Skip to main content


Front. Mol. Biosci., 18 March 2021
Sec. Biological Modeling and Simulation
Volume 8 - 2021 |

Low Diversity of Human Variation Despite Mostly Mild Functional Impact of De Novo Variants

  • 1Department of Biochemistry and Microbiology, Rutgers University, New Brunswick, NJ, United States
  • 2Department of Genetics, Rutgers University, Piscataway, NJ, United States

Non-synonymous Single Nucleotide Variants (nsSNVs), resulting in single amino acid variants (SAVs), are important drivers of evolutionary adaptation across the tree of life. Humans carry on average over 10,000 SAVs per individual genome, many of which likely have little to no impact on the function of the protein they affect. Experimental evidence for protein function changes as a result of SAVs remain sparse – a situation that can be somewhat alleviated by predicting their impact using computational methods. Here, we used SNAP to examine both observed and in silico generated human variation in a set of 1,265 proteins that are consistently found across a number of diverse species. The number of SAVs that are predicted to have any functional effect on these proteins is smaller than expected, suggesting sequence/function optimization over evolutionary timescales. Additionally, we find that only a few of the yet-unobserved SAVs could drastically change the function of these proteins, while nearly a quarter would have only a mild functional effect. We observed that variants common in the human population localized to less conserved protein positions and carried mild to moderate functional effects more frequently than rare variants. As expected, rare variants carried severe effects more frequently than common variants. In line with current assumptions, we demonstrated that the change of the human reference sequence amino acid to the reference of another species (a cross-species variant) is unlikely to significantly impact protein function. However, we also observed that many cross-species variants may be weakly non-neutral for the purposes of quick adaptation to environmental changes, but may not be identified as such by current state-of-the-art methodology.


The vast majority of human genomic variants are single nucleotide variants (SNVs) (Durbin, et al., 2010). Coding region variants trivially make up a much smaller fraction of all variation than do non-coding variants (Lander, et al., 2001). However, the former affect protein structure/function and thus have a disproportionate effect of molecular function of the cellular machinery. For example, each individual genome contains approximately ten thousand of nsSNVs (non-synonymous SNVs, which change the amino acid sequence (Shen, et al., 2013), a combination of which is responsible for a variety of observed phenotypes, including disease (Peterson, et al., 2013; Hassan, et al., 2019). Establishing the effect of any given nsSNV, however, is a difficult task. One gold-standard experimental approach is saturated mutagenesis (SM) (Wells, et al., 1985), which induces variants of interest in a gene and measures the change of resulting protein molecular function. However, SM is too inefficient to thoroughly study the entirety of genomic variation. While the recent development of the deep mutational scanning techniques (Fowler and Fields, 2014) has facilitated high-throughput functional analysis of coding variants, experimental annotation of millions of possible nsSNVs in human genome still remains elusive, Given the inefficiency of large-scale experimental measurements computational methods for variant effect interpretation offer a plausible alternative for the exploration of the human genome.

Genome-wide association study (GWAS) (Visscher, et al., 2017), as well as the post hoc polygenetic risk scoring (Torkamani, et al., 2018), has been extensively deployed to establish the associations between complex phenotypes and genetic background. GWAS results, however, are by definition association (not causation) evaluations and are specific to a phenotype. Evaluating variant effect on molecular function requires a different type of techniques. Machine learning models are often used to classify variants into neutral/deleterious (e.g., CADD (Kircher, et al., 2014), DANN (Quang, et al., 2014)), benign/pathogenic (e.g., MutPred2 (Pejaver, et al., 2017), PhD-SNP (Capriotti and Fariselli, 2017)), stable/unstable (e.g., I-Mutant2.0 (Capriotti, et al., 2005)), and effect/no-effect (e.g., Envision (Gray, et al., 2018), SNAP (Bromberg and Rost, 2007), SNAP2 (Hecht, et al., 2015)).

Conservation of residues across homologs is often assumed to indicate structural or functional importance of these residues and their intolerance to substitution (Kumar, et al., 2009). Thus, conservation is used as a proxy for variant effect evaluation, e.g. by tools like SIFT (Ng, 2003) and PROVEAN (Choi and Chan, 2015), and has been widely incorporated as one of the features in many other variant effect predictors (e.g., CADD, DANN, SNAP, PhD-SNP). We previously proposed the concept of cross-species variants (CSV) analysis (Mahlich, et al., 2017), which is similar to but intuitively different from conservation evaluation. Conservation can be directly computed from a multiple sequence alignment (MSA) of homologs built for CSV analysis. However, CSVs specifically describe only the difference between two orthologous reference sequences and do not summarize overall conservation. For example, if the amino acid residue at a specific position of a human protein is glycine, and if the MSA-corresponding position of a mouse ortholog is leucine, then a CSV at this position of this human protein would be glycine > leucine. If this particular glycine > leucine variant also occurs in the human population, the variant is an observed CSV. As a rule, these types of human variants, i.e. to residues found in other species, have been presumed to carry no effect on protein function (Ng and Henikoff, 2001; Ng, 2003; Calabrese, et al., 2009; Adzhubei, et al., 2010; Shihab, et al., 2013; Kircher, et al., 2014; Schwarz, et al., 2014; Pejaver, et al., 2020). After all, if an amino acid is observed in a functional protein of an ortholog, its substitution into the human version cannot be expected to drastically affect the function.

Pathogenic amino acid substitutions are, on average, functionally more radical than CSVs (Briscoe, et al., 2004; Miller and Kumar, 2001; Subramanian and Kumar, 2006). A study of the rhodopsin protein, for example, has revealed that variants corresponding to CSVs among vertebrates are less likely to be pathogenic (Briscoe, et al., 2004). Of the 7,293 human-mouse CSVs in 687 human disease genes, only a small fraction (2.2%) corresponds to known human disease variants (Waterston, et al., 2002). Other studies have also estimated that only about 10% of the human-to-other-species amino acid substitutions are involved in disease (Kondrashov, et al., 2002; Subramanian and Kumar, 2006). However, this type of logic may have precipitated a self-fulfilling prophecy, where CSVs that were annotated to be neutral in the development of variant effect-prediction methods (Bromberg and Rost, 2007; Adzhubei, et al., 2010; Kircher, et al., 2014; Pejaver, et al., 2017; Pejaver, et al., 2020) could bias the prediction of previously unseen CSV effects toward neutrality. While unlikely pathogenic, intuitively, a yeast version of the human protein may be less or more functionally efficient, may have unexpected structural effects given the rest of the protein sequence, or may participate in different/additional molecular pathways. Incorporating taxonomic distances between the species included in an alignment improves identification of variant effect (Malhis, et al., 2019). A deeper evaluation of CSVs in terms of their functional effects may thus be warranted.

We previously reported (Mahlich, et al., 2017) that amino acid CSVs have less predicted molecular functional effects on average than human variation recorded by the Exome Aggregation Consortium (Lek, et al., 2016). Here we extend this analysis, by investigating human variation in 1,265 proteins that have orthologs in 20 species spread across the eukaryotic branch of the tree of life. We evaluate the differences in functional impact of the variants that are observed within the human population against those not yet observed, but genetically possible. We show that common variants favor less conserved positions than rare variants, indicating a potential need for flexibility in sequence for the purposes of environment-driven adaptation. We also assessed the differences in predicted impacts on the function of human protein of cross-species variants (CSVs; variant amino acid is found in one of the 20 orthologs) and non-CSVs. We finally suggest that the lack of functional impact of CSVs might be overestimated by the current presumption that evolutionary persistence suggests functional neutrality.


Variant Collection

A total of 93,437 human protein-coding transcripts were extracted from GRCh37 p.13 assembly (Church, et al., 2011) in Ensembl BioMart (Kinsella, et al., 2011). From these, we selected 22,346 longest transcripts per gene. We removed transcripts from patches/alternate sequences (, retaining 19,971 transcripts. For these, we artificially generated all possible non-synonymous single nucleotide variants (73,813,560 nsSNVs). We downloaded the Genome Aggregation Database (gnomAD v2, exome data (Karczewski, et al., 2020) and, using SAMtools (Li, et al., 2009), mapped the generated nsSNVs to the corresponding variant allele frequencies where available. We thus collected 2,951,998 variants with gnomAD allele count = 1 and 2,561,015 gnomAD variants with larger allele counts. The remaining 68,300,547 variants were not found in gnomaAD. Note that at the time of data collection gnomAD v2 was the most current version available. The current v3 version of gnomAD is only slightly different in relevant content as its reference genome, GRCh38, recapitulates 99% of GRCh37 (Pan, et al., 2019) and most differences between the two are in the non-coding regions, an area outside this study. We thus expect that results and conclusions reported here would not change with this update.

The allele counts of all nsSNVs causing the same single amino acid substitution (SAV) were further aggregated to represent the frequencies of individual SAVs (Eq. 1):


where for any codon, n1nk are counts of the specific SAV-causing alleles and N is the total numbers of sequenced alleles of that codon. Note that in the process of aggregation some observed (allele count >1) SAVs could be derived from the aggregation from multiple single allele nsSNVs. The aggregation of nsSNV frequencies into SAV frequencies, resulted in 2,564,652 observed (allele count >1), 2,918,355 singletons (allele count =1), and 60,601,329 synthetic SAVs in the 19,971 transcripts. Observed variants were further classified as common (freq (SAV) ≥ 0.01) and rare (freq (SAV) <0.01).

Collection of Cross-Species Variants

Cross-species variants (CSVs) are the amino acid differences between the human reference protein sequence and the orthologous protein sequence of another species. For example, if the amino acid residue at the third position of the human protein sequence P is leucine, and if the amino acid residue at the same position in mouse orthologous protein sequence is glycine, then the CSV at this position in P would be L3G. Aiming to span the tree of life with species available in Ensembl BioMart (GRCh37), we considered 20 species for CSV analysis: yeast (Saccharomyces cerevisiae), worm (Caenorhabdiis elegans), fruitfly (Drosophila melanogaster), zebrafish (Danio rerio), xenopus (Xenopus laevis), anole lizard (Anolis carolinensis), chicken (Gallus gallus), platypus (Ornithorhynchus anatinus), opossum (Monodelphis domestica), dog (Canis familiaris), pig (Sus scrofa), dolphin (Tursiops truncatus), mouse (Mus musculus), rabbit (Oryctolagus cuniculus), tree shrew (Tupaia belangeri), tarsier (Carlito syrichta), gibbon (Nomascus leucogenys), gorilla (Gorilla gorilla), bonobo (Pan paniscus), and chimpanzee (Pan troglodytes). We identified the evolutionary distances of these species from Homo sapiens using the TimeTree database (Kumar, et al., 2017). All protein coding DNA sequences (CDS) of these 20 species were downloaded from the Ensembl database (Zerbino, et al., 2018) (release 94, For every human protein coding transcript T, the available orthologous CDS for each of the 20 species was extracted using the Ensembl BioMart (Kinsella, et al., 2011). Each species may have multiple protein coding sequences orthologous to T, but only the longest one was selected. We performed multiple sequence alignment (MSA) of T and all its orthologs using PRANK (Löytynoja and Goldman, 2005), which translates CDS and aligns protein sequences. Of the 19,971 human transcripts in our set, 1,342 had a full set of the 20 species orthologs in the MSA. In these transcripts (940,328 amino acids) there were 183,540 observed (49,541 CSVs/133,999 non-CSVs), 228,774 singleton (52,550 CSVs/176,224 non-CSVs), and 5,118,164 synthetic SAVs (873,011 CSVs/4, 245,153 non-CSVs).

Cross-Species Variant Effect Predictions

We generated SNAP (Bromberg and Rost, 2007) predictions for all variants in the 1,342 transcripts. SNAP predictions could be made for 1,265 of the proteins; a set of 77 sequences (832,697 variants) did not yield any predictions due to SNAP’s sequence length constraints (63 sequences), variant to sequence mapping errors (3 sequences), and unresolvable errors in the SNAP input feature extraction pipeline (11 sequences) as well as an additional 46,840 variants on the remaining proteins. Note that, as in all other proteins in our set, the vast majority (93%) of these variants were synthetic (4% singleton and 3% observed), suggesting that our analyses of effect trends should be largely unaffected by this missing subset. Thus, the final SNAP effect prediction dataset contained 4,650,941 variants in 791,040 positions among 1,265 proteins (Supplementary Table S1). Note that for this study we used the original SNAP tool instead of the more recent version SNAP2 (Hecht, et al., 2015). There were two reasons for this choice: 1) SNAP2 used OMIM (Amberger, et al., 2009) disease variants in training, a choice which does not directly reflect variant functional effects, and 2) SNAP effect prediction reliability scores strongly correlate with the functional effect strength (Bromberg, et al., 2013), an observation that has not been explicitly made for SNAP2.

Variant Conservation Scores

For all residues of all proteins in our set we computed two types of conservation scores:

1. We used the PredictProtein pipeline (Yachdav, et al., 2014) to compute ConSurf (Glaser, et al., 2003) conservation scores. ConSurf scores are based on MSAs of up to 150 homologous sequences. Reported scores are normalized so that the average score over all residues of one protein is zero and the standard deviation is one. Lower scores indicate more conserved residues.

2. We extracted from the list of SNAP input features the position-specific independent counts (PSIC) (Sunyaev, et al., 1999). PSIC scores reflect per-residue position-specific weights considering the MSA-based overall level of sequence similarity.

We only retained the conservation scores for those variant positions (104,375) that had both ConSurf and PSIC annotations.

Conservation scores across variant subsets were used only once per variant position in the subset. That is, if two rare CSVs were present at one protein position, conservation for this position was only used once toward establishing the distribution of the rare CSV dataset. On the other hand, if a position contained both a common CSV and a rare CSV, the conservation score was included separately into distributions of each subset.

Per-Residue Funtrp Scores

funtrp (Miller, et al., 2019) is a prediction tool that assesses the expected range of functional effects due to the possible variants at a given protein position. funtrp classifies sequence positions as neutral (most variants at this position show weak or no effect), rheostatic (a full range of variant effects) and toggle (most variants have a severe effect). funtrp was trained with deep-mutagenesis data and uses sequence-based features to differentiate between the three residue classes. We used our publicly available webservice ( to identify funtrp classes for each position of 1,254 of our protein sequences; predictions for the remaining 11 sequences were not returned by the method.

Evaluating Statistical Significance Distribution Differences

For all comparisons of score distributions (e.g. SNAP scores) across variant classes (e.g. rare vs common), we re-sampled said distributions 1,000 times to extract 1,000 observations each time. For each resampling instance, we performed the Kolmogorov-Smirnov test to test the equity of the distributions, reporting the associated p-value; the median p-val over 1,000 iterations was reported.

Results and Discussion

Many Variants Remain to be Sequenced

Single amino acid variant (SAV) effects were determined by SNAP (Bromberg and Rost, 2007) (predicted score range for our variants [−94, + 88]), with negative scores identifying neutral SAVs (no change in function) and positive scores identifying non-neutrals/effect SAVs (activating or deactivating changes in function); score absolute values indicate the reliability of prediction and, for non-neutral variants, the size of the effect (Bromberg, et al., 2013). Note that our definition of effect does not specify whether the effect is detrimental or beneficial to the organism, but rather reports on the change in wild-type functionality of the affected protein.

Overall, more variants were predicted to be neutral than effect, with some difference in fractions of effect variants between synthetic, singleton, and observed variant subsets (Supplementary Table S1). The distribution of synthetic variant SNAP scores was significantly different from that of singleton and observed variant scores (Kolmogorov-Smirnov, KS, test p-value; synthetic vs. singleton = 8.7e−04, synthetic vs. observed = 1.1e−06), while singleton and observed scores were only slightly different (singleton vs. observed p-val = 0.14). For synthetic variants (median SNAP score = −12; Figure 1A), i.e. those that have not been seen in the population, the majority (60%) were predicted to be neutral. These variants are, thus, technically observable and may be identified in future sequencing efforts. Those 40% of the synthetic variants predicted to have an effect, had on average more severe impact than the effect variants seen in the human population (combined observed and singleton sets; 31% effect; Figure 1B). Increased predicted effect of synthetic variants is in line with the expectation that these are subject to purifying selection.


FIGURE 1. Higher prevalence of effect among the synthetic as compared to observed and singleton variants. (A) The distribution of effect predictions for synthetic variants (dark orange; median SNAP = -12) is significantly more right-shifted toward effect (SNAP ≥0; horizontal line) than that of observed variants (green; median SNAP = -24) and singletons (yellow; median SNAP = -20). For all distributions, however, the majority of predictions are neutral (SNAP <0) (B) Additionally, synthetic variants show an enrichment of moderate to severe functional effects (SNAP ≥ 23) vs. singletons and observed variants.

Earlier (Bromberg, et al., 2013), we observed a similar trend of more effect variants in the synthetic than in the observed/singleton set; i.e. 55% effect in synthetic SAVs in 100 randomly selected enzymes vs. 46% effect variants in 1000Genomes data (Auton, et al., 2015). However, the fractions of both the synthetic and observed/singleton of effect variants in our earlier study were significantly higher than the corresponding numbers reported here. Furthermore, the SNAP scores of the synthetic variants reported here and those in Bromberg were significantly different (p-val 4.0e−15); the scores of our combined observed/singleton variants also differed from the scores of 1000Genomes variants (p-val = 3.0e−12).

While 1000Genomes variants were observed in 85% (1,072 of 1,265) of the transcripts used in this study, our variant set for these proteins was larger, suggesting improved sequencing coverage and accounting for some effect prediction differences. Notably, only 36% of the 1000Genome variants in our proteins had an effect–in line with the 31% effect variants in our observed/singleton set and 10% less than in the complete 1000Genomes variant set. Furthermore, of the set of 100 enzymes used in the Bromberg et al. study to generate synthetic variants, only four were present in our protein set. Thus, the difference in effect scores between our earlier study and the current work is most likely due to the specific genes/proteins selected for this study. Genes/proteins in our set have orthologs in each of our selected species, i.e. these are likely ancient and rarely disease-associated (Moreau and Tranchevent, 2012). As the functions of these proteins are important for organism survival, they likely harbor the variants necessary for environment-driven functional adaptation but do not allow for severe disruption upon mutation (Key, et al., 2014; Key, et al., 2014; Ilardo and Nielsen, 2018; Rees, et al., 2020). While the variants in these proteins may still be extremely deleterious, less than three percent in our set were of severe effect (SNAP score ≥50; 130,870 variants; 7.2% of all effect variants) and, as expected, most were synthetic (123,962 variants, 3% of all synthetic), with few found in the population (6,908 variants, 2% of all singleton/observed).

Given these fractions of effect variants, we expect at least half a million (neutral synthetic CSVs) and possibly over four million (any synthetic neutrals and milds/moderates) variants to be possibly observable, i.e. they may be found with more sequencing. As the genes considered here are likely ancient and evolutionarily optimized to resist drastic changes upon mutation, this 12-fold possible increase in the observable variants (vs those already observed) suggests an upper bound of increase in the number of observed/singleton SAVs that may be collected in the future.

Common Variants May Drive Environmental Adaptations

Despite the fact that common variation is, by definition, widespread in the population, trivially, the vast majority of unique population variants are rare. Variant effect trends are therefore dominated by observations for rare variants, effectively drowning out signal from common variants. We thus aimed to elucidate the difference between common (≥1% SAV frequency) and rare variants. For this part of the analysis we excluded from consideration the singleton variants, which are a special case of rare variation and may be disproportionately sequencing errors. We note that common variants are unlikely to be very deleterious/disease-causing as they would not stay common. On the other hand, variants that have no impact on function (neutrals) and very weak nonneutrals can be fixed in the population at about the same rate via genetic drift (Kimura and Ohta, 1969).

We also considered the differences between observed cross-species variants (CSVs) and non-CSVs (Methods). We expected different evolutionary drivers for the existence of different variant types (e.g. common CSV vs. rare non-CSV) and, in turn, potential differences in their impact on protein function. Note that variants labeled as non-CSV may still be present in the orthologs of species that were not assessed here. However, using more species could also reduce our total protein set if some of the currently used transcripts are absent in the new species transcriptome.

Common variants are as frequently CSVs as non-CSVs (691 CSVs vs. 683 non-CSVs, Supplementary Table S2). For common CSVs (reference substituted by variant amino acid), the human reference amino acid is present in a minority (40%) of all 20 species orthologs, but more frequently in mammals (48%) and great apes (59%; Table 1). Note that these fractions were computed as the number of shared reference amino acids of all residues aligned, e.g. if for one variant ten of 15 orthologs aligned at the variant position have the human reference amino acid, while for another variant four of the 20 orthologs do, the total fraction of reference amino acid across these variants is 40% (14/35). Given these fairly low fractions, the variant amino acids of common CSVs are possibly ancestral, i.e. human variant amino acid could have been the reference of a potential ancestor. Thus, for humans reinstating the ancestral residue at this position is likely to be detrimental, as it would otherwise remain fixed as reference.


TABLE 1. Prevalence of human reference amino acids in CSV positions across orthologs.

For common non-CSVs the corresponding fractions of reference amino acids across orthologs are 75% (all), 86% (mammals), and 98% (apes; Table 1). Thus, variant amino acids of common non-CSVs likely represent somewhat newer evolutionary developments and are 1) likely to be beneficial (still effect!) for humans as a whole but may have not been around long enough to become the reference or 2) are non-universal adaptations to persistent environmental conditions, e.g. ethnicity-specific variants (Rees, et al., 2020).

Unlike common variants, rare CSV variants are nearly three-fold less commonplace than non-CSVs. However, just as for common variants, rare non-CSV reference amino acids are present in orthologs at a higher frequency than CSV references (83% non-CSV vs. 68% CSV). The preponderance of non-CSV reference amino acids across all species highlights these variants as likely of recent origin, and therefore possibly of any amount (a full range) of effect. Rare CSV variant amino acids, on the other hand, may be ancestral, although the likelihood of this is greatly diminished as compared to common variants (68% rare vs. 40% common reference amino acid across orthologs). If they are ancestral, their extensive elimination from the population would suggest deleterious effects (purifying selection). Independent appearances of the variant in human (as rare variant) and in another species (as reference) is unlikely, but also possible. In this case, the variant amino acid would likely be neutral or slightly deleterious in human.

Further comparing the frequencies of occurrence of reference amino acids across orthologs suggests that rare variants occur at more conserved positions than common variants; reference amino acids of CSVs vs. non-CSVs were present across all species for 68% vs. 83% for rare variants and 40% vs. 75% for common ones. Evaluation of conservation of variant positions using ConSurf (Glaser, et al., 2003) confirmed this observation (Figure 2; lower score means more conserved position; KS p-val CSV rare vs. common = 3.2e−08, non-CSV rare vs. common = 1.5e−09). The protein positions harboring rare variants were on average more conserved (103,609 positions; median ConSurf score = −0.11) than positions with common variants (1,013 positions; median ConSurf score = 0.36). Note that there are only a few 247) positions for which both rare and common variants are present, and these are also only weakly conserved (median ConSurf score = 0.34). A similar trend was observed using PSIC scores (Sunyaev, et al., 1999) of variant positions (Figure 2; higher score means more conserved position; median PSIC score of: rare = 0.80, common = 0.55, both = 0.59; KS p-val CSV rare vs. common = 4.4e−16, non-CSV rare vs. common = 4.2e−07).


FIGURE 2. Rare variants more frequently found in conserved protein positions. Rare variants (blue) are more frequently found in conserved positions (ConSurf ≤0) than common variants (purple). Furthermore, non-CSVs (hatched fill) are more frequently present in conserved positions than CSVs (solid fill). Similarly, rare variants carry higher PSIC scores than common variants.

This is an unexpected result, as variants in conserved positions are often assumed to have an effect, while rare variants, both CSV and non-CSV, are less frequently predicted to have an effect than the corresponding common variants (rare vs. common effect variants: 10% vs. 20% CSVs and 36% vs. 40% non-CSVs; Supplementary Table S2). Here we point out that more severe effect (several high score outliers) vs. more frequent effects (many variants have some effect) indicate different score distributions but may result in similar summary statistics (e.g. distribution means). Thus, although common variants have an effect more frequently than rare variants (Figure 3A), the former are less frequently severe (SNAP ≥50; 6% rare vs 3.6% common effect variants; Figure 3B). Furthermore, rare non-CSVs are enriched in moderate effect variants (SNAP ≥25) vs. common non-CSVs that are mostly mild. Common CSVs, on the other hand, carry more moderate effects than rare CSVs (Figure 3B). Note that as CSVs in general score tend to be predicted neutral more often than non-CSVs (Supplementary Figure S1, the preponderance of high-scoring common CSVs vs. non-CSVs reinforces the likely adaptational value of common CSVs proposed above. The propensity of rare variants to cause severe effects highlights them as likely culprits of disease. However, rare variants make up nearly three quarters of variation overall and are clearly not restricted to being disease-causing. In fact, they cover a complete range of effect–from strongly effect to reliably neutral (Figure 3A).


FIGURE 3. Common variants are more frequently effect than rare variants, but rare variants are more frequently severe. (A) The distribution of common CSV and non-CSV predictions (purple) is more right-shifted (more effect) than that of rare variants (blue). Furthermore, (B) rare non-CSVs (blue dashed line) are more often of moderate and severe effect than common non-CSVs (purple dashed line). However, common CSVs are more often of mild-moderate effect than rare CSVs. Due to small numbers of variants at each SNAP score (x-axis), frequencies are calculated in intervals of 10, e.g. 0 ≤ SNAP <10; points are centered in the interval.

In an effort to validate our observations of effects of common variants we used funtrp (Miller, et al., 2019) – a method that trained to recognize the range of variant effects possible at a single protein position. It classifies positions into 1) neutrals, where most variants have no effect on protein function, 2) toggles, where most variants have severe or knockout effects, and 3) rheostats, where variants cover a range of effect strengths. Overall, funtrp classes reflected SNAP predictions well; median SNAP scores of variants in neutral, rheostat, and toggle positions were −33, −18, and 12, respectively. Common variants were more often found in neutral positions as compared to rare ones (66% vs. 56%, Figure 4A). However, of the effect positions (i.e. rheostat and toggle), common variants preferred rheostats (77% common vs. 69% rare variants). As most toggles are conserved (Miller, et al., 2019), this observation is in line with the above finding that rare variants 1) are more likely than common ones to be in conserved positions and 2) that they carry more severe functional effects. Common variants in rheostatic positions, on the other hand, were likely used in evolution to fine-tune functions of affected proteins.


FIGURE 4. Common variants prefer neutral positions more than rare variants. Neutral positions (green shading) are enriched in common variants (purple) as opposed to rare variants (blue) (66% vs. 56% - actual variant counts shown as numbers in the bars). The fraction of rare variants in rheostatic positions (blue shading) is higher than the corresponding fraction of common variants. However, the ratio of common variants in rheostat positions vs. toggles (pink shading) is higher than that of rare variants.

Variant Effect Reflects Evolutionary Time of Reference Amino Acid Origin

We asked whether variant effect is related to the likely evolutionary time of appearance of the human reference. For each species X, we collected all effect variants in our dataset where the human and X reference amino acids were identical. For mammals, the median effect strengths of the variants affecting these positions were similar. For other species, however, the variant effect was correlated with increasing evolutionary distance between human and the specific species (Figure 5). This correlation held true for CSVs and non-CSVs, as well as for synthetic, singleton or observed variants.


FIGURE 5. Impact of variants sharing reference amino acids with other species correlates with evolutionary distance. Mean SNAP scores (y‐axis) are computed for CSV (green line) and non‐CSV (red line) synthetic (left panel), singleton (middle panel), and common (right panel) variants, according to per-species human-shared reference amino acids. Species are placed along the x‐axis (logarithmic) according to their distance to ancestor shared with human.

Notably for non-CSVs, median effect scores increased more rapidly over evolutionary time than for CSVs. This trend was expected, as variants whose reference amino acids are present in evolutionarily distant species likely disproportionately affect conserved ancestral amino acids. For example, a shared human and yeast reference amino acid is likely present across all or most species in our set. Thus, a CSV at this position (if say, fly amino acid is different) would indicate some flexibility at the position, but a non-CSV would elicit the functional effect associated with the disruption of stringent conservation. However, we found that conservation of the variant position is unlikely the sole contributor to the observed effect gradient. The trend, albeit less pronounced, remained visible if only the variants in positions of low conservation (ConSurf score ≥0.5) were used in the analysis (Supplementary Table S2). Importantly, a clear distinction between CSVs and non-CSVs was also still evident, indicating that even in non-conserved positions CSVs and non-CSVs are distinguishable.

Self-Fulfilling Prophecy: Are Cross-Species Variants Really Neutral?

As mentioned previously, CSVs were less often predicted to have an effect than mutations to an amino acid that is not present in other species (non-CSVs); this observation was true for both synthetic and observed human variation (Supplementary Table S1). The absolute difference in median SNAP scores between CSVs and non-CSVs was 38 (mean =30) for synthetic variants and 32 (mean =27) for the observed–a full 14–21% of the entire scoring range ([−94, + 88]). CSV scores are most often neutral across all three categories of variation (i.e. synthetic, singleton, observed), while the distribution of non-CSV scores is much more widespread (Figure 6). An biological explanation for this observation is that CSVs are indeed more likely to be neutral with respect to protein function, as is expected from their persistence in homologs (Kondrashov, 1995; Sunyaev, et al., 2001). However, another explanation for this stark difference could then be the fact that SNAP was trained using a dataset of cross-species orthologous enzyme variants deemed neutral. Only 30 of these enzymes were in our set of 1,265 proteins and, thus, are not expected to dramatically impact our observations. However, if SNAP learned input feature patterns specific to CSVs, others could be labeled neutral without ever being seen in training. Thus, SNAP could fail to recognize CSVs that have a functional impact without introducing the organism to selection pressures, i.e. functionally non-neutral, but physiologically neutral. In fact, these may be the so called “fuel for evolution” (Bromberg, et al., 2013; Fu, et al., 2013) – the pool of weakly nonneutral variants necessarily present in the population for the purposes of quick adaptation to a changing environment.


FIGURE 6. Observed variants enriched in mild effects. Both observed CSVs (green solid line) as well as non-CSVs (green dashed line) are enriched in mild effect variants over their synthetic counterparts (orange CSVs–solid line, non-CSVs–dashed line).

In our earlier work we had determined a SNAP threshold of 23 as the upper functional impact limit to the absence of physiological visibility. We have confirmed this threshold for this data set as well, as the score where the fraction of possible/expected variants exceeds those observed (Figure 6). Of the observed effect CSVs, 76% are in this mild functional effect range, while 58% of all effect non-CSVs are as well. This significantly larger fraction of mild effect CSVs than effect non-CSVs suggests that the former are more likely the functional variants necessary for adaptation.

Although CSVs are more frequently (vs non-CSVs) predicted to be mild in effect, they also vastly outnumber non-CSVs in the neutral score range. Curiously, there is almost no difference between the synthetic and observed CSV score distributions. However, only 5% of all possible CSVs in our set are observed in the human population–not much more (percentage-wise) than all possible non-CSVs (3%; and fewer in the absolute sense with ∼42K observed CSVs and ∼144K observed non-CSVs). It thus remains unclear whether functional constraints are indeed weaker for (often biochemically similar substitutions of amino acids in) CSVs.

Evaluating prediction bias is difficult in the absence of a gold-standard data set and one of neutral CSVs doesn’t exist. While funtrp uses site conservation as input, it was not trained to recognize individual variant effect and thus could be used to elucidate our findings. In other words, funtrp forgoes the broad generalization of assigning neutrality to cross-species variants on the basis of the evolution-guided inference (e.g. SNAP and other methods (Ng and Henikoff, 2001; Ng, 2003; Calabrese, et al., 2009; Adzhubei, et al., 2010; Shihab, et al., 2013; Kircher, et al., 2014; Schwarz, et al., 2014; Pejaver, et al., 2020).

In line with our earlier observations, funtrp found that most protein positions in our set are neutral. The distribution of synthetic, singleton, and observed variants across position classes was very similar for CSVs (62/30/8% neutral/rheostat/toggle; Supplementary Table S3). Non-CSVs maintained an average 50/33/17% ratio of neutrals/rheostats/toggles, with observed non-CSVs more frequently found in neutral and rheostat positions than singletons or synthetic variants (Supplementary Table S3). Thus, both CSVs and non-CSVs were about as likely to localize to rheostatic positions, but non-CSVs were less frequently found in neutrals and twice as often in toggles. Note that while not all variants in neutral positions are necessarily functionally neutral, and non-neutral positions may have some neutral variants, only 62% of observed CSVs are found in neutral positions, while SNAP predicts 90% of observed CSVs to be functionally neutral.

Two conclusions from these results are salient: 1) as expected, CSVs are indeed more frequently neutral than non-CSVs and 2) it appears that SNAP (and likely other predictors) tends to overestimate CSV neutrality. Thus, we suggest that cross-species variants may carry mild to moderate functional effects and should be evaluated accordingly.


We investigated a set of single amino acid substitutions (SAVs) in evolutionarily persistent, likely ancient, proteins, i.e. those that we expect to be optimized to tolerate variation. We found that despite the enrichment in severe effects of synthetic vs observed variants, a large proportion of SAVs might still be found upon broader sequencing of the population. Moreover, we expect that only a small fraction of variants that have yet to be sequenced will have a severe impact and/or be disease causing. We further observed that common variants favor poorly conserved sites. This lower conservation, indicative of more tolerance toward variation, might be providing enough “wiggle” room for environmental adaptations. Rare variants are, on the other hand, are often found in more conserved positions, explaining their enrichment in severe effects in comparison to common SAVs. Curiously, it appears that our ancient proteins have been optimized to the point where disrupting a conserved site does not immediately cause a functional disruption, as seen in the majority of rare variants predicted to be neutral. Finally, we suggest that cross-species variants (CSVs) might indeed be more often neutral than non-CSVs however not as consistently as currently expected. Ultimately, however, this question can only be answered through the development of an effect predictor that is does not make a priori assumptions of CSV neutrality and, which is somewhat harder, does not rely on conservation.

Data Availability Statement

The original contributions presented in the study are included in the article/Supplementary Material, further inquiries can be directed to the corresponding author.

Author Contributions

YB and YM conceived the presented ideas and designed the study. YM conducted the variant effect analysis. MM produced the effect predictions. ZZ produced the variant and cross-species dataset. YM, MM, and ZZ contributed equally to this work as first authors. All authors contributed to writing the manuscript.


YB, MM, YM, and ZZ were supported by the NIH (National Institutes of Health) grant U01 GM115486 and YB was also supported by the NIH grant R01 MH115958.

Conflict of Interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

The reviewer PLM declared a past co-authorship with one of the authors YB to the handling editor.


We would like to thank Burkhard Rost (Technical University Munich) for all discussions of the topics covered here. We are grateful to Nir Ben-Tal (Tel-Aviv University), Daisuke Kihara (Purdue University), and Arun Prasad Pandurangan (Cambridge Biomedical Campus) for the invitation to contribute our thoughts to this collection of articles. Many thanks go to the reviewers, whose comments and suggestions helped improve the paper. Finally, we would also like to express gratitude to all researchers working in genome analysis and to the people who have made their data available to science.

Supplementary Material

The Supplementary Material for this article can be found online at:


Adzhubei, I. A., Schmidt, S., Peshkin, L., Ramensky, V. E., Gerasimova, A., Bork, P., et al. (2010). A method and server for predicting damaging missense mutations. Nat. Methods 7 (4), 248–249. doi:10.1038/nmeth0410-248

PubMed Abstract | CrossRef Full Text | Google Scholar

Amberger, J., Bocchini, C. A., Scott, A. F., and Hamosh, A. (2009). McKusick's Online mendelian inheritance in man (OMIM). Nucleic Acids Res. 37, D793–D796. doi:10.1093/nar/gkn665

PubMed Abstract | CrossRef Full Text | Google Scholar

Auton, A., Auton, A., Brooks, L. D., Durbin, R. M., Garrison, E. P., Kang, H. M., et al. (2015). A global reference for human genetic variation. Nature 526 (7571), 68–74. doi:10.1038/nature15393

PubMed Abstract | CrossRef Full Text | Google Scholar

Briscoe, A. D., Gaur, C., and Kumar, S. (2004). The spectrum of human rhodopsin disease mutations through the lens of interspecific variation. Gene 332, 107–118. doi:10.1016/j.gene.2004.02.037

PubMed Abstract | CrossRef Full Text | Google Scholar

Bromberg, Y., Kahn, P. C., and Rost, B. (2013). Neutral and weakly nonneutral sequence variants may define individuality. Proc. Natl. Acad. Sci. USA 110 (35), 14255. doi:10.1073/pnas.1216613110

PubMed Abstract | CrossRef Full Text | Google Scholar

Bromberg, Y., and Rost, B. (2007). SNAP: predict effect of non-synonymous polymorphisms on function. Nucleic Acids Res. 35 (11), 3823–3835. doi:10.1093/nar/gkm238

PubMed Abstract | CrossRef Full Text | Google Scholar

Calabrese, R., Capriotti, E., Fariselli, P., Martelli, P. L., and Casadio, R. (2009). Functional annotations improve the predictive score of human disease-related mutations in proteins. Hum. Mutat. 30 (8), 1237–1244. doi:10.1002/humu.21047

PubMed Abstract | CrossRef Full Text | Google Scholar

Capriotti, E., Fariselli, P., and Casadio, R. (2005). I-Mutant2.0: predicting stability changes upon mutation from the protein sequence or structure. Nucleic Acids Res. 33 (Suppl. l–2), W306–W310. doi:10.1093/nar/gki375

PubMed Abstract | CrossRef Full Text | Google Scholar

Capriotti, E., and Fariselli, P. (2017). PhD-SNPg: a webserver and lightweight tool for scoring single nucleotide variants. Nucleic Acids Res. 45 (W1), W247–W252. doi:10.1093/nar/gkx369

PubMed Abstract | CrossRef Full Text | Google Scholar

Choi, Y., and Chan, A. P. (2015). PROVEAN web server: a tool to predict the functional effect of amino acid substitutions and indels. Bioinformatics 31 (16), 2745–2747. doi:10.1093/bioinformatics/btv195

PubMed Abstract | CrossRef Full Text | Google Scholar

Church, D. M., Schneider, V. A., Graves, T., Auger, K., Cunningham, F., et al. (2011). Modernizing Reference Genome Assemblies. PLoS Biol. 9 (7), e1001091. doi:10.1371/journal.pbio.1001091

PubMed Abstract | CrossRef Full Text | Google Scholar

Durbin, R. M., Abecasis, G. R., Altshuler, D., Auton, A., Brooks, L. D., Durbin, R. M., et al. (2010). A map of human genome variation from population-scale sequencing. Nature 467 (7319), 1061–1073. doi:10.1038/nature09534

PubMed Abstract | CrossRef Full Text | Google Scholar

Fowler, D. M., and Fields, S. (2014). Deep mutational scanning: a new style of protein science. Nat. Methods 11 (8), 801–807. doi:10.1038/nmeth.3027

PubMed Abstract | CrossRef Full Text | Google Scholar

Fu, W., O'Connor, T. D., Jun, G., Kang, H. M., Abecasis, G., Leal, S. M., et al. (2013). Analysis of 6,515 exomes reveals the recent origin of most human protein-coding variants. Nature 493 (7431), 216–220. doi:10.1038/nature11690

PubMed Abstract | CrossRef Full Text | Google Scholar

Glaser, F., Pupko, T., Paz, I., Bell, R. E., Bechor-Shental, D., Martz, E., et al. (2003). ConSurf: identification of functional regions in proteins by surface-mapping of phylogenetic information. Bioinformatics 19 (1), 163–164. doi:10.1093/bioinformatics/19.1.163

PubMed Abstract | CrossRef Full Text | Google Scholar

Gray, V. E., Hause, R. J., Luebeck, J., Shendure, J., and Fowler, D. M. (2018). Quantitative missense variant effect prediction using large-scale mutagenesis data. Cell Syst 6 (1), 116–e3. doi:10.1016/j.cels.2017.11.003

PubMed Abstract | CrossRef Full Text | Google Scholar

Hassan, M. S., Shaalan, A. A., Dessouky, M. I., Abdelnaiem, A. E., and ElHefnawi, M. (2019). A review study: computational techniques for expecting the impact of non-synonymous single nucleotide variants in human diseases. Gene 680, 20–33. doi:10.1016/j.gene.2018.09.028

PubMed Abstract | CrossRef Full Text | Google Scholar

Hecht, M., Bromberg, Y., and Rost, B. (2015). Better prediction of functional effects for sequence variants. BMC genomics 16 Suppl 8 (S8), S1. doi:10.1186/1471-2164-16-S8-S1

PubMed Abstract | CrossRef Full Text | Google Scholar

Ilardo, M., and Nielsen, R. (2018). Human adaptation to extreme environmental conditions. Curr. Opin. Genet. Development 53, 77–82. doi:10.1016/j.gde.2018.07.003

CrossRef Full Text | Google Scholar

Karczewski, K. J., Francioli, L. C., Tiao, G., Cummings, B. B., Alföldi, J., Wang, Q., et al. (2020). The mutational constraint spectrum quantified from variation in 141,456 humans. Nature 581 (7809), 434–443. doi:10.1038/s41586-020-2308-7

PubMed Abstract | CrossRef Full Text | Google Scholar

Key, F. M., Peter, B., Dennis, M. Y., Huerta-Sánchez, E., Tang, W., Prokunina-Olsson, L., et al. (2014) Selection on a variant associated with improved viral clearance drives local, adaptive pseudogenization of interferon lambda 4 (IFNL4). Plos Genet. 10 (10), e1004681. doi:10.1371/journal.pgen.1004681

PubMed Abstract | CrossRef Full Text | Google Scholar

Key, F. M., Teixeira, J. C., de Filippo, C., and Andrés, A. M. (2014) Advantageous diversity maintained by balancing selection in humans. Curr. Opin. Genet. Dev. 29:45–51. doi:10.1016/j.gde.2014.08.001

PubMed Abstract | CrossRef Full Text | Google Scholar

Kimura, M., and Ohta, T. (1969). The average number of generations until fixation of a mutant gene in a finite population. Genetics ;61 (3), 763-771.

PubMed Abstract | CrossRef Full Text | Google Scholar

Kinsella, R. J., Kähäri, A., Haider, S., Zamora, J., Proctor, G., Spudich, G., et al. (2011). Ensembl BioMarts: a hub for data retrieval across taxonomic space. Database 2011, bar030. doi:10.1093/database/bar030

PubMed Abstract | CrossRef Full Text | Google Scholar

Kircher, M., Witten, D. M., Jain, P., O’Roak, B. J., Cooper, G. M., and Shendure, J. (2014) A general framework for estimating the relative pathogenicity of human genetic variants. Nat. Genet. 46 (3):310. doi:10.1038/ng.2892

PubMed Abstract | CrossRef Full Text | Google Scholar

Kondrashov, A. S., Sunyaev, S., and Kondrashov, F. A. (2002). Dobzhansky-Muller incompatibilities in protein evolution. Proc. Natl. Acad. Sci. USA 99(23), 14878–14883. doi:10.1073/pnas.232565499

PubMed Abstract | CrossRef Full Text | Google Scholar

Kondrashov, A. S. (1995). Contamination of the genome by very slightly deleterious mutations: why have we not died 100 times over? J. Theor. Biol. 175 (4), 583–594. doi:10.1006/jtbi.1995.0167

PubMed Abstract | CrossRef Full Text | Google Scholar

Kumar, P., Henikoff, S., and Ng, P. C. (2009). Predicting the effects of coding non-synonymous variants on protein function using the SIFT algorithm. Nat. Protoc. 4 (7), 1073–1081. doi:10.1038/nprot.2009.86

PubMed Abstract | CrossRef Full Text | Google Scholar

Kumar, S., Stecher, G., Suleski, M., and Hedges, S. B. (2017). TimeTree: a resource for timelines, timetrees, and divergence times. Mol. Biol. Evol. 34 (7), 1812–1819. doi:10.1093/molbev/msx116

PubMed Abstract | CrossRef Full Text | Google Scholar

Löytynoja, A., and Goldman, N. (2005). An algorithm for progressive multiple alignment of sequences with insertions. Proc. Natl. Acad. Sci. 102 (30), 10557–10562. doi:10.1073/pnas.0409137102

CrossRef Full Text | Google Scholar

Lander, E. S., Linton, L. M., Birren, M., Nusbaum, C., Zody, M. C., Baldwin, J., et al. (2001). Initial sequencing and analysis of the human genome. Nature 409 (6822), 860–921. doi:10.1038/35057062

PubMed Abstract | CrossRef Full Text | Google Scholar

Lek, M., Karczewski, K. J., Minikel, E. V., Samocha, K. E., Banks, E., Fennell, T., et al. (2016). Analysis of protein-coding genetic variation in 60,706 humans. Nature 536 (7616), 285–291. doi:10.1038/nature19057

PubMed Abstract | CrossRef Full Text | Google Scholar

Li, H., Handsaker, B., Wysoker, A., Fennell, T., Ruan, J., Homer, N., et al. (2009). The sequence alignment/map format and SAMtools. Bioinformatics 25 (16), 2078–2079. doi:10.1093/bioinformatics/btp352

PubMed Abstract | CrossRef Full Text | Google Scholar

Mahlich, Y., Reeb, J., Hecht, M., Schelling, M., De Beer, T. A. P., Bromberg, Y., et al. (2017). Common sequence variants affect molecular function more than rare variants? Sci. Rep. 7 (1), 1608–1613. doi:10.1038/s41598-017-01054-2

PubMed Abstract | CrossRef Full Text | Google Scholar

Malhis, N., Jones, S. J. M., and Gsponer, J. (2019). Improved measures for evolutionary conservation that exploit taxonomy distances. Nat. Commun. 10 (1), 1556. doi:10.1038/s41467-019-09583-2

PubMed Abstract | CrossRef Full Text | Google Scholar

Miller, M., Vitale, D., Kahn, P. C., Rost, B., and Bromberg, Y. (2001). Funtrp: identifying protein positions for variation driven functional tuning. Nucleic Acids Res. 47 (21), e142. doi:10.1093/nar/gkz818

CrossRef Full Text | Google Scholar

Miller, M. P., and Kumar, S. (2001). Understanding human disease mutations through the use of interspecific genetic variation. Hum. Mol. Genet. 10 (21), 2319–2328. doi:10.1093/hmg/10.21.2319

PubMed Abstract | CrossRef Full Text | Google Scholar

Moreau, Y., and Tranchevent, L. C. (2012). Computational tools for prioritizing candidate genes: boosting disease gene discovery. Nat. Rev. Genet. 13 (8), 523–536. doi:10.1038/nrg3253

PubMed Abstract | CrossRef Full Text | Google Scholar

Ng, P. C., and Henikoff, S. (2001). Predicting deleterious amino acid substitutions. Genome Res. 11(5):863–874.doi:10.1101/gr.176601

PubMed Abstract | CrossRef Full Text | Google Scholar

Ng, P. C., and Henikoff, S. (2019). SIFT: predicting amino acid changes that affect protein function. Nucleic Acids Res. 31 (13):3812–3814. doi:10.1093/nar/gkg509

CrossRef Full Text | Google Scholar

Pan, B., Kusko, R., Xiao, W., Zheng, Y., Liu, Z., Xiao, C., et al. (2019) Similarities and differences between variants called with human reference genome HG19 or HG38. BMC Bioinformatics 20 (2), 101. doi:10.1186/s12859-019-2620-0

PubMed Abstract | CrossRef Full Text | Google Scholar

Pejaver, V., Urresti, J., Lugo-Martinez, J., Pagel, K. A., Lin, G. N., Nam, H. J., et al. 2020. Inferring the molecular and phenotypic impact of amino acid variants with MutPred2. Nat. Commun. 11 (1), 5918. doi:10.1038/s41467-020-19669-x

PubMed Abstract | CrossRef Full Text | Google Scholar

Pejaver, V., Urresti, J., Lugo-Martinez, J., Pagel, K. A., Lin, G. N., Nam, H.-J., et al. (2017). MutPred2: inferring the molecular and phenotypic impact of amino acid variants. doi:10.1101/134981

CrossRef Full Text | Google Scholar

Peterson, T. A., Doughty, E., and Kann, M. G. (2013). Towards precision medicine: advances in computational approaches for the analysis of human variants. J. Mol. Biol. 425 (21), 4047–4063. doi:10.1016/j.jmb.2013.08.008

PubMed Abstract | CrossRef Full Text | Google Scholar

Quang, D., Chen, Y., and Xie, X. (2014). DANN: a deep learning approach for annotating the pathogenicity of genetic variants. Bioinformatics 31 (5), 761–763. doi:10.1093/bioinformatics/btu703

PubMed Abstract | CrossRef Full Text | Google Scholar

Rees, J. S., Castellano, S., and Andrés, A. M. (2020). The genomics of human local adaptation. Trends Genet. 36 (6), 415–428. doi:10.1016/j.tig.2020.03.006

PubMed Abstract | CrossRef Full Text | Google Scholar

Schwarz, J. M., Cooper, D. N., Schuelke, M., and Seelow, D. (2014). MutationTaster2: mutation prediction for the deep-sequencing age. Nat. Methods 11 (4), 361–362. doi:10.1038/nmeth.2890

PubMed Abstract | CrossRef Full Text | Google Scholar

Shen, H. (2013). Comprehensive characterization of human genome variation by high coverage whole-genome sequencing of forty four caucasians. PLoS One 8 (4), e59494. doi:10.1371/journal.pone.0059494

PubMed Abstract | CrossRef Full Text | Google Scholar

Shihab, H. A., Gough, J., Cooper, D. N., Stenson, P. D., Barker, G. L., Edwards, K. J., et al. (2013). Predicting the functional, molecular, and phenotypic consequences of amino acid substitutions using hidden Markov models. Hum. Mutat. ;34 (1), 57–65. doi:10.1002/humu.22225

PubMed Abstract | CrossRef Full Text | Google Scholar

Subramanian, S., and Kumar, S. (2006). Evolutionary anatomies of positions and types of disease-associated and neutral amino acid mutations in the human genome. BMC genomics 7 (1), 306. doi:10.1186/1471-2164-7-306

PubMed Abstract | CrossRef Full Text | Google Scholar

Sunyaev, S., Ramensky, V., Koch, I., Lathe, W., Kondrashov, A. S., and Bork, P. (2001). Prediction of deleterious human alleles. Hum. Mol. Genet. 10 (6):591–597. doi:10.1093/hmg/10.6.591

PubMed Abstract | CrossRef Full Text | Google Scholar

Sunyaev, S. R., Eisenhaber, F., Rodchenkov, I. V., Eisenhaber, B., Tumanyan, V. G., and Kuznetsov, E. N. (1999). PSIC: profile extraction from sequence alignments with position-specific counts of independent observations. Protein Eng. 12 (5):387–394.

PubMed Abstract | CrossRef Full Text | Google Scholar

Torkamani, A., Wineinger, N. E., and Topol, E. J. (2018). The personal and clinical utility of polygenic risk scores. Nat. Rev. Genet. 19 (9):581–590. doi:10.1038/s41576-018-0018-x

PubMed Abstract | CrossRef Full Text | Google Scholar

Visscher, P. M., Wray, N. R., Zhang, Q., Sklar, P., McCarthy, M. I., Brown, M. A., et al. (2018) 10 years of GWAS discovery: biology, function, and translation. Am. J. Hum. Genet. 101 (1):5–22. doi:10.1016/j.ajhg.2017.06.005

CrossRef Full Text | Google Scholar

Waterston, R. H., Waterston, R. H., Lindblad-Toh, K., Birney, E., Rogers, J., Abril, J. F., et al. (2002). Initial sequencing and comparative analysis of the mouse genome. Nature 420 (6915), 520–562. doi:10.1038/nature01262

PubMed Abstract | CrossRef Full Text | Google Scholar

Wells, J. A., Vasser, M., and Powers, D. B. (1985). Cassette mutagenesis: an efficient method for generation of multiple mutations at defined sites. Gene 34 (2–3), 315–323. doi:10.1016/0378-1119(85)90140-4

PubMed Abstract | CrossRef Full Text | Google Scholar

Yachdav, G., Kloppmann, E., Kajan, L., Hecht, M., Goldberg, T., Hamp, T., et al. (2014) PredictProtein--an open resource for online prediction of protein structural and functional features. Nucleic Acids Res. 42 W337–W343. doi:10.1093/nar/gku366

PubMed Abstract | CrossRef Full Text | Google Scholar

Zerbino, D. R., Achuthan, P., Akanni, W., Amode, M. R., Barrell, R., Bhai, J., et al. (2018). Ensembl. Nucleic Acids Res. 46 (D1), D754–D761. doi:10.1093/nar/gkz966

PubMed Abstract | CrossRef Full Text | Google Scholar

Keywords: variation, adaptation, evolution, nsSNVs, SNAP, cross-species variation, common variation

Citation: Mahlich Y, Miller M, Zeng Z and Bromberg Y (2021) Low Diversity of Human Variation Despite Mostly Mild Functional Impact of De Novo Variants. Front. Mol. Biosci. 8:635382. doi: 10.3389/fmolb.2021.635382

Received: 30 November 2020; Accepted: 01 February 2021;
Published: 18 March 2021.

Edited by:

Arun Prasad Pandurangan, MRC Laboratory of Molecular Biology (LMB), Cambridge, United Kingdom

Reviewed by:

Pier Luigi Martelli, University of Bologna, Italy
Octav Caldararu, Technical Institute of Denmark, Denmark

Copyright © 2021 Mahlich, Miller, Zeng and Bromberg. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

*Correspondence: Yana Bromberg,; Yannick Mahlich,