{"publication":{"abstract":"**Background:** Autoimmune and cardiovascular diseases are diagnostically complex, especially when multiple rare disorders co-occur in a single patient. This study presents a comprehensive genomic analysis of a 50-year-old female patient with mixed ancestry and a history of thrombotic thrombocytopenic purpura (TTP), systemic lupus erythematosus (SLE), and left ventricular non-compaction cardiomyopathy (LVNC).\n\n**Methods:** Whole genome sequencing (WGS) data was analyzed, and followed by a tiered variant prioritization strategy using the SEQ platform. A symptom-guided filtering approach was applied using Human Phenotype Ontology (HPO) terms associated with each of the patient’s conditions. Additional layers of interpretation included ACMG classification, minor allele frequency thresholds, and pathway enrichment analysis via g:Profiler.\n\n**Results:** Out of 5.9 million single nucleotide variants (SNVs), 209 of them passed initial filtering based on tier-based classification and population allele frequency (\\<1%). Furthermore, 42 candidate variants across 25 genes met ACMG criteria (excluding benign categories) and HPO-based phenotype filters aligned with the patient’s symptoms. Several variants were classified as VUS, yet mapped to symptom-relevant genes such as *DOCK11, C4A, C4B, ADA2,* and *DNASE1*, indicating potential modifier or overlapping effects between SLE and TTP. Notably, *TTN, RPL3L,* and *GET3* variants aligned with the patient’s cardiac features, while HLA gene variants appeared relevant to her autoimmune and thrombotic manifestations. SV overlaps across SLE, TTP, and LVNC are consistent with convergent mechanisms but lack statistical validation in this single patient case.\n\n**Conclusion:** This study demonstrates feasibility of a symptom-to-variant filtering strategy to generate phenotype-aligned candidate variants in a diagnostically challenging case. While many of the identified variants are classified as VUS, the integrated analysis of SNVs, SVs, and functional enrichment highlights candidate genes for further investigation. The findings support a polygenic or synergistic model underlying the patient’s complex presentation, requiring follow-up validation and testing across broad cohort studies.\n\n**Full paper download:** please contact, christine@researchtothepeople.org","body":"# Introduction\n\nAutoimmune diseases represent a diagnostic and therapeutic challenge due to their heterogeneous clinical presentations, overlapping symptoms, and multifactorial etiologies involving both genetic predisposition and environmental triggers.[](https://doi.org/10.1056/nejm200108023450506) [](https://doi.org/10.1016/0167-5699\\(93\\)90244-f) [](https://doi.org/10.1016/j.jaut.2009.09.008) Advances in genomic technologies, particularly next-generation sequencing (NGS), provides a crucial utility for deeper exploration of the genetic contributions to these complex conditions. Although NGS technologies such as whole genome sequencing (WGS) are now more accessible, interpretation of sequencing data remains limited in complex cases, particularly when disease phenotypes are atypical, rare, or incompletely characterized.[](https://doi.org/10.1038/gim.2013.99) [](https://doi.org/10.1038/nrg3555) [](https://doi.org/10.1038/s41436-018-0408-7)\n\nThis case involves a 50-year-old female patient of mixed Filipino and Caucasian ancestry with overlapping rare autoimmune and cardiovascular conditions. Her clinical history began at age five with an episode of epiglottitis, marking the onset of systemic immune dysregulation. Over the subsequent decades, she was diagnosed with thrombotic thrombocytopenic purpura (TTP), systemic lupus erythematosus (SLE), autoimmune autonomic ganglionopathy (AAG), and left ventricular non-compaction cardiomyopathy (LVNC). TTP was diagnosed at the age of 14 and followed a relapsing course, requiring hundreds of therapeutic plasma exchange treatments with fresh frozen plasma to manage recurrent episodes. The multifaceted nature of her conditions, coupled with persistent autoimmune features, raised the possibility of underlying genetic and immunological contributions. Therefore, WGS was obtained to investigate potential molecular mechanisms driving disease susceptibility, chronicity, and co-occurrence.[](https://doi.org/10.21428/a2fa6306.98b1af9a)\n\nUsing WGS data from the patient, a gene-based variant prioritization strategy was previously applied by Shen and colleagues, revealing several rare pathogenic and likely pathogenic variants across multiple genes.[](https://doi.org/10.21428/a2fa6306.98b1af9a) While that analysis provided foundational insight into possible genetic contributors, it primarily focused on predefined gene-disease associations. In this follow-up study, we expanded upon those findings by applying a symptom-driven variant filtering framework, querying symptoms relevant to SLE, TTP, and LVNC. This approach is based on the premise that clinical symptoms represent the phenotypic outcomes of underlying genetic variations. Therefore, integrating symptom-based filtering can reveal variants that contribute to overlapping or modifying effects across related conditions.[](https://doi.org/10.1038/s41467-023-39306-7) [](https://doi.org/10.1101/2023.08.02.23293212)\n\nIn this approach, we mapped the patient’s symptoms to Human Phenotype Ontology (HPO) terms.[](https://doi.org/10.1093/nar/gkaa1043) This is combined with a tiered prioritization strategy as described by Genomize,[](https://genomize.com/ai-driven-ngs-data-analysis-a-variant-prioritization-pipeline-for-precision-medicine/) and established classification criteria from the American College of Medical Genetics and Genomics (ACMG).[](https://doi.org/10.1038/gim.2015.30) Together, these layers enabled us to re-rank variants based not only on predicted pathogenicity but also on clinical relevance to the patient’s presentation, thereby retaining candidate variants potentially missed in prior gene-centric analyses.[](https://doi.org/10.1101/gr.160325.113)\n\n# Methods\n\n## Patient Sample and Sequencing\n\nGenomic DNA was extracted from a saliva sample obtained from a 50-year-old female patient. WGS was performed using the Illumina platform with PCR-free library preparation. Raw sequencing data were delivered in FASTQ format, as previously described,[](https://doi.org/10.21428/a2fa6306.98b1af9a) for downstream analysis.\n\n### Data Processing and Variant Calling\n\nSequencing data were processed and analyzed using the SEQ platform (Genomize Bilisim ve Biyoteknoloji AS, Istanbul, Turkey), a cloud-based, AI-supported system designed for clinical-grade NGS analysis.[](https://seq.genomize.com/) The platform integrates public and proprietary databases to enable dynamic, phenotype-driven variant interpretation, including HPO, gnomAD, dbSNP, ACMG and ClinVar.[](https://doi.org/10.1093/nar/gkaa1043) [](https://doi.org/10.1038/gim.2015.30) [](https://doi.org/10.1038/s41586-020-2308-7) [](https://doi.org/10.1093/nar/29.1.308) [](https://doi.org/10.1093/nar/gkx1153) A complete list of the resources and database versions used during the analysis is available in the **Supplementary Methods**.\n\nAnnotated variants were first categorized using the SEQ platform’s five-tier prioritization framework, which integrates clinical and population-level data to rank variants by predicted pathogenicity and phenotypic relevance.[](https://genomize.com/ai-driven-ngs-data-analysis-a-variant-prioritization-pipeline-for-precision-medicine/) For downstream analysis, only Tier I-III variants (high-priority or disease-relevant candidates) were retained. Variants classified as Tier IV or V, which typically represent benign or low-confidence findings, were deprioritized to improve interpretive focus.\n\n### Symptom-to-Variant Filtering Strategy\n\nTo improve the precision of variant prioritization, we employed a symptom-guided strategy. Symptom-specific virtual gene panels were constructed using relevant HPO terms mapped to the patient’s three core disease domains: (i) SLE, (ii) TTP, and (iii) LVNC. Relevant HPO terms are detailed in **Supplementary Methods**. Briefly, we employed a lenient keyword-based filtering strategy for HPO terms, and identified those containing the phrases “lupus”, “thrombo”, and “left ventricular”. This initial filtering was intended to capture a broad phenotype spectrum and avoid early exclusion of potentially relevant features in this complex case.\n\nAdditional criteria for filtering the variants were based on:\n\n- **ACMG classification,** including pathogenic (P), likely pathogenic (LP), variants of uncertain significance (VUS), and subcategories such as VUS+ and VUS++.[](https://doi.org/10.1038/gim.2015.30)Particularly, VUS+ and VUS++ criteria represent the variants with some evidence of pathogenicity, but not enough to meet the formal LP criteria.[](https://doi.org/10.1038/gim.2015.30)\n- **Exonic consequence**, focusing on coding variants as defined in **Supplementary Methods***.*\n- **Population frequency thresholds,** with a minor allele frequency (MAF) cutoff of \\<1% across all gnomAD subpopulations.[](https://doi.org/10.1038/s41586-020-2308-7) This threshold was applied to prioritize rare variants more likely to have functional or clinical impact, while excluding common polymorphisms unlikely to explain a rare or severe phenotype.[](https://doi.org/10.1038/nrg3555) [](https://doi.org/10.1038/gim.2015.30) [](https://doi.org/10.1038/s41586-020-2308-7) [](https://doi.org/10.1038/nature13127)\n\nWorkflow followed during the analyses for each disease area are detailed in **Figure 1.**\n\n## Pathway and Functional Enrichment Analysis\n\nFunctional enrichment analysis was conducted using g\\:Profiler[](https://doi.org/10.1093/nar/gkad347) to identify overrepresented Gene Ontology (GO) terms (Biological Process, Molecular Function, and Cellular Component), KEGG and Reactome pathways, and HPO terms.[](https://doi.org/10.1093/nar/gkaa1043) [](https://doi.org/10.1093/nar/gkv1070) [](https://doi.org/10.1093/nar/gkab1028) The input gene list included all prioritized variants for each condition (SLE, TTP, and LVNC) identified in this study. Numbers of input genes and the prioritized variants for these conditions are provided in their respective results sections. The analysis used the g:SCS algorithm to correct for multiple testing, with a significance threshold set at an adjusted p-value \\< 0.05. Only annotated genes from the *Homo sapiens* genome were included in the background.[](https://doi.org/10.1093/nar/gkad347)\n\n## Results\n\n### Overview of Variant Detection\n\nWGS of the patient yielded data suitable for clinical variant interpretation. The sequencing achieved an average depth of 45.51×, with 92.79% of the reference genome covered (5x: 91.91%, 20x: 90.81%, 50x: 43.80%).\n\nOut of **5,900,401 single nucleotide variants (SNVs)** initially identified, **209 variants** passed tier-based and frequency (\\<1%) filtering (full list in **Supplementary Results**). In parallel, **12,127 structural variants (SVs)** were detected during initial variant calling.\n\nTo further refine candidate variants, we applied a **symptom-guided filtering strategy**, integrating patient-specific features and HPO terms across three diagnostic categories: SLE, TTP, and LVNC. The analytical workflow is summarized in **Figure 1**. This follow-up approach extends prior findings and is designed as a feasibility demonstration of symptom-guided filtering in a single complex case.[](https://doi.org/10.21428/a2fa6306.98b1af9a)\n\n::::::figure{#fig-1 align=\"center\" type=\"image\" label=\"Figure 1\"}\n:::::image{width=\"100%\" alt=\"Analysis pipeline and obtained results for SNVs and SVs of the patient’s genome. Starting from the FASTQ file, patient’s WGS data was analyzed by using SEQ platform and promising variants (SNVs and SV\" src=\"/api/files/publications/full-text-symptom-guided-genomic/fig-01-63b82b30.png\"}\n:::::\n\n:::::figcaption\n**Figure 1.** **Analysis pipeline and obtained results for SNVs and SVs of the patient’s genome.** Starting from the FASTQ file, patient’s WGS data was analyzed by using SEQ platform and promising variants (SNVs and SVs) were identified. \\*: standardized HPO terms based on three main disease areas (**Supplementary Methods**), #: Prioritized ACMG classes including VUS, VUS+, VUS++, LP, and P.\n:::::\n::::::\n\n### Symptom-Guided Filtering: SLE-Related Phenotype\n\nFrom lupus-related symptom filtering, seven candidate variants were shortlisted (**Table 1**). Among these, ***C4A, C4B,*** and ***DNASE1*** were notable. While classified as **VUS** under ACMG criteria, they align with **known SLE biology** and HPO annotations. In addition, immune-deficiency-related genes such as ***ADA2, CFTR,*** and ***DOCK11*** were implicated. Interestingly, ***CFTR***, primarily associated with cystic fibrosis, was also annotated with **HP:0002725 (systemic lupus erythematosus)** in HPO.\n\n::::::figure{#table-1 type=\"table\" label=\"Table 1\"}\n| **Gene** | **Consequences** | **Variant ID** | **dbSNP** | **Pathogenicity** | **Associated Diseases** | **GT** |\n| --- | --- | --- | --- | --- | --- | --- |\n| *DOCK11* | Splice Polypyrimidine Tract Variant | chrX:118680470 C-\\>T | rs199582396 | VUS | · XL,XR \\| Autoinflammatory disease, multisystem, with immune dysregulation
· XR \\| Early-onset immune dysregulation due to dock11 complete deficiency
· XR \\| Early-onset immune dysregulation with autoimmunity due to dock11 partial deficiency | HT |\n| *C4A* | Splice Region Variant | chr6:31991593 C-\\>T | rs879873173 | VUS | · Blood group, rodgers;
· AR \\| C4a deficiency
· AD,AR \\| Autosomal systemic lupus erythematosus
· Mu \\| Behçet disease
· Immunodeficiency due to a classical component pathway complement deficiency | HT |\n| *C4B* | Splice Region Variant | chr6:32024331 C-\\>T | rs369924 | VUS | · AR \\| Complement component 4b deficiency
· Immunodeficiency due to a classical component pathway complement deficiency
· Systemic lupus erythematosus | HT |\n| *C1QB* | Missense Variant | chr1:22660957 T-\\>C | rs148746097 | VUS | · AR \\| C1q deficiency
· Immunodeficiency due to a classical component pathway complement deficiency | HT |\n| *CFTR* | Missense Variant | chr7:117592032 G-\\>A | rs121908759 | VUS | · AD \\| Bronchiectasis with or without elevated sweat chloride 1
· AR \\| Cystic fibrosis
· Hypertrypsinemia, neonatal
· AD \\| Pancreatitis, hereditary
· Sweat chloride elevation without cf
· AR,Mu \\| Congenital bilateral absence of vas deferens | HT |\n| *DNASE1* | Missense Variant | chr16:3657022 C-\\>G | rs1799891 | VUS | · AD,AR \\| Autosomal systemic lupus erythematosus | HT |\n| *ADA2* | Splice Polypyrimidine Tract Variant | chr22:17191821 AG-\\>A | rs767647109 | VUS | · AR \\| Vasculitis, autoinflammation, immunodeficiency, and hematologic defects syndrome
· AR \\| Adenosine deaminase 2 deficiency
· AD \\| Diamond-blackfan anemia
· AR \\| Polyarteritis nodosa
· AD,AR \\| Sneddon syndrome | HT |\n\n:::::figcaption\n**Table 1.** **Lupus-Related Variants from Symptom-Guided Analysis**\n:::::\n::::::\n\n### Symptom-Guided Filtering: TTP-Related Phenotype\n\nSymptom-based filtering for thrombotic/thrombocytopenic features yielded **40 variants across 23 genes**:\n\n- A **homozygous stop-gained/frameshift** variant in ***CBS*** was classified as **LP** due to its impact on protein truncation and known association with **hyperhomocysteinemia and thrombosis**.[](https://doi.org/10.1007/springerreference_109524) [](https://doi.org/10.1002/humu.10089)\n- A **heterozygous frameshift variant** in ***NKX2-6*** (VUS+) has prior links to cardiac malformations and vascular pathology.[](https://doi.org/10.1002/ajmg.a.61550) [](https://doi.org/10.3892/ijmm.2014.1971)\n\nAdditional variants in ***TTN***, ***HLA-B***, ***HLA-DQA1***, and ***HLA-DQB1*** indicated **compound heterozygosity** with multiple missense changes, suggesting **additive pathogenic potential.** Overlap was also noted with six lupus-related genes (*DOCK11, C4A, C4B, CFTR, DNASE1, ADA2*), hinting at shared mechanisms.\n\n::::::figure{#table-2 type=\"table\" label=\"Table 2\"}\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n

Gene

Consequences

Variant ID

dbSNP

Pathogenicity

Associated Diseases

GT

CBS

Stop Gained;
Frameshift Variant

chr21:43063074 A->ATGATCTGCAGAGGGCGCGGCTTCAGGGCTCAAGCCCAGCAAAAGCCCCACCTGGATGATCCACCCCAG

-

LP

· AR | Homocystinuria, b6-responsive and nonresponsive types

· AR | Thrombosis, hyperhomocysteinemic

· AR | Classic homocystinuria

· AR | Homocystinuria due to cystathionine beta-synthase deficiency

HM

NKX2-6

Frameshift Variant

chr8:23702476 GTGGCTGCCAGA->G

-

VUS+

· Conotruncal heart malformations

· Persistent truncus arteriosus

· Common arterial trunk

· AR | congenital heart disease

· AR | Conotruncal heart malformations

· AD,Mu | Tetralogy of fallot

HT

TCIRG1

Missense Variant

chr11:68050648 G->A

rs764312517

VUS

· AR | Osteopetrosis, autosomal recessive 1

· AD | Autosomal dominant severe congenital neutropenia

·AR | Autosomal recessive osteopetrosis

·AR,XR | Dysosteosclerosis

· AR | Intermediate osteopetrosis

HT

KARS1

Missense Variant

chr16:75631490 C->T

rs149772470

VUS

· AR | Charcot-marie-tooth disease, recessive intermediate, b

· AR | Deafness, congenital, and adult-onset progressive leukoencephalopathy

· AR | Leukoencephalopathy, progressive, infantile onset, with or without deafness

· AR | Autosomal recessive nonsyndromic hearing loss 89

· AR | Early-onset progressive leukoencephalopathy-central nervous system calcification-deafness-visual impairment syndrome

· AR | Rare autosomal recessive non-syndromic sensorineural deafness type dfnb

HT

DOCK11

Splice Polypyrimidine Tract Variant

chrX:118680470 C->T

rs199582396

VUS

· XL,XR | Autoinflammatory disease, multisystem, with immune dysregulation

· XR | Early-onset immune dysregulation with autoimmunity due to dock11 partial deficiency

HT

C4A

Splice Region Variant

chr6:31991593 C->T

rs879873173

VUS

· Blood group, rodgers

· AR | C4a deficiency

· AD,AR | Autosomal systemic lupus erythematosus

· Mu | Behçet disease

· AR | Complement component 4a deficiency

· Immunodeficiency due to a classical component pathway complement deficiency

· Systemic lupus erythematosus

HT

C4B

Splice Region Variant

chr6:32024331 C->T

rs369924

VUS

· C4b deficiency

· AR | Complement component 4b deficiency

· Immunodeficiency due to a classical component pathway complement deficiency

· Systemic lupus erythematosus

HT

CFTR

Missense Variant

chr7:117592032 G->A

rs121908759

VUS

· AD | Bronchiectasis with or without elevated sweat chloride 1

· AR | Cystic fibrosis

· Hypertrypsinemia, neonatal

· AD | Pancreatitis, hereditary

· Sweat chloride elevation without cf

· AR,Mu | Congenital bilateral absence of vas deferens

· AD,AR,XR | Male infertility with azoospermia or oligozoospermia due to single gene mutation

HT

TTN

Missense Variant

chr2:178552794 C->A

rs959063938

VUS

· AD | Cardiomyopathy, dilated, 1g

· AD | Cardiomyopathy, familial hypertrophic, 9

· AR | Congenital myopathy 5 with cardiomyopathy

· AR | Muscular dystrophy, limb-girdle, autosomal recessive 10

· AD | Myopathy, myofibrillar, 9, with early respiratory failure

· AD | Tibial muscular dystrophy, tardive

· AD | Arrhythmogenic right ventricular cardiomyopathy

· AR | Autosomal recessive centronuclear myopathy

· Classic multiminicore myopathy

· AR | Early-onset myopathy with fatal cardiomyopathy

· AD | Familial atrial fibrillation

· Familial isolated arrhythmogenic ventricular dysplasia, biventricular form /left dominant form /right dominant form

· AD,AR,M,XR | Familial isolated dilated cardiomyopathy

· AD | Hereditary myopathy with early respiratory failure

· AD | Hypertrophic cardiomyopathy

· Inherited isolated arrhythmogenic cardiomyopathy, dominant-left variant /dominant-right variant /biventricular variant

· AD | Myopathy, myofibrillar, 9, with early respiratory failure

· AR | Titin-related limb-girdle muscular dystrophy r10

· AR | Ttn-related myopathy

c. HT

TTN

Missense Variant

chr2:178563172 C->T

rs1027584774

VUS

c. HT

TTN

Missense Variant;
Splice Region Variant

chr2:178688679 T->C

rs976519701

VUS

c. HT

TTN

Missense Variant

chr2:178528761 T->C

rs781108499

VUS

c. HT

HLA-B

Splice Polypyrimidine Tract Variant

chr6:31356966 G->C

rs41563412

VUS

· Mu | Ankylosing spondylitis, susceptibility to, 1

· Severe cutaneous adverse reaction, susceptibility to

· Stevens-johnson syndrome, susceptibility to

· Toxic epidermal necrolysis, susceptibility to

· Mu | Behçet disease

· Mu | Giant cell arteritis

· Mu | Reactive arthritis

· Takayasu arteritis

c. HT

HLA-B

Frameshift Variant

chr6:31356824 C->CA

rs9281379

VUS

c. HT

HLA-B

Missense Variant

chr6:31356280 C->A

rs12697943

VUS

c. HT

HLA-B

Missense Variant

chr6:31356822 T->G

rs1050538

VUS

c. HT

HLA-DQA1

Missense Variant

chr6:32641456 T->C

rs3188043

VUS

· AR,Mu | Celiac disease, susceptibility to

· Mu | Adult-onset myasthenia gravis

· AR | Idiopathic achalasia

c. HT

HLA-DQA1

Missense Variant

chr6:32641444 G->C

rs12722061

VUS

c. HT

HLA-DQA1

Missense Variant

chr6:32641450 A->C

rs3207985

VUS

c. HT

HLA-DQA1

Missense Variant

chr6:32641436 G->A

rs3207983

VUS

c. HT

HLA-DQA1

Missense Variant

chr6:32641445 A->T

rs3208105

VUS

c. HT

HLA-DQA1

Missense Variant

chr6:32641502 C->T

rs9272709

VUS

c. HT

HLA-DQA1

Missense Variant

chr6:32641451 G->A

rs28383449

VUS

c. HT

HLA-DQA1

Missense Variant

chr6:32641454 A->G

rs12722069

VUS

c. HT

HLA-DQA1

Missense Variant

chr6:32641501 G->C

rs1142335

VUS

c. HT

HLA-DQB1

Missense Variant

chr6:32664926 G->A

rs9274395

VUS

· AR,Mu | Celiac disease, susceptibility to

· AD | Creutzfeldt-jakob disease, variant, resistance to

· Mu | Multiple sclerosis, susceptibility to

· AR | Idiopathic achalasia

· Narcolepsy type 1

· Narcolepsy type 2

c. HT

HLA-DQB1

Missense Variant

chr6:32664992 T->C

rs767838657

VUS

c. HT

HLA-DQB1

Missense Variant

chr6:32664971 T->A

rs750855857

VUS

c. HT

HLA-DQB1

Missense Variant

chr6:32664993 A->T

rs281862065

VUS

c. HT

DNASE1

Missense Variant

chr16:3657022 C->G

rs1799891

VUS

· AD | Systemic lupus erythematosus

HT

SCN11A

Splice Polypyrimidine Tract Variant

chr3:38946916 C->CA

rs552650164

VUS

· AD | Episodic pain syndrome, familial, 3

· AD | Hereditary sensory and autonomic neuropathy

· AD | Familial episodic pain syndrome with predominantly lower limb involvement

· AD | Hereditary sodium channelopathy-related small fibers neuropathy

HT

SEC61A1

Splice Polypyrimidine Tract Variant

chr3:128066940 G->A

rs773757716

VUS

· AD | Immunodeficiency, common variable, 15

· AD | Neutropenia, severe congenital, 11

· AD | Tubulointerstitial kidney disease, autosomal dominant, 5

· AD | Hyperuricemic nephropathy, familial juvenile type 4

· AD | Sec61a1 deficiency

HT

TBXAS1

Missense Variant

chr7:139953397 C->A

rs5768

VUS

· AR | Ghosal hematodiaphyseal syndrome

HT

CUBN

Splice Polypyrimidine Tract Variant

chr10:16835211 G->T

rs190546478

VUS

· AR | Imerslund-grasbeck syndrome

· AR | Proteinuria, chronic benign

HT

RPL3L

Missense Variant

chr16:1946720 G->A

rs374470658

VUS

· AR | Cardiomyopathy, dilated, 2d

· AR | Dilated cardiomyopathy

· AD,AR,M,XR | Familial isolated dilated cardiomyopathy

HT

DTNBP1

Splice Region Variant;
Splice Polypyrimidine Tract Variant

chr6:15593088 G->GAA

rs199770715

VUS

· AR | Hermansky-pudlak syndrome 7

· Hermansky-pudlak syndrome due to bloc-1 deficiency

HT

ADA2

Splice Polypyrimidine Tract Variant

chr22:17191821 AG->A

rs767647109

VUS

· AD,AR | Sneddon syndrome

· AR | Vasculitis, autoinflammation, immunodeficiency, and hematologic defects syndrome

· AR | Adenosine deaminase 2 deficiency

· AD | Diamond-blackfan anemia

· AR | Polyarteritis nodosa

HT

GET3

Missense Variant

chr19:12737533 G->T

rs749041448

VUS

· AR | Cardiomyopathy, dilated, 2h

· AD,AR,M,XR | Familial isolated dilated cardiomyopathy

HT

GBA1

Missense Variant;
Splice Region Variant

chr1:155236246 G->A

rs75548401

VUS

· AR | Gaucher disease, perinatal lethal

· AD | Lewy body dementia, susceptibility to

· AD,Mu | Parkinson disease, late-onset, susceptibility to

· Fetal gaucher disease

· AR | Gaucher disease

· AR | Gaucher disease type 1

· AR | Gaucher disease type 2

· AR | Gaucher disease type 3

· AR | Gaucher disease-ophthalmoplegia-cardiovascular calcification syndrome

· AD | Parkinson disease

HT

GALC

Missense Variant

chr14:87986597 T->C

rs147313927

VUS

· AR | Krabbe disease

· Adult krabbe disease

· Infantile krabbe disease

· Late-infantile/juvenile krabbe disease

HT

\n\n:::::figcaption\n**Table 2.** **Variants associated with TTP-related phenotypes.**\n:::::\n::::::\n\n### Symptom-Guided Filtering: LVNC-Related Phenotype\n\nSeven variants were associated with LVNC-related HPO terms. Of these:\n\n- **Four variants** resided in ***TTN***, a key gene encoding titin and heavily implicated in **cardiomyopathies and arrhythmias**.[](https://doi.org/10.3389/fphys.2019.01436)\n- ***RPL3L*** and ***GET3*** also emerged with cardiomyopathy annotations.[](https://doi.org/10.1186/s12920-023-01567-y) [](https://doi.org/10.1038/s41467-023-37838-6)\n- All variants caused **missense substitutions** and were classified as **VUS**, highlighting uncertain but plausible relevance.\n\nNotably, six of the seven LVNC-related variants were shared with the TTP gene set, potentially reflecting on a **phenotypic and genetic overlap** between the two conditions.\n\n::::::figure{#table-3 type=\"table\" label=\"Table 3\"}\n\n\n\n\n\n\n\n\n\n\n\n

Gene

Consequences

Variant ID

dbSNP

Pathogenicity

Associated Diseases

GT

TTN

Missense Variant

chr2:178552794 C->A

rs959063938

VUS

· AD | Cardiomyopathy, dilated, 1g

· AD | Cardiomyopathy, familial hypertrophic, 9

· AR | Congenital myopathy 5 with cardiomyopathy

· AR | Muscular dystrophy, limb-girdle, autosomal recessive 10

· AD | Myopathy, myofibrillar, 9, with early respiratory failure

· AD | Tibial muscular dystrophy, tardive

· AD | Arrhythmogenic right ventricular cardiomyopathy

· AR | Autosomal recessive centronuclear myopathy

· Classic multiminicore myopathy

· AR | Early-onset myopathy with fatal cardiomyopathy

· AD | Familial atrial fibrillation

· Familial isolated arrhythmogenic ventricular dysplasia, biventricular form /left dominant form /right dominant form

· AD,AR,M,XR | Familial isolated dilated cardiomyopathy

· AD | Hereditary myopathy with early respiratory failure

· AD | Hypertrophic cardiomyopathy

· Inherited isolated arrhythmogenic cardiomyopathy, dominant-left variant /dominant-right variant /biventricular variant

· AD | Myopathy, myofibrillar, 9, with early respiratory failure

· AR | Titin-related limb-girdle muscular dystrophy r10

· AR | Ttn-related myopathy

c.HT

TTN

Missense Variant

chr2:178563172 C->T

rs1027584774

VUS

c.HT

TTN

Missense Variant;
Splice Region Variant

chr2:178688679 T->C

rs976519701

VUS

c.HT

TTN

Missense Variant

chr2:178528761 T->C

rs781108499

VUS

c.HT

RPL3L

Missense Variant

chr16:1946720 G->A

rs374470658

VUS

· AR | Cardiomyopathy, dilated, 2d

· AR | Dilated cardiomyopathy

· AD,AR,M,XR | Familial isolated dilated cardiomyopathy

HT

GET3

Missense Variant

chr19:12737533 G->T

rs749041448

VUS

· AR | Cardiomyopathy, dilated, 2h

· AD,AR,M,XR | Familial isolated dilated cardiomyopathy

HT

AIP

Missense Variant

chr11:67483205 G->A

rs145047094

VUS

· AD,SMu | Pituitary adenoma 1, multiple types

· Acromegaly

· AD | Growth hormone secreting pituitary adenoma 1

· AD,XD | Pituitary gigantism

· AD | Prolactinoma

· Silent pituitary adenoma

HT

\n\n:::::figcaption\n**Table 3.** **LVNC-related variants identified via HPO symptom filtering.**\n:::::\n::::::\n\n### Structural Variants (SV)\n\nSV analysis identified:\n\n- **9 SLE-related**, **76 TTP-related**, and **52 LVNC-related** SVs (**Figure 2**; **Supplementary Results**).\n- We observed 7 and 17 SV overlaps respectively; these counts are descriptive and consistent with shared mechanisms but not statistically tested.\n- One lupus-specific **duplication (*IGHG2, MIR8071-2, COPDA1*)** implicates regulatory disruption of humoral immune response and immune signaling.\n\n::::::figure{#fig-2 align=\"center\" type=\"image\" label=\"Figure 2\"}\n:::::image{width=\"76%\" alt=\"Venn Diagram showing distinct and mutual SVs across patient’s phenotypes. SVs among SLE, TTP, and LVNC phenotypes are represented with blue, red and green circles, respectively. No overlapping SVs bet\" src=\"/api/files/publications/full-text-symptom-guided-genomic/fig-02-a6764288.png\"}\n:::::\n\n:::::figcaption\n**Figure 2.** **Venn Diagram showing distinct and mutual SVs across patient’s phenotypes.** SVs among SLE, TTP, and LVNC phenotypes are represented with blue, red and green circles, respectively. No overlapping SVs between SLE and LVNC features are observed.\n:::::\n::::::\n\n::::::figure{#table-4 type=\"table\" label=\"Table 4\"}\n| **Position** | **SV Type** | **Gene/Transcript Info** | **Pathogenicity** |\n| --- | --- | --- | --- |\n| chr14 105630632-105649462*\\** | DUP | *IGHG2,* ENST00000641095, Exon 1-6 *MIR8071-2*, ENST00000616772, Exon 1 COPDA1, ENST00000497397, Exon 1-3 | VUS |\n| chr16 88646281-88646336*\\** | DUP | *CYBA*, ENST00000261623, Intron 4 | VUS |\n| chr22 17184893-17184948 | DEL | *ADA2*, ENST00000399837, Intron 7 | VUS |\n| chrX 118494897-118494967 | DEL | *DOCK11*, ENST00000276202, Upstream (-848) | VUS |\n| chr2 203873328-203873369 | DEL | *CTLA4*, ENST00000648405, Exon 4 | VUS |\n| chr4 101556664-101556697 | DEL | *BANK1*, ENST00000504592, Intron 2 | VUS |\n| chr11 128478355-128478381 | DEL | *ETS1*, ENST00000392668, Intron 8 | VUS |\n| chr13 102652398-102652435 | DEL | *TPP2*, ENST00000376052, Intron 24 | VUS |\n| chr19 33458552-33458580 | DEL | *PEPD*, ENST00000244137, Intron 9 | VUS |\n\n:::::figcaption\n**Table 4.** **Lupus-Thrombotic/Thrombocytopenic**\n:::::\n::::::\n\n*\\*: Variants that are unique for lupus-related features only*\n\n::::::figure{#table-5 type=\"table\" label=\"Table 5\"}\n| **Position** | **SV Type** | **Gene/Transcript Info** | **Pathogenicity** |\n| --- | --- | --- | --- |\n| chr1 236755585-236755675 | DEL | *ACTN2*, ENST00000366578, Intron 17 | VUS |\n| chr12 21863283-21863328 | DEL | *ABCC9*, ENST00000261200, Intron 19 ENSG00000257022, ENST00000539874, Intron 2 | VUS |\n| chr15 71199892-71199918 | DEL | *THSD4*, ENST00000261862, Intron 3 | VUS |\n| chr5 156187513-156187555 | DEL | *SGCD*, ENST00000517913, Intron 3 | VUS |\n| chr11 2458634-2458661 | DEL | *KCNQ1*, ENST00000155840, Intron 1 | VUS |\n| chr11 2699488-2699514 | DEL | *KCNQ1*, ENST00000155840, Intron 11 *KCNQ1OT1*, ENST00000597346, Exon 1 | VUS |\n| chr10 68193811-68193840 | DEL | *MYPN*, ENST00000358913, Intron 13 | VUS |\n| chr4 47617955-47617984 | DEL | *CORIN*, ENST00000273857, Intron 19 | VUS |\n| chr2 43835238-43835264 | DEL | *ABCG5*, ENST00000405322, Intron 2 | VUS |\n| chr11 19197365-19197396 | DEL | *CSRP3*, ENST00000265968, Intron 1 *CSRP3-AS1*, ENST00000527978, Intron 1 | VUS |\n| chrX 32554865-32554900 | DEL | *DMD*, ENST00000357033, Intron 16 | VUS |\n| chrX 32601661-32601690 | DEL | *DMD*, ENST00000357033, Intron 12 | VUS |\n| chr19 38539893-38539920 | DEL | *RYR1*, ENST00000359596, Intron 84 | VUS |\n| chr20 44162766-44162791 | DEL | *JPH2*, ENST00000372980, Intron 1 | VUS |\n| chr1 3105928-3181110 | DEL, DUP | *PRDM16*, ENST00000270722, Intron 1 | VUS |\n| chr15 48547722-48547755 | DEL | *FBN1*, ENST00000316623, Intron 6 | VUS |\n| chr12 112442831-112442858 | DEL | *PTPN11*, ENST00000351677, Intron 1 | VUS |\n\n:::::figcaption\n**Table 5.** **Thrombotic/Thrombocytopenic - Left Ventricular**\n:::::\n::::::\n\n### Functional Enrichment Analysis\n\nTo contextualize the biological roles of the prioritized variant-associated genes, we conducted **functional enrichment analysis** using **g:Profiler** across multiple biological and phenotypic ontologies including **GO**, **HPO**, **KEGG**, **Reactome**, and **WikiPathways** databases.\n\n##### *Analysis of SNV-Associated Genes*\n\nWe first analyzed the SNV-containing gene set derived from the patient's genome data. This included 24 genes (*C4A*, *C4B*, *C1QB*, *HLA-DQB1*, *DNASE1*, *HLA-B*, *ADA2*, *SEC61A1*, *GBA1*, *DOCK11*, *TCIRG1*, *CFTR*, *CBS*, *CUBN*, *AIP*, *SCN11A*, *TBXAS1*, *KARS1*, *GALC*, *NKX2-6*, *TTN*, *RPL3L*, *GET3*, *DTNBP1*), excluding ***HLA-DQA1*** which was not recognized by g:Profiler.\n\n**HPO:** A total of **65 significant HPO terms** were enriched (**Figure 3A**). Among the most significant were:\n\n- *Abnormality of blood and blood-forming tissues* (HP:0001871), all 24 genes matched.\n- *Abnormality of the cardiovascular system* (HP:0001626), 23 genes matched.\n- Other HPO terms included oral ulcers, complement system defects, thrombocyte and platelet abnormalities, lupus, and autoantibody positivity.\n\nUpon counting which genes appeared the most across the identified terms, we saw: *C4A* (annotated to 63 HPO terms), followed by *DNASE1* (51), *C4B* (50), *HLA-B* (49), and *ADA2* (46). Interestingly, *CBS* and *NKX2-6*, which were previously flagged as TTP-associated variants, were involved in 21 and 13 HPO terms respectively, related to vasculature and inflammatory features in skin and joints. In addition, cardiovascular-related terms included *AIP* (18 terms), *TTN* and *RPL3L* (11 each), and *GET3* (9).\n\nIn total, 5 KEGG Pathways terms were enriched, including *systemic lupus erythematosus*, *phagosome*, *pertussis, Staphylococcus aureus infection and Vibrio cholera infection* **(Figure 3B)**. Notably, *C4A*, *C4B*, *C1QB*, and *HLA-DQB1* were commonly implicated in immune-related pathways. *TCIRG1* and *SEC61A1* were enriched in the *phagosome*-related pathways, suggesting an immunoregulatory axis independent of the complement genes.\n\n::::::figure{#fig-u3 align=\"center\" type=\"image\"}\n:::::image{width=\"100%\" alt=\"\" src=\"/api/files/publications/full-text-symptom-guided-genomic/fig-03-3e3629ba.png\"}\n:::::\n::::::\n\n::::::figure{#fig-3 align=\"center\" type=\"image\" label=\"Figure 3\"}\n:::::image{width=\"100%\" alt=\"Functional enrichment analyses depicting the top list of (A) HPO terms, and (B) KEGG pathways associate with the genes harboring variants observed in the patients WGS data.\" src=\"/api/files/publications/full-text-symptom-guided-genomic/fig-04-b772fab8.png\"}\n:::::\n\n:::::figcaption\n**Figure 3.** Functional enrichment analyses depicting the top list of (A) HPO terms, and (B) KEGG pathways associate with the genes harboring variants observed in the patients WGS data.\n:::::\n::::::\n\n#### Incorporating SVs, Summary and Comparative Insights\n\nWe also conducted a parallel enrichment analysis using genes affected by SVs, which is provided in **Supplementary Results**. While our main results emphasized SNVs due to their higher resolution and immediate clinical interpretability, the SV-inclusive analysis offered an additional layer to validate and broaden the findings from the SNV-based results. This combined view allowed us to identify shared and distinct patterns in genes, pathways, and ontologies impacted by either variant type, and presented potential contributors that may have been overlooked when analyzing SNVs or SVs in isolation.\n\nThe SV gene sets showed enrichment of similar categories including immune dysfunction, platelet and thrombocyte defects, abnormal heart morphology, and autoimmune diseases, consistent with SNV-only analyses - and interpreted as exploratory rather than confirmatory. Several SV-associated genes were enriched for abnormalities in thrombocyte physiology and cardiac structure, in line with the patient’s clinical diagnoses of TTP and LVNC. However, immune-related enrichments, especially *autoimmunity* and *HLA-related pathways*, were more prominent in SNV-driven results, likely due to the presence of *C4A, C4B, HLA-DQB1*, and *HLA-B* in the SNV set. Looking into both SV- and SNV-based enrichment, we did not observe any additional pathways or ontologies of significance.\n\nThus, we primarily focused our reporting and discussion on the SNV-based results to maintain clinical interpretability, but we acknowledge that SVs contribute meaningfully to the broader genomic profile. The enrichment patterns remained convergent across both variant types, and are consistent with known immunologic and cardiovascular biology.\n\n# Discussion\n\nThis study demonstrates the utility of a symptom-to-variant approach for whole genome interpretation in a diagnostically complex case involving multiple rare and autoimmune conditions. Previously, Shen and colleagues performed genomic analysis of this patient, and employed a predefined gene-disease association strategy. The approach identified numerous variants (allele frequency \\<5%) associated with SLE, TTP, and LVNC when considered as separate disease entities.[](https://doi.org/10.21428/a2fa6306.98b1af9a) Our current study aimed to complement those findings by shedding light on possible interconnections between diseases and underlying symptoms, rather than treating each in isolation. While doing so, a stringent MAF threshold (\\<1%) was employed to exclude common polymorphisms that remain limited in explaining rare or severe phenotypes.\n\nIn this patient’s case, the majority of variants identified through WGS were classified as VUS, making it challenging to interpret. This is partially also because pathogenicity alone is not sufficient for clinical relevance, particularly in patients with atypical or overlapping phenotypes. For example, genetic pathogenicity may indicate a predisposition to certain pathologies but does not guarantee disease manifestation, as clinical expression can depend on environmental influences or the presence of additional genetic modifiers that may be protective.[](https://doi.org/10.1038/s41525-023-00386-5) [](https://doi.org/10.1073/pnas.2516291123) Conversely, disease may still develop even when major known genetic drivers appear within normal population ranges.[](https://doi.org/10.3390/genes13071275)\n\nBy applying ACMG guidelines in combination with symptom-driven filtering, we were able to systematically evaluate and reprioritize variants including VUS according to their potential clinical relevance.[](https://doi.org/10.1038/gim.2015.30) This framework allowed us to highlight candidate variants that align with the patient’s disease relevant features, including potential modifiers and cross-condition variants that may have been overlooked in strictly disease-focused workflows. However, leveraging VUS also carries the risk of false positives, as many of these variants lack sufficient functional data or clear genotype-phenotype correlations. In particular, the impact of a VUS on gene function, expression, or genomic stability often remains poorly characterized. To mitigate this risk, we applied stringent filtering criteria, prioritizing rare variants based on their genomic location and allele frequency, including variants from incidental findings, especially in the case of single nucleotide variants (SNVs).[](https://doi.org/10.1101/gr.183483.114) [](https://doi.org/10.1056/nejmra1312543) Structural variants (SVs), while considered, were filtered with slightly less stringency, as discussed later.\n\nIn line with this, heterozygous variants in autosomal recessive (AR) genes were also considered due to their potential clinical relevance. While AR conditions typically require biallelic pathogenic variants for full disease manifestation, numerous studies have reported cases where heterozygous carriers exhibit partial phenotypes or an increased risk of related conditions due to modifier effects, incomplete penetrance, or oligogenic inheritance.[](https://doi.org/10.1038/gim.2013.99) [](https://doi.org/10.1016/j.cell.2011.09.008) [](https://doi.org/10.1186/s13059-016-1107-9) For example, although *ADA2, DNASE1*, and *CTLA4* are generally associated with recessive inheritance, heterozygous variants have been implicated in broader or atypical immune phenotypes.[](https://doi.org/10.1056/nejmoa1307361) [](https://doi.org/10.1038/ng.975) Moreover, in immune-mediated and cardiomyopathic conditions, heterozygous variants in genes typically associated with autosomal recessive inheritance have been reported to contribute to atypical or subclinical phenotypes, such as in genes *ADA2, CTLA4, PTPN11, DSG2,* and *PKP2*.[](https://doi.org/10.1056/nejmoa1307361) [](https://doi.org/10.1126/science.1255904) [](https://doi.org/10.1161/circgenetics.113.000338) [](https://doi.org/10.1016/j.jacc.2018.04.078) [](https://doi.org/10.1038/s41436-020-00946-5)\n\nAn additional consideration was given for deep intronic variants and SVs, in support of the SNV based approach, and to account for compound heterozygosity. Accordingly, compound heterozygosity indicates the presence of two distinct pathogenic variants on separate alleles of the same gene. Compound heterozygosity can have causal roles, but standard short-read WGS may detect only one allele due to technical limitations, such as structural variation or coverage gaps.[](https://doi.org/10.1093/nar/gkx1153) [](https://doi.org/10.3389/fgene.2019.00426) Thus, even a single heterozygous variant in a symptom-relevant gene may warrant clinical consideration. In line with this, heterozygous variants and SVs were retained in our analysis when found in genes with known relevance to the patient’s symptoms, especially when rare and classified as P or LP, *pathogenic* and *likely pathogenic*, respectively. Moreover, the filtering approach for SNVs was initially confined to exonic variants. This decision was made because the sample size was limited, and drawing assumptions from non-coding regions would have been speculative and difficult to interpret. Therefore, in this context, WGS was not necessarily superior to WES for small variant analysis. However, a primary advantage of using WGS data was the ability to evaluate SVs.\n\nOut of 5.9 million SNVs, we identified 209 variants, despite most being annotated as VUS. We further refined this list using disease-relevant ontologies and ACMG pathogenicity criteria. This allowed a degree of stringency, and presented a list to 42 candidate variants. Two main observations emerged based on these variants: (i) they were distributed across 25 genes which are known to associate with the patient’s disease phenotypes, and (ii) they were shared across the TTP, SLE and LVNC-features. This tempted us to consider whether some of these variants have unknown pathogenicity, and more importantly whether there are shared mechanisms or modifier effects across the patient’s diseases. Most prioritized variants are VUS and are not clinically actionable without orthogonal confirmation (e.g., Sanger sequencing, polymerase chain reaction (PCR), segregation, and functional assays). Therefore, preclinical validations and analyses across large cohort studies are warranted, given that rare and complex nature of her conditions.[](https://doi.org/10.1016/j.ajhg.2017.07.014) Some of the genes that emerged from the analyses, thus require further attention. For instance, the genes *DOCK11, C4A, C4B, CFTR*, *DNASE1*, and *ADA2* represented these potential overlapping features between SLE and TTP.[](https://doi.org/10.1056/nejmoa1307361) [](https://doi.org/10.1186/s12979-021-00259-4) [](https://doi.org/10.1056/nejme2305431) [](https://doi.org/10.1172/jci96273) [](https://doi.org/10.1186/s12931-019-1103-1) [](https://doi.org/10.1136/ard.45.4.319) [](https://doi.org/10.1016/j.autrev.2023.103297)\n\nFunctional review of these genes supports their roles in immune pathways. *DOCK11* encodes a guanine nucleotide exchange factor involved in immune cell signaling, and both SNV and SV findings in this locus may contribute to dysregulated B- and T-cell function observed in lupus.[](https://doi.org/10.1186/s12979-021-00259-4) [](https://doi.org/10.1056/nejme2305431) Complement-related genes including *C4A, C4B, C1QB,* and *C1QC* are well-recognized lupus susceptibility loci, with deficiencies in classical complement components strongly linked to impaired immune complex clearance and increased risk of autoimmunity.[](https://doi.org/10.3389/fimmu.2016.00036) *ADA2* deficiency has been associated with systemic vasculopathy and immune dysregulation, and both small variants and intronic SVs in this gene may contribute to the vascular and inflammatory dysfunctions in this patient.[](https://doi.org/10.1056/nejmoa1307361) *DNASE1* encodes an endonuclease critical for clearing extracellular DNA; reduced function is a known lupus risk factor due to impaired clearance of apoptotic material and subsequent autoantibody generation.[](https://doi.org/10.1191/0961203302lu242oa) [](https://doi.org/10.1172/jci.insight.177003) *CFTR*, while primarily associated with cystic fibrosis, has been implicated in immune regulation, and variants in this gene may contribute to inflammatory processes that overlap with lupus phenotypes.[](https://doi.org/10.1172/jci96273) [](https://doi.org/10.1186/s12931-019-1103-1) Among SVs, common intronic changes were observed in *BANK1, ETS1, ADA2, PEPD,* and *CTLA4*, all of which have established or putative roles in B-cell signaling, T-cell regulation, and immune tolerance in lupus pathogenesis.[](https://doi.org/10.1056/nejmoa1307361) [](https://doi.org/10.1038/ng.79) [](https://doi.org/10.1615/critrevimmunol.2017020284) [](https://doi.org/10.1016/j.immuni.2018.12.023) [](https://doi.org/10.3389/fmolb.2021.723003) [](https://doi.org/10.1007/s00439-004-1244-1) [](https://doi.org/10.1186/s13075-018-1604-1) [](https://doi.org/10.3389/fimmu.2024.1447995) Of particular note, a lupus-specific duplication was identified spanning *IGHG2, MIR8071-2,* and *COPDA1*. Alterations in immunoglobulin heavy chain genes such as *IGHG2* may affect humoral responses, while the presence of a microRNA gene (*MIR8071-2*) and long noncoding RNA (*COPDA1*) suggests additional layers of post-transcriptional and regulatory effects that could influence immune function.[](https://doi.org/10.3389/fimmu.2018.01555) [](https://doi.org/10.1038/s41392-025-02168-0) [](https://doi.org/10.1177/20406223231153572) [](https://doi.org/10.1038/s41467-020-19242-6) Hence, the findings suggest convergent mechanisms involving complement pathways and immune cell regulation that may underlie both lupus and thrombotic phenotypes that require functional assays and further data to confirm relevance of the VUS in these cases.[](https://doi.org/10.1016/j.ajhg.2017.07.014)\n\nSeveral genes identified in this analysis were also found relevant to the patient’s cardiomyopathic and thrombotic phenotypes. *TTN*, which encodes titin, is an established gene in LVNC and dilated cardiomyopathy, and the presence of multiple variants including compound heterozygous changes suggests that titin dysfunction may contribute significantly to the patient’s cardiac manifestations.[](https://doi.org/10.3389/fphys.2019.01436) Supporting this, functional enrichment linked *TTN* to cardiovascular system abnormalities alongside *RPL3L* and *GET3*, both of which are involved in muscle contraction and protein homeostasis, respectively, and have been implicated in cardiomyopathic processes.[](https://doi.org/10.1186/s12920-023-01567-y) [](https://doi.org/10.1038/s41467-023-37838-6) [](https://doi.org/10.1161/circgen.119.002507) In the immune and thrombotic domain, *HLA-B*, *HLA-DQA1*, and *HLA-DQB1* were strongly represented in the enrichment analysis and are known to influence autoimmune susceptibility and thrombocytopenia, highlighting their potential relevance to the patient’s history of TTP.[](https://doi.org/10.3389/fimmu.2024.1447995) [](https://doi.org/10.1182/blood.2020005395) [](https://doi.org/10.1111/iji.70003) Two genes emerged as particularly attractive TTP-associated candidates: *CBS*, where the patient carries a homozygous stop-gained/frameshift variant classified as LP (likely pathogenic), and *NKX2-6*, which harbors a heterozygous frameshift variant classified as VUS+. Both were recurrently annotated in enrichment terms related to vascular and inflammatory abnormalities.[](https://doi.org/10.1007/springerreference_109524) [](https://doi.org/10.1002/humu.10089) [](https://doi.org/10.1002/ajmg.a.61550) [](https://doi.org/10.3892/ijmm.2014.1971) In addition, common structural variants were identified in cardiomyopathy-associated genes that code for ion channels, as well as musculoskeletal and connective tissue proteins. These genes include *ACTN2, ABCC9, THSD4, SGCD, KCNQ1, MYPN, CORIN, CSRP3, DMD, RYR1, JPH2, PRDM16,* and *FBN1*, though their individual contribution in this case remains uncertain. Together, these findings emphasize that both small variants and compound heterozygous events, particularly in *TTN* and *CBS*, along with immune-modulatory loci such as *HLA* genes, may underlie the overlapping cardiac and thrombotic features observed in this patient. Yet, read-level quality control metrics and loss-of-function confidence scoring (e.g., transcript-level nonsense-mediated decay[](https://doi.org/10.1002/humu.23626)) were not provided for the respective variants; confirmations on these metrics and zygosity are pending.\n\nIn addition to SLE, TTP, and LVNC, the patient also carries a diagnosis of autoimmune autonomic ganglionopathy (AAG), a rare condition characterized by dysautonomia and neuropathic features. Using phenotype terms related to AAG such as “neuropathy” and “dysautonomia” in functional queries, several genes from the patient’s variant set emerged with potential relevance.[](https://doi.org/10.3390/ijms25042296) *SCN11A*, a sodium channel gene implicated in autonomic dysfunction and sensory neuropathies, represents a plausible candidate for the autonomic features observed.[](https://doi.org/10.1007/s00424-020-02419-9) [](https://doi.org/10.1172/jci92373) *SEC61A1*, encoding a key component of the endoplasmic reticulum translocation machinery, has been linked to immune-related neuropathies and impaired cellular stress responses.[](https://doi.org/10.3389/fphar.2025.1580086) [](https://doi.org/10.3390/cells10051036) Although no autoimmune role has been observed, lysosomal storage-related genes *GBA1* and *GALC* also appeared in the analysis, consistent with their known associations with neurodegeneration and peripheral nerve dysfunction as in Parkinson’s disease.[](https://doi.org/10.1016/j.jmb.2023.168023) [](https://doi.org/10.1186/s40035-024-00437-6) [](https://doi.org/10.1111/ene.70206) Additional hits included *KARS1* which has been implicated in neuromuscular disorders,[](https://doi.org/10.1186/s13023-018-0788-4) as well as immune regulatory genes such as *ADA2, HLA-DQB1, HLA-DQA1*, and *HLA-B*, which reinforce the role of autoimmunity in AAG pathogenesis. While these associations remain exploratory, it is tempting to consider these variants may influence neuronal and immune regulatory pathways to contribute to the patient’s autonomic neuropathy, complementing the autoimmune basis of her broader disease spectrum.\n\nA central critique emerging from this case is whether the patient’s presentation reflects a multifactorial interplay of genetic and environmental influences.[](https://doi.org/10.1056/nejm200108023450506) [](https://doi.org/10.1056/nejmra1100359) Accordingly, we observed hints at disrupted complement activation, coagulation cascades, and platelets and immune cell functions, which can explain some mechanistic overlaps between SLE, TTP, LVNC, and even aspects of autonomic neuropathy.[](https://doi.org/10.4049/jimmunol.177.7.4794) [](https://doi.org/10.2353/ajpath.2007.070166)\n\nTogether, these patterns suggest a likely polygenic or synergistic disease model,[](https://doi.org/10.1093/nar/gky1120) in which variants across interconnected immune, vascular, and cardiac pathways could be additive and may collectively contribute to the complex phenotype observed in this patient.\n\n# Limitations and Future Directions\n\nThis study builds upon earlier analyses demonstrating the feasibility of symptom-guided variant filtering in a single, diagnostically complex case. Consequently, formal benchmarking against alternative analytical pipelines and validation across larger cohorts were beyond the scope of the current work and will be addressed in future studies.\n\nThis study is limited by reliance on existing public variant annotations (e.g., ClinVar, gnomAD), which may underrepresent rare or population-specific alleles. Additionally, most VUS variants, as well as the Tier IIB and V variants in this study, remain difficult to interpret and draw strong associations without broader cohort-level evidence. Sequencing errors and false positive variant calls should be taken into account, particularly for rare or low-frequency variants which can hinder interpretation.\n\nA key finding of this study was a homozygous stop-gained/frameshift variant in *CBS*, classified as likely pathogenic and central to the thrombotic interpretation. However, this variant, as well as several other prioritized findings, have not yet undergone locus-specific technical validation. In particular, read-level quality control metrics, multi-caller concordance, and high-confidence loss-of-function annotation were not independently verified. Therefore, the robustness of zygosity, variant calling accuracy, and predicted loss-of-function impact require further confirmation before definitive clinical conclusions can be drawn. At the same time, the symptom-based filtering approach employed here has some intrinsic limitations. Mapping clinical HPO terms allowed for a structured, phenotype-driven workflow, but the granularity and accuracy of HPO annotations are variable. Many features overlap across diseases, and broad terms can dilute specificity, leading to challenges in distinguishing disease-specific signals.[](https://doi.org/10.3390/jpm11080730) [](https://doi.org/10.1038/s41431-019-0412-7) While HPO remains a valuable starting point, future analyses may benefit from incorporating clinical coding systems such as ICD-10 or algorithmically defined phenotypes from resources like PheKB.[](https://doi.org/10.1093/jamia/ocv202) These frameworks provide more fine-grained, population-anchored features that could sharpen symptom-driven variant interpretation and reduce ambiguity.\n\nGiven the single-case scope of this study, the analyses remain descriptive and hypothesis-generating and necessitate formal statistical evaluation. For example, overlaps of SV across symptom-guided panels require confirmation to determine whether the observed overlap exceeds random expectation given the overall SV burden and panel sizes. Similarly, enrichment results may be influenced by pleiotropic loci (e.g., complement and HLA genes), annotation density, and the choice of background set.[](https://doi.org/10.1038/s41598-018-19333-x) [](https://doi.org/10.1186/s13059-015-0761-7) Thus, enrichment should be interpreted as supportive rather than confirmatory, and matched-background or permutation-based testing is warranted. Methodological and analytical future directions aimed at improving the overall workflow include:\n\n• **Validating the prioritized variants** through orthogonal assays, including Sanger sequencing and PCR.[](https://doi.org/10.1056/nejmra1312543)\n\n• **Performing locus-specific validation of high-priority variants (e.g., *CBS*)** by recomputing read-level quality control metrics (depth, allele balance, mapping quality, strand bias), assessing multi-caller concordance, re-annotating across canonical transcripts using Ensembl VEP,[](https://doi.org/10.1186/s13059-016-0974-4) applying structured ACMG PVS1 criteria,[](https://doi.org/10.1002/humu.23626) and conducting ancestry-aware population frequency checks to strengthen loss-of-function evidence and confirm zygosity.[](https://doi.org/10.1038/s41586-020-2308-7) [](https://doi.org/10.1093/bioinformatics/btu356) [](https://doi.org/10.1186/gm432)\n\n• **Expanding structural variant analysis**, which may uncover larger deletions, duplications, or regulatory element disruptions not captured by small variant filtering.\n\n• **Utilizing long-read whole genome sequencing** to provide improved resolution of structural and complex variants that are not fully resolved by short-read sequencing.[](https://doi.org/10.1001/jamapediatrics.2025.3320) [](https://doi.org/10.64898/2026.01.13.26343759) [](https://doi.org/10.1101/2025.10.29.25339045)\n\n• **Incorporating multi-omics data** (e.g., transcriptomic profiling) to support functional interpretation of prioritized variants.\n\n• **Leveraging large patient repositories and evaluating additional patients** with similar overlapping autoimmune or cardiac phenotypes to assess recurrence and potential clinical relevance of key VUS findings.\n\n• **Future benchmarking efforts** focusing on comparing symptom-guided prioritization against alternative variant ranking strategies, including phenotype-driven frameworks (e.g., Exomiser[](https://doi.org/10.1038/nprot.2015.124)) and the previously applied gene-centric workflow.[](https://doi.org/10.21428/a2fa6306.98b1af9a)\n\n• **Conducting formal statistical testing** to evaluate SV overlaps across symptom-guided panels and to validate gene enrichment results.\n\n• **Re-run g:Profiler using custom backgrounds** (genes covered ≥20× in this WGS; union of SLE/TTP/LVNC HPO panels) and perform sensitivity analyses excluding pleiotropic loci (*C4A, C4B, C1QB, HLA-B, HLA-DQB1*) plus leave-one-out tests.[](https://doi.org/10.1093/bioinformatics/btm051) This will provide an assessment of enrichment robustness to background choice and the influence of widely annotated genes.\n\n**For this patient**, given the high number of variants affecting immune and vascular regulation, follow-up recommendations may include:\n\n• **Segregation testing in family members,** if available, to help clarify the pathogenicity of select variants (especially heterozygous recessive findings or compound heterozygous candidates).\n\n• **Referral to a clinical geneticist** for integrated review of candidate variants and prior genetic reports, and consideration of enrollment in rare disease programs or research registries.\n\n• **Monitoring and/or screening for potential comorbidities** related to high-priority VUS genes (e.g., immunological, thrombotic, or cardiac conditions).\n\n• **Periodic re-analysis**, as variant classifications, public databases, and AI-supported tools continue to improve.\n\n• **Preclinical testing of the respective genes** across in vitro and in vivo models, would help evaluating the relevance and causality of the prioritized genes and gene sets. This can also allow further testing genetic/pharmacological intervention strategies as potential avenues for managing the patient’s conditions.\n\nIn conclusion, our results support the value of symptom-guided variant filtering as a complementary layer to standard genomics interpretation. This strategy can be especially useful in diagnostically challenging cases, such as the ones involving rare autoimmune and multi-systemic conditions, and offers a personalized route toward prioritizing phenotype-aligned candidate variants for follow-up evaluation.\n\n## Acknowledgements\n\nThis case study was conducted in collaboration with **Research to the People**, an open, patient-partnered research initiative focused on rare diseases and oncology.[](https://rttp.stanford.edu/christine) For variant interpretation and prioritization were conducted using SEQ platform provided by Genomize.[](https://seq.genomize.com/) We thank Dr. Gaia Andreoletti, Wendy Erler, and Stefanos Koutsoukos for their valuable input on the analyses and their contributions to the revision of the study findings.\n","contributors":[{"user_id":154,"role":"Writing","first_name":"Derya","last_name":"Karaarslan","avatar":"avatars/users/pubpub-095c1842.jpg","in_byline":1,"priority":1,"affiliations":[]},{"user_id":155,"role":"Writing","first_name":"Pete","last_name":"Kane","avatar":"avatars/users/pubpub-e4e837d9.jpg","in_byline":1,"priority":2,"affiliations":[]},{"user_id":156,"role":"Writing","first_name":"Christine","last_name":"Von Raesfeld","avatar":"avatars/users/pubpub-f3858890.jpg","in_byline":1,"priority":3,"affiliations":[]},{"user_id":157,"role":"Writing","first_name":"Murat","last_name":"Yaman","avatar":"avatars/users/pubpub-825bec86.jpg","in_byline":1,"priority":4,"affiliations":[]}],"created_at":"2026-10-18T12:00:00.000Z","doi":null,"version_doi":null,"feedback_form_embed_src":null,"id":45,"license":"CC BY","linked_assets":[],"orgs":[{"org_id_int":87,"priority":1,"slug":"research-to-the-people","name":"Research to the People","avatar":null}],"pdf_url":null,"publisher":"Research to the People","references":[{"url":"https://doi.org/10.1056/nejm200108023450506","text":"Davidson A, Diamond B. (2001). Autoimmune Diseases."},{"url":"https://doi.org/10.1016/0167-5699(93)90244-f","text":"Rose NR, Bona C. (1993). Defining criteria for autoimmune diseases (Witebsky's postulates revisited)."},{"url":"https://doi.org/10.1016/j.jaut.2009.09.008","text":"Cooper GS, Bynum ML, Somers EC. (2009). Recent insights in the epidemiology of autoimmune diseases: Improved prevalence estimates and understanding of clustering of diseases."},{"url":"https://doi.org/10.1038/gim.2013.99","text":"Shashi V, McConkie-Rosell A, Rosell B, Schoch K, Vellore K, McDonald M, Jiang Y, Xie P, Need A, Goldstein DB. (2014). The utility of the traditional medical genetics diagnostic evaluation in the context of next-generation sequencing for undiagnosed genetic disorders."},{"url":"https://doi.org/10.1038/nrg3555","text":"Boycott KM, Vanstone MR, Bulman DE, MacKenzie AE. (2013). Rare-disease genetics in the era of next-generation sequencing: discovery to translation."},{"url":"https://doi.org/10.1038/s41436-018-0408-7","text":"Posey JE, O’Donnell-Luria AH, Chong JX, Harel T, Jhangiani SN, Coban Akdemir ZH, Buyske S, Pehlivan D, Carvalho CM, Baxter S, Sobreira N, Liu P, Wu N, Rosenfeld JA, Kumar S, Avramopoulos D, White JJ, Doheny KF, Witmer PD, Boehm C, Sutton VR, Muzny DM, Boerwinkle E, Günel M, Nickerson DA, Mane S, MacArthur DG, Gibbs RA, Hamosh A, Lifton RP, Matise TC, Rehm HL, Gerstein M, Bamshad MJ, Valle D, Lupski JR. (2019). Insights into genetics, human biology and disease gleaned from family based genomic studies."},{"url":"https://doi.org/10.21428/a2fa6306.98b1af9a","text":"SHEN Y, Von Raesfeld C, Kane P, Raijman J, Lin Z, Zhu X. (2025). Genetic Analysis of a Rare Patient Case with Complex Autoimmune, Neurological, Blood, and Heart Disease Comorbidities."},{"url":"https://doi.org/10.1038/s41467-023-39306-7","text":"Danzi MC, Dohrn MF, Fazal S, Beijer D, Rebelo AP, Cintra V, Züchner S. (2023). Deep structured learning for variant prioritization in Mendelian diseases."},{"url":"https://doi.org/10.1101/2023.08.02.23293212","text":"Stenton SL, O’Leary M, Lemire G, VanNoy GE, DiTroia S, Ganesh VS, Groopman E, O’Heir E, Mangilog B, Osei-Owusu I, Pais LS, Serrano J, Singer-Berk M, Weisburd B, Wilson M, Austin-Tse C, Abdelhakim M, Althagafi A, Babbi G, Bellazzi R, Bovo S, Carta MG, Casadio R, Coenen P, De Paoli F, Floris M, Gajapathy M, Hoehndorf R, Jacobsen JO, Joseph T, Kamandula A, Katsonis P, Kint C, Lichtarge O, Limongelli I, Lu Y, Magni P, Mamidi TKK, Martelli PL, Mulargia M, Nicora G, Nykamp K, Pejaver V, Peng Y, Pham THC, Podda MS, Rao A, Rizzo E, Saipradeep VG, Savojardo C, Schols P, Shen Y, Sivadasan N, Smedley D, Soru D, Srinivasan R, Sun Y, Sunderam U, Tan W, Tiwari N, Wang X, Wang Y, Williams A, Worthey EA, Yin R, You Y, Zeiberg D, Zucca S, Bakolitsa C, Brenner SE, Fullerton SM, Radivojac P, Rehm HL, O’Donnell-Luria A. (2023). Critical assessment of variant prioritization methods for rare disease diagnosis within the Rare Genomes Project."},{"url":"https://doi.org/10.1093/nar/gkaa1043","text":"Köhler S, Gargano M, Matentzoglu N, Carmody LC, Lewis-Smith D, Vasilevsky NA, Danis D, Balagura G, Baynam G, Brower AM, Callahan TJ, Chute CG, Est JL, Galer PD, Ganesan S, Griese M, Haimel M, Pazmandi J, Hanauer M, Harris NL, Hartnett MJ, Hastreiter M, Hauck F, He Y, Jeske T, Kearney H, Kindle G, Klein C, Knoflach K, Krause R, Lagorce D, McMurry JA, Miller JA, Munoz-Torres MC, Peters RL, Rapp CK, Rath AM, Rind SA, Rosenberg AZ, Segal MM, Seidel MG, Smedley D, Talmy T, Thomas Y, Wiafe SA, Xian J, Yüksel Z, Helbig I, Mungall CJ, Haendel MA, Robinson PN. (2020). The Human Phenotype Ontology in 2021."},{"url":"https://genomize.com/ai-driven-ngs-data-analysis-a-variant-prioritization-pipeline-for-precision-medicine/","text":"Tolga Aslan P. AI-Driven NGS Data Analysis: A Variant Prioritization Pipeline for Precision Medicine. . Accessed 2025 July."},{"url":"https://doi.org/10.1038/gim.2015.30","text":"Richards S, Aziz N, Bale S, Bick D, Das S, Gastier-Foster J, Grody WW, Hegde M, Lyon E, Spector E, Voelkerding K, Rehm HL. (2015). Standards and guidelines for the interpretation of sequence variants: a joint consensus recommendation of the American College of Medical Genetics and Genomics and the Association for Molecular Pathology."},{"url":"https://doi.org/10.1101/gr.160325.113","text":"Robinson PN, Köhler S, Oellrich A, Sanger Mouse Genetics Project, Wang K, Mungall CJ, Lewis SE, Washington N, Bauer S, Seelow D, Krawitz P, Gilissen C, Haendel M, Smedley D. (2013). Improved exome prioritization of disease genes through cross-species phenotype comparison."},{"url":"https://seq.genomize.com/","text":"Genomize Bilisim ve Biyoteknoloji AS., SEQ: A Variant Analysis Platform for Rare Disease, Cancer and Preventive Genomics. . Accessed 2025 July"},{"url":"https://doi.org/10.1038/s41586-020-2308-7","text":"Karczewski KJ, Francioli LC, Tiao G, Cummings BB, Alföldi J, Wang Q, Collins RL, Laricchia KM, Ganna A, Birnbaum DP, Gauthier LD, Brand H, Solomonson M, Watts NA, Rhodes D, Singer-Berk M, England EM, Seaby EG, Kosmicki JA, Walters RK, Tashman K, Farjoun Y, Banks E, Poterba T, Wang A, Seed C, Whiffin N, Chong JX, Samocha KE, Pierce-Hoffman E, Zappala Z, O’Donnell-Luria AH, Minikel EV, Weisburd B, Lek M, Ware JS, Vittal C, Armean IM, Bergelson L, Cibulskis K, Connolly KM, Covarrubias M, Donnelly S, Ferriera S, Gabriel S, Gentry J, Gupta N, Jeandet T, Kaplan D, Llanwarne C, Munshi R, Novod S, Petrillo N, Roazen D, Ruano-Rubio V, Saltzman A, Schleicher M, Soto J, Tibbetts K, Tolonen C, Wade G, Talkowski ME, Genome Aggregation Database Consortium, Aguilar Salinas CA, Ahmad T, Albert CM, Ardissino D, Atzmon G, Barnard J, Beaugerie L, Benjamin EJ, Boehnke M, Bonnycastle LL, Bottinger EP, Bowden DW, Bown MJ, Chambers JC, Chan JC, Chasman D, Cho J, Chung MK, Cohen B, Correa A, Dabelea D, Daly MJ, Darbar D, Duggirala R, Dupuis J, Ellinor PT, Elosua R, Erdmann J, Esko T, Färkkilä M, Florez J, Franke A, Getz G, Glaser B, Glatt SJ, Goldstein D, Gonzalez C, Groop L, Haiman C, Hanis C, Harms M, Hiltunen M, Holi MM, Hultman CM, Kallela M, Kaprio J, Kathiresan S, Kim B, Kim YJ, Kirov G, Kooner J, Koskinen S, Krumholz HM, Kugathasan S, Kwak SH, Laakso M, Lehtimäki T, Loos RJF, Lubitz SA, Ma RCW, MacArthur DG, Marrugat J, Mattila KM, McCarroll S, McCarthy MI, McGovern D, McPherson R, Meigs JB, Melander O, Metspalu A, Neale BM, Nilsson PM, O’Donovan MC, Ongur D, Orozco L, Owen MJ, Palmer CNA, Palotie A, Park KS, Pato C, Pulver AE, Rahman N, Remes AM, Rioux JD, Ripatti S, Roden DM, Saleheen D, Salomaa V, Samani NJ, Scharf J, Schunkert H, Shoemaker MB, Sklar P, Soininen H, Sokol H, Spector T, Sullivan PF, Suvisaari J, Tai ES, Teo YY, Tiinamaija T, Tsuang M, Turner D, Tusie-Luna T, Vartiainen E, Vawter MP, Ware JS, Watkins H, Weersma RK, Wessman M, Wilson JG, Xavier RJ, Neale BM, Daly MJ, MacArthur DG. (2020). The mutational constraint spectrum quantified from variation in 141,456 humans."},{"url":"https://doi.org/10.1093/nar/29.1.308","text":"Sherry ST. (2001). dbSNP: the NCBI database of genetic variation."},{"url":"https://doi.org/10.1093/nar/gkx1153","text":"Landrum MJ, Lee JM, Benson M, Brown GR, Chao C, Chitipiralla S, Gu B, Hart J, Hoffman D, Jang W, Karapetyan K, Katz K, Liu C, Maddipatla Z, Malheiro A, McDaniel K, Ovetsky M, Riley G, Zhou G, Holmes JB, Kattman BL, Maglott DR. (2017). ClinVar: improving access to variant interpretations and supporting evidence."},{"url":"https://doi.org/10.1038/nature13127","text":"MacArthur DG, Manolio TA, Dimmock DP, Rehm HL, Shendure J, Abecasis GR, Adams DR, Altman RB, Antonarakis SE, Ashley EA, Barrett JC, Biesecker LG, Conrad DF, Cooper GM, Cox NJ, Daly MJ, Gerstein MB, Goldstein DB, Hirschhorn JN, Leal SM, Pennacchio LA, Stamatoyannopoulos JA, Sunyaev SR, Valle D, Voight BF, Winckler W, Gunter C. (2014). Guidelines for investigating causality of sequence variants in human disease."},{"url":"https://doi.org/10.1093/nar/gkad347","text":"Kolberg L, Raudvere U, Kuzmin I, Adler P, Vilo J, Peterson H. (2023). g:Profiler—interoperable web service for functional enrichment analysis and gene identifier mapping (2023 update)."},{"url":"https://doi.org/10.1093/nar/gkv1070","text":"Kanehisa M, Sato Y, Kawashima M, Furumichi M, Tanabe M. (2015). KEGG as a reference resource for gene and protein annotation."},{"url":"https://doi.org/10.1093/nar/gkab1028","text":"Gillespie M, Jassal B, Stephan R, Milacic M, Rothfels K, Senff-Ribeiro A, Griss J, Sevilla C, Matthews L, Gong C, Deng C, Varusai T, Ragueneau E, Haider Y, May B, Shamovsky V, Weiser J, Brunson T, Sanati N, Beckman L, Shao X, Fabregat A, Sidiropoulos K, Murillo J, Viteri G, Cook J, Shorser S, Bader G, Demir E, Sander C, Haw R, Wu G, Stein L, Hermjakob H, D’Eustachio P. (2021). The reactome pathway knowledgebase 2022."},{"url":"https://doi.org/10.1007/springerreference_109524","text":"(n.d.). Homocystinuria due to Cystathionine Beta-Synthase Deficiency."},{"url":"https://doi.org/10.1002/humu.10089","text":"Maclean KN, Gaustadnes M, Oliveriusová J, Janošík M, Kraus E, Kožich V, Kery V, Skovby F, Rüdiger N, Ingerslev J, Stabler SP, Allen RH, Kraus JP. (2002). High homocysteine and thrombosis without connective tissue disorders are associated with a novel class of cystathionine β-synthase (CBS) mutations."},{"url":"https://doi.org/10.1002/ajmg.a.61550","text":"Ritter A, Werner P, Latney B, Krock BL, Santani A, Bedoukian E, Skraban CM, Deardorff MA, Goldmuntz E. (2020). NKX2‐6 related congenital heart disease: Biallelic homeodomain‐disrupting variants and truncus arteriosus."},{"url":"https://doi.org/10.3892/ijmm.2014.1971","text":"WANG J, ZHANG D, SUN Y, LI R, QIU X, QU X, LIU X, FANG W, YANG Y. (2014). NKX2-6 mutation predisposes to familial atrial fibrillation."},{"url":"https://doi.org/10.3389/fphys.2019.01436","text":"Tharp CA, Haywood ME, Sbaizero O, Taylor MRG, Mestroni L. (2019). The Giant Protein Titin’s Role in Cardiomyopathy: Genetic, Transcriptional, and Post-translational Modifications of TTN and Their Contribution to Cardiac Disease."},{"url":"https://doi.org/10.1186/s12920-023-01567-y","text":"Yang Q, Zhang Q, Qin Z, Zhang S, Yi S, Yi S, Zhang Q, Luo J. (2023). Novel compound heterozygous variants in the RPL3L gene causing dilated cardiomyopathy type-2D: a case report and literature review."},{"url":"https://doi.org/10.1038/s41467-023-37838-6","text":"Shiraishi C, Matsumoto A, Ichihara K, Yamamoto T, Yokoyama T, Mizoo T, Hatano A, Matsumoto M, Tanaka Y, Matsuura-Suzuki E, Iwasaki S, Matsushima S, Tsutsui H, Nakayama KI. (2023). RPL3L-containing ribosomes determine translation elongation dynamics required for cardiac function."},{"url":"https://doi.org/10.1038/s41525-023-00386-5","text":"Ciesielski TH, Sirugo G, Iyengar SK, Williams SM. (2024). Characterizing the pathogenicity of genetic variants: the consequences of context."},{"url":"https://doi.org/10.1073/pnas.2516291123","text":"Tang M, Cromie GA, Kabir A, Timour MS, Ashmead J, Lo RS, Corley N, DiMaio F, Morizono H, Caldovic L, Ah Mew N, Gropman A, Shehu A, Dudley AM. (2026). Predicting epistasis across proteins by structural logic."},{"url":"https://doi.org/10.3390/genes13071275","text":"Rosina E, Pezzani L, Pezzoli L, Marchetti D, Bellini M, Pilotta A, Calabrese O, Nicastro E, Cirillo F, Cereda A, Scatigno A, Milani D, Iascone M. (2022). Atypical, Composite, or Blended Phenotypes: How Different Molecular Mechanisms Could Associate in Double-Diagnosed Patients."},{"url":"https://doi.org/10.1101/gr.183483.114","text":"Amendola LM, Dorschner MO, Robertson PD, Salama JS, Hart R, Shirts BH, Murray ML, Tokita MJ, Gallego CJ, Kim DS, Bennett JT, Crosslin DR, Ranchalis J, Jones KL, Rosenthal EA, Jarvik ER, Itsara A, Turner EH, Herman DS, Schleit J, Burt A, Jamal SM, Abrudan JL, Johnson AD, Conlin LK, Dulik MC, Santani A, Metterville DR, Kelly M, Foreman AKM, Lee K, Taylor KD, Guo X, Crooks K, Kiedrowski LA, Raffel LJ, Gordon O, Machini K, Desnick RJ, Biesecker LG, Lubitz SA, Mulchandani S, Cooper GM, Joffe S, Richards CS, Yang Y, Rotter JI, Rich SS, O’Donnell CJ, Berg JS, Spinner NB, Evans JP, Fullerton SM, Leppig KA, Bennett RL, Bird T, Sybert VP, Grady WM, Tabor HK, Kim JH, Bamshad MJ, Wilfond B, Motulsky AG, Scott CR, Pritchard CC, Walsh TD, Burke W, Raskind WH, Byers P, Hisama FM, Rehm H, Nickerson DA, Jarvik GP. (2015). Actionable exomic incidental findings in 6503 participants: challenges of variant classification."},{"url":"https://doi.org/10.1056/nejmra1312543","text":"Biesecker LG, Green RC. (2014). Diagnostic Clinical Genome and Exome Sequencing."},{"url":"https://doi.org/10.1016/j.cell.2011.09.008","text":"Lupski JR, Belmont JW, Boerwinkle E, Gibbs RA. (2011). Clan Genomics and the Complex Architecture of Human Disease."},{"url":"https://doi.org/10.1186/s13059-016-1107-9","text":"Katsanis N. (2016). The continuum of causality in human genetic disorders."},{"url":"https://doi.org/10.1056/nejmoa1307361","text":"Zhou Q, Yang D, Ombrello AK, Zavialov AV, Toro C, Zavialov AV, Stone DL, Chae JJ, Rosenzweig SD, Bishop K, Barron KS, Kuehn HS, Hoffmann P, Negro A, Tsai WL, Cowen EW, Pei W, Milner JD, Silvin C, Heller T, Chin DT, Patronas NJ, Barber JS, Lee CR, Wood GM, Ling A, Kelly SJ, Kleiner DE, Mullikin JC, Ganson NJ, Kong HH, Hambleton S, Candotti F, Quezado MM, Calvo KR, Alao H, Barham BK, Jones A, Meschia JF, Worrall BB, Kasner SE, Rich SS, Goldbach-Mansky R, Abinun M, Chalom E, Gotte AC, Punaro M, Pascual V, Verbsky JW, Torgerson TR, Singer NG, Gershon TR, Ozen S, Karadag O, Fleisher TA, Remmers EF, Burgess SM, Moir SL, Gadina M, Sood R, Hershfield MS, Boehm M, Kastner DL, Aksentijevich I. (2014). Early-Onset Stroke and Vasculopathy Associated with Mutations in ADA2."},{"url":"https://doi.org/10.1038/ng.975","text":"Al-Mayouf SM, Sunker A, Abdwani R, Abrawi SA, Almurshedi F, Alhashmi N, Al Sonbul A, Sewairi W, Qari A, Abdallah E, Al-Owain M, Al Motywee S, Al-Rayes H, Hashem M, Khalak H, Al-Jebali L, Alkuraya FS. (2011). Loss-of-function variant in DNASE1L3 causes a familial form of systemic lupus erythematosus."},{"url":"https://doi.org/10.1126/science.1255904","text":"Kuehn HS, Ouyang W, Lo B, Deenick EK, Niemela JE, Avery DT, Schickel J, Tran DQ, Stoddard J, Zhang Y, Frucht DM, Dumitriu B, Scheinberg P, Folio LR, Frein CA, Price S, Koh C, Heller T, Seroogy CM, Huttenlocher A, Rao VK, Su HC, Kleiner D, Notarangelo LD, Rampertaap Y, Olivier KN, McElwee J, Hughes J, Pittaluga S, Oliveira JB, Meffre E, Fleisher TA, Holland SM, Lenardo MJ, Tangye SG, Uzel G. (2014). Immune dysregulation in human subjects with heterozygous germline mutations in CTLA4."},{"url":"https://doi.org/10.1161/circgenetics.113.000338","text":"Rasmussen TB, Nissen PH, Palmfeldt J, Gehmlich K, Dalager S, Jensen UB, Kim WY, Heickendorff L, Mølgaard H, Jensen HK, Baandrup UT, Bross P, Mogensen J. (2014). Truncating Plakophilin-2 Mutations in Arrhythmogenic Cardiomyopathy Are Associated With Protein Haploinsufficiency in Both Myocardium and Epidermis."},{"url":"https://doi.org/10.1016/j.jacc.2018.04.078","text":"Bagnall RD, Ingles J, Dinger ME, Cowley MJ, Ross SB, Minoche AE, Lal S, Turner C, Colley A, Rajagopalan S, Berman Y, Ronan A, Fatkin D, Semsarian C. (2018). Whole Genome Sequencing Improves Outcomes of Genetic Testing in Patients With Hypertrophic Cardiomyopathy."},{"url":"https://doi.org/10.1038/s41436-020-00946-5","text":"Walsh R, Lahrouchi N, Tadros R, Kyndt F, Glinge C, Postema PG, Amin AS, Nannenberg EA, Ware JS, Whiffin N, Mazzarotto F, Škorić-Milosavljević D, Krijger C, Arbelo E, Babuty D, Barajas-Martinez H, Beckmann BM, Bézieau S, Bos JM, Breckpot J, Campuzano O, Castelletti S, Celen C, Clauss S, Corveleyn A, Crotti L, Dagradi F, de Asmundis C, Denjoy I, Dittmann S, Ellinor PT, Ortuño CG, Giustetto C, Gourraud J, Hazeki D, Horie M, Ishikawa T, Itoh H, Kaneko Y, Kanters JK, Kimoto H, Kotta M, Krapels IP, Kurabayashi M, Lazarte J, Leenhardt A, Loeys BL, Lundin C, Makiyama T, Mansourati J, Martins RP, Mazzanti A, Mörner S, Napolitano C, Ohkubo K, Papadakis M, Rudic B, Molina MS, Sacher F, Sahin H, Sarquella-Brugada G, Sebastiano R, Sharma S, Sheppard MN, Shimamoto K, Shoemaker M, Stallmeyer B, Steinfurt J, Tanaka Y, Tester DJ, Usuda K, van der Zwaag PA, Van Dooren S, Van Laer L, Winbo A, Winkel BG, Yamagata K, Zumhagen S, Volders PG, Lubitz SA, Antzelevitch C, Platonov PG, Odening KE, Roden DM, Roberts JD, Skinner JR, Tfelt-Hansen J, van den Berg MP, Olesen MS, Lambiase PD, Borggrefe M, Hayashi K, Rydberg A, Nakajima T, Yoshinaga M, Saenen JB, Kääb S, Brugada P, Robyns T, Giachino DF, Ackerman MJ, Brugada R, Brugada J, Gimeno JR, Hasdemir C, Guicheney P, Priori SG, Schulze-Bahr E, Makita N, Schwartz PJ, Shimizu W, Aiba T, Schott J, Redon R, Ohno S, Probst V, Arnaout AA, Amelot M, Anselme F, Billon O, Defaye P, Dupuis J, Jesel L, Laurent G, Maury P, Pasquie J, Wiart F, Behr ER, Barc J, Bezzina CR. (2021). Enhancing rare variant interpretation in inherited arrhythmias through quantitative analysis of consortium disease cohorts and population controls."},{"url":"https://doi.org/10.3389/fgene.2019.00426","text":"Mantere T, Kersten S, Hoischen A. (2019). Long-Read Sequencing Emerging in Medical Genetics."},{"url":"https://doi.org/10.1016/j.ajhg.2017.07.014","text":"Starita LM, Ahituv N, Dunham MJ, Kitzman JO, Roth FP, Seelig G, Shendure J, Fowler DM. (2017). Variant Interpretation: Functional Assays to the Rescue."},{"url":"https://doi.org/10.1186/s12979-021-00259-4","text":"Sugiyama Y, Fujiwara M, Sakamoto A, Tsushima H, Nishikimi A, Maruyama M. (2022). The immunosenescence-related factor DOCK11 is involved in secondary immune responses of B cells."},{"url":"https://doi.org/10.1056/nejme2305431","text":"Tangye SG, Meyts I. (2023). DOCK11 and Immune Disease."},{"url":"https://doi.org/10.1172/jci96273","text":"Borcherding DC, Siefert ME, Lin S, Brewington J, Sadek H, Clancy JP, Plafker SM, Ziady AG. (2019). Clinically approved CFTR modulators rescue Nrf2 dysfunction in cystic fibrosis airway epithelia."},{"url":"https://doi.org/10.1186/s12931-019-1103-1","text":"Polverino F, Lu B, Quintero JR, Vargas SO, Patel AS, Owen CA, Gerard NP, Gerard C, Cernadas M. (2019). CFTR regulates B cell activation and lymphoid follicle development."},{"url":"https://doi.org/10.1136/ard.45.4.319","text":"Fox D, Faix J, Coblyn J, Fraser P, Smith B, Weinblatt M. (1986). Thrombotic thrombocytopenic purpura and systemic lupus erythematosus."},{"url":"https://doi.org/10.1016/j.autrev.2023.103297","text":"Pamuk ON, Ali SM, Hasni S. (2023). Development of systemic lupus erythematosus in patients with immune thrombocytopenic purpura: A systematic meta-analysis."},{"url":"https://doi.org/10.3389/fimmu.2016.00036","text":"Lintner KE, Wu YL, Yang Y, Spencer CH, Hauptmann G, Hebert LA, Atkinson JP, Yu CY. (2016). Early Components of the Complement Classical Activation Pathway in Human Systemic Autoimmune Diseases."},{"url":"https://doi.org/10.1191/0961203302lu242oa","text":"Jacob M, Napirei M, Ricken A, Dixkens C, Mannherz HG. (2002). Histopathology of lupus-like nephritis in Dnase1-deficient mice in comparison to NZB/W F1 mice."},{"url":"https://doi.org/10.1172/jci.insight.177003","text":"Stabach PR, Sims D, Gomez-Bañuelos E, Zehentmeier S, Dammen-Brower K, Bernhisel A, Kujawski S, Lopez SG, Petri M, Goldman DW, Lester ER, Le Q, Ishaq T, Kim H, Srivastava S, Kumar D, Pereira JP, Yarema KJ, Koumpouras F, Andrade F, Braddock DT. (2024). A dual-acting DNASE1/DNASE1L3 biologic prevents autoimmunity and death in genetic and induced lupus models."},{"url":"https://doi.org/10.1038/ng.79","text":"Kozyrev SV, Abelson A, Wojcik J, Zaghlool A, Linga Reddy MVP, Sanchez E, Gunnarsson I, Svenungsson E, Sturfelt G, Jönsen A, Truedsson L, Pons-Estel BA, Witte T, D'Alfonso S, Barizzone N, Danieli MG, Gutierrez C, Suarez A, Junker P, Laustrup H, González-Escribano MF, Martin J, Abderrahim H, Alarcón-Riquelme ME. (2008). Functional variants in the B-cell gene BANK1 are associated with systemic lupus erythematosus."},{"url":"https://doi.org/10.1615/critrevimmunol.2017020284","text":"Garrett-Sinha LA, Kearly A, Satterthwaite AB. (2016). The Role of the Transcription Factor Ets1 in Lupus and Other Autoimmune Diseases."},{"url":"https://doi.org/10.1016/j.immuni.2018.12.023","text":"Kim CJ, Lee C, Jung J, Ghosh A, Hasan SN, Hwang S, Kang H, Lee C, Kim G, Rudra D, Suh C, Im S. (2019). The Transcription Factor Ets1 Suppresses T Follicular Helper Type 2 Cell Differentiation to Halt the Onset of Systemic Lupus Erythematosus."},{"url":"https://doi.org/10.3389/fmolb.2021.723003","text":"Eni-Aganga I, Lanaghan ZM, Balasubramaniam M, Dash C, Pandhare J. (2021). PROLIDASE: A Review from Discovery to its Role in Health and Disease."},{"url":"https://doi.org/10.1007/s00439-004-1244-1","text":"Lee YH, Harley JB, Nath SK. (2005). CTLA-4 polymorphisms and systemic lupus erythematosus (SLE): a meta-analysis."},{"url":"https://doi.org/10.1186/s13075-018-1604-1","text":"Julià A, López-Longo FJ, Pérez Venegas JJ, Bonàs-Guarch S, Olivé À, Andreu JL, Aguirre-Zamorano MÁ, Vela P, Nolla JM, de la Fuente JLM, Zea A, Pego-Reigosa JM, Freire M, Díez E, Rodríguez-Almaraz E, Carreira P, Blanco R, Taboada VM, López-Lasanta M, Corbeto ML, Mercader JM, Torrents D, Absher D, Marsal S, Fernández-Nebro A. (2018). Genome-wide association study meta-analysis identifies five new loci for systemic lupus erythematosus."},{"url":"https://doi.org/10.3389/fimmu.2024.1447995","text":"Posadas-Cantera S, Mitsuiki N, Emmerich F, Patiño V, Lorenz H, Neth O, Dybedal I, Taskén K, Schäffer AA, Grimbacher B, Gámez-Díaz L. (2025). The effect of HLA genotype on disease onset and severity in CTLA-4 insufficiency."},{"url":"https://doi.org/10.3389/fimmu.2018.01555","text":"Cachat J, Deffert C, Alessandrini M, Roux-Lombard P, Le Gouellec A, Stasia M, Hugues S, Krause K. (2018). Altered Humoral Immune Responses and IgG Subtypes in NOX2-Deficient Mice and Patients: A Key Role for NOX2 in Antigen-Presenting Cells."},{"url":"https://doi.org/10.1038/s41392-025-02168-0","text":"Dai X, Fan Y, Zhao X. (2025). Systemic lupus erythematosus: updated insights on the pathogenesis, diagnosis, prevention and therapeutics."},{"url":"https://doi.org/10.1177/20406223231153572","text":"Afrashteh Nour M, Ghorbaninezhad F, Asadzadeh Z, Baghbanzadeh A, Hassanian H, Leone P, Jafarlou M, Alizadeh N, Racanelli V, Baradaran B. (2023). The emerging role of noncoding RNAs in systemic lupus erythematosus: new insights into the master regulators of disease pathogenesis."},{"url":"https://doi.org/10.1038/s41467-020-19242-6","text":"Moroney JB, Vasudev A, Pertsemlidis A, Zan H, Casali P. (2020). Integrative transcriptome and chromatin landscape analysis reveals distinct epigenetic regulations in human memory B cells."},{"url":"https://doi.org/10.1161/circgen.119.002507","text":"Verhagen JM, van den Born M, van der Linde HC, G.J. Nikkels P, Verdijk RM, Kivlen MH, van Unen LM, Baas AF, ter Heide H, van Osch-Gevers L, Hoogeveen-Westerveld M, Herkert JC, Bertoli-Avella AM, van Slegtenhorst MA, Wessels MW, Verheijen FW, Hassel D, Hofstra RM, Hegde RS, van Hasselt PM, van Ham TJ, van de Laar IM. (2019). Biallelic Variants in ASNA1 , Encoding a Cytosolic Targeting Factor of Tail-Anchored Proteins, Cause Rapidly Progressive Pediatric Cardiomyopathy."},{"url":"https://doi.org/10.1182/blood.2020005395","text":"Sakai K, Kuwana M, Tanaka H, Hosomichi K, Hasegawa A, Uyama H, Nishio K, Omae T, Hishizawa M, Matsui M, Iwato K, Okamoto A, Okuhiro K, Yamashita Y, Itoh M, Kumekawa H, Takezako N, Kawano N, Matsukawa T, Sano H, Ohshiro K, Hayashi K, Ueda Y, Mushino T, Ogawa Y, Yamada Y, Murata M, Matsumoto M. (2020). HLA loci predisposing to immune TTP in Japanese: potential role of the shared ADAMTS13 peptide bound to different HLA-DR."},{"url":"https://doi.org/10.1111/iji.70003","text":"van Rensburg WJJ, Marle Av, Geertsema L. (2025). Co‐Occurrence of HIV‐Susceptibility and ‐Protective HLA Alleles Is a Possible Contributor to the Development of Primary HIV‐Associated Thrombocytopenia (PHAT): A Cross‐Sectional Study."},{"url":"https://doi.org/10.1002/humu.23626","text":"Abou Tayoun AN, Pesaran T, DiStefano MT, Oza A, Rehm HL, Biesecker LG, Harrison SM, ClinGen Sequence Variant Interpretation Working Group (ClinGen SVI). (2018). Recommendations for interpreting the loss of function PVS1 ACMG/AMP variant criterion."},{"url":"https://doi.org/10.3390/ijms25042296","text":"Nakane S, Koike H, Hayashi T, Nakatsuji Y. (2024). Autoimmune Autonomic Neuropathy: From Pathogenesis to Diagnosis."},{"url":"https://doi.org/10.1007/s00424-020-02419-9","text":"Baker MD, Nassar MA. (2020). Painful and painless mutations of SCN9A and SCN11A voltage-gated sodium channels."},{"url":"https://doi.org/10.1172/jci92373","text":"Huang J, Vanoye CG, Cutts A, Goldberg YP, Dib-Hajj SD, Cohen CJ, Waxman SG, George AL. (2017). Sodium channel NaV1.9 mutations associated with insensitivity to pain dampen neuronal excitability."},{"url":"https://doi.org/10.3389/fphar.2025.1580086","text":"Xin J, Yin K, Li S, Gu P, Shao S. (2025). Exploring the ER channel protein Sec61: recent advances in pathophysiological significance and novel pharmacological inhibitors."},{"url":"https://doi.org/10.3390/cells10051036","text":"Sicking M, Lang S, Bochen F, Roos A, Drenth JPH, Zakaria M, Zimmermann R, Linxweiler M. (2021). Complexity and Specificity of Sec61-Channelopathies: Human Diseases Affecting Gating of the Sec61 Complex."},{"url":"https://doi.org/10.1016/j.jmb.2023.168023","text":"Chatterjee D, Krainc D. (2023). Mechanisms of Glucocerebrosidase Dysfunction in Parkinson’s Disease."},{"url":"https://doi.org/10.1186/s40035-024-00437-6","text":"Zhang X, Wu H, Tang B, Guo J. (2024). Clinical, mechanistic, biomarker, and therapeutic advances in GBA1-associated Parkinson’s disease."},{"url":"https://doi.org/10.1111/ene.70206","text":"Feo F, Tramacere L, Ramat S, Govoni A, Caremani L, Grigioni G, Mei D, Falliano S, Marin F, Ferri L, Paoli A, Rinaldi M, la Marca G, Ombrone D, Procopio E, Guerrini R, Morrone A, Caciotti A. (2025). High Prevalence of GALC Gene Variants in Adults With Neurodegenerative Conditions."},{"url":"https://doi.org/10.1186/s13023-018-0788-4","text":"Ardissone A, Tonduti D, Legati A, Lamantea E, Barone R, Dorboz I, Boespflug-Tanguy O, Nebbia G, Maggioni M, Garavaglia B, Moroni I, Farina L, Pichiecchio A, Orcesi S, Chiapparini L, Ghezzi D. (2018). KARS-related diseases: progressive leukoencephalopathy with brainstem and spinal cord calcifications as new phenotype and a review of literature."},{"url":"https://doi.org/10.1056/nejmra1100359","text":"Tsokos GC. (2011). Systemic Lupus Erythematosus."},{"url":"https://doi.org/10.4049/jimmunol.177.7.4794","text":"Ritis K, Doumas M, Mastellos D, Micheli A, Giaglis S, Magotti P, Rafail S, Kartalis G, Sideras P, Lambris JD. (2006). A Novel C5a Receptor-Tissue Factor Cross-Talk in Neutrophils Links Innate Immunity to Coagulation Pathways."},{"url":"https://doi.org/10.2353/ajpath.2007.070166","text":"Markiewski MM, Lambris JD. (2007). The Role of Complement in Inflammatory Diseases From Behind the Scenes into the Spotlight."},{"url":"https://doi.org/10.1093/nar/gky1120","text":"Buniello A, MacArthur JAL, Cerezo M, Harris LW, Hayhurst J, Malangone C, McMahon A, Morales J, Mountjoy E, Sollis E, Suveges D, Vrousgou O, Whetzel PL, Amode R, Guillen JA, Riat HS, Trevanion SJ, Hall P, Junkins H, Flicek P, Burdett T, Hindorff LA, Cunningham F, Parkinson H. (2018). The NHGRI-EBI GWAS Catalog of published genome-wide association studies, targeted arrays and summary statistics 2019."},{"url":"https://doi.org/10.3390/jpm11080730","text":"Rojano E, Córdoba-Caballero J, Jabato FM, Gallego D, Serrano M, Pérez B, Parés-Aguilar Á, Perkins JR, Ranea JAG, Seoane-Zonjic P. (2021). Evaluating, Filtering and Clustering Genetic Disease Cohorts Based on Human Phenotype Ontology Data with Cohort Analyzer."},{"url":"https://doi.org/10.1038/s41431-019-0412-7","text":"Tomar S, Sethi R, Lai PS. (2019). Specific phenotype semantics facilitate gene prioritization in clinical exome sequencing."},{"url":"https://doi.org/10.1093/jamia/ocv202","text":"Kirby JC, Speltz P, Rasmussen LV, Basford M, Gottesman O, Peissig PL, Pacheco JA, Tromp G, Pathak J, Carrell DS, Ellis SB, Lingren T, Thompson WK, Savova G, Haines J, Roden DM, Harris PA, Denny JC. (2016). PheKB: a catalog and workflow for creating electronic phenotype algorithms for transportability."},{"url":"https://doi.org/10.1038/s41598-018-19333-x","text":"Haynes WA, Tomczak A, Khatri P. (2018). Gene annotation bias impedes biomedical research."},{"url":"https://doi.org/10.1186/s13059-015-0761-7","text":"Timmons JA, Szkop KJ, Gallagher IJ. (2015). Multiple sources of bias confound functional enrichment analysis of global -omics data."},{"url":"https://doi.org/10.1186/s13059-016-0974-4","text":"McLaren W, Gil L, Hunt SE, Riat HS, Ritchie GRS, Thormann A, Flicek P, Cunningham F. (2016). The Ensembl Variant Effect Predictor."},{"url":"https://doi.org/10.1093/bioinformatics/btu356","text":"Li H. (2014). Toward better understanding of artifacts in variant calling from high-coverage samples."},{"url":"https://doi.org/10.1186/gm432","text":"O'Rawe J, Jiang T, Sun G, Wu Y, Wang W, Hu J, Bodily P, Tian L, Hakonarson H, Johnson WE, Wei Z, Wang K, Lyon GJ. (2013). Low concordance of multiple variant-calling pipelines: practical implications for exome and genome sequencing."},{"url":"https://doi.org/10.1001/jamapediatrics.2025.3320","text":"Thiffault I, Farrow E, Barrett C, Scott M, Ross A, Means JC, Cheung WA, Johnson AF, Koseva B, McLennan R, Grundberg E, Bi C, Schwendinger-Schreck C, Yoo B, Johnston JJ, Del Viso F, Paolillo V, Herriges J, Zhang L, Gibson M, Cohen ASA, Alaimo J, Saunders CJ, Pastinen T. (2025). Clinical Long-Read Sequencing Test for Genetic Disease Diagnosis."},{"url":"https://doi.org/10.64898/2026.01.13.26343759","text":"de Bitter TJ, van der Sanden B, Sagath L, Höps W, Arts P, de Groot M, Weiss MM, Derks R, den Ouden A, van den Heuvel S, Timmermans RG, van Leeuwen T, Corominas Galbany J, Smits J, Snijders Blok L, Hofste T, Steehouwer M, Zomer N, Sabbagh Q, Kamsteeg E, Lugtenberg D, Bosgoed EA, Rodenburg RJ, Sun SM, Mensenkamp AR, Ligtenberg MJ, de Leeuw N, Hellebrekers DM, Stegmann AP, Paulussen AD, Blok MJ, van Zelst-Stams WA, van den Wijngaard A, Yntema HG, Gilissen C, Hoischen A, Vissers LE. (2026). Clinical long-read genome sequencing for rare disease diagnostics."},{"url":"https://doi.org/10.1101/2025.10.29.25339045","text":"van der Sanden B, Betz C, Herzog K, Schamschula E, Wimmer K, Vater I, Balachandran S, Chen X, Corominas Galbany J, Timmermans R, Derks R, HiFi Solves EMEA Consortium, Spielmann M, Eberle MA, Gilissen C, Vissers LE, Zschocke J, Bolz HJ, Hoischen A. (2025). HiFi sequencing accurately identifies clinically relevant variants in paralogous genes."},{"url":"https://doi.org/10.1038/nprot.2015.124","text":"Smedley D, Jacobsen JOB, Jäger M, Köhler S, Holtgrewe M, Schubach M, Siragusa E, Zemojtel T, Buske OJ, Washington NL, Bone WP, Haendel MA, Robinson PN. (2015). Next-generation diagnostics and disease-gene discovery with the Exomiser."},{"url":"https://doi.org/10.1093/bioinformatics/btm051","text":"Goeman JJ, Bühlmann P. (2007). Analyzing gene expression data in terms of gene sets: methodological issues."},{"url":"https://rttp.stanford.edu/christine","text":"RTTP. Case Study: Christine. . Accessed 2025 July"}],"slug":"full-text-symptom-guided-genomic","social_posts_count":null,"social_posts_embed_src":null,"state":"PUBLISHED","subtitle":"This study applies a symptom-driven variant prioritization strategy using whole genome sequencing to uncover potential genetic contributors across overlapping autoimmune, thrombotic, and cardiac conditions in a diagnostically complex patient.","tags":[],"title":"Full Text: Symptom-Guided Genomic Analysis in a Complex Autoimmune Case: Integrating Whole Genome Sequencing and Phenotype Mapping","version_desc":null,"version_number":1,"versions":[{"id":45,"version_number":1,"version_desc":null,"doi":null,"created_at":"2026-10-18T12:00:00.000Z"}]}}