Diversity in Clinical Genetics Remains Poorly Defined

Diversity in Clinical Genetics Remains Poorly Defined but the Clinical Genome Resource (ClinGen) Ancestry and Diversity Working Group is working to address this important issue. In clinical genetics and genomics, many approaches depend on the ability to identify genetic variation that appears to be non-randomly distributed in a population. However, genetic variation often clusters in ways that reflect how peoples’ ancestors were grouped together. These historical associations are often summarized by the terms Race, Ethnicity, and Ancestry but what these terms mean, both semantically and biologically, are still very unclear. The paper below provides no clear solutions but is an excellent introduction and discussion of this problem and the challenges we face in addressing it.

Clinical Genetics Lacks Standard Definitions and Protocols for the Collection and Use of Diversity Measures

Abstract

Genetics researchers and clinical professionals rely on diversity measures such as race, ethnicity, and ancestry (REA) to stratify study participants and patients for a variety of applications in research and precision medicine. However, there are no comprehensive, widely accepted standards or guidelines for collecting and using such data in clinical genetics practice. Two NIH-funded research consortia, the Clinical Genome Resource (ClinGen) and Clinical Sequencing Evidence-generating Research (CSER), have partnered to address this issue and report how REA are currently collected, conceptualized, and used. Surveying clinical genetics professionals and researchers (n = 448), we found heterogeneity in the way REA are perceived, defined, and measured, with variation in the perceived importance of REA in both clinical and research settings. The majority of respondents (>55%) felt that REA are at least somewhat important for clinical variant interpretation, ordering genetic tests, and communicating results to patients. However, there was no consensus on the relevance of REA, including how each of these measures should be used in different scenarios and what information they can convey in the context of human genetics. A lack of common definitions and applications of REA across the precision medicine pipeline may contribute to inconsistencies in data collection, missing or inaccurate classifications, and misleading or inconclusive results. Thus, our findings support the need for standardization and harmonization of REA data collection and use in clinical genetics and precision health research.

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Here’s a link to the group that published this paper: https://www.clinicalgenome.org/working-groups/ancestry/

What your genome won't tolerate

This is one of those projects that’s so clearly interesting and important that it’s surprising nobody has done it already: specifically, this is a very thorough and well-executed analysis of all the places in the human genome that do not appear to tolerate being mutated. If you have access, it’s worth reading. —RPR

Measuring intolerance to mutation in human genetics

Zachary L. Fuller, Jeremy J. Berg, Hakhamanesh Mostafavi, Guy Sella & Molly Przeworski

Nature Genetics (Research Article)

Abstract—In numerous applications, from working with animal models to mapping the genetic basis of human disease susceptibility, knowing whether a single disrupting mutation in a gene is likely to be deleterious is useful. With this goal in mind, a number of measures have been developed to identify genes in which protein-truncating variants (PTVs), or other types of mutations, are absent or kept at very low frequency in large population samples—genes that appear ‘intolerant’ to mutation. One measure in particular, the probability of being loss-of-function intolerant (pLI), has been widely adopted. This measure was designed to classify genes into three categories, null, recessive and haploinsufficient, on the basis of the contrast between observed and expected numbers of PTVs. Such population-genetic approaches can be useful in many applications. As we clarify, however, they reflect the strength of selection acting on heterozygotes and not dominance or haploinsufficiency.

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Where mutations are not tolerated: a good summary of an outstanding study

Big datasets pinpoint new regions to explore the genome for disease

A dataset of more than 100,000 individuals allows researchers to identify genetic regions that are intolerant to change and may underlie developmental disorders.

Imagine rain falling on a square of sidewalk. While the raindrops appear to land randomly, over time a patch of sidewalk somehow remains dry. The emerging pattern suggests something special about this region. This analogy is akin to a new method devised by researchers at University of Utah Health. They explored more than 100,000 healthy humans to identify regions of our genes that are intolerant to change. They believe that DNA mutations in these "constrained" regions may cause severe pediatric diseases.

"Instead of focusing on where DNA changes are, we looked for parts of genes where DNA changes are not," said Aaron Quinlan, Ph.D., associate professor of Human Genetics and Biomedical Informatics at U of U Health and associate director of the USTAR Center for Genetic Discovery. "Our model searches for exceptions to the rule of dense genetic variation in this massive dataset to reveal constrained regions of genes that are devoid of variation. We believe these regions may be lethal or cause extreme phenotypes of disease when mutated."

While this approach is conceptually simple, only recently has there been enough human genomes available to make it happen. These new, invariable stretches may reveal new disease-causing genes and can be used to help pinpoint the cause of disease in patients with developmental disorders. The results of this study are available online in the December 10 issue of the journal Nature Genetics.

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Next Next Gen Detection of Structural Variants

Accurate detection of complex structural variations using single-molecule sequencing

Fritz J. Sedlazeck, Philipp Rescheneder, Moritz Smolka, Han Fang, Maria Nattestad, Arndt von Haeseler, and Michael C. Schatz

Nature Methods (Research article)

Abstract—Structural variations are the greatest source of genetic variation, but they remain poorly understood because of technological limitations. Single-molecule long-read sequencing has the potential to dramatically advance the field, although high error rates are a challenge with existing methods. Addressing this need, we introduce open-source methods for long-read alignment (NGMLR; https://github.com/philres/ngmlr) and structural variant identification (Sniffles; https://github.com/fritzsedlazeck/Sniffles) that provide unprecedented sensitivity and precision for variant detection, even in repeat-rich regions and for complex nested events that can have substantial effects on human health. In several long-read datasets, including healthy and cancerous human genomes, we discovered thousands of novel variants and categorized systematic errors in short-read approaches. NGMLR and Sniffles can automatically filter false events and operate on low-coverage data, thereby reducing the high costs that have hindered the application of long reads in clinical and research settings.

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Connecting chromatin states (Epigenetics) to structural variation in human genomes

Chromatin organization modulates the origin of heritable structural variations in human genome 

Tanmoy Roychowdhury and Alexej Abyzov

Nucleic Acids Research (Article)

Abstract

“Structural variations (SVs) in the human genome originate from different mechanisms related to DNA repair, replication errors, and retrotransposition. Our analyses of 26 927 SVs from the 1000 Genomes Project revealed differential distributions and consequences of SVs of different origin, e.g. deletions from non-allelic homologous recombination (NAHR) are more prone to disrupt chromatin organization while processed pseudogenes can create accessible chromatin. Spontaneous double stranded breaks (DSBs) are the best predictor of enrichment of NAHR deletions in open chromatin. This evidence, along with strong physical interaction of NAHR breakpoints belonging to the same deletion suggests that majority of NAHR deletions are non-meiotic i.e. originate from errors during homology directed repair (HDR) of spontaneous DSBs. In turn, the origin of the spontaneous DSBs is associated with transcription factor binding in accessible chromatin revealing the vulnerability of functional, open chromatin. The chromatin itself is enriched with repeats, particularly fixed Alu elements that provide the homology required to maintain stability via HDR. Through co-localization of fixed Alus and NAHR deletions in open chromatin we hypothesize that old Alu expansion had a stabilizing role on the human genome.”

Population-specific structural variation

Genome maps across 26 human populations reveal population-specific patterns of structural variation

Abstract—Large structural variants (SVs) in the human genome are difficult to detect and study by conventional sequencing technologies. With long-range genome analysis platforms, such as optical mapping, one can identify large SVs (>2 kb) across the genome in one experiment. Analyzing optical genome maps of 154 individuals from the 26 populations sequenced in the 1000 Genomes Project, we find that phylogenetic population patterns of large SVs are similar to those of single nucleotide variations in 86% of the human genome, while ~2% of the genome has high structural complexity. We are able to characterize SVs in many intractable regions of the genome, including segmental duplications and subtelomeric, pericentromeric, and acrocentric areas. In addition, we discover ~60 Mb of non-redundant genome content missing in the reference genome sequence assembly. Our results highlight the need for a comprehensive set of alternate haplotypes from different populations to represent SV patterns in the genome.

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A discussion of the limitations of a single, static reference genome

Buffalo gave us spicy wings and the ‘book of life.’ Here’s why that’s undermining personalized medicine

“The human reference genome, largely completed in 2001, has achieved near-mythic status. It is “the book of life,” the “operating manual for Homo sapiens.” But the reference genome falls short in ways that have become embarrassing, misleading, and, in the worst cases, emblematic of the white European dominance of science — shortcomings that are threatening the dream of genetically based personalized medicine.“

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Disease risk estimates need more samples from more populations (Genome Biology)

Genetic disease risks can be misestimated across global populations

Michelle S. Kim, Kane P. Patel, Andrew K. Teng, Ali J. Berens, and Joseph Lachance

Genome Biology (Research article)

Accurate assessment of health disparities requires unbiased knowledge of genetic risks in different populations. Unfortunately, most genome-wide association studies use genotyping arrays and European samples. Here, we integrate whole genome sequence data from global populations, results from thousands of genome-wide association studies (GWAS), and extensive computer simulations to identify how genetic disease risks can be misestimated. In contrast to null expectations, we find that risk allele frequencies at known disease loci are significantly different for African populations compared to other continents. 

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Alzheimers insights from the desk of the NIH Director, Dr. Francis Collins

Largest-Ever Alzheimer’s Gene Study Brings New Answers

Predicting whether someone will get Alzheimer’s disease (AD) late in life, and how to use that information for prevention, has been an intense focus of biomedical research. The goal of this work is to learn not only about the genes involved in AD, but how they work together and with other complex biological, environmental, and lifestyle factors to drive this devastating neurological disease.

It’s good news to be able to report that an international team of researchers, partly funded by NIH, has made more progress in explaining the genetic component of AD. Their analysis, involving data from more than 35,000 individuals with late-onset AD, has identified variants in five new genes that put people at greater risk of AD [1]. It also points to molecular pathways involved in AD as possible avenues for prevention, and offers further confirmation of 20 other genes that had been implicated previously in AD.

The results of this largest-ever genomic study of AD suggests key roles for genes involved in the processing of beta-amyloid peptides, which form plaques in the brain recognized as an important early indicator of AD. They also offer the first evidence for a genetic link to proteins that bind tau, the protein responsible for telltale tangles in the AD brain that track closely with a person’s cognitive decline.

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Population-specific genome structure variation coverage in GenomeWeb

Human Genome Structural Variation Patterns Vary by Population, Optical Mapping Study Shows

NEW YORK (GenomeWeb) – Some large structural variants in the human genome exhibit population-specific patterns, according to a new analysis of more than 150 genome maps.

Large structural variants — those that are bigger than 2 kilobases — are difficult to detect, especially as short-read sequencing technologies are the most commonly used tools in genomic analysis.

For their study, Pui-Yan Kwok from the University of California, San Francisco and his colleagues analyzed optical genome maps generated for more than 150 individuals representing more than two dozen populations. A phylogenetic analysis of these maps indicated that some SVs and CNVs show variable population patterns. The researchers were also able to characterize SVs in typically intractable regions of the genome, including spots not covered by the human reference genome. Their results were published yesterday in Nature Communications.

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