Textbook Chapters Flashcards
Candidate gene approaches have allowed identification of human disease genes based on prior information of the cause of a related phenotype. Give an example of successful disease gene identification based don prior knowledge of (a) a related human phenotype (b) a related mouse phenotype
Identification of the gene for congenital contactural arachnodactyly . This disorder shows overlapping features with Marfan syndrome, a dominantly inherited disorder of fibrous connective tissue. After the FBN1 fibrillin gene was shown to be a locus for Marfan syndrome, the related FBN2 fibrillin gene was investigated and shown to be mutated in congenital contactural arachnodactyly.
The Dominant megacolon (Dom) mouse is a model of Hirschprung disease in which there is congenital absence of ganglion cells in regions of the gastrointestinal tract. The mutant mice were observed to have pigmentary abnormalities resembling those in Waardenburg syndrome. After the Sox10 gene was identified as the disease gene in the Dom mouse mutant, the corresponding human gene, SOX10, was screened for mutations and shown to be the gene mutated in Waardenburg-Hirschsprung disease.
Strategies to identify the genes that underlie single gene disorders have often relied on first obtaining a subchromosomal location for the disease gene. List two approaches that have been taken to identify subchromosomal locations for these disorders.
- Linkage analyses. Testing markers from across all the chromosomes to see if alleles at any of the marker loci show a tendency to co-segregate with the disease in families.
- Cytogenetic analysis to look for disease-associated chromosomal rearrangements. Disease-associated translocations and inversions offer a potentially rapid way of identifying the disease gene: the hope is that one of the breakpoints has resulted in a change of expression by cleaving within or close to the disease gene. And in some cases interstitial or terminal deletions have been profitably used to identify a gene underlying a monogenic disorder.
The risk of developing a disease is sometimes expressed as a risk ratio, l. What is meant by this ratio?
The risk ratio, l, is the disease risk for a relative of an affected person divided by the disease risk for an unrelated person. The risk ratio is a measure of the contribution made by genetic factors to the etiology of the disease.
Illustrate how the heritability of a disease can change over time using an example of a) a monogenic disorder and b) a complex disease. ( This question makes no question)
a) Phenylketonuria. The deficiency of phenylalanine hydroxylase in phenylketonuria produces elevated phenylalanine and toxic by-products that can result in cognitive disability. In the recent past the disease was almost wholly due to genetic factors, and so the heritability was extremely high. In modern times, neonatal screening programs in many countries allow early detection and treatment using low-phenylalanine diets. Now, in societies with advanced health care, phenylketonuria results mostly from environmental factors that lead to failure to deliver the treatment (inefficiency in health care systems, reluctance of families to seek out treatment, non-compliance with the diet, and so on).
b) Type 2 diabetes. There has been a huge recent increase in type 2 diabetes in many populations, mostly as a result of increasingly unhealthy diets and lack of exercise. As a result of the major changes in environmental factors (diet, exercise) the heritability of this disorder in many populations is now much reduced when compared with just a few decades ago.
Genome-wide linkage studies can often be carried out with just a few hundred DNA markers, but a genome-wide association study typically uses several hundreds of thousands of markers. Explain why this difference exists by explaining the very different designs of these two approaches.
Essentially, association is an effect that can be observed over very short distances only, whereas linkage between two loci can be observed over quite long regions of a chromosome. Using, say, 400 markers on a genome-wide linkage analysis means that there is on average 1 marker for each 8 Mb of DNA. That is possible because linkage tracks co-segregation of alleles in families over just a few generations. There will be a few meioses only where recombination might separate a disease locus from a marker locus.
Association, however, is a statistical property that looks at co-segregation of alleles at different loci in populations. The DNA analysed comes from individuals who last had a common ancestor many generations ago. There will have been a comparatively large numbers of meioses in which there will have been an opportunity for recombination to separate a marker locus from a locus where there is a genetic susceptibility factor. To have any chance at all of detecting association, therefore, a marker locus and a disease susceptibility locus have to very closely linked.
What is a haplotype block, and how are they organized in the human genome?
A haplotype block is a region of the genome where genetic variations are highly correlated and inherited together due to low recombination rates. These blocks range from a few to hundreds of kilobases in size and are characterized by high linkage disequilibrium (LD).
- Size and Structure: Varies from a few to hundreds of kilobases, with genetic variants inherited together.
- Boundaries: Defined by recombination hotspots, where recombination occurs more frequently.
- Tag SNPs: Specific SNPs within the block represent the genetic variation of the entire block, simplifying genotyping.
- Distribution: Spread throughout the genome, with patterns varying between populations.
- Evolutionary Significance: Provide insights into human evolutionary history and population structure, aiding in the study of genetic diseases and traits.
Before genomewide association (GWA) studies became successful, association studies used to rely on candidate gene approaches. How successful were the candidate gene approaches?
The success of candidate gene association studies was limited in the sense that only a few types of variant were identified to be disease susceptibility factors or protective factors, whereas very large numbers of disease susceptibility and protective factors were subsequently identified by GWA scans. On the other hand, however, candidate gene association studies have identified extremely important disease-associated variants, notably various HLA proteins that make the largest genetic contribution to diverse autoimmune diseases, plus the APOE*e4 allele that makes the largest known genetic contribution to Alzheimer disease, and CTLA4 variants that are important in Graves diseases and type I diabetes mellitus.
In the years when association studies were limited to candidate gene approaches what were the technological drawbacks that prevented genomewide association (GWA) studies and what developments made GWA studies possible?
Unlike linkage analysis, which can work over quite long distances across chromosomes, association analysis works only over very short distances. Thus, whereas human genomewide linkage analyses can easily be conducted with just a few hundred polymorphic DNA markers, carrying out GWA studies requires a very high density of markers – several hundreds of thousands of polymorphic DNA markers are required so that the spacing of neighboring markers across the genome gets down to kilobases (instead of the several megabases of DNA that separates neighboring markers in genomewide linkage analyses). The problem early on was two-fold: defining a huge number of human polymorphic markers, and then finding a way of assaying such a huge number of polymorphic markers quickly.
For a long time, we simply didn’t have enough polymorphic DNA markers, but that changed after strenuous attempts were made to identify human single nucleotide polymorphisms (SNPs). SNPs have the drawback that they are not very polymorphic, compared to microsatellites, but they have the big advantage that they occur extremely frequent in the human genome, and millions of human SNPs have been identified. The second technological development was a system of conveniently assaying hundreds of thousands of SNPs. That became possible when microarray technology was developed , allowing hundreds of thousands of different SNPs to be typed in parallel in a single experiment
How successful have genomewide association studies been in identifying genetic susceptibility to complex disease? What has been the main value of these studies?
In one sense genomewide association studies have not been as successful as initially hoped. For diseases where the ls relative risk factor is high there have been early successes such as for inflammatory bowel diseases and age-related macular degeneration, and more recently large meta-analyses of GWA studies have been very successful in identifying novel genetic variants associated with various diseases such as Alzheimer disease. For some others, including highly heterogeneous neuropsychiatric disorders such as autism spectrum disorder, GWA studies have, however, had more limited success.
Even where GWA studies have been very successful in identifying novel disease-susceptibility variants, however, the variants identified have frequently been of very weak effect, often with an odds ratio of 1.2 or less. disorders). That means GWA studies are often not going to provide that much help in predicting disease risk.
In another sense, however, GWA studies have been quite successful. GWA studies have provided large numbers of susceptibility loci for many common diseases, allowing remarkable new insights into the molecular pathogenesis of complex disease. Sometimes, whole new pathways of disease have been suggested. In addition to disease susceptibility factors, GWA studies have had a measure of success in identifying new protective factors that might be valuable in future approaches to provide resistance to infectious diseases
List three possible explanations for the general failure of GWA studies to identify genetic factors that collectively might explain the heritability of complex diseases.
- Very large numbers of common variants with very weak effect. GWA studies with a few thousand cases and controls are well suited to detecting susceptibility factors with odds ratios of 1.5 or more, but many genuine susceptibility factors might be missed if they have weaker effects (odds ratios of less than 1.2). To have a high chance of detecting these variants, GWA studies need to use much larger numbers of cases and controls, either directly or in meta-analyses using aggregate data from multiple individual studies.
- Rare variants of large effect. A major limitation of GWA studies is that they are restricted to identifying associations with common (frequent) variants. Much of the disease susceptibility might conceivably be due to a heterogeneous set of rare variants with individually strong effects (high odds ratios).
- Gene–gene and gene–environment interactions. The concept of heritability is flawed. It assumes additive effects by different loci, and the proportion of heritability explained by known GWA variants does not take into account genetic interactions between loci. Heritability is also traditionally separated into genetic and environmental components, but this is simplistic: genes interact with the environment.
Certain common alleles are known to be associated with specific complex diseases. Why has purifying selection not led to these alleles being eliminated from the population?
Many complex diseases are of late onset, and because short human lifespans used to be common until quite recently, susceptibility alleles for aging-related disorders might have had very little effect on reproductive rates over large numbers of generations. Alleles causing diseases that manifest only later in life might therefore have been protected to a very considerable degree from natural selection.
Many common disease alleles also seem to have some advantages. Balancing selection is most probably involved—the deleterious haplotypes might confer some heterozygote advantage.
Common disease variants may also have conferred some advantages in the recent past. The ‘thrifty gene’ hypothesis proposes that certain genetic variants confer a selective advantage in populations exposed to famine, and that they were advantageous in the past when food supplies were limited.
Naturally occurring genetic variants are known to act as protective factors to confer reduced risk of infectious disease. List some examples.
• Certain genetic variants of the ATP2B4 gene confer reduced risk of malaria. Red blood cells are the host cells for the pathogenic stage of the malarial parasite Plasmodium falciparum and the ATP2B4 gene is known to make a protein that works as the main calcium pump of red blood cells.
• The bs-globin allele associated with HbS and sickle cell disease also confers resistance to Plasmodium falciparum malaria by making the red blood cell an inhospitable environment for the P. falciparum parasite.
• Genetic variants that inactivate the gene encoding the Duffy blood group antigen act as a protective factors to reduce the risk of Plasmodium vivax malaria. The Duffy blood group gene makes a chemokine receptor that is needed for Plasmodium vivax to gain access to red blood cells
Outside of cancers and infectious diseases, various other complex diseases are known to be strongly influenced by environmental factors. Give three examples of environmental factors that are known to increase the risk of specific complex diseases excluding cancers and infectious diseases.
- Over-consumption and excess of fatty foods as a risk factor for type 2 diabetes
- Smoking as a risk factor for coronary artery disease, Crohn’s disease and age- related macular disease
- Gut microorganisms in inflammatory bowel disease and type 1 diabetes
