Albright et al. BMC Cancer 2012, 12:138 http://www.biomedcentral.com/1471-2407/12/138 new sites which have gained sufficient sample sizes since 1994 to be analyzed for the first time. Methods and data resources Utah population database The Utah genealogy data was originally linked to Utah cancer registry records in the late seventies. The UPDB has been an important research population database for the discovery and elucidation of heritable factors for a wide variety of diseases, including cancer [2-6], influenza [11], asthma [12], diabetes [13], rotator cuff [14], aneur- ysm [15], and brain [16], among others. This resource and the high-risk pedigrees identified within it have been key for the localization and isolation of multiple cancer genes, including CDKN2A (melanoma) [17], BRCA1 [18], BRCA2 [19], and HPC2/ELAC2 (prostate cancer) [20]. The original Utah genealogy including 1.6 million Utah individuals has grown to include more than 6.5 million individual records, including 2.3 million indivi- duals who belong to families of 3 to 15 generations deep, which connect to the original Utah genealogy. We have taken advantage of the extended genealogy data to include more rigorous control of the amount of ances- tral genealogical data required for an individual to be included in genetic analyses. To ensure high quality gen- ealogical records of sufficient depth and breadth for both cases and for controls, we only consider those indi- viduals who have ancestral genealogy data for their par- ents, all four grandparents, and at least 6 of their 8 great grandparents. There were just over 1.2 million such individuals in the database used for this analysis. There are 85,550 of these 1.2 million individuals who also have a cancer record in the UCR between 1966 and 2009. We used ICD-oncology coding as indicated in Table 1 to assign these 85,550 individuals to 36 different cancer site groups. Cancer rates in Utah are recognized to be generally lower than U.S. rates. Table 2 shows Utah/U.S. inci- dence rate ratios for cancers as defined and compared by SEER. Few cancer sites have significantly higher rates in Utah than the U.S. This is probably due to low rates of smoking in the Utah population [21]. Genealogical Index of Familiality (GIF) method The genealogical index of familiality (GIF) is a well-pub- lished method to test hypotheses of excess relatedness within sets of individuals selected from the UPDB [22,23]. The GIF test estimates the average pair-wise relatedness between all possible pairs within a defined group of individuals (cases) and compares it to the expected average relatedness in the UPDB. The average pairwise relatedness measured by the GIF is based upon the Malécot coefficient of kinship [24]; the coefficient Page 2 of 7 gives the probability that randomly selected homologous genes from 2 individuals are identical by descent from a common ancestor. For parent-child relationships the Malécot coefficient is 1/2; for siblings or grandparent relationships the coefficient is ¼; for avunculars (e.g., aunt/niece or aunt/nephew) the coefficient is ⅛; for first cousins the coefficient is (1/2)4, or 1/16 , and so forth. The value of the GIF statistic decreases with increasing genetic distance between individuals; unrelated indivi- duals have 0 relatedness. All possible genetic paths between cases are identified and the average pairwise relatedness is summed and then averaged for all pairs, then multiplied by 105. The GIF statistic, or average pair-wise relatedness, for a set of cases is compared to GIF statistics estimated for 10,000 independent sets of matched controls; controls are matched by sex, 5 year birth cohorts, and birth state (Utah or not). The significance of the test of hypothesis is measured empirically based on the position of the case GIF in the distribution of 10,000 control GIF statis- tics. The overall GIF statistic, utilizing all pairwise rela- tionships between all cases, allows a test of the alternative hypothesis of no excess clustering over that expected in the Utah population. A significant result for the Overall GIF test supports excess relatedness of the cases; however, it does not distinguish genetic from familial clustering resulting from shared environment or risk. For this reason we have expanded the GIF method to include a comparison of average relatedness that ignores close relationships (with a relationship closer than first cousins). This revised “distant” GIF, or dGIF, statistic measures relatedness for distant relationships only, ignoring close relationships where shared environmental exposures are expected to be highest. The dGIF statistic is calculated similarly to the origi- nal GIF, but ignores close relationships among cases, and among controls. A significant result for the dGIF test suggests an excess of distant relationships among case pairs and supports a genetic contribution to the observed excess relatedness. For the GIF test, since we have performed multiple tests (on 36 different cancer sites), we have adjusted our threshold for significance using the Bonferroni correc- tion (noted to be conservative). We use p < 0.0013 (0.05/36) to identify significant results. The University of Utah Institutional Review Board, and the Resource for Genetic Epidemiological Research approved the research. Results The GIF test for excess relatedness was performed for the 36 different cancer groups shown in Table 1. Even though we have increased our stringency on the cases to