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Spectrum of Human Characters Non-Mendelian Inheritance: Multifactoril, … Traits/ diseases: ¾ Monogenic/ Mendelian ¾ Polygenic ¾ Environmental ¾ Multifactorial Mohammad Keramatipour MD, PhD Mode of inheritance: ¾ Mendelian/ monogenic ¾ Non Non--classical monogenic g ¾ Oligogenic (digenic), polygenic ¾ Multifactorial [email protected] M Keramatipour 1 Genetics Disorders M Keramatipour 2 Classical Monogenic Inheritance Definition and classification?? Simple comparison of frequency of different types of genetic di disease ((population l ti prevalence/1000 prevalence/ l /1000)) /1000 Properties: ¾ Different phenotype result from alternative genotypes of a single gene ¾ Number of genotypes & phenotypes are small ¾ Disorders due to genome and chromosome mutations: 3.8 ¾ Single Single--gene disorders: 20 ¾ Disorders with multifactorial inheritance: ~ 600 8 Also known as complex p or common disorders ¾ Relationship between genotype and phenotype is simple The main genetic contribution to morbidity and mortality is through disorders with multifactorial inheritance M Keramatipour ¾ Well Well--suited for analysis using crosses and pedigree 3 M Keramatipour NonNon -classic Monogenic Inheritance Multifactorial Inheritance Multiple genetic and environmental factors are involved ¾ Environmental influence , much more than single gene traits Monogenic g disorders with nonnon-classic ((non(non-mendelian)) inheritance ¾ Triplet Triplet--repeat disorders (unstable repeat expansions) Complex p inheritance: ¾ Single genotype: many possible phenotypes ¾ Single phenotype: caused by many possible genotypes ¾ Effects of segregation of alleles of one gene may be masked by effects of other genes ¾ Such traits can not be studied with usual pedigree methods ¾ Disorders caused by mutations in mitochondrial genes ¾ Disorders associated with genomic imprinting ¾ Disorders associated with uniparental disomy ¾ Disorders associated with mosaicism 8 Somatic mosaicism 8 Germline mosaicism M Keramatipour 4 5 M Keramatipour 6 Traits with Complex Inheritance Traits with Complex Inheritance Three categories of traits show complex inheritance: ¾ Threshold traits (discontinuous, discontinuous dichotomous, dichotomous discrete discrete, ¾ Continuous traits (quantitative): 8 No clear breaks between few discrete phenotypic variation 8 Measurable: blood pressure, body mass index, height, weight qualitative): 8 Have onlyy two or few classes 8 Either present or absent (affected, nonnon-affected) 8 Each individual has an underlying risk or liability to express it 8 If the th underlying d l i liability li bilit iis hi high h enough h ((over th the th threshold), h ld) th the trait will be expressed ¾ Meristic traits traits:: 8 Phenotype is found by counting 8 Such as number of fingerprint ridges, number of bristles on a fly M Keramatipour 7 M Keramatipour A Bit of History 8 A Bit of History Galton experiments: Around 1900 1900,, there were two camps: p ((1900 1900--1918) 1918) ¾ Francis Galton & studying family resemblances, quantifying observation and applying statistical analysis ¾ “Hereditary “ Talent and C Character”,article ” in 1865 1865,, and then a book “Hereditary Genius” in 1869 ¾ Biometricians: 8 Continuous C i or quantitative i i traits i ¾ Mendelians: 8 Discrete traits ¾ 1884 1884:: Galton compared the physical attributes of parents and children and established of the degree of correlation between relative ¾ By 1900 the idea of continuous or quantitative characters introduced M Keramatipour ¾ Are quantitative traits inherited in the same way as discrete traits? 9 1918,, Reconciliation ! 1918 M Keramatipour Polygenic Theory of Quantitative Traits What does it say? ¾ Any character that depend on the additive action of a large Reconciliation: ¾ Multiple loci (genes) contribute to variation number of individually small independent causes (genes??) shows a Normal (Gaussian) distribution in the population R. A. Fisher, 1918: 1918: ¾ Traits governed by a large number of Mendelian factors (polygenic) display the continuous nature, quantitative variation and family correlation ¾ Falconer extended this model to dichotomous characters A Normal distribution is characterized by two parameters: ¾ The mean: mean: the value that is the peak of the curve ¾ The variance (or its square root, the standard deviation): deviation): is a measure of the degree of spread of values to either side of the mean ¾ Results of Fisher’s and Falconer’s analysis: Polygenic Theory The variance of a measured quantity in the population is called the total phenotypic variance of Inheritance for discrete traits M Keramatipour 10 11 M Keramatipour 12 Heights, A Quantitative Trait Studying Quantitative Traits Human heights They show a continuum of phenotypic variation (range of phenotype) p yp ) Can not be described by small number of discrete categories Alternative way to describe a population is to use a frequency distribution ¾ Shows the proportion of individuals that fall within a certain range g of p phenotype yp ¾ Frequency histogram ¾ Normal distribution M Keramatipour 13 M Keramatipour Frequency Distribution 14 The Normal Distribution Type of frequency distribution ¾ Symmetric (bell(bell-shaped) ¾ Asymmetric (skewed) The normal distribution curve: ¾ A distribution for an infinite sample in which the trait of interest varies in a symmetric way around an average value ¾ First recognized in seventeenth century by an English mathematician, th ti i d de M Moivre i 8 Positive 8 Negative N ti ¾ Bimodal symmetry Normal distribution can be characterized by two parameters: ¾ The mean ¾ The variance (or its square root, the standard deviation) deviation) M Keramatipour 15 M Keramatipour Mean & Variance Normal Distribution of Quantitative Traits The figure shows a simple model for distribution in the population of a 16 The mean shows what the average individual looks like ¾ But all individuals don’t don t look the same character determined by one, two, three or many loci: ¾ Mean value of the character is 100 unit Symbolically: ¾ All alleles have The variance show how much individuals differ, or vary from frequency of 0.5 and additive/co--dominant additive/co effects each other (indirectly by showing how much on average they vary from the mean The sum of squared deviation from the mean divided by the degrees of freedom ¾ Each E h upper case allele ll l adds 5 units to the value and each lower case allele subtracts 5 units M Keramatipour X = ∑ƒi X / N Symbolically: 17 M Keramatipour Vx = ∑ƒi (X – X ) 2 / (N (N--1) 18 Variance Standard Deviation (SD) SD is the square root of variance Variances are additive ¾ Total variance for a trait can be predicted by adding variances for different factors contribute to the trait ¾ But is difficult to use Standard deviation shows the proportion of individuals in a normal distribution with certain differences from the mean Mean and variance can explain a normal distribution M Keramatipour 19 M Keramatipour The Normal Range 20 Regression to the Mean Comparing the heights of sons and their fathers by F Francis i G Galton: lt The concept of normal range of physiological quantities are fundamental to clinical medicine Specially in assessing health status in children, height, weight head circumference and … are compared with the weight, normal expected measurements for a child’s sex and age Normal range of a quantitative trait is consider between the two standard deviation above and below the population mean In a Normal distribution approximately pp y 5% of p population p will have measurements more than 2 SD above or below the population mean M Keramatipour ¾ Taller Taller--thanthan-average fathers tended to have taller--than taller than--average sons ¾ The sons tended to be nearer the h average h height i h of all men than their fathers He called this: “regression Distribution among children to the mediocrity” 21 Regression to the Mean M Keramatipour 22 Studying Quantitative Traits From figure in previous slide: ¾ For each class of fathers, mean for the children is halfway y between the fathers value and the population mean ¾ For each class of children, mean for the fathers is half way between the children children’s s value and the population mean ¾ The distribution in children is the same as the distribution in the fathers Main statistics and methods for studying quantitative traits: ¾ Heritability ¾ Familial correlation: r: correlation coefficient ¾ Regression analysis Attention: ¾ This is an introduction to these only!!! ¾ We do not have enough time to discuss these in details ¾ The purpose is to make you just familiar with them ¾ Caution: regression to the mean is a purely statistical phenomenon not a genetic mechanism ¾ Assumptions to this model: 8 Random mating 8 No dominance M Keramatipour Distribution among fathers 23 M Keramatipour 24 Heritability (h2) Partitioning Heritability Definition: the proportion of phenotypic variation within a group of individuals that is due to genetic variation Heritability values are relevant only to particular groups raised in a particular environment Heritability values change between 0 to 1 Then: p phenotypic yp variance is due to the additive effects of ¾ 1 means all phenotypic variability is due to genetic variation genetic variance and environmental variance ¾ 0 means all the variation is due to environmental effects ¾ [ V p = VE + VG ] Aim: analysis of the genetic and environmental components Heritability of a trait is the proportion of the total variance that that affect quantitative traits M Keramatipour Assumptions: ¾ Genetic and environmental factors are the only components that determine a trait ¾ Genetic and environmental factors are independent of each other is genetic, genetic that is VG / Vp 25 M Keramatipour 26 Measuring Heritability Heritability in Human Comparing the variation in traits between genetically identical Heritability of human traits can be estimated by comparing them among relatives of known degrees of relatedness (parents, children, MZ and DZ twins,…) and g genetically y disparate p g groups p can determine VE and VG Example: ¾ Inbreeding in mice to develop homogeneous strains An example is using twin studies ¾ h2 = (Variance in DZ pairs - variance in MZ pairs) / Variance in DZ pairs ¾ Examples: h2 = 0.8 for stature, & h2 = 0.70 - 0.80 for body mass index ¾ When heritability is known, it is easy to determine VE 8 VG = 0, so Vp = VE ¾ Try an example: 8 Determine VE and VG for weight in mice 8 Any idea? Proposal for such study?? M Keramatipour 27 M Keramatipour Limitations of Heritability 28 Examples of Heritabilities Difficulties in measurement and interpretation ¾ Genetic and environmental factors are not independent because genetic and social (environmental) disadvantages go together and the equation [ Vp = VE + VG ], cannot be accurate ¾ Correlation between relatives may not simply reflect their familial genetic relationship because they share their environment as well as their genes ¾ It is not accurate to extend the h2 obtained from twins or from an ethnic group to the whole population or to another population ¾ If socioeconomic conditions change, even h2 obtained from the same group is not applicable again M Keramatipour 29 M Keramatipour 30 Regression Analysis Familial Correlation of Quantitative Traits Correlation coefficient: coefficient: statistic that show how traits are It determines the nature of the relationship (between the two correlated variables) and enables us to make prediction from that ¾ Correlation concerns the strength of association (relationship) between the values of two variables (trait variable among two groups of individuals) One way is to reduce the data to a straight line, called “line of best fit” or “regression line” 8 Example: comparing height between fathers and theirs sons ¾ Ranges from -1 to + +1 1, ziro means no correlation ¾ Positive value: shows direct correlation ¾ Negative value: shows inverse correlation ¾ The absolute number measure the strength of correlation ¾ Correlations do not imply causecause-andand-effect ¾ Correlation C l ti reflects fl t th the iinfluence fl off both b th heredity h dit and d common environment M Keramatipour 31 M Keramatipour 32 SexSex -specific Threshold Polygenic Theory of Discontinuous Traits Falconer’s Polygenic Threshold Model Due ot Falconer theory If a disease ¾ Liability for a trait (disease) is multifactorial/polygenic and follow a is more common in one sex that is because of a lower threshold for that sex and the higher threshold for the other sex Example: congenital pyloric stenosis five times more common stenosis, in boys Normal distribution in the population ¾ There is a threshold that whenever exceeded the trait expresses ¾ Medical applications: 8 Recurrence risk in relatives of a proband with such disorders ¾ Threshold is higher for girls than boys ¾ Relatives of an affected girl have a higher average susceptibility than relatives fo an affected boy M Keramatipour 33 Measuring Familial Aggregation 34 Measuring Familial Aggregation The primary characteristic of diseases with complex Case Case--Control studies (mutifactorial) inheritance is their familial aggregation ¾ The frequency of disease in families of the cases is compared Assessing familial aggregation: ¾ Measuring relative risk ratio (λr) ¾ Case Case--control studies with the its frequency among families of suitable controls who do not have the disease 8 Parkinson disease: in a study frequency of PD among first and second degree g relatives of p patients found to be 6.3, and 1.2 among relatives of controls Measuring relative risk ratio (λr) ¾ λr = prevalence of the disease in a relative “r” of a proband / ¾ Limitations: 8 Ascertainment bias 8 Association does not prove causation population l i prevalence l off the h di disease 8 Autism: λ = 2000 for MZ twins & λ = 150 for siblings g 8 Type 1 diabetes mellitus: λ = 80 for MZ twins & λ = 12 for siblings M Keramatipour M Keramatipour 35 M Keramatipour 36 Genes/Environment Contribution Complex Disorders in Pedigree Assessing the relative contribution of genes and environment to complex disease trait Do not follow simple mendelian pattern of inheritance ¾ Concordance among relatives ¾ Comparison of disease incidence in biological relatives to Demonstrate familial aggregation Disease is more common among the close relative of the unrelated family members ¾ Twin studies proband b d and db become lless common iin relatives l i who h are lless closely related Greater concordance for disease is expected among MZ versus DZ twins Pairs of relatives who share diseasedisease-predisposing genotypes at relevant loci may still be discordant for phenotype because of the crucial role of nonnon-genetic factors 8 Disease concordance in MZ twins 8 Comparing concordance of MZ versus DZ twins 8 Twin reared apart ¾ Limitations of twin studies 8 MZ twins do not have precisely identical gene expression 8 Environmental exposure may not be the same 8 Measuring g disease concordance in MZ twins g gives an average g estimate only 8 Ascertainment bias M Keramatipour 37 Counseling for Complex Diseases M Keramatipour 38 Empiric Risk/ General Points Assessment of recurrent risk: empiric risks Empiric p risks obtained through g p population p surveys y not based Some g general rules in empiric p risk assessments: ¾ Risk to first first--degree relatives equates to the square square--root of the population incidence ¾ Risk Ri k to sibs ib and d offspring ff i should h ld b be about b equall ¾ Risk to more distant relatives declines rapidly ¾ Risk increases when there are multiple affected family members ¾ Risk increases with the severity of the disorders ¾ Risk is greater among relatives of the more rarely affected sex on inheritance theory Empiric risk depend on the specific condition General G l considerations: id ti ¾ Relationship to the affected individual ¾ Severity of the disorder in the proband Example: Pyloric stenosis M Keramatipour 39 Empiric Risk For Complex Disorders M Keramatipour M Keramatipour 40 Thank You For Your Attention!!! 41 M Keramatipour 42