Information

How to interpret McDonald-Kreitman test results?

How to interpret McDonald-Kreitman test results?


We are searching data for your request:

Forums and discussions:
Manuals and reference books:
Data from registers:
Wait the end of the search in all databases.
Upon completion, a link will appear to access the found materials.

It is easy to get the numbers right and calculate neutrality index. It is easy to memorize "equals", "greater", "lesser", etc. At least on the exams, when certain level of simplicity is assumed. But instead of memorizing I'd prefer to understand. So far I struggle to get past the numbers. Could anyone, please, explain reasoning behind the them? Why certain values means the trait is advantageous or not? An easy to understand example would be helpful as well. Thank you.


A helpful way to phrase it for me is that "If there's a disproportionate fraction of non-synonymous mutations between species relative to what exists within species, it is because non-synonymous mutations are being selected for in the one species but not in the other, thus there's positive selection."

By the way I'm not an expert in this so take the explanation with a grain of salt.


(intraspecies non-synonymous polymorphisms)/(intraspecies synonymous polymorphisms) should equal the (mutation rate of non-synonymous mutations × number of non-synonymous sites)/(mutation rate of synonymous mutations × number of synonymous sites). Note that the mutation rate of synonymous mutations is just µ. Let F be the proportion of the synonymous mutations that are not deleterious so that the mutation rate of non-synonymous sites is Fµ. Let k be the ratio of synonymous sites over non-synonymous sites. Then the above fraction is equal to F/k. The same logic can be applied to find that the fraction of (interspecies non-syn polymorphisms)/(interspecies syn polymorphisms)=F/k, assuming the probability of fixation is roughly the same for synonymous and non-synonymous sites (neutral theory's assumption).


Interpret the key results for Principal Components Analysis

Principal Component Analysis: Income, Education, Age, Residence, Employ, .

Key Results: Cumulative, Eigenvalue, Scree Plot

In these results, the first three principal components have eigenvalues greater than 1. These three components explain 84.1% of the variation in the data. The scree plot shows that the eigenvalues start to form a straight line after the third principal component. If 84.1% is an adequate amount of variation explained in the data, then you should use the first three principal components.


The importance of biological analysis

Biological analysis is a scientific approach that combines analytical tools and biological content in one place, so researchers can obtain a fundamentally deeper and broader understanding of biological relationships and processes known to be connected to experimental observations, and the translation of that understanding to actionable insights and concrete hypotheses.

Biological analysis can transform basic data analysis results into useful research outcomes, so researchers can leverage what they have discovered to make informed decisions, generate well-formed, testable hypotheses, design follow-up experiments, and provide compelling biological and mechanistic evidence for results (see Figure 1).

Biological analysis represents a very natural but extraordinarily powerful extension to improve traditional data analysis and interpretation approaches. It connects molecular information coming out of various experimental platforms to help researchers understand whether the genes from their experiment work together as molecular modules, assess their impact on higher level biological processes and phenotypes, and determine whether or not those collections of events also impact diseases.

For example, if the goal is to identify molecular mechanisms that link a genotype to a phenotype, biological analysis is the crucial approach that links gene expression changes in cancer cells to the observed cellular phenotype or related disease phenotype.

Biological analysis can rapidly identify relationships already known to be involved in experimental changes. These capabilities help researchers by providing a broader biological picture when they analyse experimental results. For example, by examining a gene of interest in the context of a pathway, it becomes easier to get a sense of what is happening in an experimental model.

What are the key players? What are the known interactions? What are the top pathways involved in the data set?

Asking these kinds of questions and relating experimental data back to the larger biological picture is a key part of biological analysis.

In addition to providing a more relatable, high level biological picture, biological analysis can identify key findings and novel discoveries from large amounts of data. For example, a basic microRNA dataset might return 13,000 potential miRNA targets.

Using biological analysis, a researcher could begin to narrow down and prioritise that list, using questions like: which of those are experimentally demonstrated and involved in particular pathways of greatest interest to me? From those, which mRNAs are expressed in a relevant tissue, and have inverse expression from their matched microRNA? Which are known biomarkers?

These advantages all demonstrate another key benefit of biological analysis, which is that it significantly decreases the time it takes to obtain a novel discovery. The integration of a wide variety of structured biological content in one place, in combination with analysis tools that let researchers effectively use that content to narrow in on a targeted set of experimental findings or explore outward from their findings to other biological relationships, saves an immense amount of time over manual, piecemeal or overly specific tools and approaches.

Biological analysis also speeds the process of creating a validated and testable hypotheses, either at the end of an experiment using insights gained from experimental results, or prior to beginning a new experiment. Generating a hypothesis that can be interrogated and vetted against published research provides added confidence that wet lab testing makes sense. With biological analysis tools, researchers can challenge their hypothesis and examine it in the context of additional layers of biological and chemical knowledge before investing in the physical experiment.

By informing decisions throughout the experimental cycle, biological analysis decreases the time it takes to get from instrument to insight, and improves the ability to complete that process without dead ends, mistaken directions and other research obstacles (see Figure 2).


Sample Reports

MyHeritage reports begin with a summary page that gives an overview of your ethnicity and the number of DNA matches. From here, you can click to explore your ethnicity breakdown or read the list of possible DNA matches.

Your ethnicity estimate is shown as a color-coded map and as a list of percentages. You can choose to watch a guided, animated intro to your heritage by clicking ‘Play Intro’ or just explore it on your own by zooming in for a more detailed breakdown.

MyHeritage displays DNA matches as a list in descending order of closeness. So, the highest matches are at the top by default, although you can click on the tabs to rearrange the order. When you click on a match, the ‘Review DNA’ button gives you more information about that person and the ‘View Tree’ button shows you their family tree, if they have one on the site.

Ancestry’s DNA reports are fairly similar. You’ll first be shown an overview page, which gives a summary of your ethnicity estimate and DNA matches as well as DNA circles if you are part of any.

From here, you can click to see your ethnicity estimate, which is shown as an interactive color-coded map, like MyHeritage, and a list of percentages. When you click on a region, you can zoom in and see a more specific geographic location for your ancestors and learn more about that region.

Ancestry groups your DNA matches into expected family relationships. So, if your children have taken the DNA test, they’ll be shown together. You can click on a DNA match to learn more about their ethnicity and your shared DNA and a way to get in contact with them. Ancestry also compares your saved family tree with your match’s tree, if you both have one, and indicates your common ancestor.

Ancestry’s DNA reports include a few more interesting fields, so take the time to explore them.


How Do I Interpret My Ethnicity Results?

One of the key results that you get back from AncestryDNA is an estimate of your ethnicity. The keyword here is obviously “estimate“, where your DNA is compared to genetic profiles that have been gathered from a reference panel.

Credit: AncestryDNA

The people that make up the reference panel have been chosen as they have deep-rooted ancestry within a particular region.

In all, Ancestry DNA covers 150 regions all around the world, (updated from 26 regions).

Your DNA is compared to each of these different regions and so the results, given as percentages, that you get back from the testing can tell you where your ancestors likely came from.

Interpret your ethnicity results?

Now that you understand about how your ethnicity is calculated I will explain how you can interpret the results of this testing.

The results of your ethnicity testing is displayed to you in the form of a color coded pie chart. Here the chart is divided into wedges which are given percentages as to the likelihood of where your ancestors originated.

So, it may show you that you have a score of 99% ethnicity originating from Europe. This can be broken down even further to include areas such as Great Britain, Ireland, or even Scandinavia.

Interactive map showing results!

To help you interpret these results there is also a map showing you these locations which are color coordinated based on the results of the pie chart. This map is also interactive!

So, you can click around the map, zoom in and gather even more details. Zooming in will then show you areas of varying degrees of shading. The darker the shading then the higher the chance that your ancestor originated from that area.

This is a great tool to help you show you where next that you can look for further information.

As your results are presented in a simple layout there will be some information that is omitted. The reason for this is just to make the presentation of your results as clear as possible.

To gain further insights into your ancestry you just need to click onto each of the separate regions that you have a result for to see further information.

See all 150+ regions!

If you click on the See all 150+ regions link you will then see all of the ethnicity regions. And you will also see them marked on the map.

The numbers that appear to the right of each of these regions refer to Genetic Communities. These numbers will be the same for everybody and are NOT specific to you.

They are merely a link to groups that can help you further with your research.

From this section you will see the same color-coded dots for the regions that you have been identified with. Any region that you are not identified with will be colored with a grey dot.


Definitions:

Quantitative analysis
Analysis where a microbial count is determined

Qualitative analysis
Analysis where the presense or absence of an organism is determined

CFUs
Colony Forming Units

COA
Certificate of Analysis

This portion is a representative sample and will provide an accurate and comprehensive result.

When reporting a result where no organisms are detected, the reporting standard dictates that you cannot report zero, but that you need to report <1.

Since samples are diluted for testing purposes, this dilution also needs to be taken into account when reporting the result. For example, where no colonies are detected in a dilution of 1:10, the result would equal <10.

This result is the lowest result reportable where no organisms are detected.

Refer to the extract from ISO 7218, ‘Microbiology of Food and Animal Feeding Stuffs — General Rules for Microbiological Examinations’:

‘9.3.5.3.2 If the two dishes at the level of the test sample (liquid products) or of the initial suspension (other products) do not contain any colonies, express the result as follows:
less than 1 microorganism per millilitre (liquid products)
less than 1/d microorganisms per gram (other products)
(Where d is the dilution factor of the initial suspension)’

These results indicate that the microbial count reached the upper countable range of the test method. The counts are TNTC (Too Numerous to Count) and therefore are reported as more than (>) the upper countable range of the test.

Where counts exceed the recommended countable range, but are able to be counted, the result will be reported with an ‘E’ to indicate that the count is outside of the recommended countable range.

The reporting format of results is dictated by the ISO Standard 7218, ‘Microbiology of Food and Animal Feeding Stuffs — General Rules for Microbiological Examinations’.

This standard dictates the following:
In liquid samples, the lowest limit of detection for absence of growth is <1.

As a dilution of solid foodstuffs is made in order to test the sample, the lowest limit of detection for absence of growth is <10.

Since a representative portion of a sample is analysed, the results indicate that no growth was obtained in the sample portion tested.
A result of ‘no growth’ or ‘zero’ is therefore not scientifically accurate because the sample dilution is not reported, or taken into account.

The ‘E’ = Estimated
Most quantitative reference methods have a recommended range of reportable results. When counts outside of this range are reported, it is indicated using the symbol ‘E’. This count represents the actual colonies counted where the final count is outside of the recommended reporting range.

Example:
The countable range for TVC (Total Viable Count) is 30-300 CFUs/g per dilution. The results on the COA will read as follows for the following counts:
Average CFU count = 10 – Results = 10 E
Average CFU count = 350 – Results = 350 E
(For ease of interpretation, no dilution factor has been included in the example above)

Please note that we report all of our results as per international standard, ISO 7218, ‘Microbiology of Food and Animal Feeding Stuffs – General Rules for Microbiological Examinations’.

Please refer to point 9.3.5.3.1 and 9.3.5.3.2, wherein the following is stated with regards to calculation and reporting of results (see below):

‘9.3.5 Expression of results
9.3.5.3 Estimated counts
9.3.5.3.1 If the two dishes, at the level of the test sample (liquid products) or of the initial suspension (other products), contain less than 15 colonies, calculate the arithmetical mean y of the colonies counted on two dishes.
Express the result as follows:
for liquid products: estimated number of microorganisms per millilitre NE = y
for the other products: estimated number of microorganisms per gram NE = y/d
(where d is the dilution factor of the initial suspension).’

9.3.5.3.2 If the two dishes at the level of the test sample (liquid products) or of the initial suspension (other products) do not contain any colonies, express the result as follows:
less than 1 microorganism per millilitre (liquid products)
less than 1/d microorganisms per gram (other products)
(where d is the dilution factor of the initial suspension).’

Preliminary results indicate analyses which are complete. They may also show results which have not been completed, and are pending.

Preliminary results can be provided upon request of the client. They may be used by the client to establish further testing requirements. They may be used by the laboratory to convey results to the client before the final report can be issued.

When preliminary results are provided in the form of a Certificate of Analysis, they will not be signed. A signature indicates that the results and request have been verified by a technical signatory, and that the results are complete.

Certain reference methods stipulate that samples with presumptive positive results require further confirmation. Confirmation is therefore a obligation to complete the method, and report the final result as ‘Positive’ or ‘Detected’.

Results which have no presumptive colonies are complete according to the method. No further confirmation can be performed, and no additional result or charge for confirmation is therefore needed for these samples.

The results, and the additional cost associated with performing the confirmations are therefore separated.

NR indicates a non-reportable result, and will be indicated in the results column of the COA. Results may not be reportable for a number of reasons.

NR is reported when results obtained are not microbiologically sound, for example, the counts are not within laboratory acceptable limits or the food matrix affected the test media, resulting in colonies enumerated not portraying typical bacterial characteristics, or results.

In the case of NR, the cost for that particular analysis is removed and it is recommended to re-submit that particular sample for analysis so as to obtain conclusive results.


Learn To Read Your Wellness Blood Test Results!

1. The urea nitrogen BUN test is used to evaluate kidney function. The test measures levels of urea or nitrogen in your urine.

Understanding lab test results…

  • In general, normal BUN levels range from 6-20 (mg/dL) in adults.
  • Higher BUN levels can indicate dehydration, kidney failure or disease, high protein levels, heart failure, gastrointestinal bleeding, or obstruction in the urinary tract.
  • Lower BUN levels can indicate liver failure, malnutrition or severe lack of protein in your diet.

So, if you find yourself with a number over 21, check with your doctor about the possibility of the first set of diseases. And if it’s under 6, check about the second set of diseases.

2. The total protein test measures how much of the proteins albumin and globulin are in your body.

Understanding your blood test results when it comes to protein…

  • The normal range for total protein is between 6 and 8.3 (g/dL).
  • High protein could mean inflammation or infections, such as viral hepatitis B or C, or HIV. It can also be an indicator of bone marrow disorders.
  • Low protein could indicate bleeding, liver disorder, kidney disorder, malnutrition, or inflammatory conditions.

Bet you didn’t even think you could have higher or lower levels of protein. Now, you know what it means when you don’t get enough or have too much.

3. The sodium serum test measures the amount of sodium in your blood.

    levels is 136-145 (mmol/L) .
  • High sodium could mean that you are dehydrated, not drinking enough water and eating too much sodium in your diet. Low sodium could be a symptom of under active adrenal glands, kidney failure, heart failure or thyroid gland.
  • Low sodium can also be caused by sweating, burns, diarrhea or poor nutrition.

There’s so much hidden sodium in food. This is really easy to overdo!

4. Cholesterol total test measures the amount of the waxy, fat-like Cholesterol in your blood. The cholesterol total test will look at both LDL (bad) and HDL (good) cholesterol.

  • The normal range for overall cholesterol is less than 200 (mg/dL).
  • High total cholesterol is 240 (mg/dL) and above and is considered risky.
  • And borderline high risk is in a range of (200-239 mg/dL)

Not all cholesterol is bad for you. Make sure you get enough of the good kind (HDL).

5. The calcium test measures the amount of calcium in your body that is not stored in your bones.

  • The normal range for calcium levels is 8.8-10.4 (mg/dL).
  • High calcium might be caused by long term bed rest after a broken bone, hyperparathyroidism , cancer, Paget’s disease, and tuberculosis.
  • Low calcium might occur from a low level of the blood protein albumin, hypoparathyroidism , high levels of phosphate in the blood, rickets, and malnutrition from celiac disease, pancreatitis, and alcoholism.

Calcium isn’t just about having strong bones, although that IS an important part!

6. A Urinalysis complete test detects abnormalities in the urine. Problems with your lungs, urinary tract, skin, kidneys and bladder can all be detected by testing your urine.

  • High protein, presence of crystals, infectious bacteria or yeast, epithelial cells, sugar, blood, pH levels or acidity and abnormalities in red or white blood cells, are all indications of disease that can be detected through urinalysis. basically is the absence of the above…there should be no protein, yeast, bacteria, ketones or glucose and very few red or white blood cells and crystals. The normal pH for your urine is 6.

7. The thyroid panel indicates the health of your thyroid gland. It tests the level of TSH in your blood and tells you whether your thyroid is overactive or under active.

  • A high TSH level, above (2.0 mIU/L), indicates an under active thyroid gland and causes health problems like weight gain, brittle hair and nails, joint pain, infertility, depression and heart disease.
  • A low TSH level indicates an overactive thyroid gland that produces too much thyroid hormone.
  • You might experience weight loss, high levels of anxiety or tremors.

If your levels are too high or too low means the difference in being hypothyroid and hyperthyroid. Treatments vary for both despite each being a thyroid disease.

8. The complete blood count, or CBC test, looks at the red blood cells, white blood cells and platelets.

A normal CBC consists of white, red, platelet, hematocrit and hemoglobin counts.

And the blood test results…

  • Normal for red blood cell count: 3.90-5.72 trillion cells/L
  • Normal for white blood cell count: 3.5-10.5 billion cells/L
  • Normal for platelet count: 150-450 billion/L
  • Normal for hemoglobin: 12.0-17.5 grams/dL
  • Normal for hematocrit: 34.9-50.0 percent
  • If your red blood count, including hemoglobin and hematocrit, is low, you are likely anemic.
  • If the same red blood count is high, you could have heart disease. Causes of high red blood count are smoking, kidney disease, heart disease, alcoholism and liver disease.
  • A high white blood cell count can indicate inflammation, infection or immune disorder.
  • A low white blood cell count can be caused by autoimmune disorders, cancer, or bone marrow problems.

The CBC is an extremely useful test that provides your doctor a lot of information regarding your health.

9. The vitamin D 25-hydroxy test measures how much vitamin D is in your body.

  • The normal range for vitamin D is between 20 and 40 (ng/mL).
  • High vitamin D may be due to too much vitamin D and a condition called hypervitaminosis D which can lead to symptoms of kidney damage.
  • Low vitamin D can happen with liver and kidney disease, lack of vitamin D in the diet, lack of exposure to sunlight, poor food absorption and use of certain medicines.

Most people suffer from a lack of Vitamin D, which is a shame because you can get it from simply going outside more. Order a vitamin, mineral, or nutrition test

10. The folate or folic acid test measures the amount of folic acid in the body one of many forms of vitamin B.

  • The normal range for folic acid in the blood is is between 2.7 and 17.0 (ng/mL).
  • Higher-than-normal folic acid levels usually aren’t problematic, but they might indicate a vitamin B-12 deficiency.
  • A lower level of folic acid might indicate anemia, malabsorption or problems absorbing vitamins and minerals or just a folic acid deficiency.

Now that you have a better understanding of what you’ll see from your results, you can take bigger control over your healthcare. Get your Wellness Test today!

If you seek additional information regarding test results, you may choose to visit Lab Tests Online, a public resource for clinical lab testing and an excellent resource.

Though Walk-In Lab’s Medical Director reviews all lab results, testing through Walk-In Lab does not replace the need for your primary care physician. Walk-In Lab testing is intended for information and education, and not diagnostic purposes. When you order an online blood test, you should follow up with your physician to review your laboratory results, especially those which are flagged in the out-of-normal reference range.


Abstract

The McDonald–Kreitman (MK) test, which compares the ratio of polymorphism to divergence at nonsynonymous and synonymous sites, is frequently used to detect adaptive evolution in protein-coding sequences. Because the two classes of sites share a common evolutionary history, the MK test is thought to be robust to most demographic factors. However, weak selection on nonsynonymous sites can bias the MK test, especially when a species’ effective population size has not been constant. Here, we present an empirical analysis of the influence of demography on the MK test by comparing test results for a common set of 136 genes, including a set of sex-biased genes that shows a strong signal of adaptive evolution, in two Drosophila melanogaster populations: an ancestral population from Africa and a derived population from Europe. The latter has undergone a relatively recent bottleneck, which has reduced its effective population size. We find that the MK test has less power to detect positive selection in the European population for two reasons. First, the overall reduced level of standing variation decreases the statistical power of the test. Second, the segregation of slightly deleterious nonsynonymous mutations biases the MK test away from detecting positive selection. The latter effect is stronger for X-linked genes, which have experienced the greatest reduction in effective population size outside of Africa, and also leads to the underestimation of rates of adaptive protein evolution by multilocus implementations of the MK test. Interestingly, a subset of autosomal female-biased genes shows an increased signal of adaptive evolution in the European population. This is inconsistent with currently accepted demographic scenarios and may reflect female-specific changes in selective constraint following the colonization of non-African habitats.


Interpreting Chi-Square Values

Determine the degrees of freedom of your chi-square value. If you are comparing results for a single sample with multiple categories, the degrees of freedom is the number of categories minus 1. For example, if you were evaluating the distribution of colors in a jar of jellybeans and there were four colors, the degrees of freedom would be 3. If you are comparing tabular data the degrees of freedom equals the number of rows minus 1 multiplied by the number of columns minus 1.

Determine the critical p value that you will use to evaluate your data. This is the percent probability (divided by 100) that a specific chi-square value was obtained by chance alone. Another way of thinking about p is that it is the probability that your observed results deviated from the expected results by the amount that they did solely due to random variation in the sampling process.

Look up the p value associated with your chi-square test statistic using the chi-square distribution table. To do this, look along the row corresponding to your calculated degrees of freedom. Find the value in this row closest to your test statistic. Follow the column that contains that value upwards to the top row and read off the p value. If your test statistic is in between two values in the initial row, you can read off an approximate p value intermediate between two p values in the top row.

Compare the p value obtained from the table to the critical p value earlier decided upon. If your tabular p value is above the critical value, you will conclude that any deviation between the sample category values and the expected values was due to random variation and was not significant. For example, if you chose a critical p value of 0.05 (or 5%) and found a tabular value of 0.20, you would conclude there was no significant variation.


U waves

U waves are not a common finding.

The U wave is a > 0.5mm deflection after the T wave best seen in V2 or V3.

These become larger the slower the bradycardia – classically U waves are seen in various electrolyte imbalances, hypothermia and secondary to antiarrhythmic therapy (such as digoxin, procainamide or amiodarone).


Watch the video: StatHand - Calculating and interpreting a weighted kappa in SPSS (May 2022).


Comments:

  1. Fibh

    This brilliant phrase is necessary just by the way

  2. Shaktitaxe

    I congratulate, what words ..., the brilliant idea

  3. Audley

    In it something is. Thanks for the help in this question how I can thank you?

  4. Joris

    I'm sorry, but in my opinion, you are wrong. I'm sure. Let us try to discuss this. Write to me in PM.

  5. Egeslic

    I can not participate now in discussion - it is very occupied. But I will be released - I will necessarily write that I think.

  6. Smedley

    Everything is just superb.



Write a message