- Research article
- Open Access
Selective constraint, background selection, and mutation accumulation variability within and between human populations
© Hodgkinson et al.; licensee BioMed Central Ltd. 2013
Received: 19 March 2013
Accepted: 11 July 2013
Published: 23 July 2013
Regions of the genome that are under evolutionary constraint across multiple species have previously been used to identify functional sequences in the human genome. Furthermore, it is known that there is an inverse relationship between evolutionary constraint and the allele frequency of a mutation segregating in human populations, implying a direct relationship between interspecies divergence and fitness in humans. Here we utilise this relationship to test differences in the accumulation of putatively deleterious mutations both between populations and on the individual level.
Using whole genome and exome sequencing data from Phase 1 of the 1000 Genome Project for 1,092 individuals from 14 worldwide populations we show that minor allele frequency (MAF) varies as a function of constraint around both coding regions and non-coding sites genome-wide, implying that negative, rather than positive, selection primarily drives the distribution of alleles among individuals via background selection. We find a strong relationship between effective population size and the depth of depression in MAF around the most conserved genes, suggesting that populations with smaller effective size are carrying more deleterious mutations, which also translates into higher genetic load when considering the number of putatively deleterious alleles segregating within each population. Finally, given the extreme richness of the data, we are now able to classify individual genomes by the accumulation of mutations at functional sites using high coverage 1000 Genomes data. Using this approach we detect differences between ‘healthy’ individuals within populations for the distributions of putatively deleterious rare alleles they are carrying.
These findings demonstrate the extent of background selection in the human genome and highlight the role of population history in shaping patterns of diversity between human individuals. Furthermore, we provide a framework for the utility of personal genomic data for the study of genetic fitness and diseases.
Regions of the genome that are under constraint across multiple species have previously been used to identify functional sequences in the human genome [1–3], with the idea being that areas that remain conserved over large evolutionary time scales are likely to be involved in key biological processes. Projects such as ENCODE use information about evolutionary constraint, together with laboratory techniques, to identify putative functional regions in the human genome [2, 4–6] and various metrics have been developed that attempt to formalise the level of constraint at a particular site or region of the genome, sometimes including structural information, to predict the functional consequences of mutations at those sites [7–13]. Since many metrics provide a fine scaled measure of the level of constraint, they may allow identification of the most functionally important sites and guide our understanding of fundamental evolutionary processes; for example less conserved regions may be prime targets for balancing or positive selection [14, 15], whereas more highly conserved sites may show signatures of negative selection [15, 16].
Furthermore, within individual genomes, less highly conserved sites may point to regions that can tolerate mutations without affecting the fitness of the individual, whereas mutations at more highly conserved sites may be lethal. Indeed, numerous studies on disease phenotypes attempt to rank putative disease causing mutations by levels of constraint or predicted impact on protein structure in order to prioritise mutations for further study (for review see ) and it has been suggested that an individual that carries the minor allele within a population at a highly conserved site may have a greater mutation load [18, 19]; an accumulation of such events across the entire genome may well impact on the overall fitness of the individual. Similarly, the frequency of an allele segregating at a site also appears to be a good indicator of functional importance, and it has been shown that variants segregating at non-synonymous sites, which are putatively functional, tend to be at lower frequency than those at synonymous sites, regardless of the function of the gene . Thus, the large proportion of rare variants identified in recent studies [21, 22] may have an impact on fitness.
Recent work has also sought to utilise information from an individual genome to better understand the causes of disease, and many studies have been successful in identifying causal variants on a case-by-case basis [23–26], although often only in the context of Mendelian disorders. Beyond this, ‘personal’ genomic approaches using various types of information from whole-genome sequencing, transcriptomics, proteomics and metabolomics seek to assess disease risk and tailor therapeutics [27, 28]. Although in their infancy, these types of approaches are already proving successful in identifying risk factors and pre-empting symptoms through early treatment [29, 30], highlighting the potential of considering individual genomes in the context of population genetics.
In this study we utilise the fine-scaled nature of one particular measure of evolutionary constraint, Genomic Evolutionary Rate Profiling (GERP), together with the substantial information now available from a large-scale genome sequencing effort, The 1000 Genomes Project, to both validate and utilize comparative and population level information to capture critical genetic events. It has previously been shown that there is a relationship between GERP and derived allele frequency (DAF) using a few individuals or considering a small proportion of the genome [18, 19]. Using polymorphism data across 1,092 individuals from 14 populations in phase 1 of the 1000 Genomes Project  we confirm the relationship between GERP and the minor allele frequency (MAF) of a site, both in coding regions and genome-wide (see Additional file 1: Figures S1-S7). Given that we find a strong relationship between levels of evolutionary constraint and human genetic diversity, we consider how evolutionary constraint can be used to infer patterns of selection and potentially represent genetic fitness in human populations. We make three observations: 1) The depression in average MAF around coding regions is more severe for genes that are most highly conserved across species and decreases as genes become less conserved, a pattern that is repeated at conserved sites in non-coding regions. This direct evidence suggests that negative selection is the primary mechanism shaping patterns of diversity within functionally important regions of the human genome and in the surrounding sequences via background selection, 2) Effective population size correlates with patterns of allele frequencies in the regions surrounding genes, with more extreme depressions in MAF observed in populations with a smaller effective size. This suggests that selection may be less efficient in these populations, allowing more putatively deleterious alleles to segregate, which translates into higher individual mutation load 3) We detect significant differences between some individuals within populations for the number of putatively deleterious rare alleles they are carrying by comparing the distributions of constraint scores for rare alleles on an individual level. This implies that there are differences in the accumulation of putatively deleterious alleles between supposedly healthy individuals.
Variability in constraint distinguishes modes of selection
Variation in mutation accumulation within 1000 Genomes populations
Individuals with significantly different distributions of GERP scores within populations for singletons at nonsynonymous sites
Mann–Whitney U p-value
Variation in mutation accumulation between 1000 Genomes populations
Since we confirm a relationship between GERP and MAF in phase 1 data from the 1000 Genomes Project, measures of constraint can be used to capture critical genetic events. Previously, Hernandez et al. described a reduction in diversity around exons using 1000 Genomes pilot data  and concluded that it is at least partly consistent with background selection. Furthermore, Loehmueller et al. inferred that background selection is shaping human diversity by comparing various genomic parameters in genic and non-genic regions. However, in both cases simulations were used to implicate background selection as the model that best fits the observed diversity data. In this study we present genome-wide empirical evidence of deeper depressions in MAF around the most conserved genes, which is most consistent with purifying selection being the primary mechanism driving allele frequencies in and around (via background selection) coding regions. As a consequence, it may be difficult to detect genuine evidence for selective sweeps and previous scans for such events, which rely on detecting reduced diversity around genes [40–42], may be contaminated with the effects of background selection. Our approach is robust to demographic effects and recombination, since we group genes by a measure that is calculated over long evolutionary time scales and thus is not affected by short term phenomenon, and also to variations in mutation rate, since we consider MAF rather than SNP density in the regions surrounding genes.
We also observe similar patterns in sequences surrounding the most conserved sites in non-coding regions and although Drake et al. have previously shown that alleles segregating within conserved non-coding elements have lower MAF on average, this study provides evidence that conserved non-coding sites are not only under purifying selection but also affect alleles in the surrounding sequences via background selection to shape human diversity. The patterns are less extreme than for coding regions, but that is likely due to the spatial distribution of conserved sites being denser within a gene than in non-coding regions. Furthermore, since the variants analysed here are far away from known genes, it is unlikely that these patterns are driven by linkage to coding sites and although some regions are likely to contain functional elements such as transcription factor binding sites, these results provide yet more evidence that background selection is highly prevalent throughout the genomes of humans.
The demographic history of a population is known to impact on the frequency of alleles segregating among individuals [19, 36, 37]. By comparing 14 worldwide populations from the 1000 Genomes Project we can measure fine-scaled differences in the effect that demography has on the efficiency of selection. First, we observe differences in the depression in MAF around the most conserved genes for different populations that correlates with Ne and subsequently it is likely that deleterious alleles are segregating more readily in populations with a smaller effective size. Over time, selection has been more efficient in removing deleterious variants in populations with larger effective size, such as those from Africa, and this is clear from the reduced MAF of variants segregating in all parts of the genome in African populations (for example see Figure 2A) and the fact that African populations carry fewer putatively deleterious homozygous derived alleles (see Figure 4C). However, it is possible that there is now more potential for selection to act on variants in populations with smaller effective size, since they are at higher frequency due to past demographic events. As a consequence, perhaps as a result of recent population expansion or even a changing environment, it is possible that previously freely segregating alleles are now being driven to lower frequency in regions close to conserved genes, leading to a larger difference in the depression in MAF for populations with a smaller Ne. Second, by considering the average distribution of GERP scores for mutations segregating in each population we find differences across 1000 Genomes populations for the entire positive GERP score range. In general, we find that populations with smaller Ne tend to carry fewer heterozygous alleles, but have accumulated more variants in homozygous form. The implications for fitness depend highly on the level of penetrance at functionally important sites; if the majority of mutations are dominant, individuals from populations with more heterozygous mutations will have a higher genetic load, yet if the majority of mutations act in a recessive manner, which seems most likely since alleles at putatively damaging sites tend to persist in the population in the face of selective pressures, individuals from populations carrying more homozygous derived alleles may carry more load.
Finally, measures of constraint can also be used to classify individual genomes by the accumulation of mutations at functional sites. Within populations we infer very little variation in the accumulation of deleterious mutations across individuals when considering all nonsynonymous variants. However, it has been suggested that rare alleles drive more of the differences in phenotype between individuals, an idea that is supported by a large fraction of ‘missing heritability’ in many genome-wide association studies that focus mostly on common variation (for a discussion on this see ). Considering only rare alleles we detect a number of significantly different pairs of individuals that show a difference in the distributions of putatively deleterious alleles they are carrying. Although these only represent a small fraction of the total number of pairs of individuals compared across all populations, it shows that the framework of comparing distributions of constraint scores on the individual level is able to detect significant differences, even for supposedly ‘healthy’ individuals from the 1000 Genomes populations. Additionally, our approach to detect differences is conservative and may only detect the most extreme differences in mutation accumulation. First, the statistical thresholds applied in our study are likely overly conservative given that the pairwise comparisons of individual distributions of GERP scores are not independent. Second, it is also possible that by limiting this analysis to high-coverage exome data we are not accounting for the effects exerted by sites further away from the exons that may contribute further to fitness differences between individuals. Third, we predict that individual differences in the distributions of putatively deleterious variants are likely to be higher in diseased groups. As such, individuals that, for example, develop complex diseases may be more likely to come from the tails of the distribution.
Using polymorphism data to infer fitness is not a new idea [45–47], however by confirming the relationship between interspecific constraint and MAF it allows us to make inferences about key genetic processes and gain a greater insight into how selection operates in human populations. By doing so we have highlighted the extent of background selection across the human genome and the interplay of selection and demographic history in shaping human diversity. Furthermore, the utility of this relationship also allows us to make predictions about disease and fitness based on a score that is not dependent on having population level data. By applying this method and using a stringent statistical threshold we were able to detect differences between ‘healthy’ individuals from the 1000 Genomes populations. Subsequently, it may be promising to consider the constraint profiles of individuals with complex diseases, a strategy which may be effective in capturing the signatures of damaging mutations that don’t necessarily occur at the same sites for a given phenotype (and are thus missed by more traditional approaches), but instead act in a cumulative manner across the genome. This approach is a useful first step to characterise the nature of mutation load on an individual level, and may have important implications in studying human fitness and disease.
Phase 1 data from the 1000 Genomes Project were downloaded from the 1000 genomes ftp site (http://ftp-trace.ncbi.nih.gov/1000genomes/ftp/) and consists of 1092 individuals from 14 populations. 1000 genomes data used here consists of two types: whole-genome low coverage data that is sequenced to an average depth of 2-6× and high coverage exon-targeted data that is sequenced to an average coverage of 50-100×. The false discovery rate of exome and non-coding SNPs is 1.6% and 1.8% respectively . Details on 1000 Genomes populations, sequencing protocol, snp calling, and validation can be found in the 1000 Genomes pilot  and phase 1  publications. Low coverage variant call format (vcf) files were used to calculate allele frequency data across all, and within each, population. For high coverage exome data, SNPs falling within targetted exons were extracted from exome vcf files and allele frequency data was collected only for these sites. Throughout the analysis, sites were annotated using the SeattleSeq SNP Annotation tool (http://snp.gs.washington.edu/SeattleSeqAnnotation134/). GERP++ (referred to as GERP in the main text) scores, which measure evolutionary constraint based on the number of substitutions in orthologous sequences of up to 34 mammalian species [9, 13], and elements were downloaded from the Sidow laboratory website (http://mendel.stanford.edu/SidowLab/downloads/gerp/index.html). Throughout our analyses when GERP scores are directly compared to MAF we exclude any sites with a GERP score of exactly 0; these represent sites where there are less than three species that could be aligned in the calculation of the GERP score  and are therefore uninformative. Throughout the analysis, whenever multiple distributions are compared we use a Bonferroni correction for multiple testing.
Polymorphism patterns in coding and non-coding regions
The genomic locations of each gene were obtained from version Hs37.2 of the CCDS dataset (http://www.ncbi.nlm.nih.gov/projects/CCDS/CcdsBrowse.cgi) as these represent a collaborative and high quality annotation of protein coding regions. To calculate the average MAF of polymorphisms surrounding coding regions, genes were sorted into quartiles based on the average GERP score per gene, which represents the sum of GERP scores for all coding sites (regardless of whether they contain a SNP), divided by the total number of coding sites, and the average MAF was calculated in one hundred non-overlapping windows of 10 kb in the sequences surrounding each group of genes, using low coverage data across all populations. The process was also repeated by splitting genes into ten groups based on average GERP score. To measure the depth of depression in MAF surrounding genes we calculated the difference between the average MAF in the windows spanning from 500 KB to 1 MB and -500 KB to -1 MB relative to the position of each gene, as it is in these regions where the average MAF appears to level off, and the lowest MAF value in the central 100 KB. To consider the patterns of polymorphism around the most highly conserved genes for each population we calculated the average MAF in regions surrounding the top 10% of genes by average GERP scores as before and calculated the depth of the depression in MAF around these genes in the same manner as described above. These values were then compared to estimates of Ne for as many of the old world populations as were available in a study by Mele et al. (included estimates for the YRI, LWK, CEU, GBR, TSI, CHB, JPT and ASW populations). We excluded the IBS population from this analysis due to the small number of individuals sampled.
For non-coding regions we consider only those sites that are at least 200 KB away from a known coding region, as determined by the ensembl gene data set (http://genome.ucsc.edu/), which represents probably the most exhaustive set of coding region annotations. In these regions, SNPs were sorted by GERP score into quartiles and the average MAF was calculated in one hundred non-overlapping windows of 100 bp in the sequences surrounding each group of mutations, using low coverage data across all populations. To consider whether the decrease in average MAF around conserved and non-conserved sites is significant, we tested whether the average MAF closest to the focal sites (in the surrounding 200 bp) is significantly different to the average MAF in more distal regions (in the windows spanning from 5 kb to 10 kb and from -5 kb to -10 kb, relative to the focal SNP).
Comparison of individuals
To compare individual GERP distributions we constructed a distribution of GERP scores for each individual by including all nonsynonymous sites where the individual carried the minor allele. We assumed that fitness was additive, and thus included the GERP score twice if an individual was homozygous for the minor allele at a given site. Examples of GERP score distributions are shown in Additional file 1: Figures S24 and S25. To compare 1000 Genomes populations, we averaged the distribution of GERP scores per individual within each population for both the proportion and the absolute number of mutations falling into each GERP bin. To compare groups of populations we again averaged the distributions of all individuals within each group. We focussed on the positive GERP score range, since it is likely that only these mutations have an impact on minor allele frequency and thus fitness (see Additional file 1). For homozygous sites we use the derived allele (inferred from a six-way primate alignment obtained from the ensembl website, http://www.ensembl.org), since using the minor allele will introduce a bias due to the different sample sizes for each continental group. To compare the average number of singletons carried by individuals between populations, we took the same approach as detail above, but randomly selected the same number of individuals from each population to ensure that a singleton represented the same MAF in each population and therefore was not biased by sample size. Again, the IBS population was excluded from this analysis due to the small number of individuals sequenced.
We would like to thank Julie Hussin and Gil McVean for useful discussions and the Canadian Foundation for Innovation, Genome Quebec and the Ministry of Development, Exploration, Innovation and Economics (MDEIE) grants to PA for funding.
- Boffelli D, McAuliffe J, Ovcharenko D, Lewis KD, Ovcharenko I, Pachter L, Rubin EM: Phylogenetic shadowing of primate sequences to find functional regions of the human genome. Science. 2003, 299 (5611): 1391-1394. 10.1126/science.1081331.View ArticlePubMedGoogle Scholar
- Consortium TEP: A user’s guide to the encyclopedia of DNA elements (ENCODE). PLoS Biol. 2011, 9 (4): e1001046-10.1371/journal.pbio.1001046.View ArticleGoogle Scholar
- Cooper SJ, Trinklein ND, Anton ED, Nguyen L, Myers RM: Comprehensive analysis of transcriptional promoter structure and function in 1% of the human genome. Genome Res. 2006, 16 (1): 1-10.PubMed CentralView ArticlePubMedGoogle Scholar
- Birney E, Stamatoyannopoulos JA, Dutta A, Guigo R, Gingeras TR, Margulies EH, Weng Z, Snyder M, Dermitzakis ET, Thurman RE: Identification and analysis of functional elements in 1% of the human genome by the ENCODE pilot project. Nature. 2007, 447 (7146): 799-816. 10.1038/nature05874.View ArticlePubMedGoogle Scholar
- Ep C: The ENCODE (ENCyclopedia Of DNA Elements) Project. Science. 2004, 306 (5696): 636-640.View ArticleGoogle Scholar
- Bernstein BE, Birney E, Dunham I, Green ED, Gunter C, Snyder M: An integrated encyclopedia of DNA elements in the human genome. Nature. 2012, 489 (7414): 57-74. 10.1038/nature11247.View ArticleGoogle Scholar
- Adzhubei IA, Schmidt S, Peshkin L, Ramensky VE, Gerasimova A, Bork P, Kondrashov AS, Sunyaev SR: A method and server for predicting damaging missense mutations. Nat Methods. 2010, 7 (4): 248-249. 10.1038/nmeth0410-248.PubMed CentralView ArticlePubMedGoogle Scholar
- Cooper GM, Stone EA, Asimenos G, Green ED, Batzoglou S, Sidow A: Distribution and intensity of constraint in mammalian genomic sequence. Genome Res. 2005, 15 (7): 901-913. 10.1101/gr.3577405.PubMed CentralView ArticlePubMedGoogle Scholar
- Davydov EV, Goode DL, Sirota M, Cooper GM, Sidow A, Batzoglou S: Identifying a high fraction of the human genome to be under selective constraint using GERP++. PLoS Comput Biol. 2010, 6 (12): e1001025-10.1371/journal.pcbi.1001025.PubMed CentralView ArticlePubMedGoogle Scholar
- Pollard KS, Hubisz MJ, Rosenbloom KR, Siepel A: Detection of nonneutral substitution rates on mammalian phylogenies. Genome Res. 2010, 20 (1): 110-121. 10.1101/gr.097857.109.PubMed CentralView ArticlePubMedGoogle Scholar
- Ramensky V, Bork P, Sunyaev S: Human non-synonymous SNPs: server and survey. Nucleic Acids Res. 2002, 30 (17): 3894-3900. 10.1093/nar/gkf493.PubMed CentralView ArticlePubMedGoogle Scholar
- Siepel A, Bejerano G, Pedersen JS, Hinrichs AS, Hou M, Rosenbloom K, Clawson H, Spieth J, Hillier LW, Richards S: Evolutionarily conserved elements in vertebrate, insect, worm, and yeast genomes. Genome Res. 2005, 15 (8): 1034-1050. 10.1101/gr.3715005.PubMed CentralView ArticlePubMedGoogle Scholar
- Stone EA, Sidow A: Physicochemical constraint violation by missense substitutions mediates impairment of protein function and disease severity. Genome Res. 2005, 15 (7): 978-986. 10.1101/gr.3804205.PubMed CentralView ArticlePubMedGoogle Scholar
- Bustamante CD, Fledel-Alon A, Williamson S, Nielsen R, Hubisz MT, Glanowski S, Tanenbaum DM, White TJ, Sninsky JJ, Hernandez RD: Natural selection on protein-coding genes in the human genome. Nature. 2005, 437 (7062): 1153-1157. 10.1038/nature04240.View ArticlePubMedGoogle Scholar
- Hellmann I, Mang Y, Gu Z, Li P, de la Vega FM, Clark AG, Nielsen R: Population genetic analysis of shotgun assemblies of genomic sequences from multiple individuals. Genome Res. 2008, 18 (7): 1020-1029. 10.1101/gr.074187.107.PubMed CentralView ArticlePubMedGoogle Scholar
- Cai JJ, Macpherson JM, Sella G, Petrov DA: Pervasive hitchhiking at coding and regulatory sites in humans. PLoS Genet. 2009, 5 (1): e1000336-10.1371/journal.pgen.1000336.PubMed CentralView ArticlePubMedGoogle Scholar
- Cooper GM, Shendure J: Needles in stacks of needles: finding disease-causal variants in a wealth of genomic data. Nat Rev Genet. 2011, 12 (9): 628-640. 10.1038/nrg3046.View ArticlePubMedGoogle Scholar
- Cooper GM, Goode DL, Ng SB, Sidow A, Bamshad MJ, Shendure J, Nickerson DA: Single-nucleotide evolutionary constraint scores highlight disease-causing mutations. Nat Methods. 2010, 7 (4): 250-251. 10.1038/nmeth0410-250.PubMed CentralView ArticlePubMedGoogle Scholar
- Goode DL, Cooper GM, Schmutz J, Dickson M, Gonzales E, Tsai M, Karra K, Davydov E, Batzoglou S, Myers RM: Evolutionary constraint facilitates interpretation of genetic variation in resequenced human genomes. Genome Res. 2010, 20 (3): 301-310. 10.1101/gr.102210.109.PubMed CentralView ArticlePubMedGoogle Scholar
- Blekhman R, Man O, Herrmann L, Boyko AR, Indap A, Kosiol C, Bustamante CD, Teshima KM, Przeworski M: Natural selection on genes that underlie human disease susceptibility. Current Biology: CB. 2008, 18 (12): 883-889. 10.1016/j.cub.2008.04.074.PubMed CentralView ArticlePubMedGoogle Scholar
- Keinan A, Clark AG: Recent explosive human population growth has resulted in an excess of rare genetic variants. Science. 2012, 336 (6082): 740-743. 10.1126/science.1217283.PubMed CentralView ArticlePubMedGoogle Scholar
- Nelson MR, Wegmann D, Ehm MG, Kessner D, St Jean P: An abundance of rare functionalvariants in 202 drugtargetgenessequenced in 14,002 people. Science. 2012, 337: 100-104. 10.1126/science.1217876.PubMed CentralView ArticlePubMedGoogle Scholar
- Ng SB, Bigham AW, Buckingham KJ, Hannibal MC, McMillin MJ, Gildersleeve HI, Beck AE, Tabor HK, Cooper GM, Mefford HC: Exome sequencing identifies MLL2 mutations as a cause of Kabuki syndrome. Nat Genet. 2010, 42 (9): 790-793. 10.1038/ng.646.PubMed CentralView ArticlePubMedGoogle Scholar
- Ng SB, Buckingham KJ, Lee C, Bigham AW, Tabor HK, Dent KM, Huff CD, Shannon PT, Jabs EW, Nickerson DA: Exome sequencing identifies the cause of a mendelian disorder. Nat Genet. 2010, 42 (1): 30-35. 10.1038/ng.499.PubMed CentralView ArticlePubMedGoogle Scholar
- Bainbridge MN, Wiszniewski W, Murdock DR, Friedman J, Gonzaga-Jauregui C, Newsham I, Reid JG, Fink JK, Morgan MB, Gingras MC: Whole-genome sequencing for optimized patient management. Sci Transl Med. 2011, 3 (87): 87re83-View ArticleGoogle Scholar
- Roach JC, Glusman G, Smit AF, Huff CD, Hubley R, Shannon PT, Rowen L, Pant KP, Goodman N, Bamshad M: Analysis of genetic inheritance in a family quartet by whole-genome sequencing. Science. 2010, 328 (5978): 636-639. 10.1126/science.1186802.PubMed CentralView ArticlePubMedGoogle Scholar
- Chen R, Snyder M: Systems biology: personalized medicine for the future?. Curr Opin Pharmacol. 2012, 12 (5): 623-628. 10.1016/j.coph.2012.07.011.PubMed CentralView ArticlePubMedGoogle Scholar
- Cordero P, Ashley EA: Whole-genome sequencing in personalized therapeutics. Clin Pharmacol Ther. 2012, 91 (6): 1001-1009. 10.1038/clpt.2012.51.View ArticlePubMedGoogle Scholar
- Ashley EA, Butte AJ, Wheeler MT, Chen R, Klein TE, Dewey FE, Dudley JT, Ormond KE, Pavlovic A, Morgan AA: Clinical assessment incorporating a personal genome. Lancet. 2010, 375 (9725): 1525-1535. 10.1016/S0140-6736(10)60452-7.PubMed CentralView ArticlePubMedGoogle Scholar
- Chen R, Mias GI, Li-Pook-Than J, Jiang L, Lam HY, Chen R, Miriami E, Karczewski KJ, Hariharan M, Dewey FE: Personal omics profiling reveals dynamic molecular and medical phenotypes. Cell. 2012, 148 (6): 1293-1307. 10.1016/j.cell.2012.02.009.PubMed CentralView ArticlePubMedGoogle Scholar
- Abecasis GR, Auton A, Brooks LD, DePristo MA, Durbin RM: An integrated map of genetic variation from 1,092 human genomes. Nature. 2012, 491: 56-65. 10.1038/nature11632.View ArticlePubMedGoogle Scholar
- Hernandez RD, Kelley JL, Elyashiv E, Melton SC, Auton A, McVean G, Sella G, Przeworski M: Classic selective sweeps were rare in recent human evolution. Science. 2011, 331 (6019): 920-924. 10.1126/science.1198878.PubMed CentralView ArticlePubMedGoogle Scholar
- Leffler EM, Bullaughey K, Matute DR, Meyer WK, Segurel L, Venkat A, Andolfatto P, Przeworski M: Revisiting an old riddle: what determines genetic diversity levels within species?. PLoS Biol. 2012, 10 (9): e1001388-10.1371/journal.pbio.1001388.PubMed CentralView ArticlePubMedGoogle Scholar
- Sattath S, Elyashiv E, Kolodny O, Rinott Y, Sella G: Pervasive adaptive protein evolution apparent in diversity patterns around amino acid substitutions in Drosophila simulans. PLoS Genet. 2011, 7 (2): e1001302-10.1371/journal.pgen.1001302.PubMed CentralView ArticlePubMedGoogle Scholar
- Ward LD, Kellis M: Evidence of abundant purifying selection in humans for recently acquired regulatory functions. Science. 2012, 337: 1675-1678. 10.1126/science.1225057.PubMed CentralView ArticlePubMedGoogle Scholar
- Tishkoff SA, Williams SM: Genetic analysis of African populations: human evolution and complex disease. Nat Rev Genet. 2002, 3 (8): 611-621.PubMedGoogle Scholar
- Lohmueller KE, Indap AR, Schmidt S, Boyko AR, Hernandez RD, Hubisz MJ, Sninsky JJ, White TJ, Sunyaev SR, Nielsen R: Proportionally more deleterious genetic variation in European than in African populations. Nature. 2008, 451 (7181): 994-997. 10.1038/nature06611.PubMed CentralView ArticlePubMedGoogle Scholar
- Consortium G: A map of human genome variation from population-scale sequencing. Nature. 2010, 467 (7319): 1061-1073. 10.1038/nature09534.View ArticleGoogle Scholar
- Lohmueller KE, Albrechtsen A, Li Y, Kim SY, Korneliussen T, Vinckenbosch N, Tian G, Huerta-Sanchez E, Feder AF, Grarup N: Natural selection affects multiple aspects of genetic variation at putatively neutral sites across the human genome. PLoS Genet. 2011, 7 (10): e1002326-10.1371/journal.pgen.1002326.PubMed CentralView ArticlePubMedGoogle Scholar
- Akey JM: Constructing genomic maps of positive selection in humans: where do we go from here?. Genome Res. 2009, 19 (5): 711-722. 10.1101/gr.086652.108.PubMed CentralView ArticlePubMedGoogle Scholar
- Kaplan NL, Hudson RR, Langley CH: The “hitchhiking effect” revisited. Genetics. 1989, 123 (4): 887-899.PubMed CentralPubMedGoogle Scholar
- Smith JM, Haigh J: The hitch-hiking effect of a favourable gene. Genet Res. 1974, 23 (1): 23-35. 10.1017/S0016672300014634.View ArticlePubMedGoogle Scholar
- Drake JA, Bird C, Nemesh J, Thomas DJ, Newton-Cheh C, Reymond A, Excoffier L, Attar H, Antonarakis SE, Dermitzakis ET: Conserved noncoding sequences are selectively constrained and not mutation cold spots. Nat Genet. 2006, 38 (2): 223-227. 10.1038/ng1710.View ArticlePubMedGoogle Scholar
- Gibson G: Rare and common variants: twenty arguments. Nat Rev Genet. 2011, 13 (2): 135-145.PubMed CentralView ArticleGoogle Scholar
- Keightley PD, Eyre-Walker A: Joint inference of the distribution of fitness effects of deleterious mutations and population demography based on nucleotide polymorphism frequencies. Genetics. 2007, 177 (4): 2251-2261. 10.1534/genetics.107.080663.PubMed CentralView ArticlePubMedGoogle Scholar
- Eyre-Walker A, Woolfit M, Phelps T: The distribution of fitness effects of new deleterious amino acid mutations in humans. Genetics. 2006, 173 (2): 891-900. 10.1534/genetics.106.057570.PubMed CentralView ArticlePubMedGoogle Scholar
- Boyko AR, Williamson SH, Indap AR, Degenhardt JD, Hernandez RD, Lohmueller KE, Adams MD, Schmidt S, Sninsky JJ, Sunyaev SR: Assessing the evolutionary impact of amino acid mutations in the human genome. PLoS Genet. 2008, 4 (5): e1000083-10.1371/journal.pgen.1000083.PubMed CentralView ArticlePubMedGoogle Scholar
- Mele M, Javed A, Pybus M, Zalloua P, Haber M, Comas D, Netea MG, Balanovsky O, Balanovska E, Jin L: Recombination gives a new insight in the effective population size and the history of the old world human populations. Mol Biol Evol. 2012, 29 (1): 25-30. 10.1093/molbev/msr213.View ArticlePubMedGoogle Scholar
This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.