millironx/beefblup
1
0
Fork 0
mirror of https://github.com/MillironX/beefblup.git synced 2024-12-22 09:08:16 +00:00
Code Issues Releases Packages Activity

Convert wiki to docs

Browse source
This commit is contained in:
Thomas A. Christensen II 2021-08-28 19:25:17 -05:00
parent b4e0591ed2
commit 97bd43323d
Signed by: millironx
GPG key ID: 139C07724802BC5D
3 changed files with 666 additions and 0 deletions
Show stats Download patch file Download diff file Expand all files Collapse all files

3
docs/src/.markdownlint.json Normal file
View file

@ -0,0 +1,3 @@
{
"MD041": false
}

105
docs/src/beefblup-cli.md Normal file
View file

@ -0,0 +1,105 @@
```@meta
CurrentModule = BeefBLUP
```
beefblup Command Line Interface (CLI) documentation
> _A work in progress_
**Notice:** This document is a draft for what the command-line interface for
beefblup would look like as of version 1.0, if beefblup was even a command-line
application to begin with (it's not). It is modeled (loosely) after the man page
format. It is not intended to be taken seriously, but instead to serve as a
useful thought experiment and brainstorming ground on the future of beefblup.
Please use it if it clarifies things for you. If it doesn't, ignore it.
## Input file
beefblup requires a very specific format of input file. The format may be in
comma-separated values (CSV) or Excel 2007+ (XLSX) format. CSV files should not
be quoted (and therefore cannot have commas within cell values). Other formats
may be forthcoming.
A beefblup data file must have at least six columns appearing in this order:
- ID
- Sire ID
- Dam ID
- Birthdate
- Fixed effect(s)
- Response variable(s)
The first row always contains column names. The values of column names are
unimportant for the first four columns, as they will always be treated the same
regardless of the name. The generated report will use the column names
of fixed effects and response variables as given.
Each fixed effect should have its own column, to as many as are needed. There is
no limit to the number of fixed effects as defined by beefblup, however its
dependencies might have some. The same rules apply to response variables.
Unknown values should be left blank (`,,`). Do not substitute null placeholders
(e.g. `NULL`, `NA`, `0`, `nothing`, `undefined`, etc.) for unknown values.
An example spreadsheet might have the following format
| ID | Sire ID | Dam ID | Birthdate | Sex | Weaning Weight |
| --- | ------- | ------ | --------- | ------ | -------------- |
| 1 | | | 1/1/1990 | Male | 354 |
| 2 | 1 | | 1/1/1990 | Female | 251 |
| 3 | 1 | | 1/1/1991 | Male | 327 |
| 4 | 1 | 2 | 1/1/1991 | Female | 328 |
| 5 | | 2 | 1/1/1991 | Male | 301 |
| 6 | | | 1/1/1991 | Female | 270 |
| 7 | | | 1/1/1992 | Male | 330 |
## Synopsis
```bash
beefblup [-G SNPs_file] [-M num_response_vars] [-o report_spreadsheet]
[--no-aod] [--no-year] [--no-season] [--no-autob] [--maternal] input_file
[report_file]
```
## Command line basic syntax
The most basic input is to simply pass the input file name to the program.
```bash
beefblup filename.csv
```
In this case beefblup will insert fixed-effects for age-of-dam, year, and
season, and will calculate the EBVs for the response variable in the final
column. The report will then be saved as `filename_report.txt`.
## Suppressing automatically-calculated fixed effects
If you don't wish to include one of the automatically calculated fixed-effects
from your model, you can pass arguments to suppress them.
### Suppress Age-of-dam
```bash
beefblup --no-aod filename.csv
```
### Suppress year
```bash
beefblup --no-year filename.csv
```
### Suppress season
```bash
beefblup --no-season filename.csv
```
### Suppress all calculated fixed effects
```bash
beefblup --no-autob filename.csv
```
The argument `--no-autob` comes from the nomenclature of assigning fixed-effects
to the matrix _b_ in Henderson's mixed-model equations.

558
docs/src/how-to-calculate-epds.md Normal file
View file

@ -0,0 +1,558 @@
```@meta
CurrentModule = BeefBLUP
```
# How to Calculate EPDs
Not to exclude our Australian comrades or our dairy friends, this guide could
alternately be called
- How to Calculate Expected Breeding Values (EBVs)
- How to Calculate Predicted Transmitting Abilities (PTAs)
- How to Calculate Expected Progeny Differences (EPDs)
Since I'm mostly talking to American beef producers, though, we'll stick with
EPDs for most of this discussion.
Expected Breeding Values (EBVs) (which are more often halved and published as
Expected Progeny Differences [EPDs] or Predicted Transmitting Abilities [PTAs]
in the United States) are generally found using Charles Henderson's linear
mixed-model equations. Great, you say, what is that? I'm glad you asked...
## The mathematical model
Every genetics textbook starts with the following equation
```math
P = G + E
```
Where:
- _P_ = phenotype
- _G_ = genotype (think: breeding value)
- _E_ = environmental factors
Now, we can't identify _every_ environmental factor that affects phenotype, but
we can identify some of them, so let's substitute _E_ with some absolutes. A
good place to start is the "contemporary group" listings for the trait of
interest in the [BIF Guidelines], though for the purposes of this example, I'm
only going to consider sex, and birth year.
```math
P = G + E_{year} + E_{sex}
```
Where:
- _E<sub>n</sub>_ is the effect of _n_ on the phenotype
Now let's say I want to find the weaning weight breeding value (_G_) of my
favorite herd bull. I compile his stats, and then plug them into the equation
and solve for G, right? Let's try that.
### Calf Records
ID | Birth Year | Sex | YW (kg)
-- | - | - | -
1 | 1990 | Male | 354
```math
354 \textup{kg} &= G_1 + E_{1990} + E_{male}
```
Hmm. I just realized I don't know any of those _E_ values. Come to think of it,
I remember from math class that I will need as many equations as I have
unknowns, so I will add equations for other animals that I have records for.
### Calf Records
ID | Birth Year | Sex | YW (kg)
-- | - | - | -
1 | 1990 | Male | 354
2 | 1990 | Female | 251
3 | 1991 | Male | 327
4 | 1991 | Female | 328
5 | 1991 | Male | 301
6 | 1991 | Female | 270
7 | 1992 | Male | 330
```math
\begin{aligned}
251 \textup{kg} &= G_2 + E_{1990} + E_{female} \\
327 \textup{kg} &= G_3 + E_{1991} + E_{male} \\
328 \textup{kg} &= G_4 + E_{1991} + E_{female} \\
301 \textup{kg} &= G_5 + E_{1991} + E_{male} \\
270 \textup{kg} &= G_6 + E_{1991} + E_{female} \\
330 \textup{kg} &= G_7 + E_{1992} + E_{male}
\end{aligned}
```
Drat! Every animal I added brings more variables into the system than it
eliminates! In fact, since each cow brings in _at least_ one term
(_G<sub>n</sub>_), I will never be able to write enough equations to solve for
_G_ numerically. I will have to use a different approach.
## The statistical model: the setup
Since I can never solve for _G_ directly, I will have to find some way to
estimate it. I can switch to a statistical model and solve for _G_ that way. The
caveat with a statistical model is that there will be some level of error, but
so long as we know and can control the level of error, that will be better than
not knowing _G_ at all.
Since we're switching into a statistical space, we should also switch the
variables we're using. I'll rewrite the first equation as
```math
y = b + u + e
```
Where:
- _y_ = Phenotype
- _b_ = Environment
- _u_ = Genotype
- _e_ = Error
It's not as easy as simply substituting _b_ for every _E_ that we had above,
however. The reason for that is that we must make the assumption that
environment is a **fixed effect** and that genotype is a **random effect**. I'll
go over why that is later, but for now, understand that we need to transform the
environment terms and genotype terms separately.
We'll start with the environment terms.
## The statistical model: environment as fixed effects
To properly transform the equations, I will have to introduce
_b<sub>mean</sub>_ terms in each animal's equation. This is part of the fixed
effect statistical assumption, and it will let us obtain a solution.
Here are the transformed equations:
```math
\begin{aligned}
354 \textup{kg} &= u_1 + b_{mean} + b_{1990} + b_{male} + e_1 \\
251 \textup{kg} &= u_2 + b_{mean} + b_{1990} + b_{female} + e_2 \\
327 \textup{kg} &= u_3 + b_{mean} + b_{1991} + b_{male} + e_3 \\
328 \textup{kg} &= u_4 + b_{mean} + b_{1991} + b_{female} +e_4 \\
301 \textup{kg} &= u_5 + b_{mean} + b_{1991} + b_{male} + e_5 \\
270 \textup{kg} &= u_6 + b_{mean} + b_{1991} + b_{female} + e_6 \\
330 \textup{kg} &= u_7 + b_{mean} + b_{1992} + b_{male} + e_7
\end{aligned}
```
Statistical methods work best in matrix form, so I'm going to convert the set of
equations above to a single matrix equation that means the exact same thing.
```math
\begin{bmatrix}
354 \textup{kg} \\
251 \textup{kg} \\
327 \textup{kg} \\
328 \textup{kg} \\
301 \textup{kg} \\
270 \textup{kg} \\
330 \textup{kg}
\end{bmatrix}
=
\begin{bmatrix}
u_1 \\
u_2 \\
u_3 \\
u_4 \\
u_5 \\
u_6 \\
u_7
\end{bmatrix}
+
b_{mean}
+
\begin{bmatrix}
b_{1990} \\
b_{1990} \\
b_{1991} \\
b_{1991} \\
b_{1991} \\
b_{1991} \\
b_{1992}
\end{bmatrix}
+
\begin{bmatrix}
b_{male} \\
b_{female} \\
b_{male} \\
b_{female} \\
b_{male} \\
b_{female} \\
b_{male}
\end{bmatrix}
+
\begin{bmatrix}
e_1 \\
e_2 \\
e_3 \\
e_4 \\
e_5 \\
e_6 \\
e_7
\end{bmatrix}
```
That's a nice equation, but now my hand is getting tired writing all those _b_
terms over and over again, so I'm going to use [the dot product] to condense
this down.
```math
\begin{bmatrix}
354 \textup{kg} \\
251 \textup{kg} \\
327 \textup{kg} \\
328 \textup{kg} \\
301 \textup{kg} \\
270 \textup{kg} \\
330 \textup{kg}
\end{bmatrix}
=
\begin{bmatrix}
u_1 \\
u_2 \\
u_3 \\
u_4 \\
u_5 \\
u_6 \\
u_7
\end{bmatrix}
+
\begin{bmatrix}
1 & 1 & 0 & 0 & 1 & 0 \\
1 & 1 & 0 & 0 & 0 & 1 \\
1 & 0 & 1 & 0 & 1 & 0 \\
1 & 0 & 1 & 0 & 0 & 1 \\
1 & 0 & 1 & 0 & 1 & 0 \\
1 & 0 & 0 & 1 & 1 & 0
\end{bmatrix}
+
\begin{bmatrix}
b_{mean} \\
b_{1990} \\
b_{1991} \\
b_{1992} \\
b_{male} \\
b_{female}
\end{bmatrix}
+
\begin{bmatrix}
e_1 \\
e_2 \\
e_3 \\
e_4 \\
e_5 \\
e_6 \\
e_7
\end{bmatrix}
```
That matrix in the middle with all the zeros and ones is called the **incidence
matrix**, and essentially reads like a table with each row corresponding to an
animal, and each column corresponding to a fixed effect. For brevity, we'll just
call it _**X**_, though. One indicates that the animal and effect go together,
and zero means they don't. For our record, we could write a table to go with
_**X**_, and it would look like this:
Animal | mean | 1990 | 1991 | 1992 | male | female
-- | - | - | - | - | - | -
1 | yes | yes | no | no | yes | no
2 | yes | yes | no | no | no | yes
3 | yes | no | yes | no | yes | no
4 | yes | no | yes | no | no | yes
5 | yes | no | yes | no | yes | no
6 | yes | no | yes | no | no | yes
7 | yes | no | no | yes | yes | no
Now that we have _**X**_, we have the ability to start making changes to allow
us to solve for _u_. Immediately, we see that _**X**_ is **singular**, meaning
it can't be solved directly. We kind of already knew that, but now we can
quantify it. We calculate the [rank of _**X**_], and find that there is only
enough information contained in it to solve for 4 variables, which means we need
to eliminate two columns.
There are several ways to effectively eliminate fixed effects in this type of
system, but one of the simplest and the most common methods is to declare a
**base population**, and lump the fixed effects of animals within the base
population into the mean fixed effect. Note that it is possible to declare a
base population that has no animals in it, but that gives weird results. For
this example, we'll follow the convention built into `beefblup` and pick the
last occuring form of each variable.
### Base population
<dl>
<dt>Year</dt>
<dd>1992</dd>
<dt>Sex</dt>
<dd>Male</dd>
</dl>
Now in order to use the base population, we simply drop the columns representing
conformity with the traits in the base population from _**X**_. Our new
equation looks like
```math
\begin{bmatrix}
354 \textup{kg} \\
251 \textup{kg} \\
327 \textup{kg} \\
328 \textup{kg} \\
301 \textup{kg} \\
270 \textup{kg} \\
330 \textup{kg}
\end{bmatrix}
=
\begin{bmatrix}
u_1 \\
u_2 \\
u_3 \\
u_4 \\
u_5 \\
u_6 \\
u_7
\end{bmatrix}
+
\begin{bmatrix}
1 & 1 & 0 1 \\
1 & 1 & 0 0 \\
1 & 0 & 1 1 \\
1 & 0 & 1 0 \\
1 & 0 & 1 1 \\
1 & 0 & 0 1
\end{bmatrix}
+
\begin{bmatrix}
b_{mean} \\
b_{1990} \\
b_{1991} \\
b_{male} \\
\end{bmatrix}
+
\begin{bmatrix}
e_1 \\
e_2 \\
e_3 \\
e_4 \\
e_5 \\
e_6 \\
e_7
\end{bmatrix}
```
And the table for humans to understand:
Animal | mean | 1990 | 1991 | female
-- | - | - | - | -
1 | yes | yes | no | no
2 | yes | yes | no | yes
3 | yes | no | yes | no
4 | yes | no | yes | yes
5 | yes | no | yes | no
6 | yes | no | yes | yes
7 | yes | no | no | no
Even though each animal is said to participate in the mean, the result for the
mean will now actually be the average of the base population. Math is weird
sometimes.
Double-checking, the rank of _**X**_ is still 4, so we can solve for the average
of the base population, and the effect of being born in 1990, the effect of
being born in 1991, and the effect of being female (although I think [Calvin
already has an idea about that one]).
Whew! That was some transformation. We still haven't constrained this model
enough to solve it, though. Now on to the genotype.
## The statistical model: genotype as random effect
Remember I said above that genotype was a **random effect**? Statisticians say
"_a random effect is an effect that influences the variance and not the mean of
the observation in question._" I'm not sure exactly what that means or how that
is applicable to genotype, but it does let us add an additional constraint to
our model.
The basic gist of genetics is that organisms that are related to one another are
similar to one another. Based on a pedigree, we can even say how related to one
another animals are, and quantify that as the amount that the genotype terms
should be allowed to vary between related animals.
We'll need a pedigree for our animals:
### Calf Records
ID | Sire | Dam | Birth Year | Sex | YW (kg)
-- | - | - | - | - | -
1 | NA | NA | 1990 | Male | 354
2 | NA | NA | 1990 | Female | 251
3 | 1 | NA | 1991 | Male | 327
4 | 1 | NA | 1991 | Female | 328
5 | 1 | 2 | 1991 | Male | 301
6 | NA | 2 | 1991 | Female | 270
7 | NA | NA | 1992 | Male | 330
Now, because cows sexually reproduce, the genotype of one animal is halfway the
same as that of either parent.<sup>[a](#a)</sup> It should go without saying
that each animal's genotype is identical to that of itself. From this we can
then find the numerical multiplier for any relative (grandparent = 1/4, full
sibling = 1, half sibling = 1/2, etc.). Let's write those values down in a
table.
ID | 1 | 2 | 3 | 4 | 5 | 6 | 7
-- | - | - | - | - | - | - | -
1 | 1 | 0 | 1/2 | 1/2 | 1/2 | 0 | 0
2 | 0 | 1 | 0 | 0 | 1/2 | 1/2 | 0
3 | 1/2 | 0 | 1 | 1/4 | 1/4 | 0 | 0
4 | 1/2 | 0 | 1/4 | 1 | 1/4 | 0 | 0
5 | 1/2 | 1/2 | 1/4 | 1/4 | 1 | 1/4 | 0
6 | 0 | 1/2 | 0 | 0 | 1/4 | 1 | 0
7 | 0 | 0 | 0 | 0 | 0 | 0 | 1
Hmm. All those numbers look suspiciously like a matrix. Why don't I put them
into a matrix called _**A**_?
```math
\begin{bmatrix}
1 & 0 & \frac{1}{2} & \frac{1}{2} & \frac{1}{2} & 0 & 0 \\
0 & 1 & 0 & 0 & \frac{1}{2} & \frac{1}{2} & 0 \\
\frac{1}{2} & 0 & 1 & \frac{1}{4} & \frac{1}{4} & 0 & 0 \\
\frac{1}{2} & 0 & \frac{1}{4} & 1 & \frac{1}{4} & 0 & 0 \\
\frac{1}{2} & \frac{1}{2} & \frac{1}{4} & \frac{1}{4} & 1 & \frac{1}{4} & 0 \\
0 & \frac{1}{2} & 0 & 0 & \frac{1}{4} & 1 & 0 \\
0 & 0 & 0 & 0 & 0 & 0 & 1
\end{bmatrix}
```
Now I'm going to take the matrix with all of the _u_ values, and call it
_**μ**_. To quantify the idea of genetic relationship, I will then say that
```math
\textup{var}(μ) = \mathbf{A}σ_μ^2
```
Where:
- _**A**_ = the relationship matrix defined above
- _σ<sub>μ</sub><sup>2</sup>_ = the standard deviation of all the genotypes
To fully constrain the system, I have to make two more assumptions: 1) that the
error term in each animal's equation is independent from all other error terms,
and 2) that the error term for each animal is independent from the value of the
genotype. I will call the matrix holding the _e_ values _**ε**_ and then say
```math
\textup{var}(ϵ) = \mathbf{I}σ_ϵ^2
```
```math
\textup{cov}(μ, ϵ) = \textup{cov}(ϵ, μ) = 0
```
Substituting in the matrix names, our equation now looks like
![\Large Figure 25. Nearly complete mixed-model
equation](https://latex.codecogs.com/svg.latex?%5Cinline%20%5Cbegin%7Bbmatrix%7D%20354%5Ctextup%7B%20kg%7D%5C%5C%20251%5Ctextup%7B%20kg%7D%5C%5C%20327%5Ctextup%7B%20kg%7D%5C%5C%20328%5Ctextup%7B%20kg%7D%5C%5C%20301%5Ctextup%7B%20kg%7D%5C%5C%20270%5Ctextup%7B%20kg%7D%5C%5C%20330%5Ctextup%7B%20kg%7D%20%5Cend%7Bbmatrix%7D%20%3D%20%5Cmu%20&plus;%20X%20%5Cbegin%7Bbmatrix%7D%20b_%7Bmean%7D%5C%5C%20b_%7B1990%7D%5C%5C%20b_%7B1991%7D%5C%5C%20b_%7Bfemale%7D%5C%5C%20%5Cend%7Bbmatrix%7D%20&plus;%20%5Cvarepsilon)
```math
\begin{bmatrix}
354 \textup{kg} \\
251 \textup{kg} \\
327 \textup{kg} \\
328 \textup{kg} \\
301 \textup{kg} \\
270 \textup{kg} \\
330 \textup{kg}
\end{bmatrix}
= μ + X
\begin{bmatrix}
b_{mean} \\
b_{1990} \\
b_{1991} \\
b_{male} \\
\end{bmatrix}
+ ϵ
```
We are going to make three changes to this equation before we are ready to solve
it, but they are cosmetic details for this example.
1. Call the matrix on the left side of the equation _**Y**_ (sometimes it's
called the **matrix of observations**)
2. Multiply _**μ**_ by an identity matrix called _**Z**_. Multiplying by the
identity matrix is the matrix form of multiplying by one, so nothing changes,
but if we later want to find one animal's genetic effect on another animal's
performance (e.g. a **maternal effects model**), we can alter _**Z**_ to
allow that/
3. Call the matrix with all the _b_ values _**β**_.
With all these changes, we now have
```math
Y = Z μ + X β + ϵ
```
This is the canonical form of the mixed-model equation, and the form that
Charles Henderson used to first predict breeding values of livestock.
## Solving the equations
Henderson proved that the mixed-model equation can be solved by the following:
![\Large Figure 27. Solution to mixed-model
equation](https://latex.codecogs.com/svg.latex?%5Cinline%20%5Cbegin%7Bbmatrix%7D%20%5Chat%7B%5Cbeta%7D%5C%5C%20%5Chat%7B%5Cmu%7D%20%5Cend%7Bbmatrix%7D%20%3D%5Cbegin%7Bbmatrix%7D%20X%27X%26X%27Z%5C%5C%20Z%27X%26Z%27Z&plus;A%5E%7B-1%7D%5Clambda%20%5Cend%7Bbmatrix%7D%5E%7B-1%7D%20%5Cbegin%7Bbmatrix%7D%20X%27Y%5C%5C%20Z%27Y%20%5Cend%7Bbmatrix%7D)
```math
\begin{bmatrix}
\hat{β} \\
\hat{μ}
\end{bmatrix}
=
\begin{bmatrix}
X'X & X'Z \\
Z'X & Z'Z&plus;A^{-1}\lambda
\end{bmatrix}^{-1}
\begin{bmatrix}
X'Y \\
Z'Y
\end{bmatrix}
```
Where
- The variables with hats are the statistical estimates of their mixed-model
counterparts
- The predicted value of _**μ**_ is called the _Best Linear Unbiased
Predictor_ or _BLUP_
- The estimated value of _**β**_ is called the _Best Linear Unbiased Estimate_
or _BLUE_
- ' is the transpose operator
- λ is a single real number that is a function of the heritability for the trait
being predicted. It can be left out in many cases (λ = 1).
- λ = (1-h<sup>2</sup>)/h<sup>2</sup>
What happened to
## Footnotes
### a
An animal **can** share its genome with itself by a factor of more than one:
that's called inbreeding! We can account for this, and `beefblup` does as it
calculates _**A**_. This is an area that actually merits a good deal of study:
see chapter 2 of _Linear Models for the Prediction of Animal Breeding Values_ by
Raphael A. Mrode (ISBN 978 1 78064 391 5).
[BIF Guidelines]:
https://beefimprovement.org/wp-content/uploads/2018/03/BIFGuidelinesFinal_updated0318.pdf
[The Dot Product]:
https://www.khanacademy.org/math/precalculus/x9e81a4f98389efdf:matrices/x9e81a4f98389efdf:multiplying-matrices-by-matrices/v/matrix-multiplication-intro
[rank of _**X**_]: https://math.stackexchange.com/a/2080577 [Calvin already has
an idea about that one]: https://www.gocomics.com/calvinandhobbes/1992/12/02