G > A located in the MT-12SrRNA with the metabolite ratio of C2/C10:1 . The second most significant mtSNV was found for mt3714A > G located in the MT-ND1 with the metabolite ratio of phosphatidylcholine (PC) ae C42:5/PC ae C44:5 . A large number of significant metabolite ratios were observed involving PC aa C36:6 and the variant mt10689G > A, located in the MT-ND4L gene. These results show an important interconnection between mitochondria and metabolite concentrations. Considering that some of the significant metabolites found in this study have been previously related to complex diseases, such as neurological disorders and metabolic conditions, these associations found here might play a crucial role for further investigations of such complex diseases. Understanding the mechanisms that control human health and disease, in particular, the role of genetic predispositions and their interaction with environmental factors is a prerequisite for the development of safe and efficient therapies for complex disorders.In the era of personalized medicine with more and more patient-specific targeted therapies being used, we need reliable, dynamic, faster and sensitive biomarkers both to track the causes of disease and to develop and evolve therapies during the course of treatment. Metabolomics recently has shown substantial evidence to support its emerging role in disease diagnosis and prognosis. Aside from biomarkers and development of therapies, it is also an important goal to understand the involvement of mitochondrial DNA (mtDNA) in metabolic regulation, aging and disease development. Somatic mutations of the mitochondrial genome are also heavily implicated in age-related disease and aging. The general hypothesis is that an alteration in the concentration of metabolite profiles  influences the increase of mutation rate in the mtDNA and thereby contributes to a range of pathophysiological alterations observed in complex diseases. We performed an inverted mitochondrial genome-wide association analysis between mitochondrial nucleotide variants (mtSNVs) and concentration of metabolites. We used 151 metabolites and the whole sequenced mitochondrial genome from 2718 individuals to identify the genetic variants associated with metabolite profiles. Because of the high coverage, next-generation sequencing-based analysis of the mitochondrial genome allows for an accurate detection of mitochondrial heteroplasmy and for the identification of variants associated with the metabolome. The strongest association was found for mt715 Genome-wide association studies (GWASs) have identified a number of genetic polymorphisms that convey an increased risk for developing diseases . HoweverRecently, GWAS on the mitochondrial genome (mtGWAS) have also provided some new insights into the mechanisms underlying several complex diseases . GeneticMitochondrial DNA (mtDNA), which has a length of ~16.6 kb, codes for 13 subunits of the mitochondrial respiratory chain complexes, 2 ribosomal (rRNA) genes and 22 transfer RNA (tRNA) genes that are required for mitochondrial protein synthesis. Mitochondria consume oxygen and substrates to generate energy in the form of adenosine triphosphate (ATP) while producing reactive oxygen species (ROS), also known as free radicals, in the process. While ROS have important roles in cell signaling and homeostasis, ROS levels can increase drastically and cause significant damage to the DNA, thereby altering the membrane permeability and calcium homeostasis along with increasing the heteroplasmic mtDNA. This damage is termed oxidative stress. High levels of oxidative stress may damage cellular structures which, in turn, can lead to mitochondrial dysfunction, possibly even to apoptosis.Mitochondrial mutations can either be somatic or inherited through the maternal line . Since mA relationship that has not been investigated to date is the one between mitochondrial genetic variants and metabolomics (mtGWAS-metabolomics). The most prominent role of mitochondria is not only to generate large quantities of ATP but also to regulate many metabolic tasks ,11, suchThe general hypothesis is that an alteration in the concentration of metabolite profiles  influences the increase of mutation rate in the mtDNA and thereby contributes to a range of pathophysiological alterations observed in complex diseases. Based on this hypothesis, the rationale for the present research is the establishment of mitochondrial and metabolomic signatures as a panel of candidate biomarkers for the prediction and early diagnosis of disease as well as monitoring of disease progression.In this study, we conducted association analyses between 151 metabolites and the whole sequenced mitochondrial genome from 1163 individuals to identify genetic variants influencing metabolite profiles. Because of the high coverage, next-generation sequencing (NGS) allows us a deeper and more accurate analysis of the mitochondrial genome for identification of disease-causing variants and heteroplasmy than mitochondrial nucleotide variants (mtSNV) allele intensities measured by genotyping arrays.We conducted a GWAS with metabolite ratios using 151 metabolic traits in a group of 1163 KORA-F4 individuals with sequenced mitochondria covering 9172 mtSNVs. Linear regression analysis was also conducted for each of 151 metabolites, but the results were by all individual metabolite not significant.P-values are plotted in x-axis represents the mitochondrial genome, showing the position and relative size of each of the 13 major mitochondrial genes. An mtSNV was considered as significant not only when the P-value was <1.257545*10−05 after Meff correction but also when the metabolite ratio P-gain was >151, i.e. the total number of metabolites. In total, we observed 404 mtSNVs that display metabolite ratio associations at a genome-wide significance level. The significant G > A located in the MT-12SrRNA, was associated with the metabolite ratio acetylcarnitine/decenoylcarnitine . Following in significance is mt3714A > G located in the MT-ND1, which was associated with the metabolite ratio phosphatidylcholine (PC) acyl-alkyl C42:5/PC acyl-alkyl C44:5 .The most significant mtSNV, mt715More details about the estimated model parameters for each significant mtSNV are given in
Table S1
G > A, located in the ND4L gene is of special interest because it is associated with 16 different metabolite ratios, making it the most common multi-associated mtSNV in our dataset. Moreover, in all these 16 ratios, the metabolite PC diacyl C36:6 (PC aa C36:6) is involved (It can be observed that a large percentage (15%) of the most significant mtSNVs is located in the MT-ND4L . They arinvolved .This study was carried out with the aim of investigating the relationship between the metabolite ratios and the genetic variants of the mtDNA. The general hypothesis is that an alteration in the concentration of metabolite profiles  influences the increase of mutation rate in the mtDNA and thereby contributes to a range of pathophysiological alterations observed in complex diseases.G > A located in the MT-12SrRNA with the metabolite ratio acetylcarnitine (C2)/decenoylcarnitine (C10:1) . This association may play a crucial role in the regulation of insulin secretion.The strongest association was found for mt715C2 is broken down in the blood by plasma esterases to carnitine which is used by the body to transport fatty acids into the mitochondria for breakdown and thus may have a role in normalizing intracellular lipid metabolism. It is known that C2 levels are low in patients with type 1 and type 2 diabetes mellitus . FurtherC10:1 is considered to be a fatty ester lipid molecule. This metabolite was found to be associated not only with type 2 diabetes but also with prediabetic states . In addide novo purine biosynthesis, which leads to the accumulation of the de novo purine synthesis intermediate 5-aminoimidazole-4-carboxamide and the activation of the metabolic regulator 5′-AMP-activated protein kinase.In humans, MT-12SrRNA is encoded by the MT-RNR1 gene. The MT-RNR1 gene encodes for a protein responsible for regulating insulin sensitivity and metabolic homeostasis . The proInterestingly, the concentration of C2 and C10:1 has been observed to increase during physical exercise ,24. It iG > A which, in turn, can lead to MT-12SrRNA dysfunction, possibly even to affect insulin regulation.In this context, our results show that an alteration of C2/C10:1 levels in serum increases the ratio of heteroplasmy at mt715A > G located in the MT-ND1 with the metabolite ratio PC ae C42:5/PC ae C44:5 . This association may play a crucial role in disorders related to damage or disease that affects the brain.The second most significant mtSNV was found for mt3714Both metabolites involved in this ratio are PC, which are included in the glycerophospholipid class. Glycerophospholipids are the main component of biological membranes, providing them with stability, fluidity and permeability. Glycerophospholipids are actively catabolized by brain tissue and are involved in the regulation of several molecular functions, such as generation of second messengers, apoptosis, antioxidant and membrane fusion and regulation of enzyme activities . A markeThe MT-ND1 gene encodes for the NADH–ubiquinone oxidoreductase chain 1 protein, a subunit of NADH dehydrogenase, which is located in the mitochondrial inner membrane. Genetic mutations of the human MT-ND1 gene have been associated with several inborn genetic disorders, such as MELAS, Leigh’s syndrome and Leber’s hereditary optic neuropathy ,33. All A > G which, in turn, can lead to MT-ND1 dysfunction. This represents a pathway that may help to understand molecular aspects of neurodegeneration diseases.Based on our results, an alteration in PC ae C42:5 and PC ae C44:5 increases heteroplasmy at mt3714G > A, located in the ND4L gene, which is associated with 16 metabolite ratios is the most common multi-associated mtSNV in our dataset. Moreover, in all these 16 ratios, the metabolite PC diacyl C36:6 (PC aa C36:6) is involved.It should be noted that the largest number of significant associations between metabolite ratios and mtSNVs are located in the MT-ND4L gene  and 3. TDespite the crucial importance of PC for brain functioning, disturbance of PC concentration has also been found to be associated with various metabolic disorders, such as atherosclerosis , insulinPC aa C36:6 was also involved in three metabolite ratios that were previously shown to be associated with Fat-Free Mass Index .MT-ND4L is a subunit of NADH dehydrogenase, which is the component of the electron transport chain that is responsible for the oxidative phosphorylation process. Its dysfunction may cause energy deficiency in cells, resulting in metabolic disorders such as obesity and diabetes . In factG > A, located in the ND4L gene. Put together, this association may explain an interesting pathway to understand the development of metabolic conditions.Based on our results in 16 metabolite ratios where PC aa C36:6 is involved, alteration of its concentration changes significantly the heteroplasmy in the variant mt10689We presented the results of the first association analysis between genetic positions in the mitochondrial genome and metabolite profiles using sequencing data of the mitochondrial genome and an inverted mtGWAS. Although further analyses are needed to follow up on the present results, these findings highlight the important role of the mtDNA and the field of metabolomics among the factors that contribute to the balance of the human metabolism and suggest that variants in the mitochondrial genome may be more important than has previously been suspected.Our results support the idea that a strong relationship exists between mtDNA heteroplasmy and metabolite levels. Both are likely to play a crucial role as discriminating cofactors in the etiology of common multi-factorial diseases. A conceivable hypothesis is that high levels of oxidative stress might be produced when altered levels of certain metabolites  alter the permeability of cell membranes along with increasing the heteroplasmic mtDNA and weakening the mitochondrial defense systems. High levels of oxidative stress damage cellular structures, including the mitochondria themselves, which, in turn, can lead to mitochondrial dysfunction and possibly even to apoptosis.In the era of personalized medicine with more and more patient-specific targeted therapies being used, we need reliable, dynamic, faster and sensitive biomarkers both to track the causes of disease and to develop and evolve therapies during the course of treatment. Understanding the mechanisms that control human health and disease, in particular, the role of genetic predispositions and their interaction with environmental factors, is a prerequisite for the development of safe and efficient therapies for complex disorders.We conclude that recent advances in metabolomics and NGS technology along with novel strategies to analyze and understand the metabolic pathways, as well as to integrate metabolite networks with mitochondrial genetic data, opens this window of opportunity to identify new biomarkers related to the human mtDNA, both to track complex diseases and to develop and evolve the option of treatment.The Cooperative Health Research in the Region of Augsburg (KORA) study is a series of independent population-based epidemiological surveys and follow-up studies of participants living in the region of Augsburg, in southern Germany, an area with demographic and socioeconomic characteristics roughly reflecting those of an average central European population. All participants are residents of German nationality who were identified through the registration office, and written informed consent was obtained from each participant . The stuMetabolomic measurements have been performed for 3061 individuals of KORA F4 population-based sample. Men and women were collected in a random order and samples were randomly put on plates to reduce batch effects. Metabolites were measured in serum using the AbsoluteIDQ™ p150 Kit , as described elsewhere . This NGS library preparation kit employs an engineered Transposome™ to randomly fragment and tag amplicons and small genomes with Illumina® specific adapters. After library preparation, samples were sequenced on the Illumina® MiSeq™. This method generated whole mitochondrial genome NGS data, which accurately reflect the Sanger sequence. The 16 569 positions have been sequenced with mtSNV covered with 3500-fold on average. Because of this high coverage, NGS will allow us a much deeper analysis of the mitochondrial genome and heteroplasmy for identification of disease-causing variants than array-based genotyping of mitochondrial SNVs. It has been observed that NGS data are highly reproducible and very reliable, and, overall, they has been found to be superior to the data produced by microarray technology  were also removed together with the five positions  in blacklisted sites as recommended by GATK . Further<fig id="f5">Supplementary Material S1<graphic mimetype="image" xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="ddab312f5.tif"/></fig>During QC step2, metabolites were imputed using multiple imputation by chained equations with the method called ‘predictive mean matching’. Missing values are frequently observed in high-throughput, mass spectrometry-based metabolomics measurements, which is the approach used by Biocrates. To this end, we have used the same approach as in previous analyses of the Biocrates metabolomics data in KORA . FollowiAfter quality control, individuals with available mtDNA data and metabolite values were considered in the analysis. Association analyses was performed in a total number of 9172 mtSNVs and 151 metabolites in a sample of 1163 KORA-F4 individuals.GWAS have been developed as a method to identify genetic loci associated with disease by scanning multiple markers across the genome. The advance in genotyping technology has led to significant advances in the genetics of complex diseases. Recently, NGS has gained popularity through its capacity to analyze a much greater number of markers across the genome. One of the peculiarities of the mtDNA is the heteroplasmy. Owing to heteroplasmy, mtDNA tends to be heterogeneous in the sense that different mitochondria can have different genotypes such that a genotype at an mtDNA locus may not be restricted to zero, one or two minor alleles. This issue affects the possibility of estimating genotypes and makes calling algorithms useless. The finest way to approach mitochondrial heteroplasmy is to utilize high-throughput sequencing data, which readily provides data of the entire mitochondrial genome and offers the prerequisite for the detection of low-level mitochondrial genetic heteroplasmy.2-transformed ratio, y = log2(B/A), where A denotes the number of counts of the reference allele and B the second most frequent allele. In the ratios, to avoid undefined logarithms in those individuals who only carry the reference allele for the mtSNV under study, a term of two additional counts was added in the denominator and numerator. Considering that the average number of counts is about 3500, adding two counts to A and B will not have any impact in the final results.In our analysis, we take the heteroplasmy into account by using the raw sequence reads, i.e. the number of counts for each variant, instead of calling the variants into a few categories. The best way to assess the association of metabolites with the mtSNV is to apply linear regression analysis. The mtSNV enters the model as the response via the logmtDNA) was included in the model aswell.Sex and age at examination are introduced in the model as covariates. To improve the convergence properties of model estimates, the total coverage (Cov) of the mtSNV (i.e. A + B) was added in the model. In addition, to avoid false positives owing to a batch effect during mtDNA sequencing, the effect of the index of the sequencing plate )/2(N), where N is the number of single metabolites, with the linear model for association analysis with all mtSNVs. This hypothesis-free approach highlights pairs of metabolites that are more likely to be coupled either biochemically or physiologically )/2) and s the number of mtSNVs to be tested. The aim of this analysis is to evaluate how the mitochondrial heteroplasmy is influenced by the metabolite ratio, representing the phenotype of interest, taking into account the age and sex of a particular person; and therefore, the model adjusts for individual differences regarding these two variables in the study population. GWAS has always been referred to analyses that use genetic variance as predictors in models. In our analysis, the general hypothesis is that an alteration in the concentration of metabolite profiles influences the increase of mutation rate in the mtDNA. In this situation, it is more appropriate to use the genetic variants as outcome variables. For that reason, we will refer to this approach as ‘inverted mtGWAS’.where P-values are obtained from a Wald test based on the asymptotic normality of regression coefficient estimates and are corrected for multiple comparisons, with the correction factor being equal to the effective number of independent tests (Meff)  from Nyholt et al. . To estimate whether deviation from normality of metabolite ratios may have biased our results, we tested associations for both untransformed and log-scaled ratios, not detecting substantial differences. Quantile-quantile plots were used to examine the P-value distribution, and the lambda (λ) ranged from 0.93 to 1.26 (see s (Meff) . We calct et al.  with thet et al. . We usedatistics  to quant1.26 see . All sta1.26 see .supplementary_Figure_1_ddab312Click here for additional data file.supplementary_Material_1_ddab312Click here for additional data file.supplementary_Table1_ddab312Click here for additional data file.


BRCA1 and BRCA2 susceptibility genes predispose carriers to breast and/or ovarian cancer. The use of germline testing panels containing these genes has grown dramatically, but the interpretation of the results has been complicated by the identification of many sequence variants of undefined cancer relevance, termed “Variants of Uncertain Significance (VUS).” We have developed functional assays and a statistical model called VarCall for classifying BRCA1 and BRCA2 VUS. Here we describe a multifactorial extension of VarCall, called VarCall XT, that allows for co–analysis of multiple forms of genetic evidence. We evaluated the accuracy of models defined by the combinations of functional, in silico protein predictors, and family data for VUS classification. VarCall XT classified variants of known pathogenicity status with high sensitivity and specificity, with the functional assays contributing the greatest predictive power. This approach could be used to identify more patients that would benefit from personalized cancer risk assessment and management.Loss-of-function variants in the  However, while a genetic variant may be identified in individuals with the disease, the likelihood of pathogenicity of the variant may not be sufficient to justify an intervention. Thus, genetic variants of uncertain clinical significance (VUS) present a critical challenge to the effective use of clinical genetic informationBRCA1 and BRCA2 are among the genes most frequently subjected to clinical genetic testing because of the clinical importance of pathogenic variants. Carriers of pathogenic variants in BRCA1 or BRCA2 are at significantly increased risk (Relative Risk (RR) > 4) for breast and ovarian cancer and this knowledge can guide screening for early detection and the use of risk-reducing prophylactic surgeries. Importantly, the discovery of pathogenic variants also identifies patients likely to benefit from PARP inhibitors, a class of therapeutic agents that target cancers with homologous recombination DNA repair deficiency. Thus, the determination of the clinical relevance of VUS in BRCA1 and BRCA2 has significant implications for clinical care.BRCA1 and BRCA2 VUS. For example, loss-of-function variants in these tumor suppressors, for which the effect on protein function can be inferred from the genetic code , are generally classified as pathogenic. A multifactorial model that includes family history, co-segregation, and co-occurrence with another known pathogenic variant in the same gene  has been used for the remaining variant types 3. It is also possible to classify variants based on the ACMG/AMP  rule-based guidelines, in which pathogenicity may be predicted using multiple sources of the available evidence, including results from reproducible and clinically validated functional assays5. However, current BRCA1 and BRCA2 variant classification models either do not incorporate functional data or use these data-only as supporting evidence, for or against pathogenicity6. The potential for the application of functional data has not been fully realized because there is not an adequate statistical framework with which to (a) optimize the discrimination between pathogenic and non-pathogenic variants using functional data; and (b) integrate functional data with family history and other sources of data to obtain a global likelihood of pathogenicity.Currently, several non-mutually exclusive approaches exist for classifying BRCA1 and BRCA2 in the context of hereditary breast and ovarian cancer syndrome (HBOC), and to examine the effect on predictive accuracy realized by adding the available family or in silico data to the functional data.Here, we present VarCall XT , a Bayesian statistical framework that incorporates data from established functional assays with family data and sequence-based in silico prediction models. This model was used to determine the likelihood of pathogenicity for individual missense variants in BRCA1 or BRCA2 described above. While VarCall analyses of the functional data used here have been previously reported8, we refit these models using our VarCall XT software and summarize these fits here for the sake of consistency and to provide a basis of comparison for the multifactorial extensions of these models.VarCall models make a binary prediction of pathogenic versus benign classification for each variant. In Fig. BRCA1, had enough family data for classification  based on the family data alone  were uniformly higher than for those models excluding the functional data . Finally, Brier scores and the no-call rates were systematically lower among the models with function data than those without. Thus, when considering sensitivity and specificity jointly, the least accurate model with functional data was significantly more accurate than the most accurate model without functional data.Because resolution to estimate predictive accuracy was limited by the number of variants of known status it was difficult to compare VarCall XT models. However, the contrast between models with and without the functional data was clear. The 95% CI limits for sensitivity among the models that excluded the functional data all fell below the lower 95% CI limits of all models that included functional data, while those for specificity did so in the majority of circumstances. Further, overall accuracy estimates (Supplementary Table BRCA1 variants and 112 (45%) of the BRCA2 variants. Among the classified variants there were 17 BRCA1 (seven benign and 10 pathogenic) and 12 BRCA2 variants (two benign and 10 pathogenic) with family history summary data. When available, the number of families contributing to these data was often small . This highlights the fact that even when testing is widespread and prevalent, there will be no available family data on a large fraction of VUS that arise and the data that are available will often provide modest information overall.Family history data were available for 86 (25%) of the We computed ratios of the estimated odds (ORs) in favor of pathogenicity, given both the function and family data, to the estimated odds given the functional data alone. The estimated mean OR among benign variants across both genes was <1.0 as expected  and was >1.0 among known pathogenic variants . On average, the effect of adding the family data moved the associated classification probabilities marginally in the correct direction; however, this pattern was clearer and more consistent among the known benign variants than the known pathogenic variants, as shown by the wider interval estimates. Note that the inclusion of the family data changes the posterior odds of pathogenicity of all variants, not just those with the data. Estimated classifications of the variants with those data shift causing estimates of the population (of variants) level parameters that specify the Gaussian classification model to adjust in response, thereby altering the mapping, applicable to all variants, between functional effects estimates and their associated likelihoods of pathogenicity.BRCA1 no-calls became benign and one pathogenic call became a no-call  versus the combined functional and family data provide more resolution  and BRCA2 variants (n = 6) shift into or out of a no-call region  than in the corresponding BRCA1 region (n = 19)), the fraction of BRCA1 variants to shift classifications is larger than for BRCA2 . No variants changed from pathogenic to benign or benign to pathogenic  of BRCA2 variants had decreased odds of pathogenicity with the addition of the family data. Likewise, among variants predicted to be pathogenic, 22% (7/32) of BRCA1 and 74% (26/35) BRCA2 variants had increased odds of pathogenicity. These patterns are reflected in the plotted loess regression curves in Supplementary Fig. The classification status of the majority of variants was unchanged with the addition of the family data. However, four all Fig. A. All ofBRCA2  and BRCA2 . In BRCA1, the mean difference was 0.22 (SE = 0.050) in the BRCT1 domain, 0.28 (SE = 0.049) in the BRCT2 domain, 0.13 (SE = 0.10) in the Coiled Coil domain, 0.42 (SE = 0.043) in the Linker region and 0.073 (SE = 0.034) in the remaining, unannotated regions. In BRCA2, the mean difference was 0.17 (SE = 0.048) in the Helical region of the DNA Binding Domain, 0.087 (SE = 0.059) in OB1, 0.052 (SE = 0.053) in OB2, 0.34 (SE = 0.082) in OB3 and 0.015 (SE = 0.16) in the unlabeled regions. These results, coupled with the poor operating characteristics of the protein predictor model ; among the VUS by 0.179  and, in contrast, among BRCA1 VUS by −0.00272 .The effects of the Align-GVGD score were similar to those associated with the in silico protein predictor data. The effects on BRCA1 VUS, but had little effect on the likely pathogenic variants or on BRCA2 variants. The results we observe here may overstate Align-GVGD’s potential to improve predictive accuracy since it was also used as a prior probability in the multifactorial model3 used to establish the IARC classifications of the variants we use to assess accuracy.The Align-GVGD score also strengthened the original VarCall estimates for likely benign We have extended the VarCall model to incorporate additional forms of data related to the pathogenicity of VUS and have characterized the predictive performance based on two functional data-only and 13 unique combinations of functional assay data, in silico protein predictions, Align-GVGD score, and a summary of cancer family history. The resulting model, referred to as VarCall XT, is structured to facilitate the addition of further forms of evidence, including data from additional functional assays, genetic analyses, and tumor pathology characteristics. VarCall XT provides a prediction of the probability that each VUS is pathogenic based on data from multiple sources, thus providing a measure for assessing the strength of evidence in favor of or against pathogenicity and for inclusion in VUS classification schema.Available data for variant classification are expected to become richer over time as more individuals are tested, new models for in silico prediction of protein function are developed, other functional assays are developed and standardized, assays are extended to more VUS, and other forms of evidence become available. VarCall XT provides a flexible platform for updating predictions of pathogenicity as these new data arise. VarCall XT assumes that VUS have binary risk profiles, behaving like the wild-type sequence or pathogenic, protein-truncating variants. However, there are likely to be hypomorphic variants and distribution of penetrance values intermediate to these two extremes. The VarCall model can be easily adapted to allow for a different distribution of risk profiles.BRCA1 variants of known disposition were correctly called  and 97% of BRCA2 variants were correctly called . This level of accuracy is not surprising as these functional assays are well-calibrated8. While it is difficult to identify a single best model, the contribution of the functional data is clear. The least accurate model that included the functional data was (much) more accurate than the most accurate model that did not include functional data. This can be explained, at least in part, as an artifact of statistical power: the variants that we focus on each gene have multiple measurements made by the functional assays. The available family data is much weaker: because many variants are seen rarely, truly informative family data is the exception, not the rule. The utility of in silico protein predictors and Align-GVGD score for the prediction of individual VUS is far less clear. Coverage of the protein predictor data is complete, but its ability to separate pathogenic from neutral BRCA1 and BRCA2 variants is imperfect, this may be because the protein predictor models have been developed to identify broader classes of likely functional variation.As currently configured, VarCall identifies variants of known pathogenicity with high sensitivity and specificity with functional assays contributing the greatest predictive power to the model. Indeed, considering the functional data alone, 98.4% of Further, the family data corroborated the functional assays despite their limited extent and the lack of co-segregation data. With continued growth in testing, there is the potential for family history summaries to become much more informative, but this outcome will depend on the availability and quality of these data. Our results highlight the fact that, despite the current shortcomings of these sources of data, accurate classification of VUS can rely heavily on functional assay data. This is and will continue to be especially important for rare VUS that lack informative family data.BRCA1 and BRCA2 may be needed for the classification of potential splice variants. Importantly, splicing data can be incorporated into the VarCall XT model as they become available.Importantly, the functional assays described above cannot be used to classify splice variants. Thus, specific RNA-based splicing assays or functional assays that allow for endogenous splicing within BRCA1 and BRCA2 genes. Both the BRCA1 transcriptional assay and the BRCA2 homologous recombination assay were clinically calibrated using controls with properties that were well established in the literature. It is therefore not surprising that the most informative predictive model contains functional data, and hence the lowest Brier scores since that score reflects the calibratedness of the predictor. Family history and protein prediction scores contribute some amount of measurement error and bias to classification schemes. However, these effects will diminish as testing-based family data increases in scope and coverage and as protein prediction models increase in accuracy.An inherent limitation in the VarCall XT approach is the historical definition of pathogenic missense variants in the BRCA1 and BRCA2 missense variants can overcome this limitation and lead to a more accurate determination of whether a variant results in increased cancer risk.DNA testing is a powerful tool used to tailor medical care based on individual cancer risks. However, DNA testing can produce inconclusive results when a VUS is identified. In this common scenario, healthcare providers might not have the information needed to recommend appropriate preventive and early detection steps, or certain therapeutic treatments, such as PARP inhibitors, and family members may not be referred for genetic testing or enhanced screening. Our results indicate that combining functional assay with other forms of data regarding BRCA1 transcriptional assay dataset has been described in detail elsewhere9. Briefly, the assay is based on the ability of the BRCA1 C–terminal region to activate the transcription of a reporter gene when fused to a heterologous transcription factor DNA binding domain (GAL4 or LexA)10. This validated assay has been shown previously to have high predictive sensitivity and specificity7.The BRCA1 VUS, 42 IARC class 1 or 2 (benign and likely benign by the IARC sequence variant classification scheme11) and 21 IARC class 4 or 5 variants (likely pathogenic and pathogenic), and two controls were assayed for transcriptional activity in 126 experimental batches . Each batch included the wild-type (WT) as a positive control and p.(Met1775Arg) (IARC Class 5) as a negative control. Typically, three replicates of each variant were made within each batch. As previously12, we averaged the within-batch replicate log-ratio values and analyzed the resulting variant- and batch-level mean values.In this dataset, 281 BRCA2 homologous recombination assay and the associated experimental protocol have been described previously8. The BRCA2 HR dataset analyzed here comprised 2233 individual log-ratio measurements made on a total of 248 variants. Of these, 22 were IARC class 1 or 2 variants that, together with the wild-type (WT) control, were labeled as benign in the analysis. In addition to the loss-of-function (LOF) control p.(Asp2723His), 11 IARC class 4 and 5 likely pathogenic and pathogenic variants were labeled as damaging to protein function. The remaining 168 variants were VUS and were unlabeled in the analysis. The data were collected in 162 experimental batches. The WT and LOF variants were replicated twice in every batch, while all other variants were replicated at least twice in at least one batch. As with the BRCA1 assay data, we averaged the within-batch replicate log-ratio values for each variant and analyzed the 984 resulting within-batch means.The BRCA1 and BRCA2 were obtained from the dbNSFP13 (v4.0b2) database using the BioR toolkit14. For simplicity, the predictions from each model were expressed as rank scores and scaled to a range between zero and one. Hence a score of 0.9 implied that the variant was more likely to be damaging than 90% of all other variants predicted by that method15. Because these metrics are correlated, we carried out a principal components analysis to identify the ten orthogonal linear combinations (PCs) summarizing a majority (90%) of variation in the metrics. Additional detail is available in the Supplement.In silico predictions from 27 protein prediction models for all assayed variants in 16 as a predictor of variant pathogenicity. Tavtigian et al.16 provided point and 95% interval estimates of the probability that a given missense substitution in BRCA1 or BRCA2 was pathogenic based on a multiple sequence alignment ranging from humans to sea urchin.In addition, we separately investigated Align-GVGD17, we estimated Bayes factors (BFs) in favor of variant pathogenicity based on family history summaries obtained from ~140,000 patients tested by Ambry Genetics for the presence of pathogenic variants in BRCA1 and BRCA2. We included these quantities in our multifactorial models as described below.Using methods described elsewhereAmbry Genetics declares that the Western Institutional Review Board has issued a regulatory opinion based on federal regulation 45 CFR 46 guidance that the research, which is based on de-identified data, does not involve human subjects. Therefore, informed consent was not required.18 models comprise a family of models for functional assay data, have been applied to several different functional assays for BRCA1 and BRCA2, and share the following common structure. They are formulated as Bayesian hierarchical models constructed around a core random-effects model for the functional data. The random-effects model decomposes variation in the assay readout into three sources: that explained by the variant, that related to the experimental batch and background variation. Variant-specific random effects are assumed to be normally distributed with a mean and variance parameter that depends on whether the variant is damaging or not. This implies a two-component normal mixture model. Hence, while the first level of the model is a mixed-effects regression model, the second level is a Gaussian classification model. Classification models provide a more powerful alternative to approaches based on discriminant analysis and clustering, allowing for more accurate classifications with modest to small numbers of labeled samples23 .VarCallD, that indicates whether it is damaging (D = 1) or not (D = 0). The subset of variants assayed as positive or negative controls, i.e., those that are known or very likely pathogenic (Class 4/5) or known or very likely benign (IARC Class 1/2) have D set to 0 or 1 as appropriate, i.e., are “labeled,” while the values of D for the VUS are set to missing, i.e., are “unlabeled.” To avoid circularity, no functional data were used to establish the IARC classification of pathogenic and benign known variants. The IARC classification is based on the multifactorial model3 which does not include functional data. The Align-GVGD score was, however, used as a prior probability in the multifactorial model3 to obtain the IARC classification, raising the possibility that we may overestimate the accuracy of VarCall XT models that include Align-GVGD. To examine the sensitivity of our classifications to the fraction of labeled variants, we fit the BRCA1 and BRCA2 function-only VarCall models with only the WT and negative controls (p.(Met1775Arg) for BRCA1 and p.(Asp2723His) for BRCA2) labeled for comparison. The formal description of VarCall and the extensions found in the supplement are based on those presented in refs. 24.The posterior probability that a given variant is pathogenic based on this classification model is the probability that the variant’s random effect parameter is drawn from the pathogenic component of the mixture model. To estimate this probability, we assign to each variant a binary variable, The multifactorial models investigated here extend the VarCall model and are distinguished by the variant-specific metrics . Future models that involve additional forms of evidence can be obtained by extending the model by following the approach described in the Supplement.25. To identify the most reliable model we evaluated the remaining models that include at least one of the four data types, for a total of 15 classification models.For each gene, we evaluated two progression series of multifactorial VarCall models: (1) beginning with the family data BFs and cumulatively adding the in silico sequence-based predictor data, the Align-GVGD score and, finally, the functional assay data in order to assess how functional data add to classification by family and in silico data; (2) beginning with the standard VarCall model for the functional assay data and cumulatively adding the family data, the in silico data and Align-GVGD score to assess whether adding family and in silico data improve classification based on functional data. Both series culminate in the full multifactorial model. The Align-GVGD in silico predictor was added separately because it is currently part of the established multifactorial model for BRCA1 and BRCA2 VUS classification11, we classified the variant as ‘benign’ if Pr(D = 1 ∣ Data) < 0.05, as ‘pathogenic’ if Pr(D = 1 ∣ Data) ≥ 0.99 and left it unclassified otherwise; to distinguish these from the known classifications, we refer to these categories as ‘called benign’, ‘called pathogenic’ and ‘not called,’ respectively.For each gene, we compared each of the 15 VarCall models on basis of the classification probabilities assigned to the variants of known disposition in the context of a leave-one-variant-out analysis. We systematically left each unlabeled, treating it as if it were a VUS, refit the model, and recorded the variant’s estimated classification probability. Based on this value and thresholds established in the literature26, which summarizes the discordance between the binary classifications, Dv, and the predicted probabilities, Pr(Dv ∣ Data) that result from a given model. We also compute Bayesian estimates of the sensitivity, specificity, false-positive, false-negative, and no-call rates associated with each VarCall model. In particular, for each model, we estimate the conditional distributions over the estimated classification categories given known disposition based on a Dirichlet-multinomial model for the observed counts assuming Jeffrey’s prior on the multinomial probability vector. We compute and report the posterior mean and 95% Bayesian high-density region (HDR) interval estimates for each operating characteristic based on the fitted model.Using these estimated classifications, we compute and report model- and empirical, count-based estimates of the operating characteristics of the VarCall models. The empirical predictive summaries include tabulations of the estimated classifications conditional on known pathogenicity status; raw predictive accuracy rates that treat variants in the no-call category as incorrectly classified; and the scaled Brier scoreBRCA1 and BRCA2; the protein predictor data; a figure of density plots of the top ten principal components of the protein predictor data; a figure of scatter plots of the log-odds of pathogenicity computed using the function data alone versus the function data combined with the Align-GVGD score; and a figure comparing classification probabilities when all known variants are labeled to those computed when only the control variants are labeled.The supplemental data file includes a detailed description of the models; results of an analysis of sensitivity to the fraction of labeled variants; a table of empirical summaries of the 15 VarCall models for Further information on research design is available in the Supplementary MaterialReporting SummarySupplemental Data 1