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Methods & Techniques in Genetic Psychology

  • Topic Overview: Exploring how genetics influence brain structure and behavior.

  • Institution: University of Bath

Week 20: Contents

  • Main Topics:

    • Assessing brain and behavior & heritability (H2) in genetic studies

    • Genome-wide association studies (GWAS)

    • Downstream GWAS derivatives:

      1. SNP (h2) heritability

      2. Genetic correlations using SNP/DNA data

      3. Functional genomics

      4. Polygenic scoring

      5. Mendelian randomization

Genetic Study Requirements

  • Sample Size: Large samples needed to identify small effects; typically 10,000 minimum, ideally over 1,000,000.

  • Quantitative Traits: MRI/behavioral data must be:

    • Easy to collect

    • Reliable (test-retest)

    • Generalizable across populations

Variables in Genetic Psychology

  • Types of Data Collected:

    • Socio-demographics and lifestyle factors

    • Brain imaging (e.g., MRI)

    • Cognitive tests

    • Physiology and health records linked to various databases, including health and mortality registries.

Genetic Markers and Properties

  • Specific Data Points:

    • Approximately 125,000 rare markers

    • 47,000 markers related to specific phenotypes

    • Use of the UK Biobank Axiom genotype array for genome-wide coverage and improved performance in imputation (~630,000 markers).

Dependent Variables in Genetic Psychology

  • Behavioral Measures: Survey data related to personality, mood, temperament.

  • Cognitive Measures: Psychometric assessments focusing on language, memory, etc.

  • Neuroimaging Data: MRI-derived metrics such as brain morphometry and microstructure assessments.

  • Psychiatric Evaluation Types: Includes case/control studies based on specific symptoms.

Cognitive Domain Structure

  • Levels of Variance:

    • Level 3: Variance in general cognitive ability (g)

    • Level 2: Variance across cognitive domains (e.g., reasoning, speed, memory, spatial)

    • Level 1: Specific test errors across various individual tests.

Genetic Studies of Personality

  • The Big Five Model:

    • Openness: Closed vs. Open

    • Conscientiousness: Spontaneous vs. Conscientious

    • Extroversion: Introverted vs. Extroverted

    • Agreeableness: Hostile vs. Agreeable

    • Neuroticism: Stable vs. Neurotic

Recent Genetic Studies Focus Areas

  • Subjective well-being (e.g., loneliness, happiness)

  • Sleep behaviors (e.g., insomnia, circadian rhythm)

  • Linguistic abilities (e.g., reading, dyslexia)

  • Dietary choices and sexual preferences.

Neuroimaging Traits

  • Types of Measurement:

    • Anatomical parcellation from various imaging techniques (fMRI, SMRI)

    • Different measures such as GMV (grey matter volume), WMV (white matter volume), FA (fractional anisotropy), MD (mean diffusivity), and RD (radial diffusivity)

Genetic Heritability Assessment

  • Twin Studies:

    • Identification of differences between monozygotic (MZ) and dizygotic (DZ) twins to assess heritability (broad sense).

    • Falconer’s formula used to calculate heritability based on twin correlations:

      • H2 = 2 x (MZ correlation - DZ correlation)

Genome-Wide Association Studies (GWAS)

  • Purpose: Accelerate genetic investigation in behavioral sciences by identifying SNP-phenotype associations.

  • Study Design:

    1. Data collection and genotyping

    2. Quality control of datasets

    3. Association analyses using logistic or linear regression depending on trait categorization.

    4. Meta-analysis and replication of findings.

GWAS Data Representation

  • Data Formats:

    • '.ped', '.map', '.fam', '.bed', and '.bim' files summarizing individual genotypes and allele frequencies.

    • Sample data formatted to reflect phenotype associations through logistic and linear regression analyses based on SNP categorizations.

Analysis of GWAS Results

  • Manhattan Plots: Visual representation of SNP associations across the genome, showing significance levels on the Y-axis.

  • Functional Genomics: Identification of biological pathways through which significant SNPs influence traits.

Polygenic Risk Scoring (PRS)

  • Overview: A summary measure calculated by summing individual risk alleles weighted by their associated effect sizes from GWAS results. Requires both a discovery and a validation dataset.

  • Applications: Used for predicting genetic susceptibility to diseases and behavioral traits.

Strengths and Limitations of PRS

  • Strengths:

    • Stable across the lifespan; potential for early detection and intervention.

    • Explains more variance than single SNP studies.

  • Limitations:

    • Relies heavily on GWAS sample size for power.

    • Currently explains a small proportion of variance in outcomes (~1-2%).

    • Methodological considerations need to be addressed.

Genetic Correlations

  • Bivariate Analysis: Used to understand the genetic relations between two traits and their causal pathways.

  • Horizontal Pleiotropy: A genetic variation affects multiple traits.

  • Vertical Pleiotropy: Indicates a direct or indirect causal influence of one trait on another.

Summary of Heritability and Correlational Studies

  • Traits Assessed: Various traits include autism spectrum disorder, mood regulation, and cognitive capacity.

  • Research Focus: Investigating shared genetic variations to better understand complex behavioral and physiological traits.

Conclusion and Forward-Looking Statements

  • The exploration of SNP heritability, through techniques like GREML-SC, GREML-KIN, and LD Score Regression (LDSR), continues to enhance our understanding of genetics in behavior and cognition and informs future research directives.