Albright et al. BMC Cancer 2012, 12:138 http://www.biomedcentral.com/1471-2407/12/138 4.90). Larger sample sizes in future years may clarify these results. The GIF statistic values for cancer cases range from 3.66 for bone cancer to 7.90 for chronic lymphocytic leukemia (this subgroup of leukemia cases was also one of the highest GIF measures observed in the 1994 analy- sis). Mean GIF statistics for the 10,000 sets of matched controls for each site had a smaller range (4.14 - 4.98); this is as expected in light of the overall similarity we expect for the estimated pairwise relatedness of ran- domly selected UPDB controls matched to cancer cases for 5-year birth cohort and sex. The control GIF mea- sure represents the average expected relatedness in the Utah population for older individuals (those represent- ing the at-risk cancer population). There is also some expected variability of the control GIF values based on the birth year distributions for different cancer sites. Individuals in the UPDB with earlier birth years have lower relatedness than those born more recently. The lowest control GIF average (4.14) was for acute lympho- cytic leukemia; only 20 of 409 ALL cases were born before 1900; the highest control GIF statistic (4.98) was for cancer of the anus, 117 of the 396 anus cancer patients were born before 1900. Discussion This analysis of cancer familial clustering provides a comprehensive review of evidence for familial clustering of cancer by site using a population-based genealogical resource linked to statewide cancer data. This analysis serves to confirm previously reported conclusions sug- gesting that the majority of cancer sites show some evi- dence of familial clustering in excess of expected. The only cancer sites not showing overall significant excess familial clustering are those with the smaller sample sizes, suggesting that it may be prudent to await more data analysis before a conclusion is reached for these rarer cancers. Using a more stringent, conservative test for a genetic contribution to cancer predisposition (the dGIF), this analysis has identified some cancer sites with significant evidence for a genetic contribution to predis- position, including very strong evidence for chronic lym- phocytic leukemia, thyroid cancer, lip cancer, lung cancer, prostate cancer, and melanoma. In addition to adding multiple new cancer sites to this analysis, and multiple new subgroups of cancers, we additionally used a more stringent selection criteria for cases and controls, based on amount and quality of gen- ealogical data available. We propose that this increases the fidelity of the results and serves to eliminate the noise in the genealogical resource that comes from ana- lysis of a greater number of families with incomplete genealogy compared to analysis of individuals known to have multiple relatives whose cancer can be observed in Page 6 of 7 our window of view from 1966. In addition, this filtering based on quantity of ancestral genealogy has resulted in slightly higher case GIF statistics than were observed in, for example, the 1994 analysis. The results for the overall GIF test show that for most of the cancer sites examined (28 of 36) there is a signifi- cant excess overall relatedness observed (without correc- tion). This is similar to the results shown in Cannon- Albright et al., 1994, the most recent published Utah GIF analysis. In that previous analysis, all cancer sites except small intestine, gallbladder, kidney, liver, pan- creas, and uterus (termed endometrial in this analysis) showed significant overall excess relatedness (at p < 0.05). With larger sample sizes, we now see significant excess cancer clustering of the small intestine, kidney, liver, pancreas, and uterus (endometrial). Of the new sites we have added to this analysis, larynx, anus, sali- vary, pharynx and female genitals show significant over- all excess familial clustering. The list of cancers that failed to show excess overall familial clustering primarily includes those with the smallest sample sizes. Separate analyses of cancers have identified subgroups with evi- dence for a genetic contribution [16,25-27]. The familiality analyses reported here has been limited in terms of the availability of data. Cancers diagnosed before 1966 or outside of Utah are censored; similarly individuals whose genealogy data was not included in the UPDB, or whose data did not appropriately link to their cancer data are also censored. We assume such censoring to be unbiased in nature. Because the GIF analysis considers relationships that are observed, it is robust to such censoring, but may be conservative in its identification of strong evidence for a heritable predis- position to disease. Increases in computing power have allowed us to increase the number of matched controls groups ana- lyzed from n = 6 in 1982, to 100 in 1994, to 10,000 in this analysis. It has similarly allowed us to consider both overall excess relatedness, as well as excess relatedness due only to distant relationships, the key to being able to separate what could be clustering due to shared environment from clustering that appears much more likely to be due to shared genetic factors. Conclusions Although we report several cancer sites with strong evi- dence for a genetic contribution, the implications of the results reported here go beyond genetic predisposition to disease. For almost all cancer sites analyzed, we observed an excess of familial clustering; most of this evidence is based on an excess of close relationships among cases. Whether based on the existence of shared environment or of shared genetics or a combination, the clinical implications are the same: close relatives of