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Biopsych - Molecular Biology (RM) - Y2 - Coggle Diagram
Biopsych - Molecular Biology (RM) - Y2
Structure and function of genes
DNA - 23 pairs of chromosomes, tightly wrapped double helix
Histones are proteins that package and order the DNA
DNA - Deoxyibronucleic acid; it is a molecule in every cell in your body
Able to copy itself
Transcription DNA -> RNA
-> The process of producing RNA from the DNA
-> For each gene, this is only from one strand of the DNA
-> The pre-mRNA is spliced to form mature mRNA (removal of introns)
What part of the genome influences the variation between people?
-> Several types of genetic variation - large (whole extra chromosome) and medium (copy number variants - how many copies you have)
-> Simplest, smallest and most commonly studied are SNPs (Single Nucleotide Polymorphisms) - variation in one gene
-> Genetic variation - found approximately every 1000th base pair - Variation between people is called a polymorphism - account for genetically-influenced differences between people
History of DNA:
First time a chromosome was discovered - X-ray diffraction - Rosalind Franklin
DNA identified by Watson and Crick
Why study genetics?
What makes us different from one another - SNPs
Understanding and treating disorders, specifically genetic syndromes
Understanding evolution - lactose tolerance developed 3000-5000 years ago
Crime detection - being able to identify fingerprints etc
Complex traits and disorders v monogenic disorders
In genetics, we can split up phenotypes into two main categories
Monogenic disorders - these are disorders caused by one genetic loci; Huntingdon’s disease for example
-> Sometimes caused by genetic variation and sometimes not, known as the level of penetrance
Complex traits and disorders - phenotypes caused by multiple genetic loci and environments in their interplay
-> In psychology, most of the phenotypes we study are in this category e.g. educational attainment, neurodevelopmental conditions, mental health, personality and temperament
Methods for identifying specific genes associated with a complex trait or disorder
Linkage studies
Candidate gene association studies
Genome-wide association studies
Next generation sequencing
Single nucleotide polymorphisms - one pair of genes
Linkage disequilibrium -
Non-random association of alleles at different loci on the same chromosome
Forms a map of squares in which the numbers in the squares denote the correlation between alleles and two loci
Genome-wide association studies - their rationale
Point to new biological mechanisms through discovery of genes and biology
Can allow prediction using genetic information
Benefits -
-> Intervention and treatment strategies (therapeutics; drug discovery)
-> Better understanding of brain development (basic neuroscience)
-> Refined phenotyping and classifications (diagnostics)
-> Causal paths between correlated outcomes (Mendelian Randomisation)
-> Understanding of environmental risk factors and gene-environment interplay
Genome Wide Association studies
What is it?
It is an association test between a genetic variant (SNP, the IV) and the outcome (the phenotype being studied, the DV)
However, it involves millions of association tests at the same time - test millions of SNPs across the whole genome for their association with the phenotype
Find out which genetic variants are associated with our phenotype of interest
How to conduct a GWAS - Uffelman et al, 2021 -
Collect data
Each individual in a population of unrelated individuals is genotyped for 1+ million common genetic variants, and each variant is tested for the association with the measured trait or disease
Research participant donates saliva sample and behavioural data
DNA Array - captures the majority of the common genetic variation of the human genome
Genotyping
Quality control
Imputation
Association testing
Meta-analysis
Regress phenotype on each SNP - conduct regressions of the genetic variant on the phenotype, performed for thousands to millions of variants genome wide
Manhattan plot of all SNP p-values - robust and replicable statistical associations identified as variants with p values <=5x10-8 (above a red line)
Replication
Post-GWAS analyses
Evaluation
Strengths -
Can find genes associated with human behaviour
Finds the mechanisms underlying behaviour and conditions
Genetic information can contribute to drug development
Learn more about why conditions sometimes co-occur with each other
Can be conducted by combining samples through meta-analysis
Rather than having to rely on a single large sample, we can combine smaller samples to obtain a larger total meta-analysed sample
GWAS is therefore most successful when scientists collaborate and share data (Tam et al, 2019)
GWAS includes studying genetic variation across the entire genome in one analysis
Design does not rely on guessing where in the genome to look
Genotyping the entire genome of a participants is very fast due to the technological advancement of microarrays
Challenges
Very large sample sizes are needed - Weedon et al (2007) used almost 5,000 participants for one genetic variants, and Wood et al, 2014 used 253, 288 participants for 679 variants
Common genetic variants have small effect, as the biggest signal only accounts for millimetres of height difference between individuals
Solution - studies rely on combining existing sample data
Phenotypes sometimes poorly defined due to the need for large samples
Large studies with thousands or millions of participants usually cannot afford in depth phenotyping
Cheap phenotyping usually means relying on questionnaires
Current research is starting to find cheaper solutions to obtaining better phenotyping for GWAS - portable experimental equipment can be taken to schools or homes and used on large samples
Solution - use cheap questionnaire assessments on large samples
Solution - big investments in large studies with deeper phenotyping; UK Biobank and the Adolescent Brain Cognitive Development (ABCD) cohorts were assessed on MRI and genotyped and a GWAS was conducted
GWAS identifies genetic variants, but are they a causal variant for the phenotype
Even if a GWAS identifies a genetic variant as associated with the phenotype being studied, it might not be the causal variant - sometimes, the actual one is nearby
Fine mapping - technique used after a GWAS is conducted to locate the specific variant that plays a mechanistic role
GWAS does not explain the complete mechanistic pathway from gene to phenotype
Solution - once GW significant associations are found, further work is needed to find out how the genetic variant influences protein function and brain function (and other levels of analysis) in order to understand the mechanistic pathway to the phenotype
Limitations
Samples are not representative of the regional / national / global population
Samples in research are not representative because not everyone is willing to participate in research studies
Some participants drop out over time (termed sample attrition)
The above two factors are not random but associated with personal characteristics
Most GWAS research conducted has focused on European ancestry individuals due to
-> More scientific and research investment in Western countries in which the majority of individuals had European ancestry
-> The annotation of the European ancestry genome is further along than other ancestral genomes
-> Mixing ancestral samples was problematic from an analysis point of view due to population stratification, but statistical solutions are being developed to solve this
Solution to this - major efforts to ensure GWAS research focuses equally across diverse ancestral groups
The bright and dark side of GWAS: Tam et al, 2019 -
Bright side - can help with:
Identification of novel SNV-trait association
Discovery of novel biological mechanisms
Diverse clinical applications
Insight into ethnic variation of complex traits
Relevant to low-frequency rare variants
Identification of novel monogenic and oligogenic disease genes
Relevant to the study of structural variation
Multiple applications beyond gene identification
Straightforward GWAS generation, management and analysis
Easy-to-share and publicly available data
Dark side -
Disease prediction
True signals
Population stratification
Ultra rare mutations
Epistasis
Causal variants or genes
Missing heritability
Key insights from GWAS studies
Many genetic variants are pleiotropic - they are associated with more than one phenotype
Most complex traits and disorders are polygenic which means they are influenced by thousands of different genetic variants, each having a very small effect on the phenotype
Thousands of genetic variants have been associated with phenotypes related to many types of human behaviour and disorders
Some mechanistic insights in terms of what genes do in terms of actual function
Any individual genetic variant has a very small effect on the phenotype - this is why such large samples are needed
Large GWAS samples -
Biobanks provide large samples across ancestries
For example - White British, Japanese, Chinese, British South Asian, African ancestries, and multiple ancestries in the USA
Examples of GWAS discoveries:
42 genome-wide significant loci associated with dyslexia (Doust et al, 2022)
-> Twin heritability estimated of 40-80%
-> Dyslexia might represent extreme end of genetic liability for reading ability
GWAS sample -
-> 51,800 adults self reporting a dyslexia diagnosis and 1,087,070 controls
-> > 18 years of age, mean age of 46.9
ADHD GWAS -
Common and highly heritable behaviourally defined condition
Demontis et al, Nat Genet, 2019
20,183 ADHD cases and 35,191 controls
SNP heritability - 22%
12 independent genome-wide significant loci