遗传 进化与生态学 16 - Detecting Evolution

本期的内容为进化检测。本文集的这一部分是遗传、进化与生态学 Genetics, Evolution, and Ecology. 这门课理论上建议在阅读完文集的第一部分的内容之后再开始学习，但基础不足的朋友也可以尝试阅读喔~

这一部分的主要内容均来自 Prof. Angela J. Roles 的 BIOL 200 课程，因此本文集的这一部分均不会标记为原创。但由于文本来源不清晰，UP主还是一个字一个字码出来的文章，本文禁止非授权的转载，谢谢！

Lesson 16: Detecting Evolution

The Hardy Weinberg Principle: Modeling stasis

    ▸Assumptions for no change at a locus: random mating; no mutation; no selection; no gene flow; no genetic drift;

    If the assumptions are met, then we predict:

[1] HW Equilibrium: Allele frequencies do not change over generations, p time1 = p time2;

[2] HW Proportions: Genotype frequencies within a generation are a simple function of allele frequencies.

So, how do we know whether or not a population is in HW proportions right now?

Statistics!

We can compare what we’d expect from Hardy-Weinberg to the observed values, using a chi-square (χ2) goodness-of-fit test.

Step 1: Figure out the expected numbers of individuals of each genotype.

Is our population of plants in HW proportions?

Recall:

    We had 40 pink (LL), 40 orange (LC), and 20 red-flowered (CC) plants.

Giving us observed genotype frequencies of:

    P obs = freq. of LL = 40/100 = 0.4 And:

    H obs = freq. of LC= 40/100 = 0.4 p = 0.6

    Q obs = freq. of CC = 20/100 = 0.2 q = 0.4

What are our expected genotype frequencies for these allele frequencies?

    P exp = 0.6 × 0.6 = 0.36   

    H exp = 2 × 0.6 × 0.4 = 0.48

    Q exp = 0.4 × 0.4 = 0.16

Not the same! Why? Does this mean we are not meeting the assumptions? We use a chi-square test to figure out if the difference is statistically significant.

Chi-square goodness-of-fit test

▸The chi-square test compares observed numbers of individuals to an expected number of individuals.

    - The test statistic (χ2) measures the difference between the observed and expected values.

For this data, the probability of seeing a difference of this size is P = 0.25 (25%). That’s NOT statistically significant. We conclude this population is in HW proportions.

Interpreting statistics and P-values

▸Statistical methods are used to compare the data we observe to what we might have expected assuming a particular hypothesis is correct (sometimes a null hypothesis).

▸There is always a test statistic that estimates how different the observed data are from the expectations for the hypothesis.

▸Given certain assumptions and features of the data (e.g., distribution of data, sample sizes), we can then determine the P-value: the probability that the difference we found is due to chance alone.

▸By convention in biology, P ≤ 0.05 is considered the cutoff for statistical significance.

▸If P > 0.05, we conclude that our observed data match the predictions of our [null] hypothesis.

▸If P ≤ 0.05, we conclude there IS a statistically significant difference between our data and the predictions of the hypothesis.

Predictions of hypotheses

Testing for the outcome of genetic crosses

▸Imagine you observed 3 flower colors in a population of plants.

    - You hypothesize there is 1 gene with 2 alleles encoding flower color.

▸You cross Aa × Aa.

    - You expect to see offspring ratios: 25% AA, 50% Aa, 25% aa

    - You observe 19 AA, 47 Aa, and 31 aa offspring.

▸What are the possible outcomes of the chi-square test?

    - If observed ≈ expected, and P > 0.05 then you conclude that your hypothesis (1 gene, 2 alleles) is supported by the data. Differences are due to chance.

    - If observed ≠ expected, and P ≤ 0.05, then you conclude that the 1 gene, 2 alleles hypothesis must be wrong.

▸In this case, the test yields P = 0.216. Your hypothesis is supported.

Testing a population for Hardy-Weinberg Proportions

▸In a population of 100 individuals, you observe genotype frequencies:

    P obs = 0.2

    H obs = 0

    Q obs = 0.8

▸Which gives you p = 0.2, q = 0.8

▸Under HW (hypothesizes no evolution at this locus), you expected to see:

    P exp = 0.04

    H exp = 0.32

    Q exp = 0.64

▸A difference this size in a population of N N 100 is highly statistically significant (P << 0.001).

▸You conclude that this population is NOT in HW proportions and you now hypothesize that the population may be inbreeding

Evolution & Changes

Now then, what about HW equilibrium? How do we detect when evolution is occurring across generations?

“Genes mutate, individuals are selected, and populations evolve.” – David Hull

▸Evolution = change over time in the genetic makeup of a population

    - can diagnose by change in allele and genotype frequencies;

    - occurs within populations;

    - occurs independently in populations isolated in space/time.

▸Pattern: appearance of fossils; current distribution of phenotypes in nature

▸Process: forces that cause genetic change

    - Adaptive evolution: natural selection acts causing the population’s average survival and reproductive success to increase over time.

    - Neutral (or non-adaptive) evolution: genetic drift, gene flow, non-random mating, and mutation are all neutral evolutionary forces.

    - Sometimes multiple forces will act at the same time. For example, under some circumstances, non-random mating will be favored by natural selection.

Patterns in time: 30 years of data

    Body size and beak size over time for populations of Darwin’s finches in the Galapagos (Grant and Grant, 2002).

▸Horizontal lines = range of body sizes expected if no change occurs (confidence interval).

▸Possible selective event (drought) in 1977.

▸How do we know if this phenotypic change is also genetic? How do we know if fossil record change is genetic?

▸Time scale: expectations for millions of years will differ from those for decades.

General pattern types

Examples from the fossil record (Hunt 2007)

▸The x-axis is time, Ma = millions of years ago

    - Note that the range of the x-axis is different in each graph

How do we measure genetic change over time?

▸We study patterns of change in allele frequencies over generations;

▸MUCH shorter time scale than in the fossil record...

▸Time (500 generations) on the x-axis, frequency of one allele on the y-axis.

▸Each line shows a separate simulation with the same starting conditions.

How do we infer the cause of change?

▸We use our understanding of HW to infer likely causes of observed patterns;

▸We ask: “If evolutionary force X is acting, how is that likely to change allele or genotype frequencies over time?”

▸In some cases, we can experimentally test hypothesized mechanisms;

▸In upcoming lectures, we’ll explore each of the evolutionary forces and the types of genetic change we expect to result from each.

本期内容到此结束，感谢您的阅读~