Utah family-based analysis: past, present and future.
ABSTRACT A unique genealogical resource linked to phenotype data was created in Utah over 30 years ago. Here we review the history and content of this resource. In addition, we review three current methodologies used in conjunction with this resource to define the heritable contribution to phenotypes and to identify predisposition genes responsible for these phenotypes. Example analyses and high-risk pedigrees are presented. Finally we briefly review ways this resource, or others like it, may expand in future.
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ABSTRACT: Background: Prostate cancer is a common and often deadly cancer. Decades of study have yet to identify genes that explain much familial prostate cancer. Traditional linkage analysis of pedigrees has yielded results that are rarely validated. We hypothesize that there are rare segregating variants responsible for high-risk prostate cancer pedigrees, but recognize that within-pedigree heterogeneity is responsible for significant noise that overwhelms signal. Here we introduce a method to identify homogeneous subsets of prostate cancer, based on cancer characteristics, which show the best evidence for an inherited contribution. Methods: We have modified an existing method, the Genealogical Index of Familiality (GIF) used to show evidence for significant familial clustering. The modification allows a test for excess familial clustering of a subset of prostate cancer cases when compared to all prostate cancer cases. Results: Consideration of the familial clustering of eight clinical subsets of prostate cancer cases compared to the expected familial clustering of all prostate cancer cases identified three subsets of prostate cancer cases with evidence for familial clustering significantly in excess of expected. These subsets include prostate cancer cases diagnosed before age 50 years, prostate cancer cases with body mass index (BMI) greater than or equal to 30, and prostate cancer cases for whom prostate cancer contributed to death. Conclusions: This analysis identified several subsets of prostate cancer cases that cluster significantly more than expected when compared to all prostate cancer familial clustering. A focus on high-risk prostate cancer cases or pedigrees with these characteristics will reduce noise and could allow identification of the rare predisposition genes or variants responsible.Frontiers in Genetics 01/2013; 4:152. DOI:10.3389/fgene.2013.00152
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ABSTRACT: Background. Population-based studies are needed to estimate the prevalence of underweight or overweight/obese childhood cancer survivors. Procedure. Adult survivors (diagnosed ≤20 years) were identified from the linked Utah Cancer Registry and Utah Population Database. We included survivors currently aged ≥20 years and ≥5 years from diagnosis (N = 1060), and a comparison cohort selected on birth year and sex (N = 5410). BMI was calculated from driver license data available from 2000 to 2010. Multivariable generalized linear regression models were used to calculate prevalence relative risks (RR) and 95% confidence intervals (95% CI) of BMI outcomes for survivors and the comparison cohort. Results. Average time since diagnosis was 18.5 years (SD = 7.8), and mean age at BMI for both groups was 30.5 (survivors SD = 7.7, comparison SD = 8.0). Considering all diagnoses, survivors were not at higher risk for being underweight or overweight/obese than the comparison. Male central nervous system tumor survivors were overweight (RR = 1.12, 95% CI 1.01-1.23) more often than the comparison. Female survivors, who were diagnosed at age 10 and under, had a 10% higher risk of being obese than survivors diagnosed at ages 16-20 (P < 0.05). Conclusion. While certain groups of childhood cancer survivors are at risk for being overweight/obese, in general they do not differ from population estimates.Journal of Cancer Epidemiology 01/2014; 2014:531958. DOI:10.1155/2014/531958
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ABSTRACT: The low body mass index (BMI) phenotype of less than 18.5 has been linked to medical and psychological morbidity as well as increased mortality risk. Although genetic factors have been shown to influence BMI across the entire BMI, the contribution of genetic factors to the low BMI phenotype is unclear. We hypothesized genetic factors would contribute to risk of a low BMI phenotype. To test this hypothesis, we conducted a genealogy data analysis using height and weight measurements from driver's license data from the Utah Population Data Base. The Genealogical Index of Familiality (GIF) test and relative risk in relatives were used to examine evidence for excess relatedness among individuals with the low BMI phenotype. The overall GIF test for excess relatedness in the low BMI phenotype showed a significant excess over expected (GIF 4.47 for all cases versus 4.10 for controls, overall empirical p-value<0.001). The significant excess relatedness was still observed when close relationships were ignored, supporting a specific genetic contribution rather than only a family environmental effect. This study supports a specific genetic contribution in the risk for the low BMI phenotype. Better understanding of the genetic contribution to low BMI holds promise for weight regulation and potentially for novel strategies in the treatment of leanness and obesity.PLoS ONE 12/2013; 8(12):e80287. DOI:10.1371/journal.pone.0080287 · 3.53 Impact Factor