Mitigating Risk: Smartphone Notifications, Adaptive Surveying, and Genetics.

Modern information technologies have revolutionized how we can study risk taking. I leverage datasets produced by three of these technologies to investigate the mitigation of risk associated with obesity, saving for retirement, and smoking. First, I investigate how smartphone notifications, namely those sent by wearable activity trackers and associated mobile "apps," can mitigate risks associated with unhealthy living. The notifications suggest temporary, modestly ambitious goals to randomly treated users. They frame not meeting the temporary goal as a loss, tapping into loss aversion, and thereby promoting physical activity in the short-term (on the order of 0.10 more miles of walking for 2/3 step interventions) and reported sleep in the medium-term (on the order of a few more minutes for the sole sleep intervention). Second, I focus on mitigating risks associated with Americans saving inadequately or incorrectly for retirement. Framing biases permeate measurements of risk preferences, clouding what to assume in constructing portfolios and default policy rules, and potentially causing large welfare losses. We develop an adaptive surveying procedure to parse framing biases from measures of risk aversion by allowing subjects to reason through inconsistencies among differently framed (but axiomatically equivalent) hypothetical retirement savings plans. The procedure induces an overall reduction in decision errors and an increase in consistency across frames. It also engenders mild convergence in population estimates of mean risk aversion among frames, suggesting resultant "reasoned" preferences better represent true, underlying preferences. Third, using data from the Health and Retirement Study (HRS), I explore whether genetic predisposition can mitigate the extent to which people response to changes in cigarette excise taxes. I focus on two sorts of genetic predisposition, namely to high daily cigarette consumption and high educational attainment. I construct these predisposition measures, or polygenic scores, by performing out-of-sample prediction on the HRS using effect size estimates associated with millions of genetic markers from past, large genome-wide association studies (which exclude HRS). Both polygenic scores are predictive of smoking status and cigarettes per day. However, along both consumption margins and with respect to either polygenic score, I find no evidence of heterogeneity in responsiveness to cigarette excise taxes.PhDEconomicsUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/116648/1/fontanam_1.pd

Genomic analysis of diet composition finds novel loci and associations with health and lifestyle

We conducted genome-wide association studies (GWAS) of relative intake from the macronutrients fat, protein, carbohydrates, and sugar in over 235,000 individuals of European ancestries. We identified 21 unique, approximately independent lead SNPs. Fourteen lead SNPs are uniquely associated with one macronutrient at genome-wide significance (P < 5 × 10−8), while five of the 21 lead SNPs reach suggestive significance (P < 1 × 10−5) for at least one other macronutrient. While the phenotypes are genetically correlated, each phenotype carries a partially unique genetic architecture. Relative protein intake exhibits the strongest relationships with poor health, including positive genetic associations with obesity, type 2 diabetes, and heart disease (rg ≈ 0.15–0.5). In contrast, relative carbohydrate and sugar intake have negative genetic correlations with waist circumference, waist-hip ratio, and neighborhood deprivation (|rg| ≈ 0.1–0.3) and positive genetic correlations with physical activity (rg ≈ 0.1 and 0.2). Relative fat intake has no consistent pattern of genetic correlations with poor health but has a negative genetic correlation with educational attainment (rg ≈−0.1). Although our analyses do not allow us to draw causal conclusions, we find no evidence of negative health consequences associated with relative carbohydrate, sugar, or fat intake. However, our results are consistent with the hypothesis that relative protein intake plays a role in the etiology of metabolic dysfunction

International genome-wide meta-analysis identifies new primary biliary cirrhosis risk loci and targetable pathogenic pathways.

Primary biliary cirrhosis (PBC) is a classical autoimmune liver disease for which effective immunomodulatory therapy is lacking. Here we perform meta-analyses of discovery data sets from genome-wide association studies of European subjects (n=2,764 cases and 10,475 controls) followed by validation genotyping in an independent cohort (n=3,716 cases and 4,261 controls). We discover and validate six previously unknown risk loci for PBC (Pcombined<5 × 10(-8)) and used pathway analysis to identify JAK-STAT/IL12/IL27 signalling and cytokine-cytokine pathways, for which relevant therapies exist

The CAFA challenge reports improved protein function prediction and new functional annotations for hundreds of genes through experimental screens

Background The Critical Assessment of Functional Annotation (CAFA) is an ongoing, global, community-driven effort to evaluate and improve the computational annotation of protein function. Results Here, we report on the results of the third CAFA challenge, CAFA3, that featured an expanded analysis over the previous CAFA rounds, both in terms of volume of data analyzed and the types of analysis performed. In a novel and major new development, computational predictions and assessment goals drove some of the experimental assays, resulting in new functional annotations for more than 1000 genes. Specifically, we performed experimental whole-genome mutation screening in Candida albicans and Pseudomonas aureginosa genomes, which provided us with genome-wide experimental data for genes associated with biofilm formation and motility. We further performed targeted assays on selected genes in Drosophila melanogaster, which we suspected of being involved in long-term memory. Conclusion We conclude that while predictions of the molecular function and biological process annotations have slightly improved over time, those of the cellular component have not. Term-centric prediction of experimental annotations remains equally challenging; although the performance of the top methods is significantly better than the expectations set by baseline methods in C. albicans and D. melanogaster, it leaves considerable room and need for improvement. Finally, we report that the CAFA community now involves a broad range of participants with expertise in bioinformatics, biological experimentation, biocuration, and bio-ontologies, working together to improve functional annotation, computational function prediction, and our ability to manage big data in the era of large experimental screens.Peer reviewe

Genetic variants associated with subjective well-being, depressive symptoms, and neuroticism identified through genome-wide analyses

Very few genetic variants have been associated with depression and neuroticism, likely because of limitations on sample size in previous studies. Subjective well-being, a phenotype that is genetically correlated with both of these traits, has not yet been studied with genome-wide data. We conducted genome-wide association studies of three phenotypes: subjective well-being (n = 298,420), depressive symptoms (n = 161,460), and neuroticism (n = 170,911). We identify 3 variants associated with subjective well-being, 2 variants associated with depressive symptoms, and 11 variants associated with neuroticism, including 2 inversion polymorphisms. The two loci associated with depressive symptoms replicate in an independent depression sample. Joint analyses that exploit the high genetic correlations between the phenotypes (|ρ^| ≈ 0.8) strengthen the overall credibility of the findings and allow us to identify additional variants. Across our phenotypes, loci regulating expression in central nervous system and adrenal or pancreas tissues are strongly enriched for association.</p

Polygenic prediction of educational attainment within and between families from genome-wide association analyses in 3 million individuals

We conduct a genome-wide association study (GWAS) of educational attainment (EA) in a sample of ~3 million individuals and identify 3,952 approximately uncorrelated genome-wide-significant single-nucleotide polymorphisms (SNPs). A genome-wide polygenic predictor, or polygenic index (PGI), explains 12-16% of EA variance and contributes to risk prediction for ten diseases. Direct effects (i.e., controlling for parental PGIs) explain roughly half the PGI's magnitude of association with EA and other phenotypes. The correlation between mate-pair PGIs is far too large to be consistent with phenotypic assortment alone, implying additional assortment on PGI-associated factors. In an additional GWAS of dominance deviations from the additive model, we identify no genome-wide-significant SNPs, and a separate X-chromosome additive GWAS identifies 57

Genome-wide analysis identifies 12 loci influencing human reproductive behavior.

The genetic architecture of human reproductive behavior-age at first birth (AFB) and number of children ever born (NEB)-has a strong relationship with fitness, human development, infertility and risk of neuropsychiatric disorders. However, very few genetic loci have been identified, and the underlying mechanisms of AFB and NEB are poorly understood. We report a large genome-wide association study of both sexes including 251,151 individuals for AFB and 343,072 individuals for NEB. We identified 12 independent loci that are significantly associated with AFB and/or NEB in a SNP-based genome-wide association study and 4 additional loci associated in a gene-based effort. These loci harbor genes that are likely to have a role, either directly or by affecting non-local gene expression, in human reproduction and infertility, thereby increasing understanding of these complex traits

Goodbye Hartmann trial: a prospective, international, multicenter, observational study on the current use of a surgical procedure developed a century ago

Background: Literature suggests colonic resection and primary anastomosis (RPA) instead of Hartmann's procedure (HP) for the treatment of left-sided colonic emergencies. We aim to evaluate the surgical options globally used to treat patients with acute left-sided colonic emergencies and the factors that leading to the choice of treatment, comparing HP and RPA. Methods: This is a prospective, international, multicenter, observational study registered on ClinicalTrials.gov. A total 1215 patients with left-sided colonic emergencies who required surgery were included from 204 centers during the period of March 1, 2020, to May 31, 2020. with a 1-year follow-up. Results: 564 patients (43.1%) were females. The mean age was 65.9 ± 15.6&nbsp;years. HP was performed in 697 (57.3%) patients and RPA in 384 (31.6%) cases. Complicated acute diverticulitis was the most common cause of left-sided colonic emergencies (40.2%), followed by colorectal malignancy (36.6%). Severe complications (Clavien-Dindo ≥ 3b) were higher in the HP group (P &lt; 0.001). 30-day mortality was higher in HP patients (13.7%), especially in case of bowel perforation and diffused peritonitis. 1-year follow-up showed no differences on ostomy reversal rate between HP and RPA. (P = 0.127). A backward likelihood logistic regression model showed that RPA was preferred in younger patients, having low ASA score (≤ 3), in case of large bowel obstruction, absence of colonic ischemia, longer time from admission to surgery, operating early at the day working hours, by a surgeon who performed more than 50 colorectal resections. Conclusions: After 100&nbsp;years since the first Hartmann's procedure, HP remains the most common treatment for left-sided colorectal emergencies. Treatment's choice depends on patient characteristics, the time of surgery and the experience of the surgeon. RPA should be considered as the gold standard for surgery, with HP being an exception

Genomic analysis of diet composition finds novel loci and associations with health and lifestyle

Abstract: We conducted genome-wide association studies (GWAS) of relative intake from the macronutrients fat, protein, carbohydrates, and sugar in over 235,000 individuals of European ancestries. We identified 21 unique, approximately independent lead SNPs. Fourteen lead SNPs are uniquely associated with one macronutrient at genome-wide significance (P < 5 × 10−8), while five of the 21 lead SNPs reach suggestive significance (P < 1 × 10−5) for at least one other macronutrient. While the phenotypes are genetically correlated, each phenotype carries a partially unique genetic architecture. Relative protein intake exhibits the strongest relationships with poor health, including positive genetic associations with obesity, type 2 diabetes, and heart disease (rg ≈ 0.15–0.5). In contrast, relative carbohydrate and sugar intake have negative genetic correlations with waist circumference, waist-hip ratio, and neighborhood deprivation (|rg| ≈ 0.1–0.3) and positive genetic correlations with physical activity (rg ≈ 0.1 and 0.2). Relative fat intake has no consistent pattern of genetic correlations with poor health but has a negative genetic correlation with educational attainment (rg ≈−0.1). Although our analyses do not allow us to draw causal conclusions, we find no evidence of negative health consequences associated with relative carbohydrate, sugar, or fat intake. However, our results are consistent with the hypothesis that relative protein intake plays a role in the etiology of metabolic dysfunction