studies have shown substantial differences between the eastern and western parts of the country, especially in the male-mediated Y chromosome. This divergence is evident in non-neutral genetic variation also and it is usually explained to stem from founder effects occurring in the settlement of eastern Finland as late as in the 16th century. Here, we have reassessed this population historical scenario using Y-chromosomal, mitochondrial and autosomal markers and geographical sampling covering entire Finland. The obtained results suggest substantial Scandinavian gene flow into south-western, but not into the eastern, Finland. Male-biased Scandinavian gene flow into the south-western parts of the country would plausibly explain the large inter-regional differences observed in the Y-chromosome, and the relative homogeneity in the mitochondrial and autosomal data. On the basis of these results, we suggest that the expression of 'Finnish Disease Heritage' illnesses, more common in the eastern/north-eastern Finland, stems from long-term drift, rather than from relatively recent founder effects.

    So you've got a cline of genetic variation. How do you explain it? This paper reminds us that for a single locus there are always multiple explanations: asymmetric migration, natural selection, founder effect and population growth are the simple unicausal scenarios. Considering a cline by itself, there's no reason to prefer any of these except for assumptions that come from outside that gene -- maybe you know something about the history, maybe the gene's function gives you a clue.

    If you're going to test these hypotheses with genes alone, then you need to sample multiple loci, and you need to make an adequate spatial sampling of the population. And when you do, sometimes the evidence points in a different way than you had expected.

    References:

    Palo JU, Ulmanen I, Lukka M, Ellonen P, Sajantila A. 2009. Genetic markers and population history: Finland revisited. Eur J Hum Genet 17:1336-1346. doi:10.1038/ejhg.2009.53

    Tags: 
    mtDNA
    Europe
    founder effect
    genetics
    mtDNA migrations
    Finland
    genetic drift

  • Genetic drift eliminated rare mtDNA haplotypes from Iceland

    Tue, 2009-01-20 12:33 -- John Hawks

    How powerful has genetic drift been in recent human evolution? That's the question I raised the other day with reference to the claim that a heart disease risk-inducing allele had become common by drift in India during the last 30,000 years.

    Another paper released earlier this week in PLoS Genetics claims that mtDNA haplotypes have been recently lost from the Icelandic population by strong genetic drift. The evidence for such changes in haplotypes comes from sequencing the mtDNA of thousand-year-old skeletons unearthed in Iceland during the last 150 years. These ancient remains have haplotypes that are found elsewhere in Europe today, but not in Iceland. The conclusion is that the modern-day descendants of these early Iceland settlers have experienced genetic drift within the last 1000 years, relieving them of of a load of rare mtDNA haplotypes.

    Could genetic drift have accomplished this loss of haplotypes? Although the paper does not present any analysis of this question, a quick consideration of some theory will show that genetic drift could easily have caused the observed results. It also shows a contrast between this case and others where genetic drift has been described as "strong". Even in this case, on an island with a limited human population, genetic drift is only "strong" in the sense of eliminating alleles that are already quite rare in the population.

    Today, Iceland is a large population of more than 300,000 individuals. As in many countries, the current size is a result of twentieth-century population growth. From Wikipedia:

    The population of the island is believed to have varied from 40,000 to 60,000 in the period from initial settlement until the mid-19th century. During that time, cold winters, ashfall from volcanic eruptions, and bubonic plagues adversely affected the population several times. The first census was carried out in 1703 and revealed that the population was then 50,358. After the destructive volcanic eruptions of the Laki volcano during 1783–1784 the population reached a low of about 40,000. Improving living conditions have triggered a rapid increase in population since the mid-19th century - from about 60,000 in 1850 to 320,000 in 2008.

    Forty thousand is a large population, in evolutionary terms. But not all these 40,000 people would have counted toward the variance in reproduction of gene lineages (See my discussion of the Wright-Fisher model). If we took a census of the medieval Iceland population, we would find that a large fraction of individuals, maybe half, were children. A rather small fraction would be postreproductive adults. So at most half the population, and probably less, were of reproductive age at any given time. At a first approximation, this population of 40,000 individuals would reproduce like a Wright-Fisher population of less than 20,000. Around half that number will be females -- although probably a bit more than half, since reproductive variance is usually higher among men. So less than 10,000 mating females per generation. Other factors, such as within-family fitness correlations, may limit the effective number even further. I'll assume a value of 5000 effective women, which is certainly an underestimate for the last 300 years, but may not be too much of an underestimate for earlier times.

    We should keep in the back of our minds that the population did not grow instantaneously to this value -- it would have taken a couple hundred years to reach its ultimate medieval size. So genetic drift might have been substantially stronger when these skeletons were being buried than it would have been 300 or 400 years later.

    Helgason et al. (2009) applied an unusual test to demonstrate the genetic difference between early and current Icelanders. They don't consider the frequencies of haplotypes. Nor do they use a measure that would ultimately be influenced by frequencies, like FST. Instead, they consider the number of shared haplotypes between the two samples as a measure of similarity.

    The statistical question is whether the modern and ancient samples share significantly fewer haplotypes than expected if the ancient and modern samples had been randomly drawn from a single population. The authors tested this hypothesis with a randomization test: they repeatedly drew random samples of the same sizes as the ancient and modern samples, from a distribution including both samples. It's a clever technique.

    It doesn't yield an easy answer to the question of how strong genetic drift needs to be to explain the data. But we can address this question by considering the data and nature of the comparison.

    Here, we're talking about the presence or absence of haplotypes that each have a very low frequency. For example, the modern Scandinavian sample in the paper includes 337 distinct haplotypes. The modern Icelandic sample, with a slightly larger same sample size (947 versus 898 individuals), has many fewer haplotypes -- only 172. That's a substantial deficit in the Icelandic sample.

    Now, let's consider the ancient sample, with 73 individuals and 58 haplotypes. Obviously, nearly all the haplotypes were present in only a single sampled individual. This implies that many haplotypes existed in that population that were not sampled in these 73 individuals. It also implies that the average frequency of a unique mtDNA haplotype in that population was less than 1.4 percent (less, in other words, than 1/73).

    We don't know that these alleles were lost in Iceland; what we know is that they weren't sampled today. So a real answer to this question would include some considerations of sampling theory.

    We can do a quick calculation to figure out the chances that an allele of 1.5 percent would be eliminated by drift in less than 1000 years (around 40 generations). Here's a small piece of Mathematica code that begins with an allele at 0.015 frequency, a population of 5000 effective individuals, runs it under drift for 40 generations:

    drift := Module[{popSize, a, result, count, b},
    count = 0;
    For[i = 1, i popSize = 5000;
    a = N[RandomInteger[BinomialDistribution[popSize, 0.015]]];
    For[j = 1, j a = N[RandomInteger[BinomialDistribution[popSize, a/popSize]]]];
    If[a == 0, count++]];
    count]

    The variable count returns the number of trials out of 10,000 that the allele disappeared. For an initial frequency of 1.5 percent, roughly 3 percent of trials (308 out of 10000) end in loss of the allele. That's not a very high proportion of loss by drift.

    Still, we're not interested in loss from the population; we're interested in why haplotypes might not show up in the present-day sample. That might happen for two reasons:

    1. The alleles weren't so common to begin with -- many haplotypes were present in the early population that weren't in the skeletal sample. We can use the simulation to see what happens to rarer haplotypes. For example, if the initial frequency was only half a percent (0.005), then they are lost from the population after 40 generations nearly a third of the time (3142 out of 10000).

    2. The alleles are still in the Icelandic population, but rare enough they didn't get sampled. A sample of 900 people will miss roughly half of haplotypes with frequencies of 0.005 or less. Altering the simulation again, we find that drift achieves this result in roughly 5 percent (526 out of 10000) of cases, beginning at an initial frequency of 0.015.

    So altogether, the observed result is not at all unlikely. Genetic drift in Iceland had the power to eliminate rare alleles, even after the population was founded and grew to its medieval size.

    Still, this "strength" of genetic drift is manifested in its ability to eliminate initially rare alleles. If we ask what effect it would have on a common allele, we see a somewhat different dynamic. For mtDNA, the variance of allele frequency after one generation of drift is p(1-p)/(N), where N is the female effective population size (for autosomal genes with two copies, this expression would include 2N instead of N). After 40 generations, we expect a variance of 40p(1-p)/(N). What that means is that in 95 percent of cases, an allele that begins at 20 percent frequency will be between 13 and 27 percent after 40 generations.

    In other words, even in the small population of medieval Iceland, genetic drift is not strong enough to cause large increases or losses of common mtDNA haplotypes. And the effect of drift is four times greater for mtDNA than for autosomal genes.

    As a result of the loss of rare haplotypes, the medieval Iceland sample shares more alleles with Eastern Europe, Russia, and Spain and Portugal than with today's Icelanders. But if we look at a measure like FST, which is dominated by common alleles, the differences between these populations are very slight.

    I've gone through the example to point out that some kinds of observations are explained well by drift, and others aren't. Genetic drift has been an important cause of some evolutionary changes, even in recent human populations. But in any given case it is a hypothesis. We test this hypothesis by applying our knowledge of demographic history, mathematical models, and other genes. The hypothesis of drift might well be useful to explain one kind of observation (loss of rare alleles), while it is useless to explain others (elevation of one rare allele to a high frequency).

    References:

    Helgason A, Nicholson G, Stefánsson K, Donnelly P. 2003. A reassessment of genetic diversity in Icelanders: Strong evidence from multiple loci for relative homozygosity caused by genetic drift. Ann Hum Genet 67:281-297. doi:10.1046/j.1469-1809.2003.00046.x

    Helgason A, Lalueza-Fox C, Ghosh S, Sigurðardóttir S, Sampietro ML, Gigli E, Baker A, Bertranpetit J, Arnadottir L, Þorsteinsdotter U, Stefánsson K. 2009. Sequences From First Settlers Reveal Rapid Evolution in Icelandic mtDNA Pool. PLoS Genet 5(1): e1000343. doi:10.1371/journal.pgen.1000343

    Tags: 
    Iceland
    genetic drift
    mtDNA
    founder effect

  • The Amish heart-protecting triglyceride-busting null mutation

    Sun, 2008-12-14 18:51 -- John Hawks

    Toni Pollin and colleagues (2008) report one of the simplest medical research studies you'll ever see:

    Apolipoprotein C-III (apoC-III) inhibits triglyceride hydrolysis and has been implicated in coronary artery disease. Through a genome-wide association study, we have found that about 5% of the Lancaster Amish are heterozygous carriers of a null mutation (R19X) in the gene encoding apoC-III (APOC3) and, as a result, express half the amount of apoC-III present in noncarriers. Mutation carriers compared with noncarriers had lower fasting and postprandial serum triglycerides, higher levels of HDL-cholesterol and lower levels of LDL-cholesterol. Subclinical atherosclerosis, as measured by coronary artery calcification, was less common in carriers than noncarriers, which suggests that lifelong deficiency of apoC-III has a cardioprotective effect.

    Gina Kolata covers the story in the NY Times:

    For the sake of heart disease research, 809 members of the Old Order Amish community agreed to go to a clinic in Lancaster, Pa., near their homes, and drink a rich milkshake that was made mostly of heavy cream. Over the next six hours, a group of investigators took samples of their blood, determining how much fat was churning through their bloodstreams.

    Most of the study participants responded as expected — their levels of triglycerides, a common form of fat in the blood, rose steadily for three to four hours and then declined. But about 5 percent had an extraordinary reaction: their triglyceride levels started out low and hardly budged.

    I'm generally interested in novel protective mutations, and this is clearly one -- and far from the only one. Its current frequency is 5 percent in the Old Order Amish. Neither the article nor the paper report on its frequency in the general population; although there is the intimation that it is rare. The Amish individuals carrying the mutation all share a common haplotype, apparently (based on pedigree and LD) from a single 18th-century founder.

    It remains an open question whether homozygotes for the null allele are better or worse off than normal APOC3 homozygotes. With a frequency of 5%, the allele is rare enough that homozygotes are as few as one in 400 people. They were not included in the present study. I can't find any indication that homozygote nulls for APOC3 are a known Mendelian disorder.

    I wonder to what extent the allele frequency in the Amish is due to selection.

    The Amish have high frequencies of certain otherwise rare mutations. This is one of the textbook examples of founder effects -- extreme genetic drift due to sampling a small number of founders from a much larger population. Today's Old Order Amish in the United States trace most of their ancestry to an initial population of approximately 200 people in the eighteenth century. That means that any of the alleles carried by those 200 people, even if it was vanishingly rare in the European population, has a good chance of being half a percent or higher in today's Amish.

    But founder effect is only part of the story -- there is also subsequent population growth. Those initial 200 people have more than 200,000 descendants today within the Old Order Amish. This number doesn't count descendants who may belong to other sects that splintered during the nineteenth-century (like the Mennonites [see update below]), or descendants of people who left the church. These values suggest that the Amish population has increased by some 2.3% annually during the last 300 years; it's current rate of growth is estimated at 4%.

    This is very rapid population growth on an evolutionary time scale, equalling roughly 46% per generation. With this kind of population growth, strongly deleterious alleles may come to occur in a large number of individuals, even as they decline in frequency in the population. The susceptible population grows faster than selection can remove alleles. Hence, we find a number of rare genetic disorders within the Old Order Amish as a consequence not only of founder effect but also subsequent population growth.

    The APOC3 mutation in this study was evidently not deleterious. Its current frequency of 5% suggests it may have been advantageous.

    It's not too hard to hypothesize why a mutation that decreases the risk of heart disease might have conferred a benefit in an agrarian religious sect over the last 300 years. To the extent that heart disease affects men in their 30's and older, these are still active reproductive years for men who may have family sizes of eight children or more. Further, this is a time when men may come into property from their aging parents, may become leaders of new settlements, or may begin to affect the marriages of their children -- a time when young people formally join the church. Being alive would seem like a significant fitness advantage for men in this society. Or perhaps other effects of the gene determined its success.

    The question is just how strong such an effect might be. If the mutation began with a single copy in a population of 200 founders, its initial frequency would be 0.5 percent, or 0.005. Its present frequency in the Amish is ten times that, or 0.05. If we assume that 15 generations have passed, that growth would be consistent with a fitness advantage of around 15 percent for carriers of the null mutation. In other words, the Amish population grew around 46% per generation over the last 300 years; this mutation grew around 60% per generation.

    That kind of differential increase is unlikely to have been driven by genetic drift. Considering the rarity of the mutation in the non-Amish population today, it is unlikely to have been carried by more than a single founder, although we can't exclude the hypothesis that some number of founders were relatives who carried it. That hypothesis is the most likely way for an otherwise rare mutation to hit 5% by founder effect alone. Later, after the Amish population numbered more than a thousand or so, strong differential growth of a rare mutation by chance alone would be impossible. Still, we might imagine that in the initial few generations, one or two founders might have had a predominant effect on the subsequent Amish gene pool. We would need to suppose that the genes of such fecund founders now account for more than 10% of the present Amish gene pool. That's a testable hypothesis. Selection is simpler -- mainly because its effect can be spread across many more generations.

    The interesting thing about selection in the Amish is that their population growth greatly affects the fixation rate of new advantageous mutations. In a constant-sized population, the fixation probability of a new advantageous mutation is roughly twice the heterozygote fitness advantage, denoted as 2s. But in a growing population, the fixation probability is 2(s + r) -- when s and r are both small. If we assume a growth rate of 46% and a heterozygote fitness advantage of 15% for this null allele, it should be obvious that we've entered the territory where our small-value approximation no longer holds. New adaptive mutations are unlikely to exit the Amish population by genetic drift.

    The subject of positive selection in founder populations is under-explored, from a theoretical perspective. Especially considering the very rapid growth of some human founder populations -- measured against the generational time scale -- there is a good chance that we'll find many new adaptive mutations in such populations.

    UPDATE (2008-12-15): A reader writes:

    It is a common mistake to think that the Mennonites as a group broke off from the Amish. It is actually the other way around with the split occurring in Europe before both groups came to the Americas.

    He kindly provided a couple of sites with more information (here and here). I appreciate the correction!

    References:

    Pollin TI and 13 others. 2008. A null mutation in human APOC3 confers a favorable plasma lipid profile and apparent cardioprotection. Science 322:1702-1705. doi:10.1126/science.1161524

    Tags: 
    heart disease
    founder effect
    health
    recent selection
    genetic drift
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Neandertals

For years, I've worked on their bones. Now I'm working on their genes. Read more about the science studying these ancient people.

Denisova

From a finger bone of an ancient human came the record of a completely unexpected population. My lab is working on the science of the Denisova genome.

Acceleration

The advent of agriculture caused natural selection to speed up greatly in humans. We're uncovering some of the ways that populations have rapidly changed during the last 10,000 years.

Malapa

Just outside Johannesburg, the Malapa site is producing some of the most exciting finds in human evolution. This site is the headquarters of the Malapa Soft Tissue Project.