Mendelian Genetics What we learned Mendels contributions Mendelian

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Mendelian Genetics: What we learned? Mendel’s contributions?

Mendelian Genetics: What we learned? Mendel’s contributions?

Mendelian Genetics: What we learned? How many traits are considered for Aa. Bb. Cc

Mendelian Genetics: What we learned? How many traits are considered for Aa. Bb. Cc

Mendelian Genetics: What we learned? How many genotypic different gametes are produced by Aa.

Mendelian Genetics: What we learned? How many genotypic different gametes are produced by Aa. Bb. Cc?

Mendelian Genetics: What we learned? What are the gametes produced by Aa. Bb?

Mendelian Genetics: What we learned? What are the gametes produced by Aa. Bb?

Mendelian Genetics: What we learned? What cross yields A-: aa 1: 1? What cross

Mendelian Genetics: What we learned? What cross yields A-: aa 1: 1? What cross yields A-: aa 3: 1? What cross yields A-: aa 0: 1?

Mendelian Genetics: What we learned? When are there only two phenotypes among the offspring?

Mendelian Genetics: What we learned? When are there only two phenotypes among the offspring?

A cross yields a corn cob with 705 blue and 224 yellow kernels (blue>yellow).

A cross yields a corn cob with 705 blue and 224 yellow kernels (blue>yellow). What is the genotype of the parents? (Or, what cross yielded these results? )

For a single-trait cross with dominant-recessive relationship between A and a:

For a single-trait cross with dominant-recessive relationship between A and a:

Consider monohybrid cross: Trait blue yellow total Expected Observed

Consider monohybrid cross: Trait blue yellow total Expected Observed

Consider test cross: Trait blue yellow total Expected Observed

Consider test cross: Trait blue yellow total Expected Observed

How do we evaluate difference between expected and observed? Trait Expected Observed blue 3

How do we evaluate difference between expected and observed? Trait Expected Observed blue 3 1500 yellow 1 500 total 4 2000

How do we evaluate difference between expected and observed? Trait Expected Observed blue 1

How do we evaluate difference between expected and observed? Trait Expected Observed blue 1 465 705 yellow 1 464 224 total 2 929

Expectations and observations may differ for two reasons: 1 st. Happy accidents.

Expectations and observations may differ for two reasons: 1 st. Happy accidents.

Expectations and observations may differ for two reasons: 1 st. Happy accidents.

Expectations and observations may differ for two reasons: 1 st. Happy accidents.

Expectations and observations may differ for two reasons: 2 nd The model is wrong.

Expectations and observations may differ for two reasons: 2 nd The model is wrong.

Expectations and observations may differ for two reasons: 2 nd The model is wrong.

Expectations and observations may differ for two reasons: 2 nd The model is wrong.

Calculate Chi-square:

Calculate Chi-square:

Calculate Chi-square: Example: Trait Blue yellow Total Expected 697 232 929 Observed 705 224

Calculate Chi-square: Example: Trait Blue yellow Total Expected 697 232 929 Observed 705 224 929

Calculate Chi-square: Example: Trait Blue yellow Total Expected 465 464 929 Observed 705 224

Calculate Chi-square: Example: Trait Blue yellow Total Expected 465 464 929 Observed 705 224 929

Calculate Chi-square: Example: Trait Red Pink White Expected Observed 225 400 215

Calculate Chi-square: Example: Trait Red Pink White Expected Observed 225 400 215

Calculate Chi-square: Example: Trait Red Pink White Expected Observed 225 400 215

Calculate Chi-square: Example: Trait Red Pink White Expected Observed 225 400 215

Calculate df = (n-1):

Calculate df = (n-1):

Determine probability, P, for obtaining such a chi -square by chance alone:

Determine probability, P, for obtaining such a chi -square by chance alone:

Determine probability, P, for obtaining such a chi -square by chance alone: When difference

Determine probability, P, for obtaining such a chi -square by chance alone: When difference is small: When difference is large:

Determine probability, P, for obtaining such a chi -square by chance alone: Example: chi-square:

Determine probability, P, for obtaining such a chi -square by chance alone: Example: chi-square: 0. 366 (df = 1)

Determine probability, P, for obtaining such a chi -square by chance alone: Example: chi-square:

Determine probability, P, for obtaining such a chi -square by chance alone: Example: chi-square: 248 (df = 1)

Determine probability, P, for obtaining such a chi -square by chance alone: Example: chi-square:

Determine probability, P, for obtaining such a chi -square by chance alone: Example: chi-square: 2. 14 (df = 2)