Introduction

FlyLab will allow you to play the role of a research geneticist. You will use FlyLab to study important introductory principles of genetics by developing hypotheses and designing and conducting matings between fruit flies with different mutations that you have selected. Once you have examined the results of a simulated cross, you can perform a statistical test of your data by chi-square analysis and apply these statistics to accept or reject your hypothesis for the predicted phenotypic ratio of offspring for each cross. With FlyLab, it is possible to study multiple generations of offspring, and perform testcrosses and backcrosses. FlyLab is a very versatile program; it can be used to learn elementary genetic principles such as dominance, recessiveness, and Mendelian ratios, or more complex concepts such as sex-linkage, epistasis, recombination, and genetic mapping.

Objectives

The purpose of this laboratory is to:

  • Simulate basic principles of genetic inheritance based on Mendelian genetics by designing and performing crosses between fruit flies.
  • understand the relationship between an organism's genotype and its phenotype.
  • Demonstrate the importance of statistical analysis to accept or reject a hypothesis.

Before You Begin: Prerequisites

Before beginning FlyLab you should be familiar with the following concepts:

  • Chromosome structure, and the stages of gamete formation by meiosis
  • The basic terminology and principles of Mendelian genetics, including complete and incomplete dominance, epistasis, lethal mutations, recombination, autosomal recessive inheritance, autosomal dominant inheritance, and sex-linked inheritance.
  • Predicting the results of monohybrid and dihybrid crosses by constructing a Punnett square
  • How genetic mutations produce changes in phenotype, and beneficial and detrimental results of mutations in a population

After doing this lab you should be able to:

  • Investigate the independent assortment of two genes and determine whether the two genes are autosomal or sexlinked using a multigeneration experiment
  • Analyze the data from your genetic crosses using chi-square analysis

Procedure:

To begin an experiment, you must first design the phenotypes for the flies that will be mated. In addition to wild-type flies, 29 different mutations of the common fruit fly, Drosophila melanogaster, are included in FlyLab. The 29 mutations are actual known mutations in Drosophila. These mutations create phenotypic changes in bristle shape, body color, antennae shape, eye color, eye shape, wing size, wing shape, wing vein structure, and wing angle. For the purposes of the simulation, genetic inheritance in FlyLab follows Mendelian principles of complete dominance. Examples of incomplete dominance are not demonstrated with this simulation. A table of the mutant phenotypes available in FlyLab can be viewed by clicking on the Genetic Abbreviations tab which appears at the top of the FlyLab homepage. When you select a particular phenotype, you are not provided with any information about the dominance or recessiveness of each mutation. FlyLab will select a fly that is homozygous for the particular mutation that you choose, unless a mutation is lethal in the homozygous condition in which case the fly chosen will be heterozygous. Two of your challenges will be to determine the zygosity of each fly in your cross and to determine the effects of each allele by analyzing the offspring from your crosses.

One advantage of FlyLab is that you will have the opportunity to study inheritance in large numbers of offspring. FlyLab will also introduce random experimental deviation to the data as would occur in an actual experiment! As a result, the statistical analysis that you will apply to your data when performing chi-square analysis will provide you with a very accurate and realistic analysis of your data to confirm or refute your hypotheses.

  • Use the buttons to choose a combination of genetic traits for the parent flies
  • Wild type is the default, which represents a normal fly
  • Set Offspring # to 1000 Offspring
  • Click the button labeled “ Mate Designed Flies” to perform the cross
  • If no mutation within a group is selected, both flies will be homozygous for the wild type for all the mutations within that group
  • Be sure that when you conduct a second cross or move on to another question, reset the mutations to default setting for all other traits.
  • Use the “AnalyzeResults” feature to collect quantitative data
  • Record all qualitative and quantitative data in a Titled table (ex below)
  • For each of the following crosses, identify the cross by number, present the results (Parental, F1 and F2 generation) numbers and ratios for phenotype and genotype and show a Punnett square for the cross

You will do 4 sets of crosses: [each cross will consist of a parental cross and F1 cross]

Cross #1 & 2 – Assigned monohybrid cross

Cross # 3 & 4 – design your own monohybrid cross

Cross # 5 – 8 Assigned dihybrid cross

Example:

Cross # _____

______♀ x ______♂ (known P1phenotypes)

______♀ x ______♂ (expected P1genotypes)

Punnett Square:

(Note: the # of squares will vary based on type of gametes)

F1Generation Data

Phenotype and Sex / Phenotype and Sex….
Expected Number
Expected Ratio
Observed Number
Observed Ratio

F2 Generation Data

Phenotype and Sex / Phenotype and Sex….
Expected Number
Expected Ratio
Observed Number
Observed Ratio

(Note: the # of columns to each table will vary based on types of offspring)

1. Is the mutation sex-linked or autosomal?

2. Is the mutation a dominant or recessive?

3. Are the deviations for the phenotypic ratio of the F2 generation within the limits expected by chance? To answer this question, statistically analyze the data using the chi-square analysis (Do this by hand, you may use the online source as reference only). Calculate the chi-square statistic for the F2 generation in the chart below. Refer to Table 7.5 to determine the p (probability) value that is associated with your χ2statistic.

Phenotype / # observed (o) / # expected (e) / (o - e) / (o - e)2 / (o - e)2
e
χ2 =
  1. How many degrees of freedom are there?
  2. What is the critical value for the # of degrees of freedom?
  3. Is χ2 greater than, equal to, or less than this critical value (bold)?
  4. Can you accept or reject your null hypothesis?

Table 7.5

Accept the null hypothesis← / Reject the null → hypothesis
Degrees of Freedom / Probability
0.90 / 0.50 / 0.25 / 0.10 / 0.05 / 0.01
1 / 0.016 / 0.46 / 1.32 / 2.71 / 3.84 / 6.64
2 / 0.21 / 1.39 / 2.77 / 4.61 / 5.99 / 9.21
3 / 0.58 / 2.37 / 4.11 / 6.25 / 7.82 / 11.35
4 / 1.06 / 3.36 / 5.39 / 7.78 / 9.49 / 13.28
5 / 1.61 / 4.35 / 6.63 / 9.24 / 11.07 / 15.09

Genetic Abbreviations: Geneticists working with Drosophila have worked out a convention for labeling mutations. If the mutation is recessive, the abbreviation begins with a lowercase letter. If the mutation is dominant, the first letter of the abbreviation is capitalized. For example, the dominant mutant gene for sternopleural bristles is abbreviated as Sp and the recessive mutant gene for mahogany eyes is abbreviated as mah. The normal "wild type" version of the gene is denoted using the same abbreviation as the mutant version, but with a "+" superscript. For example, Sp+ and mah+. Sometimes, when the meaning is obvious, a "+" by itself is used as a shorthand for the wild-type gene. For example, the heterozygote genotypes +/Sp and +/mah. Different variations of the mutant gene (different alleles) are sometimes distinguished by appending superscript abbreviations to the main abbreviation.

FlyLab does not follow this protocol. Since the idea behind FlyLab is for users to discover the genetic characteristics of the mutations by "experimentation," no clues as to dominance or recessiveness are provided by the use of standard abbreviations. Instead, all the letters of the abbreviation are capitalized. In most cases, the letters used are the same as in the standard notation; however, in a few cases it was necessary to modify the choice of letters because capitalization resulted in two different mutations (a dominant and a recessive) with the same abbreviation. A table of the genetic abbreviations used in FlyLab appears below.

Abbreviation / Mutant Phenotype
AP / Apterous Wings
AR / Aristapedia Antennae
B / Bar Eyes
BL / Black Body
BW / Brown Eyes
C / Curved Wings
CV / Crossveinless Wings
CY / Curly Wings
D / Dichaete Wings
E / Ebony Body
EY / Eyeless Eyes
DP / Dumpy Wings
F / Forked Bristles
L / Lobe Eyes
M / Miniature Wings
PR / Purple Eyes
RI / Radius Incompletus Wings
S / Sable Body
SB / Stubble Bristles
SD / Scalloped Wings
SE / Sepia Eyes
SN / Singed Bristles
SS / Spineless Bristles
ST / Star Eyes
SV / Shaven Bristles
T / Tan Body
VG / Vestigial Wings
W / White Eyes
Y / Yellow Body

Chi-square analysisis a statistical method that can be used to evaluate how observed ratios for a given cross compare with predicted ratios. Chi-square analysis considers the chance deviation for an observed ratio, and the sample size of the offspring, and expresses these data as a single value. Based on this value, data are converted into a single probability value (p=value), which is an index of the probability that the observed deviation occurred by random chance alone. Biologists generally agree on a p=value of 0.05 as a standard cutoff value, known as the level of significance, for determining if observed ratios differ significantly from expected ratios. A p=value below 0.05 indicates that it is unlikely that an observed ratio is the result of chance alone. When we predict that data for a particular cross will fit a certain ratiofor example, expecting a 3:1 phenotypic ratio for a monohybrid cross between heterozygotesthis assumption is called a null hypothesis. Chi-square analysis is important for determining whether a null hypothesis is an accurate prediction of the results of a cross. Based on a p=value generated by chi-square analysis, a null hypothesis may either be rejected or fail to be rejected. If the level of significance is small (p < 0.05), it is unlikely (low probability) that the observed deviation from the expected ratio can be attributed to chance events alone. This means that your hypothesis is probably incorrect and that you need to determine a new ratio based on a different hypothesis. If, however, the level of significance is high (p > 0.05), then there is a high probability that the observed deviation from the expected ratio is simply due to random error and chi-square analysis would fail to reject your hypothesis.