Genetic Association Analysis Using Data from Triads and Unrelated Subjects

Abstract

The selection of an appropriate control sample for use in association mapping requires serious deliberation. Unrelated controls are generally easy to collect, but the resulting analyses are susceptible to spurious association arising from population stratification. Parental controls are popular, since triads comprising a case and two parents can be used in analyses that are robust to this stratification. However, parental controls are often expensive and difficult to collect. In some situations, studies may have both parental and unrelated controls available for analysis. For example, a candidate-gene study may analyze triads but may have an additional sample of unrelated controls for examination of background linkage disequilibrium in genomic regions. Also, studies may collect a sample of triads to confirm results initially found using a traditional case-control study. Initial association studies also may collect each type of control, to provide insurance against the weaknesses of the other type. In these situations, resulting samples will consist of some triads, some unrelated controls, and, possibly, some unrelated cases. Rather than analyze the triads and unrelated subjects separately, we present a likelihood-based approach for combining their information in a single combined association analysis. Our approach allows for joint analysis of data from both triad and case-control study designs. Simulations indicate that our proposed approach is more powerful than association tests that are based on each separate sample. Our approach also allows for flexible modeling and estimation of allele effects, as well as for missing parental data. We illustrate the usefulness of our approach using SNP data from a candidate-gene study of psoriasis.

Family-based vs. unrelated case-control designs for genetic associations

“…Allowing for these caveats, both types of design can yield useful and complementary information. Methods to improve efficiency of design and combination of data from both designs are welcome [12][13][14][15][16] and such methods should be tested more widely in the field. The applicability of methods of adjustment for population stratification also needs further empirical study [6][7][8][9][10][11].…”

“…Methods have also been developed to merge estimates of association from the two types of design [12,13]. Moreover, in an effort to maximize efficiency, several investigators have also proposed methods for hybrid designs that utilize data from both types of study designs [14][15][16]. Besides theoretical considerations, it would be interesting to obtain some empirical data on the extent to which these designs agree or disagree with each other.…”

Analysis of Family‐ and Population‐Based Samples Using Multiple Linkage Disequilibrium Mapping

“…Thus, situations may arise where a study collects both parental and unrelated controls for association analysis rather than choosing one specific type of control. For example, studies might collect a sample of trios to confirm previous association results that were identified in case-control studies (Epstein et al, 2005). Also, studies recruiting both parental and unrelated controls might focus on utilization of parental data, since use of unrelated controls may yield spurious associations due to stratification.…”

“…Nagelkerke et al (2004) proposed a joint analysis of such data using a likelihood-based approach, demonstrating that power is increased in comparison to methods that analyze trios and unrelated subjects separately. Epstein et al (2005) showed that power is improved after relaxing the assumption of parental mating-type distribution compared to the more general parental mating-type used by Nagelkerke et al (2004). In addition, they developed formal tests to determine whether data types can be combined, as well as for analyses of incomplete triad data.…”

Association Mapping of Complex Trait Loci With Context-Dependent Effects and Unknown Context Variable

“…Techniques such as stratified analysis (Clayton 2001), matching (Hinds et al 2004), genomic controls (Devlin and Roeder 1999;Marchini et al 2004), structured association (Pritchard et al 2000a;Sillanpää et al 2001;Hoggart et al 2003), smoothing (Conti and Witte 2003;Sillanpää and Bhattacharjee 2005), use of secondary samples (Epstein et al 2005;Kazeem and Farrell 2005), or approaches based on knowledge of relatives (Ewens and Spielman 1995;Thomson 1995;Knapp and Becker 2003) have been used to overcome the problem of population stratification. (For extensive comparison, see Setakis et al 2006.…”