Whole genome sequence dataset

Whole genome sequence (WGS) data was obtained for 12 healthy adult participants in the Center for Health Discovery and Well Being (CHDWB) study, including 4 African American women, 4 Caucasian women, and 4 Caucasian men. None of the individuals has any known complex or Mendelian diseases, but they cover a variety of profiles with respect to overall physical and mental health. Prediction of disease risk based on common variants and clinical profiles is described for the Caucasians in [7]. The participants have provided written consent to publication of their whole genome sequence data for research purposes only. They do discuss their clinical profile with a health professional following annual visits to the Center, but are not currently permitted to receive the genetic data generated during the study. In order to protect participant identities, their identifying numbers have been randomized for this study.

WGS was performed on Illumina HiSeq2000 automated sequencers at the University of Washington facility under contract to the Illumina Genome Network. Approximately 125 billion bases passing the Illumina analysis filter were obtained for each genome. Mean non-N reference coverage (after excluding gaps) is ~36X with 95.5% of the positions having an average coverage of at least 10X. The genome sequences were aligned against the human reference genome assembly (hg19 sequence) using CASAVA (Consensus Assessment of Sequence And VAriation, Illumina, Inc, San Diego, CA). On average, 87% of each individual’s quality filtered reads were aligned. High-confidence variants with a quality score above 20 were retained. Accuracy of the generated genome sequences was confirmed by comparison with previously determined genotypes from Illumina OmniQuad arrays, which showed >99% concordance for all individuals.

Amino acid indices for the alternate residues were mapped to the corresponding proteins using transcript IDs for the major isoform. All protein sequences and related information including protein functions and sequence features were obtained from the UniProt database [13].

Sequence annotation using published algorithms

Evidence for association of each SNP or gene with diseases or traits was obtained from public repositories of amino acid polymorphisms (MSV3d, July 2012 release [14] and SwissVar, accessed December 2012) [15], from Online Mendelian Inheritance in Man (OMIM) [16], and from the NHGRI genome wide association studies (GWAS) [17] catalog. Initially, each nsSNP was assigned as disease-causing, probably disease causing, unclassified, or neutral. Functional predictions, and information on disease- and trait-associations to the gene were collected from dbNSFP [10]. In addition, we used UniProt sequence feature records [13] to annotate whether the mutated amino acid is localized to any structurally/functionally important sites (molecule processing sites, binding sites, modification sites, etc.).

In addition to the deleterious predictions, protein regions under evolutionary constraint were detected using three evolutionary conservation-based indicators: GERP++ [22], phyloP [23], and SiPhy [24]. We also used Grantham scores [25] to reflect the degree of physicochemical differences between pairs of amino acids. These four indicators were included in the analysis for comparison purposes but were not utilized for nsSNP categorization.

Other popular variant annotation tools might also be useful but were considered to be redundant with respect to our purposes. For example, ANNOVAR [26] has the ability to perform variant annotation (intronic, intergenic, untranslated region, exonic: non-synonymous, synonymous, etc.), but this information was already available from the VAT output. At the time of our analysis, ANNOVAR provided dbSNP build 135 mapping, not the dbSNP build 137 [11] used in our analysis pipeline. Note that gene definitions from ANNOVAR refer to the nucleotide reference sequence where a SNP is located; the format is not directly applicable for working with records from the two selected SNP databases (MSV3d [14] and SwissVar [15]), in which UniProt accessions were used to identify gene products. Similar to ANNOVAR, SnpEff [27] is another popular program that can assign structural annotations of variants. This function would be valuable when one wants to analyze different types of genetic variations within a genome. Because we only focused on analysis of mis-sense mutations, the annotation feature of SnpEff was deemed unnecessary.

Supervised and automated structure-based predictions of variant function

High quality protein 3D structures are essential to identify functional impacts of nsSNPs. Due to the limited availability of experimentally-determined human protein structures [28], an assortment of 3D structure sources was used to manually evaluate the effects of single point mutations found in specific proteins (Additional file 2: Table S1). Crystal structures were retrieved from the RCSB Protein Data Bank (PDB) [29]. Homology models were retrieved from Protein Model Portal (PMP) [30] repository, or were built manually by joining multiple structures into a single model of a protein. Steric conflicts found within homology models were resolved by energy minimization with explicit solvent using YASARA force field [31]. Structural validation of homology models was evaluated by using two independent scores: QMEAN6 [32] and ModFOLD4 [33]. All 3D structures were visualized and rendered using Chimera [34].

The analysis began with a visualization of wild type proteins in the context of bound ligands. Additional variants that are known to be associated with diseases, or affect protein functionality and/or stability were also identified for each protein structure. Next, we used SDM [35] to compare the protein stability changes upon amino acid mutations with the default modeling of a mutant structure using Andante [36]. A mutation is classified as affecting protein function (stabilizing or destabilizing) using the stability cutoff of ±2 kcal mol^-1[35]. For evaluating the impact of amino acid changes on protein stability in a high throughput fashion, we obtained the tertiary classification of protein stability changes (increase, decrease, neutral) caused by a SNP from I-Mutant 2.0 [37], available from MSV3d [14]. The predictions are based on the protein (or homolog) structure or solely on the protein sequence when the structure was unavailable.

In order to expand the structure-based predictions to a larger dataset without the requirements of manual inspection or in-depth literature searches, we applied a combination of database searches and computational predictions to a larger set of proteins. In addition to the 6 protein structures described in the main text, we identified an additional 25 protein coordinates from PDB [29] (Additional file 2: Table S2). We assessed these 25 wild type proteins in 4 areas of structural analysis: protein stability, ligand binding capability, protein dynamics, and protein-protein interactions. For protein stability, we used the aforementioned approach along with predictions of amino acids with specialized roles regarding protein stability, namely long-range stabilization center (SC) residues and stabilizing residues (SRs). These residues were inferred from the SCide [38, 39] and SRide webservers [40], respectively. Ligand binding residues for each protein were retrieved from PDBe (http://www.ebi.ac.uk/pdbe/) or were predicted using 3DLigandSite [41]. Amino acid residues that are located in or near predicted binding pockets are likely to alter the binding capability for ligand(s). As disease-causing mutations that do not occur in binding sites or buried sites are predominantly found on protein interfaces [42], we used the PatchFinder program [43, 44] to computationally predict the most significant cluster of conserved residues on a protein’s surface that may indicate possible functional sites of the protein; i.e., sites of protein-protein interactions. Changes in protein dynamics were evaluated by the crystallographic B-factor of Cα atoms. In addition, we also used PredyFlexy [45] and FlexPred [46, 47] to predict the dynamic class of an amino acid residue (rigid, intermediate, flexible), and to estimate whether each residue is likely to induce conformational switches within the protein.

Assessment of functional enrichment

The g:Profiler web server [48] was used to detect enrichment of gene functions for genes whose nsSNP are homozygous. Functional profiling and statistical enrichment analysis were performed with two distinct methods. First, we compared the annotations of multiple gene lists, where each list represents the genes with known homozygous variants found in an individual, using G:Cocoa (n = 40–77 genes per genome). Then, we analyzed a gene list for each individual using G:GOSt. The second analysis was performed in two-steps: with and without genes harboring private variants. Enriched functions, such as common gene ontology, biological pathways, shared transcription factor or miRNA binding sites, were reported using the default g:SCS method for significance threshold determination. It is worth mentioning that significant enrichment of protein-protein interactions, derived from the BioGRID database [49], does not imply that all genes with significant enrichment p-value are interacting with each other, but simply indicates which query genes are present in the entire BioGRID dataset. The actual number of interactions and associated genes can be visualized from the network output. The enriched annotations and their gene members were confirmed by literature searches. Furthermore, the SNPshot text-mining tool for PubMed abstracts was used to explore if any of the private homozygous nsSNP-containing genes have clinical or experimental evidence for gene-drug or gene-disease associations [50].

Sequence-based variant description in 12 genomes

A total of 797 known homozygous non-synonymous substitutions was observed in 575 different genes (Table 1). The genomes of the four African individuals harbor on average 73 homozygous nsSNPs (range 58–88), while the eight Caucasian genomes have an average of 63 homozygous nsSNPs (range 50–82). The slight excess in African Americans is not statistically significant (p = 0.18, 2-tailed t-test). 456 of the genes (79%) have a single homozygous variant in the 12 genomes, but two genes (HLA-DRB5 and ANKRD20A4) have more than 10, detected in at least 6 individuals.

The vast majority of all of the variants have been observed previously in the 1000Genomes project, with just 29 private homozygous nsSNPs observed in 25 different genes, with a range of 0 to 5 per genome. Private variants were found in 10 out of 12 genomes of CHDWB dataset and are listed in Additional file 2: Table S3. Most are predicted to be neutral, though they affect nucleotides with a range of conservation levels. Almost one-third of the 25 genes have no defined functions, and only a minor proportion of the genes have been previously associated with a disease or trait.

Six programs were used to predict deleterious variants, and three to indicate the level of sequence conservation at the polymorphic site. The results are summarized in Figure 2, which shows the cumulative number of predicted deleterious (blue) or highly conserved (red) scores for each of the 797 variants. Although 58% of all homozygous mis-sense variants in the 12 genomes alter conserved sites in all three assessments, almost 40% of these (183/463) are predicted to be functionally neutral by all six programs. 45% of the homozygous rare variants are predicted deleterious by at least one program, and 11% (88 variants) by three or more programs.

Differences among the deleteriousness prediction algorithms are underscored by the cumulative score distribution plots in Figure 3. The solid lines in the top half of each plot are for the 294 homozygous rare variants in the African Americans (black) and 503 homozygous rare variants in the Caucasians (blue). The two curves are not significantly different, and predict that as many as 28% of variants are deleterious (MutationTaster) or as few as 2% (MutationAssessor), with the two most commonly used algorithms, SIFT and PolyPhen2, giving intermediate estimates of 80% neutral. The lower curves show cumulative distributions for a set of 24703 disease-promoting non-synonymous variants in 1789 proteins compiled from the MSV3d [14] and SwissVar [15] databases (red dashed curve), as well as from subsets of these disease variants found only in the 23 genes (348 SNPs) harboring homozygous nsSNPs in the four African Americans, or 44 genes (547 SNPs) in the eight Caucasians in our sample (black and blue-dashed curves respectively). All six programs show an elevated tendency to predict known disease-associated variants in the genes harboring homozygous variants in the African Americans as neutral. This is particularly obvious for the MutationTaster score and least pronounced for SIFT. A similar observation of differences among Asians, Caucasians and Africans in the fraction of damaging SNPs predicted deleterious was made by [51].

The notable differences in deleterious predictions given a set of disease-promoting variants found in African American and Caucasian samples (Figure 3, lower curves) suggest these prediction algorithms may have population-specific effects. To further investigate that this observation is not an artifact of the small samples, we plotted similar curves for a set of 1789 genes harboring disease SNPs (total 24703 SNPs). The genes were classified into four groups depending upon population bias of their SNPs, using %MAF difference cutoff of ± 5% between European American (EA) and African American (AA) populations. The four gene groups are: EA bias, AA bias, EA&AA bias, and no bias. Unlike Figure 3 where homozygous variants in the African Americans were consistently predicted to be more neutral, the larger-sample plots (Additional file 3: Figure S2) illustrate a small difference in the cumulative scores of population specific-disease SNPs, with the exception of a noticeable prediction bias of MutationTaster. The results highlight the need for development of mutation assessment pipelines that go beyond these algorithms, particularly when evaluating homozygous nsSNPs of non-Caucasian genomes.

Association-adjusted consensus deleterious scheme (AACDS) for variant classification

Consequently, we developed a ranking system that classifies homozygous nsSNPs into eight categories according to the overlap of (i) consensus deleterious prediction, (ii) documentation that the SNP causes a disease, and evidence that the SNP is in a gene that has been associated with (iii) a disease or (iv) a quantitative trait (Figure 1). Category 1 contains documented disease-causing nsSNPs. Categories 2A and 3A represent nsSNPs in genes that have known associations with diseases (2A) or traits (3A), and these are sub-divided into categories 2B and 3B if they are also predicted to be deleterious by three or more programs. Category 4 comprises nsSNPs that are predicted to be damaging but lie in genes that have no clinical associations. Conversely, Category 5 nsSNPs are located in genes that have disease or trait associations, but variants are predicted to be neutral. Category 6 represents neutral nsSNPs, whose genes have no clinical relations. Table 2 lists the number of variants in each category from each individual’s genome.

Among the 93 disease-associated genes with homozygous nsSNPs in our CHDWB genomes, 18 have homozygous variants present in more than one individual, and these account for 40 nsSNPs, observed at 33 different sites. Only one of these sites, G56R in the MYH6 myosin heavy chain, is predicted to be deleterious. Since it is also associated with resting heart rate, it is classified in both categories 2B and 3B. Data for the variant categorization in these 18 disease-associated genes is summarized in Additional file 2: Table S4. Similarly, we also observed 101 genes with trait associations, including 126 SNPs. Since there are a total of 14 and 21 variants in categories 2B and 3B respectively, most of these cases are restricted to a single individual in the sample of 12. Details for these predicted deleterious variants are listed in Additional file 2: Tables S5, S6, respectively. On average, each individual carries 3.33 category 1, 2B or 3B homozygous variants (range 0 to 7), and although the two individuals with no variants of this type are both African Americans, there is no significant difference in prevalence relative to the Caucasians (p = 0.21, 2-tailed t-test). All of the remaining predicted deleterious variant that do not have disease or trait associations (namely, category 4) are listed in Additional file 2: Table S7.

The set of 35 disease- or trait-associated SNPs that are also predicted to be deleterious is seven times larger than the set of 5 category 1 “known to be deleterious” mutations. They represent 15% of the 93 disease-associated and 136 trait-associated category 2A and 3A SNPs. Additional file 4: Figure S3 compares the allele frequency distributions of the 2B/3B SNPs relative to all 2A/3A SNPs and shows a tendency to reduced allele frequency, also consistent with them having deleterious effects on fitness. Another 170 homozygous nsSNPs lie in genes that have been associated with diseases or traits but are not predicted to be deleterious (category 5). Their frequency distribution is approximately equivalent to those of the category 2A and 3A SNPs. The vast majority (539) of the 797 SNPs we have considered are in category 6 and represent the subsets that are least likely to be damaging.

A limitation of our analysis is the uncertainty in the accuracy of phenotypic annotations of SNPs, as well as the variable confidence level in annotations of causal SNPs. We obtained the list of disease-promoting nsSNPs from MSV3d [14] and SwissVar [15]. Most of the variants were classified as either causal variants (to a specific disease), or as polymorphisms. In MSV3d, many variants have ambiguous annotations, e.g., probable-pathogenic, or unknown. In SwissVar, some proteins are noted to have associations with diseases, but the list of variants is not provided.

Supervised structure-based variant evaluation

Personal genome studies indicate that each healthy individual carries a large number of rare homozygous genetic variants [52–55]. While these variants can be found in any structural regions along the genome and can have diverse effects on biological function, Cooper (2010) estimated that as many as 60% of known disease-causing mutations are nsSNPs [2]. This viewpoint simply reflects the more obvious impact of nsSNPs on coding regions than regulatory regions: the substitutions tend to alter the amino acid sequences of the proteins. Amino acid changes are thought to have profound effects on the protein, impacting their structure or function. Furthermore, it is believed that there exist some structure-function relationships for each individual amino acid residue within a protein chain, and the 3D structure is an ideal resource for investigating this information [56]. Therefore, many algorithms have been developed to assess the effects of amino acid changes within the context of protein 3D structures. Numerous structural features have been used to explain/quantify the changes [57]. A few successful implementations have demonstrated that protein 3D structures add to prediction accuracy [58, 59]. In addition, in silico analysis of 3D structures can facilitate variant prioritization because it provides systematic screening of nsSNP effects in the context of the protein structure and suggests which mutations may critically alter the function of the protein.

Implementation of the AACDS classification scheme reduces the number of potentially deleterious variants to a number per genome that can feasibly be evaluated manually on a case-by-case basis. For this purpose, we have devised a further pipeline that involves sequence annotations, extracting either X-ray crystal/NMR structures or homology models from structure databases, and computing a series of predictions that capture protein features. In this way, each of the up to 5 variants in categories 1, 2B or 3B can be assessed in the context of the actual protein. While this approach requires that an individual with experience in protein structures be engaged in the personal genome evaluation, the potential gain in accuracy is likely to be meaningful.

Our preliminary analysis utilized sequence features for all amino acid residues in each protein, obtained from UniProt features records [13]. The entries had been curated and are predicted (and compatible with the protein function), experimentally proven, or determined by resolution of the protein structure. The analysis was restricted to 62% of the nsSNPs, since the remaining fractions do not currently have feature information. Figure 4 illustrates that although the annotated 494 nsSNPs are found in various sequence regions, they are predominately present in transmembrane and protein domains. These proportions are approximately equivalent to the proportions of each of the 9 types of annotated protein region for all residues in the included proteins (Additional file 5: Figure S4). Locations of homozygous variants relative to the length of each sequence feature indicate the variants are located throughout the entire sequence length (Additional file 6: Figure S5).

Subsequent detailed analyses involved manual inspection and evaluation of individual proteins. The remainder of this section discusses detailed structural evaluations of two known causative variants, two predicted deleterious variants in proteins that have been associated with a disease or trait, and two predicted deleterious variants for which the clinical associations are inconclusive. The first disease causing nsSNP is the well-known Arginine to Cysteine substitution at residue 176 (residue 158 if omitting the signal peptide) that defines the APOE2 allele of Apolipoprotein 2 (SNP category 1/2B/3B). This allele has a major influence on lipid transport and is a protective factor against late-onset Alzheimer’s disease and coronary artery disease [60–62]. Homozygosity for R176C is also associated with Type III hyperlipoproteinemia (HLPP3) in approximately 2% of cases (though 94% of HLPP3 cases have the genotype) [63]. Onset of the disorder is usually only after menopause in women and rarely manifests before the third decade in men. Several other rare variants in the gene have been annotated to disease, most of which affect intra- and inter-helical salt bridges (Figure 5A). With the neutral cysteine at position 176 in APOE2 protein, this pattern of salt bridge is eliminated. Although Arg176 does not interact with the LDL receptor, the R176C substitution has been shown to indirectly reduce the receptor-binding activity of APOE [64]. Stability prediction indicates this mutation has neutral effect to the protein stability (ΔΔG = −0.46 kcal mol^-1).

The second example of causative mutation is in Myotubularin-related protein 2 (MTMR2), a putative tyrosine kinase that is associated with Charcot-Marie-Tooth disease type 4B (CMT4B) [65]. One African individual has an Asparagine to a Serine substitution at the position 545 of the protein that has previously been reported as a rare variant in patients with CMT disease [66]. This SNP is classified as category 1. The minor allele frequency of this nsSNP is reported as 3.88% and 0.02% in African Americans, and European Americans, respectively, so the penetrance is much reduced in African Americans since disease prevalence of all forms of CMT is just one in 2500 [67]. The variant is situated in a conserved site, but only two algorithms predict it to be deleterious. The crystal structure places Asn545 in a protein domain, but it is not in close proximity with two other causative variants or part of a binding site. This mutation is also predicted to be neutral (ΔΔG = 0.36 kcal mol^-1) (Figure 5B).

One predicted damaging nsSNP was found in Thrombospondin 1 (THBS1), a glycoprotein that stabilizes fibrinogen platelet cross-bridges [68]. The homology model indicates the substitution of a Serine for Asparagine at residue 700 of THBS1 occurs at a critical position in one of the calcium-binding domains (green spheres project coordination of CA²⁺ ions) (Figure 5C). This Asn700Ser substitution in THBS1 (SNP category 4) has a prevalence of 8-10% in Europeans [69] and is associated with the occurrence of premature (age < 45) coronary heart disease in both homozygous and heterozygous individuals [68]. However, a study of Asn702Ser in THBS2 (homologous to 700 in THBS1) demonstrated that this variant does not alter calcium-binding capability. Instead, it induces a local conformational change leading to destabilization of surrounding structures [69], consistent with the computational prediction that the variant has a destabilizing ΔΔG of 0.58 kcal mol^-1.

The second example of a predicted deleterious nsSNP is a mis-sense substitution in Acidic mammalian chitinase (CHIA), an enzyme that stimulates chemokine production by pulmonary epithelial cells. Arg35Trp (SNP category 4) is located in a buried site, where it causes changes in residue side chain volume, charge, polarity, and hydrophobicity (Figure 5D). The substitution was predicted to disrupt the hydrophobicity of the protein and increase solvent accessibility of the residue [14]. The individual who is homozygous for this variant also carries three heterozygous nsSNPs (N278D, I339V, and V432G). The first two of these replacements are parts of disulfide bonds, while the third substitution resides in the chitin-binding domain. Interestingly, the Arg35Trp mutation is predicted to stabilize the protein (ΔΔG = −1.19 kcal mol^-1), a finding that may appear counter-intuitive. However, it is suggested that protein flexibility is crucial for enzyme catalysis [35]. The increase in protein stability and the dramatic change in physicochemical properties caused by this homozygous nsSNP, along with the disulfide bond reduction from heterozygous variants, strongly suggest the possibility for protein malfunction in this individual. As far as we are aware, he does not have asthma or an aberrant T-helper mediated inflammatory response, but deeper clinical investigation may be warranted.

The last two examples highlight cases where the variant is predicted deleterious but its clinical associations are inconclusive. The first example is a Glycine to Arginine substitution at residue 56 of Myosin heavy chain alpha (MYH6) (SNP category 2B/3B). As mentioned earlier, this is the only predicted deleterious variant among a set of 18 disease-associated genes with variants present in more than one individual (Additional file 2: Table S4). SNP databases indicate six well known causative variants in this gene that lead to familial hypertrophic cardiomyopathy and atrial septal defect. Although G56R is not one of them, this variant had been previously identified in affected individuals but it does not segregate perfectly with the disease in families of probands [70, 71]. With regard to the homology model (Figure 5E), many of the known variants associating with heart disease are located in the coiled-coil regions of this protein (missing from the crystal structure) and are not part of the ATPase catalytic site or actin binding site. Nonetheless, the G56R found in one of the CHDWB participants is particularly interesting, since the mutation occurs in a myosin head-like domain, a key component for muscle contraction, and with a large degree of amino acid change (Grantham score = 125). Stability prediction also suggests this variant destabilizes the protein (ΔΔG = 1.10 kcal mol^-1).

The second example of a variant with uncertain functional effect is taken from a set of 143 SNPs (18% of CHDWB dataset) that do not currently have phenotypic annotations. Among these, 117 SNPs have neither disease nor trait association at the gene level. Form 143 SNPs, we identified 15 variants predicted to be damaging, of which 8 are located in genes with no clinical associations. Our example is a Proline to Serine substitution at residue 145 of Pancreatic alpha-amylase (AMY2A) (SNP category 4). In addition to one calcium ion and one chloride ion per subunit, the protein is able to bind to several ligands throughout the structure. Mutagenesis studies identified several amino acid residues that are essential for the protein’s catalytic activity and affinity to calcium and chloride ions, but the impact of Pro145Ser has not been established [72, 73]. Despite the limited information of the variant, crystal structure indicates Pro145 is part of one, among many, binding sites for alpha-D-glucose (Figure 5F). In general, amino acids with similar physicochemical properties may substitute each other while maintaining the protein’s functionality. One study demonstrates some uncommonly predominant inter-species amino acid variations, such as serine-proline pairs or glutamic acid-alanine pairs [74]. The notable feature corresponds with the proline to serine substitution caused by this SNP. It is well known that the proline residue is sterically restricted and that it tends to disrupt regular secondary structural elements. Most proline residues are found in very tight turns or on protein surface [75]. The unusual occurrence of Pro145, especially in the extended β-loop indicates that this residue is essential for proper protein folding. Further investigation revealed that this residue is in the cis isomer, a very rare phenomenon since proline residues are exclusively synthesized as the trans isomer. In fact, AMY2A contains two cis-proline residues (Pro69 and Pro145); both are located in the loop regions. It had been suggested that the two residues help accommodate a sharp turn of the β-loop [76]. Substitution of proline to serine is predicted to be highly stabilizing for residue 145 (ΔΔG = −2.77 kcal mol^-1), but highly destabilizing for residue 69 (ΔΔG = 5.23 kcal mol^-1). In any case, strong stability changes are suggested to cause protein malfunction and may lead to disease(s) [35].

Automated structure-based variant evaluation

To facilitate high throughput evaluation of protein structures, we devised a structural analysis pipeline that assesses the functionality of protein residues using data directly obtained from the atomic coordinates or from computational predictions. Using this approach, a list of potentially deleterious variants from a structural perspective can be generated rapidly, providing a way to integrate structural analysis into the variant categorization scheme.

The four areas of automated structure-based variant analysis include stability, flexibility, and potential to disrupt protein-protein or protein-small molecule interactions. Many mutations disrupt these structural features and as a result, lead to altered protein functions or diseases. Our assumption is that the analysis may be able to identify some variants with strong effects. The results are summarized in Additional file 2: Table S8, which indicates that predicted deleterious variants show a wide variety of residue features. In general, SNPs that do not cause stability change (∆∆G < ± 0.5 kcal mol^-1) tend to be non-deleterious, but not vice versa. Four amino acid residues have B-factors of Cα atoms larger than 60 Å², a characteristic which may indicate that the atom is disordered due to dynamic motion, or may be an artifact of crystal imperfection. However, three out of these four amino acids are predicted to be at rigid sites and do not induce conformational switches. Another three residues are predicted to lie within a cluster of conserved residues on a protein surface, but none is categorized as damaging variants. Lastly, we found only one amino acid mutation that is located within the binding site, but the variant is in categories 2A and so not predicted to be damaging.

Imposing the constraint that the 3D structures must be of high quality, our initial analysis was restricted to only 24 experimentally-determined structures and 1 high quality theoretical model. Their sequence coverage ranges from 23–100% (average 69%) (Additional file 2: Table S2). However, the implementation can be further applied to any available structures. For example, using the automated Phyre2 homology modeling server with single/multiple template methodology [77], we were able to model an additional 77 full length proteins with high confidence. Each full length protein model has a percentage of residues modeled at >90% confidence in the range of 59-99% (average 86%). For larger proteins (>1300 amino acids), we truncated them into smaller domain(s) and our modeling attempt returned 10 models with confidence between 96-100%.

Enrichment for mutations disrupting protein interactions

As a parallel approach to evaluating the deleterious potential in homozygous protein substitutions, we used g:Cocoa [48] to evaluate whether there is an enrichment for proteins that have similar functions. The analysis revealed four significant gene annotations that include a significant number of the queried genes from more than one individual. These four terms are: X-linked recessive inheritance, epithelial cell signaling in H.pylori infection, microRNA miR-708 binding sites, and Rho GTPase signaling pathway (Additional file 7: Figure S6).

Although 96 homozygous nsSNPs were identified on the X chromosome, only 1 nsSNP has been documented as a causal variant (X:50658966 G → A) (Table 4). Genes involved in X-linked recessive inheritance from the 12 genome data were identified in one African female and four Caucasian males (Additional file 2: Table S9). We identified only two predicted damaging SNPs. One was found in TBX22 that is associated with X-linked cleft palate. The other is located in SYTL5, a trait gene associated with erectile dysfunction and prostate cancer treatment. The remaining X chromosome variants are predicted as neutral. Two male individuals have the same mutation in the F9 gene, for which reduced function can result in hemophilia B (HEMB). Unlike hemophilia A, symptoms of HEMB are usually milder or can be asymptomatic. Three male individuals also carry an identical SNP in FRMD7 gene. Malfunctions of this gene can cause nystagmus congenital X- linked type 1 (NYS1), a condition that appears at birth and up to three months old. The indications are spontaneous and involuntary ocular oscillations. Given that most X-chromosome variants are predicted neutral and there is no indication that either of these individuals have these conditions, it is unlikely that the associated disease or trait will develop.

Finally, we used BioGRID [49] to evaluate enrichment for proteins that form physical interaction networks, and found that one of the 12 individual’s genomes has 7 mutations potentially involved in an unusually high number of contacts (Additional file 8: Figure S7). Three of these proteins (FHL2, STK17A, and DSP) are linked together by other interacting partners. The FHL2 gene encodes a four and a half LIM domain protein that acts as a molecular transmitter between signaling pathways and transcriptional regulation. The wild type amino acid affected by the homozygous mis-sense variant of the FHL2 gene is evolutionarily conserved, but the Arg177Gln substitution is only predicted to be deleterious by one program. This variant is not predicted to affect protein stability and the protein is not associated with a disease or trait. However, we mention it as an example of how this approach may highlight networks of proteins, where subtle modification of multiple partners may result in cumulative disruption that would lead to disease under a multiplicative burden of rare variants model.