# Haplotype Discrete Survival Models

#### Klaus Holst & Thomas Scheike

#### 2024-02-16

# Haplotype Analysis for discrete TTP

Cycle-specific logistic regression of haplo-type effects with known haplo-type probabilities. Given observed genotype G and unobserved haplotypes H we here mix out over the possible haplotypes using that \(P(H|G)\) is given as input.

\[\begin{align*} S(t|x,G) & = E( S(t|x,H) | G) = \sum_{h \in G} P(h|G) S(t|z,h) \end{align*}\] so survival can be computed by mixing out over possible h given g.

Survival is based on logistic regression for the discrete hazard function of the form \[\begin{align*} \mbox{logit}(P(T=t| T >= t, x,h)) & = \alpha_t + x(h) beta \end{align*}\] where x(h) is a regression design of x and haplotypes \(h=(h_1,h_2)\).

Simple binomial data can be fitted using this function.

For standard errors we assume that haplotype probabilities are known.

We are particularly interested in the types haplotypes:

    types <- c("DCGCGCTCACG","DTCCGCTGACG","ITCAGTTGACG","ITCCGCTGAGG")

    ## some haplotypes frequencies for simulations
    data(hapfreqs)
    print(hapfreqs)
    #>             index   haplotype     freq
    #> DCGAGCTCACG     1 DCGAGCTCACG 0.010681
    #> DCGCGCTCACG     2 DCGCGCTCACG 0.138387
    #> DTGAGCTCACG     3 DTGAGCTCACG 0.000310
    #> DTGAGCTCACA     4 DTGAGCTCACA 0.006800
    #> DTGAGCTCGCG     5 DTGAGCTCGCG 0.034517
    #> DTGACCTCACG     6 DTGACCTCACG 0.001336
    #> DTGCGCTCACG     7 DTGCGCTCACG 0.009969
    #> DTGCGCTCACA     8 DTGCGCTCACA 0.011833
    #> DTGCGCTCGCG     9 DTGCGCTCGCG 0.302389
    #> DTGCGCCCGCG    10 DTGCGCCCGCG 0.001604
    #> DTGCCCTCACG    11 DTGCCCTCACG 0.003912
    #> DTCAGCTGACG    12 DTCAGCTGACG 0.001855
    #> DTCCGCTGACG    13 DTCCGCTGACG 0.103394
    #> DTCCCCTGACG    14 DTCCCCTGACG 0.000310
    #> ITCAGTTGACG    15 ITCAGTTGACG 0.048124
    #> ITCCGCTGAGG    16 ITCCGCTGAGG 0.291273
    #> ITCCGTTGACG    17 ITCCGTTGACG 0.031089
    #> ITCCGTCGACG    18 ITCCGTCGACG 0.001502
    #> ITCCCCTGAGG    19 ITCCCCTGAGG 0.000653

Among the types of interest we look up the frequencies and choose a baseline

    www <-which(hapfreqs$haplotype %in% types)
    hapfreqs$freq[www]
    #> [1] 0.138387 0.103394 0.048124 0.291273

    baseline=hapfreqs$haplotype[9]
    baseline
    #> [1] "DTGCGCTCGCG"

We have cycle specific data with \(id\) and outcome \(y\)

    data(haploX)
    dlist(haploX,.~id|id %in% c(1,4,7))
    #> id: 1
    #>   y X1 X2 X3 X4 times lbnr__id Count1
    #> 1 0 0  0  0  0  1     1        0
    #> 2 0 0  0  0  0  2     2        0
    #> 3 0 0  0  0  0  3     3        0
    #> 4 0 0  0  0  0  4     4        0
    #> 5 0 0  0  0  0  5     5        0
    #> 6 0 0  0  0  0  6     6        0
    #> ------------------------------------------------------------
    #> id: 4
    #>    y X1 X2 X3 X4 times lbnr__id Count1
    #> 19 1 0  0  0  0  1     1        0
    #> ------------------------------------------------------------
    #> id: 7
    #>    y X1 X2 X3 X4 times lbnr__id Count1
    #> 37 0 1  0  0  0  1     1        0
    #> 38 0 1  0  0  0  2     2        0
    #> 39 1 1  0  0  0  3     3        0

and a list of possible haplo-types for each id and how likely they are \(p\) (the sum of within each id is 1):

    data(hHaplos) ## loads ghaplos
    head(ghaplos)
    #>    id      haplo1      haplo2          p
    #> 1   1 DTGCGCTCGCG DTGAGCTCGCG 1.00000000
    #> 19  2 ITCCGTTGACG DTGAGCTCGCG 0.06867716
    #> 21  2 ITCAGTTGACG DTGCGCTCGCG 0.93132284
    #> 51  3 ITCCGTTGACG DTGAGCTCGCG 0.06867716
    #> 53  3 ITCAGTTGACG DTGCGCTCGCG 0.93132284
    #> 66  4 DTGCGCTCGCG DTGCGCTCGCG 1.00000000

The first id=1 has the haplotype fully observed, but id=2 has two possible haplotypes consistent with the observed genotype for this id, the probabiblities are 7% and 93%, respectively.

With the baseline given above we can specify a regression design that gives an effect if a “type” is present (sm=0), or an additive effect of haplotypes (sm=1):

    designftypes <- function(x,sm=0) {
    hap1=x[1]
    hap2=x[2]
    if (sm==0) y <- 1*( (hap1==types) | (hap2==types))
    if (sm==1) y <- 1*(hap1==types) + 1*(hap2==types)
    return(y)
    }

To fit the model we start by constructing a time-design (named X) and takes the haplotype distributions for each id

    haploX$time <- haploX$times
    Xdes <- model.matrix(~factor(time),haploX)
    colnames(Xdes) <- paste("X",1:ncol(Xdes),sep="")
    X <- dkeep(haploX,~id+y+time)
    X <- cbind(X,Xdes)
    Haplos <- dkeep(ghaplos,~id+"haplo*"+p)
    desnames=paste("X",1:6,sep="")   # six X's related to 6 cycles
    head(X)
    #>   id y time X1 X2 X3 X4 X5 X6
    #> 1  1 0    1  1  0  0  0  0  0
    #> 2  1 0    2  1  1  0  0  0  0
    #> 3  1 0    3  1  0  1  0  0  0
    #> 4  1 0    4  1  0  0  1  0  0
    #> 5  1 0    5  1  0  0  0  1  0
    #> 6  1 0    6  1  0  0  0  0  1

Now we can fit the model with the design given by the designfunction

    out <- haplo.surv.discrete(X=X,y="y",time.name="time",
          Haplos=Haplos,desnames=desnames,designfunc=designftypes)
    names(out$coef) <- c(desnames,types)
    out$coef
    #>          X1          X2          X3          X4          X5          X6
    #> -1.82153345 -0.61608261 -0.17143057 -1.27152045 -0.28635976 -0.19349091
    #> DCGCGCTCACG DTCCGCTGACG ITCAGTTGACG ITCCGCTGAGG
    #>  0.79753613  0.65747412  0.06119231  0.31666905
    summary(out)
    #>             Estimate Std.Err     2.5%   97.5%   P-value
    #> X1          -1.82153  0.1619 -2.13892 -1.5041 2.355e-29
    #> X2          -0.61608  0.1895 -0.98748 -0.2447 1.149e-03
    #> X3          -0.17143  0.1799 -0.52398  0.1811 3.406e-01
    #> X4          -1.27152  0.2631 -1.78719 -0.7559 1.346e-06
    #> X5          -0.28636  0.2030 -0.68425  0.1115 1.584e-01
    #> X6          -0.19349  0.2134 -0.61184  0.2249 3.647e-01
    #> DCGCGCTCACG  0.79754  0.1494  0.50465  1.0904 9.445e-08
    #> DTCCGCTGACG  0.65747  0.1621  0.33971  0.9752 5.007e-05
    #> ITCAGTTGACG  0.06119  0.2145 -0.35931  0.4817 7.755e-01
    #> ITCCGCTGAGG  0.31667  0.1361  0.04989  0.5834 1.999e-02

Haplotypes “DCGCGCTCACG” “DTCCGCTGACG” gives increased hazard of pregnancy

The data was generated with these true coefficients

    tcoef=c(-1.93110204,-0.47531630,-0.04118204,-1.57872602,-0.22176426,-0.13836416,
    0.88830288,0.60756224,0.39802821,0.32706859)

    cbind(out$coef,tcoef)
    #>                               tcoef
    #> X1          -1.82153345 -1.93110204
    #> X2          -0.61608261 -0.47531630
    #> X3          -0.17143057 -0.04118204
    #> X4          -1.27152045 -1.57872602
    #> X5          -0.28635976 -0.22176426
    #> X6          -0.19349091 -0.13836416
    #> DCGCGCTCACG  0.79753613  0.88830288
    #> DTCCGCTGACG  0.65747412  0.60756224
    #> ITCAGTTGACG  0.06119231  0.39802821
    #> ITCCGCTGAGG  0.31666905  0.32706859

The design fitted can be found in the output

    head(out$X,10)
    #>    X1 X2 X3 X4 X5 X6 haplo1 haplo2 haplo3 haplo4
    #> 1   1  0  0  0  0  0      0      0      0      0
    #> 2   1  1  0  0  0  0      0      0      0      0
    #> 3   1  0  1  0  0  0      0      0      0      0
    #> 4   1  0  0  1  0  0      0      0      0      0
    #> 5   1  0  0  0  1  0      0      0      0      0
    #> 6   1  0  0  0  0  1      0      0      0      0
    #> 8   1  0  0  0  0  0      0      0      1      0
    #> 10  1  1  0  0  0  0      0      0      1      0
    #> 12  1  0  1  0  0  0      0      0      1      0
    #> 14  1  0  0  1  0  0      0      0      1      0

# SessionInfo

    sessionInfo()
    #> R version 4.3.2 (2023-10-31)
    #> Platform: aarch64-apple-darwin22.6.0 (64-bit)
    #> Running under: macOS Sonoma 14.3.1
    #>
    #> Matrix products: default
    #> BLAS:   /Users/kkzh/.asdf/installs/R/4.3.2/lib/R/lib/libRblas.dylib
    #> LAPACK: /Users/kkzh/.asdf/installs/R/4.3.2/lib/R/lib/libRlapack.dylib;  LAPACK version 3.11.0
    #>
    #> locale:
    #> [1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
    #>
    #> time zone: Europe/Copenhagen
    #> tzcode source: internal
    #>
    #> attached base packages:
    #> [1] stats     graphics  grDevices utils     datasets  methods   base
    #>
    #> other attached packages:
    #> [1] mets_1.3.4     timereg_2.0.5  survival_3.5-7
    #>
    #> loaded via a namespace (and not attached):
    #>  [1] cli_3.6.2           knitr_1.45          rlang_1.1.3
    #>  [4] xfun_0.41           highr_0.10          jsonlite_1.8.8
    #>  [7] listenv_0.9.1       future.apply_1.11.1 lava_1.7.4
    #> [10] htmltools_0.5.6.1   sass_0.4.7          rmarkdown_2.25
    #> [13] grid_4.3.2          evaluate_0.23       jquerylib_0.1.4
    #> [16] fastmap_1.1.1       mvtnorm_1.2-4       yaml_2.3.7
    #> [19] numDeriv_2016.8-1.1 compiler_4.3.2      codetools_0.2-19
    #> [22] ucminf_1.2.0        Rcpp_1.0.12         future_1.33.1
    #> [25] lattice_0.22-5      digest_0.6.34       R6_2.5.1
    #> [28] parallelly_1.37.0   parallel_4.3.2      splines_4.3.2
    #> [31] bslib_0.5.1         Matrix_1.6-5        tools_4.3.2
    #> [34] globals_0.16.2      cachem_1.0.8