Kinship linking file for pairs of relatives in the NLSY97

Source: R/links-97-pair-doc.R

This dataset specifies the relatedness coefficient (ie, 'R') between subjects in the same extended family. Each row represents a unique relationship pair.

NOTE: Two variable names changed in November 2013. Subject1Tag and Subject2Tag became SubjectTag_S1 and SubjectTag_S2.

Format

A data frame with 2,519 observations on the following 5 variables. There is one row per unique pair of subjects, irrespective of order.

  • ExtendedID Identity of the extended family of the pair; it corresponds to the HHID in the NLSY97. See References below.

  • SubjectTag_S1 Identity of the pair's first subject. See Details below.

  • SubjectTag_S2 Identity of the pair's second subject. See Details below.

  • R The pair's Relatedness coefficient. See Details below.

  • RelationshipPath Specifies the relationship category of the pair. This variable is a factor, with level Housemates=1.

Source

Information comes from the Summer 2018 release of the NLSY97 sample. Data were extracted with the NLS Investigator (https://www.nlsinfo.org/investigator/).

Details

The variable ExtendedID corresponds to the NLSY97 variable [SIDCODE] (e.g., R11930.00), which uniquely identifies a household that may contain multiple NLSY97 subjects.

The variables SubjectTag_S1 and SubjectTag_S2 uniquely identify subjects. It corresponds to the NLSY97 variable [PUBID], (e.g., R00001.00).

The RelationshipPath variable is not useful with this dataset, but is included to be consistent with the Links97Pair dataset.

An extended family with \(k\) subjects will have \(k\)(\(k\)-1)/2 rows. Typically, Subject1 is older while Subject2 is younger.

MZ twins have R=1. DZ twins and full-siblings have R=.5. Half-siblings have R=.25. Typical first cousins have R=.125. Unrelated subjects have R=0 (this occasionally happens for Housemates, but never for the other paths). Other R coefficients are possible.

There are several other uncommon possibilities, such as half-cousins (R=.0625) and ambiguous aunt-nieces (R=.125, which is an average of 1/4 and 0/4). The variable coding for genetic relatedness,R, in Links97Pair contains only the common values of R whose groups are likely to have stable estimates. However the variable RFull in Links97PairExpanded contains all R values. We strongly recommend using R in this base::data.frame. Move to RFull (or some combination) only if you have a good reason, and are willing to carefully monitor a variety of validity checks. Some of these excluded groups are too small to be estimated reliably.

References

For more information on R (ie, the Relatedness coefficient), please see Rodgers, Joseph Lee, & Kohler, Hans-Peter (2005). Reformulating and simplifying the DF analysis model. Behavior Genetics, 35 (2), 211-217.

See also

The LinksPair97 dataset contains columns necessary for a basic BG analysis. The Links97PairExpanded dataset contains further information that might be useful in more complicated BG analyses.

A tutorial that produces a similar dataset is http://www.nlsinfo.org/childya/nlsdocs/tutorials/linking_mothers_and_children/linking_mothers_and_children_tutorial.html. It provides examples in SAS, SPSS, and STATA.

The current dataset (ie, Links97Pair) can be saved as a CSV file (comma-separated file) and imported into in other programs and languages. In the R console, type the following two lines of code:

library(NlsyLinks) write.csv(Links97Pair, "C:/BGDirectory/Links97Pair.csv")

where "C:/BGDirectory/" is replaced by your preferred directory. Remember to use forward slashes instead of backslashes; for instance, the path "C:\BGDirectory\Links97Pair.csv" can be misinterpreted.

Download CSV If you're using the NlsyLinks package in R, the dataset is automatically available. To use it in a different environment, download the csv, which is readable by all statistical software. links-metadata-2017-97.yml documents the dataset version information.

Author

Will Beasley

Examples

library(NlsyLinks)     # Load the package into the current R session.
summary(Links97Pair)   # Summarize the five variables.

#>    ExtendedID   SubjectTag_S1  SubjectTag_S2        R         
#>  Min.   :   8   Min.   :   6   Min.   :   7   Min.   :0.2500  
#>  1st Qu.:2038   1st Qu.:2402   1st Qu.:2402   1st Qu.:0.5000  
#>  Median :4148   Median :4939   Median :4940   Median :0.5000  
#>  Mean   :3987   Mean   :4767   Mean   :4768   Mean   :0.4924  
#>  3rd Qu.:5991   3rd Qu.:7174   3rd Qu.:7175   3rd Qu.:0.5000  
#>  Max.   :7477   Max.   :9021   Max.   :9022   Max.   :1.0000  
#>                                               NA's   :284     
#>    RelationshipPath
#>  Housemates:2519   
#>                    
#>                    
#>                    
#>                    
#>                    
#>                    
hist(Links97Pair$R)    # Display a histogram of the Relatedness coefficients.

table(Links97Pair$R)   # Create a table of the Relatedness coefficients for the whole sample.

#> 
#> 0.25  0.5    1 
#>  124 2083   28 

#Create a dataset of only monozygotic sibs.
mz_sibs <- subset(Links97Pair, R > .9)
summary(mz_sibs)       # Create a table MZ sibs.

#>    ExtendedID   SubjectTag_S1  SubjectTag_S2        R       RelationshipPath
#>  Min.   :  67   Min.   :  75   Min.   :  76   Min.   :1   Housemates:28     
#>  1st Qu.:1536   1st Qu.:1788   1st Qu.:1789   1st Qu.:1                     
#>  Median :3293   Median :3925   Median :3926   Median :1                     
#>  Mean   :3465   Mean   :4145   Mean   :4146   Mean   :1                     
#>  3rd Qu.:5699   3rd Qu.:6856   3rd Qu.:6856   3rd Qu.:1                     
#>  Max.   :7282   Max.   :8792   Max.   :8793   Max.   :1