scholarly journals From Genome-Wide to Environment-Wide: Capturing the Environome

2021 ◽  
pp. 174569162097980
Author(s):  
Sophie von Stumm ◽  
Katrina d’Apice
Keyword(s):  
Individual Differences ◽  
Dna Microarrays ◽  
Genetic Research ◽  
Effect Sizes ◽  
Genetic Influences ◽  
Psychological Traits ◽  
Genome Wide ◽  
Dna Variants ◽  
Gwa Studies ◽  
Behavior And Cognition

Genome-wide association (GWA) studies have shown that genetic influences on individual differences in affect, behavior, and cognition are driven by thousands of DNA variants, each with very small effect sizes. Here, we propose taking inspiration from GWA studies for understanding and modeling the influence of the environment on complex phenotypes. We argue that the availability of DNA microarrays in genetic research is comparable with the advent of digital technologies in psychological science that enable collecting rich, naturalistic observations in real time of the environome, akin to the genome. These data can capture many thousand environmental elements, which we speculate each influence individual differences in affect, behavior, and cognition with very small effect sizes, akin to findings from GWA studies about DNA variants. We outline how the principles and mechanisms of genetic influences on psychological traits can be applied to improve the understanding and models of the environome.

Genetics of eating disorders in the genome-wide era

2021 ◽  
pp. 1-11
Author(s):  
Hunna J. Watson ◽  
Alish B. Palmos ◽  
Avina Hunjan ◽  
Jessica H. Baker ◽  
Zeynep Yilmaz ◽  
...  
Keyword(s):  
Eating Disorders ◽  
Eating Disorder ◽  
Association Studies ◽  
Genetic Research ◽  
Genome Wide Association Studies ◽  
Psychological Traits ◽  
Compulsive Disorder ◽  
Risk Variants ◽  
Genome Wide ◽  
Common Genetic Variants

Abstract Enabled by advances in high throughput genomic sequencing and an unprecedented level of global data sharing, molecular genetic research is beginning to unlock the biological basis of eating disorders. This invited review provides an overview of genetic discoveries in eating disorders in the genome-wide era. To date, five genome-wide association studies on eating disorders have been conducted – all on anorexia nervosa (AN). For AN, several risk loci have been detected, and ~11–17% of the heritability has been accounted for by common genetic variants. There is extensive genetic overlap between AN and psychological traits, especially obsessive-compulsive disorder, and intriguingly, with metabolic phenotypes even after adjusting for body mass index (BMI) risk variants. Furthermore, genetic risk variants predisposing to lower BMI may be causal risk factors for AN. Causal genes and biological pathways of eating disorders have yet to be elucidated and will require greater sample sizes and statistical power, and functional follow-up studies. Several studies are underway to recruit individuals with bulimia nervosa and binge-eating disorder to enable further genome-wide studies. Data collections and research labs focused on the genetics of eating disorders have joined together in a global effort with the Psychiatric Genomics Consortium. Molecular genetics research in the genome-wide era is improving knowledge about the biology behind the established heritability of eating disorders. This has the potential to offer new hope for understanding eating disorder etiology and for overcoming the therapeutic challenges that confront the eating disorder field.

scholarly journals Genetics and psychosis

2014 ◽  
Vol 20 (1) ◽  
pp. 69-70
Author(s):  
Alastair G. Cardno
Keyword(s):  
Psychotic Disorders ◽  
Association Studies ◽  
Molecular Genetic ◽  
Genetic Research ◽  
Copy Number Variants ◽  
Genetic Influences ◽  
Genome Wide Association Studies ◽  
Adoption Studies ◽  
Genome Wide ◽  
Partial Overlap

SummaryGenetic research into psychotic disorders is advancing rapidly. On the basis of general evidence for genetic influences from family, twin and adoption studies, molecular genetic studies, particularly genome-wide association studies (GWAS), are identifying a range of common genetic risk factors that each have a small effect on risk, while certain chromosomal copy number variants (CNVs) are rarer, but have a larger effect on risk. There is also evidence for partial overlap of genetic influences among psychotic disorders and with non-psychotic disorders. This brief article summarises the main themes, current findings and potential future directions.

scholarly journals Pathfinder: A gamified measure to integrate general cognitive ability into the biological, medical and behavioural sciences

2021 ◽  
Author(s):  
Margherita Malanchini ◽  
Kaili Rimfeld ◽  
Agnieszka Gidziela ◽  
Rosa Cheesman ◽  
Andrea G. Allegrini ◽  
...  
Keyword(s):  
Cognitive Ability ◽  
Twin Studies ◽  
Genome Wide Association ◽  
General Cognitive Ability ◽  
Major Barrier ◽  
Genome Wide ◽  
Dna Variants ◽  
Advance Research ◽  
Gwa Studies ◽  
Behavioural Sciences

AbstractGenome-wide association (GWA) studies have uncovered DNA variants associated with individual differences in general cognitive ability (g), but these are far from capturing heritability estimates obtained from twin studies. A major barrier is measurement heterogeneity. In a series of four studies, we created a 15-minute, online, gamified measure of g that is highly reliable, psychometrically valid and scalable. In a fifth study, we administered this measure to 4,751 young adults from the Twins Early Development Study. This novel g measure, which also yields verbal and nonverbal scores, showed substantial twin heritability (57%) and SNP heritability (37%). A polygenic score computed from GWA studies of five cognitive and educational traits accounted for 12% of the variation in g, the strongest DNA-based prediction of g to date. Widespread use of this engaging new measure will advance research not only in genomics but throughout the biological, medical and behavioural sciences.

scholarly journals Continuity of Genetic Risk for Aggressive Behavior Across the Life-Course

2021 ◽  
Author(s):  
Camiel M. van der Laan ◽  
José J. Morosoli-García ◽  
Steve G. A. van de Weijer ◽  
Lucía Colodro-Conde ◽  
Hill F. Ip ◽  
...  
Keyword(s):  
Individual Differences ◽  
The Netherlands ◽  
Life Course ◽  
Aggressive Behavior ◽  
Meta Analysis ◽  
Genetic Influences ◽  
Genome Wide ◽  
A Genome ◽  
Polygenic Scores ◽  
The Life Course

AbstractWe test whether genetic influences that explain individual differences in aggression in early life also explain individual differences across the life-course. In two cohorts from The Netherlands (N = 13,471) and Australia (N = 5628), polygenic scores (PGSs) were computed based on a genome-wide meta-analysis of childhood/adolescence aggression. In a novel analytic approach, we ran a mixed effects model for each age (Netherlands: 12–70 years, Australia: 16–73 years), with observations at the focus age weighted as 1, and decaying weights for ages further away. We call this approach a ‘rolling weights’ model. In The Netherlands, the estimated effect of the PGS was relatively similar from age 12 to age 41, and decreased from age 41–70. In Australia, there was a peak in the effect of the PGS around age 40 years. These results are a first indication from a molecular genetics perspective that genetic influences on aggressive behavior that are expressed in childhood continue to play a role later in life.

Genetics and Psychology: Beyond Heritability

2001 ◽  
Vol 6 (4) ◽  
pp. 229-240 ◽  
Author(s):  
Robert Plomin ◽  
Essi Colledge
Keyword(s):  
Individual Differences ◽  
Genetic Factors ◽  
Behavioral Genetics ◽  
Genetic Research ◽  
Genetic Influences ◽  
Behavioral Traits ◽  
Nonshared Environment ◽  
Genetic Mechanisms ◽  
Asking Questions ◽  
Psychological Dimensions

The questions whether and how much genetic factors affect psychological dimensions and disorders represent important first steps in understanding the origins of individual differences. Because it is now widely accepted that genetic influences contribute importantly to individual differences throughout psychology, genetic research is moving beyond merely estimating heritability to asking questions about how genetic mechanisms work. We focus on two examples of ways in which genetic research is going beyond heritability. The first is to use genetically sensitive designs to identify specific environmental influences, taking into account two of the most important findings from behavioral genetics: nonshared environment and genotype-environment correlation. The second is to use the new tools of molecular genetics to identify specific genes responsible for the substantial heritability of a variety of behavioral traits.

Analysis of genetic influences on the consistency and variability of the Big Five across different stressful situations

2001 ◽  
Vol 15 (5) ◽  
pp. 355-371 ◽  
Author(s):  
Gerty Lensvelt‐Mulders ◽  
Joop Hettema
Keyword(s):  
Individual Differences ◽  
Personality Traits ◽  
Big Five ◽  
Daily Life ◽  
Genetic Research ◽  
Identical Twin ◽  
Genetic Influences ◽  
Normal Range ◽  
Main Effect ◽  
Total P

Several studies have demonstrated that individual differences in personality traits, known as the Big Five, have a genetic component. These personality traits are considered important predictors of everyday behaviour. In addition to personality traits there are also factors in the environment that govern behaviour. This dual influence on behaviour is statistically reflected in a P × S interaction. This study examines the genetic and environmental influences on the interactions between a person and his daily life environment for the Big Five. Fifty‐seven identical twin pairs and 43 fraternal twin pairs participated in this study. Trait related behaviour was measured in 30 different situations with the aid of an SR inventory. The heritability coefficients for the main effect of P were in the normal range, varying between 0.35 for Agreeableness and 0.53 for Conscientiousness. The heritability coefficients for the P × S interactions were moderately high, explaining between 26% and 69% of the total P × S variance. The consequences of these results for general and behavioural genetic research on the Big Five will be discussed. Copyright © 2001 John Wiley & Sons, Ltd.

Individual differences and academic success: A multivariate model of affect, behavior, and cognition

2014 ◽  
Author(s):  
Jason D. Ferrell ◽  
Elliot M. Tucker-Drob ◽  
Samuel D. Gosling ◽  
James W. Pennebaker
Keyword(s):  
Academic Success ◽  
Individual Differences ◽  
Multivariate Model ◽  
Behavior And Cognition

scholarly journals A likelihood ratio approach for identifying three-quarter siblings in genetic databases

Heredity ◽  
2021 ◽  
Author(s):  
Iván Galván-Femenía ◽  
Carles Barceló-Vidal ◽  
Lauro Sumoy ◽  
Victor Moreno ◽  
Rafael de Cid ◽  
...  
Keyword(s):  
Likelihood Ratio ◽  
Family Relationships ◽  
Genetic Research ◽  
Data Set ◽  
Scientific Disciplines ◽  
Genome Wide ◽  
Standard Quality ◽  
Control Procedures ◽  
Identity By State ◽  
Second Degree

AbstractThe detection of family relationships in genetic databases is of interest in various scientific disciplines such as genetic epidemiology, population and conservation genetics, forensic science, and genealogical research. Nowadays, screening genetic databases for related individuals forms an important aspect of standard quality control procedures. Relatedness research is usually based on an allele sharing analysis of identity by state (IBS) or identity by descent (IBD) alleles. Existing IBS/IBD methods mainly aim to identify first-degree relationships (parent–offspring or full siblings) and second degree (half-siblings, avuncular, or grandparent–grandchild) pairs. Little attention has been paid to the detection of in-between first and second-degree relationships such as three-quarter siblings (3/4S) who share fewer alleles than first-degree relationships but more alleles than second-degree relationships. With the progressively increasing sample sizes used in genetic research, it becomes more likely that such relationships are present in the database under study. In this paper, we extend existing likelihood ratio (LR) methodology to accurately infer the existence of 3/4S, distinguishing them from full siblings and second-degree relatives. We use bootstrap confidence intervals to express uncertainty in the LRs. Our proposal accounts for linkage disequilibrium (LD) by using marker pruning, and we validate our methodology with a pedigree-based simulation study accounting for both LD and recombination. An empirical genome-wide array data set from the GCAT Genomes for Life cohort project is used to illustrate the method.

scholarly journals Challenges of Adjusting Single-Nucleotide Polymorphism Effect Sizes for Linkage Disequilibrium

2021 ◽  
pp. 1-11
Author(s):  
Valentina Escott-Price ◽  
Karl Michael Schmidt
Keyword(s):  
Linkage Disequilibrium ◽  
Association Studies ◽  
Statistical Significance ◽  
Ordinary Least Squares ◽  
Effect Sizes ◽  
Risk Scores ◽  
Genome Wide Association Studies ◽  
Single Nucleotide ◽  
Genome Wide ◽  
Tikhonov Regularisation

<b><i>Background:</i></b> Genome-wide association studies (GWAS) were successful in identifying SNPs showing association with disease, but their individual effect sizes are small and require large sample sizes to achieve statistical significance. Methods of post-GWAS analysis, including gene-based, gene-set and polygenic risk scores, combine the SNP effect sizes in an attempt to boost the power of the analyses. To avoid giving undue weight to SNPs in linkage disequilibrium (LD), the LD needs to be taken into account in these analyses. <b><i>Objectives:</i></b> We review methods that attempt to adjust the effect sizes (β<i>-</i>coefficients) of summary statistics, instead of simple LD pruning. <b><i>Methods:</i></b> We subject LD adjustment approaches to a mathematical analysis, recognising Tikhonov regularisation as a framework for comparison. <b><i>Results:</i></b> Observing the similarity of the processes involved with the more straightforward Tikhonov-regularised ordinary least squares estimate for multivariate regression coefficients, we note that current methods based on a Bayesian model for the effect sizes effectively provide an implicit choice of the regularisation parameter, which is convenient, but at the price of reduced transparency and, especially in smaller LD blocks, a risk of incomplete LD correction. <b><i>Conclusions:</i></b> There is no simple answer to the question which method is best, but where interpretability of the LD adjustment is essential, as in research aiming at identifying the genomic aetiology of disorders, our study suggests that a more direct choice of mild regularisation in the correction of effect sizes may be preferable.

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