The GenoVA study: Equitable implementation of a pragmatic randomized trial of polygenic-risk scoring in primary care.

Polygenic risk scores (PRSs) hold promise for disease risk assessment and prevention. The Genomic Medicine at Veterans Affairs (GenoVA) Study is addressing three main challenges to the clinical implementation of PRSs in preventive care: defining and determining their clinical utility, implementing them in time-constrained primary care settings, and countering their potential to exacerbate healthcare disparities. The study processes used to test patients, report their PRS results to them and their primary care providers (PCPs), and promote the use of those results in clinical decision-making are modeled on common practices in primary care. The following diseases were chosen for their prevalence and familiarity to PCPs: coronary artery disease; type 2 diabetes; atrial fibrillation; and breast, colorectal, and prostate cancers. A randomized clinical trial (RCT) design and primary outcome of time-to-new-diagnosis of a target disease bring methodological rigor to the question of the clinical utility of PRS implementation. The study's pragmatic RCT design enhances its relevance to how PRS might reasonably be implemented in primary care. Steps the study has taken to promote health equity include the thoughtful handling of genetic ancestry in PRS construction and reporting and enhanced recruitment strategies to address underrepresentation in research participation. To date, enhanced recruitment efforts have been both necessary and successful: participants of underrepresented race and ethnicity groups have been less likely to enroll in the study than expected but ultimately achieved proportional representation through targeted efforts. The GenoVA Study experience to date offers insights for evaluating the clinical utility of equitable PRS implementation in adult primary care.

Actionability of unanticipated monogenic disease risks in newborn genomic screening: Findings from the BabySeq Project.

Newborn genomic sequencing (NBSeq) to screen for medically important genetic information is of considerable interest but data characterizing the actionability of such findings, and the downstream medical efforts in response to discovery of unanticipated genetic risk variants, are lacking. From a clinical trial of comprehensive exome sequencing in 127 apparently healthy infants and 32 infants in intensive care, we previously identified 17 infants (10.7%) with unanticipated monogenic disease risks (uMDRs). In this analysis, we assessed actionability for each of these uMDRs with a modified ClinGen actionability semiquantitative metric (CASQM) and created radar plots representing degrees of penetrance of the condition, severity of the condition, effectiveness of intervention, and tolerability of intervention. In addition, we followed each of these infants for 3-5 years after disclosure and tracked the medical actions prompted by these findings. All 17 uMDR findings were scored as moderately or highly actionable on the CASQM (mean 9, range: 7-11 on a 0-12 scale) and several distinctive visual patterns emerged on the radar plots. In three infants, uMDRs revealed unsuspected genetic etiologies for existing phenotypes, and in the remaining 14 infants, uMDRs provided risk stratification for future medical surveillance. In 13 infants, uMDRs prompted screening for at-risk family members, three of whom underwent cancer-risk-reducing surgeries. Although assessments of clinical utility and cost-effectiveness will require larger datasets, these findings suggest that large-scale comprehensive sequencing of newborns will reveal numerous actionable uMDRs and precipitate substantial, and in some cases lifesaving, downstream medical care in newborns and their family members.

Returning actionable genomic results in a research biobank: Analytic validity, clinical implementation, and resource utilization.

Over 100 million research participants around the world have had research array-based genotyping (GT) or genome sequencing (GS), but only a small fraction of these have been offered return of actionable genomic findings (gRoR). Between 2017 and 2021, we analyzed genomic results from 36,417 participants in the Mass General Brigham Biobank and offered to confirm and return pathogenic and likely pathogenic variants (PLPVs) in 59 genes. Variant verification prior to participant recontact revealed that GT falsely identified PLPVs in 44.9% of samples, and GT failed to identify 72.0% of PLPVs detected in a subset of samples that were also sequenced. GT and GS detected verified PLPVs in 1% and 2.5% of the cohort, respectively. Of 256 participants who were alerted that they carried actionable PLPVs, 37.5% actively or passively declined further disclosure. 76.3% of those carrying PLPVs were unaware that they were carrying the variant, and over half of those met published professional criteria for genetic testing but had never been tested. This gRoR protocol cost approximately $129,000 USD per year in laboratory testing and research staff support, representing $14 per participant whose DNA was analyzed or $3,224 per participant in whom a PLPV was confirmed and disclosed. These data provide logistical details around gRoR that could help other investigators planning to return genomic results.

Recommendations for risk allele evidence curation, classification, and reporting from the ClinGen Low Penetrance/Risk Allele Working Group.

PURPOSE: Genetic variants at the low end of the penetrance spectrum have historically been challenging to interpret because their high population frequencies exceed the disease prevalence of the associated condition, leading to a lack of clear segregation between the variant and disease. There is currently substantial variation in the classification of these variants, and no formal classification framework has been widely adopted. The Clinical Genome Resource Low Penetrance/Risk Allele Working Group was formed to address these challenges and promote harmonization within the clinical community. METHODS: The work presented here is the product of internal and community Likert-scaled surveys in combination with expert consensus within the Working Group. RESULTS: We formally recognize risk alleles and low-penetrance variants as distinct variant classes from those causing highly penetrant disease that require special considerations regarding their clinical classification and reporting. First, we provide a preferred terminology for these variants. Second, we focus on risk alleles and detail considerations for reviewing relevant studies and present a framework for the classification these variants. Finally, we discuss considerations for clinical reporting of risk alleles. CONCLUSION: These recommendations support harmonized interpretation, classification, and reporting of variants at the low end of the penetrance spectrum.

The landscape of reported VUS in multi-gene panel and genomic testing: Time for a change.

PURPOSE: Variants of uncertain significance (VUS) are a common result of diagnostic genetic testing and can be difficult to manage with potential misinterpretation and downstream costs, including time investment by clinicians. We investigated the rate of VUS reported on diagnostic testing via multi-gene panels (MGPs) and exome and genome sequencing (ES/GS) to measure the magnitude of uncertain results and explore ways to reduce their potentially detrimental impact. METHODS: Rates of inconclusive results due to VUS were collected from over 1.5 million sequencing test results from 19 clinical laboratories in North America from 2020 to 2021. RESULTS: We found a lower rate of inconclusive test results due to VUSs from ES/GS (22.5%) compared with MGPs (32.6%; P < .0001). For MGPs, the rate of inconclusive results correlated with panel size. The use of trios reduced inconclusive rates (18.9% vs 27.6%; P < .0001), whereas the use of GS compared with ES had no impact (22.2% vs 22.6%; P = ns). CONCLUSION: The high rate of VUS observed in diagnostic MGP testing warrants examining current variant reporting practices. We propose several approaches to reduce reported VUS rates, while directing clinician resources toward important VUS follow-up.

Data sharing to improve concordance in variant interpretation across laboratories: results from the Canadian Open Genetics Repository.

BACKGROUND: This study aimed to identify and resolve discordant variant interpretations across clinical molecular genetic laboratories through the Canadian Open Genetics Repository (COGR), an online collaborative effort for variant sharing and interpretation. METHODS: Laboratories uploaded variant data to the Franklin Genoox platform. Reports were issued to each laboratory, summarising variants where conflicting classifications with another laboratory were noted. Laboratories could then reassess variants to resolve discordances. Discordance was calculated using a five-tier model (pathogenic (P), likely pathogenic (LP), variant of uncertain significance (VUS), likely benign (LB), benign (B)), a three-tier model (LP/P are positive, VUS are inconclusive, LB/B are negative) and a two-tier model (LP/P are clinically actionable, VUS/LB/B are not). We compared the COGR classifications to automated classifications generated by Franklin. RESULTS: Twelve laboratories submitted classifications for 44 510 unique variants. 2419 variants (5.4%) were classified by two or more laboratories. From baseline to after reassessment, the number of discordant variants decreased from 833 (34.4% of variants reported by two or more laboratories) to 723 (29.9%) based on the five-tier model, 403 (16.7%) to 279 (11.5%) based on the three-tier model and 77 (3.2%) to 37 (1.5%) based on the two-tier model. Compared with the COGR classification, the automated Franklin classifications had 94.5% sensitivity and 96.6% specificity for identifying actionable (P or LP) variants. CONCLUSIONS: The COGR provides a standardised mechanism for laboratories to identify discordant variant interpretations and reduce discordance in genetic test result delivery. Such quality assurance programmes are important as genetic testing is implemented more widely in clinical care.

Interpretation of Genomic Sequencing Results in Healthy and Ill Newborns: Results from the BabySeq Project.

Genomic sequencing provides many opportunities in newborn clinical care, but the challenges of interpreting and reporting newborn genomic sequencing (nGS) results need to be addressed for its broader and effective application. The BabySeq Project is a pilot randomized clinical trial that explores the medical, behavioral, and economic impacts of nGS in well newborns and those admitted to a neonatal intensive care unit (NICU). Here we present childhood-onset and actionable adult-onset disease risk, carrier status, and pharmacogenomics findings from nGS of 159 newborns in the BabySeq Project. nGS revealed a risk of childhood-onset disease in 15/159 (9.4%) newborns; none of the disease risks were anticipated based on the infants' known clinical or family histories. nGS also revealed actionable adult-onset disease risk in 3/85 (3.5%) newborns whose parents consented to receive this information. Carrier status for recessive diseases and pharmacogenomics variants were reported in 88% and 5% of newborns, respectively. Additional indication-based analyses were performed in 29/32 (91%) NICU newborns and 6/127 (5%) healthy newborns who later had presentations that prompted a diagnostic analysis. No variants that sufficiently explained the reason for the indications were identified; however, suspicious but uncertain results were reported in five newborns. Testing parental samples contributed to the interpretation and reporting of results in 13/159 (8%) newborns. Our results suggest that nGS can effectively detect risk and carrier status for a wide range of disorders that are not detectable by current newborn screening assays or predicted based on the infant's known clinical or family history, and the interpretation of results can substantially benefit from parental testing.

Reanalysis of eMERGE phase III sequence variants in 10,500 participants and infrastructure to support the automated return of knowledge updates.

PURPOSE: The clinical genomics knowledgebase is dynamic with variant classifications changing as newly identified cases, additional population data, and other evidence become available. This is a challenge for the clinical laboratory because of limited resource availability for variant reassessment. METHODS: Throughout the Electronic Medical Records and Genomics phase III program, clinical sites associated with the Mass General Brigham/Broad sequencing center received automated, real-time notifications when reported variants were reclassified. In this study, we summarized the nature of these reclassifications and described the proactive reassessment framework we used for the Electronic Medical Records and Genomics program data set to identify variants most likely to undergo reclassification. RESULTS: Reanalysis of 1855 variants led to the reclassification of 2% (n = 45) of variants, affecting 0.6% (n = 67) of participants. Of these reclassifications, 78% (n = 35) were high-impact changes affecting reportability, with 8 variants downgraded from likely pathogenic/pathogenic to variants of uncertain significance (VUS) and 27 variants upgraded from VUS to likely pathogenic/pathogenic. Most upgraded variants (67%) were initially classified as VUS-Favor Pathogenic, highlighting the benefit of VUS subcategorization. The most common reason for reclassification was new published case data and/or functional evidence. CONCLUSION: Our results highlight the importance of periodic sequence variant reevaluation and the need for automated approaches to advance routine implementation of variant reevaluations in clinical practice.

A Model for Genome-First Care: Returning Secondary Genomic Findings to Participants and Their Healthcare Providers in a Large Research Cohort.

There is growing interest in communicating clinically relevant DNA sequence findings to research participants who join projects with a primary research goal other than the clinical return of such results. Since Geisinger's MyCode Community Health Initiative (MyCode) was launched in 2007, more than 200,000 participants have been broadly consented for discovery research. In 2013 the MyCode consent was amended to include a secondary analysis of research genomic sequences that allows for delivery of clinical results. Since May 2015, pathogenic and likely pathogenic variants from a set list of genes associated with monogenic conditions have prompted "genome-first" clinical encounters. The encounters are described as genome-first because they are identified independent of any clinical parameters. This article (1) details our process for generating clinical results from research data, delivering results to participants and providers, facilitating condition-specific clinical evaluations, and promoting cascade testing of relatives, and (2) summarizes early results and participant uptake. We report on 542 participants who had results uploaded to the electronic health record as of February 1, 2018 and 291 unique clinical providers notified with one or more participant results. Of these 542 participants, 515 (95.0%) were reached to disclose their results and 27 (5.0%) were lost to follow-up. We describe an exportable model for delivery of clinical care through secondary use of research data. In addition, subject and provider participation data from the initial phase of these efforts can inform other institutions planning similar programs.

Discordant results between conventional newborn screening and genomic sequencing in the BabySeq Project.

PURPOSE: Newborn screening (NBS) is performed to identify neonates at risk for actionable, severe, early-onset disorders, many of which are genetic. The BabySeq Project randomized neonates to receive conventional NBS or NBS plus exome sequencing (ES) capable of detecting sequence variants that may also diagnose monogenic disease or indicate genetic disease risk. We therefore evaluated how ES and conventional NBS results differ in this population. METHODS: We compared results of NBS (including hearing screens) and ES for 159 infants in the BabySeq Project. Infants were considered "NBS positive" if any abnormal result was found indicating disease risk and "ES positive" if ES identified a monogenic disease risk or a genetic diagnosis. RESULTS: Most infants (132/159, 84%) were NBS and ES negative. Only one infant was positive for the same disorder by both modalities. Nine infants were NBS positive/ES negative, though seven of these were subsequently determined to be false positives. Fifteen infants were ES positive/NBS negative, all of which represented risk of genetic conditions that are not included in NBS programs. No genetic explanation was identified for eight infants referred on the hearing screen. CONCLUSION: These differences highlight the complementarity of information that may be gleaned from NBS and ES in the newborn period.

Randomized prospective evaluation of genome sequencing versus standard-of-care as a first molecular diagnostic test.

PURPOSE: To evaluate the diagnostic yield and clinical relevance of clinical genome sequencing (cGS) as a first genetic test for patients with suspected monogenic disorders. METHODS: We conducted a prospective randomized study with pediatric and adult patients recruited from genetics clinics at Massachusetts General Hospital who were undergoing planned genetic testing. Participants were randomized into two groups: standard-of-care genetic testing (SOC) only or SOC and cGS. RESULTS: Two hundred four participants were enrolled, 202 were randomized to one of the intervention arms, and 99 received cGS. In total, cGS returned 16 molecular diagnoses that fully or partially explained the indication for testing in 16 individuals (16.2% of the cohort, 95% confidence interval [CI] 8.9-23.4%), which was not significantly different from SOC (18.2%, 95% CI 10.6-25.8%, P = 0.71). An additional eight molecular diagnoses reported by cGS had uncertain relevance to the participant's phenotype. Nevertheless, referring providers considered 20/24 total cGS molecular diagnoses (83%) to be explanatory for clinical features or worthy of additional workup. CONCLUSION: cGS is technically suitable as a first genetic test. In our cohort, diagnostic yield was not significantly different from SOC. Further studies addressing other variant types and implementation challenges are needed to support feasibility and utility of broad-scale cGS adoption.

An assessment of the role of vinculin loss of function variants in inherited cardiomyopathy.

The ACMG/AMP variant classification framework was intended for highly penetrant Mendelian conditions. While it is appreciated that clinically relevant variants exhibit a wide spectrum of penetrance, accurately assessing and expressing the pathogenicity of variants with lower penetrance can be challenging. The vinculin (VCL) gene illustrates these challenges. Model organism data provide evidence that loss of function of VCL may play a role in cardiomyopathy and aggregate case-control studies suggest low penetrance. VCL loss of function variants, however, are rarely identified in affected probands and therefore there is a paucity of family studies clarifying the clinical significance of individual variants. This study, which aggregated data from >18,000 individuals who underwent gene panel or exome testing for inherited cardiomyopathies, identified 32 probands with VCL loss-of-function variants and confirmed enrichment in probands with dilated cardiomyopathy (odds ratio [OR] = 9.01; confidence interval [CI] = 4.93-16.45). Our data revealed that the majority of these individuals (89.5%) had pediatric onset of disease. Family studies demonstrated that heterozygous loss of function of VCL alone is insufficient to cause cardiomyopathy but that these variants do contribute to disease risk. In conclusion, VCL loss-of-function variants should be reported in a diagnostic setting but need to be clearly distinguished as having lower penetrance.

Multiple GYPB gene deletions associated with the U- phenotype in those of African ancestry.

BACKGROUND: The MNS blood group system is defined by three homologous genes: GYPA, GYPB, and GYPE. GYPB encodes for glycophorin B (GPB) carrying S/s and the "universal" antigen U. RBCs of approximately 1% of individuals of African ancestry are U- due to absence of GPB. The U- phenotype has long been attributed to a deletion encompassing GYPB exons 2 to 5 and GYPE exon 1 (GYPB*01N). STUDY DESIGN AND METHODS: Samples from two U-individuals underwent Illumina short read whole genome sequencing (WGS) and Nanopore long read WGS. In addition, two existing WGS datasets, MedSeq (n = 110) and 1000 Genomes (1000G, n = 2535), were analyzed for GYPB deletions. Deletions were confirmed by Sanger sequencing. Twenty known U- donor samples were tested by a PCR assay to determine the specific deletion alleles present in African Americans. RESULTS: Two large GYPB deletions in U- samples of African ancestry were identified: a 110 kb deletion extending left of GYPB (DEL_B_LEFT) and a 103 kb deletion extending right (DEL_B_RIGHT). DEL_B_LEFT and DEL_B_RIGHT were the most common GYPB deletions in the 1000 Genomes Project 669 African genomes (allele frequencies 0.04 and 0.02). Seven additional deletions involving GYPB were seen in African, Admixed American, and South Asian samples. No samples analyzed had GYPB*01N. CONCLUSIONS: The U- phenotype in those of African ancestry is primarily associated with two different complete deletions of GYPB (with intact GYPE). Seven additional less common GYPB deletion backgrounds were found. GYPB*01N, long assumed to be the allele commonly encoding U- phenotypes, appears to be rare.

Overcoming the challenges of interpreting complex and uncommon RH alleles from whole genomes.

BACKGROUND AND OBJECTIVES: Rh is one of the most diverse and complex blood group systems. Recently, next generation sequencing (NGS) has proven to be a viable option for RH genotyping. We have developed automated software (bloodTyper) for determining alleles encoding RBC antigens from NGS-based whole genome sequencing (WGS). The bloodTyper algorithm has not yet been optimized and evaluated for complex and uncommon RH alleles. MATERIALS AND METHODS: Twenty-two samples with previous polymerase chain reaction (PCR) and Sanger sequencing-based RH genotyping underwent WGS. bloodTyper was used to detect RH alleles including those defined by structural variation (SV) using a combination of three independent strategies: sequence read depth of coverage, split reads and paired reads. RESULTS: bloodTyper was programmed to identify D negative and positive phenotypes as well as the presence of alleles encoding weak D, partial D and variant RHCE. Sequence read depth of coverage calculation accurately determined RHD zygosity and detected the presence of RHD/RHCE hybrids. RHCE*C was determined by sequence read depth of coverage and by split read methods. RHD hybrid alleles and RHCE*C were confirmed by using a paired read approach. Small SVs present in RHCE*CeRN and RHCE*ceHAR were detected by a combined read depth of coverage and paired read approach. CONCLUSIONS: The combination of several different interpretive approaches allowed for automated software based-RH genotyping of WGS data including RHD zygosity and complex compound RHD and RHCE heterozygotes. The scalable nature of this automated analysis will enable RH genotyping in large genomic sequencing projects.

Performance of ACMG-AMP Variant-Interpretation Guidelines among Nine Laboratories in the Clinical Sequencing Exploratory Research Consortium.

Evaluating the pathogenicity of a variant is challenging given the plethora of types of genetic evidence that laboratories consider. Deciding how to weigh each type of evidence is difficult, and standards have been needed. In 2015, the American College of Medical Genetics and Genomics (ACMG) and the Association for Molecular Pathology (AMP) published guidelines for the assessment of variants in genes associated with Mendelian diseases. Nine molecular diagnostic laboratories involved in the Clinical Sequencing Exploratory Research (CSER) consortium piloted these guidelines on 99 variants spanning all categories (pathogenic, likely pathogenic, uncertain significance, likely benign, and benign). Nine variants were distributed to all laboratories, and the remaining 90 were evaluated by three laboratories. The laboratories classified each variant by using both the laboratory's own method and the ACMG-AMP criteria. The agreement between the two methods used within laboratories was high (K-alpha = 0.91) with 79% concordance. However, there was only 34% concordance for either classification system across laboratories. After consensus discussions and detailed review of the ACMG-AMP criteria, concordance increased to 71%. Causes of initial discordance in ACMG-AMP classifications were identified, and recommendations on clarification and increased specification of the ACMG-AMP criteria were made. In summary, although an initial pilot of the ACMG-AMP guidelines did not lead to increased concordance in variant interpretation, comparing variant interpretations to identify differences and having a common framework to facilitate resolution of those differences were beneficial for improving agreement, allowing iterative movement toward increased reporting consistency for variants in genes associated with monogenic disease.

Considerations for clinical curation, classification, and reporting of low-penetrance and low effect size variants associated with disease risk.

PURPOSE: Clinically relevant variants exhibit a wide range of penetrance. Medical practice has traditionally focused on highly penetrant variants with large effect sizes and, consequently, classification and clinical reporting frameworks are tailored to that variant type. At the other end of the penetrance spectrum, where variants are often referred to as "risk alleles," traditional frameworks are no longer appropriate. This has led to inconsistency in how such variants are interpreted and classified. Here, we describe a conceptual framework to begin addressing this gap. METHODS: We used a set of risk alleles to define data elements that can characterize the validity of reported disease associations. We assigned weight to these data elements and established classification categories expressing confidence levels. This framework was then expanded to develop criteria for inclusion of risk alleles on clinical reports. RESULTS: Foundational data elements include cohort size, quality of phenotyping, statistical significance, and replication of results. Criteria for determining inclusion of risk alleles on clinical reports include presence of clinical management guidelines, effect size, severity of the associated phenotype, and effectiveness of intervention. CONCLUSION: This framework represents an approach for classifying risk alleles and can serve as a foundation to catalyze community efforts for refinement.

Correction: Variant classification changes over time in BRCA1 and BRCA2.

In the original version of this Article, the affiliation details for Drs. Jordan Lerner-Ellis and George Charames did not include the Department of Pathology and Laboratory Medicine at the University of Toronto. In addition, Drs. Jordan Lerner-Ellis and George Charames were incorrectly affiliated with the Institute of Health Policy, Management and Evaluation at the University of Toronto. These errors have now been corrected in both the PDF and HTML versions of the Article.

Variant classification changes over time in BRCA1 and BRCA2.

PURPOSE: To report BRCA1 and BRCA2 (BRCA1/2) variant reassessments and reclassifications between 2012 and 2017 at the Advanced Molecular Diagnostics Laboratory (AMDL) in Toronto, Canada, which provides BRCA1/2 testing for patients in Ontario, and to compare AMDL variant classifications with submissions in ClinVar. METHODS: Variants were assessed using a standardized variant assessment tool based on the American College of Medical Genetics and Genomics/Association for Molecular Pathology's guidelines and tracked in an in-house database. Variants were shared through the Canadian Open Genetics Repository and submitted to ClinVar for comparison against other laboratories. RESULTS: AMDL identified 1209 BRCA1/2 variants between 2012 and 2017. During this period, 32.9% (398/1209) of variants were reassessed and 12.4% (150/1209) were reclassified. The majority of reclassified variants were downgraded (112/150, 74.7%). Of the reclassified variants, 63.3% (95/150) were reclassified to benign, 20.7% (31/150) to likely benign, 10.0% (15/150) to variant of uncertain significance, 2.0% (3/150) to likely pathogenic, and 4.0% (6/150) to pathogenic. Discordant ClinVar submissions were found for 40.4% (488/1209) of variants. CONCLUSION: BRCA1/2 variants may be reclassified over time. Reclassification presents ethical and practical challenges related to recontacting patients. Data sharing is essential to improve variant interpretation, to help patients receive appropriate care based on their genetic results.

A whole genome approach for discovering the genetic basis of blood group antigens: independent confirmation for P1 and Xga.

BACKGROUND: Although P1 and Xga are known to be associated with the A4GALT and XG genes, respectively, the genetic basis of antigen expression has been elusive. Recent reports link both P1 and Xga expression with nucleotide changes in the promotor regions and with antigen-negative phenotypes due to disruption of transcription factor binding. STUDY DESIGN AND METHODS: Whole genome sequencing was performed on 113 individuals as part of the MedSeq Project with serologic RBC antigen typing for P1 (n = 77) and Xga (n = 15). Genomic data were analyzed by two approaches, nucleotide frequency correlation and serologic correlation, to find A4GALT and XG changes associated with P1 and Xga expression. RESULTS: For P1, the frequency approach identified 29 possible associated nucleotide changes, and the serologic approach revealed four among them correlating with the P1+/P1- phenotype: chr22:43,115,523_43,115,520AAAG/delAAAG (rs66781836); chr 22:43,114,551C/T (rs8138197); chr22:43,114,020 T/G (rs2143918); and chr22:43,113,793G/T (rs5751348). For Xga , the frequency approach identified 82 possible associated nucleotide changes, and among these the serologic approach revealed one correlating with the Xg(a+)/Xg(a-) phenotype: chrX:2,666,384G/C (rs311103). CONCLUSION: A bioinformatics analysis pipeline was created to identify genetic changes responsible for RBC antigen expression. This study, in progress before the recently published reports, independently confirms the basis for P1 and Xga . Although this enabled molecular typing of these antigens, the Y chromosome PAR1 region interfered with Xga typing in males. This approach could be used to identify and confirm the genetic basis of antigens, potentially replacing the historical approach using family pedigrees as genomic sequencing becomes commonplace.