Identification of twin pairs from large population-based samples.

The basis of most twin studies is the ascertainment of twins, often through twin registries, and determination of zygosity. The current rate of twin births in many industrialized countries implies that in the near future around 3% or more of individuals will be a twin. Hence, there are and will be a lot of twins around and many of those will not participate in twin studies. However, if large population-based samples are available that include appropriate identifiers, then twins can be detected and twin studies performed, even in the absence of zygosity information. We quantified the number of twin pairs that could be detected from a longitudinal survey in the Netherlands, which aims to answer questions about educational strategies and performance in primary education in the Netherlands. We detected 2865 twin pairs if we used a coded name identifier, date of birth, school, grade and year of survey, which is 2.01% of 284,945 pupils in five cohorts. Relaxing our selection criteria increased the number of apparent twin pairs identified, most of which are false positives due to chance matching of identification criteria. We show that the intraclass correlation on measured phenotypes can be used as a quality control measure for twin identification, and quantify the proportion of false negatives (true twin pairs not identified) due to missing data and data coding errors. We compared our estimated rate of twins in the sample to census data and estimate that with our most stringent selection criteria we detect more than 80% of all twin pairs in the sample. We conclude that the identification of twin pairs from large population-based samples is feasible, rapid and accurate if the appropriate identifiers are available, and that twin pairs from such sources are a valuable resource for studies to answer scientific question about twins versus nontwins and about genetic and environmental factors of twin resemblance.