Tutorials

Installing this Package

To install, using REPL:

julia> using Pkg; Pkg.add("GenLib")

Or in the Pkg REPL mode (]):

add GenLib

You may then use the library with using GenLib or, to mimic the behavior of R's GENLIB, import GenLib as gen.

Loading a Pedigree

From a DataFrame

Here is a pedigree with full first-degree cousins.

import GenLib as gen
using DataFrames
inds = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]
fathers = [0, 0, 0, 1, 1, 0, 3, 3, 6, 6]
mothers = [0, 0, 0, 2, 2, 0, 4, 4, 5, 5]
sexes = [1, 2, 1, 2, 2, 1, 2, 1, 1, 2]
df = DataFrame([inds, fathers, mothers, sexes], [:ind, :father, :mother, :sex])
ped = gen.genealogy(df)

A pedigree with:
10 individuals;
12 parent-child relations;
5 men;
5 women;
4 subjects;
3 generations.

From a CSV File

The original GENLIB package for R contains two sample pedigrees. They are also available in GenLib.jl as GenLib.genea140 and GenLib.geneaJi.

julia> import GenLib as gen
julia> genea140 = gen.genea140"/home/runner/work/GenLib.jl/GenLib.jl/data/genea140.csv"
julia> ped = gen.genealogy(genea140)A pedigree with:
41523 individuals;
68248 parent-child relations;
20773 men;
20750 women;
140 subjects;
18 generations.

julia> import GenLib as gen
julia> geneaJi = gen.geneaJi"/home/runner/work/GenLib.jl/GenLib.jl/data/geneaJi.csv"
julia> ped = gen.genealogy(geneaJi)A pedigree with:
29 individuals;
44 parent-child relations;
15 men;
14 women;
3 subjects;
8 generations.

Accessing an Individual

A pedigree is an ordered dictionary where the key is the ID and the value corresponds to the GenLib.Individual.

The individual's parents and children are accessed by reference.

julia> import GenLib as gen
julia> genea140 = gen.genea140;
julia> ped = gen.genealogy(genea140);
julia> ped[33724]ind: 33724
father: 10086
mother: 10087
sex: 1
julia> ped[33724].motherind: 10087
father: 0
mother: 0
sex: 2
julia> ped[33724].fatherind: 10086
father: 0
mother: 0
sex: 1
julia> ped[33724].children2-element Vector{GenLib.Individual}:
 ind: 10033
father: 33724
mother: 35178
sex: 2
 ind: 113470
father: 33724
mother: 35178
sex: 1
julia> ped[33724].children[2].fatherind: 33724
father: 10086
mother: 10087
sex: 1

Getting Founders and Probands

This is done using the GenLib.founder and GenLib.pro functions, respectively.

julia> import GenLib as gen
julia> genea140 = gen.genea140;
julia> ped = gen.genealogy(genea140);
julia> founder = gen.founder(ped)7399-element Vector{Int64}:
  10086
  10087
  10102
  10103
  10128
  10129
  10136
  10137
  10150
  10151
      ⋮
 870618
 870619
 871681
 872039
 872040
 872102
 872103
 872106
 872107
julia> pro = gen.pro(ped)140-element Vector{Int64}:
 217891
 218089
 219947
 288708
 288764
 302710
 302711
 302716
 302717
 302718
      ⋮
 676521
 677273
 717537
 717634
 717709
 718567
 802424
 868387
 868572

Finding Most Recent Common Ancestors

julia> import GenLib as gen
julia> genea140 = gen.genea140;
julia> ped = gen.genealogy(genea140);
julia> pro = gen.pro(ped);
julia> pro1 = pro[1]217891
julia> pro2 = pro[2]218089
julia> genMatrix = gen.findMRCA(ped, [pro1, pro2]);
julia> genMatrix.individuals2-element Vector{Int64}:
 217891
 218089
julia> genMatrix.ancestors108-element Vector{Int64}:
  11171
  11601
  11602
  13234
  13235
  16910
  16911
  17060
  17087
  18215
      ⋮
  65345
  68161
  68162
  70305
  70306
  77206
 203641
 253080
 253081
julia> genMatrix.meioses2×108 Matrix{Int64}:
 11   9   9   9   9  11  11  8   9   9  …  11  11  11  10  10   9  9  10  10
 12  11  11  10  10  11  11  8  10  13     10  11  11  10  10  11  9  10  10

Computing Genetic Contributions

This is done with the GenLib.gc function.

julia> import GenLib as gen
julia> genea140 = gen.genea140;
julia> ped = gen.genealogy(genea140);
julia> contributions = gen.gc(ped)140×7399 Matrix{Float32}:
 0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  …  0.0  0.0       0.0       0.0  0.0
 0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0     0.0  0.015625  0.015625  0.0  0.0
 0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0     0.0  0.0       0.0       0.0  0.0
 0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0     0.0  0.0       0.0       0.0  0.0
 0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0     0.0  0.0       0.0       0.0  0.0
 0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  …  0.0  0.0       0.0       0.0  0.0
 0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0     0.0  0.0       0.0       0.0  0.0
 0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0     0.0  0.0       0.0       0.0  0.0
 0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0     0.0  0.0       0.0       0.0  0.0
 0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0     0.0  0.0       0.0       0.0  0.0
 ⋮                        ⋮              ⋱       ⋮
 0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0     0.0  0.0       0.0       0.0  0.0
 0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0     0.0  0.0       0.0       0.0  0.0
 0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0     0.0  0.0       0.0       0.0  0.0
 0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0     0.0  0.0       0.0       0.0  0.0
 0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  …  0.0  0.0       0.0       0.0  0.0
 0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0     0.0  0.0       0.0       0.0  0.0
 0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0     0.0  0.0       0.0       0.0  0.0
 0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0     0.0  0.0       0.0       0.0  0.0
 0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0     0.0  0.0       0.0       0.0  0.0
julia> sum(contributions, dims=2)140×1 Matrix{Float32}:
 1.0
 1.0
 1.0
 1.0
 1.0
 1.0
 1.0
 1.0
 1.0
 1.0
 ⋮
 1.0
 1.0
 1.0
 1.0
 1.0
 1.0
 1.0
 1.0
 1.0

Computing Kinship Coefficients

This is done using one of the GenLib.phi functions.

Pairwise Coefficient

Let's take the two siblings above for example.

import GenLib as gen
genea140 = gen.genea140;
ped = gen.genealogy(genea140);
pro1 = ped[10033]
pro2 = ped[113470]
gen.phi(pro1, pro2)

0.25

Square Matrix

import GenLib as gen
geneaJi = gen.geneaJi
ped = gen.genealogy(geneaJi)
gen.phi(ped)

3×3 Matrix{Float32}:
 0.591797   0.371094   0.0722656
 0.371094   0.591797   0.0722656
 0.0722656  0.0722656  0.535156