Class 9 BIO3320 Complex Traits PDF

Recap Class 8: human karyotype  Recap Class 8: Dosage compensation of X-linked genes Drosophila: an increase of transcriptional activity on X chromosome of males C. elegans: a decrease of transcriptional activity of both X chromosomes of females Mammals: X-chromosome inactivation  Re...

Recap Class 8: human karyotype  Recap Class 8: Dosage compensation of X-linked genes Drosophila: an increase of transcriptional activity on X chromosome of males C. elegans: a decrease of transcriptional activity of both X chromosomes of females Mammals: X-chromosome inactivation  Recap Class 8: Chromosome abnormalities in human pregnancy Aneuploid: Possessing an abnormal number of one chromosome (or region of chromosome) Monosomy: One copy (not compatible with life, very early pregnancy failure) Trisomy: Three copies (most not compatible with life, trisomy 13, 18, 21) Sex chromosome abnormalities (not detrimental)  Recap Class 8: Chromosomal abnormalities Deletions Duplications (red-green color blindness) Inversions Translocations (Robertsonian translocation)  Recap Class 8: Hybridization and polyploidization in origin of wheat Complex Traits Genetics BIO3320 10/04/2024  Outline Complex traits Causes of variation Identifying genes affecting complex traits  Outline Complex traits Causes of variation Identifying genes affecting complex traits  What are complex traits Affected by the alleles of two or more genes (genetic factors) Also affected by environmental factors Three categories of complex traits: continuous traits (quantitative traits) categorical traits threshold traits Categorical and threshold traits Categorical: the phenotype corresponds to any one of a number of discrete categories e.g, the # of puppies in a litter; flower colors; animal fur colors Threshold: a few phenotypic classes, determined by multiple genes + the environment. When a threshold is reached, it shows one phenotype; otherwise, it shows another phenotype adult-onset diabetes  Examples of continuous traits Heights Weights Milk production in cattle Yield in corn Blood pressure in humans Distribution of height among British women Distribution of height in a graph X axis: height intervals xi Y axis: # of people within the range fi  Useful parameters of this distribution – the mean The mean: average, the peak of the distribution xi : the midpoint of height interval numbered i fi : the number of women in the height interval numbered i the summation of all classes of data N : the total sample size  Useful parameters of this distribution – the variance A measure of the spread of the distribution Estimated in terms of the squared deviation of each observation from the mean A large variance: the distribution is spread out A small variance: the distribution is clustered around the mean  Useful parameters of this distribution – the variance The standard deviation: the square root of variance In the British women height example: s2 = 7.24 in2 s = 2.69 inch  Parameters of the distribution μ, the mean of the population, estimated by σ, the standard deviation of the population, estimated by s σ2, the variance of the population, estimated by s2  The Normal Distribution When the data are symmetrical, the distribution can be approximated by a smooth, bell-shaped curve known as the normal distribution Figure 7.2: Graphs showing that the variance of a distribution measures the spread of the distribution around the mean. The area under each curve covering any range of phenotypes equals the proportion of individuals having phenotypes within that range. Features of a normal distribution  In-class exercise 1 The distribution of height among 1000 newborn boys has a mean of 50 cm and a standard deviation of 2 cm. How many boys are expected to be between 46 and 54 cm tall? A. 100 B. 680 C. 950 D.990 E. 1000 In-class exercise 2  Reconciling Mendelian inheritance with continuous traits For continuous traits, how can they be determined by discrete particles proposed by Mendel (genes)?  Reconciling Mendelian inheritance with continuous traits Three Generations of a Single Two parental strains of distinct seed oil content Gene Trait Both homozygotes (assuming the trait is determined by a single gene; F1 generation has intermediate seed oil content no dominance between two different alleles) F2 generation has 1:2:1 ratio of low, intermediate and high seed oil content Low High seed oil content  Reconciling Mendelian inheritance with continuous traits Hypothetical distributions of seed oil content in cases of: One gene two genes three genes  Reconciling Mendelian inheritance with continuous traits Hypothetical distributions of seed oil content in cases of: One gene two genes When a trait is affected by a large # of loci, the distribution of phenotypes in a population approximates a normal continuous distribution three genes  Outline Complex traits Causes of variation Identifying genes affecting complex traits  Causes of variation Genotypic variation Environmental variation Variation due to genotype-by-environment interaction Variation due to genotype-by-environment association  Causes of variations #1: genotypes Genotypic variance: differences in genotypes that cause phenotypic variance Segregation of three genes affecting a quantitative trait Assumption: Each uppercase allele adds one unit to phenoty Each lowercase has no effect Result: 7 phenotypic categories, mean = 3; variance = Segregation of three genes affecting a quantitative trait The distribution of phenotypes as determined by 3 genes or 30 genes  Genotypic variations Even in the absence of environmental variation, the distribution of phenotypes alone provides only limited information about the # of genes involved, and no information about the dominance relations of alleles  Causes of variations #2: environment Environmental variance: differences in the environment that cause phenotypic variance Fig. 7.8 Seed weight distribution in a homozygous line of edible beans  Causes of variations #3: genotype-by-environment interaction and association Take a step back: If genotype and environment separately and independently affect phenotype: Phenotypic variance = genotypic variance + environmental variance σp2 = σg2 +σe2  Causes of variations #3: genotype-by-environment interaction and association However: Genotype and environment may NOT act separately and independently: G-E interaction G-E association  G-E interaction Environmental effects on phenotype differ according to genotype  Example of G-E interaction Figure 7.10: Strain A is superior when environmental quality is low (negative numbers), but strain B is superior when environmental quality is high.  Example #2 of G-E interaction Mineral supplementation has the biggest growth effect on UDUD genotype and has no effect on UNUN genotype  G-E association Different genotypes are not distributed at random in all possible environments Certain genotypes are preferentially associated with certain environments Farmers feed their cows in proportion to their milk production levels  Outline Complex traits Causes of variation Identifying genes affecting complex traits  Identifying genes affecting complex traits Linkage analysis in mapping QTLs GWAS Candidate genes for complex traits  Quantitative-trait Loci mapping QTL: a gene that affects a complex trait QTL cannot usually be identified in pedigrees (WHY?) Identifying QTL through linked genetic marker Quantitative Trait Loci Mapped in the Tomato Genome © Photos.com Data from A. H. Paterson, et al., Nature 335 (1988): 721-725. Genome-wide association studies  Figure 05.F26: Results of Genome-Wide Association Studies involving seven traits. Each line is a whole-genome Manhattan plot showing associations between SNPs and a particular condition. SNPs shown in green are ones for which significant associations with the trait in question were determined. Reproduced from WTCCC. (2007). Genome-wide association study of 14,000 cases of seven common diseases and 3,000 shared  Common alleles affecting complex traits account for a small fraction of total heritability Crohn’s disease GWAS identified 71 associating loci 71 loci account for only 23% of phenotypic variation Yes, GWAS are informative in the physiological basis of disease, but some of the diseases / conditions are complex  Candidate genes for complex traits Based on previous knowledge, we suspect that one gene could be contributing to a trait We test the function of these genes (candidate genes) Example: Identifying natural genetic polymorphism in depression Candidate gene for depression: a gene that encode a serotonin transporter (SLC6A4)  Candidate genes for complex traits Serotonin: a neurotransmitter that influences anxiety and depression Serotonin transporter SLC6A4: transport serotonin from neurons that make it to neurons that receive it; also recycles serotonin (uptake) SLC6A4 is a target of antidepressant drugs (inhibiting uptake)  Candidate genes for complex traits S form: the short allele L form: the long allele They differ in the # of tandem repeats in the promoter region L/L genotype: cells make more mRNA, and more SLC6A4 protein S/L and S/S genotype: higher risk of depression  Candidate genes for complex traits G-E interaction: Among Ecstasy users (environment factor), depression score highest in S/S genotype In other words: depression score difference between users and nonusers biggest in the S/S genotype