This answer is mainly copy-pasted from my answers here and here.
The importance of polymorphism
To understand what generation to backcross, it is central that you understand why we need polymorphic loci.
If at least one locus is homozygous
If a recombination event happens between two loci where at least one is homozygous, then you would not see anything. Consider for example the following strand sequences in a diploid individual
-----A-----B----
-----a-----B----
Whether a recombination event occurs or not, the two possible chromosomes passed down to an offspring are
-----A-----B----
-----a-----B----
Therefore, you cannot tell whether recombination has occurred.
If both loci are heterozygous
Now consider the following individual
-----A-----B----
-----a-----b----
If no recombination event has occurred between the two loci of interest then the two possible chromosomes that will be passed down are
-----A-----B----
-----a-----b----
If on the other hand a recombination event has occurred between the two loci of interest then the two possible chromosomes that will be passed down are
-----A-----b----
-----a-----B----
Therefore, you can tell whether a recombination has occurred or not.
Statistics of recombination
There is a tiny bit of maths below. These equations are mainly for curiosity as one can understand the answer without understanding the maths behind it.
Definitions of $r$ and $M$
You are getting confused between two different statistics
- The rate of recombination $r$ between two loci
- $r$ is the probability for two sequences found at two loci to remain in the same gamete after recombination occurred. This probability cannot be greater than 0.5 ($0 \le r \le 0.5$).
- The distance in Morgans $M$ (or more commonly in centiMorgans) between two loci
- $M$ is the expected number of cross-over that occurs between the two loci.
Morgans and centiMorgans
You will note that I talk in Morgans rather than centimorgans which is unusual in the literature but it helps at conveying the intuition of what it means. if $M= 150$ centiMorgans $= 1.5$ Morgans, then the expected number of cross-over between the two loci is 1.5. Below are some more explanations on these two definitions with some drawing :)
Case study with loci A
and B
While $M$ and $r$ are closely related, they are not exactly the same thing. Consider the following sequence with the loci A
and B
---[A]------------------[B]---
Let's assumed the two loci are very far apart and $M=2$. The probability of having exactly $k$ crossovers is therefore given by a Poisson distribution with rate $M=2$
$$P(k) = \frac{e^{-M}M^k}{k!}$$
Let's say for a given case that $k=1$ (a single cross-over occurred). This cross over is represented by a "/" below
---[A]-------------/----[B]---
Here clearly the two sequences at loci A
and B
will be separated. Let's say now that $k=2$ (two cross-over occurred).
---[A]---/-----/--------[B]---
Here, even if crossovers have occurred, the two sequences at loci A
and B
will remain together. Only the sequence in between the two cross-overs will come from the homologous chromosome.
Relationship between $r$ and $M$ - in words
You might see it coming from the previous section. The probability $r$ of these two loci A
and B
to be separated via recombination is the probability of an odd number of recombination events to occur between them (knowing that $M$ is the expected number of cross-overs).
Relationship between $r$ and $M$ - in equation
Let's calculate first the probability $p_{even}$ that an even number of cross-overs occur. This probability is just
$$p_{even} = \sum_{k = 0}^{\infty}{e^{-M}M^{2k} \over (2k)!}$$
, where I just added the constant $2$ before $k$ at both the numerator and denominator. With some algebra and trig, one can show that
$$p_{even} = \sum_{k = 0}^{\infty}{e^{-M}M^{2k} \over (2k)!} =
e^{-M}\sum_{k = 0}^{\infty}{M^{2k} \over (2k)!} = $$
$$e^{-M} \: cosh(m) = e^{-M}\left ( \frac{e^{M} + e^{-M}}{2}\right ) =
{1 + e^{-2M}\over 2} $$
As, $r = p_{odd} = 1 - p_{even}$,
$$r = 1 - {1 + e^{-2M}\over 2} = {1 - e^{-2M}\over 2}$$
Here we go! We have our relationship between $r$ and $M$! Let's graph it
Relationship between $r$ and $M$ - on a graph
I just graphed the above equation in Mathematica (Plot[y = (1 - Exp[-2 M ])/2, {M, 0, 5}]
)

Here is the same graph but zoomed on lower values of $r$ and $M$ (Plot[y = (1 - Exp[-2 M ])/2, {M, 0, 0.1}]
)

We clearly see from the graph that for low values of $M$, as $M$ increases $r$ increase quasi linearly ($M≈r$). For greater values of $M$, $r$ still increases but slower and slower until reaching an asymptote / plateau at $\frac{1}{2}$. $r$ is indeed bounded between 0 (when $M=0$) and $\frac{1}{2}$ (when $M=\infty$).
Note, the fact that the sum of probabilities of every even $k$ in a Poisson distribution is always lower or equal to 0.5 is an interesting math fact in itself!