Dataset Information

How Well Does the Family Longevity Selection Score Work: A Validation Test Using the Utah Population Database.

ABSTRACT: The Family Longevity Selection Score (FLoSS) was used to select families for the Long Life Family Study (LLFS) but has never been validated in other populations. The goal of this paper is to validate how well the FLoSS-based selection procedure works in an independent dataset. In this paper, we computed FLoSS using the lifespan data of 234,155 individuals from a large comprehensive genealogically-based resource, the Utah Population Database (UPDB), born between 1779 and 1910 with mortality follow-up through 2012-2013. Computations of FLoSS in a specific year (1980) confirmed the survival advantage of the "exceptional" sibships (defined by LLFS FLoSS threshold, FLoSS ? 7). We found that the subsample of the UPDB participants born after 1900 who were from the "exceptional" sibships had survival curves similar to that of the US participants from the LLFS probands' generation. Comparisons between the offspring of parents with "exceptional" and "ordinary" survival showed the survival advantage of the "exceptional" offspring. Investigators seeking to explain the extent genetics and environment contribute to exceptional survival will benefit from the use of exceptionally long-lived individuals and their relatives. Appropriate ranking of families by survival exceptionality and their availability for the purposes of providing genetic and phenotypic data is critical for selecting participants into such studies. This study validated the FLoSS as selection criteria in family longevity studies using UPDB.

SUBMITTER: Arbeeva LS

PROVIDER: S-EPMC6174319 | biostudies-literature | 2018

REPOSITORIES: biostudies-literature

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How Well Does the Family Longevity Selection Score Work: A Validation Test Using the Utah Population Database.

Arbeeva Liubov S LS Hanson Heidi A HA Arbeev Konstantin G KG Kulminski Alexander M AM Stallard Eric E Ukraintseva Svetlana V SV Wu Deqing D Boudreau Robert M RM Province Michael A MA Smith Ken R KR Yashin Anatoliy I AI

Frontiers in public health 20181001

The Family Longevity Selection Score (FLoSS) was used to select families for the Long Life Family Study (LLFS) but has never been validated in other populations. The goal of this paper is to validate how well the FLoSS-based selection procedure works in an independent dataset. In this paper, we computed FLoSS using the lifespan data of 234,155 individuals from a large comprehensive genealogically-based resource, the Utah Population Database (UPDB), born between 1779 and 1910 with mortality follo ...[more]

PMID: 30327761

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Project description:BackgroundAlthough human longevity tends to cluster within families, genetic studies on longevity have had limited success in identifying longevity loci. One of the main causes of this limited success is the selection of participants. Studies generally include sporadically long-lived individuals, i.e. individuals with the longevity phenotype but without a genetic predisposition for longevity. The inclusion of these individuals causes phenotype heterogeneity which results in power reduction and bias. A way to avoid sporadically long-lived individuals and reduce sample heterogeneity is to include family history of longevity as selection criterion using a longevity family score. A main challenge when developing family scores are the large differences in family size, because of real differences in sibship sizes or because of missing data.MethodsWe discussed the statistical properties of two existing longevity family scores: the Family Longevity Selection Score (FLoSS) and the Longevity Relatives Count (LRC) score and we evaluated their performance dealing with differential family size. We proposed a new longevity family score, the mLRC score, an extension of the LRC based on random effects modeling, which is robust for family size and missing values. The performance of the new mLRC as selection tool was evaluated in an intensive simulation study and illustrated in a large real dataset, the Historical Sample of the Netherlands (HSN).ResultsEmpirical scores such as the FLOSS and LRC cannot properly deal with differential family size and missing data. Our simulation study showed that mLRC is not affected by family size and provides more accurate selections of long-lived families. The analysis of 1105 sibships of the Historical Sample of the Netherlands showed that the selection of long-lived individuals based on the mLRC score predicts excess survival in the validation set better than the selection based on the LRC score .ConclusionsModel-based score systems such as the mLRC score help to reduce heterogeneity in the selection of long-lived families. The power of future studies into the genetics of longevity can likely be improved and their bias reduced, by selecting long-lived cases using the mLRC.

Project description:BACKGROUND:Work addiction is a significant public health problem with a growing prevalence. The Work Addiction Risk Test (WART) is the gold standard questionnaire to detect workaholism. OBJECTIVE:The main objective of this study was to validate the French version of the WART. METHODS:Questionnaires were proposed to voluntary French workers using the WittyFit software. There were no exclusion criteria. The questionnaire was administered anonymously for initial validity testing and readministered one week later for test-retest reliability. We also assessed the workers' sociodemographic characteristics, as well as other measurements for external validity, such as stress, well-being, and coaddictions to tobacco, alcohol, and cannabis. Several psychometric properties of the French-WART were explored: acceptability, reliability (internal consistency [Cronbach alpha coefficient] and reproducibility [Lin concordance coefficient]), construct validity (correlation coefficients and principal component analysis), and external validity (correlation coefficients). RESULTS:Among the 1580 workers using WittyFit, 187 (11.83%) agreed to complete the WART questionnaire. Of those, 128 completed the test-retest survey (68.4%). Acceptability found that all respondents had fully completed the questionnaire, with few floor or ceiling effects. Reliability was very good with a Cronbach alpha coefficient at .90 (internal consistency) and Lin concordance coefficient at .90 (95% CI .87-.94] with a difference on the retest of .04 (SD 4.9) (95% CI -9.6 to 9.7) (reproducibility). We identified three main dimensions (construct validity). Relationships between WART and stress and well-being confirmed its external validity. CONCLUSIONS:The French version of the WART is a valid and reliable instrument to assess work addiction with satisfactory psychometric properties. Used in occupational medicine, this tool would allow the diagnosis of work addiction and can be easily implemented in current practice.

Project description:BACKGROUND:Previously, family-based designs and high-risk pedigrees have illustrated value for the discovery of high- and intermediate-risk germline breast cancer susceptibility genes. However, genetic heterogeneity is a major obstacle hindering progress. New strategies and analytic approaches will be necessary to make further advances. One opportunity with the potential to address heterogeneity via improved characterization of disease is the growing availability of multisource databases. Specific to advances involving family-based designs are resources that include family structure, such as the Utah Population Database (UPDB). To illustrate the broad utility and potential power of multisource databases, we describe two different novel family-based approaches to reduce heterogeneity in the UPDB. METHODS:Our first approach focuses on using pedigree-informed breast tumor phenotypes in gene mapping. Our second approach focuses on the identification of families with similar pleiotropies. We use a novel network-inspired clustering technique to explore multi-cancer signatures for high-risk breast cancer families. RESULTS:Our first approach identifies a genome-wide significant breast cancer locus at 2q13 [P = 1.6 × 10-8, logarithm of the odds (LOD) equivalent 6.64]. In the region, IL1A and IL1B are of particular interest, key cytokine genes involved in inflammation. Our second approach identifies five multi-cancer risk patterns. These clusters include expected coaggregations (such as breast cancer with prostate cancer, ovarian cancer, and melanoma), and also identify novel patterns, including coaggregation with uterine, thyroid, and bladder cancers. CONCLUSIONS:Our results suggest pedigree-informed tumor phenotypes can map genes for breast cancer, and that various different cancer pleiotropies exist for high-risk breast cancer pedigrees. IMPACT:Both methods illustrate the potential for decreasing etiologic heterogeneity that large, population-based multisource databases can provide.See all articles in this CEBP Focus section, "Modernizing Population Science."

Project description:Background:Subjective Well-Being (SWB) is usually conceptualized in terms of an affective (i.e., judgements of biological emotional reactions and experiences) and a cognitive component (i.e., judgements of life satisfaction in relation to a psychological self-imposed ideal). Recently, researchers have suggested that judgements of harmony in life can replace or at least complement the cognitive component of SWB. Here, however, we go beyond that suggestion and propose that harmony in life should be seen as SWB's social component since it is the sense of balance between the individual and the world around her-a process that comprises acceptance, adaptation, and balance. By adding judgements of one's social interactions (i.e., harmony in life) to judgments of one's life satisfaction (psycho) and judgements of one's emotional reactions (bio), we propose a tentatively biopsychosocial model of SWB. As a first step, we used different factorial models in order to determine if both a general factor and specific sub-factors contribute to the biopsychosocial model of SWB. Method:A total of 527 participants responded to the Positive Affect Negative Affect Schedule (PANAS; 20 items), the Satisfaction with Life Scale (SWLS; five items), and the Harmony in life Scale (HILS; five items). We conducted exploratory and confirmatory factor analyses to validate the biopsychosocial model of subjective well-being and a general factor (SWBS). Results:The 20 PANAS items reflected a mixture of general latent structure saturation and specific latent structure saturation, but contributed to their respective specific latent factor (PA: 48%; NA: 49%) more than to the general latent SWBS factor (positive affect: 25%; negative affect: 32%). The five SWLS items contributed to a larger degree to the general SWBS factor (72%) than to life satisfaction itself (22%), while the five HILS items contributed to even a larger degree to the general SWBS factor (98%) than to harmony in life (0%). The bifactor model was the best model compared with all other models we tested (?2 = 1,660.78, df = 375, p < 0.001); Satorra Bentler ?2 = 1,265.80, df = 375, p < 0.001; CFI = 0.92; Tucker-Lewis Index = 0.91; RMSEA = 0.067. This model of a general SWBS factor explained about 64% of the total variance in the model, while specific SWBS components together explained 15% of the total variance. Conclusion:Our study suggests SWB as a general factor in a multidimensional biopsychosocial model. Indeed, as much as 64% of the variance of SWB was explained by this general factor. The SWB components, however, contributed to a different degree to each corresponding factor in the model. For instance, while the affective and cognitive components seem to be their own constructs and also part of the general SWB factor, the social component tested here contributed 0% to its own variance but 98% to the general factor.

Dataset Information

How Well Does the Family Longevity Selection Score Work: A Validation Test Using the Utah Population Database.

Publications

How Well Does the Family Longevity Selection Score Work: A Validation Test Using the Utah Population Database.

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