Number 59 symbolism,license plate light passat,portable power pack for christmas lights qvc,birth chart juno - Tips For You

15.07.2014

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In plant breeding and genetics research, plant breeders establish a hypothesis to explain how they think a particular trait is inherited, such as if it is due to one gene with complete dominance, an interaction of more than one gene, or quantitative inheritance, with many genes contributing, etc. This lesson is written for undergraduate and graduate students studying plant breeding, as well as agriculture professionals unfamiliar with the use of the chi-square analysis.
Click on the bold blue highlighted phrases to be linked to another lesson or animation in a new window.
Click on small images, as indicated, to play short video clips which demonstrate how to do calculations. Fig 1: On the left is a tomato line susceptible to bacterial spot disease, but has other desireable qualities. We will follow a plant breeding example to demonstrate how to calculate a chi-square analysis, a statistical test researchers use to determine if their experimental data supports (or doesn’t support) the hypothesis being tested. The resulting F1 offspring from this OH88119 x 6.8068 cross all showed resistance to bacterial spot.
There are two types of chi-square analysis methods: goodness of fit (such as our tomato example) and chi-square test for independence (which determines if there is an association between variables).
There is also a chi-square test for independence which compares two sets of categories to determine whether the two groups are distributed differently among the categories. Following along with our tomato bacterial spot case, let’s walk through the steps in calculating a chi-square analysis. The second chi-square step is where the researchers go out to the field or greenhouse to count the number of F2 tomato plants which were resistant and those which were susceptible. Next, in this case study, we will calculate how much the observed phenotypes differed from those that were expected. Click image below to watch a tutorial video clip further explaining where these numbers came from if it is not clear to you.
We are now ready for the final step, interpreting the results of our chi-square calculation.
Statisticians calculate certain possibilities of occurrence (P values) for a X2 value depending on degrees of freedom. The calculated value of X2 from our results can be compared to the values in the table aligned with the specific degrees of freedom we have. Therefore in our tomato breeding example, we failed to reject our hypothesis that resistance to bacterial spot in this set of crosses is due to a single dominantly inherited gene (Rx-4). Click on image below to launch a video tutorial showing how to use a chi-square distribution chart, using this tomato breeding example. Next we will go through a genotyping example, or you can click here to skip to a discussion about computer programs available when you have extensive data, as well as the strengths and weaknesses of the chi-square test.
Following along with our tomato bacterial spot case, let’s next walk through the steps in calculating a chi-square analysis for a genotyping study. Click on the video clip image below to watch a tutorial further explaining how to calculate the expected frequencies and numbers of plants expected in each F2 genotype class.
Each F2 individual plant is then scored with an A, B, or H code, as outlined in Table 4 below.
We then total up our scoring data and enter it into a chi-square table, much like we did in the phenotyping example.The observed column is the data collected from the gel (Table 5).
Next, just as in the phenotype example, in this genotyping example we will now calculate how much the observed DNA marker patterns differed from that which were expected.
Table 6: Calculating deviations between what we expected for genotypic data and what was observed. Once again we continue to expand our chi-square calculation table and determine a final chi-square value for our experimental data. The next page of this lesson has a videoclip tutorial showing in more detail how these numbers are calculated, as well as how to interpret the final chi-square value.


Once again, we are ready for the final step, interpreting the results of our chi-square calculation. Click here to watch a tutorial video clip further explaining how to calculate the  X2 value and read the probability distribution chart. Chi-square also assumes random sampling so tomato plants being measured must be selected randomly from the total population. In this lesson you have learned how to calculate a statistical analysis known as chi-square to determine if the differences seen between expected and observed numbers of resistant and susceptible tomato plants was significant. Contact the seller- opens in a new window or tab and request a shipping method to your location.
Next the breeder sets up crosses and observes the resulting progeny to test that inheritance hypothesis. Next the breeder sets up some crosses and observes the resulting progeny to test that inheritance hypothesis.
On the right, is a line derived from wild germplasm that is resistant to bacterial spot, but lacks other cultivated tomato traits. These were then allowed to self-pollinate and a hypothesis was devised to describe the mode of inheritance for the Rx-4 gene. In our example this would be the number of tomato plants expected to be resistant to bacterial spot and those which really were resistant in our experiment, as well as those expected to be susceptible compared with those which we observed to be susceptible in the field or greenhouse. As we will see with this tomato bacterial spot resistance example, the chi-square test for goodness of fit compares the expected and observed numbers of resistant and susceptible plants to determine how well the researcher’s predictions fit the data. In the first step, we need to figure out what phenotypic classes are expected and the number of individual plants in each class.
Rather than scoring plants or categorizing them based upon their phenotype (resistant or susceptible to bacterial spot), the plants are scored based upon patterns seen for a DNA marker. Put together a table such as the following, where the expected numbers are subtracted from the observed for each phenotype class. Notice that for each class, we square the deviations and then divide that number by the expected number in that particular class. Degrees of freedom is simply the number of classes that can vary independently minus one, (n-1). This will tell us the probability that the deviations (between what we expected to see and what we actually saw) are due to chance alone and our hypothesis or model can be supported.
First read down column 1 to find the 1 degree of freedom row and then go to the right to where 1.2335 would occur. We can assume that the deviations we saw between what we expected and actually observed in terms of the number of resistant and susceptible plants could be due to mere chance. In the first step, we will focus on one DNA maker and hypothesize what banding patterns are expected and the number of individual plants for each pattern in the same F2 population we were working with for the phenotyping.
7) shows the results of screening F2 individuals from our same OH88119 x 6.8068 cross with the CosOH57 DNA marker.
One of the limitations is that all participants measured must be independent, meaning that an individual cannot fit in more than one category.
For example if you are just counting how many tomato plants show resistance to bacterial spot versus how many show susceptiblity, than a chi-square is appropriate. Researchers also need to remember that the chi-square test does not give much information about the strength of the relationship.
There might be times where the data set is much more complex and you’ll want to use a software program to do the calculation, instead of by hand.
We have talked about how to accurately interpret results and the impact it has on our experimental hypothesis. If you reside in an EU member state besides UK, import VAT on this purchase is not recoverable.
However, when the data is collected, oftentimes the plant breeder discovers the number of plants observed in each class is not exactly what was expected from the hypothesis. However, when the data is collected, oftentimes the breeder discovers the number of plants observed in each class is not exactly what was expected from the hypothesis.
A tomato line, 6.8068, was chosen as one parent for use in a crossing scheme because it is resistant to bacterial spot and contains this gene. After calculating a chi-square value we can obtain a probability that any differences seen between expected and observed numbers are simply due to chance. Based upon our observations in the F1 where all showed resistance, we decided to test the hypothesis that resistance is controlled by one dominant gene.
To follow an example of genotyping, click here to skip ahead in this lesson to that section.


Strictly defined, chi-square is the sum (from each phenotypic class) of the squares of observed values minus expected values, divided by expected values. In this case the degrees of freedom = 1 because we have 2 phenotype classes: resistant and susceptible. Lane 1 is the ladder, lanes 2 and 3 are DNA patterns from the parents and lanes 4 - 33 are DNA samples from the F2 individuals. Also when calculating the number of expected individuals for each class, there should be greater than 5 for each class for the most appropriate use of chi-square. For example one cannot say that a tomato plant height is correlated with its leaf size simply by running a chi-square statistic. For example one could see if there is an association between the size of a tomato fruit and the number of fruit produced on a single plant. We looked at both a phenotyping and genotyping example, as well as some computer-aided tools.
The question then is how do plant breeders determine if the data is close enough to what they expected to determine the hypothesis is supported or not? The question then is how do plant breeders determine if the data supports their hypothesis or not?
This line also closely resembles cultivated tomato, therefore not as much backcrossing would be needed to remove unwanted traits.
The observed results in the F2 generation though, deviated from what the researchers expected in that there were more susceptible F2 plants than they anticipated. Or alternatively, if the differences are found to be significant, the plant breeder may revisit the genetic hypothesis. Again, the null hypothesis is what we reject or fail to reject based upon our calculations. Although this sounds extremely intimidating to many people the first time they see that, it often seems less so after we plug our values into the formula below. The deviation could occur because our marker, CosOH57 is linked with bacterial spot resistance. Another consideration one must make is that the chi-square statistic is sensitive to sample size. Another strength is that chi-square makes no assumptions about the distribution of the population. Finally, the strengths and weaknesses of using the chi-square test in plant breeding applications were discussed briefly. Following a tomato disease resistance example in this lesson, you will learn a simple statistical test that breeders can use to conclude if the experimental data supports their hypothesis of single gene, completely dominant inheritance. Following a tomato disease resistance example in this lesson, you will learn a simple statistical test that breeders can use to conclude if the experimental data supports their hypothesis. OH88119 was chosen as the other parent because even though susceptible to bacterial spot, it was a suitable parent for commercial hybrids due to other desireable traits in its genome such as fruit size, fruit color, time to maturity, etc. The question remains, as to how far can observed results deviate from what was expected before they should be considered significant? Further testing in other crosses and populations will be used to provide additional evidence that our hypothesis accurately explains the mode of inheritance for Rx-4.
Therefore, we can characterize each of the the F1 as heterozygous, because those plants have a banding pattern like each of the parents. Most recommend that chi-square not be used if the sample size is less than 50, or in this example, 50 F2 tomato plants. Other statistics assume certain characteristics about the distribution of the population such as normality. In other words, is the difference in the number of susceptible plants seen and those expected simply due to chance or is something else going on such as a wrong genetic hypothesis, high influence of the environment, etc.? In order to use chi-square an individual plant (in this example) must fit in one and only one category (ie.
If you have a 2x2 table with fewer than 50 cases many recommend using Fisher’s exact test. However, the deviation from the expected results could also be due to other genetic and evolutionary factors including selection which have led to a higher frequency of one banding pattern in the tomato population.



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