Genome evolution: a sequence-centric approach

Genome evolution: a sequence-centric approach Lecture 8-9: Concepts in population genetics

(Probability, Calculus/Matrix theory, some graph theory, some statistics) Tree of life Genome Size Elements of genome structure Elements of genomic information Simple Tree Models HMMs and variants PhyloHMM,DBN Context-aware MM Factor Graphs Probabilistic models Genome structure Inference Mutations DP Sampling Variational apx. LBP Parameter estimation Population EM Generalized EM (optimize free energy) Inferring Selection Today refs: Hartl and Clark, Topics from Chapters 3-7 See Gruer/Li chapter 2 (easy to read overview) and lynch chapter 4 (more advanced)

Studying Populations Models: A set of individuals, genomes Ancestry relations or hierarchies Experiments: Fields studies, diversity/genotyping Experimental evolution mtDNA human migration patterns Åland Islands, Glanville fritillary population

Species and populations What is a species? Multiple definitions, most of them rely on free flow of genetic information within and weak flow of information outside/inside Species 2 Species 1 Species can emerge through the formation of reproductive barriers Allopatric speciation – occurs through geographical separation Parapatric speciation – occurs without geographical separation but with weak flow of genetic information Sympatric speciation – occurs while information is flowing - controversial Barriers can be genetic, physical, behavioral

Population dynamics We think of a species genome as representing the population “average” genomic information Individuals have genomes that are closely related to the “species genomes”, but differ from it in certain loci (alleles) As the population evolve there are continuous changes in allele frequencies, which may result in ultimate changes in the genome (fixation) In haploid populations (bacteria), genotypes are determined by one haplotype and ancestral relations are simple trees In diploid populations things are a bit more complex, as genotypes can be homozygous or heterozygous at each locus. We can measure and quantify just few aspect of this evolutionary dynamics: Size of populations Allele frequencies The average homozygosity/heterozygosity of an allele How many alleles at a locus Population genetics is dealing with theories that predict the behavior of these quantities using simple assumption on the evolutionary dynamics

Frequency estimates We will be dealing with estimation of allele frequencies. To remind you, when sampling n times from a population with allele of frequency p, we get an estimate that is distributed as a binomial variable. This can be further approximated using a normal distribution: When estimating the frequency out of the number of successes we therefore have an error that looks like:

Simplest model: Hardy-Weinberg • Studying dynamics of the frequencies of two alleles A/a of a gene • Assume: • Diploid organisms • Sexual Reproduction • Non-overlapping generations • Random mating • Male-females have the same allele frequencies • Large population, No migration • No mutations, no selection on the alleles under study Hardy-Weinberg equilibrium: Random mating AA aa AA aa Aa aA Aa aA Non overlapping generations With the model assumption, equilibrium is reached within one generation

Testing Hardy-Weinberg using chi-square statistics HW is over simplifying everything, but can be used as a baseline to test if interesting evolution is going on for some allele Classical example is the blood group genotypes M/N (Sanger 1975) (this genotype determines the expression of a polysaccharide on red blood cell surfaces – so they were quantifiable before the genomic era..): Observed HW Chi-square significance can be computed from the chi-square distribution with df degrees of freedom. Here: df = #classes - #parameters – 1 = 3(MN/NN/MM) – 1 (p) – 1 = 1

Recombination and linkage Assume two loci have alleles A1,A2, B1,B2 Only double Heterozygous can allow recombination to change allele frequencies: Linkage equilibrium: A1 B1 A2 B2 A1B1/ A2B2 A1 B2 A1B2/ A1B2 A2 B1 The recombination fraction r: proportion of recombinant gametes generated from double heterozygote For different chromosomes: r = 0.5 For the same chromosome, function of the distance and possibly other factors

A1 B2 A2 B1 r A1 B1 A2 B2 A1 B1 A2 B2 1-r A1 B1 A2 B2 Linkage disequilibrium (LD) Recombination on any A1- / -B1 No recomb Next generation: Define the linkage disequilibrium parameter D as: D r=0.05 r=0.5 r=0.2 Generation

Linkage disequilibrium (LD) - example blood group genotypes M/N and S/s. Both alleles in Hardy-Weinberg For M/N – p1 = 0.5425 p2 = 0.4575 For S/s – q1 = 0.3080 q2 = 0.6920 Observed unlinked Linkage equilibrium highly unlikely!

Sources of Linkage disequilibrium LD in original population that was not stabilized due to low r Genetic coadaptation: regions of the genome that are not subject to recombination (for example, inverted chromosomal fragments) Admixture of populations with different allele frequencies:

Population substructure The HW theory assumed population are randomly mating We mentioned that species are suppose to be isolated genetically, but even inside a species, the flow of information is never uniform Subpopulation structure would result in low heterozygosity This is because (different) alleles would be fixated in different sub-populations We can compute the average heterozygosity predicted by HWE from allele frequencies: H=2pq HS – in each population use frequency to compute HWE heterozygosity and average HR – in each region use frequency to compute HWE heterzygosity and take a weighted average HT – for the entire population use frequency to compute HWE heterzygosity and average Wrights fixation index F Comparing one level in the hierarchy to another Provide indication to the level of genetic differentiation in the population 0<F<1, F<0.05 is considered quite low, F>0.25 is considered very high

Population substructure – (Dobzhansky and Epling 1942) Frequency of recessive allele (blue flower color) in “desert snow” flowers (Lynanthus parruae) 0.717 0.005 0.000 0.000 0.032 0.573 0.657 0.000 More significant difference among regions than inside them 0.009 0.000 0.002 0.302 0.007 0.004 0.000 0.000 0.126 0.504 0.005 0.106 0.008 0.000 0.339 0.000 0.224 0.068 0.010 0.000 0.014 0.411 Each point represent ~4000 plants over 30 square miles of the Mohave desert

Inbreeding A population with inbreeding will undergo reduction in heterozygosity For example, self-fertilization in plants The inbreeding coefficient: H0 – the random mating heterozygosity HI – observed (inbreeding) heterozygosity In fact F is identical to the Fixation index F and can be interpreted as measuring the probability that two alleles are identical by descent - autozygotes The increase in rare-alleles homozygosity for inbreeded population is frequently detrimental Regular mating schemes in the lab and field: Selfing, Sib-mating, Backcrossing to single individual from a random bred strain Assortative mating: positive (height in human) negative (cases in plants)

The hapmap project 1 million SNPs (single nucleotide polymorphisms) 4 populations: 30 trios (parents/child) from Nigeria (Yoruba - YRI) 30 trios (parents/child) from Utah (CEU) 45 Han chinease (Beijing) 44 Japanease (Tokyo) Haplotyping – each SNP/individual No just determining heterozygosity/homozygosity – haplotyping completely resolve the genotypes (phasing) Because of linkage, the partial SNP Map largely determine all other SNPs!! The idea is that a group of “tag SNPs” Can be used for representing all genetic Variation in the human population. This is extremely important in association studies that look for the genetic cause of disease.

Correlation on SNPs between populations

Recombination rates in the human population: LD blocks

Recombination rates in the human population Recombination rates are highly non uniform – with major effects on genome structure!

Mutations Simplest model: assume two alleles, and mutations probabilities: If the process is running long enough, we will converge to a stationary distribution: A a Populations are however finite, and this create random genetic drift A random allele have a significance change to be eliminated, even in one generation: sampling

Drift Figure 7.4 Experiments with drifting fly populations: 107 Drosophila melanogaster populations. Each consisted orignally of 16 brown eys (bw) heterozygotes. At each generation, 8 males and 8 females were selected at random from the progenies of the previous generation. The bars shows the distribution of allele frequencies in the 107 populations

Drift, fixation, and the neutral theory If sampling is random, the chance of ultimate fixation is Simply because one allele must become fixated (and there are 2N to begin with). According to the neutral theory fixation of neutral alleles play a major role in driving divergence of populations. This is in contrast to the selectionist view that stress adaptive evolution as the major force for fixation of new alleles. The controversy around the neutral theory seems like something that belongs to the past, since it was heated around question of evolution in protein coding loci, and densely coded genomes. Today we realize that genomic information is distributed in a way that should certainly allow neutral or almost neutral mutations a considerable freedom in large parts of the genome.. There are still critically important questions on how strong is the neutrality assumption in different parts of the genome – we’ll look at this question later.

Wright-Fischer model for genetic drift N individuals ∞ gametes N individuals ∞ gametes We follow the frequency of an allele in the population, until fixation (f=2N) or loss (f=0) We can model the frequency as a Markov process with transition probabilities: Sampling j alleles from a population 2N population with i alleles. In larger population the frequency would change more slowly (the variance of the binomial variable is pq/2N – so sampling wouldn’t change that much)

Diffusion approximation and Kimura’s solution Fischer, and then Kimura approximated the drift process using a diffusion equation. The density of population with frequency x..x+dx at time t The flux of probability at time t and frequency x The change in the density equals the differences between the fluxes J(x,t) and J(x+dx,t), taking dx to the limit we have: The if M(x) is the mean change in allele frequency when the frequency is x, and V(x) is the variance of that change, then the probability flux equals: Heat diffusion Fokker-Planck Kolmogorov Forward eq.

Changes in allele-frequencies, Fischer-Wright model After about 4N generations, just 10% of the cases are not fixed and the distribution becomes flat.

Absorption time and Time to fixation According to Kimura’s solution, the mean time for allele fixation, assuming initial probability p and assuming it was not lost is: The mean time for allele loss is (the fixation time of the complement event):

Effective population size 4N generations looks light a huge number (in a population of billions!) But in fact, the wright-fischer model (like the hardy-weinberg model) is based on many non-realistic assumption, including random mating – any two individuals can mate The effective population size is defined as the size of an idealized population for which the predicted dynamics of changes in allele frequency are similar to the observed ones For each measurable statistics of population dynamics, a different effective population size can be computed For example, the expected variance in allele frequency is expressed as: But we can use the same formula to define the effective population size given the variance:

Effective population size: changing populations If the population is changing over time, the dynamics will be affect by the harmonic mean of the sizes: So the effective population size is dominated by the size of the smallest bottleneck Bottlenecks can occur during migration, environmental stress, isolation Such effects greatly decrease heterozygosity (founder effect – for example Tay-Sachs in “ashkenazim”) Bottlenecks can accelerate fixation of neutral or even deleterious mutations as we shall see later. Human effective population size in the recent 2My is estimated around 10,000 (due to bottlenecks).

Effective population size: unequal sex ratio, and sex chromosomes If there are more females than males, or there are fewer males participating in reproduction then the effective population size will be smaller: Any combination of alleles from a male and a female So if there are 10 times more females in the population, the effective population size is 4*x*10x/(11x)=4x, much less than the size of the population (11x). Another example is the X chromosome, which is contained in only one copy for males.

Testing neutrality The drift process have clear dynamics. We are usually interested in these dynamics as a baseline for testing hypotheses on non-neutral evolution Such tests require predictions on the behavior of concrete statistics that we can measure from a population For example, we can sequence alleles and count how many polymorphic sites exist in a gene and what are their frequencies. We can also perform evolutionary comparisons among different sites – we will focus on these later in the course. Non neutral population dynamics sp1 sp2 sp3 sp4 sp5 Slow evolution

Infinite alleles model Assuming a gene with multiple loci, we can think of the number of possible alleles as much larger than the population In this model, the probability of generating the same mutation twice is considered 0 One can then ask how many distinct alleles should we observe given a neutral process and a certain mutation probability Alternatively, one can ask what will be the probability of autozygosity F (identity by descent) (picking up two autozygous alleles and not mutating them, or picking up the same allele twice) Looking for steady state and neglecting factors that depends on m2, m/N: Because of our model, F is also the fraction of homozygous individuals 4Nm

Testing the infinite alleles model The Ewens formula enable us to predict the number of alleles (k) we should observe when sampling n times from a population with q=4Nm, assuming the infinite allele model : The Chinese restaurant process

Testing the infinite alleles model We can estimate F from k (by finding q from the E(k) formula) – We use this statistics to test if a given gene behave neutrally (or at least according to the model): Not quite neutral Highly non neutral Figure 7.16,7.17 VNTR locus in humans: observed (open columns) and Ewens predicted allele counts. F computed from the number of Xdhalleles in 89 D. pseudoobscura lines gene: 52 had a common allele, 8 singletons. Compared to a simulation assuming the infinite allele model.

Infinite sites model Instead of looking at an entire gene with many alleles, consider the many loci consisting the gene and assume that these are changing slowly: most loci are monomorphic or dimorphic. Probability of i mismatches in two random sequences: In particular, autozygosity: Just like we had for the infinite allele model. If we sample n allele, the number of segregating sites is distributed like: Assuming no intragenic recombination So we can test neutrality by looking at the number of alleles in a certain sample.

Coalescent theory Any set of individuals in a population are a consequence of a coalescence process: a common ancestor giving rise to multiple alleles through mutation, duplication and recombination. Such models are in wide use for simulating populations Application for inferring selection/neutrality or other population dynamics are becoming reasonable as more data becomes available. A simple coalescent model look at the gene tree of the k observed alleles Past Present

Selection Fitness: the relative reproductive success of an individual (or genome) Fitness is only defined with respect to the current population. Fitness is unlikely to remain constant in all conditions and environments Sampling probability is multiplied by a selection factor 1+s Mutations can change fitness A deleterious mutation decrease fitness. It would therefore be selected against. This process is called negative or purifying selection. A advantageous or beneficial mutation increase fitness. It would therefore be subject to positive selection. A neutral mutation is one that do not change the fitness. For mono-allelic populations, selection directly observe the fitness of an allele For diploid organisms, we should define how the combination of alleles affect fitness.

Selection in haploid populations Example (Hartl Dykhuizen 81): E.Coli with two gnd alleles. One allele is beneficial for growth on Gluconate. A population of E.coli was tracked for 35 generations, evolving on two mediums, the observed frequencies were: Gluconate: 0.4555  0.898 Ribose: 0.594  0.587 For Gluconate: log(0.898/0.102)-log(0.455/0.545)=35logw log(w) = 0.292, w=1.0696 Compare to w=0.999 in Ribose. Allele Frequency Relative fitness Gamete after selection Generation t: Ratio as a function of time: Consider continuous time model The change in allele frequency:

Selection and allele frequency dynamics Assume: Genotype Fitness Frequency (Hardy Weinberg!) Change in frequency is given by: In the case of codominance:

Selection and fixation An allele with a beneficial mutation will have an increased frequency in the gamete pool: Its chances to avoid immediate extinction are: This is a rather modest increase, so even beneficial allele are likely to be eliminated. For example, s=0.1 would have a loss probability of 0.333 compared to 0.368 for a neutral allele. For a diploid population, if we assume the fitness of a heterozygous if 1+s and of a homozygous is 1+2s, it can be computed from the diffusion approximation that the overall fixation probability will be:

Selection and fixation The fixation time for a neutral allele (assuming fixation was achieved), as we said before, is averaging at: With a selective advantage, the fixation time is approximated by:

Substitutions Considering now the entire population, the rate of substitution at a loci equals the number of mutations times their fixation probability. In the neutral case, this is very simple: So neutral evolution is unaffected by the size of the population. With a selective advantage, the fixation probability is approximated by: So evolution will be more efficient when population is larger, mutation rate is faster and selection is stronger. The parameter 4Nes is describing the speed up.

Other types of selection Over-dominance: heterozygous are better, so there is a possibility for equilibrium in allele frequencies: few examples, but on famous is resistance ot malaria and sickle cell anemia in Africa Frequency-, Density-dependent selection: when the fitness depend on the frequency of the allele or the population size. Fecundity selection: different reproductive potential for mating pairs. Effects of heterogeneous environment: (overdominance?) Different effects in males and femeals Effects that apply directly to the haplotype: gametic selection/meiotic drive (e.g., killing your homologous chromosome reproductive potential) Kin selection: origin of altruism?

Recombination and selection

Linkage and selection Linkage interfere with the purging of deleterious mutations and reduce the efficiency of positive selection! Beneficial Beneficial Beneficial Weakly deleterious Selective sweep/Hitchhiking effect /“genetic draft” Hill-Robertson effect

Linkage and selection The variance in allele frequency is used to define the effective population size Simplistically, assume a neutral locus is evolving such that a selective sweep is affecting a fully linked locus at rate d. A sweep will fixate the allele with probability p, and we further assume that the sweep happens instantly: This is very rough, but it demonstrates the basic intuition here: sweeps reduce the effective selection in a way that can be quantified through reduction in the effective population size. C – the average frequency of the neutral allele after the sweep

Genome evolution: a sequence-centric approach