本期的內容為進化檢測。本文集的這一部分是遺傳、進化與生態(tài)學 Genetics, Evolution, and Ecology. 這門課理論上建議在閱讀完文集的第一部分的內容之后再開始學習，但基礎不足的朋友也可以嘗試閱讀喔~

這一部分的主要內容均來自 Prof. Angela J. Roles 的 BIOL 200 課程，因此本文集的這一部分均不會標記為原創(chuàng)。但由于文本來源不清晰，UP主還是一個字一個字碼出來的文章，本文禁止非授權的轉載，謝謝！

Lesson 16: Detecting Evolution

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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.

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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.

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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.

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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.

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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.

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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

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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

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?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.

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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.

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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.

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本期內容到此結束，感謝您的閱讀~