Negative Selection. Ryan D. Hernandez. 1

Transcription

1 Negative Selection Ryan D. Hernandez 1

2 2

3 Kennewick 9,500 years ago Spirit Cave 9,500-9,400 years ago 6 20,000-15,000 years ago NORTH AMERICA Yana River 30,000 years ago Clovis 13,500 years ago Meadowcroft 19,000-12,000 years ago 40,000 years ago 5 Zhoukoudian (Shandingdong) 11,000 years ago Human colonization of the world 40,000-30,000 years ago 4 AFRICA EUROPE Pestera cu Oase 35,000 years ago Nile River 1 Qafzeh 100,000 years ago Red Sea 2 Omo Kibish Oldest modern human 195,000 years ago 200,000 years ago 70,000-50,000 years ago ASIA Andaman Islands EQUATOR Minatogawa 18,000 years ago Niah Cave 40,000 years ago SOUTH AMERICA Malakunanja 50,000 years ago 15,000-12,000 years ago Monte Verde 14,800 years ago Human Migration Fossil or artifact site Klasies River Mouth 120,000 years ago 40,000 years ago Migration date Generalized route SOURCES: SUSAN ANTON, NEW YORK UNIVERSITY; ALISON BROOKS, GEORGE WASHINGTON UNIVERSITY; PETER FORSTER, UNIVERSITY OF CAMBRIDGE; JAMES F. O'CONNELL, UNIVERSITY OF UTAH; STEPHEN OPPENHEIMER, OXFORD UNIVERSITY; SPENCER WELLS, NATIONAL GEOGRAPHIC SOCIETY; OFER BAR-YOSEF, HARVARD UNIVERSITY NGM MAPS 2006 National Geographic Society. All rights reserved. AUSTRALIA 3 Lake Mungo 45,000 years ago 50,000 years ago 3

4 Growth of European Effective Population Size Ancient Expansion Out of Africa Founding of Europe Exponential Growth 4 Tennessen, J. A. et al. Science (2012).

5 Growth of European Effective Population Size 5 Tennessen, J. A. et al. Science (2012).

6 Vast Majority of human genetic variation is rare Fraction of variants Variants with frequency <1% missense synonymous noncoding neutral model Number of non reference alleles (log scale) Class Fraction of variants Missense 92.6% Synonymous 88.5% Non-coding 82.3% 6

7 Rare variation is highly population-specific 1000 Genomes exon pilot experiment 7 Gravel et al. PNAS (2011)

8 The Effect of Positive Selection Adaptive Neutral Nearly Neutral Mildly Deleterious Fairly Deleterious Strongly Deleterious 8

9 The Effect of Positive Selection Adaptive Neutral Nearly Neutral Mildly Deleterious Fairly Deleterious Strongly Deleterious 9

10 Coding regions tend to have the lowest levels of diversity in the genome 10

11 What are the predominant evolutionary forces driving human genomes?! Eyre-Walker & Keightley (2009) Boyko et al (2008) Williamson et al (2007) ~40% of amino acid were advantageous 10-20% of amino acid were advantageous 10% of the genome affected by selec3ve sweeps 11

12 Diversity levels around a selective sweep Thornton et al (2007): Simulation of patterns of neutral diversity around a selective sweep 12

13 The footprint of adaptive amino acid substitutions Neutral diversity levels Amino acid substitution Goal: compare the pattern around amino acid substitutions to the pattern around synonymous substitutions. Reflects the typical strength of selection Reflects the fraction of amino acid substitutions that are adaptive n substitutions 13

14 Other organisms... Drosophila Sattath et al (2011) estimate ~13% of amino acid substitutions were adaptive. 14

15 Our approach is highly powered to detect positive selection 15

16 Observed Patterns of Diversity Around Human Substitutions 16 Hernandez, et al. Science (2011)

17 The Effect of Negative Selection Adaptive Neutral Nearly Neutral Mildly Deleterious Fairly Deleterious Strongly Deleterious 17

18 Site-Frequency Spectrum Proportion of SNPs Neutral Model Derived count in sample of 16 chromosomes 18

19 Site-Frequency Spectrum Proportion of SNPs Excess of rare mutations Neutral Model Deleterious Derived alleles in sample of 16 chromosomes 19

20 Site-Frequency Spectrum Both population expansions as well as negative selection causes an excess of rare alleles. Proportion of SNPs Excess of rare mutations Neutral Model Deleterious Growth Derived alleles in sample of 16 chromosomes 20

21 Site-Frequency Spectrum Selection is more efficient in large populations, and more deleterious mutations are introduced. Proportion of SNPs Neutral Model Growth Deleterious + Growth Derived count in sample of 16 chromosomes 21

22 Inferred Distribution of Fitness Effects Synonymous Neutral: 10 5 <s<0 Nearly Neut.: 10 4 <s< 10 5 Weak: 10 3 <s< 10 4 Moderate: 10 2 <s< 10 3 Strong: s< % 0.5% 1% 2% 5% 10% 100% 22

23 Observed Effect of Selection 100% Probably Damaging Possibly Damaging Benign 80% % of coding mutations 60% 40% 20% 0% Zach Szpiech (0.10,1.0) (0.01,0.10] ( ] (0,0.001] Thyroid Glioblastoma AF AF AF AF TGP Germline Random Liver Kidney Papillary CLL Kidney Clear Cell Melanoma Bladder Pancreas Breast Ovary Cervix Neuroblastoma 23 Lung Small Cell Head and Neck Cancer Somatic Kidney Chromophobe Myeloma Esophageal Stomach Prostate Lung Squamous Colorectum AML Lymphoma B-Cell Glioma Low Grade Uterus Lung Adeno ALL Medulloblastoma Pilocytic Astrocytoma PolyPhen2

24 Observed Effect of Selection 100% Probably Damaging Possibly Damaging Benign 80% % of coding mutations 60% 40% 20% 0% Zach Szpiech (0.10,1.0) (0.01,0.10] ( ] (0,0.001] Thyroid Glioblastoma AF AF AF AF TGP Germline Random Liver Kidney Papillary CLL Kidney Clear Cell Melanoma Bladder Pancreas Breast Ovary Cervix Neuroblastoma 24 Lung Small Cell Head and Neck Cancer Somatic Kidney Chromophobe Myeloma Esophageal Stomach Prostate Lung Squamous Colorectum AML Lymphoma B-Cell Glioma Low Grade Uterus Lung Adeno ALL Medulloblastoma Pilocytic Astrocytoma PolyPhen2

25 Observed Effect of Selection 100% Probably Damaging Possibly Damaging Benign 80% % of coding mutations 60% 40% 20% 0% (0.10,1.0) (0.01,0.10] ( ] (0,0.001] Thyroid Glioblastoma AF AF AF AF Random Liver Kidney Papillary CLL Kidney Clear Cell Melanoma Bladder Pancreas Breast Ovary Cervix Neuroblastoma Zach Szpiech TGP Germline 25 Lung Small Cell Head and Neck Cancer Somatic Kidney Chromophobe Myeloma Esophageal Stomach Prostate Lung Squamous Colorectum AML Lymphoma B-Cell Glioma Low Grade Uterus Lung Adeno ALL Medulloblastoma Pilocytic Astrocytoma PolyPhen2

26 The Effect of Negative Selection Consequences: Background selection Some proportion of chromosomes eliminated each generation { Decreased effective population size (f0ne) Decreased neutral variation ( f0π ) While neutral variation can be lost, some neutral mutations may increase in frequency 26

27 Background selection (BGS) Definition: The reduction of diversity at a neutral locus due to the effects of linked deleterious selection Can estimate the effect of BGS by comparing observed diversity at neutral sites compared to the level of diversity you would expect under neutrality! π/π 0 27

28 Earlier Theoretical Work Hudson & Kaplan (1995) f 0 =exp U s + R U = deleterious mutation rate s = selection coefficient R = recombination rate 28

29 Effect of Recombination With recombination, neutral mutations can escape the grip of deleterious mutations. 29

30 Multiple Targets of Deleterious Mutations Consider a chromosome composed of neutral loci and deleterious loci. 30

31 Drosophila Chromosome 3 l o : 4 z n oo e 8 Observed Predicted A sh = sh = sh =0.005 A n A D 0.ooOot....,... P..,.... I Physical Position (band) Hudson & Kaplan (1995) 31

32 Distribution of Ultraconserved Elements in the Human Genome 32 Bejerano et al. (2004)

33 Background Selection The effects of the continual removal of deleterious mutations by natural selection on variability at linked sites. Observed human-chimp div. Exp. w/o mut. rate variation Exp w/mut. rate variation 33 McVicker, G. et al. PLoS Genet (2009).

34 Diversity levels around sites subject to natural selection relative diversity θ W θ π θ H genomic location (kb) Thornton et al (2007): Simulation of patterns of neutral diversity around a selective sweep Simulation of patterns of neutral diversity around a 700bp deleterious locus with γ=-5. 34

35 Modeling the data } Observed } Predicted 35

36 Other organisms... Drosophila Chimpanzee Sattath et al (2011) estimate ~13% of amino acid substitutions were adaptive. PanMap Project: full genome sequencing on 10 western chimps yields identical results 36

37 Genetic Load Genetic load is the reduction in population mean fitness due to deleterious mutations compared to a (hypothetical) mutation-free population. Load is the outcome of the evolutionary process of a population. But, unlike other features of genetic variation, it cannot be directly observed. Must be indirectly inferred. 37 Kirk Lohmueller

38 Inferring Genetic Load Empirical counting approaches: Under an additive model, the number of derived deleterious alleles will be proportional to genetic load Under a recessive model, the number of homozygous derived genotypes will be proportional to load 38 Kirk Lohmueller

39 Inferring Genetic Load a S NS b S NS It is widely appreciated that African ancestry individuals have more variation overall than individuals with European ancestry. However, European individuals have more homozygous variation. No. of heterozygous genotypes per individual 3,000 2,500 2,000 c P < P < AA EA AA EA PO PR P < P < No. of homozygous derived genotypes per individual No. of homozygous damaging genotypes per individual 1,600 1, d P < P < AA EA AA EA PO PR P < P < 0.2 Increased load? AA EA AA EA AA EA AA EA 39 Lohmueller, et al. (Nature, 2008)

40 Inferring Genetic Load However, het. and hom. derived alleles appear to balance between African and European Americans. All individuals have same number of Mean derived allele frequency Number per individual, AA: Number per individual, EA: Noncoding 21,421 21,345 Mean derived allele frequencies at different types of SNVs African American Synonymous Benign nonsynonymous 15,401 15,231 5,373 5,338 European American Possibly damaging 1,695 1,682 Probably damaging 2,002 1,969 derived alleles! 40 Lohmueller, et al. (Nature, 2008)

41 Serial Founder Effects on Genetic Load C Ramachandran, et al. (2005) 41 Henn, et al. (2015)

42 The Effect of Negative Selection Adaptive Neutral Nearly Neutral Mildly Deleterious Fairly Deleterious Strongly Deleterious 42

43 BGS Features A B C 0.03 Strong selection 0.03 Weak selection π/π normalized diversity normalized diversity relative diversity ESN YRI LWK MSL GWD CEU IBS GBR FINTSI Population ITU PJLGIH BEB STU KHV CHB CDX JPT CHS ESN YRI LWK MSL GWD CEU IBS GBR FINTSI Population ITU PJLGIH BEB STU KHV CHB CDX JPT CHS 0.25 ESN YRI LWK MSL GWD CEU IBS GBR FINTSI Population ITU PJLGIH BEB STU KHV CHB CDX JPT CHS Neutral sites in Fig All pops, Genomes correcting Project for divergence data: only 20 -non-admixed truncated populations The strength of background selection varies across populations! 43

44 Background Selection & Disease? Background selection drives patterns of genetic variation. But does it matter? Does it have implications for studying disease? To find out, we looked at the NHGRI GWAS database: 44

45 Published Genome-Wide Associations through 12/2013 Published GWA at p 5X10-8 for 17 trait categories NHGRI GWA Catalog

46 Effects of Linked Selection Greater reduction stronger negative selection Less reduction weaker negative selection QQ-plot of the reduction in diversity around GWAS hits compared to background. 46

47 Effects of Linked Selection Greater reduction in diversity around GWAS hits indicates a strong, local burden of negative selection. 47 Maher, et al. Human Heredity (2012).

48 Implications Human evolution has not occurred by single, large-effect mutations. Natural selection operates on complex traits (e.g., disease), not simply mendelian genetics! Before selection Next: Apply detailed evolutionary models to disease! Hard sweep Polygenic adaptation After selection 48 Current Biology Pritchard, Pickrell, & Coop. Curr Biol (2010).

49 Importance of Simulations When studying population genetics, simulations are an incredibly useful tool: Gain insight into evolutionary processes Improve null models of evolution Develop goodness of fit tests for data For complex simulations, the forward simulator sfs_code can be a useful tool! 49