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Thread: QPADM runs

  1. #111
    Registered Users
    Posts
    548
    Sex
    Location
    Libya
    Ethnicity
    North African
    Nationality
    Libyan
    aDNA Match (1st)
    Austria_Wels:R10667
    aDNA Match (2nd)
    Algeria_Necropole_Orientale:R10770
    aDNA Match (3rd)
    Guanche:gun008
    Y-DNA (P)
    E-FT458078
    mtDNA (M)
    H65a

    African Union
    Quote Originally Posted by Qrts View Post
    Modeling Guanches with this very simple model:
    Code:
    > results$weights
    # A tibble: 2 x 5
      target                   left                        weight     se     z
      <chr>                    <chr>                        <dbl>  <dbl> <dbl>
    1 CanaryIslands_Guanche.SG Morocco_LN.SG               0.910  0.0141 64.4 
    2 CanaryIslands_Guanche.SG Congo_Kindoki_Protohistoric 0.0903 0.0141  6.40
    > 
    > results$popdrop 
    # A tibble: 3 x 13
      pat      wt   dof   chisq            p f4rank Morocco_LN.SG Congo_Kindoki_Protohistoric feasible best  dofdiff chisqdiff p_nested
      <chr> <dbl> <dbl>   <dbl>        <dbl>  <dbl>         <dbl>                       <dbl> <lgl>    <lgl>   <dbl>     <dbl>    <dbl>
    1 00        0     5    5.27 0.383             1         0.910                      0.0903 TRUE     NA         NA       NA        NA
    2 01        1     6   44.7  0.0000000547      0         1                         NA      TRUE     TRUE        0    -5455.        1
    3 10        1     6 5499.   0                 0        NA                          1      TRUE     TRUE       NA       NA        NA
    Do they not require Steppe however?
    would it also be possible to run the egyptian average, would be interesting to see how they score on qpadm compared to g25

    Target: Egyptian
    Distance: 0.9545% / 0.00954504
    47.8 Levant_IA
    32.0 EGY_Late_Period
    10.8 SSA
    9.4 Canary_Islands_Guanche
    Last edited by Abceff; 11-12-2022 at 09:51 AM.
    Target: abceff
    Distance: 1.6043% / 0.01604301
    43.6 Tunisian_Berber_Zraoua
    26.2 Tunisian_Jew
    24.0 Berber_Tunisia_Sen
    3.8 Bantu_Kenya
    2.4 Dinka


    Distance to: abceff
    0.01873718 23.40% Tunisian_Jew:TunisianJew1511 + 76.60% Moroccan:MCA16

  2. The Following User Says Thank You to Abceff For This Useful Post:

     Gentica277282 (11-12-2022)

  3. #112
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    Omitted

    Quote Originally Posted by Abceff View Post
    would it also be possible to run the egyptian average, would be interesting to see how they score on qpadm compared to g25

    Target: Egyptian
    Distance: 0.9545% / 0.00954504
    47.8 Levant_IA
    32.0 EGY_Late_Period
    10.8 SSA
    9.4 Canary_Islands_Guanche
    Unfortunately I could not model using your example above. I first modeled ancient Egyptians and some models passed but could not get substantially lower standard errors than 6-7%, which is expected considering Natufians shouldn't be the exact same as Egyptian Hunter-gatherers/Neolithic farmers.

    rightpops:
    right = c('Mbuti.DG', 'Ami.DG', 'Mixe.DG', 'RUS_Kostenki14', 'RUS_Samara_HG', 'ITA_Villabruna', 'Morocco_Taforalt_EpiP')
    Code:
    > results$weights
    # A tibble: 2 x 5
      target                        left                    weight     se     z
      <chr>                         <chr>                    <dbl>  <dbl> <dbl>
    1 Egypt_ThirdIntermediatePeriod Levant_Natufian_EpiP     0.513 0.0645  7.94
    2 Egypt_ThirdIntermediatePeriod TUR_Hatay_Alalakh_MLBA   0.487 0.0645  7.55
    > 
    > results$popdrop 
    # A tibble: 3 x 13
      pat      wt   dof  chisq        p f4rank Levant_Natufian_EpiP TUR_Hatay_Alalakh_MLBA feasible best  dofdiff chisqdiff p_nested
      <chr> <dbl> <dbl>  <dbl>    <dbl>  <dbl>                   <dbl>                  <dbl> <lgl>    <lgl>   <dbl>     <dbl>    <dbl>
    1 00        0     5   5.50 3.58e- 1      1                   0.513                  0.487 TRUE     NA         NA       NA        NA
    2 01        1     6  48.3  1.04e- 8      0                   1                     NA     TRUE     TRUE        0     -121.        1
    3 10        1     6 170.   5.58e-34      0                  NA                      1     TRUE     TRUE       NA       NA        NA
    Egyptian female outlier from Beirut models the same but with additional ~5% Dinka, which the above samples do not need:
    Code:
    > results$weights
    # A tibble: 3 x 5
      target            left                    weight     se     z
      <chr>             <chr>                    <dbl>  <dbl> <dbl>
    1 LBN_IA_Egyptian_o Levant_Natufian_EpiP    0.481  0.0748  6.44
    2 LBN_IA_Egyptian_o TUR_Hatay_Alalakh_MLBA  0.467  0.0723  6.46
    3 LBN_IA_Egyptian_o Dinka.DG                0.0515 0.0135  3.80
    > 
    > results$popdrop 
    # A tibble: 7 x 14
      pat      wt   dof   chisq        p f4rank Levant_Natufian_EpiP TUR_Hatay_Alalakh_MLBA Dinka.DG feasible best  dofdiff chisqdiff p_nested
      <chr> <dbl> <dbl>   <dbl>    <dbl>  <dbl>                   <dbl>                  <dbl>    <dbl> <lgl>    <lgl>   <dbl>     <dbl>    <dbl>
    1 000       0     4    6.35 1.74e- 1      2                   0.481                  0.467   0.0515 TRUE     NA         NA      NA         NA
    2 001       1     5   23.5  2.69e- 4      1                   0.653                  0.347  NA      TRUE     TRUE        0     -13.9        1
    3 010       1     5   37.4  5.05e- 7      1                   0.972                 NA       0.0283 TRUE     TRUE        0     -58.6        1
    4 100       1     5   96.0  3.73e-19      1                  NA                      0.925   0.0747 TRUE     TRUE       NA      NA         NA
    5 011       2     6   40.1  4.42e- 7      0                   1                     NA      NA      TRUE     NA         NA      NA         NA
    6 101       2     6  293.   2.95e-60      0                  NA                      1      NA      TRUE     NA         NA      NA         NA
    7 110       2     6 7761.   0             0                  NA                     NA       1      TRUE     NA         NA      NA         NA

    As for modern Egyptians, this is the best model:
    Code:
    > results$weights
    # A tibble: 2 x 5
      target   left                     weight     se     z
      <chr>    <chr>                     <dbl>  <dbl> <dbl>
    1 Egyptian Lebanon_Beirut_IA3_o1.SG 0.934  0.0136 68.5 
    2 Egyptian Dinka.DG                 0.0660 0.0136  4.84
    > 
    > results$popdrop 
    # A tibble: 3 x 13
      pat      wt   dof   chisq        p f4rank Lebanon_Beirut_IA3_o1.SG Dinka.DG feasible best  dofdiff chisqdiff p_nested
      <chr> <dbl> <dbl>   <dbl>    <dbl>  <dbl>                    <dbl>    <dbl> <lgl>    <lgl>   <dbl>     <dbl>    <dbl>
    1 00        0     5    5.56 0.351         1                    0.934   0.0660 TRUE     NA         NA       NA        NA
    2 01        1     6   25.9  0.000228      0                    1      NA      TRUE     TRUE        0    -8923.        1
    3 10        1     6 8949.   0             0                   NA       1      TRUE     TRUE       NA       NA        NA
    Modern Egyptians seem to require additional East African input, and they can only be successfully modeled with the female outlier from Beirut who already has ~5% East African, but not with the the Late Period Egyptians who don't seem to require any.
    Last edited by Qrts; 11-13-2022 at 06:29 PM.

  4. The Following User Says Thank You to Qrts For This Useful Post:

     Gentica277282 (11-13-2022)

  5. #113
    Registered Users
    Posts
    2,527
    Ethnicity
    Arab
    Y-DNA (P)
    J2a-J-L210-J-y15222
    mtDNA (M)
    L1b2a

    With high coverage guanche sample 011

    Code:
    > qp$weights
    # A tibble: 3  5
      target     left                weight     se     z
      <chr>      <chr>                <dbl>  <dbl> <dbl>
    1 GenticaAnc Guanche.SG_11        0.575 0.0714  8.05
    2 GenticaAnc COG_MatangaiTuru_IA  0.112 0.0201  5.55
    3 GenticaAnc LBN_IA               0.314 0.0604  5.19
    > qp$popdrop
    # A tibble: 7  14
      pat      wt   dof  chisq         p f4rank Guanche.SG_11 COG_MatangaiTuru_IA LBN_IA feasible best  dofdiff chisqdiff p_nested
      <chr> <dbl> <dbl>  <dbl>     <dbl>  <dbl>         <dbl>               <dbl>  <dbl> <lgl>    <lgl>   <dbl>     <dbl>    <dbl>
    1 000       0     8   7.59 4.75e-  1      2         0.575              0.112   0.314 TRUE     NA         NA     NA          NA
    2 001       1     9  46.5  4.83e-  7      1         0.931              0.0693 NA     TRUE     TRUE        0      5.26        0
    3 010       1     9  41.3  4.49e-  6      1         0.874             NA       0.126 TRUE     TRUE        0    -55.2         1
    4 100       1     9  96.5  8.01e- 17      1        NA                  0.260   0.740 TRUE     TRUE       NA     NA          NA
    5 011       2    10  67.7  1.21e- 10      0         1                 NA      NA     TRUE     NA         NA     NA          NA
    6 101       2    10 352.   1.72e- 69      0        NA                  1      NA     TRUE     NA         NA     NA          NA
    7 110       2    10 578.   9.38e-118      0        NA                 NA       1     TRUE     NA         NA     NA          NA
    > 
    > `|`=`%>%`
    > p=""
    > o=""
    > rm(t)
    Warning message:
    In rm(t) : object 't' not found
    > #t=qp$popdrop|dplyr::filter(f4rank!=0&feasible)|arrange(desc(p),chisq)
    > t=qp$popdrop|dplyr::filter(f4rank!=0&feasible)|arrange(desc(p),chisq)
    > 
    > p=t|select(7:last_col(5))|apply(1,\(x)na.omit(100*x)|sort(T)|sprintf("%.0f %s",.,names(.))|paste(collapse=" "))
    > o=sub("^0","",sprintf(ifelse(t$p<.001,"%.0g","%.3f"),t$p))|paste0((ifelse(t$p > 0.05,"SUCCESS ","FAILED  ")),"p=",.," ",p,collapse="\n")
    > paste0("Target: ",target,"\nLeft: ",paste(sort(left),collapse=", "),"\nRight: ",paste(right,collapse=", "),"\nFeasible Results:","\n",o)|writeLines
    Target: GenticaAnc
    Left: COG_MatangaiTuru_IA, Guanche.SG_11, LBN_IA
    Right: Ju_hoan_North.DG, RUS_DevilsCave_N, IRN_Ganj_Dareh_N, TUR_Marmara_Barcın_N, RUS_Karelia_HG, MAR_Taforalt_EpiP, LUX_Loschbour.DG, RUS_Kolyma_M, RUS_Sunghir6.SG, KAZ_Botai, RUS_Yana_RHS
    Feasible Results:
    SUCCESS p=.475 57 Guanche.SG_11 31 LBN_IA 11 COG_MatangaiTuru_IA
    FAILED  p=4e-06 87 Guanche.SG_11 13 LBN_IA
    FAILED  p=5e-07 93 Guanche.SG_11 7 COG_MatangaiTuru_IA
    FAILED  p=8e-17 74 LBN_IA 26 COG_MatangaiTuru_IA
    >
    SUCCESS p=.475
    57 Guanche.SG_11
    31 LBN_IA
    11 COG_MatangaiTuru_IA

    It seems like COG_MatangaiTuru_IA is working better then Kindoki for me
    Last edited by Gentica277282; 11-20-2022 at 10:36 PM.
    With high coverage Guanche sample 011
    SUCCESS p=.985
    55 Guanche.SG_11
    32 Canaanite_MLBA
    13 COG_MatangaiTuru_IA

    SUCCESS p=.978
    50 Guanche.SG_11
    40 BedouinB.DG
    11 COG_MatangaiTuru_IA

    G25 results

    Target: Me
    Distance: 1.4691% / 0.01469064 | R5P
    52.8 Berber_MAR_TIZ
    21.2 Yemenite_Mahra
    17.8 Greek_Cyclades_Amorgos
    5.4 Yoruba
    2.8 Bulala





    R11109 MALE 1 CE 1749.5 400 CE ARCHAEOLOGY Isola_Sacra Y-DNA: J-Y15222 mtDNA: X2m'n

  6. #114
    Registered Users
    Posts
    2,527
    Ethnicity
    Arab
    Y-DNA (P)
    J2a-J-L210-J-y15222
    mtDNA (M)
    L1b2a

    some new models with bedouin B

    Code:
     qp$weights
    # A tibble: 3  5
      target     left                weight     se     z
      <chr>      <chr>                <dbl>  <dbl> <dbl>
    1 GenticaAnc Guanche.SG           0.532 0.0692  7.69
    2 GenticaAnc COG_MatangaiTuru_IA  0.104 0.0174  5.98
    3 GenticaAnc BedouinB.DG          0.364 0.0637  5.71
    > qp$popdrop
    # A tibble: 7  14
      pat      wt   dof  chisq        p f4rank Guanche.SG COG_MatangaiTuru_IA BedouinB.DG feasible best  dofdiff chisqdiff p_nested
      <chr> <dbl> <dbl>  <dbl>    <dbl>  <dbl>      <dbl>               <dbl>       <dbl> <lgl>    <lgl>   <dbl>     <dbl>    <dbl>
    1 000       0     9   3.00 9.64e- 1      2      0.532              0.104        0.364 TRUE     NA         NA     NA          NA
    2 001       1    10  52.6  8.67e- 8      1      0.917              0.0829      NA     TRUE     TRUE        0      1.35        0
    3 010       1    10  51.3  1.54e- 7      1      0.770             NA            0.230 TRUE     TRUE        0    -22.7         1
    4 100       1    10  74.0  7.61e-12      1     NA                  0.185        0.815 TRUE     TRUE       NA     NA          NA
    5 011       2    11  98.4  3.72e-16      0      1                 NA           NA     TRUE     NA         NA     NA          NA
    6 101       2    11 339.   4.66e-66      0     NA                  1           NA     TRUE     NA         NA     NA          NA
    7 110       2    11 314.   7.72e-61      0     NA                 NA            1     TRUE     NA         NA     NA          NA
    > 
    > `|`=`%>%`
    > p=""
    > o=""
    > rm(t)
    > #t=qp$popdrop|dplyr::filter(f4rank!=0&feasible)|arrange(desc(p),chisq)
    > t=qp$popdrop|dplyr::filter(f4rank!=0&feasible)|arrange(desc(p),chisq)
    > 
    > p=t|select(7:last_col(5))|apply(1,\(x)na.omit(100*x)|sort(T)|sprintf("%.0f %s",.,names(.))|paste(collapse=" "))
    > o=sub("^0","",sprintf(ifelse(t$p<.001,"%.0g","%.3f"),t$p))|paste0((ifelse(t$p > 0.05,"SUCCESS ","FAILED  ")),"p=",.," ",p,collapse="\n")
    > paste0("Target: ",target,"\nLeft: ",paste(sort(left),collapse=", "),"\nRight: ",paste(right,collapse=", "),"\nFeasible Results:","\n",o)|writeLines
    Target: GenticaAnc
    Left: BedouinB.DG, COG_MatangaiTuru_IA, Guanche.SG
    Right: Mbuti.DG, TUR_C_Boncuklu_PPN, CHG, Natufian, IRN_N.SG, ITA_Mesolithic.SG, RUS_Karasuk, RUS_DevilsCave_N, KAZ_Botai, PER_RioUncallane_1800BP, Indian_GreatAndaman_100BP.SG, MAR_Taforalt_EpiP
    Feasible Results:
    SUCCESS p=.964 53 Guanche.SG 36 BedouinB.DG 10 COG_MatangaiTuru_IA
    FAILED  p=2e-07 77 Guanche.SG 23 BedouinB.DG
    FAILED  p=9e-08 92 Guanche.SG 8 COG_MatangaiTuru_IA
    FAILED  p=8e-12 82 BedouinB.DG 18 COG_MatangaiTuru_IA
    >
    Code:
    > qp$weights
    # A tibble: 3  5
      target     left                weight     se     z
      <chr>      <chr>                <dbl>  <dbl> <dbl>
    1 GenticaAnc Guanche.SG           0.589 0.0629  9.37
    2 GenticaAnc COG_MatangaiTuru_IA  0.119 0.0186  6.43
    3 GenticaAnc ISR_MLBA             0.292 0.0538  5.42
    > qp$popdrop
    # A tibble: 7  14
      pat      wt   dof  chisq         p f4rank Guanche.SG COG_MatangaiTuru_IA ISR_MLBA feasible best  dofdiff chisqdiff p_nested
      <chr> <dbl> <dbl>  <dbl>     <dbl>  <dbl>      <dbl>               <dbl>    <dbl> <lgl>    <lgl>   <dbl>     <dbl>    <dbl>
    1 000       0     9   4.43 8.81e-  1      2      0.589              0.119     0.292 TRUE     NA         NA     NA          NA
    2 001       1    10  49.4  3.49e-  7      1      0.916              0.0838   NA     TRUE     TRUE        0     -7.81        1
    3 010       1    10  57.2  1.23e-  8      1      0.898             NA         0.102 TRUE     TRUE        0    -32.7         1
    4 100       1    10  89.9  5.70e- 15      1     NA                  0.260     0.740 TRUE     TRUE       NA     NA          NA
    5 011       2    11 101.   1.32e- 16      0      1                 NA        NA     TRUE     NA         NA     NA          NA
    6 101       2    11 346.   1.85e- 67      0     NA                  1        NA     TRUE     NA         NA     NA          NA
    7 110       2    11 619.   1.21e-125      0     NA                 NA         1     TRUE     NA         NA     NA          NA
    > 
    > `|`=`%>%`
    > p=""
    > o=""
    > rm(t)
    > #t=qp$popdrop|dplyr::filter(f4rank!=0&feasible)|arrange(desc(p),chisq)
    > t=qp$popdrop|dplyr::filter(f4rank!=0&feasible)|arrange(desc(p),chisq)
    > 
    > p=t|select(7:last_col(5))|apply(1,\(x)na.omit(100*x)|sort(T)|sprintf("%.0f %s",.,names(.))|paste(collapse=" "))
    > o=sub("^0","",sprintf(ifelse(t$p<.001,"%.0g","%.3f"),t$p))|paste0((ifelse(t$p > 0.05,"SUCCESS ","FAILED  ")),"p=",.," ",p,collapse="\n")
    > paste0("Target: ",target,"\nLeft: ",paste(sort(left),collapse=", "),"\nRight: ",paste(right,collapse=", "),"\nFeasible Results:","\n",o)|writeLines
    Target: GenticaAnc
    Left: COG_MatangaiTuru_IA, Guanche.SG, ISR_MLBA
    Right: Mbuti.DG, TUR_C_Boncuklu_PPN, CHG, Natufian, IRN_N.SG, ITA_Mesolithic.SG, RUS_Karasuk, RUS_DevilsCave_N, KAZ_Botai, PER_RioUncallane_1800BP, Indian_GreatAndaman_100BP.SG, MAR_Taforalt_EpiP
    Feasible Results:
    SUCCESS p=.881 59 Guanche.SG 29 ISR_MLBA 12 COG_MatangaiTuru_IA
    FAILED  p=3e-07 92 Guanche.SG 8 COG_MatangaiTuru_IA
    FAILED  p=1e-08 90 Guanche.SG 10 ISR_MLBA
    FAILED  p=6e-15 74 ISR_MLBA 26 COG_MatangaiTuru_IA

    Code:
    > qp$weights
    # A tibble: 3  5
      target     left                weight     se     z
      <chr>      <chr>                <dbl>  <dbl> <dbl>
    1 GenticaAnc Guanche.SG           0.582 0.0610  9.54
    2 GenticaAnc COG_MatangaiTuru_IA  0.121 0.0180  6.72
    3 GenticaAnc ISR_Canaanite_MLBA   0.297 0.0522  5.68
    > qp$popdrop
    # A tibble: 7  14
      pat      wt   dof  chisq         p f4rank Guanche.SG COG_MatangaiTuru_IA ISR_Canaanite_MLBA feasible best  dofdiff chisqdiff p_nested
      <chr> <dbl> <dbl>  <dbl>     <dbl>  <dbl>      <dbl>               <dbl>              <dbl> <lgl>    <lgl>   <dbl>     <dbl>    <dbl>
    1 000       0     9   2.47 9.82e-  1      2      0.582              0.121              0.297  TRUE     NA         NA     NA          NA
    2 001       1    10  52.4  9.60e-  8      1      0.914              0.0862            NA      TRUE     TRUE        0     -4.77        1
    3 010       1    10  57.2  1.24e-  8      1      0.902             NA                  0.0979 TRUE     TRUE        0    -33.2         1
    4 100       1    10  90.3  4.58e- 15      1     NA                  0.260              0.740  TRUE     TRUE       NA     NA          NA
    5 011       2    11 114.   2.97e- 19      0      1                 NA                 NA      TRUE     NA         NA     NA          NA
    6 101       2    11 352.   7.31e- 69      0     NA                  1                 NA      TRUE     NA         NA     NA          NA
    7 110       2    11 816.   5.74e-168      0     NA                 NA                  1      TRUE     NA         NA     NA          NA
    > 
    > `|`=`%>%`
    > p=""
    > o=""
    > rm(t)
    > #t=qp$popdrop|dplyr::filter(f4rank!=0&feasible)|arrange(desc(p),chisq)
    > t=qp$popdrop|dplyr::filter(f4rank!=0&feasible)|arrange(desc(p),chisq)
    > 
    > p=t|select(7:last_col(5))|apply(1,\(x)na.omit(100*x)|sort(T)|sprintf("%.0f %s",.,names(.))|paste(collapse=" "))
    > o=sub("^0","",sprintf(ifelse(t$p<.001,"%.0g","%.3f"),t$p))|paste0((ifelse(t$p > 0.05,"SUCCESS ","FAILED  ")),"p=",.," ",p,collapse="\n")
    > paste0("Target: ",target,"\nLeft: ",paste(sort(left),collapse=", "),"\nRight: ",paste(right,collapse=", "),"\nFeasible Results:","\n",o)|writeLines
    Target: GenticaAnc
    Left: COG_MatangaiTuru_IA, Guanche.SG, ISR_Canaanite_MLBA
    Right: Mbuti.DG, TUR_C_Boncuklu_PPN, CHG, Natufian, IRN_N.SG, ITA_Mesolithic.SG, RUS_Karasuk, RUS_DevilsCave_N, KAZ_Botai, PER_RioUncallane_1800BP, Indian_GreatAndaman_100BP.SG, MAR_Taforalt_EpiP
    Feasible Results:
    SUCCESS p=.982 58 Guanche.SG 30 ISR_Canaanite_MLBA 12 COG_MatangaiTuru_IA
    FAILED  p=1e-07 91 Guanche.SG 9 COG_MatangaiTuru_IA
    FAILED  p=1e-08 90 Guanche.SG 10 ISR_Canaanite_MLBA
    FAILED  p=5e-15 74 ISR_Canaanite_MLBA 26 COG_MatangaiTuru_IA

    Results from Highest P rate

    SUCCESS p=.982
    58 Guanche.SG
    30 ISR_Canaanite_MLBA
    12 COG_MatangaiTuru_IA

    SUCCESS p=.964
    53 Guanche.SG
    36 BedouinB.DG
    10 COG_MatangaiTuru_IA

    SUCCESS p=.881
    59 Guanche.SG
    29 ISR_MLBA
    12 COG_MatangaiTuru_IA
    With high coverage Guanche sample 011
    SUCCESS p=.985
    55 Guanche.SG_11
    32 Canaanite_MLBA
    13 COG_MatangaiTuru_IA

    SUCCESS p=.978
    50 Guanche.SG_11
    40 BedouinB.DG
    11 COG_MatangaiTuru_IA

    G25 results

    Target: Me
    Distance: 1.4691% / 0.01469064 | R5P
    52.8 Berber_MAR_TIZ
    21.2 Yemenite_Mahra
    17.8 Greek_Cyclades_Amorgos
    5.4 Yoruba
    2.8 Bulala





    R11109 MALE 1 CE 1749.5 400 CE ARCHAEOLOGY Isola_Sacra Y-DNA: J-Y15222 mtDNA: X2m'n

  7. #115
    Registered Users
    Posts
    2,527
    Ethnicity
    Arab
    Y-DNA (P)
    J2a-J-L210-J-y15222
    mtDNA (M)
    L1b2a

    with high coverage 011

    Code:
    > qp$weights
    # A tibble: 3  5
      target     left                weight     se     z
      <chr>      <chr>                <dbl>  <dbl> <dbl>
    1 GenticaAnc Guanche.SG_11        0.554 0.0630  8.79
    2 GenticaAnc COG_MatangaiTuru_IA  0.125 0.0187  6.70
    3 GenticaAnc ISR_Canaanite_MLBA   0.321 0.0542  5.92
    > qp$popdrop
    # A tibble: 7  14
      pat      wt   dof  chisq         p f4rank Guanche.SG_11 COG_MatangaiTuru_IA ISR_Canaanite_MLBA feasible best  dofdiff chisqdiff p_nested
      <chr> <dbl> <dbl>  <dbl>     <dbl>  <dbl>         <dbl>               <dbl>              <dbl> <lgl>    <lgl>   <dbl>     <dbl>    <dbl>
    1 000       0     9   2.34 9.85e-  1      2         0.554              0.125              0.321  TRUE     NA         NA     NA          NA
    2 001       1    10  42.5  6.12e-  6      1         0.919              0.0815            NA      TRUE     TRUE        0     -6.25        1
    3 010       1    10  48.7  4.54e-  7      1         0.919             NA                  0.0807 TRUE     TRUE        0    -35.0         1
    4 100       1    10  83.7  9.40e- 14      1        NA                  0.257              0.743  TRUE     TRUE       NA     NA          NA
    5 011       2    11  76.6  6.79e- 12      0         1                 NA                 NA      TRUE     NA         NA     NA          NA
    6 101       2    11 347.   9.34e- 68      0        NA                  1                 NA      TRUE     NA         NA     NA          NA
    7 110       2    11 649.   5.73e-132      0        NA                 NA                  1      TRUE     NA         NA     NA          NA
    > 
    > `|`=`%>%`
    > p=""
    > o=""
    > rm(t)
    > #t=qp$popdrop|dplyr::filter(f4rank!=0&feasible)|arrange(desc(p),chisq)
    > t=qp$popdrop|dplyr::filter(f4rank!=0&feasible)|arrange(desc(p),chisq)
    > 
    > p=t|select(7:last_col(5))|apply(1,\(x)na.omit(100*x)|sort(T)|sprintf("%.0f %s",.,names(.))|paste(collapse=" "))
    > o=sub("^0","",sprintf(ifelse(t$p<.001,"%.0g","%.3f"),t$p))|paste0((ifelse(t$p > 0.05,"SUCCESS ","FAILED  ")),"p=",.," ",p,collapse="\n")
    > paste0("Target: ",target,"\nLeft: ",paste(sort(left),collapse=", "),"\nRight: ",paste(right,collapse=", "),"\nFeasible Results:","\n",o)|writeLines
    Target: GenticaAnc
    Left: COG_MatangaiTuru_IA, Guanche.SG_11, ISR_Canaanite_MLBA
    Right: Mbuti.DG, TUR_C_Boncuklu_PPN, CHG, Natufian, IRN_N.SG, ITA_Mesolithic.SG, RUS_Karasuk, RUS_DevilsCave_N, KAZ_Botai, PER_RioUncallane_1800BP, Indian_GreatAndaman_100BP.SG, MAR_Taforalt_EpiP
    Feasible Results:
    SUCCESS p=.985 55 Guanche.SG_11 32 ISR_Canaanite_MLBA 13 COG_MatangaiTuru_IA
    FAILED  p=6e-06 92 Guanche.SG_11 8 COG_MatangaiTuru_IA
    FAILED  p=5e-07 92 Guanche.SG_11 8 ISR_Canaanite_MLBA
    FAILED  p=9e-14 74 ISR_Canaanite_MLBA 26 COG_MatangaiTuru_IA
    Code:
    > qp$weights
    # A tibble: 3  5
      target     left                weight     se     z
      <chr>      <chr>                <dbl>  <dbl> <dbl>
    1 GenticaAnc Guanche.SG_11        0.498 0.0698  7.14
    2 GenticaAnc COG_MatangaiTuru_IA  0.106 0.0179  5.96
    3 GenticaAnc BedouinB.DG          0.395 0.0645  6.13
    > qp$popdrop
    # A tibble: 7  14
      pat      wt   dof  chisq        p f4rank Guanche.SG_11 COG_MatangaiTuru_IA BedouinB.DG feasible best  dofdiff chisqdiff p_nested
      <chr> <dbl> <dbl>  <dbl>    <dbl>  <dbl>         <dbl>               <dbl>       <dbl> <lgl>    <lgl>   <dbl>     <dbl>    <dbl>
    1 000       0     9   2.62 9.78e- 1      2         0.498              0.106        0.395 TRUE     NA         NA     NA          NA
    2 001       1    10  41.2  1.05e- 5      1         0.919              0.0806      NA     TRUE     TRUE        0     -4.15        1
    3 010       1    10  45.3  1.90e- 6      1         0.793             NA            0.207 TRUE     TRUE        0    -20.3         1
    4 100       1    10  65.6  3.09e-10      1        NA                  0.180        0.820 TRUE     TRUE       NA     NA          NA
    5 011       2    11  69.2  1.74e-10      0         1                 NA           NA     TRUE     NA         NA     NA          NA
    6 101       2    11 339.   4.97e-66      0        NA                  1           NA     TRUE     NA         NA     NA          NA
    7 110       2    11 257.   1.16e-48      0        NA                 NA            1     TRUE     NA         NA     NA          NA
    > 
    > `|`=`%>%`
    > p=""
    > o=""
    > rm(t)
    > #t=qp$popdrop|dplyr::filter(f4rank!=0&feasible)|arrange(desc(p),chisq)
    > t=qp$popdrop|dplyr::filter(f4rank!=0&feasible)|arrange(desc(p),chisq)
    > 
    > p=t|select(7:last_col(5))|apply(1,\(x)na.omit(100*x)|sort(T)|sprintf("%.0f %s",.,names(.))|paste(collapse=" "))
    > o=sub("^0","",sprintf(ifelse(t$p<.001,"%.0g","%.3f"),t$p))|paste0((ifelse(t$p > 0.05,"SUCCESS ","FAILED  ")),"p=",.," ",p,collapse="\n")
    > paste0("Target: ",target,"\nLeft: ",paste(sort(left),collapse=", "),"\nRight: ",paste(right,collapse=", "),"\nFeasible Results:","\n",o)|writeLines
    Target: GenticaAnc
    Left: BedouinB.DG, COG_MatangaiTuru_IA, Guanche.SG_11
    Right: Mbuti.DG, TUR_C_Boncuklu_PPN, CHG, Natufian, IRN_N.SG, ITA_Mesolithic.SG, RUS_Karasuk, RUS_DevilsCave_N, KAZ_Botai, PER_RioUncallane_1800BP, Indian_GreatAndaman_100BP.SG, MAR_Taforalt_EpiP
    Feasible Results:
    SUCCESS p=.978 50 Guanche.SG_11 40 BedouinB.DG 11 COG_MatangaiTuru_IA
    FAILED  p=1e-05 92 Guanche.SG_11 8 COG_MatangaiTuru_IA
    FAILED  p=2e-06 79 Guanche.SG_11 21 BedouinB.DG
    FAILED  p=3e-10 82 BedouinB.DG 18 COG_MatangaiTuru_IA
    results

    SUCCESS p=.985
    55 Guanche.SG_11
    32 ISR_Canaanite_MLBA
    13 COG_MatangaiTuru_IA

    SUCCESS p=.978
    50 Guanche.SG_11
    40 BedouinB.DG
    11 COG_MatangaiTuru_IA
    Last edited by Gentica277282; 11-21-2022 at 11:55 PM.
    With high coverage Guanche sample 011
    SUCCESS p=.985
    55 Guanche.SG_11
    32 Canaanite_MLBA
    13 COG_MatangaiTuru_IA

    SUCCESS p=.978
    50 Guanche.SG_11
    40 BedouinB.DG
    11 COG_MatangaiTuru_IA

    G25 results

    Target: Me
    Distance: 1.4691% / 0.01469064 | R5P
    52.8 Berber_MAR_TIZ
    21.2 Yemenite_Mahra
    17.8 Greek_Cyclades_Amorgos
    5.4 Yoruba
    2.8 Bulala





    R11109 MALE 1 CE 1749.5 400 CE ARCHAEOLOGY Isola_Sacra Y-DNA: J-Y15222 mtDNA: X2m'n

  8. #116
    Registered Users
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    Ethnicity
    Arab
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    J2a-J-L210-J-y15222
    mtDNA (M)
    L1b2a

    Doing something new with comparing fst distances

    Lets see fst distances against ppnb. Me vs Bedouin B and Samaritan

    Samaritan

    # A tibble: 1 4
    pop1 pop2 est se
    <chr> <chr> <dbl> <dbl>
    1 Levant_PPNB Samaritan.DG 0.0869 0.00700

    Beoduin B
    pop1 pop2 est se
    <chr> <chr> <dbl> <dbl>
    1 Levant_PPNB BedouinB.DG 0.0513 0.00272

    Me
    pop1 pop2 est se
    <chr> <chr> <dbl> <dbl>
    1 Levant_PPNB GenticaAnc 0.0240 0.00326

    With natufian
    me:

    pop1 pop2 est se
    <chr> <chr> <dbl> <dbl>
    1 Natufian GenticaAnc 0.0196 0.0142


    Bedouin B:

    pop1 pop2 est se
    <chr> <chr> <dbl> <dbl>
    1 Natufian BedouinB.DG 0.0776 0.00733


    Somali:
    pop1 pop2 est se
    <chr> <chr> <dbl> <dbl>
    1 Natufian Somali.DG 0.115 0.0107

    With the I072

    Me
    pop1 pop2 est se
    <chr> <chr> <dbl> <dbl>
    1 Natufian_I072 GenticaAnc 0.489 0.00517


    Bedouin B
    pop1 pop2 est se
    <chr> <chr> <dbl> <dbl>
    1 Natufian_I072 BedouinB.DG 0.528 0.00317

    Somali
    pop1 pop2 est se
    <chr> <chr> <dbl> <dbl>
    1 Natufian_I072 Somali.DG 0.537 0.00610


    me and bedouin b
    pop1 pop2 est se
    <chr> <chr> <dbl> <dbl>
    1 BedouinB.DG GenticaAnc 0.0309 0.00371
    Last edited by Gentica277282; Yesterday at 12:29 AM.
    With high coverage Guanche sample 011
    SUCCESS p=.985
    55 Guanche.SG_11
    32 Canaanite_MLBA
    13 COG_MatangaiTuru_IA

    SUCCESS p=.978
    50 Guanche.SG_11
    40 BedouinB.DG
    11 COG_MatangaiTuru_IA

    G25 results

    Target: Me
    Distance: 1.4691% / 0.01469064 | R5P
    52.8 Berber_MAR_TIZ
    21.2 Yemenite_Mahra
    17.8 Greek_Cyclades_Amorgos
    5.4 Yoruba
    2.8 Bulala





    R11109 MALE 1 CE 1749.5 400 CE ARCHAEOLOGY Isola_Sacra Y-DNA: J-Y15222 mtDNA: X2m'n

  9. #117
    Registered Users
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    My fst distance to MAR EN and Taforalt

    pop1 pop2 est se
    <chr> <chr> <dbl> <dbl>
    1 MAR_EN.SG GenticaAnc 0.116 0.00646


    Bedouin B
    pop1 pop2 est se
    <chr> <chr> <dbl> <dbl>
    1 MAR_EN.SG BedouinB.DG 0.179 0.00482



    pop1 pop2 est se
    <chr> <chr> <dbl> <dbl>
    1 MAR_Taforalt_EpiP GenticaAnc 0.135 0.00358


    Bedouin B
    pop1 pop2 est se
    <chr> <chr> <dbl> <dbl>
    1 MAR_Taforalt_EpiP BedouinB.DG 0.192 0.00300
    With high coverage Guanche sample 011
    SUCCESS p=.985
    55 Guanche.SG_11
    32 Canaanite_MLBA
    13 COG_MatangaiTuru_IA

    SUCCESS p=.978
    50 Guanche.SG_11
    40 BedouinB.DG
    11 COG_MatangaiTuru_IA

    G25 results

    Target: Me
    Distance: 1.4691% / 0.01469064 | R5P
    52.8 Berber_MAR_TIZ
    21.2 Yemenite_Mahra
    17.8 Greek_Cyclades_Amorgos
    5.4 Yoruba
    2.8 Bulala





    R11109 MALE 1 CE 1749.5 400 CE ARCHAEOLOGY Isola_Sacra Y-DNA: J-Y15222 mtDNA: X2m'n

  10. #118
    Registered Users
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    mtDNA (M)
    L1b2a

    With the fst distances lower is closer but you also have to take the standard errors into account
    With high coverage Guanche sample 011
    SUCCESS p=.985
    55 Guanche.SG_11
    32 Canaanite_MLBA
    13 COG_MatangaiTuru_IA

    SUCCESS p=.978
    50 Guanche.SG_11
    40 BedouinB.DG
    11 COG_MatangaiTuru_IA

    G25 results

    Target: Me
    Distance: 1.4691% / 0.01469064 | R5P
    52.8 Berber_MAR_TIZ
    21.2 Yemenite_Mahra
    17.8 Greek_Cyclades_Amorgos
    5.4 Yoruba
    2.8 Bulala





    R11109 MALE 1 CE 1749.5 400 CE ARCHAEOLOGY Isola_Sacra Y-DNA: J-Y15222 mtDNA: X2m'n

  11. #119
    Registered Users
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    Ethnicity
    Arab
    Y-DNA (P)
    J2a-J-L210-J-y15222
    mtDNA (M)
    L1b2a

    Against Sardinians, Guanche and other North African samples

    pop1 pop2 est se
    <chr> <chr> <dbl> <dbl>
    1 Sardinian.DG GenticaAnc 0.0201 0.00304


    # A tibble: 1 4
    pop1 pop2 est se
    <chr> <chr> <dbl> <dbl>
    1 Guanche.SG GenticaAnc 0.0297 0.00324


    pop1 pop2 est se
    <chr> <chr> <dbl> <dbl>
    1 Mozabite.DG GenticaAnc 0.0114 0.00334


    pop1 pop2 est se
    <chr> <chr> <dbl> <dbl>
    1 Saharawi.DG GenticaAnc 0.00491 0.00303


    Saharawi with the lowest distance of course because I have ancestry from the south of morocco

    Us against Guanche
    pop1 pop2 est se
    <chr> <chr> <dbl> <dbl>
    1 Guanche.SG GenticaAnc 0.0296 0.00326
    2 Guanche.SG Mozabite.DG 0.0379 0.00223
    3 Guanche.SG Saharawi.DG 0.0333 0.00210
    4 Guanche.SG Sardinian.DG 0.0495 0.00198


    I need to download the new dataset, once I do I will run more North African populations but that's the only NA populations I have in my current dataset
    Last edited by Gentica277282; Yesterday at 01:20 AM.
    With high coverage Guanche sample 011
    SUCCESS p=.985
    55 Guanche.SG_11
    32 Canaanite_MLBA
    13 COG_MatangaiTuru_IA

    SUCCESS p=.978
    50 Guanche.SG_11
    40 BedouinB.DG
    11 COG_MatangaiTuru_IA

    G25 results

    Target: Me
    Distance: 1.4691% / 0.01469064 | R5P
    52.8 Berber_MAR_TIZ
    21.2 Yemenite_Mahra
    17.8 Greek_Cyclades_Amorgos
    5.4 Yoruba
    2.8 Bulala





    R11109 MALE 1 CE 1749.5 400 CE ARCHAEOLOGY Isola_Sacra Y-DNA: J-Y15222 mtDNA: X2m'n

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