Utah Family-Based Analysis: Past, Present and Future

Division of Genetic Epidemiology, Department of Biomedical Informatics, University of Utah School of Medicine, Salt Lake City, Utah 84108, USA.
Human Heredity (Impact Factor: 1.47). 02/2008; 65(4):209-20. DOI: 10.1159/000112368
Source: PubMed


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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    • "The UPDB is a University of Utah resource that contains over seven million individual records from statewide datasets [29] [30]. The UPDB includes all driver license records (as well as identification cards for nondrivers), which we used to ascertain self-reported height and weight. "
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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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    • "The Genealogical Index of Familiality (GIF) statistic was used to test the hypothesis of excess relatedness among individuals in the low BMI phenotype. The GIF was developed specifically for the UPDB [39]–[40]. Briefly, the GIF measures the average pair-wise relatedness of a set of individuals and compares that measurement to the average pair-wise relatedness expected in the Utah population. The GIF test differs from relative risk (RR) in that it includes analysis of all genetic relationships, both close and distant. "
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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 · 3.23 Impact Factor
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    • "All other individuals were considered “unknown”, and were not genotyped in this study. These pedigrees are defined as high risk because they contain significantly more female breast cancer than expected using cancer rates calculated from the UPDB (see [12]). "
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    ABSTRACT: Background We applied a new weighted pairwise shared genomic segment (pSGS) analysis for susceptibility gene localization to high-density genomewide SNP data in three extended high-risk breast cancer pedigrees. Results Using this method, four genomewide suggestive regions were identified on chromosomes 2, 4, 7 and 8, and a borderline suggestive region on chromosome 14. Seven additional regions with at least nominal evidence were observed. Of particular note among these total twelve regions were three regions that were identified in two pedigrees each; chromosomes 4, 7 and 14. Follow-up two-pedigree pSGS analyses further indicated excessive genomic sharing across the pedigrees in all three regions, suggesting that the underlying susceptibility alleles in those regions may be shared in common. In general, the pSGS regions identified were quite large (average 32.2 Mb), however, the range was wide (0.3 – 88.2 Mb). Several of the regions identified overlapped with loci and genes that have been previously implicated in breast cancer risk, including NBS1, BRCA1 and RAD51L1. Conclusions Our analyses have provided several loci of interest to pursue in these high-risk pedigrees and illustrate the utility of the weighted pSGS method and extended pedigrees for gene mapping in complex diseases. A focused sequencing effort across these loci in the sharing individuals is the natural next step to further map the critical underlying susceptibility variants in these regions.
    BMC Genomics 11/2012; 13(1):676. DOI:10.1186/1471-2164-13-676 · 3.99 Impact Factor
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