week 10 - genetic analysis Flashcards

Question

Deletions, duplications and insertions types

Answer 1

deletion novel sequence insertion mobile element insertion tandem duplication interspersed duplication inversion translocation

Answer 2

- 4-5 million differences between any 2 humans o 1 in 1000 bases - Most differences occur at common locations o 4-5 million common SNPs (>0.5%) o 50K rare mutations (<0.5%) o 40-80 de novo mutations

Answer 3

So what we will attempt to do with QTL mapping is to relate these differences to differences in the phenotype of a trait of interest.

Answer 4

- Physical distance in nucleotide bases (kb) - The actual distance in bp between two variants

Answer 5

- RF between two markers - Based on the number of recombination events occurring in a region - RF is relation to genetic distance in cM via a mapping function - Physical distance is usually correlated with genetic distance - Markers are closer together in regions with low RF - Markers are further apart in regions of high RF

Answer 6

- Not always accurate o Hotspots o Regions where you would expect to find more recombination that others

Answer 7

- Reason: o Because there might be no recombination between them o So genetically look far away from each other (even though they are physically close)

Answer 8

- Find regions of the genome that are variable (markers) - Map and order these regions to produce a genetic marker map - Map traits of interest to these markers

Answer 9

- There are many types of DNA sequence variation in populations of plant, animal or microbial species - By assembling a map of variation we can link this map to variation in phenotypic traits of interest

Answer 10

- Variation in humans, plants and animals is broadly divided into 2 types: qualitative quantitate

Answer 11

 Blood groups, eye colour, flower colour  Only a few genotypes

Answer 12

 Height, weight  Many genotypes

Answer 13

- Deals with the study of inheritance of the quantitatively varying characters (complex or quantitative traits) that are controlled by many genes and also to a considerable extent by the environment

Answer 14

- Yield - Quality - Maturity - Size (height, girth, biomass)

Answer 15

- Abiotic stress responses (e.g. drought tolerance) - Biotic stress responses (e.g. disease resistance, photosynthetic capability)

Answer 16

- Size (e.g. weight and height) - Productivity (e.g. milk and egg production) - Quality (e.g. meat/wool) - Fecundity

Answer 17

- Growth rate - Abiotic stress responses (e.g. heat tolerance) - Biotic stress responses (e.g. disease resistance) - Strength

Answer 18

- Intelligence - personality

Answer 19

- size (weight, height, ect) - colour

Answer 20

- metabolic rates - diabetes - hypertension

Answer 21

- intelligence - personality

Answer 22

- trait which has complex/polygenic inheritance, but only two obvious phenotypes o e.g. affected or not affected by disease - E.g. type II diabetes o Individuals who exceed a certain number of risk factors (genetic and/or environmental) will develop the disease and others will not.

Answer 23

- Flow of information DNA --> RNA --> protein --> produces trait - Goal in genetic is to relate genetic variation to phenotypic variation in a trait

Answer 24

- Debate between mendelian and biometrician Willian Ratson - Quantitative traits do not follow discreet patterns and cannot be inherited Francis Galton - Quantitative traits can be inhertited and the degree of inheritance can be estimated by pure stats

Answer 25

Ronald Fisher came up with the polygenic model - To reconcile these two opposing views on quantitative traits

Answer 26

- Fisher first highlighted the polygenic nature of quantitative trait o The random sampling of alleles at each gene produces a continuous normally distributed phenotype in the population

Answer 27

several genes o Polygenes or QLT (quantitative trait loci)

Answer 28

a mendelian gene o Each gene can segregate independently

Answer 29

from environmental variance

Answer 30

- There is also interaction within each gene (dominance and co-dominance) - And interaction between genes (linkage and epistasis) - And interaction between the gene and the environment

Answer 31

o Measure the quasi-independent effects of alleles on a trait

Answer 32

o Measure the interactions between alleles at a single locus o E.g. complete dominance (one allele can mask the effect of the other)

Answer 33

produce variability of the phenotype.

Answer 34

When the heterozygote phenotypic value is half way between that of the two homozygotes, gene action is defined as additive. o Can see in the graph that each A2 allele contributes an increase of i to the phenotype value, in this case +1.

Answer 35

2) Complete dominance – the phenotype is the same whether you have 1 or 2 A2 alleles. o Can see in the graph how there is an underlying additive genetic component as shown by the slope of the line – but the values deviate from additivity due to dominance effects. In this case we can see that A1A2 and A2A2 phenotypes are quite similar.

Answer 36

o e.g. Mendels crosses between pea plants with purple flowers or white flowers – all progeny of F1 are purple as Purple P allele is dominant.

Answer 37

o heterozygote value is over half way but not quite as high as the A2 homozygote. o E.g. snapdragons – cross red to white and see pink as neither allele is dominant – a blending of the phenotypes

Answer 38

o rare- phenotype of the heterozygote is beyond the range of either homozygote.

Answer 39

- Or the “heterozygote advantage” where the heterozygote has better fitness than either of the homozygotes. o Sickle cell anaemia – where the heterozygote has partial resistance to malaria.

Answer 40

- Genetic architecture will differ in different populations. - Different populations will have different environmental exposures and different alleles segregating.

Answer 41

- Genotype not identifiable from phenotype - Epistasis (gene-gene interaction) - Genotype x environment interaction (G x E)

Answer 42

- If we could see which individuals had which genes at QTL locus B - The distributions for the 3 genotypes overlap so we cannot determine genotype just by looking at phenotype. - An individual with avergae height could be either of the 3 genotypes. - Let’s say height = 30cm – could be any of the three genotypes!

Answer 43

- Different genotypes responding in different ways to changes in the environment e.g. a. Trait is not sensitive to environment b. Trait value higher in environment 2 c. Some genotypes have higher trait value in environment 2, others have lower trait value in environment 2.

Answer 44

- Phenotype of the trait - Marker genotype - Genetic structure of mapping populations

Answer 45

- Use statistical methods to bring together the 3 sources of data and identify regions of the chromosome, which we call QTL, that are associated with variation in the trait.

Answer 46

several genes and by the environment o Rare combination of polygenes effect can lead to unexpected phenotypes

Answer 47

Mendelian phenotype ratios to work out what is going on and will need new methods of modelling these traits

Answer 48

1) linkage analysis 2) genome wide association analysis

Answer 49

o Uses defined population either created using crosses or with familial relationships known (pedigrees)

Answer 50

o Uses naturally occurring populations of individuals o Used in human, plant and animal populations

Answer 51

use a segregating population large recombinant blocks (recent recombination) F2 mosaic - due to recombination makes use of recombination to define where gene of interest might be

Answer 52

take natural population as they are and use for analysis make use of historic recombination over thousands of years - lots of small recombination blocks - narrows down region of interest to a smaller interval can find commonalities between genotype and phenotype - looking for shared phenotypes between individuals

Answer 53

- Association – e.g. human populations are the results of many generation of recombination in meiosis, producing genomes with short blocks from ancestral individuals.

Answer 54

- Collected genotype and phenotype data as shown in the table. - Use statistical methods to generate graphs like these in which we have the locations of the markers on the genetic map (x-axis) and on the y-axis, the evidence for a QTL. - As with all statistical tests we define a threshold for deciding whether or not a result is significant (link to minitab workshops). - Can see a broad peak. This is a QTL. - The results of association analysis are presented in a slightly different way. Individual dots = markers. - See narrow band of markers with significant evidence for QTL. - Note the band is narrower than with linkage analysis.

Answer 55

- So then the next step is to look at which genes are in the region of the significant markers and perform experiments to confirm that these genetic markers really affect the trait.

Answer 56

- Previous knowledge of the genetic of the trait not required - Can fine map QTL to 10-100kb because many recombination events have occurred in the history of the population - Can reveal causal genes in an unbiased way

Answer 57

- A population that segregates for a trait of interest - Mostly case-control groups o Case: individuals with the traits (e.g. patients with disease, personality etc.)  Have the things you are trying to find the genetic basis of o Controls: individuals without the traits (healthy individual) o Want case and control groups to be similar to each other (avoids confounding factors)  Want a mix of population in both groups - Genotype data o Mostly SNP arrays that genotype a pre-selected subset of SNPs o Whole genome or exome sequencing - Statistical analysis to pick up association signal

Answer 58

- Compares prevalence of polymorphism between subjects who have that condition (cases) with patients who do not have the condition (control) - In theory, the case-control study can be described simply. o First, identify the cases (a group known to have the outcome) and the controls (a group known to be free of the outcome). o Then, look back in time to learn which subjects in each group had the exposure(s), comparing the frequency of the exposure in the case group to the control group.

Answer 59

- Null hypothesis: there is no association between the marker (e.g. SNP) genotype and the trait - Alternative hypothesis: there is association between the marker (e.g. SNP) genotype and the trait - For categorical traits o Can use statistics similar to chi-square to compare the frequency of genetic variants in the two groups (case/control)

Answer 60

o Can use parametric analyses like ANOVA and regression to compare normally distributed traits between the genotype group

Answer 61

association of thousands to millions of markers (polymorphism,, e.g. SNP, individual points) with trait status in hundreds to ten of thousands of individuals

Answer 62

- Looking for the peaks that stand out o Tells us that there is a specific association - Reach dot represents a variant (a SNP) o Have many SNP spread across the chromosome - Significant markers (red points) with statistical support (log(p)) above the statistical threshold (red line) are causal of the trait variation or are in LD with the unknown causes variations

Answer 63

- Linkage disequilibrium (LD) refer to correlation between SNPs - If equilibrium would be a random association look at notes for table

Answer 64

- Many associations are still hidden and can be uncovered by: o Studying a larger sample population (> 1 million) o Studying more diverse populations (non-European populations) o Incorporating gene x gene and gene x environment interaction o Considering non-additive models of gene actions

Answer 65

- GWAS have revealed “molecular sub-phenotypes” or ‘heterogenetiy’ of disease - Increased understanding of disease pathways will provide new drug targets and promote personalised medicine o i.e. target treatment to underlying subtype - e.g. Can be many genes responsible for e.g. cancer o These can differ between individuals o So understanding which genetic pathway in a particular individual will allow for targeted drug treatment

Answer 66

- The associated SNP may be within or close to a gene that Is relevant to the trait of interest - Goal is to identify this gene and its variants (alleles) that conder different disease risks: o Quantitative trait nucleotide - Some associations are nowhere near a functional gene o E.g. a variant on chr9p21 associated with heart attack is 150kb from the nearest gene

Answer 67

- The bulk of the genetic variance underlying the trait heritability has still not been explained - E.g. > 30 markers associated with Crohn’s disease explains < 10% of genetic variance - The so-called “missing heritability” problem

Answer 68

- Not very useful for very rare disease o Small amount of population o Statistics do not really work on small populations - Disease prediction - True signals - Population stratification - Ultra-rare mutations - Epistasis - Cause variants or gene - Missing heritability

Answer 69

- Identification of novel SNV-trait associations - 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

Answer 70

Complex o Highly polygenic

Answer 71

mapping complex traits in natural populations

Answer 72

direct or indirect o Typical genetic effect sizes are small o GWAS has helped in understanding disease pathways but only account for small proportion of variation

Answer 73

discover the mechanisms for how specific genetic variants contribute to disease risk

Answer 74

rare variant hypothesis

Answer 75

The Rare Variant Hypothesis proposes that much of the unexplained genetic variation in complex traits is due to many rare variants, each with larger individual effects, rather than common variants with small effects.

Answer 76

GWAS studies have identified many common variants (minor allele frequency >1%), but they explain only a small portion of heritability for most traits — a problem known as "missing heritability." The rare variant hypothesis suggests that this missing heritability could be due to rare variants that are: Not well captured by standard SNP arrays Specific to families or populations Functionally important, often in coding or regulatory regions

Answer 77

Frequency: <0.5% (often much lower) Effect Size: Medium to large Detection: Requires whole genome/exome sequencing Origin: Often recent, can be de novo Population-specific: Yes — rarely shared across populations

Answer 78

BRCA1/2 mutations in breast cancer — rare but high risk Lipid metabolism disorders — single-gene rare variants Autism spectrum disorders — multiple rare, high-impact mutations in neuronal genes

Answer 79

"The rare variant hypothesis suggests that complex traits and diseases may be driven by many rare genetic variants with moderate-to-large effects. These are not well captured by GWAS, contributing to the problem of missing heritability and requiring sequencing-based approaches for discovery."