X²

The function:

To decide whether the difference between observed and expected values is actually significant.

The example:

You’ve been told that in 1995, the average successful first kiss rate was 15%. With some digging, you found that last year, 27 of 476 young men successfully landed their first kiss in your hometown.  Is this consistent with the average?

To solve this problem:

1. You’ll want to establish all of the categories. In this problem, we are comparing kissers to missers. This is a success-failure scenario, so those are the ONLY two categories.

2. Establish your null and alternative hypotheses. If you are still kinda cloudy as to how exactly to do that, I’ll write a post on that later, which you will find HERE. But for now, just know that

• Null Hypothesis = The average 15% success rate holds true because … (insert hypothesized reason here).
• Alternative Hypothesis = Due to (insert hypothesized reason here), the success rate has changed from the 15% average.

REMEMBER: These are hypotheses. You don’t have to get them exactly right every time. That’s what experimentation is for. Essentially, the null is just supposed to say “there was no change” and the alternative hypothesis is supposed to say “there was a change.” Don’t forget what the function of a chi square test is.

3. Since you now know what is to be compared, you’ll want to make a table of expected versus observed values. I’ve taken the liberty of completing the table for this problem. Enjoy. :)

	Successful Kisses	Failed Kisses
Observed	27	449
Expected	67.35	449
(observed - expected)	-40.35	0
(o-e)^2	1628.1225	0
[(o-e)^2]/e	24.174	0

REMEMBER: You MUST MUST MUST use actual data (numbers) rather than percentages or ratios. AND don’t use the data if any of the points are less than 5.

4. Use the data you found by completing the table along with your knowledge of the X²equation to calculate the X² value. If this is the first time that you’ve encountered X², I’ve included a copy of the equation below. The sigma (the funny looking E) means that you make the calculation for each category separately and then you ADD THEM ALL TOGETHER to get the final value.

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5. Use a probability chart like the one found here to find the P value. I’d explain how to do it, but if you follow the link, you’ll see that those people do a pretty good job of explaining it themselves. Anyway, in the context of this problem, you’ll find that X²=24.174 and P<0.0005. Using the standard P> 0.05, we can compare to find that our experimental P value of 0.0005. Since our value is waaaaaaaay smaller than 0.05, we can reject the null hypothesis and conclude that THE AVERAGE KISS SUCCESS RATE IN YOUR HOMETOWN IS LESS THAN IT IT WAS IN 1995.

Boom Shakka Lakka!

Maybe you don’t know it, but you’ve had more than one: Hox Genes. Just like the guys with great hair and perfect taste in wine, hox genes are one of many mechanisms responsible for the way you get mapped out while mom is pregnant with you.

Let’s start at the beginning… the PG13 version. So mom and dad flirt… FASTFORWARD… you’re now a zygote. Welcome to the world. You’ve got nine months of preparation before you can come out to play, so there’s not a moment to waste. You’ve got a few million cells to produce… time for crazy amounts of mitosis!!

But wait… mitosis produces identical cells. Right? And if all the cells are the same, then you end up with a blob and not a baby… right?

Yup! So there has to be something done to make the cells different. Almost immediately after the signals are sent through the cell that say “HEY! We’ve been fertilized!”, the genes are expressed that say “LISTEN LISTEN LISTEN!! We need to set some stuff straight. I don’t want to have to explain this to you stupid cells again. Over here is going to be the top and over there is going to be the bottom. Over there is going to be left, and right is going to be on that side. Front is going to be over here and back is going to be over there. GOT IT?!”

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Okokok, so proteins don’t actually talk. But essentially, that’s what the first regulatory genes do – make every spot in the fetus different somehow. They set up the “chemical environment” of the cells, and the other genes that make a baby obey the gradients of…. you know what? I’m getting tongue tied here, so I know you probably don’t know what’s going on.

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Genes are dumb little ugly gremlins that do only what they’re told and nothing more. They all already know their instructions: When your room is painted blue and someone upgrades your cable to digital, make this protein… Don’t ask questions, just do it.
Obviously, all the genes can’t be given those same conditions, or else we’d have the same problem: all the genes would be making all proteins at the same time… BLOB BABY. So, many genes have different conditions, but not all (sometimes it’s better to have more than one gene respond to the same conditions… i’ll write a post on that later).

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NOW, coming full circle! Hox genes are some of the first genes (for all intensive purposes) that are expressed during development. The main purpose of the proteins that they make are to SERVE AS THE CONDITIONS THAT TELL OTHER GENES WHEN TO BE EXPRESSED. So, visualize a cell at the tail end of a puppy fetus. Remember those genes that are expressed to make every spot different? The one of the hox genes in that tail cell has been instructed to express that hox protein when it’s in that spot in the tail.

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NOW, let’s move on from here… delving a bit deeper. The proteins that hox genes produce are called transcription factors. They are responsible for turning on other genes. Remember how I said earlier that some genes are expressed at the same time? Hox genes do that. That hox gene in the puppy tail is going to turn on all the genes that make tools to make tail (versus genes for the tools to make nose or paw).

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That’s all. Hox genes simply provide the tools to make certain body parts. They’re a little more complex than other genes, though, in that once a hox gene is expressed in an area, that area takes on an identity that it can never shed. If a cell is in the area of the fetus where hox genes for arm are being expressed, that cell CAN’T BE ANYTHING BUT ARM.

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UPDATE: I’ve received some feedback from readers stating that this concept still isn’t quite clear. I’ve created a forum topic to bounce ideas and explanations. If you still need help grasping this one, click here to  head on over and check it out!

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Oh, yeah. I almost forgot. YAY! NO MORE BLOB BABY!!!

But now, on to brass tax. If you’re reading this, it’s because you either want or need to learn to use BLAST, the genetic Google hosted by the National Institutes of Health. This article is going to focus on searching if you already have an amino acid sequence, NOT A NUCLEOTIDE SEQUENCE. So, without further adieu, let’s go!

Step 1: Verification

So, the first thing you’ll need to do is to verify that you have an amino acid sequence rather than a nucleotide sequence. This is probably going to be the easiest part for those of you who are just getting started with BLAST. Your sequence is going to be a text file, just like any other research paper that you’ve ever written or read. But it’s going to be a bunch of unreadable letters. You can tell the difference between an amino acid sequence and a nucleotide sequence by just glancing at the letters you see.

Nucleotide

ATTGCGTTCGAGTCACTATGTATGGCCTCCACGGTAGGTTGAGCAGTACC
TGGCGGTATGACCACCTCCTCAGCGACGATGCTTATGGAGGCGCTGGACA
AGCGTTGACCCAGAGCTTTGGTCCCCAGAGCAAGAAGACCACTGGCCCGA
CACAAGAACACTTCCTCCTTTCCATTAGGGTTCGAGAATAAAGCTATCAG
CTGAGTCAATGCATTGCCACTTTTGAGTCCTCAAGCTAGATAAGTCTCCC
TTTTAAGAAACGCACGAGTACGCCTCTCTAGCGGTTTCTCATCGGACAGC
TCCTACGAAAGCGATCTTTATCGGGATCCACCGACTGTCGGCCTACAAGG
TGGGCCTTTTTGGACCACCCCGAGTAGATCGGCGACCTTTCTTTGTATGC
CAATTCATGAGTAACCTGAGCAGATTGAATGTACACGCAAAATGTCGATC
TAAGTGTCCCGTCCAAGAAGAATTTTTTCTTACTACCCCAGCCTGGTTTA

Notice how this is all A, T, G, and C? It’s a nucleotide sequence of a nucleic acid (which is made up of A, T, G, and C… so it’s DNA. But you already knew that.) (…even if you didn’t realize that you knew it…)

This isn’t what we’re looking for this time. We want a protein (amino acid) sequence.

Amino Acid

MDPHNPIVLDQGTGFVKIGRAGENFPDYTFPSIVGRPILRAEERASVATPLKDIMIGDEA
SEVRSYLQISYPMENGIIKNWTDMELLWDYAFFEQMKLPSTSNGKILLTEPPMNPLKNRE
KMCEVMFEKYDFGGVYVAIQAVLALYAQGLSSGVVVDSGDGVTHIVPVYESVVLSHLTRR
LDVAGRDVTRHLIDLLSRRGYAFNRTADFETVRQIKEKLCYVSYDLDLDTKLARETTALV
ESYELPDGRTIKVGQERFEAPECLFQPGLVDVEQPGVGELLFNTVQSADVDIRSSLYKAI
VLSGGSSMYPGLPSRLEKELKQLWFSRVLHNDPSRLDKFKVRIEDPPRRKHMVFIGGAVL
ASIMADKDHMWLSKQEWQESGPSAMTKFGPR*

BINGO! Since this one is all random letters (instead of A, T, G, and C), we can assume pretty safely that this is an amino acid sequence.

Sidenote: I know I said it was all random letters, but all the letters actually represent certain amino acids. Just FYI.

Just for the sake of science, I’m going to use this as my example for the rest of the article.

Step 2: Choosing the Correct Search

At the BLAST homepage, you’ll notice that under “Basic Blast,” there are five different…. ummm… things.

Each of these things are types of BLAST search that you can run. I”ll try to write a post on the differences between them all, but for now, just know that we’re going to use “protein blast” because we have a protein query (a list of amino acids) and we want to find out what protein it probably is (by searching protein databases).

So, click on “protein blast” and we’ll move on.

Step 3: The Search Interface

If you’ve found this site (MPSN), then you’re probably VERY familiar with the one pictured below.

The screen that you use for the BLAST search isn’t EXACTLY the same, but there are enough similarities that we can compare them. In fact, the basic principles are ALL the same.

Do you see where it says “Enter accession number, gi, or FASTA sequence” ? This is where you’re going to paste the amino acid sequence.

In the entry field that says “Job Title,” you can come up with something to title your search if you’d like. But it won’t ruin your search if you leave it blank. I usually leave it blank.

“Query Subrange” and “Upload File” are other tools that may someday help you in a large BLAST search. But as for now, they’re pretty much useless to you.

Moving on down the page, we find that the next significant plaything is the database chooser-thingy. If you click on it, you’ll see a dropdown menu with a cornucopia of different databases you can choose. Don’t let this discourage you. The only one that’s going to help you most of the time is “Non-redundant protein sequences.” Although there isn’t a single database that holds all of the info at NIH, the non-redundant option holds “All non-redundant GenBank CDS translations + RefSeq Proteins + PDB + SwissProt + PIR + PRF.” If you don’t know exactly what that means, that’s ok. Just realize that it means that non-redundant is the choice you want to make.

If you’re dealing with a teacher who has told you what organism you’re studying, you can put the scientific name of the organism in the input marked “Organism Optional.” Although that may seem like common sense to you, I can’t tell you how many times I’ve seen people ask for help concerning that input.

ALMOST DONE HERE…

Now, all you need to do is make sure that the “blastp” is marked in the “Algorithm” box, and then you can click BLAST at the bottom of the screen. You should be redirected to a page that includes this:

Step 4: Interpreting Results

You did it! You conducted a BLAST search like a pro, but now what? Well, let’s start with that funky-lookin’ graph in the middle of the page.

This graph shows you different results that matched your search sequence. We gave the BLAST program a sequence and it compared our sequence to a few hundred thousand others (or more) and spit out the results that it found in the form of this graph.

You’ll notice that at the top, there are five colors: black, blue, green, pink, and red. There are also numbers that go with the colors. A common mistake is to think that these numbers represent the amount of amino acids that were matched in the result. For instance, if we had searched “FJLDKJ” and the BLAST had returned with a result of “FJDDKJ,” then that would give a score of 5 rather than 6. That’s not quite right…

The numbers you see are scores. They DO compare the amount that matched between the search and the result, but the don’t do that by counting the individual number. Think about it like a chapter exam in class: the number of questions isn’t always 100, but the score is always out of 100. This means that the teacher is “standardizing” the scores so that they all compare to each other on a scale of 100.

The BLAST system does basically the same thing, although the score is out of somewhere around 200. I’m not exactly sure what the maximum possible value is, but a 200 in blast is like an A on an exam. By that logic, I’m sure you can figure out that, in your blast searches, you want to use the results in the pink and the red.

Moving down the page again, you’ll see a section called “Descriptions.” This is the first real encounter with your results. What you see here is really where you can get a lot of information. For instance, I’ll bet that you didn’t know that the organism that our protein was made in is Saccharomyces Cerevisiae (baker’s yeast).

HOW DID I KNOW THAT? Just look at the results. The highest score AND the second highest score results both come from baker’s yeast. (You can click the little blue link on the left side in order to go to the pubMed article where the results are found. Clicking on that link sent me to an article that showed me not only the organism, but also that this protein is the result of the ARP2P gene.

OH! And I almost forgot to explain the E-Value!

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The E-Value is a fairly simple concept, once you get the hang of it. For those of you that are familiar with statistics, it’s really similar to the standard deviation. For the rest of you, allow me to explain: the E-Value is the probability (the percent chance) of there being a better result than this one. The smaller the E-Value, the smaller the chance of there being a better result. Therefore, a GOOD BLAST RESULT is one that has a HIGH SCORE and a LOW E-VALUE.

I really hope that this helps somebody out there.

Best of Luck,

Grey

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Do you remember that we decided that DNA and RNA are two separate languages? Transcription factors read “TATAAA” as “park here.” There are hundreds of other examples of how enzymes recognize only certain sequences, but we’re gonna keep it simple for now.

Well during translation, Dr. Ribosome and his tRNA graduate students “translate” the mRNA into yet ANOTHER language: polypeptide. Ok, after some thought, that’s too cheesy even for me. But since it gets the point across, I’ll leave it at that.

Once the mRNA strand has left the nucleus and entered the cytoplasm (or “cytosol” to some people), it is fair game for ribosome binding. That 5′ cap is EXTREMELY important here because it marks the location for the ribosome to bind.

WAIT! I want to add a little sidenote here that would have REALLY aided me in getting the concept of why all the little proteins are necessary. Remember how we needed transcription factors to start transcription? We’re gonna need translation initiation factors in order to start translation as well. But what’s really the point? You’ll have to read that article to find out, but for now, read on.

SO, the ribosome uses initiation factors to bind on the RNA, more factors find the AUG start codon, then the ribosome uses factors to bring the tRNA molecule with Methionine to the AUG Start codon… WHEN DOES THE RIBOSOME ACTUALLY DO SOME WORK BY ITSELF!?!?

Ummmmmmm, pretty much after that first methionine. You see, once everything is set up, the rest is automatic. If you look at the picture above, you’ll see that there is an “A Site,” a “P Site,” and a tRNA molecule floating away. The “A Site” is short of aminoacyl site, or “amino ACID” site. This is where tRNA molecules land if their anticodon matches the codon that is showing.The “P Site” is short for the peptidyl, or “PEPTIDEyl” site. This is where the amino acids bound to the tRNA molecules are added to the growing protein using a peptide bond. Finally, there is the exit site, where the empty tRNA molecule is allowed to float off and become recharged with another amino acid (of the same type).

In the case of the methionine start codon, the ribosome was moved so that the AUG codon was the only one that was showing in the Aminoacyl site. Once the methionine tRNA was bound, the RIBOSOME MOVED (not the tRNA molecule) and this pushed the tRNA molecule over to the Peptidyl site. This left an empty A-Site for another tRNA to enter and bind, causing the ribosome to move again. This time, the methionine tRNA is pushed into the exit site and ejected from the ribosome.

This move-eject-bind procedure will go on until the ribosome encounters a stop codon. If you look at the chart above, you’ll see three different combinations that will result in a stop. This happens because there’s a special molecule that possess and anticodon but NOT an amino acid. When this molecule moves into the P site, the ribosome will attempt to move the growing amino acid chain to bind with the new amino acid. The only problem is that with this special molecule, there is no amino acid. So the protein just floats off, and the ribosome falls apart, thereby ending translation.

Short Version

1. Factors bind ribosome to mRNA
2. Methionine tRNA initiates reading
3. Move – peptide bond/eject – Move – peptide bond/eject
4. Stop codon ends it all

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Best of Luck,

Grey

Have you ever baked cookies? Ok, so that’s a dumb question. But how about this one — did you use a cookbook? Unless you’re one of a seriously small percentage of humans with a photographic memory, then chances are that you did. So, making cookies was a 2 step process: writing the information down and deciphering that information into a different language.

I know it’s corny, but just go with it… for the sake of education.

From that awful analogy, have you figured out that there are 2 separate forces at work here? There’s the information-writing party and the information-reading party. In your case, the cookbook writers were… oh, you get it.

So, lets take what we now know and apply it to cell biology. In THAT case, the information-writing party would be anything involved in transcription, and the information-reading party would be anything involved in translation. Since this particular article is supposed to be about transcription, let’s go into a little more detail, but always keep in mind that everything in this process is about writing information down so that it can be read later.

Think of the genome as a library, full of cookbooks. This entire library is in the DNA language, and the ribosomes (the information-reading party) don’t speak DNA. There’s a wealth of information there for making all kinds of proteins, but the ribosomes just can’t read it, so it’s necessary that the information be translated into RNA, the ribosome’s native language.

The obvious place to start is in the nucleus at the gene of interest. We know from my post on the parts of a gene that there is a part called the promoter. I’ve heard the promoter referred to as everything from an on/off switch to an airport landing strip, and they all make sense. The promoter is the part of the gene that says “HEY! Bake THIS recipe!!”

There is a specific word in the DNA language that transcription factors, proteins that do speak DNA, understand as meaning “park here.” We refer to this word as a sequence called the TATA box. These transcription factors (there are many of them that work together to start transcription) act as a pit crew for RNA Polymerase. Their job is to find the starting line, get the RNA Pol in the right spot, and then give it any starting push that it may need (from stored energy, like ATP).

The RNA Polymerase is the only enzyme that is actually bilingual. Once it is given the proper push by the transcription factors, it glides along the DNA template strand, reading the DNA words and translating them into RNA. The two languages are very similar, in fact that only difference is that whereas the DNA alphabet is A, G, C, and T, the RNA alphabet is A, G, C, and U.

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At this point in the process, we have RNA sliding down the DNA template strand translating the directions from DNA into RNA. So, what next? There must be something programmed into the system to stop it at the end. Nope. In fact, transcripts don’t even make it to the end 100% of the time.

The RNA Polymerase glides along the top of the DNA strand in much the same way as a kid running down the curb on a street. It doesn’t take much force to knock the kid off the curb, and it takes even less to knock the RNA Pol off the DNA. There are no strong magnetic forces acting that hold the RNA in place, and the enzyme itself fits over the DNA like a horseshoe on a string. Sometimes, the transcript lasts all the way to the end of the coding sequence, sometimes it doesn’t, and sometimes it may go on for many thousands of base pairs after the gene has ended. Obviously, though, enough transcripts last until the end of the coding region, or else this system wouldn’t work.

You would think that, logically, as soon as the RNA Pol falls off the template strand, the new mRNA molecule would be finished and the process would be over. However, grammar is always important. I’m sure you can think of a few instances in which bad grammar made something hard to read, or even completely unreadable. Cells use a type of grammar as well, but we call it “post-transcriptional mRNA modification.” I know it’s a big scary term, but it simply means that after the RNA Pol does its job, the cell changes the molecule a little bit.

This modification consists of three very important parts: A 5′ cap, 3′ polyadenylation, and intron excision. These are all rather simple as well, but terms can be scary. When I say 5′ cap, I mean that the cell contains a host of enzymes that add a protective molecule onto the transcript at the 5′ end that signals the ribosomes to do their job later as well as protects the transcript from being sliced up by exonuclease enzymes. The 3′ polyadenylation, which means “lots of Adenosines on the 3′ end,” also protects the mRNA from exonuclease shredding. Intron excision, however, it probably the most important. In this process, chunks of DNA words that don’t actually mean anything are taken out of the directions in the mRNA.

Why these stinkin things are even there, we don’t know, but we DO know that if they aren’t taken out of the mRNA transcript, there will be no cookies later on. You see, in the DNA language, it is customary to take chunks of sentences and overlap them with each other. In order to read the original sentence, we must remove the chunks of other sentences. We call the original sentence chunks “exons” because we want to EXPRESS them, and the INTERVENING sentence chunks are called “introns.”

Once the cell has written down the recipe in a language that the ribosomes can understand and put it in the correct grammatical format, then the mRNA transcript can leave the nucleus and move on to the second step in protein expression.

The Short Version

1. DNA attacked by Transcription Factors that recognize the TATA box
2. RNA Polymerase binds to Transcription Factor complex, then begins to synthesize mRNA strand complementary to the DNA template
3. 5′ Capping, 3′ Poly-A tail, Intron Excision
4. Move out of Nucleus

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Best of Luck,

Grey

Three Mikan

So, you’ve read the article on dihybrid crosses, you’ve red the article on monohybrid crosses, and now you want to move on to polyhybrid crosses (but take it slow). Well, far be it from me to keep you from learning. Let’s see if you’ve got it…

In the hypothetical MPSN world, goldfish only have three traits: size, color, and teeth. The size gene comes in two flavors, big (B) and small (b). The color and teeth genes are similar, coming in gold(G) or black(g) and teeth(T) or no teeth(t), respectively. Let’s cross a homozygous dominant male with a homozygous recessive female. What are the phenotypes of the F1 offspring (the children)? And if we cross the F1 offspring with each other, what are the phenotypes and ratios of the F2 offspring (the grandchildren)?

Can you handle it? Now don’t scroll down until you think you have answered all the questions. Ready? GO!

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Step 1: Gather the Known Information

• Mom = (bbggtt)
• Dad = (BBGGTT)

Step 2: Find Unknown Information

The first thing we’ll need to do, now that we know our parental phenotypes is to figure out what types of gametes that they can produce. Since we know that mom ONLY HAS (b), (g), andt (t), then (bgt) is the only gamete she can produce. Likewise for dad: only his gametes will each be dominant (BGT).

If we cross these, we end up with 100% (BbGgTt) offspring in the F1 (children) generation. Thankfully since mom and dad only have dominant or recessive alleles to give, we can deduce that the offspring will each be heterozygous WITHOUT USING A PUNNETT SQUARE! Woot!

HOWEVER, to answer the second question, we must employ the help of a punnett square. Here goes:

So, what are the F1 gametes? Since they are all (BbGgTt), then they will all have the same set of gametes. (But those gametes can combine differently, so don’t assume that all the offspring of the F2 generation (grandchildren) will be the same like in the F1 generation.) Using what we know about permutations, we can calculate that there will be 8 different types of gametes. These F1 gametes are: (BGT), (BGt), (Bgt), (bgt), (bGt), (bgT), (BgT), (bGT). Now, to find out the phenotypes, we have to cross them. 123GO.

OY. That took me 20 minutes to type up… such a pain… But anyway, here’s what we found:

• Big, Gold, and with Teeth      = 27
• Big, Gold, and toothless        = 9
• Big, black, and with Teeth     = 9
• Big, black, and toothless       = 3
• small, Gold and with Teeth    = 9
• small, Gold, and toothless     = 3
• small, black, and with Teeth  = 3
• small, black, and toothless    = 1

As you can see, both for populations of Big F2 offspring (grandchildren) and small F2 offspring (grandchildren), we can see a difinite 9:3:3:1 ratio. [As you travel up the corporate ladder into bigger polyhybrid crosses, be on the look out for that 9:3:3:1 ratio]

Step 3: Check Your Work.

The original problem asked 2 questions: “What would the F1 phenotype be?” and “What would the F2 phenotypes and ratios be?” Have we answered both of those questions? Yes? Cool. Good work.

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Best of Luck,

Grey

Yak

• Now that you guys have a fairly good understanding of the difference between dominance and recessiveness, I’ll no longer be referring to traits as dominant or recessive. I’ll simply refer to one trait as (X) and another as (x) and I’ll expect you to know the difference. You’re growing up in your biological education, so it’s time to remove some training wheels. If you fall of the bike, get a band-aid, review the last few posts, and move on!

So since you’re growing up, come right over here. Sit with me, pour yourself a glass of scotch, and I’ll tell you all about my Uncle Vlad. (NOTE: MPSN DOES NOT CONDONE UNDERAGE DRINKING. THANK YOU.) Uncle Vlad is a yak breeder in the imaginary nation of Romania, the beta version. Uncle Vlad is known all throughout the countryside for his skill in breeding healthy, white-haired (h) yak that produce a sweet bright blue milk (m)… regardless of gender. Now I’m not sure if you know anything about yak breeding, but getting a combination like that is VERY rare. You see, brown hair (H) and bitter green milk (M) are the usual traits.

He once told me of his first yak; he won her at a village fair.  You see, there was an old monk (the kids liked to call him Mr. Greg) who had this crazy theory about the inner workings of having children. To the fair, he brought a herd of yak, and he told everyone there that if they could explain why the two parent yak had yaklets that looked the way they did, the winner could choose a yak from the herd to take home as his or her own. Uncle Vlad couldn’t resist, even though he had read all of crazy old Greg’s papers.

He first noticed that the two yak that had been called the parents were both brown, but the mom produced blue milk and the dad produced green milk. He also noticed that there were only two main types of yaklets: brown with blue milk and brown with green milk. Old Greg saw him concentrating on the yak, so he decided to help out a little. Beside the dad, he placed two large combs and two small buckets. Beside the mom, he placed one large and one small comb and one large bucket. Although, Uncle Vlad’s memory escapes him as to HOW he did it, he does recall winning the yak and eventually going to medical school.

By now, you’re probably certain that this story is utter mess, but it’s an entertaining way to say: LETS EXPLORE THE GENETICS OF THE BETA-ROMANIAN YAK. Mad at me? Oh well.

So let’s pretend that the parent yak went to Vegas for their honeymoon after the wedding and soon thereafter gave birth to non-identical triplets. (They don’t all have look the same because they don’t have identical DNA.) They all have brown hair, and two have green milk. If the parents decide to go on three more honeymoons and have triplets each time, how many kids total should we theoretically expect to produce blue milk?

I know, I know. This is a long article. “Get to the point.” I have heard your cries, so I’ll condense the technical side. Here goes.

Step 1: Collect given information.

I did this on purpose. Let’s go through the article and pick up the stuff that we already know before we start calculatin’.

• Brown hair = (H), White hair = (h), Green milk = (M), Blue milk = (m)
• Mom’s genotype = (HHmm) just in case you didn’t get that from the bucket & comb hint
• Dad’s genotype = (HhM?) The (?) is because that allele could either be a (t) or a (T); we don’t know yet.
• Kid’s phenotypes = brown hair for all,  green milk for 2, blue milk for 1

Step 2: Figure out genotypes.

In order to answer the question, we’ll need to know the genotypes of the parents. We already know mom’s but we’re missing one crucial allele from dad. Let’s look at the kids in order to figure it out. We already know the genotypes for the hair gene, so lets ignore it and move on to the milk gene.

There are kids that produce blue milk and kids that produce green milk. In order for this to occur, there must be kids who are (mm) AND kids who are either (MM) or (Mm). Regardless of genotype, the kids have to get alleles from the parents – one from each. Since mom can only give (m) alleles, we know that there are no kids that have the (MM) genotype for the milk gene. [Dad has the (M) allele, but he can only give ONE to the child.] So, we know that there are kids with (mm) and kids with (Mm).

We’ve figured out the genotypes of the kids, but that doesn’t help us figure out how to answer the question… or does it? Remember our point? We have to figure out dad’s genotype. He’s (HhM?). So what is the identity of the (?) allele? Well, just like we used mom to figure out that there are no (MM) kids, we’re gonna use the (mm) kid to figure out that dad’s mysterious allele is an (m). The kid gets one (m) from mom (mm) who ONLY has (m) to give, but he had to get the other one from somewhere. That must mean that dad is (Mm) for the milk gene.

Step 3: Test by Crossing

Now that we know that mom’s genotype is (HHmm) and that dad’s is (HhMm), we can figure out what we can expect as far as kids go. Since mom only has one type of allele for each gene, she can only produce one type of gamete (Hm). There are no other combinations. Dad, however, has many different combinations. In fact, we can figure out exactly how many by using basic probability rules. He has 2 genes represented, and both of those genes have 2 alleles represented. Therefore, (2×2)=4, and he can produce these gametes: (HM), (Hm), (hM), and (hm). As a cross, we can see the progeny below:

Step 4: Interpret results

The question asked us how many children, out of 12, would we expect to produce blue milk. (We therefore exclude the actual kids that have already been born.) Since 2/4 of the progeny had the genotype (mm) for the milk gene, we can conclude that 2/4 of the 12 children, aka 6, should produce blue milk.

So to recap:

1. Collect given info
2. Decipher genotypes of all individuals involved
3. Cross with Punnett square
4. Interpret results in light of the question asked.

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Best of Luck,

Grey

Smoothie Picture by Eric Schmuttenmaer

I like smoothies. In fact, I was drinking one in a coffee shop and working on an English paper when I came up with this idea. Yumm.

So, when you think of incomplete dominance (also called semi-dominance by some), think of a strawberry (S) and banana (s) smoothie…mmmMMMMmmm. There are two things that have a big effect on the final product. (Kinda like the two alleles in genetics. Follow me here)

There are three possible combinations of the two ingredients: strawberries (S) and bananas (s). There is an all-strawberry smoothie, (SS). There is the all-banana smoothie (ss). And then, there’s the combination of the two into strawberry-bananaish goodness (Ss).

HOLD ON. This time, the (Ss) isn’t just a strawberry smoothie?!?!? Nope. And that’s what characterizes incomplete dominance. The heterozygote (Ss), in this case the strawberry-banana yummness, shows a middle ground between the two extremes.

Just for practice, let’s look at a problem that deals with incomplete dominance:

Your mom has two crepe myrtle trees, both with purple flowers. Over the next few years, more trees spring up – mostly purple, but some red and some blue. Your mom thinks the blue is the prettiest color ever, and she wants more. What cross would you perform to get the most blue-flowering trees?

First thing’s first: identify what we can. We know that mom and dad (the two beginning trees) are purple, and that the children are purple, red, and blue. We don’t know which allele is technically referred to as the dominant one, but since it’s common sense that red + blue = purple, we can deduce that this is a problem that deals with incomplete dominance… so that’s not really a big deal. We’ll call the blue allele (B) dominant because mom like it, so the red (b) would be the recessive version.

Although common sense would say that both of these alleles are on the same level of dominance to each other, we use the traditional means of writing this out. Just for the ease.

By using this method, we can call the blue flowers (BB), the red flowers (bb) and the purple one’s (Bb). Which mix will give us the most (BB) offspring?

As you can see, the most blue flowers will result if we cross a blue tree with another blue tree. We tested each instance in which there was any blue allele present. (BB) plants true breeding and can ONLY produce blue flowers, meaning 100% of offspring will show the wanted phenotype. You can’t get any better than that.

Best of Luck,

Grey

In zebras, purple eyes are dominant to yellow ones. (I don’t know if this is true, but just go with it.)  A purple-eyed zebra-man has little zebralets with yellow-eyed zebra babe: 487 purple-eyed 520 yellow-eyed. What are the genotypes of the man and his kids? What are the probabilities of their possible children?

We’ll call the purple allele (P) because it’s dominant, and we’ll call the yellow allele (p) because it’s the recessive version of the gene. Now that we know how we’re gonna write the genotype of the parents, let’s figure those genotypes out.

We know that the female genotype is (pp) because we were told that, in this example, yellow is both recessive and the color of the female’s eyes.

Dad’s genotype is going to be a little harder to decide – but not much. Remember that with dominant alleles, both homozygotes and heterozygotes will show the dominant phenotype. If you’re not really good with the genetic jargan, that means that Dad will have purple eyes whether his genotype is (PP) or (Pp). That’s where we come in; we have to figure out which one he is.

This is usually where most people have problems. “Uhhhh… what now?” What a great question. Let’s figure out the children’s genotypes – since we already figured out everybody else’s. There are both purple and yellow eyed kids. That means that there are either (Pp) or (PP) AND there are (pp) represented.

REMEMBER that the children can only get alleles from mom and dad. SO if mom and dad don’t have them, the children can’t. Since Mom’s genotype is (pp), then she can only give (p) alleles to her kids. Since we know that there is going to be at least one (p) in all the kids, we can deduce that NONE of the kids will be (PP) and that the purple-eyed kids will have (Pp) as their genotype.

HEY! Wait a sec, we missed something. OH YEAH, we can also figure out Dad’s genotype now. Remember what I said earlier about kids only being able to get alleles from the parents? Let’s look at those yellow eyed kids again. They have a genotype of (pp) and we already know that one of those (p)’s came from Mom… so what about the OTHER one? It had to come from Dad, who has purple eyes. THEREFORE, his genotype must be (Pp).

FINALLY, we did ALL THAT WORK. Phew! But let’s check the question to make sure that we answered the question. I made that mistake a lot when I took genetics- so check to make sure that you’re done. There were two parts: genotype and probability. We know that Dad’s genotype is (Pp) and that his children are (Pp) and (pp). BUT we haven’t figured out the probabilities yet. Sorry.

It’s not that bad. Quit whining. Let’s just do this. It’s all down hill from here. Watch.

Mom: (pp)

Dad: (Pp)

If you didn’t get it that easily, check out my article on understanding Punnett Squares. But as you can see on the graph, 2/4 of the children are (Pp) and the other 2/4 are (pp). THEREFORE, we know that 0.5 of the children are (Pp) and the other 0.5 are (pp). And that makes sense seeing as 487+520 are 1007, and both numbers are just about half. NOW, we’re done.

Let’s recap the rules that we learned.

1. Name the alleles with the letter of the dominant. That way, it’s easier to remember which one is dominant.
2. The DOMINANT is capitalized and the recessive is not.
3. Use the genotypes of what’s given to find out the genotypes of what’s not.
4. If statistics are given, check the given data with the stats to make sure it makes sense.

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Best of Luck,

Grey