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.
A data frame with 42,773 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 NLSY79. 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 levels Gen1Housemates=1, Gen2Siblings=2, Gen2Cousins=3, ParentChild=4, AuntNiece=5.
Will Beasley
The dataset contains Gen1 and Gen2 subjects. "Gen1" refers to subjects in the original NLSY79 sample (https://www.nlsinfo.org/content/cohorts/nlsy79). "Gen2" subjects are the biological children of the Gen1 females -ie, those in the NLSY79 Children and Young Adults sample (https://www.nlsinfo.org/content/cohorts/nlsy79-children).
Subjects will be in the same extended family if either:
they are Gen1 housemates,
they are Gen2 siblings,
they are Gen2 cousins (ie, they have mothers who are Gen1 sisters in the NLSY79,
they are mother and child (in Gen1 and Gen2, respectively), or
they are aunt|uncle and niece|nephew (in Gen1 and Gen2, respectively).
The variables SubjectTag_S1 and SubjectTag_S2 uniquely identify
subjects. For Gen2 subjects, the SubjectTag is identical to their CID (ie,
C00001.00 -the SubjectID assigned in the NLSY79-Children files). However
for Gen1 subjects, the SubjectTag is their CaseID (ie, R00001.00), with
"00" appended. This manipulation is necessary to identify subjects
uniquely in inter-generational datasets. A Gen1 subject with an ID of 43
has a SubjectTag of 4300. The SubjectTags of her four children
remain 4301, 4302, 4303, and 4304.
Level 5 of RelationshipPath (ie, AuntNiece) is gender neutral. The
relationship could be either Aunt-Niece, Aunt-Nephew, Uncle-Niece, or
Uncle-Nephew. If there's a widely-accepted gender-neutral term, please
tell me.
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
Gen1Housemates, 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 Links79Pair contains
only the common values of R whose groups are likely to have stable estimates.
However the variable RFull in Links79PairExpanded 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.
Furthermore, some of these groups have members who are more strongly genetically related than their
items would indicate. For instance, there are 41 Gen1 pairs who explicitly claim they are not biologically related
(ie, RExplicit=0), yet their correlation for Adult Height is r=0.24. This is
much higher than would be expected for two people sampled randomly; it is nearly identical to
the r=0.26 we observed among the 268 Gen1 half-sibling pairs who claim they share exactly 1
biological parent.
The LinksPair79 dataset contains columns necessary for a
basic BG analysis. The Links79PairExpanded 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.
RelationshipPath variable. Code written using this dataset should
NOT assume it contains only Gen2 sibling pairs. See below for an example of
filtering the relationship category in the in Links79Pair
documentation.
The specific steps to determine the R coefficient will be described in an upcoming publication. The following information may influence the decisions of an applied researcher.
A distinction is made between Explicit and Implicit information.
Explicit information comes from survey items that directly address the
subject's relationships. For instance in 2006, surveys asked if the
sibling pair share the same biological father (eg, Y19940.00 and
T00020.00). Implicit information comes from items where the subject
typically isn't aware that their responses may be used to determine genetic
relatedness. For instance, if two siblings have biological fathers with
the same month of death (eg, R37722.00 and R37723.00), it may be reasonable
to assume they share the same biological father.
Interpolation is our lingo when other siblings are used to leverage
insight into the current pair. For example, assume Subject 101, 102, and
103 have the same mother. Further assume 101 and 102 report they share a
biological father, and that 101 and 103 share one too. Finally, assume
that we don't have information about the relationship between 102 and 103.
If we are comfortable with our level of uncertainty of these
determinations, then we can interpolate/infer that 102 and 103 are
full-siblings as well.
The math and height scores are duplicated from ExtraOutcomes79, but are included here to make some examples more concise and accessible.
The NLSY79 variable HHID (ie, R00001.49) is the source for the
ExtendedID variable. This is discussed at
http://www.nlsinfo.org/nlsy79/docs/79html/79text/hhcomp.htm.
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.
The LinksPair79 dataset contains columns necessary for a
basic BG analysis. The Links79PairExpanded 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, Links79Pair) 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:
write.csv(Links79Pair, "C:/BGDirectory/Links79Pair.csv")
where "C:/BGDirectory/" is replaced by your preferred directory.
Remember to use forward slashes instead of backslashes; for instance, the
path "C:\BGDirectory\Links79Pair.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-2017-79.yml documents the dataset version information.
library(NlsyLinks) # Load the package into the current R session.
summary(Links79Pair) # Summarize the five variables.
hist(Links79Pair$R) # Display a histogram of the Relatedness coefficients.
table(Links79Pair$R) # Create a table of the Relatedness coefficients for the whole sample.
# Create a dataset of only Gen2 sibs, and display the distribution of R.
gen2Siblings <- subset(Links79Pair, RelationshipPath == "Gen2Siblings")
table(gen2Siblings$R) # Create a table of the Relatedness coefficients for the Gen2 sibs.
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