skater: an R package for SNP-based kinship analysis, testing, and evaluation.

Motivation: SNP-based kinship analysis with genome-wide relationship estimation and IBD segment analysis methods produces results that often require further downstream process- ing and manipulation. A dedicated software package that consistently and intuitively imple- ments this analysis functionality is needed.
Results: Here we present the skater R package for SNP-based kinship analysis, testing, and evaluation with R. The skater package contains a suite of well-documented tools for importing, parsing, and analyzing pedigree data, performing relationship degree inference, benchmarking relationship degree classification, and summarizing IBD segment data. Availability: The skater package is implemented as an R package and is released under the MIT license at https://github.com/signaturescience/skater. Documentation is available at https://signaturescience.github.io/skater. Copyright:

R version: R version 4.0.4 (2021-02-15)

skater package version: 0.1.0

Introduction

Inferring familial relationships between individuals using genetic data is a common practice in population genetics, medical genetics, and forensics. There are multiple approaches to estimating relatedness between samples, including genome-wide measures, such as those implemented in Plink or KING, and methods that rely on identity by descent (IBD) segment detection, such as GERMLINE, hap-IBD, and IBIS. Recent efforts focusing on benchmarking these methods have been aided by tools for simulating pedigrees and genome-wide SNP data. Analyzing results from genome-wide SNP-based kinship analysis or comparing analyses to simulated data for benchmarking have to this point required writing one-off analysis functions or utility scripts that are seldom distributed with robust documentation, test suites, or narrative examples of usage. There is a need in the field for a well-documented software package with a consistent design and API that contains functions to assist with downstream manipulation, benchmarking, and analysis of SNP-based kinship assessment methods. Here we present the skater package for SNP-based kinship analysis, testing, and evaluation with R.

Methods

Implementation

The skater package provides an intuitive collection of analysis and utility functions for SNP-based kinship analysis. Functions in the package include tools for importing, parsing, and analyzing pedigree data, performing relationship degree inference, benchmarking relationship degree classification, and summarizing IBD segment data, described in full in the Use Cases section below. The package adheres to “tidy” data analysis principles, and builds upon the tools released under the tidyverse R ecosystem.

The skater package is hosted in the Comprehensive R Archive Network (CRAN) which is the main repository for R packages: http://CRAN.R-project.org/package=skater. Users can install skater in R by executing the following code: install.packages("skater")

Alternatively, the development version of skater is available on GitHub at https://github.com/signaturescience/skater. The development version may contain new features which are not yet available in the version hosted on CRAN. This version can be installed using the install_github() function in the devtools package: install.packages("devtools") devtools::install_github("signaturescience/skater", build_vignettes=TRUE)

When installing skater, other packages which skater depends on are automatically installed, including magritr, tibble, dplyr, tidyr, readr, purrr, kinship2, corrr, rlang, and others.

Operation

Minimal system requirements for installing and using skater include R (version 3.0.0 or higher) and several tidyverse packages that many R users will already have installed. Use cases are demonstrated in detail below. In summary, the skater package has functions for:

Reading in various output files produced by commonly used tools in SNP-based kinship analysis

Pedigree parsing, manpulation, and analysis

Relationship degree inference

Benchmarking and assessing relationship classification accuracy

IBD segment analysis post-processing

A comprehensive reference for all the functions in the skater package is available at https://signaturescience.github.io/skater/.

Use cases

The skater package provides a collection of analysis and utility functions for SNP-based kinship analysis, testing, and evaluation as an R package. Functions in the package include tools for working with pedigree data, performing relationship degree inference, assessing classification accuracy, and summarizing IBD segment data. library(skater)

Pedigree parsing, manipulation, and analysis

Pedigrees define familial relationships in a hierarchical structure. One of the common formats used by PLINK and other genetic analysis tools is the .fam file. A .fam file is a tabular format with one row per individual and columns for unique IDs of the mother, father, and the family unit. The package includes read_fam() to read files in this format: famfile <- system.file("extdata", "3gens.fam", package="skater", mustWork=TRUE) fam <- read_fam(famfile) fam ## # A tibble: 64 x 6 ##   fid    id          dadid        momid       sex affected ##                               ## 1  testped1 testped1_g1-b1-s1 0          0           1     1 ## 2  testped1 testped1_g1-b1-i1 0          0           2     1 ## 3  testped1 testped1_g2-b1-s1 0          0           1     1 ## 4  testped1 testped1_g2-b1-i1 testped1_g1-b1-s1 testped1_g1-b1-i1 2     1 ## 5  testped1 testped1_g2-b2-s1 0          0           1     1 ## 6  testped1 testped1_g2-b2-i1 testped1_g1-b1-s1 testped1_g1-b1-i1  2     1 ## 7  testped1 testped1_g3-b1-i1 testped1_g2-b1-s1 testped1_g2-b1-i1  2     1 ## 8  testped1 testped1_g3-b2-i1 testped1_g2-b2-s1 testped1_g2-b2-i1  1     1 ## 9  testped2 testped2_g1-b1-s1 0          0           2     1 ## 10 testped2 testped2_g1-b1-i1 0          0           1     1 ## #… with 54 more rows

Family structures imported from .fam formated files can then be translated to the pedigree structure used by the kinship2 package. The “fam” format may include multiple families, and the fam2ped() function will collapse them all into a tibble with one row per family: peds <- fam2ped(fam) peds ## # A tibble: 8 x 3 ##   fid    data          ped ##              ## 1 testped1   ## 2 testped2   ## 3 testped3   ## 4 testped4   ## 5 testped5   ## 6 testped6   ## 7 testped7   ## 8 testped8  

In the example above, the resulting tibble is nested by family ID. The data column contains the individual family information, while the ped column contains the pedigree object for that family. Using standard tidyverse operations, the resulting tibble can be unnested for any particular family: peds %>%  dplyr::filter(fid=="testped1") %>%  tidyr::unnest(cols=data) ## # A tibble: 8 x 7 ##   fid    id           dadid        momid   sex affected   ped ##                             ## 1 testped1 testped1_g1-b1-s1                1       1                2       1                1       1                1       1 

A single pedigree can also be inspected or visualized (standard base R plot arguments such as mar or cex can be used to adjust aesthetics): peds$ped[[1]] ## Pedigree object with 8 subjects ## Bit size= 4 plot(peds$ped[[1]], mar=c(1,4,1,4), cex=.7)

The plot_pedigree() function from skater will iterate over a list of pedigree objects, writing a multi-page PDF, with each page containing a pedigree from family: plot_pedigree(peds$ped, file="3gens.ped.pdf")

The ped2kinpair() function takes a pedigree object and produces a pairwise list of relationships between all individuals in the data with the expected kinship coefficients for each pair.

The function can be run on a single family: ped2kinpair(peds$ped[[1]]) ## # A tibble: 36 x 3 ##   id1          id2            k ##                     ##  1 testped1_g1-b1-s1 testped1_g1-b1-s1 0.5 ##  2 testped1_g1-b1-i1 testped1_g1-b1-s1 0 ##  3 testped1_g1-b1-s1 testped1_g2-b1-s1 0 ##  4 testped1_g1-b1-s1 testped1_g2-b1-i1 0.25 ##  5 testped1_g1-b1-s1 testped1_g2-b2-s1 0 ##  6 testped1_g1-b1-s1 testped1_g2-b2-i1 0.25 ##  7 testped1_g1-b1-s1 testped1_g3-b1-i1 0.125 ##  8 testped1_g1-b1-s1 testped1_g3-b2-i1 0.125 ##  9 testped1_g1-b1-i1 testped1_g1-b1-i1 0.5 ## 10 testped1_g1-b1-i1 testped1_g2-b1-s1 0 ## # … with 26 more rows

This function can also be mapped over all families in the pedigree: kinpairs <-  peds %>%  dplyr::mutate(pairs=purrr::map(ped, ped2kinpair)) %>%  dplyr::select(fid, pairs) %>%  tidyr::unnest(cols=pairs) kinpairs ## # A tibble: 288 x 4 ##   fid     id1         id2            k ##                        ##  1 testped1 testped1_g1-b1-s1 testped1_g1-b1-s1 0.5 ##  2 testped1 testped1_g1-b1-i1 testped1_g1-b1-s1 0 ##  3 testped1 testped1_g1-b1-s1 testped1_g2-b1-s1 0 ##  4 testped1 testped1_g1-b1-s1 testped1_g2-b1-i1 0.25 ##  5 testped1 testped1_g1-b1-s1 testped1_g2-b2-s1 0 ##  6 testped1 testped1_g1-b1-s1 testped1_g2-b2-i1 0.25 ##  7 testped1 testped1_g1-b1-s1 testped1_g3-b1-i1 0.125 ##  8 testped1 testped1_g1-b1-s1 testped1_g3-b2-i1 0.125 ##  9 testped1 testped1_g1-b1-i1 testped1_g1-b1-i1 0.5 ## 10 testped1 testped1_g1-b1-i1 testped1_g2-b1-s1 0 ## # … with 278 more rows

Note that this maps ped2kinpair() over all ped objects in the input tibble, and that relationships are not shown for between-family relationships.

Relationship degree inference and benchmarking

The skater package includes functions to translate kinship coefficients to relationship degrees. The kinship coefficients could come from ped2kinpair() or other kinship estimation software.

The dibble() function creates a degree inference tibble, with degrees up to the specified max_degree (default=3), expected kinship coefficient, and lower (l) and upper (u) inference ranges as defined in Manichaikul et al. Degree 0 corresponds to self/identity/monozygotic twins, with an expected kinship coefficient of 0.5, with inference range >=0.354. Anything beyond the maximum degree resolution is considered unrelated (degree NA). Note also that while the theoretical upper boundary for the kinship coefficient is 0.5, the inference range for 0-degree (same person or identical twins) extends to 1 to allow for floating point arithmetic and stochastic effects resulting in kinship coefficients above 0.5. dibble() ## # A tibble: 5 x 4 ##  degree   k     l     u ##         ## 1   0 0.5    0.354  1 ## 2   1 0.25   0.177  0.354 ## 3   2 0.125   0.0884  0.177 ## 4   3 0.0625  0.0442 0.0884 ## 5  NA 0     -1     0.0442

The degree inference max_degree default is 3. Change this argument to allow more granular degree inference ranges: dibble (max_degree = 5) ## # A tibble: 7 x 4 ##  degree   k    l    u ##        ## 1   0 0.5   0.354  1 ## 2   1 0.25  0.177  0.354 ## 3   2 0.125  0.0884 0.177 ## 4   3 0.0625 0.0442 0.0884 ## 5   4 0.0312 0.0221 0.0442 ## 6   5 0.0156 0.0110 0.0221 ## 7  NA 0    -1     0.0110

Note that the distance between relationship degrees becomes smaller as the relationship degree becomes more distant. The dibble() function will emit a warning with max_degree >=10, and will stop with an error at >=12.

The kin2degree() function infers the relationship degree given a kinship coefficient and a max_degree up to which anything more distant is treated as unrelated. Example first degree relative: kin2degree(.25, max_degree=3) ## [1] 1

Example 4th degree relative, but using the default max_degree resolution of 3: kin2degree(.0312, max_degree=3) ## [1] NA

Example 4th degree relative, but increasing the degree resolution: kin2degree(.0312, max_degree=5) ## [1] 4

The kin2degree() function is vectorized over values of k, so it can be used inside of a mutate on a tibble of kinship coefficients: # Get two pairs from each type of relationship we have in kinpairs: kinpairs_subset <-  kinpairs %>%  dplyr::group_by(k) %>%  dplyr::slice(1:2) kinpairs_subset ## # A tibble: 10 x 4 ## # Groups:  k [5] ##   fid     id1          id2             k ##                         ##  1 testped1 testped1_g1-b1-i1 testped1_g1-b1-s1 0 ##  2 testped1 testped1_g1-b1-s1 testped1_g2-b1-s1 0 ##  3 testped1 testped1_g3-b1-i1 testped1_g3-b2-i1 0.0625 ##  4 testped2 testped2_g3-b1-i1 testped2_g3-b2-i1 0.0625 ##  5 testped1 testped1_g1-b1-s1 testped1_g3-b1-i1 0.125 ##  6 testped1 testped1_g1-b1-s1 testped1_g3-b2-i1 0.125 ##  7 testped1 testped1_g1-b1-s1 testped1_g2-b1-i1 0.25 ##  8 testped1 testped1_g1-b1-s1 testped1_g2-b2-i1 0.25 ##  9 testped1 testped1_g1-b1-s1 testped1_g1-b1-s1 0.5 ## 10 testped1 testped1_g1-b1-i1 testped1_g1-b1-i1 0.5 # Infer degree out to third degree relatives: kinpairs_subset %>%  dplyr::mutate (degree=kin2degree(k, max_degree=3)) ## # A tibble: 10 x 5 ## # Groups:  k [5] ##   fid     id1          id2            k  degree ##                          ##  1 testped1 testped1_g1-b1-i1 testped1_g1-b1-s1 0      NA ##  2 testped1 testped1_g1-b1-s1 testped1_g2-b1-s1 0      NA ##  3 testped1 testped1_g3-b1-i1 testped1_g3-b2-i1 0.0625    3 ##  4 testped2 testped2_g3-b1-i1 testped2_g3-b2-i1 0.0625    3 ##  5 testped1 testped1_g1-b1-s1 testped1_g3-b1-i1 0.125    2 ##  6 testped1 testped1_g1-b1-s1 testped1_g3-b2-i1 0.125    2 ##  7 testped1 testped1_g1-b1-s1 testped1_g2-b1-i1 0.25     1 ##  8 testped1 testped1_g1-b1-s1 testped1_g2-b2-i1 0.25     1 ##  9 testped1 testped1_g1-b1-s1 testped1_g1-b1-s1 0.5     0 ## 10 testped1 testped1_g1-b1-i1 testped1_g1-b1-i1 0.5     0

Benchmarking degree classification

Once estimated kinship is converted to degree, it may be of interest to compare the inferred degree to truth. When aggregated over many relationships and inferences, this approach can help benchmark performance of a particular kinship analysis method.

The skater package adapts a confusion_matrix() function from Clark to provide standard contingency table metrics (e.g. sensitivity, specificity, PPV, precision, recall, F1, etc.) with a new reciprocal RMSE (R-RMSE) metric. The confusion_matrix() function on its own outputs a list with four objects:

A tibble with calculated accuracy, lower and upper bounds, the guessing rate and p-value of the accuracy vs. the guessing rate.

A tibble with contingency table statistics calculated for each class. Details on the statistics calculated for each class can be reviewed on the help page for ?confusion_matrix.

A matrix with the contingency table object itself.

A vector with the reciprocal RMSE (R-RMSE). The R-RMSE represents an alternative to classification accuracy when benchmarking relationship degree estimation and is calculated using the formula in (1). Taking the reciprocal of the target and predicted degree results in larger penalties for more egregious misclassifications (e.g., classifying a first-degree relative pair as second degree) than misclassifications at more distant relationships (e.g., misclassifying a fourth-degree relative pair as fifth-degree). The +0.5 adjustment prevents division-by-zero when a 0th-degree (identical) relative pair is introduced.

To illustrate the usage, this example will start with the kinpairs data from above and randomly flip ~20% of the true relationship degrees: # Function to randomly flip levels of a factor (at 20%, by default) randomflip <- function(x, p=.2) ifelse(runif(length(x))%  dplyr::mutate(degree_truth=kin2degree(k, max_degree=3)) %>%  dplyr::mutate(degree_truth=tidyr::replace_na(degree_truth, "unrelated")) %>%  dplyr::mutate(degree_inferred=randomflip (degree_truth)) kinpairs_inferred ## # A tibble: 288 x 6 ##   fid    id1          id2         k    degree_truth degree_inferred ##                             ## 1 testped1 testped1_g1-b1-s1 testped1_g1-b1-s1 0.5  0       0 ## 2 testped1 testped1_g1-b1-i1 testped1_g1-b1-s1 0   unrelated  unrelated ## 3 testped1 testped1_g1-b1-s1 testped1_g2-b1-s1 0   unrelated  unrelated ## 4 testped1 testped1_g1-b1-s1 testped1_g2-b1-i1 0.25 1       1 ## 5 testped1 testped1_g1-b1-s1 testped1_g2-b2-s1 0   unrelated  unrelated ## 6 testped1 testped1_g1-b1-s1 testped1_g2-b2-i1 0.25 1       1 ## 7 testped1 testped1_g1-b1-s1 testped1_g3-b1-i1 0.125 2       2 ## 8 testped1 testped1_g1-b1-s1 testped1_g3-b2-i1 0.125 2       1 ## 9 testped1 testped1_g1-b1-i1 testped1_g1-b1-i1 0.5  0       0 ## 10 testped1 testped1_g1-b1-i1 testped1_g2-b1-s1 0   unrelated  unrelated ## #… with 278 more rows

Next, running the confusion_matrix() function will return all four objects noted above: confusion_matrix(prediction = kinpairs_inferred$degree_inferred,          target = kinpairs_inferred$degree_truth) ## $Accuracy ## # A tibble: 1 x 5 ##  Accuracy ‘Accuracy LL‘ ‘Accuracy UL‘ ‘Accuracy Guessing‘ ‘Accuracy P-value‘ ##                                     ## 1   0.812      0.763      0.856          0.333        1.09e-62 ## ## $Other ## # A tibble: 6 x 15 ##   Class      N ‘Sensitivity/Re~ ‘Specificity/TN~ ‘PPV/Precision‘ NPV  ‘F1/Dice‘ ##                                ## 1 0     64         0.75        0.964      0.857 0.931   0.8 ## 2 1     72         0.806        0.944      0.829 0.936   0.817 ## 3 2     48         0.833        0.967      0.833 0.967   0.833 ## 4 3      8         0.75        0.936       0.25  0.992   0.375 ## 5 unrelated 96         0.854        0.958      0.911 0.929   0.882 ## 6 Average  57.6        0.799        0.954      0.736 0.951   0.741 ## # … with 8 more variables: Prevalence , Detection Rate , ## #  Detection Prevalence , Balanced Accuracy , FDR , FOR , ## #  FPR/Fallout , FNR ## ## $Table ##       Target ## Predicted  0  1  2 3 unrelated ##  0      48  4  2 1      1 ##  1      5 58  4 0      3 ##  2      0  3 40 1      4 ##  3      8  4  0 6      6 ##  unrelated 3  3  2 0     82 ## ## $recip_rmse ## [1] 0.4665971

Standard tidyverse functions such as purrr::pluck() can be used to isolate just the contingency table: confusion_matrix(prediction = kinpairs_inferred$degree_inferred,            target = kinpairs_inferred$degree_truth) %>%  purrr::pluck("Table") ##       Target ## Predicted  0  1  2 3 unrelated ##  0      48  4  2 1      1 ##  1      5 58  4 0      3 ##  2      0  3 40 1       4 ##  3      8  4  0 6       6 ##  unrelated 3  3  2 0      82

The confusion_matrix() function includes an argument to output in a tidy ( longer=TRUE) format, and the example below illustrates how to spread contingency table statistics by class: confusion_matrix(prediction = kinpairs_inferred$degree_inferred,           target = kinpairs_inferred$degree_truth,           longer = TRUE) %>%  purrr::pluck("Other") %>%  tidyr::spread (Class, Value) %>%  dplyr::relocate (Average, .after=dplyr::last_col()) %>%  dplyr::mutate_if(rlang::is_double, signif, 2) ## # A tibble: 14 x 7 ##   Statistic           ‘0‘   ‘1‘   ‘2‘   ‘3‘  unrelated  Average ##                             ##  1 Balanced Accuracy      0.86  0.88  0.9   0.84     0.91   0.88 ##  2 Detection Prevalence    0.19  0.24  0.17  0.083    0.31   0.2 ##  3 Detection Rate       0.17  0.2   0.14  0.021    0.28   0.16 ##  4 F1/Dice            0.8   0.82  0.83  0.38     0.88   0.74 ##  5 FDR              0.14  0.17  0.17  0.75     0.089   0.26 ##  6 FNR              0.25  0.19  0.17  0.25     0.15   0.2 ##  7 FOR              0.069  0.064  0.033  0.0076    0.071   0.049 ##  8 FPR/Fallout         0.036  0.056  0.033  0.064     0.042   0.046 ##  9 N              64   72   48    8      96    58 ## 10 NPV              0.93  0.94  0.97  0.99     0.93   0.95 ## 11 PPV/Precision        0.86  0.83  0.83  0.25     0.91   0.74 ## 12 Prevalence          0.22  0.25  0.17  0.028    0.33   0.2 ## 13 Sensitivity/Recall/TPR  0.75  0.81  0.83  0.75     0.85   0.8 ## 14 Specificity/TNR       0.96  0.94  0.97  0.94     0.96   0.95

IBD segment analysis

Tools such as hap-IBD, and IBIS detect shared IBD segments between individuals. The skater package includes functionality to take those IBD segments, compute shared genomic centimorgan (cM) length, and converts that shared cM to a kinship coefficient. In addition to inferred segments, these functions can estimate “truth” kinship from simulated IBD segments. The read_ibd() function reads pairwise IBD segments from IBD inference tools and from simulated IBD segments. The read_map() function reads in genetic map in a standard format which is required to translate the total centimorgans shared IBD to a kinship coefficient using the ibd2kin() function. See ?read_ibd and ?read_map for additional details on expected format.

The read_ibd() function reads in the pairwise IBD segment format. Input to this function can either be inferred IBD segments from hap-IBD ( source="hapibd") or simulated segments ( source="pedsim"). The first example below uses data in the hap-ibd output format: hapibd_filepath <- system.file("extdata", "GBR.sim.ibd.gz",                    package="skater") hapibd_seg <- read_ibd(hapibd_filepath, source = "hapibd") hapibd_seg ## # A tibble: 3,954 x 6 ##   id1          id2           chr   start    end  length ##                            ##  1 testped1_g1-b1-s1 testped1_g3-b1-i1   1 197661576 234863602  47.1 ##  2 testped1_g2-b2-i1 testped1_g3-b1-i1   1 197661576 231017545  39.8 ##  3 testped1_g3-b1-i1 testped1_g3-b2-i1   1 197661576 212799139  20.3 ##  4 testped3_g1-b1-s1 testped3_g3-b2-i1   1  2352146  10862397  17.7 ##  5 testped3_g2-b2-i1 testped3_g3-b2-i1   1  2352146  10862397  17.7 ##  6 testped1_g1-b1-s1 testped1_g2-b1-i1   1  3328659  64123868  86.4 ##  7 testped1_g1-b1-s1 testped1_g3-b1-i1   1  3328659  33476811  51.2 ##  8 testped1_g2-b2-s1 testped1_g3-b2-i1   1  5003504  32315147  45.9 ##  9 testped2_g1-b1-i1 testped2_g3-b1-i1   1 240810528 248578622  15.9 ## 10 testped2_g1-b1-i1 testped2_g2-b2-i1   1 241186056 249170711  15.5 ## # … with 3,944 more rows

In order to translate the shared genomic cM length to a kinship coefficient, a genetic map must first be read in with read_map(). Software for IBD segment inference and simulation requires a genetic map. The map loaded for kinship estimation should be the same one used for creating the shared IBD segment output. The example below uses a minimal genetic map that ships with skater: gmap_filepath <- system.file("extdata", "sexspec-avg-min.plink.map",                   package="skater") gmap <- read_map(gmap_filepath) gmap ## # A tibble: 28,726 x 3 ##    chr value      bp ##        ##  1   1 0     752721 ##  2   1 0.0292 1066029 ##  3   1 0.0829 1099342 ##  4   1 0.157  1106473 ##  5   1 0.246  1152631 ##  6   1 0.294  1314015 ##  7   1 0.469  1510801 ##  8   1 0.991  1612540 ##  9   1 1.12  1892325 ## 10   1 1.41  1916587 ## # … with 28,716 more rows

The ibd2kin() function takes the segments and map file and outputs a tibble with one row per pair of individuals and columns for individual 1 ID, individual 2 ID, and the kinship coefficient for the pair: ibd_dat <- ibd2kin(.ibd_data=hapibd_seg, .map=gmap) ibd_dat ## # A tibble: 196 x 3 ##   id1          id2           kinship ##                      ##  1 testped1_g1-b1-i1 testped1_g1-b1-s1 0.000316 ##  2 testped1_g1-b1-i1 testped1_g2-b1-i1 0.261 ##  3 testped1_g1-b1-i1 testped1_g2-b2-i1 0.263 ##  4 testped1_g1-b1-i1 testped1_g2-b2-s1 0.000150 ##  5 testped1_g1-b1-i1 testped1_g3-b1-i1 0.145 ##  6 testped1_g1-b1-i1 testped1_g3-b2-i1 0.133 ##  7 testped1_g1-b1-i1 testped2_g1-b1-i1 0.000165 ##  8 testped1_g1-b1-i1 testped2_g1-b1-s1 0.000323 ##  9 testped1_g1-b1-i1 testped2_g2-b1-i1 0.000499 ## 10 testped1_g1-b1-i1 testped2_g2-b1-s1 0.000318 ## # … with 186 more rows

Summary

The skater R package provides a robust software package for data import, manipulation, and analysis tasks typically encountered when working with SNP-based kinship analysis tools. All package functions are internally documented with examples, and the package contains a vignette demonstrating usage, inputs, outputs, and interpretation of all key functions. The package contains internal tests that are automatically run with continuous integration via GitHub Actions whenever the package code is updated. The skater package is permissively licensed (MIT) and is easily extensible to accommodate outputs from new genome-wide relatedness and IBD segment methods as they become available.

Software availability

Software available from: http://CRAN.R-project.org/package=skater .

Source code available from: https://github.com/signaturescience/skater .

Archived source code at time of publication: https://doi.org/10.5281/zenodo.5761996.

Software license: MIT License.

Author information

SDT, VPN, and MBS developed the R package.

All authors contributed to method development.

SDT wrote the first draft of the manuscript.

All authors assisted with manuscript revision.

All authors read and approved the final manuscript.

Competing interests

No competing interests were disclosed.

Grant information

This work was supported in part by award 2019-DU-BX-0046 (Dense DNA Data for Enhanced Missing Persons Identification) to B.B., awarded by the National Institute of Justice, Office of Justice Programs, U.S. Department of Justice and by internal funds from the Center for Human Identification. The opinions, findings, and conclusions or recommendations expressed are those of the authors and do not necessarily reflect those of the U.S. Department of Justice.

This article describes an R package that provides a suite of tools for performing kinship and IBD segment analysis. Key features include methods for reading and analyzing pedigree data, inferring relationships, benchmarking relationship degree classification, and IBD segment analyses. The use cases in this article and the R reference manual on CRAN together provide exemplary documentation for skater. Users unfamiliar with pedigree-style analyses and/or who desire a clean, well documented interface for performing these analyses can find a great deal of utility in this R package.

Are the conclusions about the tool and its performance adequately supported by the findings presented in the article?

Yes

Is the rationale for developing the new software tool clearly explained?

Yes

Is the description of the software tool technically sound?

Yes

Are sufficient details of the code, methods and analysis (if applicable) provided to allow replication of the software development and its use by others?

Yes

Is sufficient information provided to allow interpretation of the expected output datasets and any results generated using the tool?

Yes

Reviewer Expertise:

Relatedness in large datasets.

I confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard.

For many researchers, myself included, R is the language-of-choice when it comes to downstream analysis and tidying-up the output of other programs. This inevitably leads to a disorganized body of ad hoc scripts for parsing, cleaning, and manipulating various result files. Arguably, the best solution to this problem is to collect related scripts into an R package, which can be properly documented, version-controlled, and potentially shared with others. The skater package presented in this paper is precisely such a package, aimed at the users of several popular software for relatedness estimation, including KING, PLINK, hap-IBD, and others. A specific goal of the package is to provide tools that work consistently across these programs.

The paper is written in clear language, well structured, and easy to follow. Parts of the paper resemble a user manual, including detailed code examples. I enjoyed playing around with the package, which is well organized and documented. In particular, I appreciate the thorough input checks and helpful error messages when things go wrong. The package may not offer a ton of novel methods yet, but I can certainly see myself using it the next time I run some of these relatedness programs.

Below are a few issues I have with the manuscript. Finally, I cannot resist offering a couple of suggestions for the skater package itself:

It would be helpful to clarify what sort of applications skater is intended for. The introduction mentions "relationship inference in population genetics, medical genetics, and forensics", but that seems overly broad. The focus is on simple measures like the kinship coefficient and relatedness degree (rather than detailed coefficients and actual genealogical relationships) suggesting large-scale applications rather than small-scale pedigree analysis as e.g. in forensic case work. Indeed, all the programs referenced in the paper are intended for large-scale population studies. Furthermore, it seems to be an underlying assumption that all individuals are noninbred (suggested e.g. by the statement "the theoretical upper boundary for the kinship coefficient is 0.5"), and also that they are human (by the hardcoded 3560 cM genome length).

To be clear, I think these limitations and assumptions are perfectly fine, but it would be better to state (or discuss) them more clearly.

A connection to "SNP-based methods" is mentioned repeatedly, but never really explained. Is there one? I note at least one of the cited programs (hap-IBD) is designed to work with sequencing data. Does it matter for skater how the kinship estimates were obtained?

The function `confusion_matrix()` produces an impressive array of summary stats when comparing inferred kinship degrees to true ones. This is great, but I wonder about the origin of this function. In my understanding, it is a modified version of a function from another package, confusionMatrix, written by Michael Clark (not an author of the paper). Several related functions appear to be copied verbatim into skater. All of this is clearly marked in the code, with Clark rightfully listed as author, and Clark's package does come with a permissive license (MIT), but I still find it a bit odd. Why not simply import it? That way, bug fixes and improvements in the original version would automatically propagate to skater, avoiding the confusion of multiple versions. If the problem is that confusionMatrix is not on CRAN, perhaps one could reach out to make this happen?

In the example illustrating `confusion_matrix()`, the authors construct a dataset by modifying a previous one, by "randomly flipping ~20% of the true degrees". The term "flip" sounds misplaced to me here, since the variable isn't dichotomous. Also, the procedure doesn't change 20%, only 0.2 * 4/5 = 16% on average, since the new values are generated from the complete set and stay unchanged with probability 1/5. Regardless of these minor issues, I must admit I found the example rather artificial. It would be much more interesting to see confusion_matrix() applied to a real dataset! That could also motivate some comments on how its output should be interpreted.

The `read_fam()` for reading .fam files is very strict, insisting on space-separated columns and disallowing any format deviations. I note that e.g. PLINK, and several other R packages that read pedigree files, allow variations like tab-separated columns and missing phenotype column; perhaps this could be useful in the skater package too.

Separating parent-offspring pairs from full sibs is a crucial step in many relatedness studies, and often possible by a simple analysis of the output of e.g. KING. Does skater offer such differentiation? If not, it might make a nice addition to the package in the future.

Are the conclusions about the tool and its performance adequately supported by the findings presented in the article?

Partly

Is the rationale for developing the new software tool clearly explained?

Yes

Is the description of the software tool technically sound?

Yes

Are sufficient details of the code, methods and analysis (if applicable) provided to allow replication of the software development and its use by others?

Yes

Is sufficient information provided to allow interpretation of the expected output datasets and any results generated using the tool?

Yes

Reviewer Expertise:

Statistical genetics, pedigree analysis, R

I confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard, however I have significant reservations, as outlined above.

The authors developed an R package to evaluate the performance of different relatedness inference tools. Both the R package and the manuscript are user-friendly and easy to follow. This tool should be timely and valuable to geneticists (or data analysts) who have a need to compare different relatedness inference tools and choose a tool that performs the best for their own genetic datasets. The manuscript is very well written.

One very minor comment is about the format requirement for the developed tool. Is the required format basically PLINK with expectations that some commonly used PLINK formats are still not allowed, or all PLINK format is acceptable? E.g., Again, the comment above is quite minor and may only require some clarification. The overall quality of this manuscript is great.

How about only one parent available, with Father ID 1234 and mother ID 0? This is allowed and considered as PLINK format. How does the proposed tool handle it, e.g., ask the user to reformat it?

How about both parents only with IDs available. E.g., Father ID is 1234 and mother ID is 5678, and none of them have appeared again in Individual IDs?

Are the conclusions about the tool and its performance adequately supported by the findings presented in the article?

Yes

Is the rationale for developing the new software tool clearly explained?

Yes

Is the description of the software tool technically sound?

Yes

Are sufficient details of the code, methods and analysis (if applicable) provided to allow replication of the software development and its use by others?

Yes

Is sufficient information provided to allow interpretation of the expected output datasets and any results generated using the tool?

Yes

Reviewer Expertise:

My area of research is statistical genetics, specifically developing methods and tools for relatedness inference.

I confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard.