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Merge pull request #47 from senresearch/main
Update dev from main
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‎.github/workflows/CompatHelper.yml

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name: CompatHelper
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on:
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schedule:
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- cron: 0 0 * * *
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workflow_dispatch:
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permissions:
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contents: write
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pull-requests: write
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jobs:
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CompatHelper:
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runs-on: ubuntu-latest
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steps:
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- name: Check if Julia is already available in the PATH
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id: julia_in_path
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run: which julia
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continue-on-error: true
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- name: Install Julia, but only if it is not already available in the PATH
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uses: julia-actions/setup-julia@v1
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with:
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version: '1'
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arch: ${{ runner.arch }}
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if: steps.julia_in_path.outcome != 'success'
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- name: "Add the General registry via Git"
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run: |
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import Pkg
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ENV["JULIA_PKG_SERVER"] = ""
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Pkg.Registry.add("General")
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shell: julia --color=yes {0}
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- name: "Install CompatHelper"
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run: |
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import Pkg
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name = "CompatHelper"
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uuid = "aa819f21-2bde-4658-8897-bab36330d9b7"
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version = "3"
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Pkg.add(; name, uuid, version)
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shell: julia --color=yes {0}
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- name: "Run CompatHelper"
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run: |
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import CompatHelper
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CompatHelper.main()
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shell: julia --color=yes {0}
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env:
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GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }}
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COMPATHELPER_PRIV: ${{ secrets.DOCUMENTER_KEY }}
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# COMPATHELPER_PRIV: ${{ secrets.COMPATHELPER_PRIV }}

‎.github/workflows/TagBot.yml

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name: TagBot
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on:
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issue_comment:
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types:
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- created
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workflow_dispatch:
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jobs:
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TagBot:
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if: github.event_name == 'workflow_dispatch' || github.actor == 'JuliaTagBot'
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runs-on: ubuntu-latest
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steps:
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- uses: JuliaRegistries/TagBot@v1
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with:
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token: ${ secrets.GITHUB_TOKEN }
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ssh: ${ secrets.DOCUMENTER_KEY }

‎.github/workflows/ci.yml

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name: CI
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on:
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pull_request:
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push:
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branches: [main]
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jobs:
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test:
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name: Julia ${{ matrix.version }} - ${{ matrix.os }} - ${{ matrix.arch }} - ${{ github.event_name }}
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runs-on: ${{ matrix.os }}
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strategy:
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fail-fast: false
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matrix:
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version:
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- '1.6'
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- '1' # automatically expands to the latest stable 1.x release of Julia
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- 'nightly'
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os:
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- ubuntu-latest
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arch:
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- x64
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steps:
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- uses: actions/checkout@v2
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- uses: julia-actions/setup-julia@v1
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with:
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version: ${{ matrix.version }}
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arch: ${{ matrix.arch }}
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- uses: actions/cache@v1
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env:
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cache-name: cache-artifacts
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with:
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path: ~/.julia/artifacts
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key: ${{ runner.os }}-test-${{ env.cache-name }}-${{ hashFiles('**/Project.toml') }}
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restore-keys: |
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${{ runner.os }}-test-${{ env.cache-name }}-
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${{ runner.os }}-test-
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${{ runner.os }}-
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- uses: julia-actions/julia-buildpkg@v1
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- uses: julia-actions/julia-runtest@v1
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env:
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JULIA_NUM_THREADS: 4
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- uses: julia-actions/julia-processcoverage@v1
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- uses: codecov/codecov-action@v1
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with:
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file: lcov.info

‎Project.toml

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BigRiverQTLPlots = "042156b7-ffc6-4e52-80f3-70a9c31dd28f"
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BulkLMM = "b8d15608-0852-4141-ae38-222578e2ed7b"
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CSV = "336ed68f-0bac-5ca0-87d4-7b16caf5d00b"
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CategoricalArrays = "324d7699-5711-5eae-9e2f-1d82baa6b597"
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DataFrames = "a93c6f00-e57d-5684-b7b6-d8193f3e46c0"
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Distributed = "8ba89e20-285c-5b6f-9357-94700520ee1b"
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JSON = "682c06a0-de6a-54ab-a142-c8b1cf79cde6"
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LinearAlgebra = "37e2e46d-f89d-539d-b4ee-838fcccc9c8e"
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Missings = "e1d29d7a-bbdc-5cf2-9ac0-f12de2c33e28"
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Reexport = "189a3867-3050-52da-a836-e630ba90ab69"
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Statistics = "10745b16-79ce-11e8-11f9-7d13ad32a3b2"
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StatsBase = "2913bbd2-ae8a-5f71-8c99-4fb6c76f3a91"
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[compat]
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BigRiverQTLPlots = "0.2"
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BulkLMM = "^1.2"
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CSV = "0.10, 1"
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DataFrames = "0.22, 1"
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JSON = "0.21, 1"
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Missings = "1"
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Reexport = "1.2"
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Statistics = "1"
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StatsBase = "0.34"
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julia = "1"
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[extras]
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DelimitedFiles = "8bb1440f-4735-579b-a4ab-409b98df4dab"
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Test = "8dfed614-e22c-5e08-85e1-65c5234f0b40"

‎README.md

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# BigRiverQTL.jl
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[![CI](https://github.com/senresearch/BigRiverQTL.jl/actions/workflows/ci.yml/badge.svg?branch=testing)](https://github.com/senresearch/BigRiverQTL.jl/actions/workflows/ci.yml)
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[![codecov](https://codecov.io/gh/senresearch/BigRiverQTL.jl/branch/testing/graph/badge.svg?token=uHM6utUQoi)](https://codecov.io/gh/senresearch/BigRiverQTL.jl)
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[![Pkg Status](https://www.repostatus.org/badges/latest/active.svg)](https://www.repostatus.org/#active)
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# BigRiverQTL.jl
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*A Statistical Toolbox for QTL Analysis*
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`BigRiverQTL.jl` is a user-friendly Julia package that supports efficient and interpretable quantitative trait locus (QTL) analysis. This comprehensive toolbox encompasses three core components tailored to streamline the entire QTL analysis workflow: preprocessing, genomic scanning, and result visualization.
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`BigRiverQTL.jl` is a user-friendly Julia package that supports
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efficient and interpretable quantitative trait locus (QTL)
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analysis. This comprehensive toolbox encompasses three core components
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tailored to streamline the entire QTL analysis workflow:
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preprocessing, genomic scanning, and result visualization.
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- **Preprocessing:** The preprocessing functions are designed to seamlessly import and convert genomic data into an efficient and memory-conservative format. This component also offers function capabilities for quickly calculating kinship matrices, ensuring data readiness for subsequent analysis phases.
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- **Preprocessing:** The preprocessing functions are designed to
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seamlessly import and convert genomic data into an efficient and
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memory-conservative format. This component also offers function
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capabilities for quickly calculating kinship matrices, ensuring data
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readiness for subsequent analysis phases.
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- **Genomic Scanning:** `BigRiverQTL.jl` provides advanced genomic scanning capabilities through `BulkLMM.jl` for swift single-trait scans, which surpass other methods in terms of computational speed. For analyses involving multiple traits, the package employs `FlxQTL.jl`, a cutting-edge approach that detects complex trait interrelations.
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- **Genomic Scanning:** `BigRiverQTL.jl` provides advanced genomic
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scanning capabilities through `BulkLMM.jl` for swift single-trait
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scans, which surpass other methods in terms of computational
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speed. For analyses involving multiple traits, the package employs
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`FlxQTL.jl`, a cutting-edge approach that detects complex trait
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interrelations.
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- **Result Visualization:** The third component of `BigRiverQTL.jl` enriches the analytical experience by offering plotting tools designed to illustrate the outcomes of genomic scans. The plotting functions are useful for interpreting the results and aid in the derivation of meaningful conclusions
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- **Result Visualization:** The third component of `BigRiverQTL.jl`
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enriches the analytical experience by offering plotting tools
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designed to illustrate the outcomes of genomic scans. The plotting
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functions are useful for interpreting the results and aid in the
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derivation of meaningful conclusions
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## Installation
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To install `BigRiverQTL.jl`, you can use Julia's package manager. Here is the command:
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To install `BigRiverQTL.jl`, you can use Julia's package manager. Here
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is the command:
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```julia
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using Pkg
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## Contribution
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Contributions to BigRiverQTL.jl are welcome and appreciated. If you'd like to contribute, please fork the repository and make changes as you'd like. If you have any questions or issues, feel free to open an
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Contributions to BigRiverQTL.jl are welcome and appreciated. If you
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would like to contribute, please fork the repository, make your
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changes, and send us a pull request. If you have a question or think
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you have found a bug, please open an issue.
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## Examples
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```julia
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using Pkg
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Pkg.activate("../")
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```
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# Example QTL
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___
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```julia
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using Revise
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```
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This example is also available as a
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[notebook](example/example_qtl.ipynb) in the 'example' directory.
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## Data
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In this example, we will use a dataset available from the `R/qtl2`
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package. Specifically, we will use the BXD dataset, which is obtained
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from the [GeneNetwork](https://genenetwork.org/) website.
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You can download the BXD genotype data from the following link:
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[Download BXD Genotype
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Data](https://raw.githubusercontent.com/rqtl/qtl2data/master/BXD/bxd.zip)
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### Example - BXD
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```julia
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using BigRiverQTLPlots
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using BulkLMM
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using Random, Statistics
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using Plots
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using Helium
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# Libraries
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using BigRiverQTL
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using CSV
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using DataFrames
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using Plots
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```
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#### Data
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```julia
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##############
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# BXD spleen #
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##############
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We assume that the data is stored in `..\data\BXD` directory.
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```julia
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########
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# Data #
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########
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data_dir = joinpath(@__DIR__, "../data/BXD/");
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file = joinpath(data_dir, "bxd.json");
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```
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Load bxd data using the function `get_geneticstudydata()`:
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```julia
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# Transforming data to a optimised and accessible data type
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# Load bxd data
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data = get_geneticstudydata(file);
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```
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```julia
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gInfo=data.gmap;
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pInfo=data.phenocov;
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pheno=data.pheno;
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pheno=data.pheno.val;
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geno=data.geno.val[1];
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geno_processed=convert(Array{Float64}, geno);
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# Data types
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# makers info
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gInfo = data.gmap;
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# pehnotype info
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pInfo = data.phenocov;
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# phenotype values
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pheno = data.pheno.val;
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# We can get the genotype matrix using the following command:
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geno = reduce(hcat, data.geno.val);
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# For computing reasons, we need to convert the geno matrix in Float64
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geno_processed = convert(Array{Float64}, geno);
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```
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#### Preprocessing
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```julia
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#################
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traitID = 1112;
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pheno_y = pheno[:, traitID];
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pheno_y2=ones(length(pheno_y));
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pheno_y2[findall(x->x!=nothing,pheno_y)]=pheno_y[findall(x->x!=nothing,pheno_y)];
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idx_nothing = findall(x->x!=nothing,pheno_y)
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pheno_y2[idx_nothing]=pheno_y[idx_nothing];
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```
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#### Kinship
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```julia
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###########
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# Kinship #
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###########
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kinship = kinship_gs(geno_processed,.99)
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kinship = kinship_gs(geno_processed,.99);
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```
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#### Scan
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```julia
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########
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);
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```
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#### Preprocessing
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#### Plots
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```julia
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########
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# Plot #
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########
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#########
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# Plots #
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#########
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plot_QTL(single_results_perms, gInfo)
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# QTL plots
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plot_QTL(single_results_perms, gInfo, mbColname = "Pos")
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```
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![image](images/QTL_example.png)
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```julia
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# Manhattan plots
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plot_manhattan(single_results_perms, gInfo, mbColname = "Pos")
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```
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[]()
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![image](images/manhattan_example.png)

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