Introduction
To start a set of simulations in the AlphaSimR package
(Gaynor, Gorjanc, and Hickey 2021), four
steps must be implemented, as follows:
- Simulate founder genomes/haplotypes.
- Set global simulation parameters for a target trait/traits.
- Model the breeding program.
- Examine results by looking into population individuals’ metrics.
In this vignette, we will implement and further explore the first two.
Genetic basis of base population: Founder genomes
First, we clean our working environment and download/load the AlphaSimR package.
## Carregando pacotes exigidos: AlphaSimR## Warning: package 'AlphaSimR' was built under R version 4.3.3## Carregando pacotes exigidos: R6To create the founder genome we may use the function
runMacs(). It uses the method from Chen et al. (Chen, Marjoram, and Wall 2009) to simulate a
set of haplotypes and, from that, a species-specific demography. This
allows us to simulate a base genome with characteristics of the target
crop that we are working with.
# Founder genome
founderGenomes = runMacs(nInd = 10, # Number of individuals that compose the base genome
nChr = 10, # Number of chromosome pairs of the target species
segSites = 100, # number of segregation sites
species = "MAIZE", # We can use the base parameters and we have available in the package MAIZE, WHEAT, CATTLE, and GENERIC.
ploidy = 2) # Setting the ploidy
# Object created
founderGenomes## An object of class "MapPop"
## Ploidy: 2
## Individuals: 10
## Chromosomes: 10
## Loci: 1000## $`1`
## 1_1 1_2 1_3 1_4 1_5 1_6 1_7
## 0.00000000 0.02350037 0.02432626 0.02974982 0.05290448 0.05314096 0.06560542
## 1_8 1_9 1_10 1_11 1_12 1_13 1_14
## 0.08164341 0.09503599 0.10937703 0.13951821 0.16434691 0.18286539 0.18543564
## 1_15 1_16 1_17 1_18 1_19 1_20 1_21
## 0.20395913 0.21627787 0.22525728 0.23458121 0.25300633 0.25645332 0.27869274
## 1_22 1_23 1_24 1_25 1_26 1_27 1_28
## 0.32536066 0.33301649 0.35411599 0.35757147 0.36195627 0.38981097 0.39692079
## 1_29 1_30 1_31 1_32 1_33 1_34 1_35
## 0.40931790 0.41801899 0.47387501 0.48572567 0.49593467 0.52930085 0.53737307
## 1_36 1_37 1_38 1_39 1_40 1_41 1_42
## 0.54870037 0.56826287 0.58620339 0.60410942 0.60545486 0.61919531 0.62927092
## 1_43 1_44 1_45 1_46 1_47 1_48 1_49
## 0.63039711 0.63145269 0.65801687 0.66893620 0.72733947 0.73574150 0.75588080
## 1_50 1_51 1_52 1_53 1_54 1_55 1_56
## 0.75998999 0.76076984 0.78947345 0.78960570 0.79190792 0.80530303 0.86323745
## 1_57 1_58 1_59 1_60 1_61 1_62 1_63
## 0.87597670 0.88284951 0.90883472 0.92426720 0.92680268 0.95547958 0.99089066
## 1_64 1_65 1_66 1_67 1_68 1_69 1_70
## 1.01446958 1.02430886 1.05077819 1.05797307 1.06205847 1.07136424 1.08092190
## 1_71 1_72 1_73 1_74 1_75 1_76 1_77
## 1.08752477 1.11680535 1.16307338 1.17631320 1.24503437 1.25326893 1.32710127
## 1_78 1_79 1_80 1_81 1_82 1_83 1_84
## 1.35471201 1.39119560 1.44171403 1.51419693 1.53110830 1.53887337 1.56165316
## 1_85 1_86 1_87 1_88 1_89 1_90 1_91
## 1.59818465 1.61192359 1.64331044 1.66137502 1.68632325 1.73494136 1.74538286
## 1_92 1_93 1_94 1_95 1_96 1_97 1_98
## 1.75326656 1.75812563 1.78346880 1.80945606 1.87307046 1.93680117 1.93690816
## 1_99 1_100
## 1.94915784 1.96574402
##
## $`2`
## 2_1 2_2 2_3 2_4 2_5 2_6
## 0.000000000 0.002250523 0.053150929 0.054760691 0.062595078 0.088302999
## 2_7 2_8 2_9 2_10 2_11 2_12
## 0.094055148 0.110004339 0.141437583 0.152219672 0.203927346 0.267739467
## 2_13 2_14 2_15 2_16 2_17 2_18
## 0.277806329 0.279649935 0.282055419 0.282317510 0.286408301 0.289003199
## 2_19 2_20 2_21 2_22 2_23 2_24
## 0.289627805 0.294817605 0.301427484 0.303128553 0.319944424 0.329861225
## 2_25 2_26 2_27 2_28 2_29 2_30
## 0.332010660 0.334382495 0.355834430 0.356450870 0.359853433 0.385147213
## 2_31 2_32 2_33 2_34 2_35 2_36
## 0.472817376 0.486589037 0.499740794 0.507808351 0.549371387 0.560197334
## 2_37 2_38 2_39 2_40 2_41 2_42
## 0.589453133 0.592389537 0.598621797 0.631338145 0.649533005 0.649554010
## 2_43 2_44 2_45 2_46 2_47 2_48
## 0.657399408 0.683593272 0.695070222 0.713840342 0.723856174 0.827309705
## 2_49 2_50 2_51 2_52 2_53 2_54
## 0.828091772 0.832391110 0.846549847 0.858922702 0.901083937 0.901311086
## 2_55 2_56 2_57 2_58 2_59 2_60
## 0.922554444 0.936183124 1.016431115 1.018335024 1.039154966 1.082428072
## 2_61 2_62 2_63 2_64 2_65 2_66
## 1.124293599 1.126252403 1.134599758 1.175175022 1.198566065 1.219100878
## 2_67 2_68 2_69 2_70 2_71 2_72
## 1.219342179 1.228408851 1.242360306 1.273167117 1.289519877 1.293850442
## 2_73 2_74 2_75 2_76 2_77 2_78
## 1.311252918 1.327745550 1.335217663 1.371477313 1.389142910 1.394287320
## 2_79 2_80 2_81 2_82 2_83 2_84
## 1.440571871 1.448929017 1.464193347 1.464734614 1.464835304 1.488507218
## 2_85 2_86 2_87 2_88 2_89 2_90
## 1.523823164 1.546671949 1.603153558 1.622093927 1.637887883 1.690208204
## 2_91 2_92 2_93 2_94 2_95 2_96
## 1.726889221 1.733664261 1.776310822 1.792025689 1.799904078 1.813999016
## 2_97 2_98 2_99 2_100
## 1.859518465 1.903309423 1.932305456 1.943339523
##
## $`3`
## 3_1 3_2 3_3 3_4 3_5 3_6
## 0.000000000 0.007974915 0.045277425 0.048546594 0.063413839 0.064830657
## 3_7 3_8 3_9 3_10 3_11 3_12
## 0.103522710 0.114612748 0.121063306 0.126562730 0.147646284 0.158633534
## 3_13 3_14 3_15 3_16 3_17 3_18
## 0.192232897 0.217687662 0.241473259 0.275912876 0.285136467 0.290808109
## 3_19 3_20 3_21 3_22 3_23 3_24
## 0.292259651 0.384248187 0.469656552 0.478931390 0.481262495 0.488742821
## 3_25 3_26 3_27 3_28 3_29 3_30
## 0.489733189 0.514670969 0.518555670 0.539821499 0.553863418 0.554038063
## 3_31 3_32 3_33 3_34 3_35 3_36
## 0.620830525 0.622454567 0.669028120 0.669603799 0.712523583 0.750320635
## 3_37 3_38 3_39 3_40 3_41 3_42
## 0.754322299 0.754588608 0.757028909 0.782382365 0.798810158 0.842372948
## 3_43 3_44 3_45 3_46 3_47 3_48
## 0.858872763 0.878230712 0.891605100 0.892455264 0.947340878 0.978577866
## 3_49 3_50 3_51 3_52 3_53 3_54
## 1.004458444 1.014860310 1.042086836 1.072344300 1.085111496 1.102441924
## 3_55 3_56 3_57 3_58 3_59 3_60
## 1.146215002 1.158887078 1.165131610 1.188633148 1.205305443 1.218313339
## 3_61 3_62 3_63 3_64 3_65 3_66
## 1.222233233 1.223694126 1.229057861 1.264616904 1.277201998 1.288045062
## 3_67 3_68 3_69 3_70 3_71 3_72
## 1.308365924 1.309689245 1.323991269 1.361833869 1.364756674 1.367415911
## 3_73 3_74 3_75 3_76 3_77 3_78
## 1.395561604 1.396525687 1.422187102 1.433948511 1.476385830 1.524399075
## 3_79 3_80 3_81 3_82 3_83 3_84
## 1.524887086 1.569361294 1.578922514 1.598008868 1.612193454 1.653870155
## 3_85 3_86 3_87 3_88 3_89 3_90
## 1.665764102 1.667753969 1.679850094 1.734450700 1.740433905 1.749262978
## 3_91 3_92 3_93 3_94 3_95 3_96
## 1.781226864 1.795777922 1.799526651 1.804985012 1.856640908 1.884201567
## 3_97 3_98 3_99 3_100
## 1.904966486 1.907313225 1.964314136 1.976493787
##
## $`4`
## 4_1 4_2 4_3 4_4 4_5 4_6 4_7
## 0.00000000 0.05412759 0.06153840 0.09838543 0.14230049 0.18569049 0.20695635
## 4_8 4_9 4_10 4_11 4_12 4_13 4_14
## 0.20779775 0.21947706 0.22507507 0.29338988 0.29506997 0.30389125 0.31543851
## 4_15 4_16 4_17 4_18 4_19 4_20 4_21
## 0.31812888 0.32709739 0.33626562 0.35142876 0.37146924 0.37290498 0.38516038
## 4_22 4_23 4_24 4_25 4_26 4_27 4_28
## 0.42782839 0.42846934 0.43362016 0.43661365 0.46796000 0.46918496 0.47344316
## 4_29 4_30 4_31 4_32 4_33 4_34 4_35
## 0.48709499 0.52217476 0.53729965 0.58286625 0.58569161 0.58848683 0.63685709
## 4_36 4_37 4_38 4_39 4_40 4_41 4_42
## 0.64441792 0.66179484 0.69077233 0.70224876 0.70795244 0.71114618 0.72302381
## 4_43 4_44 4_45 4_46 4_47 4_48 4_49
## 0.76323938 0.78222927 0.78305851 0.78891306 0.80696983 0.83668500 0.85304534
## 4_50 4_51 4_52 4_53 4_54 4_55 4_56
## 0.90837697 0.94822161 0.95010659 0.96541893 1.06316215 1.08139210 1.08630863
## 4_57 4_58 4_59 4_60 4_61 4_62 4_63
## 1.11888244 1.12317595 1.13654083 1.15948588 1.20601736 1.23693686 1.25713746
## 4_64 4_65 4_66 4_67 4_68 4_69 4_70
## 1.31194608 1.33434647 1.34004227 1.36854534 1.43038983 1.44029141 1.44718617
## 4_71 4_72 4_73 4_74 4_75 4_76 4_77
## 1.46391821 1.52774555 1.52836067 1.54277468 1.55072157 1.57132709 1.57932670
## 4_78 4_79 4_80 4_81 4_82 4_83 4_84
## 1.58084650 1.58602081 1.60317358 1.64915548 1.69433193 1.70369010 1.70683667
## 4_85 4_86 4_87 4_88 4_89 4_90 4_91
## 1.72299077 1.73243432 1.77128492 1.82161645 1.82668487 1.85842011 1.86556649
## 4_92 4_93 4_94 4_95 4_96 4_97 4_98
## 1.87015267 1.89456411 1.89738891 1.89807438 1.93034654 1.94805350 1.95161927
## 4_99 4_100
## 1.95661692 1.96946965
##
## $`5`
## 5_1 5_2 5_3 5_4 5_5 5_6 5_7
## 0.00000000 0.02902465 0.03670029 0.03816574 0.04198224 0.09998596 0.11004944
## 5_8 5_9 5_10 5_11 5_12 5_13 5_14
## 0.11038918 0.11061621 0.14924601 0.16468003 0.17718124 0.18046471 0.18495743
## 5_15 5_16 5_17 5_18 5_19 5_20 5_21
## 0.18676059 0.19750495 0.20917094 0.21207529 0.27016234 0.27616604 0.28158850
## 5_22 5_23 5_24 5_25 5_26 5_27 5_28
## 0.35325999 0.38164036 0.41150860 0.41729645 0.54102812 0.58017653 0.58308801
## 5_29 5_30 5_31 5_32 5_33 5_34 5_35
## 0.59739526 0.60780440 0.62167175 0.62182672 0.62798175 0.62824671 0.69480107
## 5_36 5_37 5_38 5_39 5_40 5_41 5_42
## 0.74206096 0.75601383 0.75996848 0.76607984 0.76960274 0.82746360 0.84885305
## 5_43 5_44 5_45 5_46 5_47 5_48 5_49
## 0.88276691 0.89601527 0.91283267 0.92587399 0.95287460 0.97014950 1.01716540
## 5_50 5_51 5_52 5_53 5_54 5_55 5_56
## 1.02394088 1.02537750 1.03046215 1.04266540 1.07583884 1.14121328 1.16844918
## 5_57 5_58 5_59 5_60 5_61 5_62 5_63
## 1.17288194 1.18131865 1.18521567 1.21710112 1.24698729 1.26517529 1.28652036
## 5_64 5_65 5_66 5_67 5_68 5_69 5_70
## 1.30044443 1.30485704 1.33990487 1.38028981 1.38796303 1.38882751 1.40326800
## 5_71 5_72 5_73 5_74 5_75 5_76 5_77
## 1.44345930 1.49969266 1.55666286 1.58227077 1.60284105 1.60800314 1.61359664
## 5_78 5_79 5_80 5_81 5_82 5_83 5_84
## 1.61799647 1.64740524 1.68479309 1.69751990 1.71831425 1.73926763 1.75554321
## 5_85 5_86 5_87 5_88 5_89 5_90 5_91
## 1.77187639 1.83136168 1.83180972 1.83732575 1.84776018 1.85554853 1.86185787
## 5_92 5_93 5_94 5_95 5_96 5_97 5_98
## 1.87058875 1.89840941 1.92586878 1.93014941 1.95460636 1.95510654 1.97212274
## 5_99 5_100
## 1.98389470 1.99265521
##
## $`6`
## 6_1 6_2 6_3 6_4 6_5 6_6
## 0.000000000 0.002760388 0.016044042 0.048704947 0.086859190 0.102655746
## 6_7 6_8 6_9 6_10 6_11 6_12
## 0.117654296 0.125029189 0.127040345 0.150783484 0.250869915 0.273480156
## 6_13 6_14 6_15 6_16 6_17 6_18
## 0.275521974 0.284430464 0.309385930 0.315502071 0.335874104 0.344389651
## 6_19 6_20 6_21 6_22 6_23 6_24
## 0.365446413 0.387316735 0.388990144 0.394086502 0.404365308 0.414369455
## 6_25 6_26 6_27 6_28 6_29 6_30
## 0.414671264 0.440428041 0.509461256 0.530329868 0.589532078 0.626812260
## 6_31 6_32 6_33 6_34 6_35 6_36
## 0.627867256 0.640664238 0.690035933 0.714678866 0.725714146 0.812717476
## 6_37 6_38 6_39 6_40 6_41 6_42
## 0.857000791 0.879005064 0.914389160 0.931679623 0.939835852 0.943531660
## 6_43 6_44 6_45 6_46 6_47 6_48
## 0.945553855 0.951229856 0.952338841 0.956231626 0.961524772 0.967481009
## 6_49 6_50 6_51 6_52 6_53 6_54
## 0.978837890 0.993377001 0.993667030 0.996663909 1.011407756 1.026570387
## 6_55 6_56 6_57 6_58 6_59 6_60
## 1.047007605 1.073459429 1.080595527 1.119324710 1.125568805 1.131683967
## 6_61 6_62 6_63 6_64 6_65 6_66
## 1.159047951 1.168366324 1.172272536 1.209004401 1.232151096 1.243293519
## 6_67 6_68 6_69 6_70 6_71 6_72
## 1.281484337 1.291793399 1.296319915 1.317898806 1.358992495 1.385614768
## 6_73 6_74 6_75 6_76 6_77 6_78
## 1.465175584 1.471478053 1.480967720 1.482444455 1.484813789 1.511853543
## 6_79 6_80 6_81 6_82 6_83 6_84
## 1.565174750 1.586995750 1.618487361 1.622761058 1.624464268 1.634322014
## 6_85 6_86 6_87 6_88 6_89 6_90
## 1.664253569 1.671066758 1.707913714 1.733309097 1.740317535 1.773979274
## 6_91 6_92 6_93 6_94 6_95 6_96
## 1.784512509 1.805802267 1.831139623 1.853990687 1.868673880 1.883045835
## 6_97 6_98 6_99 6_100
## 1.917127983 1.923337618 1.943550390 1.950290081
##
## $`7`
## 7_1 7_2 7_3 7_4 7_5 7_6
## 0.0000000000 0.0005422876 0.0117822111 0.0133531147 0.0164341515 0.0265387544
## 7_7 7_8 7_9 7_10 7_11 7_12
## 0.1341687364 0.1651588336 0.1816793216 0.1887061132 0.2035638932 0.2485284329
## 7_13 7_14 7_15 7_16 7_17 7_18
## 0.2641050765 0.2755917734 0.2949744615 0.3531681782 0.3572436245 0.3942317671
## 7_19 7_20 7_21 7_22 7_23 7_24
## 0.3962966048 0.4073923156 0.4573715866 0.5104274337 0.5295903487 0.5427403536
## 7_25 7_26 7_27 7_28 7_29 7_30
## 0.5652577312 0.6346802423 0.6500344896 0.6525062573 0.6563917645 0.7249827388
## 7_31 7_32 7_33 7_34 7_35 7_36
## 0.7614955721 0.7917109510 0.7979813695 0.8461831184 0.8481017067 0.8505767062
## 7_37 7_38 7_39 7_40 7_41 7_42
## 0.8711392206 0.8932173171 0.8968489877 0.9000682767 0.9423437028 0.9464327017
## 7_43 7_44 7_45 7_46 7_47 7_48
## 0.9546537188 0.9750633544 0.9800968739 0.9937155560 0.9965640340 1.0078631789
## 7_49 7_50 7_51 7_52 7_53 7_54
## 1.0414284859 1.0670726739 1.1023044944 1.1254266165 1.1364547470 1.1689978513
## 7_55 7_56 7_57 7_58 7_59 7_60
## 1.1968225853 1.2089359158 1.2152802296 1.2369859280 1.2456063551 1.3206972594
## 7_61 7_62 7_63 7_64 7_65 7_66
## 1.3320049404 1.3351019833 1.3556346558 1.3900861468 1.4046912257 1.4087675441
## 7_67 7_68 7_69 7_70 7_71 7_72
## 1.4244232889 1.4476603878 1.4754127442 1.4763411783 1.4975718855 1.5426197459
## 7_73 7_74 7_75 7_76 7_77 7_78
## 1.5435789294 1.5489565350 1.5986713769 1.6294509078 1.6459788962 1.6491884252
## 7_79 7_80 7_81 7_82 7_83 7_84
## 1.6616037851 1.6671236360 1.6747943231 1.6797204006 1.6800555505 1.6839434059
## 7_85 7_86 7_87 7_88 7_89 7_90
## 1.6882218123 1.7444391054 1.7473757771 1.7481635210 1.7617682957 1.7678913533
## 7_91 7_92 7_93 7_94 7_95 7_96
## 1.7785406707 1.8135507210 1.8817499108 1.9000510386 1.9001997663 1.9073124136
## 7_97 7_98 7_99 7_100
## 1.9212633100 1.9253642397 1.9381823012 1.9652826890
##
## $`8`
## 8_1 8_2 8_3 8_4 8_5 8_6
## 0.0000000000 0.0009105238 0.0086836680 0.0132159332 0.0223699763 0.0639591469
## 8_7 8_8 8_9 8_10 8_11 8_12
## 0.0786201658 0.1779539469 0.2805343394 0.2951564169 0.2993682821 0.3101526360
## 8_13 8_14 8_15 8_16 8_17 8_18
## 0.3632449283 0.3701478863 0.3892659095 0.4340518733 0.4397641736 0.4689725897
## 8_19 8_20 8_21 8_22 8_23 8_24
## 0.5111552106 0.5112979222 0.5443681476 0.5778889557 0.5829499974 0.6038367951
## 8_25 8_26 8_27 8_28 8_29 8_30
## 0.6103232835 0.6127955605 0.6168336219 0.6268650760 0.6724292115 0.7106021175
## 8_31 8_32 8_33 8_34 8_35 8_36
## 0.7307338380 0.7425289938 0.7721897112 0.7914249630 0.8290322324 0.8439722066
## 8_37 8_38 8_39 8_40 8_41 8_42
## 0.8481378703 0.8534128532 0.8790847404 0.8826264190 0.9507693438 0.9556690443
## 8_43 8_44 8_45 8_46 8_47 8_48
## 0.9645002671 0.9859159654 0.9862069192 1.0089490559 1.0510233895 1.0599552449
## 8_49 8_50 8_51 8_52 8_53 8_54
## 1.0857821586 1.1471842469 1.1873109275 1.1985990544 1.2034666353 1.2779473874
## 8_55 8_56 8_57 8_58 8_59 8_60
## 1.2783760608 1.2885837458 1.2895453576 1.2921350194 1.2999015455 1.3067864158
## 8_61 8_62 8_63 8_64 8_65 8_66
## 1.3091653030 1.3290934108 1.3441552812 1.3443500820 1.3531886466 1.3928797515
## 8_67 8_68 8_69 8_70 8_71 8_72
## 1.4066595930 1.4234660423 1.4270281707 1.4582153348 1.4645786681 1.4646261111
## 8_73 8_74 8_75 8_76 8_77 8_78
## 1.4772435967 1.4826301980 1.4872746648 1.5085300838 1.5285275034 1.5497637571
## 8_79 8_80 8_81 8_82 8_83 8_84
## 1.5700424353 1.6168305730 1.6289798583 1.6477839026 1.6794950518 1.7030224079
## 8_85 8_86 8_87 8_88 8_89 8_90
## 1.7234327842 1.7494494851 1.7590021143 1.7658868948 1.7688398033 1.7767332780
## 8_91 8_92 8_93 8_94 8_95 8_96
## 1.7888143450 1.7937355780 1.8097990983 1.8149202127 1.8273897849 1.8753803392
## 8_97 8_98 8_99 8_100
## 1.8998974787 1.9365915025 1.9707966605 1.9882375176
##
## $`9`
## 9_1 9_2 9_3 9_4 9_5 9_6
## 0.000000000 0.008641172 0.011780800 0.027316989 0.041224334 0.047080456
## 9_7 9_8 9_9 9_10 9_11 9_12
## 0.064111663 0.088436142 0.106835640 0.112063086 0.124270289 0.136752766
## 9_13 9_14 9_15 9_16 9_17 9_18
## 0.163848670 0.244677310 0.263521727 0.266294096 0.277572470 0.314312636
## 9_19 9_20 9_21 9_22 9_23 9_24
## 0.319021494 0.343017264 0.343984858 0.353434839 0.366866684 0.399141640
## 9_25 9_26 9_27 9_28 9_29 9_30
## 0.401677532 0.426044809 0.426317020 0.454949434 0.458466331 0.459039310
## 9_31 9_32 9_33 9_34 9_35 9_36
## 0.463643572 0.522033895 0.539906949 0.569607160 0.622095816 0.631271435
## 9_37 9_38 9_39 9_40 9_41 9_42
## 0.633695590 0.635907139 0.660205754 0.672102603 0.679002665 0.692507331
## 9_43 9_44 9_45 9_46 9_47 9_48
## 0.717289704 0.717353911 0.757576987 0.758465397 0.762776176 0.766654967
## 9_49 9_50 9_51 9_52 9_53 9_54
## 0.794401438 0.795882248 0.796908107 0.806266850 0.847261772 0.902421155
## 9_55 9_56 9_57 9_58 9_59 9_60
## 0.923341409 0.932382873 0.959998126 0.974736050 1.044368408 1.101017392
## 9_61 9_62 9_63 9_64 9_65 9_66
## 1.102665077 1.107309902 1.127958082 1.159372757 1.181008238 1.188297305
## 9_67 9_68 9_69 9_70 9_71 9_72
## 1.249469987 1.258378306 1.258620303 1.268507814 1.279390670 1.279470800
## 9_73 9_74 9_75 9_76 9_77 9_78
## 1.307444887 1.345041389 1.355097370 1.424554032 1.436146516 1.447932508
## 9_79 9_80 9_81 9_82 9_83 9_84
## 1.476899759 1.566798691 1.574997801 1.581078611 1.581965309 1.606131007
## 9_85 9_86 9_87 9_88 9_89 9_90
## 1.611094736 1.619654063 1.647182227 1.650609081 1.679877141 1.695844389
## 9_91 9_92 9_93 9_94 9_95 9_96
## 1.716349022 1.722723036 1.725001246 1.794967914 1.825615623 1.827172248
## 9_97 9_98 9_99 9_100
## 1.845783931 1.902507457 1.924233999 1.928909419
##
## $`10`
## 10_1 10_2 10_3 10_4 10_5 10_6 10_7
## 0.00000000 0.04051040 0.05305527 0.07036126 0.08005613 0.09978904 0.14848792
## 10_8 10_9 10_10 10_11 10_12 10_13 10_14
## 0.15909171 0.17379883 0.18235065 0.18835338 0.18889364 0.19459487 0.19996552
## 10_15 10_16 10_17 10_18 10_19 10_20 10_21
## 0.25234204 0.26588560 0.27956593 0.28099513 0.28842629 0.32121105 0.38087387
## 10_22 10_23 10_24 10_25 10_26 10_27 10_28
## 0.39128348 0.42313911 0.46864897 0.47719503 0.48119718 0.52540550 0.52721867
## 10_29 10_30 10_31 10_32 10_33 10_34 10_35
## 0.53614268 0.54901132 0.57887730 0.59259779 0.63841347 0.64008952 0.64251569
## 10_36 10_37 10_38 10_39 10_40 10_41 10_42
## 0.66478562 0.67829932 0.68562064 0.70224621 0.70300815 0.76762815 0.82066785
## 10_43 10_44 10_45 10_46 10_47 10_48 10_49
## 0.82909924 0.83676158 0.84383646 0.87726047 0.88110908 0.88169313 0.90302308
## 10_50 10_51 10_52 10_53 10_54 10_55 10_56
## 0.91662696 0.94752270 0.95040725 0.97699586 0.99366649 1.00709229 1.01751598
## 10_57 10_58 10_59 10_60 10_61 10_62 10_63
## 1.02537125 1.06040119 1.06718095 1.08611978 1.08968990 1.12482703 1.12564088
## 10_64 10_65 10_66 10_67 10_68 10_69 10_70
## 1.20033294 1.20081933 1.23809331 1.26253214 1.27532539 1.29708509 1.32884297
## 10_71 10_72 10_73 10_74 10_75 10_76 10_77
## 1.33063019 1.39714529 1.42155808 1.44873163 1.46438888 1.49695210 1.50192341
## 10_78 10_79 10_80 10_81 10_82 10_83 10_84
## 1.50856972 1.57415481 1.57661416 1.57928693 1.61680897 1.62094804 1.63119732
## 10_85 10_86 10_87 10_88 10_89 10_90 10_91
## 1.64872988 1.69857714 1.72806667 1.72933450 1.73085496 1.74780146 1.75238789
## 10_92 10_93 10_94 10_95 10_96 10_97 10_98
## 1.76861927 1.77029394 1.80639399 1.81811218 1.82888668 1.85476772 1.90823417
## 10_99 10_100
## 1.92924885 1.93949142When we choose the species in the argument species, four parameters are automatically adjusted:
# Genome length (i.e. Soybean)
genLen = 1.15 #Gb
# Effective population size (Ne)
Ne = 50
# Demographic bottlenecks
speciesParams = "8E8 -t 4E-7 -r 3.6E-7"
# Species history
speciesHist = "-eN 0.03 1 -eN 0.05 2 -eN 0.10 4 -eN 0.15 6"Obs: runMacs() functions offers four
target species with their demography already implemented in the package.
Other than that, we can use runMacs2() function to set our
own species characteristics (further discussed).
Trait characteristics
With the founder genomes in perspective, we can proceed to add the characteristics of the target trait or traits to the simulation. AlphaSimR has a vignette that we recommend you take a look at link. It gives, thoroughly, an explanation on the traits of interest and how to interpret its effects.
Single trait
Additive trait
We will start with a trait with only additive effects. For such, we have the following parameters:
# Global simulation parameters from founder genomes.
SP = SimParam$new(founderGenomes)
# Additive trait
SP$addTraitA(nQtlPerChr = 15, # Number of QTL per chromosome
mean = 10, # Trait mean
var = 10) # Trait variance
# QTL effects (for the traits)
SP$traits[[1]]@addEff## [1] 0.085565946 -0.437593711 0.203398614 -0.765935624 -0.032465806
## [6] 0.044232110 -0.473991442 0.544934027 0.119530112 -0.529582836
## [11] -0.022884257 -0.032665433 0.463425601 0.306441965 -0.597857746
## [16] 0.089172636 -0.594680464 -0.289057561 -1.132172291 0.726862440
## [21] -0.443075949 -0.461144414 0.552168175 -0.020827062 0.522046842
## [26] -0.271751303 -0.488491217 0.614012446 0.190092664 0.613821052
## [31] -0.251684523 -0.038784679 0.064384267 -0.348377377 -0.066404222
## [36] -0.106181607 0.802525974 0.098876784 -0.550690863 0.368050133
## [41] 0.392263954 0.194181205 0.000997263 0.765697273 -0.697251928
## [46] -0.075129410 -0.476404145 0.279559984 0.014631521 0.444036629
## [51] -0.198736165 0.282284217 0.059593793 0.345534932 0.410675627
## [56] 0.113421759 -0.019799552 0.319209614 0.652818662 -0.173292126
## [61] -0.305048432 0.268166932 -0.375904821 0.205511432 -0.393798529
## [66] -0.719018698 -0.175972131 -0.611367498 0.295211059 -0.132458523
## [71] 0.319610939 0.189503240 -0.418237422 0.031265120 0.478585516
## [76] 0.542797551 0.128094071 0.675666650 -0.266860179 -0.360142214
## [81] 0.171089204 0.411911604 -0.246958759 -0.249808741 -0.655187464
## [86] 0.259031995 -0.275548182 0.985861993 -0.167418803 0.552554404
## [91] -0.906867876 -0.729210088 0.834265758 0.250512213 0.285237244
## [96] -0.494364824 -0.252733968 -0.138225223 -0.545484389 -0.763655225
## [101] 0.699225516 0.172230658 0.200142575 -0.720550657 -0.184802557
## [106] -0.128681837 -0.225803808 -0.248233156 0.443527370 -0.047819353
## [111] 0.505110753 -0.048570193 0.204505499 0.077362334 0.249524296
## [116] 0.627307116 0.257669585 0.296161206 -0.951309833 0.233852492
## [121] 0.511375231 -0.102660772 -0.112685449 -0.340946958 0.253392632
## [126] -0.280668491 0.842505754 -0.445981692 -0.230430235 0.642092669
## [131] 0.266243512 0.064713263 -0.009228176 0.136110239 0.316591631
## [136] -0.078515824 -0.305115080 0.874491147 0.336274870 0.177426467
## [141] 0.054279665 -0.738748878 0.324329287 0.434409559 0.067569063
## [146] 0.421375021 0.693381404 0.341151051 0.040458101 0.031176381## [1] 5 7 9 12 31 38 41 51 53 60 68 69 80 82 94 3 17 21
## [19] 25 32 42 43 44 46 51 53 61 71 79 81 1 7 18 22 23 30
## [37] 46 47 49 53 54 67 85 89 100 4 5 17 26 30 31 32 48 51
## [55] 64 81 83 84 86 89 12 15 19 26 32 34 47 53 55 59 60 70
## [73] 85 88 90 8 13 25 39 46 49 54 55 59 70 83 93 96 97 100
## [91] 2 6 11 18 24 54 59 64 73 74 85 89 95 96 99 1 7 8
## [109] 13 18 26 30 32 42 60 75 78 80 84 92 4 7 19 28 34 43
## [127] 48 50 59 65 68 70 75 79 98 12 14 18 29 37 41 43 48 54
## [145] 59 62 64 80 97 99After simulating the base genome and to set the trait
characteristics, we can create individuals from that base genome for the
trait of interest. We will use the function newPop(), as
follows:
## $varA
## Trait1
## Trait1 10
##
## $varD
## Trait1
## Trait1 0
##
## $varAA
## Trait1
## Trait1 0
##
## $varG
## Trait1
## Trait1 10
##
## $genicVarA
## Trait1
## 9.321657
##
## $genicVarD
## Trait1
## 0
##
## $genicVarAA
## Trait1
## 0
##
## $genicVarG
## Trait1
## 9.321657
##
## $covA_HW
## Trait1
## -1.069452
##
## $covD_HW
## Trait1
## 0
##
## $covAA_HW
## Trait1
## 0
##
## $covG_HW
## Trait1
## -1.069452
##
## $covA_L
## Trait1
## 1.747795
##
## $covD_L
## Trait1
## 0
##
## $covAA_L
## Trait1
## 0
##
## $covAD_L
## Trait1
## 0
##
## $covAAA_L
## Trait1
## 0
##
## $covDAA_L
## Trait1
## 0
##
## $covG_L
## Trait1
## 1.747795
##
## $mu
## Trait1
## 10
##
## $mu_HW
## Trait1
## 10
##
## $gv
## Trait1
## [1,] 11.191696
## [2,] 6.479824
## [3,] 10.939248
## [4,] 4.902894
## [5,] 11.704637
## [6,] 5.649299
## [7,] 8.950004
## [8,] 13.425854
## [9,] 14.333984
## [10,] 12.422558
##
## $bv
## Trait1
## [1,] 1.191696
## [2,] -3.520176
## [3,] 0.939248
## [4,] -5.097106
## [5,] 1.704637
## [6,] -4.350701
## [7,] -1.049996
## [8,] 3.425854
## [9,] 4.333984
## [10,] 2.422558
##
## $dd
## Trait1
## [1,] 0
## [2,] 0
## [3,] 0
## [4,] 0
## [5,] 0
## [6,] 0
## [7,] 0
## [8,] 0
## [9,] 0
## [10,] 0
##
## $aa
## Trait1
## [1,] 0
## [2,] 0
## [3,] 0
## [4,] 0
## [5,] 0
## [6,] 0
## [7,] 0
## [8,] 0
## [9,] 0
## [10,] 0
##
## $gv_mu
## Trait1
## 7.762035
##
## $gv_a
## Trait1
## [1,] 3.429661
## [2,] -1.282211
## [3,] 3.177213
## [4,] -2.859141
## [5,] 3.942602
## [6,] -2.112736
## [7,] 1.187969
## [8,] 5.663819
## [9,] 6.571949
## [10,] 4.660523
##
## $gv_d
## Trait1
## [1,] 0
## [2,] 0
## [3,] 0
## [4,] 0
## [5,] 0
## [6,] 0
## [7,] 0
## [8,] 0
## [9,] 0
## [10,] 0
##
## $gv_aa
## Trait1
## [1,] 0
## [2,] 0
## [3,] 0
## [4,] 0
## [5,] 0
## [6,] 0
## [7,] 0
## [8,] 0
## [9,] 0
## [10,] 0## 1_1 1_2 1_3 1_4 1_5 1_6 1_7 1_8 1_9 1_10
## 1_1 1 0 1 0 0 1 1 0 0 0
## 1_2 0 1 0 0 0 0 0 1 0 1
## 2_1 0 0 1 0 0 1 1 0 0 0
## 2_2 1 1 0 1 0 0 1 0 0 0
## 3_1 0 1 0 0 0 1 0 0 0 0
## 3_2 0 0 1 0 0 1 1 0 1 0
## 4_1 0 1 0 1 0 1 0 0 0 0
## 4_2 0 1 0 0 0 1 1 0 1 0
## 5_1 0 1 0 1 0 0 1 0 1 0
## 5_2 0 0 1 0 0 1 1 0 1 0
## 6_1 1 0 1 0 0 0 1 0 0 1
## 6_2 0 1 0 1 0 0 1 0 0 0
## 7_1 1 1 0 1 0 0 1 0 0 1
## 7_2 0 1 0 1 0 1 0 0 1 0
## 8_1 1 0 1 0 0 1 0 0 0 0
## 8_2 1 0 1 0 0 1 1 0 0 1
## 9_1 0 1 0 1 0 1 1 0 1 0
## 9_2 0 1 0 0 0 0 1 0 0 0
## 10_1 1 0 1 0 0 1 1 0 0 0
## 10_2 0 1 0 0 1 0 1 0 1 0## 1_1 1_2 1_3 1_4 1_5 1_6 1_7 1_8 1_9 1_10
## 1 1 1 1 0 0 1 1 1 0 1
## 2 1 1 1 1 0 1 2 0 0 0
## 3 0 1 1 0 0 2 1 0 1 0
## 4 0 2 0 1 0 2 1 0 1 0
## 5 0 1 1 1 0 1 2 0 2 0
## 6 1 1 1 1 0 0 2 0 0 1
## 7 1 2 0 2 0 1 1 0 1 1
## 8 2 0 2 0 0 2 1 0 0 1
## 9 0 2 0 1 0 1 2 0 1 0
## 10 1 1 1 0 1 1 2 0 1 0Allele frequency
Allele frequency describes the proportion of mutations at a locus and can be estimated from the haplotypes (popHaplo object).
Non-additive effects: Trait with additive and dominance effects
# Global simulation parameters from founder genomes.
SP = SimParam$new(founderGenomes)
# Additive trait
SP$addTraitAD(nQtlPerChr = 15, # Number of QTLs controlling the trait per chromosome
# nQtlPerChr = c(15, 5), # Alternative: QTL effect by chromosome
mean = 10,
var = 10,
meanDD = 0.1, # dominance degree - it varies from 0-1 (meaning no-dominance and codominance)
varDD = 0.2) # Variance for the degree of dominance
# QTL effects for additive effects
SP$traits[[1]]@addEff## [1] 0.780247707 -0.487353940 -1.269906012 -1.247051299 -0.091761574
## [6] 0.016169526 0.173178212 0.101523270 0.397094053 0.100332951
## [11] 0.384119011 0.638624110 -0.019663702 0.301857468 0.137373422
## [16] -0.466677625 -0.092614135 0.592090521 0.017921355 -0.398389099
## [21] -0.586636164 1.000756946 0.616929715 -0.305046386 1.096198731
## [26] 1.350829459 0.104904596 1.000304763 -1.163159220 -0.281195582
## [31] -0.804928657 0.022754169 -0.087130664 -1.037365222 0.896845586
## [36] -0.520856804 -0.739784095 -0.248129122 0.072579396 -0.025582275
## [41] -1.211977842 0.007393246 0.298610274 -0.886251259 -0.551403603
## [46] 0.072123697 0.238040265 1.380606701 0.273832163 -1.102504053
## [51] -0.576032632 0.152240353 -0.453234155 0.234427949 0.534416476
## [56] 0.036658791 -1.493351563 -0.421085550 -0.662326823 -0.086441506
## [61] -1.170767932 -1.576099996 -0.737600911 -1.253461589 0.158816988
## [66] 0.032846948 0.422011837 0.217867587 -0.816754632 0.800623540
## [71] -0.789661210 0.358829573 0.738363608 -0.222951733 0.057174988
## [76] -0.390316767 -1.667055002 -0.380218481 -0.947630240 -0.891229503
## [81] 0.422954621 -0.015042713 -0.710260401 0.609043438 0.377300540
## [86] -0.018065644 -0.575593845 -0.266469383 0.052644821 0.466636177
## [91] 0.566396558 0.547807710 -0.459149470 -1.286182699 -0.723823170
## [96] 0.661168962 0.192130177 -0.465964987 0.190600821 0.426614256
## [101] 0.190210466 0.156338546 -0.872564890 0.017843902 -0.184812679
## [106] 0.271078204 -0.345839164 -0.017273015 -0.371467191 0.313561358
## [111] 0.169723095 -0.844400040 -1.109410339 0.478950223 0.403918351
## [116] -0.303570271 -0.891425680 -0.105031953 -0.551836634 1.272935118
## [121] -0.572124950 -1.791143010 -0.489568804 2.093681189 -0.786161152
## [126] 0.603276356 -2.093276363 -0.450421559 -0.144041305 -0.840477612
## [131] -0.420219277 -0.270175012 -0.901040642 1.129621204 -0.178913038
## [136] 0.112990912 -0.024300719 0.045314340 -0.142011046 0.700562633
## [141] -0.620216026 0.422033774 -0.722370969 -0.572991831 -0.343346060
## [146] 1.741171667 0.171582304 -0.284523491 -0.148360424 -0.679703969## [1] 0.047639035 0.016254900 1.181561166 -0.416166407 -0.031928687
## [6] -0.013858072 0.035124053 -0.040796749 0.057186296 0.003448641
## [11] 0.004035300 -0.443363169 -0.008013378 -0.090494433 0.045421450
## [16] 0.243038148 -0.095987437 0.081275749 -0.008572046 -0.111449185
## [21] -0.129754765 -0.090282567 -0.376269648 0.255386957 -0.453239651
## [26] 0.321909357 0.035592163 -0.401475139 0.560430954 0.051147076
## [31] 0.343639605 0.007781657 0.073663669 -0.294975764 -0.180983596
## [36] -0.269489323 -0.284546148 -0.134149895 -0.004476229 0.020436090
## [41] 0.123039736 0.003239567 -0.036258436 0.767782278 -0.169264244
## [46] -0.059277814 0.004967971 0.188372672 -0.091958476 0.598651435
## [51] 0.439159827 0.146019088 0.232309211 0.040446095 0.165686816
## [56] 0.003406726 -0.154512427 -0.058619047 0.160020183 -0.013991377
## [61] 0.849471492 0.795918738 -0.080137216 0.085989027 0.046445714
## [66] 0.007794452 0.338758666 -0.068364527 -0.251119620 0.028846377
## [71] -0.106491268 0.083481233 -0.133946972 0.051516088 0.004881393
## [76] 0.166808487 -0.081045175 0.204332069 0.778376863 -0.110171342
## [81] 0.057751770 -0.003616832 -0.485160409 0.498333035 -0.195638242
## [86] 0.017027778 -0.024922204 -0.064339284 -0.023448470 0.120825884
## [91] -0.397564542 -0.499314669 -0.328624365 0.997063605 0.168369215
## [96] 0.118323698 0.132396649 -0.047506411 -0.019464603 -0.286125284
## [101] -0.120118391 -0.105562807 0.573272405 -0.002324469 -0.035613788
## [106] 0.139463299 0.233265139 0.011146124 -0.229300715 0.030543535
## [111] 0.086583862 0.186401233 0.074273123 0.427488403 0.041393429
## [116] -0.135358676 0.610959799 0.009200015 -0.129708213 0.116261562
## [121] -0.005277494 -0.198139051 0.163003026 1.208433509 0.198359495
## [126] -0.256344955 0.986646393 -0.048936909 0.108401325 0.232356772
## [131] 0.328828032 -0.212588911 -0.018137022 0.006078439 0.027008828
## [136] 0.035478605 0.005172285 0.009565332 0.046914505 -0.010446415
## [141] -0.043787964 -0.110442409 -0.167216554 0.485567690 0.104465161
## [146] 0.148811163 0.081916413 0.104149284 -0.037432931 0.021733648The implementation of traits in AlphaSimR follows a biological model, which is responsible for converting into a genetic value each individual genotype before created. In a straightforward way, the genetic value is used to create the individuals’ phenotypes. The biological effects presented in AlphaSimR are:
A: additive effect
D: dominance effect
G: genotype by environment interaction effect
E: environmental effect
So, we can create traits with the combinations of those effects (assuming that all of them as, at least, additive) using the ADGE framework, as it follows:
# Traits that can be created in AlphaSimR:
SP$addTraitA()
SP$addTraitAD()
SP$addTraitADG()
SP$addTraitADEG()
SP$addTraitAG()
SP$addTraitAE()
SP$addTraitAEG()It is important to have in mind that for dominance effects (D) we set the mean of the dominance degree (0-1) and variance, whereas for genotype by environment effect (G) and environmental effect (E) we have just to adjust the variance.
Multi-trait framework
The AlphaSimR package allows to simulate a set of traits
for the individuals. For such, two strategies may be used.
The first strategy is to set all traits at once (using
$addTrait). In this option, a correlation matrix between
each pair of traits, for each effect, should be added, as follows:
rm(list=ls())
# Founder genome
founderGenomes = AlphaSimR::quickHaplo(nInd = 3, # LD, p=q=0.5, haplotypes from 0,1.
nChr = 3,
segSites = 100)
# From the base population
SP = SimParam$new(founderGenomes)
# Alternative one - Setting the correlation between traits (Additive trait)
SP$addTraitA(nQtlPerChr = 20,
mean = c(0, 10),
var = c(0.5, 2),
corA = matrix(c(1.0, 0.5,
0.5, 1.0), nrow = 2))
# QTL effects for trait one
SP$traits[[1]]@addEff## [1] 0.2290251069 0.2441651222 0.3029507664 0.3077077461 -0.2972007612
## [6] 0.5173174254 0.2508114831 -0.2007550496 0.0457774633 0.1905144136
## [11] 0.1466971199 0.1916791879 0.1188953808 -0.0955399642 0.0939311457
## [16] 0.1444396536 -0.2117420477 -0.2145416350 -0.4601159079 0.1549942832
## [21] 0.5817058591 -0.1107348664 0.0638352879 -0.0872512301 -0.0904934525
## [26] -0.3789265715 -0.3715813493 -0.2668805669 0.1418988183 -0.2702105328
## [31] 0.1271771921 -0.2237840918 0.0085073276 -0.2088390327 -0.1883229842
## [36] 0.1348169503 -0.2781722211 0.0470201019 0.0577347300 -0.1468260158
## [41] 0.0967544412 -0.0008380173 0.0270439441 0.1765114366 -0.2010104777
## [46] 0.0868925834 0.1816365692 -0.1884773228 -0.0941057927 -0.2356188465
## [51] -0.0332832184 0.1004036229 0.0742151686 -0.2044750533 0.1501508308
## [56] 0.1089308092 -0.1387989256 0.4798144083 0.2881120628 0.2872272680## [1] -0.034815372 -0.031756648 0.033999906 0.785947336 -0.146362306
## [6] -0.133358572 0.285649151 -0.153070063 -0.110048907 -0.417160812
## [11] 0.348114170 0.010502804 0.326338202 -0.479021971 -0.074231811
## [16] 0.403896146 -0.277967623 -0.398587988 -0.825472352 0.836460164
## [21] 0.342178719 -0.346725813 -0.003337369 0.035384180 0.073644914
## [26] -1.011656060 0.186072877 -0.411544304 0.115894375 -0.041581654
## [31] 0.065793762 0.107345411 0.157368712 -0.319349576 0.047018515
## [36] -0.008459182 -0.470528513 0.289086746 -0.105934830 -0.022827164
## [41] 0.392217921 -0.137367713 -0.150171033 -0.300671270 -0.228526801
## [46] 0.194717214 0.339439314 -0.312254829 0.165703012 -0.193181400
## [51] 0.185216263 0.004676330 -0.025210059 -0.850662717 0.643995431
## [56] 0.459467610 -0.717636845 0.113203349 0.493948156 0.850083171A second strategy in a multi-trait framework is to add each trait individually. In this case, we do not need to add the information of correlation between them. Another positive aspect of this second strategy is the possibility of setting a different number of QTLs controlling each trait simulated.
# From the base population
SP = SimParam$new(founderGenomes)
# Alternative two - One trait at a time
# Trait one
SP$addTraitA(nQtlPerChr = 20,
mean = 0,
var = 0.5)
# Trait two
SP$addTraitA(nQtlPerChr = 5,
mean = 100,
var = 20)
# QTL effects for trait one
SP$traits[[1]]@addEff## [1] -0.32770099 -0.07223123 -0.42208626 -0.05465270 -0.01272842 0.73556171
## [7] 0.09970047 -0.73730769 -0.22961957 -0.08753253 0.15112800 -0.15160323
## [13] -0.07291887 0.59192188 -0.20349971 0.67519737 0.09815017 -0.18705538
## [19] 0.10506299 -0.17366900 -0.02899719 0.15228530 -0.57771983 -0.09313978
## [25] 0.09134781 0.31440722 0.34588836 0.57655633 0.28074450 0.05297949
## [31] 0.23159002 0.04240052 0.02602039 0.30204222 0.21752080 -0.37891815
## [37] 0.29685021 -0.52981112 -0.28456329 0.02856641 0.95158188 0.03593571
## [43] -0.04098482 -0.44223218 -0.12280024 0.41138331 -0.32340035 0.79130579
## [49] 0.57229674 0.20561058 0.12645046 0.36799576 0.10128746 -0.12259787
## [55] 0.91029927 -0.03982485 -0.29831049 -0.42644228 0.20993957 -0.52476795## [1] -1.09411493 -0.43592529 -0.07113493 0.59293637 1.47692874 -0.50789459
## [7] -0.73739642 -1.64366392 -2.35271610 1.04388016 0.35536704 0.89803573
## [13] 0.05229721 -0.73050826 1.66183979Split argument
In breeding programs, such as maize, heterosis is harnessed by exploiting the crosses between heterotic groups. Usually, two distinct groups are used (i.e., maize dent and flint germplasm pools). The genetic divergence of these two pools is explained by their historic geographical separation and adaptation to different environments. It has undergone changes in the population structure and history of the base population.
For simulations that involve population separation in terms of
generations, the AlphaSimR package offers the split
argument in the runMacs() function. It represents an
optional historic population split in terms of generations ago.
Below, we will simulate three different base genomes. The first one will use the default value (NULL) for the split argument. The second and third genomes will be split 10 and 50 generations ago, respectively. We will examine the base genome genotypes and explore them through a principal component analysis (PCA).
rm(list=ls())
#------------------- Founder genome with no split
founderGenomes = runMacs(nInd = 200,
nChr = 5,
segSites = 100,
species = "MAIZE",
ploidy = 2,
split = NULL)
# From the base population
SP = SimParam$new(founderGenomes)
# Creating individuals/population
basePop = newPop(founderGenomes)
# Check the genotypes
popGeno = pullSegSiteGeno(basePop)
# PCA
pca_noSplit = prcomp(popGeno)
#------------------- Founder genome with split equals to 10
founderGenomes = runMacs(nInd = 200,
nChr = 5,
segSites = 100,
species = "MAIZE",
ploidy = 2,
split = 10)
# From the base population
SP = SimParam$new(founderGenomes)
# Creating individuals/population
basePop = newPop(founderGenomes)
# Check the genotypes
popGeno_10 = pullSegSiteGeno(basePop)
# PCA
pca_Split10 = prcomp(popGeno_10)
#------------------- Founder genome with split equals to 50
founderGenomes = runMacs(nInd = 200,
nChr = 5,
segSites = 100,
species = "MAIZE",
ploidy = 2,
split = 50)
# From the base population
SP = SimParam$new(founderGenomes)
# Creating individuals/population
basePop = newPop(founderGenomes)
# Check the genotypes
popGeno_50 = pullSegSiteGeno(basePop)
# PCA
pca_Split50 = prcomp(popGeno_50)Plotting the PCA from each base population created.
## Warning: package 'ggfortify' was built under R version 4.3.3## Carregando pacotes exigidos: ggplot2## Warning: package 'ggplot2' was built under R version 4.3.3# Plot
par(mar = c(1, 3, .1, .1))
autoplot(pca_noSplit, data = popGeno)
autoplot(pca_Split10, data = popGeno_10)
autoplot(pca_Split50, data = popGeno_50)
References
Chen, Gary K, Paul Marjoram, and Jeffrey D Wall. 2009. “Fast and
Flexible Simulation of DNA Sequence Data.” Genome
Research 19 (1): 136–42.
Gaynor, R Chris, Gregor Gorjanc, and John M Hickey. 2021.
“AlphaSimR: An r Package for Breeding Program Simulations.”
G3 11 (2): jkaa017.
Post doc, University of Florida, deamorimpeixotom@ufl.edu↩︎