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SCIENCE UNIT 3 GENETICS CORN LAB PRANAV K !1 ! GENET CS CORN LAB ! ! ! ! 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. ! //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 ! 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.