1,721,128 research outputs found
Computational and statistical approaches to analyzing variants identified by exome sequencing
Get PDFNew sequencing technology has enabled the identification of thousands of single nucleotide polymorphisms in the exome, and many computational and statistical approaches to identify disease-association signals have emerged.National Institutes of Health (U.S.) (Grant R01-MH084676)National Institutes of Health (U.S.) (Grant R01-GM078598)National Institutes of Health (U.S.) (Training grant T32-HL07604-25)Brigham and Women's Hospital (Division of Cardiovascular Medicine
The mystery of missing heritability: Genetic interactions create phantom heritability
No full textHuman genetics has been haunted by the mystery of “missing heritability” of common traits. Although studies have discovered >1,200 variants associated with common diseases and traits, these variants typically appear to explain only a minority of the heritability. The proportion of heritability explained by a set of variants is the ratio of (i) the heritability due to these variants (numerator), estimated directly from their observed effects, to (ii) the total heritability (denominator), inferred indirectly from population data. The prevailing view has been that the explanation for missing heritability lies in the numerator—that is, in as-yet undiscovered variants. While many variants surely remain to be found, we show here that a substantial portion of missing heritability could arise from overestimation of the denominator, creating “phantom heritability.” Specifically, (i) estimates of total heritability implicitly assume the trait involves no genetic interactions (epistasis) among loci; (ii) this assumption is not justified, because models with interactions are also consistent with observable data; and (iii) under such models, the total heritability may be much smaller and thus the proportion of heritability explained much larger. For example, 80% of the currently missing heritability for Crohn's disease could be due to genetic interactions, if the disease involves interaction among three pathways. In short, missing heritability need not directly correspond to missing variants, because current estimates of total heritability may be significantly inflated by genetic interactions. Finally, we describe a method for estimating heritability from isolated populations that is not inflated by genetic interactions.National Institutes of Health (U.S.) (Grant number HG003067
Recommended from our members
Evolutionary dynamics of deletions removing regulatory features in the human noncoding genome
No full textGenomic deletions provide a powerful loss-of-function model in noncoding regions to assess the role of purifying selection on human noncoding genetic variation. Regulatory element function is characterized by non-uniform tissue/cell-type activity, necessarily linking the study of fitness consequences from regulatory variants to their corresponding cellular activity. In this dissertation, I expound how I and my collaborators used a callset of deletions I generated from genomes of participants in the Alzheimer’s Disease Neuroimaging Initiative (ADNI) and deletions from the 1000 Genomes Project (1000GP) in order to examine whether purifying selection preserves noncoding sites of chromatin accessibility marked by DNase I hypersensitivity (DHS), histone modification (enhancer, transcribed, polycomb-repressed, heterochromatin), and topologically associated domain loops (TAD-loops). To examine this in a cellular activity-aware manner, I developed a statistical method, Pleiotropy Ratio Score (PlyRS), which calculates a correlation-adjusted count of "cellular pleiotropy" for each noncoding base-pair by analyzing shared regulatory annotations across tissues/cell-types. Comparing real deletion PlyRS values to simulations in a length-matched framework and using genomic covariates in analyses, we found that purifying selection acts to preserve both DHS and enhancer sites, as evident by both depletion of deletions overlapping these annotations and a shift in the allele frequency spectrum of overlapping deletions towards rare alleles. However, we did not find evidence of purifying selection for transcribed, polycomb-repressed, or heterochromatin sites. Additionally, we found evidence that purifying selection is acting on TAD-loop boundary integrity by preserving co-localized CTCF binding sites. Notably, at regions of DHS, enhancer, and CTCF within TAD-loop boundaries we found evidence that both sites of tissue/cell-type-specific activity and sites of cellularly pleiotropic activity are preserved by selection.Medical SciencesMedical Science
Deciphering mutations in actionable genes by integrating structural and evolutionary epistatic features.
Get PDFDespite the rapid advancement of sequencing technologies and although the wide diffusion of Whole Genome Sequencing (WGS) and Whole Exome Sequencing (WES) led to an increase in the diagnoses of diseases (A. C. Lionel, et al. 2018; D. J. Stavropoulos, et al. 2016; J. C. Taylor, et al. 2015) most genetic variants remain without a clear interpretation. One of the main difficulty related with the assessment of sequencing results is the abundance of Single Nucleotide Variant (SNV), around 4 million, that each healthy individual carries. Nearly all of these mutations will not produce any phenotype, that is equal to say that they have a benign or neutral effect. Only handful of those variants are potentially pathogenic, namely disease-causing. That is why computational Variant Effect Predictor (VEP) tools are used to
prioritize variants worth investigating for medical consideration. Furthermore, the evidence of computational tools is considered among the different sources for variant effect assessment according to the American College of Medical Genetics and Genomics (ACMG) and Association for Molecular Pathology (AMP) guidelines. In addition, those software tools can be recognized as medical devices according to the second article of the Medical Device Regulation (MDR) of the European Union (Regulation (EU) 2017/745). That is why building a computational tool that predicts with high accuracy variant pathogenicity might have a direct impact on the healthcare system. Since 2001 more than 100 VEPs tools have been developed. Yet, their thresholds to classify a variant as pathogenic are often set for high sensitivity, that results in high false positive rate, namely misclassification of benign variants (C. Cubuk, et al. 2021). During my PhD, I developed Deciphering Mutations in Actionable Genes (DeMAG), a supervised classifier for interpreting missense mutations, namely SNVs that alter the protein sequence, in a list of 59 actionable genes as identified by the ACMG Secondary Findings (SF) v2.0 list (S. S. Kalia, et al. 2017). DeMAG is a supervised classifier trained with a Gradient boosting machine (GBM) model that employs only 13 conservation-based and structural features derived from AlphaFold 3D models and manually curated Multiple Sequence Alignment (MSA). DeMAG yields the best performance on clinical data among other popular VEP tools, balancing sensitivity and specificity, reaching the highest Matthews Correlation Coefficient (MCC). The advancement of DeMAG is due to the assembling of a balanced and high-quality training set and to the design of the partners score, a feature that captures epistasis, both in the sequence and in the 3D space of the protein. Here, epistasis refers to residues co-evolution in the sequence and residues spatial proximity in the 3D structure of the protein. The feature is a probabilistic score obtained with a mixture discriminant analysis that predicts pathogenicity based on the phenotypic effect of co-evolving and spatially close residues. The partners score feature is a general framework to study genotype and phenotype interactions. For example, those interactions might be between hetero or homoproteins forming a complex as tertiary structure and genetic variants occurring at interfaces, already known to be disease-causing, might be enriched for the same phenotypic effect. The framework of the partners score might not be limited to protein sequence, for example, interactions in the 3D genome might reveal regions enriched with the same phenotypic effect. DeMAG has been trained only on a small set of genes and yet, without further training, it generalizes well to additional 257 genes that have enough clinical data. Because for those new genes I did not manually curate MSA, I noted that the partners score from protein 3D models seems necessary for reaching high performance, while the contribution of the partners score obtained from long-range interactions, as derived from the co-evolution analysis, does not seem crucial for variant effect predictions. DeMAG is a supervised method especially designed for clinical translation purposes. That is why it focuses on clinically actionable genes and it balances its performance between the accuracy of the pathogenic and the benign class, acknowledging the importance of minimizing both the false negatives and false positives to avoid under and over diagnosis, critical to reduce health costs and patients psychological burden. Unsupervised general VEPs are powerful tools to investigate the functional effect of genetic variants as demonstrated by their higher correlation, over supervised tools, with data from Multiplexed Assay of Variant Effect (MAVE) and Deep Mutational Scanning (DMS) experiments. Nevertheless, for targeted clinical applications, I endorse the development of specialized tools that can leverage the existing wealth of data and knowledge available to minimize predictions errors. In order to make DeMAG readily available, I developed a web application available at https://demag.org/demag_app/ that provides predictions for all amino acids substitutions in the 59 and additional 257 genes together with training and testing datasets. Moreover, the app displays all the features of DeMAG highlighting the specific value annotated for the query mutation in relation to the distribution of the features for the pathogenic and benign mutations in the training set. This provides more insights than the minimalistic prediction label
Deciphering mutations in actionable genes by integrating structural and evolutionary epistatic features.
No full textDespite the rapid advancement of sequencing technologies and although the wide diffusion of Whole Genome Sequencing (WGS) and Whole Exome Sequencing (WES) led to an increase in the diagnoses of diseases (A. C. Lionel, et al. 2018; D. J. Stavropoulos, et al. 2016; J. C. Taylor, et al. 2015) most genetic variants remain without a clear interpretation. One of the main difficulty related with the assessment of sequencing results is the abundance of Single Nucleotide Variant (SNV), around 4 million, that each healthy individual carries. Nearly all of these mutations will not produce any phenotype, that is equal to say that they have a benign or neutral effect. Only handful of those variants are potentially pathogenic, namely disease-causing. That is why computational Variant Effect Predictor (VEP) tools are used to
prioritize variants worth investigating for medical consideration. Furthermore, the evidence of computational tools is considered among the different sources for variant effect assessment according to the American College of Medical Genetics and Genomics (ACMG) and Association for Molecular Pathology (AMP) guidelines. In addition, those software tools can be recognized as medical devices according to the second article of the Medical Device Regulation (MDR) of the European Union (Regulation (EU) 2017/745). That is why building a computational tool that predicts with high accuracy variant pathogenicity might have a direct impact on the healthcare system. Since 2001 more than 100 VEPs tools have been developed. Yet, their thresholds to classify a variant as pathogenic are often set for high sensitivity, that results in high false positive rate, namely misclassification of benign variants (C. Cubuk, et al. 2021). During my PhD, I developed Deciphering Mutations in Actionable Genes (DeMAG), a supervised classifier for interpreting missense mutations, namely SNVs that alter the protein sequence, in a list of 59 actionable genes as identified by the ACMG Secondary Findings (SF) v2.0 list (S. S. Kalia, et al. 2017). DeMAG is a supervised classifier trained with a Gradient boosting machine (GBM) model that employs only 13 conservation-based and structural features derived from AlphaFold 3D models and manually curated Multiple Sequence Alignment (MSA). DeMAG yields the best performance on clinical data among other popular VEP tools, balancing sensitivity and specificity, reaching the highest Matthews Correlation Coefficient (MCC). The advancement of DeMAG is due to the assembling of a balanced and high-quality training set and to the design of the partners score, a feature that captures epistasis, both in the sequence and in the 3D space of the protein. Here, epistasis refers to residues co-evolution in the sequence and residues spatial proximity in the 3D structure of the protein. The feature is a probabilistic score obtained with a mixture discriminant analysis that predicts pathogenicity based on the phenotypic effect of co-evolving and spatially close residues. The partners score feature is a general framework to study genotype and phenotype interactions. For example, those interactions might be between hetero or homoproteins forming a complex as tertiary structure and genetic variants occurring at interfaces, already known to be disease-causing, might be enriched for the same phenotypic effect. The framework of the partners score might not be limited to protein sequence, for example, interactions in the 3D genome might reveal regions enriched with the same phenotypic effect. DeMAG has been trained only on a small set of genes and yet, without further training, it generalizes well to additional 257 genes that have enough clinical data. Because for those new genes I did not manually curate MSA, I noted that the partners score from protein 3D models seems necessary for reaching high performance, while the contribution of the partners score obtained from long-range interactions, as derived from the co-evolution analysis, does not seem crucial for variant effect predictions. DeMAG is a supervised method especially designed for clinical translation purposes. That is why it focuses on clinically actionable genes and it balances its performance between the accuracy of the pathogenic and the benign class, acknowledging the importance of minimizing both the false negatives and false positives to avoid under and over diagnosis, critical to reduce health costs and patients psychological burden. Unsupervised general VEPs are powerful tools to investigate the functional effect of genetic variants as demonstrated by their higher correlation, over supervised tools, with data from Multiplexed Assay of Variant Effect (MAVE) and Deep Mutational Scanning (DMS) experiments. Nevertheless, for targeted clinical applications, I endorse the development of specialized tools that can leverage the existing wealth of data and knowledge available to minimize predictions errors. In order to make DeMAG readily available, I developed a web application available at https://demag.org/demag_app/ that provides predictions for all amino acids substitutions in the 59 and additional 257 genes together with training and testing datasets. Moreover, the app displays all the features of DeMAG highlighting the specific value annotated for the query mutation in relation to the distribution of the features for the pathogenic and benign mutations in the training set. This provides more insights than the minimalistic prediction label
Deciphering mutations in actionable genes by integrating structural and evolutionary epistatic features.
No full textDespite the rapid advancement of sequencing technologies and although the wide diffusion of Whole Genome Sequencing (WGS) and Whole Exome Sequencing (WES) led to an increase in the diagnoses of diseases (A. C. Lionel, et al. 2018; D. J. Stavropoulos, et al. 2016; J. C. Taylor, et al. 2015) most genetic variants remain without a clear interpretation. One of the main difficulty related with the assessment of sequencing results is the abundance of Single Nucleotide Variant (SNV), around 4 million, that each healthy individual carries. Nearly all of these mutations will not produce any phenotype, that is equal to say that they have a benign or neutral effect. Only handful of those variants are potentially pathogenic, namely disease-causing. That is why computational Variant Effect Predictor (VEP) tools are used to
prioritize variants worth investigating for medical consideration. Furthermore, the evidence of computational tools is considered among the different sources for variant effect assessment according to the American College of Medical Genetics and Genomics (ACMG) and Association for Molecular Pathology (AMP) guidelines. In addition, those software tools can be recognized as medical devices according to the second article of the Medical Device Regulation (MDR) of the European Union (Regulation (EU) 2017/745). That is why building a computational tool that predicts with high accuracy variant pathogenicity might have a direct impact on the healthcare system. Since 2001 more than 100 VEPs tools have been developed. Yet, their thresholds to classify a variant as pathogenic are often set for high sensitivity, that results in high false positive rate, namely misclassification of benign variants (C. Cubuk, et al. 2021). During my PhD, I developed Deciphering Mutations in Actionable Genes (DeMAG), a supervised classifier for interpreting missense mutations, namely SNVs that alter the protein sequence, in a list of 59 actionable genes as identified by the ACMG Secondary Findings (SF) v2.0 list (S. S. Kalia, et al. 2017). DeMAG is a supervised classifier trained with a Gradient boosting machine (GBM) model that employs only 13 conservation-based and structural features derived from AlphaFold 3D models and manually curated Multiple Sequence Alignment (MSA). DeMAG yields the best performance on clinical data among other popular VEP tools, balancing sensitivity and specificity, reaching the highest Matthews Correlation Coefficient (MCC). The advancement of DeMAG is due to the assembling of a balanced and high-quality training set and to the design of the partners score, a feature that captures epistasis, both in the sequence and in the 3D space of the protein. Here, epistasis refers to residues co-evolution in the sequence and residues spatial proximity in the 3D structure of the protein. The feature is a probabilistic score obtained with a mixture discriminant analysis that predicts pathogenicity based on the phenotypic effect of co-evolving and spatially close residues. The partners score feature is a general framework to study genotype and phenotype interactions. For example, those interactions might be between hetero or homoproteins forming a complex as tertiary structure and genetic variants occurring at interfaces, already known to be disease-causing, might be enriched for the same phenotypic effect. The framework of the partners score might not be limited to protein sequence, for example, interactions in the 3D genome might reveal regions enriched with the same phenotypic effect. DeMAG has been trained only on a small set of genes and yet, without further training, it generalizes well to additional 257 genes that have enough clinical data. Because for those new genes I did not manually curate MSA, I noted that the partners score from protein 3D models seems necessary for reaching high performance, while the contribution of the partners score obtained from long-range interactions, as derived from the co-evolution analysis, does not seem crucial for variant effect predictions. DeMAG is a supervised method especially designed for clinical translation purposes. That is why it focuses on clinically actionable genes and it balances its performance between the accuracy of the pathogenic and the benign class, acknowledging the importance of minimizing both the false negatives and false positives to avoid under and over diagnosis, critical to reduce health costs and patients psychological burden. Unsupervised general VEPs are powerful tools to investigate the functional effect of genetic variants as demonstrated by their higher correlation, over supervised tools, with data from Multiplexed Assay of Variant Effect (MAVE) and Deep Mutational Scanning (DMS) experiments. Nevertheless, for targeted clinical applications, I endorse the development of specialized tools that can leverage the existing wealth of data and knowledge available to minimize predictions errors. In order to make DeMAG readily available, I developed a web application available at https://demag.org/demag_app/ that provides predictions for all amino acids substitutions in the 59 and additional 257 genes together with training and testing datasets. Moreover, the app displays all the features of DeMAG highlighting the specific value annotated for the query mutation in relation to the distribution of the features for the pathogenic and benign mutations in the training set. This provides more insights than the minimalistic prediction label
Recommended from our members
Learning Representations of Multi-Condition Single-Cell Data
No full textThe recent ability to measure gene expression, protein levels, and more at single-cell resolution is thought to have great potential for elucidating disease mechanisms. This is in part because of the prospect of comparing such data across healthy and disease states, different experimental conditions, and different genetic backgrounds. Existing approaches for comparing single-cell data across multiple conditions primarily proceed by first combining cells across all conditions, clustering the cells into condition-independent ``cell type'' clusters, and then performing downstream analyses to look for differential abundance of clusters across conditions or differential expression in a cluster across conditions. However, single-cell data are not always well modeled by clusters, and attempting to learn clusters in a condition-independent way may obscure important signal that could distinguish between the conditions. Here we introduce differential correlation analysis (DCA), a method for comparing single-cell data across multiple conditions that circumvents these difficulties by directly searching for differences in gene-gene co-expression across samples. The resulting gene-gene co-expression differences can then be summarized using principal components analysis to yield differential principal components (dPCs). We show in simulation that this more flexible approach is able to recover both changes in population abundance and population-specific expression changes. DCA is also able to account for batch effect as well as other confounders both at the individual level (e.g., patient age) and at the cellular level (e.g., mitochondrial read content in one cell). We apply DCA to a dataset of single-cell data on approximately 500,000 memory T cells sampled from a sample of 128 early tuberculosis progression cases and 131 controls. Our method powerfully identifies case-control differences, and we present evidence that these difference may point to coherent cell populations and programs with relevance to tuberculosis immunology; investigating these populations further is an important avenue of future work. Our work suggests that current methods may not realize the full potential of multi-condition single-cell data for elucidating disease biology.Health Sciences and Technolog
Mutation mapping and identification by whole-genome sequencing
Get PDFGenetic mapping of mutations in model systems has facilitated the identification of genes contributing to fundamental biological processes including human diseases. However, this approach has historically required the prior characterization of informative markers. Here we report a fast and cost-effective method for genetic mapping using next-generation sequencing that combines single nucleotide polymorphism discovery, mutation localization, and potential identification of causal sequence variants. In contrast to prior approaches, we have developed a hidden Markov model to narrowly define the mutation area by inferring recombination breakpoints of chromosomes in the mutant pool. In addition, we created an interactive online software resource to facilitate automated analysis of sequencing data and demonstrate its utility in the zebrafish and mouse models. Our novel methodology and online tools will make next-generation sequencing an easily applicable resource for mutation mapping in all model systems.Harvard Stem Cell Institute (Junior Faculty Grant)National Institutes of Health (U.S.) (Grant 1R01DK090311)National Institutes of Health (U.S.) (Grant 5R01MH084676
Recommended from our members
Statistical Inference of Signatures of Natural Selection in Human Complex Trait Genetic Architecture
No full textGenome-wide association studies have used genotype data from large study cohorts to detect large numbers of genetic variants that are significantly associated with complex traits and diseases. However, these detected variants often make up only a small and biased subset of all trait-affecting variants, and their properties are therefore not representative. Here I present novel statistical methods for inferring properties of the full set of genome-wide trait-affecting variants, show results from applying these methods to a number of different complex traits and diseases, and connect them to evolutionary models. Specifically, I present a method for estimating the frequency-dependence of genetic effects. I quantify the total proportion of genetic effects due to variants of different population frequencies and show that rare genetic variants have larger trait effects on average in all analyzed traits. I explain why this is expected under genome-wide purifying selection and analyze the consistency of the frequency-dependent architecture inference model with evolutionary model predictions. Furthermore, I use the inferred frequency-dependence to assess plausible values of relevant evolutionary parameters. I also develop a statistical method to estimate the genome-wide autocorrelation of causal minor allele effect sizes as a function of genomic distance. I show that neighboring variant effects are systematically anti-correlated using data from a range of complex traits and diseases. A possible interpretation is that linked anti-correlated variant effects cancel each other out and are therefore less affected by natural selection. These variants can hence persist in a population on longer timescales and are more likely to be observed.Systems Biolog
- …
