A Simple Method for Analyzing Exome Sequencing Data Shows Distinct Levels of Nonsynonymous Variation for Human Immune and Nervous System Genes

To measure the strength of natural selection that acts upon single nucleotide variants (SNVs) in a set of human genes, we calculate the ratio between nonsynonymous SNVs (nsSNVs) per nonsynonymous site and synonymous SNVs (sSNVs) per synonymous site. We transform this ratio with a respective factor f that corrects for the bias of synonymous sites towards transitions in the genetic code and different mutation rates for transitions and transversions. This method approximates the relative density of nsSNVs (rdnsv) in comparison with the neutral expectation as inferred from the density of sSNVs. Using SNVs from a diploid genome and 200 exomes, we apply our method to immune system genes (ISGs), nervous system genes (NSGs), randomly sampled genes (RSGs), and gene ontology annotated genes. The estimate of rdnsv in an individual exome is around 20% for NSGs and 30–40% for ISGs and RSGs. This smaller rdnsv of NSGs indicates overall stronger purifying selection. To quantify the relative shift of nsSNVs towards rare variants, we next fit a linear regression model to the estimates of rdnsv over different SNV allele frequency bins. The obtained regression models show a negative slope for NSGs, ISGs and RSGs, supporting an influence of purifying selection on the frequency spectrum of segregating nsSNVs. The y-intercept of the model predicts rdnsv for an allele frequency close to 0. This parameter can be interpreted as the proportion of nonsynonymous sites where mutations are tolerated to segregate with an allele frequency notably greater than 0 in the population, given the performed normalization of the observed nsSNV to sSNV ratio. A smaller y-intercept is displayed by NSGs, indicating more nonsynonymous sites under strong negative selection. This predicts more monogenically inherited or de-novo mutation diseases that affect the nervous system

The Human Phenotype Ontology in 2024: phenotypes around the world.

The Human Phenotype Ontology (HPO) is a widely used resource that comprehensively organizes and defines the phenotypic features of human disease, enabling computational inference and supporting genomic and phenotypic analyses through semantic similarity and machine learning algorithms. The HPO has widespread applications in clinical diagnostics and translational research, including genomic diagnostics, gene-disease discovery, and cohort analytics. In recent years, groups around the world have developed translations of the HPO from English to other languages, and the HPO browser has been internationalized, allowing users to view HPO term labels and in many cases synonyms and definitions in ten languages in addition to English. Since our last report, a total of 2239 new HPO terms and 49235 new HPO annotations were developed, many in collaboration with external groups in the fields of psychiatry, arthrogryposis, immunology and cardiology. The Medical Action Ontology (MAxO) is a new effort to model treatments and other measures taken for clinical management. Finally, the HPO consortium is contributing to efforts to integrate the HPO and the GA4GH Phenopacket Schema into electronic health records (EHRs) with the goal of more standardized and computable integration of rare disease data in EHRs

The Role of Genetics in Advancing Precision Medicine for Alzheimer’s Disease—A Narrative Review

Alzheimer’s disease (AD) is the most common type of dementia, which has a substantial genetic component. AD affects predominantly older people. Accordingly, the prevalence of dementia has been rising as the population ages. To date, there are no effective interventions that can cure or halt the progression of AD. The only available treatments are the management of certain symptoms and consequences of dementia. The current state-of-the-art medical care for AD comprises three simple principles: prevent the preventable, achieve early diagnosis, and manage the manageable symptoms. This review provides a summary of the current state of knowledge of risk factors for AD, biological diagnostic testing, and prospects for treatment. Special emphasis is given to recent advances in genetics of AD and the way genomic data may support prevention, early intervention, and development of effective pharmacological treatments. Mutations in the APP, PSEN1, and PSEN2 genes cause early onset Alzheimer’s disease (EOAD) that follows a Mendelian inheritance pattern. For late onset Alzheimer’s disease (LOAD), APOE4 was identified as a major risk allele more than two decades ago. Population-based genome-wide association studies of late onset AD have now additionally identified common variants at roughly 30 genetic loci. Furthermore, rare variants (allele frequency <1%) that influence the risk for LOAD have been identified in several genes. These genetic advances have broadened our insights into the biological underpinnings of AD. Moreover, the known genetic risk variants could be used to identify presymptomatic individuals at risk for AD and support diagnostic assessment of symptomatic subjects. Genetic knowledge may also facilitate precision medicine. The goal of precision medicine is to use biological knowledge and other health information to predict individual disease risk, understand disease etiology, identify disease subcategories, improve diagnosis, and provide personalized treatment strategies. We discuss the potential role of genetics in advancing precision medicine for AD along with its ethical challenges. We outline strategies to implement genomics into translational clinical research that will not only improve accuracy of dementia diagnosis, thus enabling more personalized treatment strategies, but may also speed up the discovery of novel drugs and interventions

The Role of Genetics in Advancing Precision Medicine for Alzheimer’s Disease—A Narrative Review

Alzheimer\u27s disease (AD) is the most common type of dementia, which has a substantial genetic component. AD affects predominantly older people. Accordingly, the prevalence of dementia has been rising as the population ages. To date, there are no effective interventions that can cure or halt the progression of AD. The only available treatments are the management of certain symptoms and consequences of dementia. The current state-of-the-art medical care for AD comprises three simple principles: prevent the preventable, achieve early diagnosis, and manage the manageable symptoms. This review provides a summary of the current state of knowledge of risk factors for AD, biological diagnostic testing, and prospects for treatment. Special emphasis is given to recent advances in genetics of AD and the way genomic data may support prevention, early intervention, and development of effective pharmacological treatments. Mutations in th

Estimates of <i>rdnsv</i> in the 200 exomes in sets of genes as defined by ontology (GO) annotations.

<p>The 10 GO-categories with the smallest (A) and the greatest (B) mean values of <i>rdnsv</i> are shown. The full list of all GO-categories with at least 1000 coding SNVs is given in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0038087#pone.0038087.s005" target="_blank">Table S3</a>. For each category the number of annotated genes, nonsynonymous and synonymous SNVs, the mean and standard deviation of individual <i>rdnsv</i> estimates across the 200 exomes, as well as the values of <i>rdnsv₀</i> and <i>rdnsv₁</i>, are shown.</p

Relative density of nonsynonymous variants (<i>rdnsv</i>).

<p>Candidate genes for the nervous system (NSG) and the immune system (ISG) are defined by tissue specific expression or keyword search and further compared with a set of randomly sampled genes (RSG). A) Overall <i>rdnsv</i> estimates for a diploid genome and 200 exome sequences, which reflect the density of nonsynonymous variants on a mixture of SNVs that range from rare to common in their population frequency. B) SNVs from the 200 exome dataset are additionally stratified by their derived allele frequency and a regression model is fitted to the values of <i>rdnsv</i>. The predicted value for the allele frequency of 0 is referred to as <i>rdnsv₀</i>, whereas the predicted value for the allele frequency of 1 is referred to as <i>rdnsv₁</i>. The interval in brackets shows the 2.5% and 97.5% quantiles from 10.000 random draws of genes.</p

Relative density of nsSNVs (<i>rdnsv</i>) in different gene sets as estimated with different SNV datasets.

<p>Nervous system genes (NSG, light grey) show a smaller <i>rdnsv</i> than immune system genes (ISG, medium grey) or randomly sampled genes (RSG, dark grey) in a European diploid genome sequence (A, C) and a pooled set of 200 European exome sequences (B, D). The greater <i>rdnsv</i> in the pooled 200 exomes than the individual genome indicates an enrichment of nsSNVs among rare SNVs.</p

Distribution of <i>rdnsv</i> estimates over 200 individual exomes.

<p>A) expression-based candidate genes and B) keyword-based candidate genes. The value of <i>rdnsv</i> is estimated separately for each of the 200 exomes and found consistently smaller for NSGs (light grey) are than ISGs (medium grey). In addition, smaller estimates of <i>rdnsv</i> for expression-based ISGs than keyword-based ISGs are seen. No difference exists between expression-based NSGs and keyword-based NSGs.</p

Single Nucleotide Variation Analysis in 65 Candidate Genes for CNS Disorders in a Representative Sample of the European Population

The detailed investigation of variation in functionally important regions of the human genome is expected to promote understanding of genetically complex diseases. We resequenced 65 candidate genes for CNS disorders in an average of 85 European individuals. The minor allele frequency (MAF), an indicator of weak purifying selection, was lowest in radical amino acid alterations, whereas similar MAF was observed for synonymous variants and conservative amino acid alterations. In noncoding sequences, variants located in CpG islands tended to have a lower MAF than those outside CpG islands. The transition/transversion ratio was increased among both synonymous and conservative variants compared with noncoding variants. Conversely, the transition/transversion ratio was lowest among radical amino acid alterations. Furthermore, among nonsynonymous variants, transversions displayed lower MAF than did transitions. This suggests that transversions are associated with functionally important amino acid alterations. By comparing our data with public SNP databases, we found that variants with lower allele frequency are underrepresented in these databases. Therefore, radical variants obtain distinctively lower database coverage. However, those variants appear to be under weak purifying selection and thus could play a role in the etiology of genetically complex diseases