genetic variations lakshmi k matukumalli. human – mouse comparison
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Single nucleotide polymorphismsShort IndelsSimple sequence repeatsCopy number variantsLoss of heterozygosity
Microsatellite
(2-9 bp core repeat)
Minisatellite
(10-60 bp core repeat)
Copy number variants
Molecular Variations
Type of polymorphisms
TC
Single-nucleotide Polymorphism (SNP)
5’ Flanking region
Promoter
5’ Untranslated
region
ATG
Coding
Nonsynonymous polymorphism
GAG Asp GUG Val
Intron
Transcript
Synonymous polymorphism
GAU Asp GAC Asp
Coding
End
3’ Untranslated
region
Insertion/deletion polymorphism (indel)
TAACGGTA GG
3’ Flanking region
Extent of Variation (Human Genome)
> 5 million SNPs (dbSNP)Recent genome analysis of diploid individual showed 4.1 million DNA variants, encompassing 12.3 Mb.
- 3,213,401 single nucleotide polymorphisms (SNPs), - 53,823 block substitutions (2–206 bp), - 292,102 heterozygous insertion/deletion events (indels)(1–571 bp), - 559,473 homozygous indels (1–82,711 bp), - 90 inversions, - Plus segmental duplications and copy number variations.
Non-SNP DNA variation accounts for 22% of all events, however they involve 74% of all variant bases. This suggests an important role for non-SNP genetic alterations in defining the diploid genome structure.
Moreover, 44% of genes were heterozygous for one or more variants.
Importance of SNPs and other variants
Study Genetic variation in diverse populations in any species to understand evolutionary origins and history, estimate population size, breeding structure, or life-history characters Migration within and between sub-populations Understand evolutionary basis for maintenance of
genetic variation and speciation. Applications
Genetic association of traits Effects on gene expression (e.g., synonymous vs
nonsynonymous / TF binding sites) DNA finger printing or sample tracking
Fine Mapping with SNP Markers
Advantages of SNPs as genetic markersas compared to microsatellites.
•High abundance
•Distribution throughout the genome
•Ease of genotyping
•Improved accuracy
•Availability of high throughput
multiplex genotyping platforms
SNP-PHAGE (Software package)
Important steps are Primer development Primer testing Sequencing Base calling, Sequence assembly Polymorphisms analysis Haplotype analysis GenBank submission of
confirmed polymorphisms
Primers
Sequence Variation
5’ amplicons
3’ amplicons
SNP Pipeline for Haplotype Analysis and GEnbank (dbSNP) submissions.
Application of Machine Learning in SNP Discovery
Inputs
Machine LearningProgram
Planning and Reasoning
Outputs
Model (Tree / Rules)
Model(Tree /Rules)
Inputs
Outputs
Training mode Testing/Prediction mode
Steps:•Parameter Selection •Parameter Optimization •Testing•Implementation.
Results:
Achieved substantial improvement in the accuracies as compared to using only polybayes or polyphred.
Objective: Reduce human intervention by using expert annotated datasetfor training a Machine learning (ML) program and use it to differentiate good/bad polymorphisms
SNP Discovery using next generation sequencers
Short sequences 23-35 bp long at a fraction of cost. Reduced Representation Sequencing
Digest genomic DNA with restriction enzyme Screen based on in silico digestion
Size select based on Repetitive DNA Number of fragments Sequencing platform
Allows “targeted” deep sequencing of pools of DNA Randomly distributed
Cost / Mb
ABI $880
454 $160
Solexa $5
SNP Discovery - Bioinformatics
Strategies to maximize performance High quality score stringencies
For each read At base for putative SNP
Require single map location of a 23-bp “tag” (and 4-bp restriction site)
Allow only one single base pair difference match for a putative SNP
Reduces repeat content Reduces gene family/paralog false positives
Require 2 copies of each allele – assembly can count as 1
Population Genetics
Population genetics is the study of the allele frequency distribution and change under the influence of the four evolutionary forces: natural selection, genetic drift, mutation and gene flow. It attempts to explain phenomena as adaptation and speciation.(www.wikipedia.org)
X
Variation
Population Genetics
Neutral theory : Rate at which new genetic variants are formed is equal to the loss of genetic diversity due to drift.
C/T C/C T/T
Genotypes : CT, CC, TT
Alleles : C and T
Genotyping of a population of 1000 individuals for a SNP resulted in 100, 500 and 400 genotypes for CC, CT and TT respectively
Genotype Frequencies: CC (0.1), CT (0.5) and TT(0.4)Allele Frequencies: C (p) = (200+500)/2000 = 0.35 (minor allele -- MAF)
T (q) = (500+800)/2000 = 0.65 (major allele)Hardy-Weinberg Equilibrium: Expected genotype frequencies are p2, 2pq and q2 (122, 422 and 455)
HWE Deviations: Drift, Selection, Admixture etc.,
Useful to partition genetic variation into components:within populationsbetween populationsamong populations
Sewall Wright’s Fixation index (Fst is a useful index of genetic differentiation and comparison of overall effect of population substructure.
Measures reduction in heterozygosity (H) expected with non-random mating at any one level of population hierarchy relative to another more inclusive hierarchical level.
Fst = (HTotal - Hsubpop)/HTotal
Fst ranges between minimum of 0 and maximum of 1:
= 0 no genetic differentiation
<< 0.5 little genetic differentiation
>> 0.5 moderate to great genetic differentiation
= 1.0 populations fixed for different alleles
Fst
Haplotype inference
The solution to the haplotype phasing problem is not straightforward due to resolution ambiguity
Computational and statistical algorithms for addressing ambiguity in Haplotype Phasing:
1) parsimony
2) phylogeny
3) maximum-likelihood
4) Bayesian inference
Linkage disequilibrium (LD)
Non-random association of alleles at two or more loci, not necessary in the same chromosome.
LD is generally caused by interactions between genes; genetic linkage and the rate of recombination; random drift or non-random mating; and population structure.
B1 B2 Total
A1 p11 = p1 q1 + D p12 = p1 q2 - D p1
A2 p21 = p2 q1 - D p22 = p2 q2 + D p2
Total q1 q2 1
Let A and B be two loci segregating two alleles each; a1 and a2 with frequencies p1 and p2 in A, and b1 and b2 with frequencies q1 and q2 in B.
A
B
D = p11 - p1q1
D depends on the allele frequencies at A and B.
D’ a scaled version of D:
Linkage disequilibrium (cont)
Dmin(p1q1 , p2q2)
D’ =
If D < 0
Dmin(p1q2 , p2q1)
If D > 0
Squared correlation coefficient
Linkage disequilibrium (cont)
r2 = D2
p1p2q1q2
* The measure preferred by population geneticists
* Is independent of of allele frequencies
* Ranges between 0 and 1
* r2 = 1 implies the markers provide exactly the same information
* r2 = 0 when they are in perfect equilibrium