Reports observed, expected and unbiased heterozygosities and FIS (inbreeding coefficient) by population or by individual from SNP data
Source:R/gl.report.heterozygosity.r
gl.report.heterozygosity.RdCalculates the observed, expected and unbiased expected (i.e. corrected for sample size) heterozygosities and FIS (inbreeding coefficient) for each population or the observed heterozygosity for each individual in a genlight object.
Usage
gl.report.heterozygosity(
x,
method = "pop",
n.invariant = 0,
plot.out = TRUE,
plot_theme = theme_dartR(),
plot_colors_pop = discrete_palette,
plot_colors_ind = two_colors,
save2tmp = FALSE,
verbose = NULL
)Arguments
- x
Name of the genlight object containing the SNP [required].
- method
Calculate heterozygosity by population (method='pop') or by individual (method='ind') [default 'pop'].
- n.invariant
An estimate of the number of invariant sequence tags used to adjust the heterozygosity rate [default 0].
- plot.out
Specify if plot is to be produced [default TRUE].
- plot_theme
Theme for the plot. See Details for options [default theme_dartR()].
- plot_colors_pop
A color palette for population plots or a list with as many colors as there are populations in the dataset [default discrete_palette].
- plot_colors_ind
List of two color names for the borders and fill of the plot by individual [default two_colors].
- save2tmp
If TRUE, saves any ggplots and listings to the session temporary directory (tempdir) [default FALSE].
- verbose
Verbosity: 0, silent or fatal errors; 1, begin and end; 2, progress log; 3, progress and results summary; 5, full report [default NULL, unless specified using gl.set.verbosity].
Value
A dataframe containing population labels, heterozygosities, FIS, their standard deviations and sample sizes
Details
Observed heterozygosity for a population takes the proportion of heterozygous loci for each individual then averages over the individuals in that population. The calculations take into account missing values.
Expected heterozygosity for a population takes the expected proportion of heterozygotes, that is, expected under Hardy-Weinberg equilibrium, for each locus, then averages this across the loci for an average estimate for the population.
Observed heterozygosity for individuals is calculated as the proportion of loci that are heterozygous for that individual.
Finally, the loci that are invariant across all individuals in the dataset
(that is, across populations), is typically unknown. This can render
estimates of heterozygosity analysis specific, and so it is not valid to
compare such estimates across species or even across different analyses. This
is a similar problem faced by microsatellites. If you have an estimate of the
number of invariant sequence tags (loci) in your data, such as provided by
gl.report.secondaries, you can specify it with the n.invariant
parameter to standardize your estimates of heterozygosity.
NOTE: It is important to realise that estimation of adjusted heterozygosity requires that secondaries not to be removed.
Heterozygosities and FIS (inbreeding coefficient) are calculated by locus within each population using the following equations:
Observed heterozygosity (Ho) = number of homozygotes / n_Ind, where n_Ind is the number of individuals without missing data.
Observed heterozygosity adjusted (Ho.adj) <- Ho * n_Loc / (n_Loc + n.invariant), where n_Loc is the number of loci that do not have all missing data and n.invariant is an estimate of the number of invariant loci to adjust heterozygosity.
Expected heterozygosity (He) = 1 - (p^2 + q^2), where p is the frequency of the reference allele and q is the frequency of the alternative allele.
Expected heterozygosity adjusted (He.adj) = He * n_Loc / (n_Loc + n.invariant)
Unbiased expected heterozygosity (uHe) = He * (2 * n_Ind / (2 * n_Ind - 1))
Inbreeding coefficient (FIS) = 1 - (mean(Ho) / mean(uHe))
Function's output
Output for method='pop' is an ordered barchart of observed heterozygosity, unbiased expected heterozygosity and FIS (Inbreeding coefficient) across populations together with a table of mean observed and expected heterozygosities and FIS by population and their respective standard deviations (SD).
In the output, it is also reported by population: the number of loci used to estimate heterozygosity(nLoc), the number of polymorphic loci (polyLoc), the number of monomorphic loci (monoLoc) and loci with all missing data (all_NALoc).
Output for method='ind' is a histogram and a boxplot of heterozygosity across individuals.
Plots and table are saved to the session temporary directory (tempdir)
Examples of other themes that can be used can be consulted in
See also
Other report functions:
gl.report.bases(),
gl.report.callrate(),
gl.report.diversity(),
gl.report.hamming(),
gl.report.hwe(),
gl.report.ld.map(),
gl.report.locmetric(),
gl.report.maf(),
gl.report.monomorphs(),
gl.report.overshoot(),
gl.report.pa(),
gl.report.parent.offspring(),
gl.report.rdepth(),
gl.report.reproducibility(),
gl.report.secondaries(),
gl.report.sexlinked(),
gl.report.taglength()
Author
Custodian: Luis Mijangos (Post to https://groups.google.com/d/forum/dartr)
Examples
# \donttest{
require("dartR.data")
df <- gl.report.heterozygosity(platypus.gl)
#> Starting gl.report.heterozygosity
#> Processing genlight object with SNP data
#> Warning: data include loci that are scored NA across all individuals.
#> Consider filtering using gl <- gl.filter.allna(gl)
#> Calculating Observed Heterozygosities, averaged across
#> loci, for each population
#> Calculating Expected Heterozygosities
#>
#>
#> pop nInd nLoc polyLoc monoLoc all_NALoc Ho HoSD
#> SEVERN_ABOVE 21.69526 991 496 504 9 0.1198739 0.1753207
#> SEVERN_BELOW 15.90918 991 461 539 9 0.1144862 0.1743597
#> TENTERFIELD 38.06036 994 581 419 6 0.1198424 0.1609034
#> He HeSD uHe uHeSD FIS
#> 0.132964 0.174096 0.136101 0.178203 0.1192294
#> 0.129830 0.174659 0.134042 0.180327 0.1458953
#> 0.142741 0.173964 0.144642 0.176279 0.1714527
#> Completed: gl.report.heterozygosity
#>
df <- gl.report.heterozygosity(platypus.gl,method='ind')
#> Starting gl.report.heterozygosity
#> Processing genlight object with SNP data
#> Warning: data include loci that are scored NA across all individuals.
#> Consider filtering using gl <- gl.filter.allna(gl)
#> Calculating observed heterozygosity for individuals
#> Note: No adjustment for invariant loci (n.invariant set to 0)
#>
#> ind.name Ho f.hom.ref f.hom.alt
#> T27 0.10108696 0.7673913 0.13152174
#> T35 0.14102564 0.7457265 0.11324786
#> SDS4 0.12000000 0.7751351 0.10486486
#> SDS12 0.13246471 0.7578719 0.10966341
#> SUS20 0.14482030 0.7452431 0.10993658
#> SUS28 0.12404788 0.7660501 0.10990207
#> SUS36 0.12459016 0.7628415 0.11256831
#> T3 0.12124464 0.7628755 0.11587983
#> T11 0.12872340 0.7542553 0.11702128
#> T19 0.12688172 0.7505376 0.12258065
#> T28 0.13841202 0.7607296 0.10085837
#> T36 0.12229437 0.7640693 0.11363636
#> SDS5 0.12742981 0.7624190 0.11015119
#> SDS13 0.12955032 0.7601713 0.11027837
#> SUS21 0.12540193 0.7652733 0.10932476
#> SUS29 0.12605932 0.7648305 0.10911017
#> SUS37 0.16230366 0.7392670 0.09842932
#> T4 0.12593383 0.7566702 0.11739594
#> T12 0.12211221 0.7535754 0.12431243
#> T20 0.13001083 0.7453954 0.12459372
#> T29 0.13057671 0.7442873 0.12513602
#> SDS6 0.10064935 0.7738095 0.12554113
#> SDS14 0.11563169 0.7591006 0.12526767
#> SUS22 0.11620469 0.7771855 0.10660981
#> SUS30 0.11111111 0.7777778 0.11111111
#> SUS38 0.11220044 0.7657952 0.12200436
#> T5 0.12327311 0.7608927 0.11583422
#> T13 0.13260870 0.7554348 0.11195652
#> T21 0.13311331 0.7667767 0.10011001
#> T30 0.11925602 0.7582057 0.12253829
#> T38 0.13782051 0.7606838 0.10149573
#> SDS7 0.10951327 0.7876106 0.10287611
#> SDS15 0.12850972 0.7678186 0.10367171
#> SUS23 0.13811563 0.7526767 0.10920771
#> SUS31 0.10825295 0.7642015 0.12754555
#> SUS39 0.11995754 0.7749469 0.10509554
#> T6 0.13024758 0.7578041 0.11194833
#> T14 0.13066955 0.7526998 0.11663067
#> T22 0.13015184 0.7559653 0.11388286
#> T31 0.11745690 0.7640086 0.11853448
#> T39 0.08255814 0.7872093 0.13023256
#> SDS8 0.12676056 0.7573131 0.11592633
#> SDS16 0.12229437 0.7673160 0.11038961
#> SUS24 0.13432836 0.7473348 0.11833689
#> SUS32 0.12030075 0.7669173 0.11278195
#> SUS40 0.12913554 0.7502668 0.12059765
#> T7 0.12995595 0.7599119 0.11013216
#> T15 0.12280702 0.7796053 0.09758772
#> T23 0.11733046 0.7621098 0.12055974
#> T32 0.13311688 0.7532468 0.11363636
#> T40 0.14004376 0.7439825 0.11597374
#> SDS9 0.12606838 0.7628205 0.11111111
#> SDS17 0.11612903 0.7655914 0.11827957
#> SUS25 0.12192513 0.7668449 0.11122995
#> SUS33 0.13856069 0.7561762 0.10526316
#> SUS41 0.11970339 0.7669492 0.11334746
#> T8 0.12284483 0.7683190 0.10883621
#> T16 0.13577586 0.7532328 0.11099138
#> T24 0.11256831 0.7726776 0.11475410
#> SDS2 0.13163482 0.7505308 0.11783439
#> T33 0.12500000 0.7586207 0.11637931
#> T41 0.12568306 0.7693989 0.10491803
#> SDS10 0.12861736 0.7491961 0.12218650
#> SDS18 0.08441558 0.7792208 0.13636364
#> SUS26 0.13291139 0.7594937 0.10759494
#> SUS34 0.14285714 0.7532468 0.10389610
#> T1 0.12850972 0.7613391 0.11015119
#> T9 0.11281490 0.7601314 0.12705367
#> T17 0.11675676 0.7783784 0.10486486
#> T25 0.13275299 0.7529924 0.11425462
#> SDS3 0.13135593 0.7627119 0.10593220
#> T34 0.12784399 0.7638137 0.10834236
#> T42 0.14761376 0.7624861 0.08990011
#> SDS11 0.12227074 0.7696507 0.10807860
#> SUS19 0.10363248 0.7777778 0.11858974
#> SUS27 0.13723404 0.7563830 0.10638298
#> SUS35 0.08884074 0.7757313 0.13542795
#> T2 0.12752391 0.7640808 0.10839532
#> T10 0.12809473 0.7674919 0.10441335
#> T18 0.13513514 0.7632432 0.10162162
#> T26 0.13588110 0.7600849 0.10403397
#> Completed: gl.report.heterozygosity
#>
n.inv <- gl.report.secondaries(platypus.gl)
#> Starting gl.report.secondaries
#> Processing genlight object with SNP data
#> Warning: data include loci that are scored NA across all individuals.
#> Consider filtering using gl <- gl.filter.allna(gl)
#> Counting ....
#> Estimating parameters (lambda) of the Poisson expectation
#> [1] 1.009082
#> [1] 0.6412174
#> [1] 0.4776479
#> [1] 0.3832083
#> [1] 0.3212201
#> [1] 0.2772315
#> [1] 0.2443198
#> [1] 0.2187315
#> [1] 0.1982468
#> [1] 0.1814658
#> [1] 0.1674603
#> [1] 0.1555902
#> [1] 0.1453987
#> [1] 0.1365515
#> [1] 0.1287978
#> [1] 0.1219458
#> [1] 0.1158462
#> [1] 0.1103813
#> [1] 0.1054565
#> [1] 0.1009953
#> [1] 0.09693515
#> [1] 0.09322415
#> [1] 0.08981909
#> [1] 0.08668362
#> [1] 0.08378692
#> [1] 0.08110273
#> [1] 0.07860852
#> [1] 0.07628483
#> [1] 0.07411477
#> [1] 0.07208364
#> [1] 0.07017853
#> [1] 0.06838812
#> [1] 0.06670239
#> [1] 0.06511244
#> [1] 0.06361039
#> [1] 0.06218918
#> [1] 0.06084248
#> [1] 0.05956463
#> [1] 0.05835052
#> [1] 0.05719552
#> [1] 0.05609546
#> [1] 0.05504654
#> [1] 0.05404531
#> [1] 0.05308862
#> [1] 0.05217359
#> [1] 0.05129759
#> [1] 0.0504582
#> [1] 0.04965321
#> [1] 0.04888057
#> [1] 0.04813839
#> [1] 0.04742493
#> [1] 0.04673858
#> [1] 0.04607786
#> [1] 0.04544136
#> [1] 0.04482782
#> [1] 0.04423602
#> [1] 0.04366486
#> [1] 0.0431133
#> [1] 0.04258036
#> [1] 0.04206513
#> [1] 0.04156677
#> [1] 0.04108448
#> [1] 0.04061751
#> [1] 0.04016516
#> [1] 0.03972677
#> [1] 0.03930173
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#> [1] 0.03848936
#> [1] 0.03810096
#> [1] 0.03772377
#> [1] 0.0373573
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#> [1] 0.03665486
#> [1] 0.03631806
#> [1] 0.03599039
#> [1] 0.03567148
#> [1] 0.035361
#> [1] 0.03505863
#> [1] 0.03476407
#> [1] 0.03447704
#> [1] 0.03419725
#> [1] 0.03392445
#> [1] 0.0336584
#> [1] 0.03339884
#> [1] 0.03314557
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#> [1] 0.0321911
#> [1] 0.03196618
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#> [1] 0.03015403
#> [1] 0.02997369
#> [1] 0.02979711
#> [1] 0.02962417
#> [1] 0.02945477
#> [1] 0.02928881
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#> [1] 0.02865743
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#> [1] 0.0265645
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#> [1] 0.02490142
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#> [1] 0.02441593
#> [1] 0.02433932
#> [1] 0.02426388
#> [1] 0.02418959
#> [1] 0.02411641
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#> [1] 0.02397333
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#> [1] 0.02369967
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#> [1] 0.02296794
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#> [1] 0.02220951
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#> [1] 0.02198865
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#> [1] 0.02120063
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#> [1] 0.02110388
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#> [1] 0.02061653
#> [1] 0.02059078
#> [1] 0.02056533
#> [1] 0.02054017
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#> [1] 0.0204907
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#> [1] 0.02044236
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#> [1] 0.0203951
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#> [1] 0.02034891
#> [1] 0.02032621
#> [1] 0.02030376
#> [1] 0.02028156
#> [1] 0.02025961
#> [1] 0.0202379
#> [1] 0.02021644
#> [1] 0.02019521
#> [1] 0.02017422
#> [1] 0.02015346
#> [1] 0.02013294
#> [1] 0.02011264
#> [1] 0.02009256
#> [1] 0.0200727
#> [1] 0.02005307
#> [1] 0.02003364
#> [1] 0.02001443
#> [1] 0.01999543
#> [1] 0.01997664
#> [1] 0.01995805
#> [1] 0.01993966
#> [1] 0.01992148
#> [1] 0.01990349
#> [1] 0.01988569
#> [1] 0.01986809
#> [1] 0.01985067
#> [1] 0.01983345
#> [1] 0.01981641
#> [1] 0.01979955
#> [1] 0.01978287
#> [1] 0.01976637
#> [1] 0.01975005
#> [1] 0.0197339
#> [1] 0.01971792
#> [1] 0.01970211
#> [1] 0.01968647
#> [1] 0.019671
#> [1] 0.01965569
#> [1] 0.01964054
#> [1] 0.01962555
#> [1] 0.01961072
#> [1] 0.01959604
#> [1] 0.01958152
#> [1] 0.01956716
#> [1] 0.01955294
#> [1] 0.01953887
#> [1] 0.01952495
#> [1] 0.01951117
#> [1] 0.01949754
#> [1] 0.01948405
#> [1] 0.01947069
#> [1] 0.01945748
#> [1] 0.01944441
#> [1] 0.01943147
#> [1] 0.01941866
#> [1] 0.01940599
#> [1] 0.01939344
#> [1] 0.01938103
#> [1] 0.01936874
#> [1] 0.01935658
#> [1] 0.01934455
#> [1] 0.01933264
#> [1] 0.01932085
#> [1] 0.01930918
#> [1] 0.01929763
#> [1] 0.0192862
#> [1] 0.01927488
#> [1] 0.01926368
#> [1] 0.0192526
#> [1] 0.01924163
#> [1] 0.01923076
#> [1] 0.01922001
#> [1] 0.01920937
#> [1] 0.01919884
#> [1] 0.01918841
#> [1] 0.01917808
#> [1] 0.01916787
#> [1] 0.01915775
#> [1] 0.01914774
#> Converged on Lambda of 0.0191378238139665
#>
#>
#> Total number of SNP loci scored: 1000
#> Number of sequence tags in total: 991
#> Estimated number of invariant sequence tags: 51288
#> Number of sequence tags with secondaries: 9
#> Number of secondary SNP loci that would be removed on
#> filtering: 9
#> Number of SNP loci that would be retained on filtering: 991
#> Number of invariant sites in sequenced tags: 65820
#> Mean length of sequence tags: 67.42684
#> Total Number of invariant sites (including invariant sequence
#> tags): 3524008
#> Completed: gl.report.secondaries
#>
gl.report.heterozygosity(platypus.gl, n.invariant = n.inv[7, 2])
#> Starting gl.report.heterozygosity
#> Processing genlight object with SNP data
#> Warning: data include loci that are scored NA across all individuals.
#> Consider filtering using gl <- gl.filter.allna(gl)
#> Calculating Observed Heterozygosities, averaged across
#> loci, for each population
#> Calculating Expected Heterozygosities
#>
#>
#> pop nInd nLoc nLoc.adj polyLoc monoLoc all_NALoc
#> SEVERN_ABOVE SEVERN_ABOVE 21.69526 991 0.0002811348 496 504 9
#> SEVERN_BELOW SEVERN_BELOW 15.90918 991 0.0002811348 461 539 9
#> TENTERFIELD TENTERFIELD 38.06036 994 0.0002819857 581 419 6
#> Ho HoSD Ho.adj Ho.adjSD He HeSD
#> SEVERN_ABOVE 0.1198739 0.1753207 3.370072e-05 0.003559680 0.132964 0.174096
#> SEVERN_BELOW 0.1144862 0.1743597 3.218607e-05 0.003496007 0.129830 0.174659
#> TENTERFIELD 0.1198424 0.1609034 3.379382e-05 0.003367792 0.142741 0.173964
#> uHe uHeSD He.adj He.adjSD FIS
#> SEVERN_ABOVE 0.136101 0.178203 3.738e-05 0.00367170 0.1192294
#> SEVERN_BELOW 0.134042 0.180327 3.650e-05 0.00364760 0.1458953
#> TENTERFIELD 0.144642 0.176279 4.025e-05 0.00377745 0.1714527
#> Completed: gl.report.heterozygosity
#>
# }
df <- gl.report.heterozygosity(platypus.gl)
#> Starting gl.report.heterozygosity
#> Processing genlight object with SNP data
#> Warning: data include loci that are scored NA across all individuals.
#> Consider filtering using gl <- gl.filter.allna(gl)
#> Calculating Observed Heterozygosities, averaged across
#> loci, for each population
#> Calculating Expected Heterozygosities
#>
#>
#> pop nInd nLoc polyLoc monoLoc all_NALoc Ho HoSD
#> SEVERN_ABOVE 21.69526 991 496 504 9 0.1198739 0.1753207
#> SEVERN_BELOW 15.90918 991 461 539 9 0.1144862 0.1743597
#> TENTERFIELD 38.06036 994 581 419 6 0.1198424 0.1609034
#> He HeSD uHe uHeSD FIS
#> 0.132964 0.174096 0.136101 0.178203 0.1192294
#> 0.129830 0.174659 0.134042 0.180327 0.1458953
#> 0.142741 0.173964 0.144642 0.176279 0.1714527
#> Completed: gl.report.heterozygosity
#>