Mendelian Genetics

Mendelian Genetics

• Genes- genetic material on a chromosome that codes for a specific trait

• Genotype- the genetic makeup of the organism

• Phenotype- the expressed trait• Allel- an alternative form of a

gene

Genetic Definitions

Dominance Mechanism

• Two alleles are carried for each trait• In true-breeding individuals, both

alleles are the same (homozygous).• Hybrids, on the other hand, have one

of each kind of allele (heterozygous).• One trait is dominant, the other trait is

recessive

Mendel’s Three Principles

• Dominance

• Segregation

• Independent Assortment

The foundation of “classical” science

(1822-1884)

• Frequency 150 purple

• Proportion 150:200

• Percentage 75% purple

• Ratio 3:1

150 purple kernels50 yellow kernelsTotal 200 kernels

This laboratory activity is designed tofamiliarize you with the statistical nature ofMendel's model.

We will attempt to understand theprobabilistic aspects of monohybrid crosses(Mendel’s Law of Segregation) by "randomly"tossing special "coins" designed to simulate thegenotypes of parents, focusing on a singlegenetic character, seed color.

Part I

We will attempt to understand the probabilistic aspects of dihybrid crosses (Mendel’s Law of Independent Assortment) by “randomly” tossing special “dice” designed to simulate the genotypes of parents, focusing on two genetic characters, seed color and plantheight.

Part II

Dihybrid Cross

In Part III, we will investigate various human genetic characters.

In Part IV, we will also evaluate the results of monohybrid and dihybrid crosses involving the characteristics of corn seeds.

In Part V, we will learn about the use of the Chi-Square test for statistical hypothesistesting.

c2 =∑(o – e)2

e

Parts I-III of this lab activity will involvecollection of class data. So we will proceedthrough these parts together as a class. Buteach round of data collection will involve workingin pairs.

Parts IV & V will not involve collection ofclass data. You will proceed through these partsworking in pairs.

Tongue Roller

R = Tongue Rollerr = Unable to Roll Tongue

Widow’s Peak

W = Widows Peakw = Lack of Widow’s Peak

Free Ear Lobe Attached Ear Lobe

E = Free Ear Lobee = Attached Ear Lobe

Hitchhiker’s Thumb

Hi = Straight Thumbhi = Hitchhiker’s Thumb

Bent Little Finger

Bf = Bent Little Fingerbf = Straight Little Finger

Mid-digital Hair

M = Mid-Digital Hairm = Absence of Mid-Digital Hair

Dimples

D = Dimplesd = Absence of Dimples

Short Hallux

Ha = Short Halluxha = Long Hallux

Short Index Finger

Ss = Short Index FingerS1 = Long Index Finger

*Sex-Influenced Trait

http://www.youtube.com/watch?v=gCPuHzbb5hA

The Chi Square Test

• A statistical method used to determine goodness of fit

• Goodness of fit refers to how close the observed data are to those predicted from a hypothesis

• Note:• The chi square test does not prove that a

hypothesis is correct• It evaluates to what extent the data and the

hypothesis have a good fit

The Chi Square Test

• The general formula is

c2 =∑(o – e)2

e

• where – o = observed data in each category– e = observed data in each category based on the

experimenter’s hypothesis– ∑ = Sum of the calculations for each category

We start with a theory for how the offspring will be distributed: the “null hypothesis”.

The null hypothesis is that the offspring will appear in a ratio of 3/4 dominant to 1/4 recessive.

Example of its use:Observed Expected*Frequency Frequency

(o) (e)red flowers 73 75white flowers 27 25

*The expected frequency depends upon the hypothesis.

o - e (o - e )2 (o - e ) 2

ered flowers -2 4 0.053white flowers 2 4 0.16

c2 =∑(O – E)2

E = 0.053 + 0.16 = 0.213

Degrees of Freedom

• A critical factor in using the chi-square test is the “degrees of freedom”, which is essentially the number of independent random variables involved.

• Degrees of freedom is simply the number of classes of offspring minus 1 (d.f. = n-1).

• For our example, there are 2 classes of offspring: red and white. Thus, degrees of freedom (d.f.) = 2 -1 = 1.

Critical Chi-Square

• Critical values for chi-square are found on tables, sorted by degrees of freedom and probability levels. Be sure to use p = 0.05.

• If your calculated chi-square value is greater than the critical value from the table, you “reject the null hypothesis”.

• If your chi-square value is less than the critical value, you “fail to reject” the null hypothesis (that is, you accept that your genetic theory about the expected ratio is correct).

d.f. 0.95 0.90 0.80 0.70 0.50 0.30 0.10 0.05 0.01

1 .004 .016 .064 .148 .455 1.07 2.71 3.84 6.642 .103 .211 .446 .713 1.38 2.41 4.6 5.99 9.203 .352 .584 1.00 1.42 2.37 3.66 6.25 7.85 11.3

probability

Chi-Square Table 2 = 0.213

Your value

Do you accept or reject the null hypothesis?