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SCIENCE UNIT 3
GENETICS CORN LAB
PRANAV K !1
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GENET CS
CORN
LAB
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by Pranav Kalra
G10A Science
MAY 15, 2014
MR TIM G
SCIENCE UNIT 3
GENETICS CORN LAB
PRANAV K !2
//BACKGROUND INFO//
INTRODUCTION TO THE LAB
Ultimately, genetics is the study of how our genes are inherited, how they vary, and what
impact they have on our phenotype and traits. To study the ideas of Mendelian and
Theoretical Genetics, we have to do something similar to what Mendel did with his Pea
Plants. Thus, to observe the inheritance of genes using Punnett squares, we will use Zea
Maize, or corn, as a test subject. It is perfect because it has been studied extensively due
to its economic importance. Beyond that, each kernel on a ear of corn is an individual
zygote, and thus an ear of corn is an entire sample. We will assume that the corn we are
studying is the fusing of two originally heterozygous gametes. We will then observe the
trait of the colour of each kernel, and thus come up with phenotypic rations, and compare
with our hypothesis.
!
//TABLE 1//
SUMMARY OF KEY GENETICS CONCEPTS
CONCEPT
NOTES
MEIOSIS
Meiosis is the process of creating sex cells, as opposed to Mitosis, which
creates somatic cells. It goes through a process similar to Mitosis twice, as
to create 4 haploid cells, rather than 2 diploid cells. It also includes
individual assortment through genetic crossover during one of the phases.
MENDELIAN
GENETICS
Genes are passed on generation to generation according to probability
1. Segregation: each gamete carries only one allele for each gene
2. Ind Assortment: Genes for traits segregate independently
3. Dominance: Some alleles are dominant, while others are recessive
CHROMOSOMAL
THEORY
Based mostly on Thomas Hunt Morgan’s research with Fruit Flies, he
realised that genes were packaged into chromosomes, and that there were
also genes that were intricately tried to the sex chromosomes (‘sex-linked’).
PUNNETT
SQUARES
A visual representation of the core of Mendelian inheritance, which involves
two alleles for each gene, and crossing two sets to the next generation, to
find the probability of each genotypic and phenotypic result.
CHI-SQUARE
ANALYSIS
!
Way to check if data variation is statistically significant. It
evaluates the observed frequencies against expected ones, to determine
the probability of the null hypothesis, and check for a significant difference.
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//NULL HYPOTHESIS//
BASED ON COUNTS OF PHENOTYPIC RATIOS
IF
THEN
BECAUSE
each corn is a cross of pure heterozygous parents (Pp x Pp),
75% of the kernels will be Purple, and 25% of the kernels will be Yellow
of Mendelian & Chromosomal theories, applied in the Punnett Square below
SCIENCE UNIT 3
GENETICS CORN LAB
F0: PP x pp
P
P
p
Pp
Pp
p
Pp
Pp
F1: Pp x Pp
P
p
P
PP
Pp
p
Pp
pp
PRANAV K !3
We start crossing
two purebred
zygotes, PP x pp
in F0.
!
Genotypic Ratio: 1 PP: 2 Pp: 1 pp Phenotypic Ratio: 3 Purple: 1 Yellow
//TABLE 2// PERSONAL DATA
The result of the
F0 cross leads to
F1, which is all
Pp. We cross two
of these together,
to create F2, our
expected values.
//TABLE 3// CLASS TOTALS DATA
# OF KERNELS
# OF KERNELS
TOTAL
404
TOTAL
6929
PURPLE
179
PURPLE
2799
YELLOW
225
YELLOW
4130
EXPECTED YELLOW
101
EXPECTED YELLOW
1732.25
EXPECTED PURPLE
303
EXPECTED PURPLE
5196.75
!
//TABLE 4//
CLASS DATA
TOTAL
#PURPLE
#YELLOW
EXPECTED YELLOW
EXPECTED PURPLE
392
211
181
98
294
430
218
212
107.5
323
308
137
171
77
231
333
131
202
83.3
250
316
140
176
79
237
438
193
245
109.5
329
326
114
212
81.5
245
352
145
207
88
264
470
149
321
117.5
353
429
220
209
107.25
322
SCIENCE UNIT 3
GENETICS CORN LAB
PRANAV K !4
396
184
212
99
297
396
106
290
99
297
414
185
229
103.5
311
433
196
237
108.25
325
414
84
330
104
311
369
141
228
92.25
277
309
66
243
77.25
232
!
//CHI SQUARE EXPLANATION I//
REASONING AND UNDERSTANDING THE CHI SQUARE TEST
The Chi Square test is used to check if there is a statistically significant difference or
similarity between the observed and expected results. It checks the probability of the Null
hypothesis being true. The test originates from the chi square distribution, a set
mathematical probability distribution. When Karl Pearson rediscovered the distribution,
he also created a test for it, the Pearson chi-squared test, with computed table of values
to check the significance or probability based on the distribution and the test.
!
!
The formula for chi square uses a sum of observed and expected values. It is the square
of the difference between the expected and observed values, divided by the expected
value. The core of the test is the difference between the expected and observed values,
which is then squared to avoid negative numbers, and redivided by the expected values
to normalise the number across all sizes of experiments.
!
We first come up with expected values based on Mendelian genetics. We expect the corn
to be the 1st generation cross of two purebred corn types, where Purple is the dominant
trait, and Yellow is recessive. This cross gives us all heterozygous zygotes to cross for the
next generation, F1, which leads to a monohybrid cross. We derived the expected values
using the phenotypic ratios of this cross, which leads to the expected yellow kernels
being ¼ of the total kernels, and purple being ¾ of the total kernels.
!
We also need to know the degrees of freedom. The degrees of freedom is defined as the
number of categories or independent variables minus one. As I will be measuring both
purple and yellow, that gives me 2-1, or 1 degree freedom.
!
I will then use the empirical table of critical values, and compare my value on the row with
1 degree of freedom, to see the probability of the Null hypothesis being true. If I have a
value greater than 8, there is less than 0.5% of my Null being true. SCIENCE UNIT 3
GENETICS CORN LAB
PRANAV K !5
//TABLE 4//TABLE 5//
PERSONAL + CLASS CHI SQUARE VALUES AND WORKINGS
TOTAL
YELLOW
# of
EXPECTED
404
225
101
392
181
98
430
212
107.5
308
171
77
333
202
83.3
316
176
79
438
245
326
Χ2
# of
EXPECTED
152.2
179
303
(179-303)
303
50.7
70.3
211
294
(211-294)
294
23.4
(212-107.5)
107.5
101.6
218
322.5
(212-322.5)
322.5
33.9
(171-77)
77
114.8
137
231
(137-231)
231
38.3
(202-83.3)
83.3
169.1
131
249.75
(131-249.75)
249.75
56.5
(176-79)
79
119.1
140
237
(140-237)
237
39.7
109.5
(245-109.5)
109.5
167.7
193
328.5
(193-328.5)
328.5
55.9
212
81.5
(212-81.5)
81.5
209.0
114
244.5
(114-244.5)
244.5
69.7
352
207
88
(207-88)
88
160.9
145
264
(145-264)
264
53.6
470
321
117.5
(321-117.5)
117.5
352.4
149
352.5
(149-352.5)
352.5
117.5
429
209
107.25
(209-107.25)
107.25
96.5
220
321.75
(220-321.75)
321.75
32.2
396
212
99
(212-99)
99
129.0
184
297
(184-297)
297
43.0
396
290
99
(290-99)
99
368.5
106
297
(106-297)
297
122.8
414
229
103.5
(229-103.5)
103.5
152.2
185
310.5
(185-310.5)
310.5
50.7
433
237
108.25
(237-108.25)
108.25
153.1
196
324.75
(196-324.75)
324.75
51.0
414
330
104
(330-104)
104
491.1
84
310.5
369
228
92.25
(228-92.25)
92.25
199.8
141
276.75
(141-276.75)
276.75
66.6
309
243
77.25
(243-77.25)
77.25
355.6
66
231.75
(66-231.75)
231.75
118.5
3318.9
2799 5197
6929 4130 1732.25
WORKING
PURPLE
(225-101)
101
(181-98)
98
(4130-1732.3)
1732.25
WORKING
(84-310.5)
310.5
(2799-5197)
5197
Χ2
165.2
1106.3
SCIENCE UNIT 3
GENETICS CORN LAB
PRANAV K !6
//CHI SQUARE EXPLANATION II//
EXPLAINING THE SIGNIFICANCE VALUE
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3318.9>7.879
!
1106.3>7.879
4425.2>7.879
The chi square values are far above anything in the table for 1 degree of freedom. The
confidence interval is less than 0.005 or less than 0.5%. This means there is a very
minuscule chance of the Null hypothesis being true, effectively disproving and rejecting
the Null hypothesis.
!
//CONCLUSION//
UNDERSTANDING THE CHI SQUARE RESULTS
While the Chi Square results don’t support the Mendelian Ratios for monohybrid crosses,
they don't disprove it either, due to errors in our experiment. The Chi Square values say
that our hypothesis, based on Mendelian values, is wrong. However, we don’t actually
know if this corn was of a second generation monohybrid cross, which came from a first
generation purebred cross. This is a huge error in our lab. On top of that, inheritance can
be a lot more complicated than a simple punnett square, as many times, traits like colour
have multiple genes impacting them. There was a range of purples and yellows on the
kernels, and thus it could be more than just one gene with dominant/recessive alleles,
and instead be polygenic trait. This would require us to factor in all the genes that lead to
colour in corn. Beyond that, genetic modification, which is quite prominent in corn for
colour, pest-resistance, and other agriculturally useful traits, could also interfere with our
prediction process, and change the results. Ultimately, while some monogenic traits can
be predicted and understood with Mendelian genetics and Punnett Squares, most
important and vague traits such as colour can be incompletely dominant, polygenic, and
now genetically modified, skewing simple Mendelian Ratios. Nonetheless, this doesn’t
disprove Mendelian Ratios for simpler traits in monohybrid crosses.