Screening And GWAS Flashcards
What is new born screening ?
Screening is there to detect potentially fatal or disabling conditions as early as possible before signs or symptoms. It allows treatment to begin immediately reducing or eliminating the effect of the condition preventing devastating outcomes
Ins why genomic sequencing should be part of newborn screening
- Conditions could be screened for simultaneously
- Allow for interventions early in life for children with treatable or preventable diseases
- As soon as a genetic condition met the criteria for the newborn screening it could be added the next day
What is babyseq?
- The idea of babyseq is to explore the impact of incorporating genomic sequencing into newborn screening they looked at: 
- The medical impact on the individual and public health
- The behavioural impact on positions and patient behaviour
- The economic impact in terms of cost for the health care system

Barriers to implementation of poly genic risk scores into clinical use
- Confrontation of terminology
- inadequate description of applications
- Failure to define key elements of test strategy
- uncertainty as to evaluation approach
- lack of consensus on nature, quality and quantity of evidence
- concerns about the performance of applications in specific populations.
- imprecise descriptions of polygenic scores
- lack of evidence for their perceived value
- Concerns that they may exacerbate health inequalities
What is necessary for the successful clinical implementation of polygenic score based applications
That’s for implementation requires establishing evidence for the performance of each component of the test and how the test performed in a given healthcare pathway 
What is the importance of context specific evaluation in the use of polygenic scores
Context- specific evaluation is important because it defines the intended purpose,role and population for the test which is essential for developing evidence of clinical validity and utility
Give a definition for screening
Screening is a process used to detect early signs of a particular condition or disease in individuals who may not yet show symptoms. It aims to identify those at risk so that further diagnostic tests or interventions can be offered.
What are the key principles of screening
- Validity
- Reliability
- Acceptability
- Feasibility
- Early Detection
- Natural history
Describe why validity is a key principle of screening
Screening tests should accurately identified individuals with the condition and exclude those without it (High specificity)
Describe why reliability is a key principle of screening
Consistent results should be obtained when the same test is repeated
Describe why Acceptability is a key principle of screening
Screening should be acceptable to the target population
Describe why feasibility is a key principle of screening
The screening program should be practical, cost -effective, and feasible to implement
Describe why early detection is a key principle of screening
Screening aims to detect conditions early, when intervention can improve outcomes
Describe why natural history is a key principle of screening
UNderstanding the natural history of the condition helps determine the optimal timing for screening
What do you need to consider when putting together population genetic screening programmes
- Define the purpose of population genetic screening
- Explain how it differed from targeted or individualised testing
- Discuss the potential impact on public health
- What types of screening programs are you doing ? Universal screening, selective screening or cascade screening
- What condition are you screening for? Hereditary disorders or carrier screening or pharmacogenomics
- Implementation and logistics
- Ethical considerations ; address informed consent, privacy, and data sharing
What is stratified screening
Screening individuals based on risk assessment results. Based no stratification each group is given a different levels of screening.
Impact of population stratification vs screening the whole population.
- Fewer people are screened
- Reduction in harm associated with screening
- Reduced anxiety and inconvenience associated with testing
- Fewer false positives and unnecessary biopsies
- Less overdiagnosis
Common features of complex diseases
- Variation in multiple genes influence the trait, and have an additive, multiplicative or interactive effect
- Familial clustering with no clear pattern of inheritance
- Presence of a high-risk genetic variant doesn’t imply that disease will develop
- Often affects homeostasis gradually, resulting in onset later in life
- Often common in the general population.
How to find complex disease gene
- Linkage vs association
- Candidate gene vs Genome -wide
- Case control vs Quantitative trait
What’s the difference between linkage analysis vs association analysis
co-occurrence of a genetic variant with a disease trait, more frequently than can be readily explained by chance
Linkage analysis: looks at the cosegregation of alleles within affected FAMILY MEMBERS.
Association analysis: Difference in allele frequency between UNRELATED groups of affected and unaffected individuals
When choosing which test to do to find genetic factors of a common disorder what factors do you have to consider:
- Phenotype: Clinical definition
- Participant: Population based - will you use case-control or cohort
- Approach: Genome-wide or candidate gene
- Analysis: Lab analysis or Data analysis
- Verification: Replication or functional studies or Meta analysis
Define endophenotype also known as intermediate phenotype
A phenotype that you can quantify in disease cohort and control cohort eg for diabetes the enodo phenotype would be insulin levels
Or
It’s a measurable biological trait that reliably shows how a specific biological system works, is somewhat inherited, and is more directly linked to the underlying cause of a disease than just the general symptoms.
You need a large enough group to have statistical power to prove your hypothesis. What influences statistical power.
- Allele frequency
- Effect size
- Number of markers genotyped
How does allele frequency affect statistical power of a GWAS
-Higher Allele Frequency: Increases statistical power. When the allele of interest is more common in the population, it’s easier to detect an association with the trait or disease.
-Lower Allele Frequency: Decreases statistical power. Rare alleles are harder to detect, as fewer individuals carry them, making it more difficult to observe a significant association.
How does effect size affect the statistical power of GWAS
-Larger Effect Size: Increases statistical power. If the allele has a strong impact on the trait or disease, it’s easier to detect, even with a smaller sample size.
-Smaller Effect Size: Decreases statistical power. Alleles with subtle effects require larger sample sizes to detect significant associations.
How does number of markers genotyped affect statistical power
-More Markers: Generally decreases statistical power for individual markers due to the multiple testing burden. When more genetic markers are tested, the threshold for significance becomes stricter (to account for multiple comparisons), making it harder to detect associations unless the sample size is large enough to compensate.
-Fewer Markers: Increases statistical power per marker but may miss important associations not covered by the limited set of markers.
What is population stratification what affect does it have
Population stratification in GWAS refers to differences in allele frequencies between subgroups within a population that arise from ancestry rather than an association with the trait or disease being studied. These differences can lead to false-positive associations, where a genetic variant appears to be linked to the trait simply because it is more common in one subgroup than another, rather than because it is truly related to the trait. This confounding effect can distort the results of a GWAS,
What do you use to identify population stratification
A PCA plot
Define confounding factors
A confounding factor (or confounder) is a variable that influences both the independent variable (the factor being studied) and the dependent variable (the outcome of interest), creating a false association or masking a real one between them.
Why does failure to adjust for multiple testing appropriately lead to excessive false positives or overlook true positive signals.
- Excessive False Positives: When multiple tests are performed simultaneously (e.g., testing thousands of genetic variants for association with a trait), the likelihood of finding a statistically significant result by chance alone increases. If no adjustment is made, many of these “significant” results may actually be false positives, meaning they appear significant due to random variation rather than a true association.
- Overlooking True Positives: On the other hand, overly strict adjustments for multiple testing (e.g., using very conservative methods) can make it more difficult to detect real associations. The stringent criteria may reduce the power of the study, causing true positive signals to be missed because they don’t meet the adjusted significance threshold.
