GEN 3022 1st Edition Lecture 3Outline of Last Lecture I. IntroductionII. VocabularyIII. Mendel and the Pea Plant Experimentsa. Advantages of using the garden peab. Seven Characters of Inheritance in Peasc. Experimental Set-upIV. Conclusions of the Experimentsa. Patterns of inheritanceb. Mendel’s Law of Segregationc. Mendel’s Law of Independent Assortmentd. Recessive vs. DominantV. Punnett SquaresOutline of Current Lecture I. Review of Mendel’s Lawsa. Law of Segregationb. Law of Independent AssortmentII. Dihybrid Test CrossIII. Pedigree AnalysisThese notes represent a detailed interpretation of the professor’s lecture. GradeBuddy is best used as a supplement to your own notes, not as a substitute.a. ExplanationIV. Probabilitya. Explanationb. General Equationc. Random Sampling Errord. Product Rulei. Example e. Binomial Expansion Equationi. ExampleV. Chi Square Testa. Goodness of Fitb. Null Hypothesisc. Equation, Current LectureI. Review of Mendel’s Lawsa. Law of Segregation: each parent contributes one copy of each gene (one allele) to each offspringb. Law of Independent Assortment: different (unlinked) genes randomly segregate into haploid cells, only works with dihybrid crossesII. Dihybrid Test Cross: used to prove the law of independent assortment and determine if an individual is homozygous or heterozygousa. The individual is crossed with a homozygous individual (homozygous recessive)b. If the two genes sort independently, there will be a 1:1:1:1 ratio among the offspringIII. Pedigree Analysisa. Another name for this is a family treeb. Used to follow a given trait (usually a disease) through generationsi. Different symbols represent the types of individuals1. Circle: female2. Square: Male3. Shaded Circle/Square: affected individual4. Blank Circle/Square: unaffected individual5. Circle/Square with a slash through it: deceased individualii. Can be used to determine if a trait (disease) is recessive or dominant based on the offspring IV. Probabilitya. Definition: chance that an event will occur in the futureb. Equation: P = # of times an event occurs/total # of eventsc. Random Sampling Error: chance that the results of a probability experiment are due to random chance and sample size (not desired)1. RSE is large for small samples 2. RSE is small for large samplesd. Product Rulei. The probability that two or more independent events (the occurrence of one event will not affect the probability of another) will occur is equal to the product of their respective probabilities. ii. Ex: a couple has four children. What is the probability that they would have four boys? Answer: ½ x ½ x ½ x ½ = 1/16e. Binomial Expansion Equationi. Probability of a given set of unordered events occurring ii. Equation: P = [n! / x!(n-x)!] px * qn-xiii. P = probability, n = number of events, x = number of events in one category, p = individual probability of x, q = individual probability of the other categoryiv. ! means factorial, for example: 4! = 4x3x2x1v. p + q = 1f. The product rule and binomial expansion equation can be used in combination i. Ex: what is the probability that a couple’s first child will be a boy and then three of their next four children will be girls?ii. Answer: use product rule first then binomial expansion equationV. Chi Square Testa. A statistical method used to determine goodness of fiti. Goodness of fit: how close the observed data are to those predicted from a hypothesisii. Null Hypothesis: used in tandem with the goodness of fit. The hypothesis is that the data obtained is due to random chance alone (random sampling error)b. Equation: X2 = (O-E/E)2i. After applying this equation, the X2 value is applied to a P value tableii. Using the degrees of freedom (n-1) find the P value in the tableiii. If the P value is greater than 0.05 then the null hypothesis will be accepted, meaning that the data is due to random sampling erroriv. If the P value is less than or equal to 0.05 then the null hypothesis will be rejected, meaning that the data is not due to random sampling
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