Lecture_23_(G).pdf

Common Diseases, Polygenic and Multifactorial Disorders Observation: most common diseases show a familial clustering, that is the they occur within some families more than what would be expected from the occurrence in the population. However, the incidence in close relatives of a...

Common Diseases, Polygenic and Multifactorial Disorders Observation: most common diseases show a familial clustering, that is the they occur within some families more than what would be expected from the occurrence in the population. However, the incidence in close relatives of affected individuals is much lower than in diseases caused by single gene mutations Explanation: these diseases are likely caused by multiple genetic (polygenic) and/or environmental factors aka. multifactorial inheritance (Figure 10.1) Figure 10.1: Human diseases represented as being on a spectrum ranging from those that are largely environmental in causation to those that are entirely genetic. Virtually all human diseases, except perhaps trauma, have a genetic component. (“Understanding Human Genetic Variation”, 2007, Types and mechanisms of genetic susceptibility Monogenic disease: A single gene mutation e.g. FH gene mutation in familial hypercholesterolemia is the main determinant of developing heart disease Polygenic diseases: determined by variation in many genes at different loci each exerting a small but additive effect PLUS environment e.g. Type 2 diabetes, where multiple-genetic loci can contribute to disease, in addition to environmental factors (lifestyle..etc) Inheritance of multifactorial diseases child (affected) Child (not affected) Father (affected) Child (not affected) Approaches to demonstrating genetic susceptibility to common diseases 1. Studying prevalence and incidence of a disease in a population- and the effects of migration – can help explain its genetics Example: a migrant group with low incidence of a disease moves to a population group with a high incidence; After some time, the disease incidence rises to the same level as the host population. What's the conclusion? What If the disease incidence remained the same in the migrant group what would be your conclusion? Approaches to demonstrating genetic susceptibility to common diseases 2. Family and Twin studies: Problem: familial aggregation of a disease can suggest, but not prove genetic susceptibility since families often share a common environment! Solution: study frequency of disease in dizygotic vs monozygotic twins. The environment should be similar for both but only monozygotic ("identical") twins will have the same genotype if both twins are affected, they a referred to as being concordant If only one is affected, then the pair are referred to being discordant So, a high concordance in monozygotic twins but a low concordance in dizygotic twins, indicates..........?  Twin studies:  Twin studies assess the contribution of genetics and/or environmental factor in traits Monozygotic twins (MZ) Dizygotic twins (DZ) Genetically identical (100%) Share 50% of their genetics (as any other siblings) If rMZ > rDZ then genetic factors are important If rDZ > ½ rMZ then shared environment is important (r = correlation) Approaches to demonstrating genetic susceptibility to common diseases 3. Polymorphism association studies: Humans differ only in ~0.1% of their genome. However, this 0.1% can explain differences in phenotypes and susceptibilities to diseases in a population. Human genome contains more than 10 million single nucleotide polymorphisms (SNPs) that occur >1% of individuals in a population (i.e., common) It is possible to associate if these SNPs variants occur more commonly in individuals affected with a particular disease than in the rest of the population However, SNP association does NOT equal SNP causation --> a genotyped SNP ('tested SNP') maybe just be closely (physically) linked to causative SNP. Estimating the genetic contribution in common diseases Heritability (h2): The proportion of the phenotypic variation in a quantitative trait that is due to genetic factors One way to calculate genetic variability is by comparing difference in concordance in identical (~100% genotype) and non-identical twins (~50% genotype) Using Falconer's formulae: h2 = 2 [r(MZ)-r(DZ)] r : correlation MZ : Monozygotic twins Assumption: environmental variation is identical (may not be true) DZ : Dizygotic twins Comparing identical twins separated at birth more accurate but rare h2 will be a value between 0 to 1. The higher the h2, the greater is the contribution of genetics differences among people to the variability of the trait in the population. Polygenic inheritance and the normal distribution Polygenic inheritance: first proposed by Ronald Fisher (1918) explains variations in certain traits that follow a continuous/normal distribution within populations due to the additive effects of multiple genes (fig 10.2) Several human characteristic show a continuous normal distribution, including: Blood Pressure Height Intelligence Body mass index Skin color Figure 10.2 The normal (Gaussian) distribution. Polygenic inheritance and the normal distribution consider if height was determined by two (equally frequent) alleles at a single locus: a = tall & b = short; this results in discontinuous phenotype of 3 groups with following ratios 1 tall (a/a) 2 average (a/b) 1 short (b/b) If determined by 2 loci, then this would result in 5 groups in the following ratios: 1 (4 tall genes), 4 (3 tall + 1 short), 6 ( 2 tall + 2 short), 4 (1 tall + 3 short), 1 (4 short) The more loci are involved the more the distribution starts to resemble a normal curve  Distribution of a non-continuous (discrete) phenotype. One gene (2 alleles A and a) Two genes (4 alleles A, a and B, b) Multiple genes (+ environment) normal distribution The more loci are involved the more the distribution starts to resemble a normal curve Medical Genetics 4th edition By Jorde, Carey and Bamshad Skin pigmentation: Skin pigmentation in humans is controlled by many separately inherited genes. To simplify, consider skin pigmentation is controlled by three genes. The dark allele for each gene are A, B and C The light alleles are a, b and c. Each dark allele contribute to one unit of darkness In this model, the darkest skin is the genotype AABBCC while the lightest is the genotype aabbcc. Mating betweeen AaBbCc couple can yelid seven possible phenotypes. (Campbell Biology, 10th Edition, page 280) Polygenic inheritance and the normal distribution It is possible to correlate a phenotype (e.g. height) with genetic loci First degree relatives share on average 50% of their genes and if height is polygenic --> assume the correlation between first-degree relatives is 0.5 (1 would be fully correlated) Reality: is height is also influenced by environment and by genes that are not additive (e.g. more dominant) This explains why tall parents can have short children Multifactorial inheritance and the liability/threshold model Disorders such as cleft lip involve the contribution of many genetic loci but the phenotype does not follow a continuous distribution: its either present or not! The liability/threshold model proposed by Sewall Wright (1934) can explain the inheritance of discontinuous multifactorial/polygenic disorders such as cleft lip All the influencing factors (genetic or environmental) are considered a single entity known as liability Liabilities of all individuals in a population form a continuous normal distribution an individual is a disease case only if an underlying accumulated liability lies above a certain threshold The genetic burden of a disease can be revealed by comparing distribution in general population vs relatives of the affected individuals (fig 10.5) Figure 10.5 Hypothetical liability curves in the general population and in relatives for a hereditary disorder in which the genetic predisposition is multifactorial. accumulated liability > threshold --> disease affected individual The shift in the to the right in the curve of relatives indicates increased genetic burden Some consequences of the liability/threshold model Incidence of a condition is greatest amongst the relatives of the most severely affected patients (fig 10.6). The risk is greatest among close relatives of the index case and decreases with more distant relatives If there are more than one affected close relatives, then the risk for other relatives increases Figure 10.6 Severe (A) and mild (B) forms of cleft lip/palate.  Risk for Cleft Lip with or without Cleft Palate in a Child Depending on the Number of Affected Parents and Other Relatives Risk for CL(P) (%) No. of Affected Parents Affected Relative 0 1 2 None 0.1 3 34 One sibling 3 11 40 Two siblings 8 19 45 One sibling and one second-degree relative 6 16 43 One sibling and one third relative 4 14 44 CL(P) risk increases with the number of affected relatives and the degree of relatedness (more risk with more affected closed relatives). CL(P): Cleft Lip and cleft Palat (Thompson & Thompson Genetics in Medicine, 8th edition, page 138) Associating genetic variants with disease Association studies: compares the frequency of a particular variant(s) in affected patients vs frequency in a control group (case-control study) If the frequencies in the two groups differ (statistically) significantly  evidence of an association For quantitative traits ( e.g. fasting glucose): the mean trait value for each genotype are compared for each genotype (fig 10.7) odd ratio: gives an indication of how much more frequently a disease occurs in individuals with a specific variant/marker (calculation, table 10.2) most markers in a multifactorial disease will give rise to a modest odds ratio (1.1-1.5) Figure 10.7 Association studies may either test allele frequency differences between cases and controls for a disease phenotype (e.g., T2D), or compare mean trait values for each genotype group (e.g. for fasting glucose). Association does not prove causation! If a variant has been associated (significantly) with a disease: a) evidence that variant is directly involved in causing the disease (a susceptibility variant) b) OR often that it is closely physically linked with a causative susceptibility variant (In this case: Association does not equal Causation) Risk of False positive in poorly designed studies Many of the associations discovered through the candidate gene approach could not be replicated in independent studies (FALSE POSITIVES). Reasons included: 1. Small sample size 2. Weak statistical support 3. Low prior probability of a selected variant being genuinely associated with the disease e.g. study showing an association between HLA-A1 variant and the ability to use chopsticks in San Francisco: it was associated with San Francisco huge Chinese population! Genome-wide association studies: identifying genes that cause multifactorial disorders Genome-wide association studies (GWAS): compares variants across the entire genome rather than looking at one variant at a time This was possible by developments of microarray genotyping technologies & the creation of a SNP variant database Advantage: Hypothesis free (no prior assumptions are needed for a biological link). Can uncover unexpected biological mechanisms.  GWAS - A method that investigate associations between DNA variants and a specific complex trait or a disease. Large sample Case Control SNPs Analysis Look for variants that are statistically associated with the phenotype. GWAS of Type 2 Diabetes (T2DM) More than 90 susceptibility SNPs have been identified Most have a small effect/contribution to disease (additive) but some will have a larger effect For example, in European populations a SNP within the TCF7L2 is the locus that has the largest effect. Inheriting 2 alleles in this gene doubles the risk of developing T2DM. TCF7L2 is involved in impaired beta cell function which is important in insulin production. GWAS of Type 2 Diabetes (T2DM) SNPs or variants associated with a disease or a trait in one population may not be associated in a different population. For example, one study found that the 2 alleles in the TCF7L2 gene are not associated with T2DM in the Saudi population. This is due to genetic and environmental differences between populations. This highlights the fact that associated SNPs can be population specific​, and caution should be taken when extrapolating genetic associations from one population to another.