Re: How do the chromosomes in ihuman sperm cells differ from those in ova?

Dear Leanne,

There are several ways in which the chromosomes in sperm differ from those in eggs. The 
most obvious is that sperm cells can include either an X or a Y chromosome, while egg cells 
can only include an X chromosome.

The reason for this is that in humans, males are XY and females are XX; the rest of the 
chromosomes consist of 22 pairs of autosomes. The special cell divisions that lead to sperm 
and egg cells (called meiosis) result in a gamete nucleus that contains half the number of 
chromosomes that are present in normal somatic cells. Normal gametes contain one 
member of each chromosome pair, including the sex chromosomes. So eggs contain 22 
autosomes and an X chromosome, while sperm contain 22 autosomes and either an X or a Y 
chromosome.

One consequence of this is that it is actually the chromosomes of the sperm that determine 
the sex of the child. If the sperm carries an X chromosome, the zygote will be XX (female); if 
the sperm carries a Y chromosome, the zygote will be XY (male). Throughout much of 
human history, folklore has this wrong, and women have been blamed for not delivering 
sons.

The other part of your question is about probability. One of the key concepts of probability 
is that of independent trials. In many probability problems, the events are independent, 
meaning that the probability of a particular outcome for any individual trial is not influenced 
by the outcome of prior trials. Here are two examples.

Consider tossing a coin. It can come up heads or tails. Every time that you toss the coin, the 
chance of heads is 50% and the chance of tails is 50%. Each trial is independent; the coin 
does not remember what happened in prior trials, although people betting on the coin seem 
to. If you and I are tossing coins, and heads comes up three times in a row, what are the 
chances that it will come up heads next time? It's still 50%. Most people seem to have a hard 
time grasping this. Evidence for this is in casino roulette, where casinos have made fortunes 
when red came up many times in a row; everyone bet on black, because they knew that the 
Law of Averages demanded that the wheel start coming up black. They were mistaken; each 
time the wheel is spun (or the coin is tossed) is an independent trial whose outcome is not 
influenced by the outcome of prior trials.

Now consider drawing cards from a deck without replacing the cards once they are drawn. A 
deck of 52 cards has 26 red cards and 26 black cards. The first time that you draw a card, 
there is a 50% chance that it will be red and a 50% chance that it will be black. After you 
drawn the first card (without replacing it) you have a 51-card deck. If the first card was red, 
the deck now has 25 red and 26 black cards. The probability of drawing a red card is 25/51 
or about 49%; the probability of drawing a black card is 26/51 or about 51%. If we draw red 
again without replacing it, we have a 50-card deck with 24 red and 26 black. The probability 
of drawing a red card now is 24/50 or 48%; the probability of drawing a black card is 26/50 
or 52%. Because we are sampling the deck without replacement, the trials are not 
independent; the outcome is influenced by the outcome of prior trials.

So, if a couple has three boys and the mother is expecting a fourth child, the probability 
that it is another boy is 50%. The father is not a deck of cards that we are sampling without 
replacement. He produces millions upon millions of sperm cells. Half of them carry X 
chromosomes and will produce daughters, while half of them carry Y chromosomes and will 
produce sons. Each fertilization event is an independent trial.

You can read more about basic genetics in an online text at:
 http://www.ncbi.nlm.nih.gov:80/books/
bv.fcgi?call=bv.View..ShowSection&rid=iga.section.200

Many of the terms of genetics are defined in the MGI Glossary at:
 http://www.informatics.jax.org/mgihome/other/glossary.shtml

Thank you for an interesting question.

Yours,

Paul Szauter
Mouse Genome Informatics