Anticipation, manifested through decreasing age of onset or increased severity in successive generations, has been noted in several genetic diseases. Statistical methods for genetic anticipation range from a simple use of the paired t-test for age of onset restricted to affected parent-child pairs to a recently proposed random effects model which includes extended pedigree data and unaffected family members [Larsen et al., 2009]. A naive use of the paired t-test is biased for the simple reason that age of onset has to be less than the age at ascertainment (interview) for both affected parent and child, and this right truncation effect is more pronounced in children than in parents. In this study, we first review different statistical methods for testing genetic anticipation in affected parent-child pairs that address the issue of bias due to right truncation. Using affected parent-child pair data, we compare the paired t-test with the parametric conditional maximum likelihood approach of Huang and Vieland [1997] and the nonparametric approach of Rabinowitz and Yang [1999] in terms of Type I error and power under various simulation settings and departures from the modeling assumptions. We especially investigate the issue of multiplex ascertainment and its effect on the different methods. We then focus on exploring genetic anticipation in Lynch syndrome and analyze new data on the age of onset in affected parent-child pairs from families seen at the University of Michigan Cancer Genetics clinic with a mutation in one of the three main mismatch repair (MMR) genes. In contrast to the clinic-based population, we re-analyze data on a population-based Lynch syndrome cohort, derived from the Danish HNPCC-register. Both datasets indicate evidence of genetic anticipation in Lynch syndrome. We then expand our review to incorporate recently proposed statistical methods that consider family instead of affected pairs as the sampling unit. These prospective censored regression models offer additional flexibility to incorporate unaffected family members, familial correlation and other covariates into the analysis. An expanded dataset from the Danish HNPCC-register is analyzed by this alternative set of methods.