# The sequencing and interpretation of the genome obtained from a Serbian
individual

Wazim Mohammed Ismail1111Contributed equally to this work. Kymberleigh A.
Pagel1∗ Vikas Pejaver1 Simo V. Zhang1 Sofia Casasa2 Matthew Mort3 David N.
Cooper3 Matthew W. Hahn1,2 and Predrag Radivojac1

###### Abstract

Recent genetic studies and whole-genome sequencing projects have greatly
improved our understanding of human variation and clinically actionable
genetic information. Smaller ethnic populations, however, remain
underrepresented in both individual and large-scale sequencing efforts and
hence present an opportunity to discover new variants of biomedical and
demographic significance. This report describes the sequencing and analysis of
a genome obtained from an individual of Serbian origin, introducing tens of
thousands of previously unknown variants to the currently available pool.
Ancestry analysis places this individual in close proximity of the Central and
Eastern European populations; i.e., closest to Croatian, Bulgarian and
Hungarian individuals and, in terms of other Europeans, furthest from
Ashkenazi Jewish, Spanish, Sicilian, and Baltic individuals. Our analysis
confirmed gene flow between Neanderthal and ancestral pan-European
populations, with similar contributions to the Serbian genome as those
observed in other European groups. Finally, to assess the burden of
potentially disease-causing/clinically relevant variation in the sequenced
genome, we utilized manually curated genotype-phenotype association databases
and variant-effect predictors. We identified several variants that have
previously been associated with severe early-onset disease that is not evident
in the proband, as well as variants that could yet prove to be clinically
relevant to the proband over the next decades. The presence of numerous
private and low-frequency variants along with the observed and predicted
disease-causing mutations in this genome exemplify some of the global
challenges of genome interpretation, especially in the context of understudied
ethnic groups.

##  1 Introduction

The genetic variation between individuals accounts for much of observed human
diversity and has the potential to provide information on phenotypic outcomes
of clinical consequence. Studies of genetic variation provided by individual
genome sequences have revealed that this variation differs both within and
between populations, and also varies considerably depending upon the
population [8]. Moreover, characterization of genetic variation of individuals
from multiple populations has revealed a correlation between genetic and
geographic distances, and has become relevant for determining genetic ancestry
and geographic origin [33, 23]. Therefore, the characterization of genetic
variation has been of major interest for diverse research fields, including
medical, biological and anthropological sciences [5, 33, 14, 23, 22, 29].

Sequencing of the first human genomes revealed that most genetic variation is
derived from single nucleotide variants (SNVs), although insertions and
deletions (indels) account for the majority of the variant nucleotides [25].
The increased accessibility of DNA sequencing has contributed to individual
efforts from a range of distinct populations. To date, individual genomes from
American [25, 51], Han Chinese [48], Russian [6], African [39], Japanese [13],
German [43], Gujarati Indian [18], Estonian [27], Pakistani [2] and Mongolian
[3] populations have been sequenced and analyzed, among many others [8].

Larger-scale efforts to characterize human genetic variation have demonstrated
that individuals from different populations carry particular combinations of
rare and low frequency variants. The 1000 Genomes Project Consortium has
estimated that 86% of all variants are confined to a single continental group
and that about 10% of variants observed in a population are private to that
population [8]. Population-specific variants have the potential to be of both
functional and biomedical importance [5]. Furthermore, evidence of
biologically meaningful population-specific variation [16] emphasizes the need
for ethnically relevant reference genomes, as has been performed, for example,
for the Korean population [7]. Although we are not claiming to have introduced
a new reference genome here, it is nevertheless important to expand our
sequencing efforts across diverse populations, particularly those that have
not been previously studied [36, 29].

In this paper, we describe the sequencing of the first genome of an individual
of Serbian origin, a member of a relatively small population in Central to
Southeastern Europe. We identify tens of thousands of novel genetic variants
in this individual, more than a hundred of which map to protein-coding regions
and several hundred of which reside in close proximity to gene coding regions.
The extent of observed genetic variation allowed comparisons with extant
European populations and reaffirms support for the hypothesis of close
correspondence between genetic and geographic distances [33]. These results
contribute to ongoing efforts to understand human genetic variation and its
geographic distribution, as well as placing the Serbian genome within the
context of the broader European population structure. Testing for Neanderthal
introgression in the genome, we find evidence suggesting gene flow from
Neanderthal to an ancestral pan-European genome, with the Serbian genome being
placed within range of other European populations. After variant annotation,
we assess the burden of potentially pathogenic variation present in this
genome and identify variants of putative clinical and pharmacogenetic
relevance. Finally, we draw conclusions pertaining to the phenotypic
consequences and biomedical interpretation of individually sequenced genomes.

##  2 Materials and Methods

### Donor information

The individual whose genome was sequenced and analyzed is of Serbian descent.
The data, both derived and raw, are publicly available through the Personal
Genome Project website [4], participant ID: hu3BDC4B.

### Sample collection and DNA sequencing

Two milliliters of saliva were self-collected by the donor and stored using
the DNA Genotek Oragene DISCOVER (OGR-500) sample collection kit. Extraction
of DNA from the sample and subsequent sequencing were performed at the BGI
(Shenzhen, China) on an Illumina HiSeq 2000 sequencer, using standard
protocols. To minimize the likelihood of systematic bias in sampling, two
libraries were prepared with an insert size of 500 bp each, with paired-end
reads of length 90 bp. Sequencing was then carried out in four lanes for each
library to ensure at least 30-fold coverage.

### Read mapping and variant calling

The paired end reads were aligned to the GRCh37 human reference genome with
the Burrows-Wheeler Aligner (BWA-MEM) [26] and Bowtie2 [21]. Picard tools [1]
were used for the removal of duplicates followed by the use of two variant
callers. GATK [30] was applied for base quality score recalibration, indel
realignment, genotyping and variant quality score recalibration according to
GATK Best Practices recommendations [44, 11]. We also generated results from
Platypus [38] applied to the output from each aligner.

As described later in Results, variants identified through a combination of
BWA and GATK were used for all analyses. Variants in the intersection of all
four pipelines were considered to be confidently identified. All variant calls
were subsequently annotated with information from NCBI RefSeq using ANNOVAR
[49].

### Principal component analysis

Principal component analysis (PCA) was carried out using the
$\mathsf{smartpca}$ program from EIGENSOFT (v6.0.1;
https://github.com/DReichLab/EIG), on the Serbian genome combined with the SNV
data (600,841 loci) from Lazaridis et al. [23]. Only the subset of European
individuals from their curated fully public dataset was used, reducing the
original set of 1,964 individuals to 260. A projection to the first two
principal components was used to establish the correspondence between genetic
and geographic distance in our results.

### Neanderthal introgression

To test for Neanderthal introgression in the Serbian genome, we computed
D-statistics [15, 41] using this genome and the dataset from Lazaridis et al.
[22]. This dataset includes 294 ancient individuals (only one of which was
used here) and a diverse set of 2,068 present-day humans, genotyped on the
Affymetrix Human Origins array. Both the archaic and modern genotype data were
provided in the PACKEDANCESTRYMAP format, and were combined using the
$\mathsf{mergeit}$ program from EIGENSOFT (v6.1.2;
https://github.com/DReichLab/EIG). The merged dataset, in total, contains
2,362 samples genotyped at 621,799 SNV loci. As requested, we completed the
consent form and obtained approval from David Reich’s laboratory before using
this dataset. We further note that some individuals from the study of
Lazaridis et al. [22] could not be included due to consent issues relating to
data distribution.

We next genotyped the Serbian genome against these predefined SNVs using GATK
UnifiedGenotyper (v3.3-0-g37228af; [11]), following the same reference-bias
free genotyping protocol as used by the Simons Genome Diversity Project [28].
We converted the resulting VCF files to the EIGENSTRAT format using VCFtools
(v0.1.12a, [9]), and integrated the Serbian genotype with the modern and
ancient datasets. Finally, we ran qpDstat from AdmixTools (default setting,
v701) to calculate D-statistics and to test for Neanderthal gene flow in the
Serbian genome [41].

### Burden of pathogenic variation

Variants of putative clinical significance were identified using genotype-
phenotype databases as well as computational variant-effect prediction.
Manually curated genotype-phenotype databases, such as the Human Gene Mutation
Database (HGMD) [42] and PharmGKB [19], annotate variants with a known
relationship to phenotype [52, 42]. Clinical Annotations from PharmGKB were
compared against dbSNP v142 rsIDs [40] obtained using the
annotate_variation.pl script in ANNOVAR and $\mathsf{avsnp142}$. Variants
identified by GATK were compared against HGMD to identify potentially disease-
causing and disease-associated mutations.

All variants in protein-coding regions were extracted and inputted to the
MutPred suite of tools [35, 34, 31]. The remaining variation observed in the
proband was interrogated using CADD [17]. For disease and gene ontology
associations, the hypergeometric test in WebGestalt was used with Benjamini-
Hochberg correction for multiple hypothesis-testing [47]. The background set
that was used for these analyses included all protein-coding genes from the
human reference genome. For significance of an ontology term, it was also
required that at least five genes were associated with it.

##  3 Results

### Effect of genotyping software

The selection of computational tools and their parameters in processing raw
sequencing reads can significantly impact the resulting genome and the
entirety of subsequent analysis. To understand the uncertainty of variant
identification, we evaluated two different read mappers, BWA [26] and Bowtie2
[21], and two different variant callers, GATK [30] and Platypus [38].

The results from four different platforms are contrasted in Figure 1. The SNV
calling shows good concordance between both read mappers and variant callers,
with a large proportion of variants identified by either platform being
identified by all platforms. Using the BWA mapper, for example,
2,991,390/3,280,434 = 91.2% of SNVs identified by GATK were also identified by
Platypus and 89.1% of SNVs identified by Platypus were also identified by GATK
(Figure 1). Indel calling, on the other hand, is less reliable, with
401,082/627,519 = 63.9% variants identified by GATK also identified by
Platypus and only 66.7% of variants identified by Platypus being also
identified by GATK. The influence of read mappers was markedly lower; i.e.,
using the GATK mapper, we found that 95.1% of SNVs and 89.3% of indels
identified with BWA were also identified with Bowtie2, and that 98.3% SNVs and
of 97.6% of indels identified with Bowtie2 were also identified with BWA.
Smaller percentages of overlap were observed for Platypus. Based on the
results observed in this work and the extent of usage of these tools in
resequencing human genomes, we selected BWA$+$GATK as our main platform.

Figure 1: Venn diagrams showing the total numbers of identified variants using
two read mappers (BWA [26], Bowtie2 [21]) and two variant callers (GATK [30],
Platypus [38]).

### Identification of genetic variants

The genome of a Serbian individual was sequenced according to the protocols
described in Materials and Methods, with all 22 autosomes having similar
coverage (and the X and Y chromosome having approximately half this coverage).
The genome sequencing and mapping achieved an average read depth of 34.7, with
98.3% of GRCh37 reference bases having coverage of 10-fold or more and 89.4%
having coverage of 20-fold or more. When only the non-zero positions were
considered, the average read depth was 34.8, with 98.5% of GRCh37 reference
bases having coverage of 10-fold or more and 89.7% having coverage of 20-fold
or more. The number of zero-depth positions were 7,649,443 (0.3%). The
coverage distribution is shown in Supplementary Figure 1.

Using the BWA$+$GATK pipeline, we identified a total of 3,908,814 variants in
the Serbian genome, of which 2,195,638 (56.2%) were heterozygous with one non-
reference allele, 23,095 (0.6%) were heterozygous with two non-reference
alleles, and 1,690,081 (43.2%) were homozygous for a non-reference allele. The
reported variants passed all quality filters of GATK (marked as “PASS”) and
were subsequently mapped to GRCh37 human reference genomic regions using
ANNOVAR [49]. It is important to mention that ANNOVAR considers all
heterozygous positions with both alternative alleles as two different
variants. Therefore, the resulting genome contains 3,931,909 total variants,
of which 2,940,042 (74.8%) were identified by all four platforms and are
considered to be confident identifications. Unsurprisingly, the majority of
identified variants were found to reside in intergenic and intronic regions
(Table 1).

To identify novel variation, we searched the identified variants against the
Genome Aggregation Database (gnomAD) [24]. We found that 1.5% (60,153) all
variants and 0.4% (12,439) of confident variants were not present in gnomAD.
We shall refer to these variants as “novel” and “confident novel” variants,
respectively. The breakdown of all variants and novel variants with respect to
genomic location is shown in Tables 1-2. The percentage of novel variants
varied across categories, comprising 0.9% (80) of nonsynonymous variants, 0.4%
of synonymous variants, 0.7% (145) of exonic variants, 1.5% (20,531) of
intronic variants, and 1.6% (34,779) of intergenic variants. We found that
45.0% (9,328/20,739) of the exonic variants were nonsynonymous, whereas 50.1%
(10,381/20,739) were synonymous. Similar fractions were observed for the
confident variants (44.1% vs. 52.3%). Of the 3,871,756 GATK variants which are
also observed in the gnomAD database, 3,805,264 (95%) of these variants are
annotated to have allele frequency greater than 1% in gnomAD and 3,676,638
(95%) with allele frequency greater than 5%.

Table 1: Summary of identified variants using BWA$+$GATK. Variants not present in gnomAD [24] are listed as novel and variants identified by all four genotyping platforms are listed as confident. Type of Variant | Variant | Novel |  Confident variants |  Confident novel
---|---|---|---|---
upstream | 23094 | 320 |  16211 |  90
upstream; downstream | 881 | 8 |  624 |  4
UTR5 | 5205 | 54 |  4055 |  22
UTR5; UTR3 | 16 | 1 |  12 |  0
exonic | 20706 | 145 |  17114 |  115
exonic; splicing | 33 | 1 |  22 |  0
splicing | 151 | 0 |  107 |  0
intronic | 1410507 | 20531 |  1078226 |  4336
UTR3 | 31066 | 409 |  24095 |  101
downstream | 26685 | 398 |  19351 |  61
ncRNA_exonic | 13064 | 129 |  9520 |  30
ncRNA_exonic; splicing | 3 | 0 |  2 |  0
ncRNA_intronic | 235936 | 3376 |  173168 |  832
ncRNA_splicing | 65 | 1 |  51 |  0
ncRNA_UTR5 | 1 | 1 |  0 |  0
intergenic | 2164496 | 34779 |  1597484 |  6848
Table 2: Summary of identified exonic variants using BWA+GATK. Variants not present in gnomAD [24] are listed as novel and variants identified by all four platforms are listed as confident. Type of Variant | Variants | Novel |  Confident variants |  Confident novel
---|---|---|---|---
synonymous SNV | 10381 | 42 |  8965 |  36
nonsynonymous SNV | 9328 | 80 |  7559 |  69
nonframeshift deletion | 137 | 2 |  62 |  0
nonframeshift insertion | 117 | 3 |  58 |  0
frameshift deletion | 103 | 6 |  45 |  4
frameshift insertion | 74 | 3 |  37 |  1
stopgain | 87 | 6 |  54 |  4
stoploss | 11 | 0 |  9 |  0
unknown | 501 | 4 |  347 |  1

### Genetic variation and geographic distance

The projection of the Serbian individual to the first and second principal
components against European groups from [23] confirms that individuals from
the same geographic region cluster together (Figure 2). We clearly distinguish
clusters of major populations composed of individuals from the same region,
approximately mirroring a map of Europe. The PCA plot demonstrates that the
genetic ancestry of the Serbian individual analyzed in the present study
corresponds to its geographic distance from other populations. It is
positioned in close proximity of the Croatian, Bulgarian, and Hungarian
populations.

A somewhat surprising finding is the similarity of distances between the
Serbian individual and other mostly Slavic populations (Russian, Belarus,
Ukrainian) relative to distances to various Central, Western, and Southern
European groups (Czech, French, English, Albanian, Greek). The average
Euclidean distance and variance between the Serbian individual and each of the
available populations in the two-dimensional space of major PCA components is
as follows: Croatian ($0.016826\pm 0.010526$), Bulgarian ($0.033603\pm
0.000225$), Hungarian ($0.037121\pm 0.000177$), Czech ($0.053687\pm
0.000033$), Albanian ($0.058875\pm 0.000117$), Ukrainian ($0.064328\pm
0.000062$), Belarusian ($0.069803\pm 0.000043$), Greek ($0.071108\pm
0.00062$), Tuscan ($0.0736441\pm 0.000028$), French ($0.083077\pm 0.000159$),
English ($0.084570\pm 0.000142$), Norwegian ($0.092721\pm 0.00088$), Russian
($0.095968\pm 0.000079$), Estonian ($0.098421\pm 0.000046$), Finnish
($0.108523\pm 0.000154$), Sicilian ($0.120370\pm 0.000481$), Spanish
($0.134602\pm 0.000776$), Ashkenazi ($0.156692\pm 0.000538$). The three
closest individuals to the Serbian genome were of Croatian ancestry (0.0038,
0.0046, and 0.0108).

We note that combining the Serbian individual with the set of 260 European
individuals from Lazaridis et al. [23] caused 50 formerly biallelic sites to
become triallelic (no monoallelic sites became triallelic). The triallelic
sites were removed from the analysis, leaving 600,791 sites in the analysis.
The $\mathsf{smartpca}$ program was applied to the 261-by-600,791 genotype
matrix.

Figure 2: Principal component analysis (PCA) plot showing the proximity of the
genome sequenced in this study to other European genomes. As observed in
previous studies [33, 23], genomic distance correlates with geographic
distance.

### Gene flow with Neanderthals

Comparisons between Neanderthals and modern humans have previously revealed
evidence of gene flow from Neanderthals to Europeans [15, 41, 37, 12]. To test
whether the Serbian genome shares an excess of alleles with the Neanderthal
genome, we integrated the Serbian genotype with a published panel of ancient
and modern humans (Materials and Methods). We calculated D-statistics as a
formal test for gene flow based on a four-taxon phylogeny,
$D(P_{1},P_{2},P_{3},O)$, where $P_{i}$ ($i\in\\{1,2,3\\}$) are populations
and $O$ is an outgroup. Given a scenario where gene flow is absent, the
derived alleles of $P_{3}$ are expected, with equal likelihood, to match those
of $P_{1}$ and $P_{2}$; i.e., $D=0$. Alternatively, either $P_{1}$ or $P_{2}$
could share alleles with $P_{3}$ more often than not, in which case $D$
deviates from zero.

We computed
$D(\textrm{Yoruba},\textrm{Serbian},\textrm{Altai},\textrm{Chimpanzee})$ for
testing for gene flow between Neanderthals (“Altai”) and the given Serbian
genome. We expected a positive $D$ value, given previous evidence that
Neanderthals exchanged more alleles with Europeans than with Africans. The
test returned a $D$ value of $0.0241\pm 0.004476$, which significantly
deviated from zero (Z-score = 5.39; Table 3), suggesting gene flow between
Neanderthal and the lineage leading to the Serbian genome. To validate this
result, we also ran the test for other European populations (Table 3).
D-statistics calculated for French, Greek, and Russian genomes were comparable
to our result, all falling within the expected range of values reported in
previous studies [15, 37, 12].

We further attempted to ensure that the calculated D-statistics were unbiased.
To do so, we repeated the analysis by replacing Yoruba with Mbuti, as some of
the Yoruba samples could have had some recent European admixture. The
calculation for $D$(Mbuti, Serbian, Altai, Chimpanzee) yielded a $D$ value of
$0.0186\pm 0.004763$ (Z-score = 3.99; Table 3), consistent with our results
using the Yoruba samples. We next checked whether the Serbian individual has
reference biases in genotyping that could have inflated the $D$ value. We
performed D-statistics tests in the form of $D$(other European population,
Serbian, Mbuti, hg19ref) and chose French, Greek and Russian as the “other
European population”. We obtained no test results indicating the bias of
Serbian genotypes toward the reference (French: $0.0038\pm 0.004078$; Greek:
$0.0090\pm 0.004182$; Russian: $0.0074\pm 0.004192$).

Table 3: Testing gene flow with Neanderthals. The results show the D-statistic ($D$), its standard error (SE) and Z-score ($Z$) for the test using the set of populations $P_{1}$, $P_{2}$, and $P_{3}$, with Chimpanzee as an outgroup ($O$). The last two columns show ABBA vs. BABA counts over the four genomes ($P_{1}$, $P_{2}$, $P_{3}$, $O$). $P_{1}$ | $P_{2}$ | $P_{3}$ | $O$ | $D$ | SE | Z-score | ABBA | BABA
---|---|---|---|---|---|---|---|---
Yoruba | Serbian | Altai | Chimpanzee | 0.0241 | 0.004476 | 5.393 | 18158 | 17302
Yoruba | French | Altai | Chimpanzee | 0.0266 | 0.003012 | 8.821 | 18284 | 17338
Yoruba | Greek | Altai | Chimpanzee | 0.0270 | 0.003034 | 8.906 | 18266 | 17305
Yoruba | Russian | Altai | Chimpanzee | 0.0288 | 0.003096 | 9.306 | 18328 | 17302
Mbuti | Serbian | Altai | Chimpanzee | 0.0186 | 0.004763 | 3.909 | 18817 | 18129
Mbuti | French | Altai | Chimpanzee | 0.0210 | 0.003532 | 5.941 | 18902 | 18125
Mbuti | Greek | Altai | Chimpanzee | 0.0214 | 0.003578 | 5.978 | 18897 | 18106
Mbuti | Russian | Altai | Chimpanzee | 0.0232 | 0.003600 | 6.434 | 18932 | 18074

### Analysis of medically relevant variants

The sequenced genome contains 2,344 genetic variants that are present in HGMD
by virtue of their having been previously associated with a risk of disease;
the proportions of variants within each effect category are shown in Table 4.
Several homozygous variants, manually annotated as disease-causing (DM) are
observed in the genome, shown in Table 5. Of these, two are youth-onset
phenotypes that are homozygous for the disease-causing allele in the proband
genome: nephropathic cystinosis (NP_004928.2:p.T260I) and Factor XIII
deficiency (NM_000129.3:c.-19+12C$>$A). The disease phenotypes associated with
these homozygous mutations typically become apparent in childhood, and
therefore their occurrence in a healthy adult is indicative of variable
penetrance. The other homozygous disease-causing variants result in phenotypes
that have not yet been observed in either the individual or in that
individual’s family history, perhaps reflecting either low expressivity or
late-onset. Observed heterozygous disease-causing mutations are primarily
childhood-onset without presentation in the individual, although they may
represent recessive conditions; thus, their failure to manifest may not
necessarily be indicative of poor reporting or curation quality.

Table 4: Amount of disease-causing and potentially disease-relevant variation
in the Serbian genome. Identified variants were searched against HGMD and
broken down into the phenotypic categories of HGMD. Variants were broken down
into exonic and noncoding as well as homozygous and heterozygous.

| Exome | Noncoding
---|---|---
| Hom | Het | Hom | Het
Disease-causing mutations (DM) | 2 | 9 | 4 | 6
Likely disease-causing mutations (DM?) | 29 | 51 | 8 | 31
Disease-associated polymorphisms with additional supporting functional evidence (DFP) | 78 | 139 | 203 | 301
Disease-associated polymorphisms (DP) | 233 | 356 | 189 | 322
Polymorphisms that affect gene/protein structure, function or expression but with no reported disease association (FP) | 63 | 95 | 95 | 130

The number of homozygous and heterozygous variants that are associated with
variants reported in HGMD. HGMD labels correspond to the strength and/or
evidence for the relationship between variant and disease.

We also identified several variants of potential pharmacogenetic relevance
using PharmGKB. Variants in PharmGKB are assigned Clinical Annotation Levels
of Evidence from variants with preliminary evidence (Level 4) to high
confidence variant-drug combinations with medically endorsed integration into
health systems (Level A1). The genome contains a single variant with a high
confidence annotation (Level 1B): rs2228001, associated with toxicity and
adverse drug reaction to cisplatin, a chemotherapeutic agent. A further 17
variants were annotated with moderate evidence to impact the dosage, efficacy,
metabolism and/or toxicity of drugs for diverse phenotypes including chronic
hepatitis C, organ transplantation rejection, glaucoma, depression,
schizophrenia, asthma, epilepsy and HIV infections, as well as several
chemotherapy drugs.

Table 5: Disease-causing variants observed in the proband. The table contains analysis of six homozygous variants form the sequenced genome that are listed in HGMD as disease-causing. Gene | Variant | rsID |  Phenotype
---|---|---|---
_MIR137HG_ |  NC_000001.10:g.98502934G$>$T | rs1625579 |  Schizophrenia increased risk
_SLC12A3_ |  NM_000339.2:c.1670-8C$>$T | NA |  Gitelman syndrome without hypomagnesaemia
_DUOXA2_ |  NM_207581.3:c.554+6C$>$T | NA |  Hypothyroidism
_F13A1_ |  NM_000129.3:c.-19+12C$>$A | rs2815822 |  Factor XIII deficiency
_CTNS_ | NP_004928.2:p.T260I | NA |  Cystinosis nephropathic
_PNPLA2_ | NP_065109.1:p.P481L | rs1138693 |  Myopathy late-onset

#### Pathogenicity prediction

In addition to known disease-associated variants, we identified missense
variants predicted to be pathogenic by MutPred2 [35]. Of the 11,206 missense
variants called by GATK, 9,329 passed all quality filters (annotated as
‘PASS’). Of these, 9,305 variants were unambiguously mapped to the correct
protein isoforms and hence were amenable for prediction by MutPred2. Based on
a score threshold of 0.8 (estimated 5% false positive rate), 95 missense
variants were predicted to be ‘pathogenic.’ Of these, 14 variants were found
in the homozygous state and 81 were found in the heterozygous state. Genes for
these variants were enriched in GO terms related to peptidase activity
(Supplementary Figure 2). A similar analysis for disease associations revealed
that the subject may be at risk for cardiovascular disorders (Supplementary
Table 1).

We assessed the pathogenicity of 180 nonsense and frameshifting insertion and
deletion variants with MutPred-LOF [34]. From this set, we identified a total
of 7 variants with scores above the 0.5 score threshold (corresponding to a 5%
false positive rate) (Supplementary Table 2). We also assessed the
pathogenicity of the 90 SNV splicing variants with MutPred Splice [31]. Of
these, 28 of the variants are scored at least 0.6 and were therefore
classified as a “Splice Affecting Variant” by MutPred Splice. Of these, one
variant is predicted to cause loss of natural 3’ splice sites, two variants
are predicted to interrupt cryptic 3’ splice sites, and three variants are
predicted to disrupt cryptic 5’ splice sites. These 28 variants are described
in Supplementary Table 3.

To ensure assessment of the complete variome of the proband, we utilized CADD
v1.3 [17] to evaluate all noncoding variants. To do so, we utilized a scaled
C-score cutoff of 20 to identify the 1% most damaging variants. In total, we
found 16 UTR variants, 1,630 intronic variants, 3,911 intergenic variants, 80
regulatory variants, 839/533 upstream/downstream variants, and 9 variants
annotated as “noncoding_change.” All of these were predicted to be
deleterious. The noncoding variants with the highest C-scores are described in
Supplementary Table 4.

##  4 Discussion

This work describes the first whole-genome sequencing of a Serbian individual.
Ancestry analysis positioned the Serbian individual in closest proximity to
the Croatian population, consistent with its South Slavic ancestry [20]. Our
analyses further support the hypothesis of gene flow between Neanderthal and
pan-European ancestral populations, with the level of introgression into the
Serbian genome being within the range observed in other European populations.
Previous genetic studies involving Slavic populations employed mitochondrial,
Y-chromosome and SNV-panel data to investigate the relationship between
geographic, genetic and linguistic distances [20, 10]. Consistent with this
work, our analyses expand the scope beyond Slavic populations and further
contribute to the understanding of human genetic variation and its geographic
distribution.

In contrast to studies using genotyping arrays [33, 23, 20, 10], the
availability of whole-genome sequences presents the opportunity for a high-
resolution individualized analysis. To this end, we found that the sequenced
genome contains a significant number of previously unobserved variants, which
emphasizes the importance of continued sequencing of a large number of
individuals, especially from smaller ethnic groups. Subsequent sequencing of
other Serbian individuals could provide further insight into these novel
variants; e.g., whether they are private to the population or to the
individual. Such results would in turn contribute important information
regarding variants that are currently considered to be rare, with implications
for improved variant interpretation. Furthermore, new algorithms and reduced
sequencing costs will have the potential to provide higher-quality analysis of
structural variants that has not been possible in this work. Our analysis also
found a number of variants of clinical and pharmacogenomic significance that
might extend beyond an individual’s disease risks to facilitate possible
future medical interventions. Such variants might contribute to better
outcomes in studies of disease penetrance, mechanistic understanding of
population risks, and database curation.

Recent advances in high-throughput sequencing and reduced costs of genotyping
have greatly facilitated whole-genome data generation, and have become key to
understanding both human phenotypes and early human history [33, 23]. However,
modern technology and cost structure continue to pose challenges in
determining and interpreting one’s genome [45]. Variation in read mapping and
variant calling contribute to the uncertainty of interpretation with different
software packages, identifying different sets of variants. We found that
inter-software discrepancies ranged from relatively small for SNVs to
considerable for insertions and deletions. Therefore, variant and genome
interpretation demand caution, since thousands of SNVs and tens of thousands
of indels may simply constitute genotyping errors [32, 46].

It is worth mentioning that in addition to the technical aspects of genome
sequencing, an important aspect of genome interpretation concerns psychosocial
uncertainty due to phenotypic and privacy-associated risks [50]. This study
has provided demographic analysis that supports the individual’s own sense of
Serbian ancestry; however, the finding of multiple predicted youth-onset
pathogenic mutations in a healthy individual provides cautionary lessons for
predictive medicine.

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