Economics and Genetics

2018 ◽  
Author(s):  
Jason M. Fletcher
Keyword(s):  
Big Data ◽  
Genetic Information ◽  
Predictive Power ◽  
Large Population ◽  
Population Based ◽  
Genetic Effects ◽  
Rapid Expansion ◽  
Economic Research ◽  
Polygenic Scores ◽  
Small Genetic Effects

Two interrelated advances in genetics have occurred which have ushered in the growing field of genoeconomics. The first is a rapid expansion of so-called big data featuring genetic information collected from large population–based samples. The second is enhancements to computational and predictive power to aggregate small genetic effects across the genome into single summary measures called polygenic scores (PGSs). Together, these advances will be incorporated broadly with economic research, with strong possibilities for new insights and methodological techniques.

scholarly journals Segregation of Familial Risk of Obesity in NHANES Cohort Supports a Major Role for Large Genetic Effects in the Current Obesity Epidemic

2019 ◽  
Author(s):  
Arthur B. Jenkins ◽  
Marijka Batterham ◽  
Lesley V. Campbell
Keyword(s):  
Kernel Smoothing ◽  
Large Population ◽  
Familial Risk ◽  
Population Based ◽  
Genetic Effects ◽  
Binary Marker ◽  
Family History Of Diabetes ◽  
Conditional Quantile ◽  
Adult Body Mass ◽  
Current Trends

AbstractThe continuing increase in many countries in adult body mass index (BMI kg/m2) and its dispersion is contributed to by interaction between genetic susceptibilities and an increasingly obesogenic environment (OE). The determinants of OE-susceptibility are unresolved, due to uncertainty around relevant genetic and environmental architecture. We aimed to test the multi-modal distributional predictions of a Mendelian genetic architecture based on collectively common, but individually rare, large-effect variants and their ability to account for current trends in a large population-based sample. We studied publicly available adult BMI data (n = 9102) from 3 cycles of NHANES (1999, 2005, 2013). A first degree family history of diabetes served as a binary marker (FH0/FH1) of genetic obesity susceptibility. We tested for multi-modal BMI distributions non-parametrically using kernel-smoothing and conditional quantile regression (CQR), obtained parametric fits to a Mendelian model in FH1, and estimated FH x OE interactions in CQR models and ANCOVA models incorporating secular time. Non-parametric distributional analyses were consistent with multi-modality and fits to a Mendelian model in FH1 reliably identified 3 modes. Mode separation accounted for ~40% of BMI variance in FH1 providing a lower bound for the contribution of large effects. CQR identified strong FH x OE interactions and FH1 accounted for ~60% of the secular trends in BMI and its SD in ANCOVA models. Multimodality in the FH effect is inconsistent with a predominantly polygenic, small effect architecture and we conclude that large genetic effects interacting with OE provide a better quantitative explanation for current trends in BMI.

Galgo: a bi-objective evolutionary meta-heuristic identifies robust transcriptomic classifiers associated with patient outcome across multiple cancer types

2020 ◽  
Vol 36 (20) ◽  
pp. 5037-5044
Author(s):  
M E Guerrero-Gimenez ◽  
J M Fernandez-Muñoz ◽  
B J Lang ◽  
K M Holton ◽  
D R Ciocca ◽  
...  
Keyword(s):  
Predictive Power ◽  
Patient Survival ◽  
Large Population ◽  
Molecular Classification ◽  
Gene Signature ◽  
Population Based ◽  
Supplementary Information ◽  
Optimization Approach ◽  
Multiple Cancer ◽  
Cancer Types

Abstract Motivation Statistical and machine-learning analyses of tumor transcriptomic profiles offer a powerful resource to gain deeper understanding of tumor subtypes and disease prognosis. Currently, prognostic gene-expression signatures do not exist for all cancer types, and most developed to date have been optimized for individual tumor types. In Galgo, we implement a bi-objective optimization approach that prioritizes gene signature cohesiveness and patient survival in parallel, which provides greater power to identify tumor transcriptomic phenotypes strongly associated with patient survival. Results To compare the predictive power of the signatures obtained by Galgo with previously studied subtyping methods, we used a meta-analytic approach testing a total of 35 large population-based transcriptomic biobanks of four different cancer types. Galgo-generated colorectal and lung adenocarcinoma signatures were stronger predictors of patient survival compared to published molecular classification schemes. One Galgo-generated breast cancer signature outperformed PAM50, AIMS, SCMGENE and IntClust subtyping predictors. In high-grade serous ovarian cancer, Galgo signatures obtained similar predictive power to a consensus classification method. In all cases, Galgo subtypes reflected enrichment of gene sets related to the hallmarks of the disease, which highlights the biological relevance of the partitions found. Availability and implementation The open-source R package is available on www.github.com/harpomaxx/galgo. Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals Balayage du génome et repérage des personnes à risque

2018 ◽  
Vol 34 (12) ◽  
pp. 1116-1122 ◽  
Author(s):  
Bertrand Jordan
Keyword(s):  
High Risk ◽  
Genetic Information ◽  
Predictive Power ◽  
Common Diseases ◽  
Polygenic Scores

The accumulation of extensive repositories linking phenotypic and genetic information, together with new computation methods, makes it possible to derive polygenic scores for susceptibility to common diseases that turn out to have strong predictive power. These will be clinically useful to identify individuals at high risk who may be eligible for protective interventions.

scholarly journals Psychiatric research platform within SAIL - Linkage of prospectively ascertained phenotypically rich and genetic data to routinely collected anonymised records.

2017 ◽  
Vol 1 (1) ◽  
Author(s):  
Joanna McGregor ◽  
Ann John ◽  
Keith Lloyd
Keyword(s):  
Mental Health ◽  
Health Research ◽  
Large Population ◽  
Genetic Data ◽  
Population Based ◽  
Mental Health Research ◽  
Response To Treatment ◽  
Psychiatric Research ◽  
Illness Onset ◽  
Polygenic Scores

ABSTRACT ObjectivesWe have conducted a feasibility study linking clinically rich survey data to routine data to create a platform for psychosis research in Wales: K Lloyd et al (2015), A national population-based e-cohort of people with psychosis (PsyCymru) linking prospectively ascertained phenotypically rich and genetic data to routinely collected records: overview, recruitment and linkage, Schizophrenia Research. Now we expand upon this through the linkage of large clinically rich cohorts with a range of mental health diagnoses along with genetic data to conduct validation exercises, develop novel methodologies, assess genetic and environment interactions and outcomes and address hypothesis-driven research questions. ApproachThrough collaborations between the Farr Institute, Cardiff University based MRC centre for Neuropsychiatric Genetics and Genomics and the National Centre for Mental Health (NCMH) clinically rich data and genetic (CNVs, SNPs & polygenic scores) data from around 6000+ participants recruited from a variety of mental health research studies including ‘PsyCymru’, ‘Genetic susceptibility to cognitive deficits study and NCMH amongst others will be loaded and linked to the datasets within SAIL. The analysis plan would firstly include validation exercises to compare the data between sources. Methodologies would be developed using this data to determine illness onset, relapse, chronicity, severity and response to treatment applied to large population-based mental health e-cohorts. ResultsBy pooling together health service data, genetic variants, environmental and lifestyle factors, phenotypic and endo-phenotypic (cognitive scores) along with the ability to ascertain temporal relationships afforded by the longitudinal perspective available in SAIL we may be able to evaluate potential risk factors, assess the complex GxE interactions that lead to disease progression, and assess outcomes such as prognosis, remission, relapse and premature mortality. The on-going routine updates provide us with the opportunity to follow-up these individuals across multiple health care settings in a cost effective and in-obtrusive manner and to carry out health services utilization/benefit and treatment surveillance in a naturalistic setting. This resource will continue to expand over the coming years in size, breadth and depth of data, with continued recruitment and additional measures planned. ConclusionTo advance mental health research by developing our understanding of the causes, course and outcomes of mental illness that may lead to the development of better diagnostic classification, predictive, preventative strategies and therapeutic approaches.

scholarly journals Phenotypic and genetic markers of psychopathology in a population-based sample of older adults

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Arianna M. Gard ◽  
Erin B. Ware ◽  
Luke W. Hyde ◽  
Lauren L. Schmitz ◽  
Jessica Faul ◽  
...  
Keyword(s):  
Older Adults ◽  
Large Population ◽  
The United States ◽  
Population Based ◽  
European Ancestry ◽  
Equation Modeling ◽  
General Factor ◽  
Genome Wide Association Studies ◽  
Genome Wide ◽  
Polygenic Scores

AbstractAlthough psychiatric phenotypes are hypothesized to organize into a two-factor internalizing–externalizing structure, few studies have evaluated the structure of psychopathology in older adults, nor explored whether genome-wide polygenic scores (PGSs) are associated with psychopathology in a domain-specific manner. We used data from 6003 individuals of European ancestry from the Health and Retirement Study, a large population-based sample of older adults in the United States. Confirmatory factor analyses were applied to validated measures of psychopathology and PGSs were derived from well-powered genome-wide association studies (GWAS). Genomic SEM was implemented to construct latent PGSs for internalizing, externalizing, and general psychopathology. Phenotypically, the data were best characterized by a single general factor of psychopathology, a factor structure that was replicated across genders and age groups. Although externalizing PGSs (cannabis use, antisocial behavior, alcohol dependence, attention deficit hyperactivity disorder) were not associated with any phenotypes, PGSs for major depressive disorder, neuroticism, and anxiety disorders were associated with both internalizing and externalizing phenotypes. Moreover, the variance explained in the general factor of psychopathology increased by twofold (from 1% to 2%) using the latent internalizing or latent one-factor PGSs, derived using weights from Genomic Structural Equation Modeling (SEM), compared with any of the individual PGSs. Collectively, results suggest that genetic risk factors for and phenotypic markers of psychiatric disorders are transdiagnostic in older adults of European ancestry. Alternative explanations are discussed, including methodological limitations of GWAS and phenotypic measurement of psychiatric outcome in large-scale population-based studies.

Genetic Factors Account for Half of the Phenotypic Variance in Liability to Sleep-Related Bruxism in Young Adults: A Nationwide Finnish Twin Cohort Study

2012 ◽  
Vol 15 (6) ◽  
pp. 714-719 ◽  
Author(s):  
Katariina Rintakoski ◽  
Christer Hublin ◽  
Frank Lobbezoo ◽  
Richard J. Rose ◽  
Jaakko Kaprio
Keyword(s):  
Cohort Study ◽  
Gender Difference ◽  
Genetic Factors ◽  
Genetic Model ◽  
Large Population ◽  
Population Based ◽  
Genetic Effects ◽  
Phenotypic Variance ◽  
Birth Cohorts ◽  
Additive Genetic Effects

Objectives: The aim of the present study was to examine the role of genetic and environmental factors in the phenotypic variance of bruxism in a large population-based cohort of young adult twins in Finland.Methods: The material of the present study derives from the FinnTwin16 cohort study consisting of five birth cohorts of twin pairs born in 1975–1979 who completed a questionnaire (at mean age 24, range 23–27 years) with data on frequency of sleep-related bruxism in 2000–2002. We used quantitative genetic modeling, based on the genetic similarity of monozygotic and dizygotic twins, to estimate the most probable genetic model for bruxism, based on decomposition of phenotypic variance into components: additive genetic effects (A), dominant genetic effects (D), and non-shared environmental effects (E).Results: On average, 8.7% experienced bruxism weekly, 23.4% rarely, and 67.9% never, with no significant gender difference (p = .052). The best fitting genetic model for bruxism was the AE-model. Additive genetic effects accounted for 52% (95% CI 0.41–0.62) of the total phenotypic variance. Sex-limitation model revealed no gender differences.Conclusions: Genetic factors account for a substantial proportion of the phenotypic variation of the liability to sleep-related bruxism, with no gender difference in its genetic architecture.

scholarly journals Idéfix: identifying accidental sample mix-ups in biobanks using polygenic scores

2021 ◽  
Author(s):  
Robert C.A. Warmerdam ◽  
Pauline Lanting ◽  
Patrick Deelen ◽  
Lude Franke ◽  
Keyword(s):  
Gene Expression ◽  
Genetic Information ◽  
Genetic Data ◽  
Population Based ◽  
Substantial Improvement ◽  
Omics Data ◽  
Summary Statistics ◽  
High Quality ◽  
Polygenic Scores

Identifying sample mix-ups in biobanks is essential to allow the repurposing of genetic data for clinical pharmacogenetics. Pharmacogenetic advice based on the genetic information of another individual is potentially harmful. Existing methods for identifying mix-ups are limited to datasets in which additional omics data (e.g. gene expression) is available. Cohorts lacking such data can only rely on the concordance of reported sex to inferred sex, which can reveal only half of the mix-ups. Here, we describe Idéfix, a method for the identification of accidental sample mix-ups in biobanks using polygenic scores. In the Lifelines population-based biobank we calculated polygenic scores (PGSs) for 25 traits for 32,786 participants. Idéfix then compares the actual phenotypes to PGSs and uses the relative discordance that is expected for mix-ups, compared to correct samples. In a simulation, using induced mix-ups, Idéfix reaches an AUC of 0.90 using 25 PGSs and sex. This is a substantial improvement over using only a sex-based concordance check, which has an AUC of 0.75. Idéfix therefore is not yet able to identify every sample mix-up. However, this will likely improve soon, with highly powered GWAS summary statistics that will likely become available for more commonly measured traits. Nevertheless, Idéfix can already be used to identify a high-quality set of participants for whom it is very unlikely that they reflect sample mix-ups, and therefore could be offered a pharmacogenetic passport using existing genetic data. For instance, when selecting the 10% of participants for whom predicted phenotypes adhere best to the actually measured phenotypes, we estimate that the proportion of sample mix-ups is reduced 250-fold.

scholarly journals Phenotypic and Genetic Markers of Psychopathology in a Population-Based Sample of Older Adults

2019 ◽  
Author(s):  
Arianna M. Gard ◽  
Erin B. Ware ◽  
Luke W. Hyde ◽  
Lauren Schmitz ◽  
Jessica Faul ◽  
...  
Keyword(s):  
Older Adults ◽  
Psychiatric Disorders ◽  
Large Population ◽  
Age Groups ◽  
The United States ◽  
Population Based ◽  
European Ancestry ◽  
General Factor ◽  
Confirmatory Factor Analyses ◽  
Polygenic Scores

AbstractAlthough psychiatric phenotypes are hypothesized to organize into a two-factor internalizing – externalizing structure, few studies have evaluated the structure of psychopathology in older adults, nor explored whether genome-wide polygenic scores (PGSs) are associated with psychopathology in a domain-specific manner. We used data from 6,216 individuals of European ancestry from the Health and Retirement Study, a large population-based sample of older adults in the United States. Confirmatory factor analyses were applied to validated measures of psychopathology and PGSs were derived from well-powered GWAS. Genomic SEM was implemented to construct latent PGSs for internalizing, externalizing, and general psychopathology. Phenotypically, the data were best characterized by a single general factor of psychopathology, a factor structure that was replicated across genders and age groups. Although externalizing PGSs (cannabis use, antisocial behavior, alcohol dependence, ADHD) were not associated with any phenotypes, PGSs for MDD, neuroticism, and anxiety disorders were associated with both internalizing and externalizing phenotypes. Moreover, the latent internalizing PGS and the latent one-factor PGS, derived using weights from Genomic SEM, explained 1% more variance in the general factor of psychopathology than any of the individual PGSs. Results support the following conclusions: genetic risk factors for and phenotypic markers of psychiatric disorders are transdiagnostic in European ancestries, GWAS-derived PGSs fail to capture genetic variation associated with disease specificity in European ancestries, and blunt phenotypic measurement in GWAS may preclude our ability to evaluate the structure and specificity of genetic contributions to psychiatric disorders.

Early productive vocabulary predicts academic achievement 10 years later

2016 ◽  
Vol 37 (6) ◽  
pp. 1461-1476 ◽  
Author(s):  
DORTHE BLESES ◽  
GUIDO MAKRANSKY ◽  
PHILIP S. DALE ◽  
ANDERS HØJEN ◽  
BURCAK AKTÜRK ARI
Keyword(s):  
Predictive Power ◽  
Longitudinal Design ◽  
Large Population ◽  
Sixth Grade ◽  
Population Based ◽  
Reading Scores ◽  
Expressive Vocabulary ◽  
Population Based Study ◽  
Late Talkers ◽  
Reading And Math

ABSTRACTWe use a longitudinal design to examine associations for a diverse sample of 2,120 Danish 16- to 30-month-old children between early expressive vocabulary and later reading and math outcomes in the sixth grade. Educational outcomes, in particular decoding and reading comprehension, can be predicted from an early vocabulary measure as early as 16 months with effect sizes (in proportion of variance accounted for) comparable to 1 year's mean growth in reading scores. The findings confirm in a relatively large population-based study that late talkers are at risk for low educational attainment because the majority of children experiencing early language delay obtain scores below average in measures of reading in the sixth grade. Low scores have the greatest predictive power, indicating that children with early delays have elevated risk for later reading problems.

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