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While liability to schizophrenia (Scz) is due to genetic and environmental factors, specific factors are largely unknown. We postulate a two-hit model for Scz, in which initial liability is generated during fetal brain development: this "hit" is precipitated by environmental stressors biologically interacting with maternal genetic vulnerability to the stress. Additional liability to Scz is generated by individual genetic vulnerability. To evaluate these putative levels of vulnerability, we search in the genome of both affected individuals and their mothers for variation that differs, statistically, from that in the general population. For parental analyses, mothers were treated as "affected," rather than their offspring, and the fathers were treated as "controls". We used a sample from the Palauan population: 175 individuals diagnosed with Scz, broadly defined; 87 mothers and 45 fathers of affected individuals. Pedigree and diagnostic data were available on 2,953 living and deceased subjects. DNA from 553 individuals was genotyped for short tandem repeats (STR) spaced approximately every 10 cM across the genome. We tested for association between affection status and STR alleles; such an approach was reasonable, despite the widely spaced markers, because this population has far-ranging linkage disequilibrium (LD). Results for the truly affected individuals were modest, whereas results from the maternal generation were promising. For a recessive model and a test for excess allele matching across mothers, significant findings occurred for D20S481, D10S1221, D6S1021, D13S317, and D18S976. Regions in which at least two adjacent markers produced substantial association statistics include 2p12-11.2, 2q24.1-32.1, 6q12-14.1, 10q23.2-24.21, 12q23.2-24.21 and 17q23.2-23.3.

Samples consisting of a mix of unrelated cases and controls, small pedigrees, and much larger pedigrees present a unique challenge for association studies. Few methods are available for efficient analysis of such a broad spectrum of data structures. In this paper we introduce a new matching statistic that is well suited to complex data structures and compare it with frequency-based methods available in the literature. To investigate and compare the power of these methods we simulate datasets based on complex pedigrees. We examine the influence of various levels of linkage disequilibrium (LD) of the disease allele with a marker allele (or equivalently a haplotype). For low frequency marker alleles/haplotypes, frequency-based statistics are more powerful in detecting association. In contrast, for high frequency marker alleles, the matching statistic has greater power. The highest power for frequency-based statistics occurs when the disease allele frequency closely matches the frequency of the linked marker allele. In contrast maximum power of the matching statistic always occurs for intermediate marker allele frequency regardless of the disease allele frequency. Moreover, the matching and frequency-based statistics exhibit little correlation. We conclude that these two approaches can be viewed as complementary in finding possible association between a disease and a marker for many different situations.