PowerPoint Slides for Chapter 16: Variation and Population Genetics Section 16.1: What causes individual variation? Integrating Concepts in Biology Copyright © 2015 by AM Campbell, LJ Heyer, CJ Paradise. All rights reserved. by A. Malcolm Campbell, Laurie J. Heyer, & Christopher Paradise

Biology Learning Objectives Evaluate the processes by which variation is generated in organisms and how this affects information at the population level and natural selection. Differentiate between independent assortment and crossing over. Copyright © 2015 by AM Campbell, LJ Heyer, CJ Paradise. All rights reserved. Section 16.1: What causes individual variation?

Figure 16.1 Relationship between height of parents and offspring Redrawn with data from Galton, 1889.

Figure 16.1 Relationship between height of parents and offspring slope of one best-fit line Size of circles proportional to the number of comparisons Redrawn with data from Galton, 1889.

BME 16.1: How does linear regression work? Bio-Math Exploration Integrating Questions 1.Add the best-fit line to the chart in galton.xlsx by right-clicking on a data point, selecting Add Trendline, and choosing type “linear.” Select the option “Display equation on chart.” Your regression equation should match the one given in Figure 16.1.galton.xlsx Copyright © 2015 by AM Campbell, LJ Heyer, CJ Paradise. All rights reserved.

BME 16.1: How does linear regression work? y = x Copyright © 2015 by AM Campbell, LJ Heyer, CJ Paradise. All rights reserved.

BME 16.1: How does linear regression work? Bio-Math Exploration Integrating Questions 1.Add the best-fit line to the chart in galton.xlsx by right-clicking on a data point, selecting Add Trendline, and choosing type “linear.” Select the option “Display equation on chart.” Your regression equation should match the one given in Figure 16.1.galton.xlsx 2.Describe what it means for a line to be the best-fit line. What makes the best-fit line better than all of the other lines that you could possibly draw through the data points? What two features of a line do you need to know to graph the line? Copyright © 2015 by AM Campbell, LJ Heyer, CJ Paradise. All rights reserved.

BME 16.1: How does linear regression work? Bio-Math Exploration Integrating Questions 1.Add the best-fit line to the chart in galton.xlsx by right-clicking on a data point, selecting Add Trendline, and choosing type “linear.” Select the option “Display equation on chart.” Your regression equation should match the one given in Figure 16.1.galton.xlsx 2.Describe what it means for a line to be the best-fit line. What makes the best-fit line better than all of the other lines that you could possibly draw through the data points? What two features of a line do you need to know to graph the line? The idea of linear regression is to find the line closest to all the data points collectively. The standard way to measure how far a line is from data points is to square each residual, and add up all the squared residuals. Copyright © 2015 by AM Campbell, LJ Heyer, CJ Paradise. All rights reserved.

BME 16.1: How does linear regression work? The residual is found by subtracting the predicted height (the y-value obtained by plugging the x-value of the data point into the equation of the line) from the actual height (the y-value of the data point) – 61.7 in first column = 8.8 The squared residuals for the orange line in galton.xlsx are in column D. (8.8 2 = 77.44) residual sum of squares = MidparentChildPredictionResidual Copyright © 2015 by AM Campbell, LJ Heyer, CJ Paradise. All rights reserved.

BME 16.1: How does linear regression work? The sum of all squared residuals (adding up column D values) = residual sum of squares = MidparentChildPredictionResidual Copyright © 2015 by AM Campbell, LJ Heyer, CJ Paradise. All rights reserved.

BME 16.1: How does linear regression work? Bio-Math Exploration Integrating Question 3.Because a line is completely determined by its slope (rise over run) and its y-intercept, you must change one or both of these two features to change the line. In galton.xlsx, experiment with changing the slope and y-intercept of the orange line to find the closest line to the data points when the slope is 0.9, 0.7 and 0.5. Give your answers for the y-intercept to the nearest integer. Are any of the resulting residual sums of squares smaller than when the slope and y-intercept are taken from the trendline? Copyright © 2015 by AM Campbell, LJ Heyer, CJ Paradise. All rights reserved.

BME 16.1: How does linear regression work? slope:0.9 y-intercept:7 residual sum of squares = Copyright © 2015 by AM Campbell, LJ Heyer, CJ Paradise. All rights reserved.

BME 16.1: How does linear regression work? slope:0.7 y-intercept:20 residual sum of squares = Copyright © 2015 by AM Campbell, LJ Heyer, CJ Paradise. All rights reserved.

BME 16.1: How does linear regression work? slope:0.5 y-intercept:34 residual sum of squares = Copyright © 2015 by AM Campbell, LJ Heyer, CJ Paradise. All rights reserved.

BME 16.1: How does linear regression work? y = x slope: y-intercept: residual sum of squares = Copyright © 2015 by AM Campbell, LJ Heyer, CJ Paradise. All rights reserved.

Figure 16.2 Mean blood pressures for rats in two colonies Data from Bianchi et al., 1994.

Figure 16.3 Portions of the β and α adducin subunit DNA sequences and corresponding amino acid sequence position along the protein letters correspond to different amino acids From Bianchi et al., 1994, Figure 1, copyright (1994) National Academy of Sciences, U.S.A.

Figure 16.3 mutations Portions of the β and α adducin subunit DNA sequences and corresponding amino acid sequence From Bianchi et al., 1994, Figure 1, copyright (1994) National Academy of Sciences, U.S.A.

Figure 16.3 Portions of the β and α adducin subunit DNA sequences and corresponding amino acid sequence top letter corresponds to allele and protein associated with high BP From Bianchi et al., 1994, Figure 1, copyright (1994) National Academy of Sciences, U.S.A.

Figure 16.2 Mean blood pressures for rats in two colonies α Y /α Y ; β R /β R α F /α F ; β _ /β _ Data from Bianchi et al., 1994.

Figure 16.4 Systolic BPs of the 3 combinations of 2 versions of the β adducin gene in rats from the low BP colony Data from Bianchi et al., 1994.

Figure 16.4 Q and R refer to the amino acid at position 529 of β subunit Systolic BPs of the 3 combinations of 2 versions of the β adducin gene in rats from the low BP colony Data from Bianchi et al., 1994.

Figure 16.5 BP of 9 combinations of two versions of the α and β adducin genes in rats after two generations of breeding low and high blood pressure rats together. From Bianchi et al., 1994, Figure 3, copyright (1994) National Academy of Sciences, U.S.A.

Figure 16.5 BP of 9 combinations of two versions of the α and β adducin genes in rats after two generations of breeding low and high blood pressure rats together. BP of high BP parental strain BP of low BP parental strain From Bianchi et al., 1994, Figure 3, copyright (1994) National Academy of Sciences, U.S.A.

Variation of combinations of the 2 adducin genes, when the genes are located on different chromosomes shows independent assortment Table 16.1 Copyright © 2015 by AM Campbell, LJ Heyer, CJ Paradise. All rights reserved.

Variation of combinations of the 2 adducin genes, when the genes are located on different chromosomes shows independent assortment Table 16.1 Copyright © 2015 by AM Campbell, LJ Heyer, CJ Paradise. All rights reserved.

Variation of combinations of the two adducin genes when the genes are on the same chromosome. assumes no crossing over Table 16.1 Copyright © 2015 by AM Campbell, LJ Heyer, CJ Paradise. All rights reserved.

Variation of combinations of the two adducin genes when the genes are on the same chromosome. assumes no crossing over Table 16.1 Copyright © 2015 by AM Campbell, LJ Heyer, CJ Paradise. All rights reserved.

Variation of combinations of the two adducin genes All parents are heterozygous Table 16.1 Copyright © 2015 by AM Campbell, LJ Heyer, CJ Paradise. All rights reserved.

Figure 16.6 Zinc contamination and pH in soils surrounding a smelting operation in Pennsylvania Data from Caiazza & Quinn, 1980, Table 1.

Figure 16.6 Copper contamination and pH in soils surrounding a smelting operation in Pennsylvania Data from Caiazza & Quinn, 1980, Table 1.

Figure 16.6 Zinc and copper contamination and pH in soils surrounding a smelting operation in Pennsylvania distance from smelter that plants were collected Data from Caiazza & Quinn, 1980, Table 1.

Figure 16.7 Stomata and hair densities of honeysuckle collected at two times and grown in controlled conditions What is the effect of distance to smelter on stomata density? Data from Caiazza & Quinn, 1980, Table 2 and 3.

Figure 16.7 Stomata and hair densities of honeysuckle collected at two times and grown in controlled conditions What is the effect of distance to smelter on hair density? Data from Caiazza & Quinn, 1980, Table 2 and 3.

Figure 16.7 Stomata and hair densities of sandwort collected at two times and grown in controlled conditions What is the effect of distance to smelter on stomata density? Data from Caiazza & Quinn, 1980, Table 2 and 3.

Figure 16.7 Stomata and hair densities of sandwort collected at two times and grown in controlled conditions What is the effect of distance to smelter on hair density? Data from Caiazza & Quinn, 1980, Table 2 and 3.

Figure 16.7 Stomata and hair densities of honeysuckle collected at two times and grown in controlled conditions What do the results of the common garden experiment show? Data from Caiazza & Quinn, 1980, Table 2 and 3.

Figure 16.7 Stomata and hair densities of sandwort collected at two times and grown in controlled conditions What do the results of the common garden experiment show? Data from Caiazza & Quinn, 1980, Table 2 and 3.

Figure 16.8 Responses of the acorn barnacle (Chthamalus anisopoma) to the snail predator (Acanthina). bent and cone shell shapes Spine used to pry barnacles From Lively, 1986, Figure 1 (a); Table 1 (b); Figure 3 (c), © 1986 Wiley. Reproduced with permission of Blackwell Publishing Ltd.

Figure 16.8 Responses of the acorn barnacle (Chthamalus anisopoma) to the snail predator (Acanthina). results of predator exclusion experiment From Lively, 1986, Figure 1 (a); Table 1 (b); Figure 3 (c), © 1986 Wiley. Reproduced with permission of Blackwell Publishing Ltd.

Figure 16.8 Responses of the acorn barnacle (Chthamalus anisopoma) to the snail predator (Acanthina). Survival of barnacles without predator From Lively, 1986, Figure 1 (a); Table 1 (b); Figure 3 (c), © 1986 Wiley. Reproduced with permission of Blackwell Publishing Ltd.

Figure 16.8 Responses of the acorn barnacle (Chthamalus anisopoma) to the snail predator (Acanthina). Survival of barnacles with predator From Lively, 1986, Figure 1 (a); Table 1 (b); Figure 3 (c), © 1986 Wiley. Reproduced with permission of Blackwell Publishing Ltd.

Figure 16.8 Responses of the acorn barnacle (Chthamalus anisopoma) to the snail predator (Acanthina). Survival of barnacles without predator with predator From Lively, 1986, Figure 1 (a); Table 1 (b); Figure 3 (c), © 1986 Wiley. Reproduced with permission of Blackwell Publishing Ltd.

Figure 16.8 Responses of the acorn barnacle (Chthamalus anisopoma) to the snail predator (Acanthina). From Lively, 1986, Figure 1 (a); Table 1 (b); Figure 3 (c), © 1986 Wiley. Reproduced with permission of Blackwell Publishing Ltd.