2 2 1 Strategies for learning GENETIC DATA ANALYSIS genetics We will begin this lecture by discussing some strategies for learning genetics Genetics is different from most other biology courses you have taken in that memorization is not very important You are expected to learn vocabulary and some examples of genetic disorders formulae etc But learning and applying concepts is much more important In particular you need to be able to think about and analyze genetic data Almost all the homework and exam questions in this part of the course will require you to examine and analyze data and to make some conclusion based upon your analysis In this way the work you do in this course is much more similar to work done by real geneticists This is one reason why students who have always done well in biology sometimes do poorly in genetics Learning how to approach a set of genetic data in a logical and consistent way takes patience and a good deal of practice I will introduce you to ways to think about problems during lectures It is therefore very important that you attend lecture I will use several techniques of active learning because these techniques have been proven to be more effective in increasing student learning than the traditional lecture only format I think these techniques also make class time more interesting for students So please do the following 1 2 3 4 5 6 7 Come to class Bring a calculator and paper with you Read assigned reading and glance over the lecture notes before coming to class During class listen and think about the material that is being presented Try to answer questions that are posed Ask questions about anything that is not clear The very best way to learn something is to teach it to someone else Get together with classmates trade off trying to explain a concept or the solution to a particular problem And please don t do the following 1 Don t spend class time furiously taking notes All lecture notes and slides will be provided to you on the web page 2 2 Genetic Data Analysis Useful Rules of Probability Sum rule The combined probability of two events that are mutually exclusive is the sum of the individual probabilities Genetic example of the sum rule Parental genotypes monohybrid cross F1 genotype F2 produced by mating F1 individuals GG x gg x indicates a genetic cross Gg 1 4 GG 1 2 Gg 1 4 gg 1 2 1 genotypic ratio 1 Q What is the probability that an F2 offspring dominant phenotype is either GG OR Gg of a monohybrid cross has the A P dominant phenotype P GG P Gg Note the P x stands for Probability of x Product rule The probability of both of two independent events is the product of the individual probabilities Genetic example using product rule and sum rule Parental genotypes dihybrid cross GGww x ggWW F1 GgWw F2 9 16 G W 3 16 G ww 3 16 ggW 1 16 ggww Note the indicates either the dominant or recessive allele G indicates an individual with the dominant phenotype either GG or Gg Q What is the probability that an F2 offspring of the following dihybrid cross will have at least one dominant allele for each trait i e that it will be G W Solution First note that segregation segregation at the W locus Mendel s of the G locus is independent of law of independent assortment So that P G W P G P W As above GG and Gg are mutually exclusive an individual not both WW and Ww are also mutually exclusive P G P Gg or P Gg 1 2 So can be GG or Gg but P GG P Gg P GG P GG 1 4 P G 1 2 1 4 3 4 P W P Ww P WW 1 2 1 4 3 4 P G W P G P W 3 4 3 4 Answer iii Complex genetic Parental genotypes F1 Q What proportion problem 6 independent AA bb CC DD ee ff Aa Bb Cc Dd Ee Ff x x of F2 progeny will be genes aa BB cc dd EE FF Aa Bb Cc Dd Ee Ff 2 AA bb Cc DD ee Ff A iv Genetic example using conditional probability Q In the F2 progeny from monohybrid cross heterozygotes among dominant progeny A P heterozygous P dominant P Gg P GG or Gg 1 2 1 4 1 2 1 2 3 4 2 3 2 3 Genetic Data Analysis what is the proportion of Goodness of Fit What if the F2 generation of a monohybrid cross has a phenotypic ratio close to 3 1 but not exactly 3 1 Q How do we tell if deviations from expected proportions in a genetic experiment are due to chance or due to the fact that our genetic hypothesis wrong is A Repeat an experiment many times E g the experiment is toss a fair coin 100 times and count how many heads are throw Repeat this experiment 100 times Q What is the most likely outcome of a single experiment A Q Will all 100 experiments have the same outcome if the coin is fair A Coin Toss Experiment 9 8 Number of Times 100 7 6 5 4 3 2 1 0 1 5 9 13 17 21 25 29 33 37 41 45 49 53 57 61 65 69 73 77 81 85 89 93 97 Number of Heads 3 Getting 65 H and 35 T is an unlikely result but not impossible How unlikely is it The probability is actually 0 0017 In other words this result is expected to occur 17 times out of every thousand times the experiment is repeated or 1 7 times out of every on hundred trials In same way consider a cross of Aa to an individual of unknown genotype that yields 100 progeny We hypothesize that the unknown individual has genotype aa Q If the progeny contain 65 of the dominant phenotype and 35 of the recessive phenotype can we reject our hypothesis that the unknown parent was aa and that the cross was therefore Aa x aa Another way to phrase the question is how likely are we to get a result this different from the expected result if our hypothesis is true First what is the expected result What is the observed result How could we measure the difference between the expected and observed results A A test commonly used to measure the goodness of fit is the CHI SQUARE with a hard C and to rhyme with pie TEST pronounced The observed number O in each category is compared to the expected number E under your hypothesis We use the square of the difference between observed and expected numbers and standardize by dividing by the expected number in a category O E is referred to as the difference d the symbol means to sum the values over each category or class of the data 2 O E 2 E d2 E So for our testcross example class …