Annual review of genomics and human genetics最新文献

Mitochondrial replacement techniques (MRTs, also referred to as mitochondrial replacement therapies) have given hope to many women who wish to have genetically related children but have mitochondrial DNA mutations in their eggs. MRTs have also spurred deep ethical disagreements and led to different regulatory approaches worldwide. In this review, we discuss the current regulation of MRTs across several countries. After discussing the basics of the science, we describe the current law and policy directions in seven countries: the United Kingdom, the United States, Canada, Australia, Germany, Israel, and Singapore. We also discuss the emerging phenomenon of medical tourism (also called medical travel) for MRTs to places like Greece, Spain, Mexico, and Ukraine. We then pull out some key findings regarding similarities and differences in regulatory approaches around the world.

In the last decade, exome and/or genome sequencing has become a common test in the diagnosis of individuals with features of a rare Mendelian disorder. Despite its success, this test leaves the majority of tested individuals undiagnosed. This review describes the Matchmaker Exchange (MME), a federated network established to facilitate the solving of undiagnosed rare-disease cases through data sharing. MME supports genomic matchmaking, the act of connecting two or more parties looking for cases with similar phenotypes and variants in the same candidate genes. An application programming interface currently connects six matchmaker nodes-the Database of Chromosomal Imbalance and Phenotype in Humans Using Ensembl Resources (DECIPHER), GeneMatcher, PhenomeCentral, seqr, MyGene2, and the Initiative on Rare and Undiagnosed Diseases (IRUD) Exchange-resulting in a collective data set spanning more than 150,000 cases from more than 11,000 contributors in 88 countries. Here, we describe the successes and challenges of MME, its individual matchmaking nodes, plans for growing the network, and considerations for future directions.

Tracing cell lineages is fundamental for understanding the rules governing development in multicellular organisms and delineating complex biological processes involving the differentiation of multiple cell types with distinct lineage hierarchies. In humans, experimental lineage tracing is unethical, and one has to rely on natural-mutation markers that are created within cells as they proliferate and age. Recent studies have demonstrated that it is now possible to trace lineages in normal, noncancerous cells with a variety of data types using natural variations in the nuclear and mitochondrial DNA as well as variations in DNA methylation status. It is also apparent that the scientific community is on the verge of being able to make a comprehensive and detailed cell lineage map of human embryonic and fetal development. In this review, we discuss the advantages and disadvantages of different approaches and markers for lineage tracing. We also describe the general conceptual design for how to derive a lineage map for humans.

Disease classification, or nosology, was historically driven by careful examination of clinical features of patients. As technologies to measure and understand human phenotypes advanced, so too did classifications of disease, and the advent of genetic data has led to a surge in genetic subtyping in the past decades. Although the fundamental process of refining disease definitions and subtypes is shared across diverse fields, each field is driven by its own goals and technological expertise, leading to inconsistent and conflicting definitions of disease subtypes. Here, we review several classical and recent subtypes and subtyping approaches and provide concrete definitions to delineate subtypes. In particular, we focus on subtypes with distinct causal disease biology, which are of primary interest to scientists, and subtypes with pragmatic medical benefits, which are of primary interest to physicians. We propose genetic heterogeneity as a gold standard for establishing biologically distinct subtypes of complex polygenic disease. We focus especially on methods to find and validate genetic subtypes, emphasizing common pitfalls and how to avoid them.

The discovery of genes underlying inherited predisposition to breast and ovarian cancer has revolutionized the ability to identify women at high risk for these diseases before they become affected. Women who are carriers of deleterious variants in these genes can undertake surveillance and prevention measures that have been shown to reduce morbidity and mortality. However, under current strategies, the vast majority of women carriers remain undetected until they become affected. In this review, we show that universal testing, particularly of the BRCA1 and BRCA2 genes, fulfills classical disease screening criteria. This is especially true for BRCA1 and BRCA2 in Ashkenazi Jews but is translatable to all populations and may include additional genes. Utilizing genetic information for large-scale precision prevention requires a paradigmatic shift in health-care delivery. To address this need, we propose a direct-to-patient model, which is increasingly pertinent for fulfilling the promise of utilizing personal genomic information for disease prevention.

I briefly describe my early life and how, through a series of serendipitous events, I became a genetic epidemiologist. I discuss how the Elston-Stewart algorithm was discovered and its contribution to segregation, linkage, and association analysis. New linkage findings and paternity testing resulted from having a genotyping lab. The different meanings of interaction-statistical and biological-are clarified. The computer package S.A.G.E. (Statistical Analysis for Genetic Epidemiology), based on extensive method development over two decades, was conceived in 1986, flourished for 20 years, and is now freely available for use and further development. Finally, I describe methods to estimate and test hypotheses about familial correlations, and point out that the liability model often used to estimate disease heritability estimates the heritability of that liability, rather than of the disease itself, and so can be highly dependent on the assumed distribution of that liability.

Accurate diagnosis is the cornerstone of medicine; it is essential for informed care and promoting patient and family well-being. However, families with a rare genetic disease (RGD) often spend more than five years on a diagnostic odyssey of specialist visits and invasive testing that is lengthy, costly, and often futile, as 50% of patients do not receive a molecular diagnosis. The current diagnostic paradigm is not well designed for RGDs, especially for patients who remain undiagnosed after the initial set of investigations, and thus requires an expansion of approaches in the clinic. Leveraging opportunities to participate in research programs that utilize new technologies to understand RGDs is an important path forward for patients seeking a diagnosis. Given recent advancements in such technologies and international initiatives, the prospect of identifying a molecular diagnosis for all patients with RGDs has never been so attainable, but achieving this goal will require global cooperation at an unprecedented scale.

Embryonic development and stem cell differentiation provide a paradigm to understand the molecular regulation of coordinated cell fate determination and the architecture of tissue patterning. Emerging technologies such as single-cell RNA sequencing and spatial transcriptomics are opening new avenues to dissect cell organization, the divergence of morphological and molecular properties, and lineage allocation. Rapid advances in experimental and computational tools have enabled researchers to make many discoveries and revisit old hypotheses. In this review, we describe the use of single-cell RNA sequencing in studies of molecular trajectories and gene regulation networks for stem cell lineages, while highlighting the integratedexperimental and computational analysis of single-cell and spatial transcriptomes in the molecular annotation of tissue lineages and development during postimplantation gastrulation.

Epilepsy encompasses a group of heterogeneous brain diseases that affect more than 50 million people worldwide. Epilepsy may have discernible structural, infectious, metabolic, and immune etiologies; however, in most people with epilepsy, no obvious cause is identifiable. Based initially on family studies and later on advances in gene sequencing technologies and computational approaches, as well as the establishment of large collaborative initiatives, we now know that genetics plays a much greater role in epilepsy than was previously appreciated. Here, we review the progress in the field of epilepsy genetics and highlight molecular discoveries in the most important epilepsy groups, including those that have been long considered to have a nongenetic cause. We discuss where the field of epilepsy genetics is moving as it enters a new era in which the genetic architecture of common epilepsies is starting to be unraveled.

Low-cost whole-genome assembly has enabled the collection of haplotype-resolved pangenomes for numerous organisms. In turn, this technological change is encouraging the development of methods that can precisely address the sequence and variation described in large collections of related genomes. These approaches often use graphical models of the pangenome to support algorithms for sequence alignment, visualization, functional genomics, and association studies. The additional information provided to these methods by the pangenome allows them to achieve superior performance on a variety of bioinformatic tasks, including read alignment, variant calling, and genotyping. Pangenome graphs stand to become a ubiquitous tool in genomics. Although it is unclear whether they will replace linearreference genomes, their ability to harmoniously relate multiple sequence and coordinate systems will make them useful irrespective of which pangenomic models become most common in the future.