Deleterious mutations

Transcription

1 Deleterious mutations Mutation is the basic evolutionary factor which generates new versions of sequences. Some versions (e.g. those concerning genes, lets call them here alleles) can be advantageous, some deleterious. Classical view was that the fate of an allele was determined solely by the fitness advantage it conferred on an individual, but more developed models can explain why even deleterious alleles can spread. In fact, our genomes harbour large numbers of deleterious mutations This lecture gives some examples which are pickings from recent literature. By these examples the concepts positive and negative selection, non-synonymous and synonymous substitutions, and certain in silico methods for prediction are intoduced. Seq evol / Deleterious mutations / SVarvio 1

2 Example 1. Seq evol / Deleterious mutations / SVarvio 2

3 Example 2. Seq evol / Deleterious mutations / SVarvio 3

4 This paper describes the importance of understanding evolutionary patterns of mutations as regards human health Untangling the mysteries of human genome variation is the essential precursor to the development of personalized medicine where the aim is to relate the genotype with the phenotype in better understanding an individual's susceptibility to disease and response to treatment. Projects of complete genome sequences from individual humans revealed that every individual carries thousands of amino acid altering (nonsynonymous) nucleotide mutations and that a large number of these mutations are novel in terms of their location and the type of amino acid change induced. Experimental and other functional information are rarely available for the association of phenotypic effect with these mutations, so computational methods are used instead. in silico prediction of mutation effects several software packages, e.g. PolyPhen (polymorphism phenotyping) SIFT (sorting intolerant from tolerant) Using various prediction tools, up to one-fourth of nonsynonymous mutations have been diagnosed to be not strictly neutral and are thus thought to harbor signatures of negative or positive selection Seq evol / Deleterious mutations / SVarvio 4

5 Accuracy of PolyPhen diagnosis of 9460 DAMs (= disease associated mutations). (A) Fraction of DAMs classified into benign, possibly-damaging, and probably-damaging categories. (B) Evolutionary timetree of 44 species used for estimating evolutionary rates. (C) Relationship of evolutionary rates and (D) incident amino acids with the correct diagnosis of DAMs (probably-damaging). Error bars, 95% confidence interval (two times the SE). (E) Correlation between the accuracy of DAM prediction from proteome-wide analysis, and one DAM-rich protein (cystic fibrosis transmembrane conductance regulator [CFTR]). Solid and open circles show data points for incident amino acids (r = 0.97; P << 0.01) and evolutionary rates (r = 0.95; P << 0.01), respectively. (F) Two examples showing the dependence of the accuracy of DAM diagnosis for constant and variables sites for arginine (an easy-todiagnose amino acid; red bars) and alanine (a difficult-to-diagnose amino acid; gray bars). Seq evol / Deleterious mutations / SVarvio 5

6 PolyPhen classification of 12,421 nsnps into benign, possiblydamaging, and probably-damaging categories. (A) Fraction of nsnps classified into the three categories. The fraction of nsnps designated to be benign at positions with (B) different evolutionary rates and (C) original amino acids. Panel B also contains the accuracy of DAM inference from the previous figure (filled squares). Seq evol / Deleterious mutations / SVarvio 6

7 Seq evol / Deleterious mutations / SVarvio 7

8 Figure in the previous page: A) Frequency distributions of DAM (red) and nsnps (blue) PSIC scores. Vertical lines show the PolyPhen PSIC cut-offs for classification of variants in the absence of structural or other information; (B) Mean PSIC values for DAMs in different evolutionary rate categories. The correlation (r) between mean PSIC values and mean evolutionary rate is 0.96 (P << 0.01 (C) Relation between mean PSIC scores for DAMs and mean PSIC scores for nsnps, by amino acid types (solid circles) and evolutionary rates (open circles). (D) Inverse relationship of the accuracy of DAMs (probably-damaging) and nsnps (benign) with the evolutionary interchangeability of amino acid pairs (original/variant pairs) as captured in the BLOSUM62 matrix. Each data point represents the average of all pairs for a given BLOSUM score, with the error bars displaying the 95% confidence intervals derived from binomial variance of the proportions. BLOSUM scores are log-odds substitution occurrences. Negative BLOSUM scores show amino acid pairs that are found to have a low probability of substitution, whereas a positive score indicates frequently observed amino acid pairs. Seq evol / Deleterious mutations / SVarvio 8

9 Analysis of evolutionarily permissible alleles, EPAs. (A) The relation of the evolutionary rate with the proportion of nsnps present in the set of EPAs in the variable sites (r = 0.91, P < 0.02). (B) The average allele frequencies of nsnps present in EPAs (closed circles) and absent from the set of EPAs (open squares) in variable positions evolving with different rates. Mean allele frequencies are significantly different between two EPA categories for each rate class (P << 0.01). (C) Relationship between nsnps frequency and the percentage of evolutionary time span (%ETS) of the corresponding EPA (r = 0.90, P << 0.01). All non-epa nsnps have an ETS of 0. (D) The biochemical severities of nsnps present in EPAs (closed circles) and absent from EPAs (open squares Seq evol / Deleterious mutations / SVarvio 9

10 Seq evol / Deleterious mutations / SVarvio 10

11 Example 3. Seq evol / Deleterious mutations / SVarvio 11

12 The paper has data from a complete catalog of SNPs were obtained for J. Craig Venter (Google who he is ) and a Han Chinese male from their respective websites ( and and for James D. Watson (Watson Crick) Nonsynonymous and synonymous SNPs were identified using known genes in Ensembl release 49. Coding SNPs in ambiguous reading frames, due to overlap of adjacent genes or frame shifts between known splice variants, or in known pseudogenes, were excluded. Seq evol / Deleterious mutations / SVarvio 12

13 Venn diagram of deleterious mutations identified in J. Craig Venter, James D. Watson, and a Han Chinese individual. The percentage of individual-specific deleterious mutations found in each genome is shown in parentheses. Seq evol / Deleterious mutations / SVarvio 13

14 Characteristics of deleterious mutations. (A) Deleterious mutations (n = 1928) are more likely to occur in recently duplicated genes relative to neutral variants (n = 8287). (B) Mutations at perfectly conserved sites, mutations that cause radical amino acid changes, defined by BLOSUM62 2, and mutations to amino acids that are not observed outside of eutherian mammals are more frequent among rare (n = 807) compared with common deleterious mutations (n = 1121). Seq evol / Deleterious mutations / SVarvio 14

15 Comparison of SIFT, PolyPhen, and the likelihood ratio test (LRT) predictions. (A) Venn diagram of the number of predictions made by the three methods. Probably damaging mutations were used for PolyPhen. Numbers below and above each line are for the complete set of 7534 high-quality variants present within the Venter genome and a subset of 4303 where all three methods generated a prediction, respectively. (B) Overlap between the LRT and SIFT predictions based on the same alignments. Seq evol / Deleterious mutations / SVarvio 15