人类身高的遗传学研究进展

人类身高是由环境和遗传因素共同决定的,遗传学研究发现遗传因素对身高差异的影响更大。身高是典型的多基因遗传性状,科研人员试图运用传统的连锁分析和关联分析寻找和发现对人类身高具有显著影响的常见DNA序列变异,但进展缓慢。近年来,随着基因分型和DNA测序技术的发展,人类身高的遗传学研究取得了很多突破性进展。全基因组关联分析(GWAS)的应用,发现和证实了上百个与人类身高相关的单核苷酸多态性位点(SNPs),拓展了人们对人类生长和发育的相关遗传学认识,同时也为研究人类其他复杂性状提供了理论依据和借鉴。本文综述了人类身高的遗传学研究进展,探讨了目前该研究领域所存在的问题和未来发展方向,以期为今后人类身高相关的遗传学研究提供参考和借鉴。     

Understanding human genetic variation in the era of high-throughput sequencing

The EMBO/EMBL symposium ‘Human Variation: Cause and Consequence’ highlighted advances in understanding the molecular basis of human genetic variation and its myriad implications for biology, human origins and disease. As high‐throughput sequencing allows us to define genetic variation and its functional consequences at genome‐wide resolution for a large number of people, important questions need to be asked about how to use new technologies to maximize the translational relevance of genetic research for society and the individual patient.     

Phased whole-genome genetic risk in a family quartet using a major allele reference sequence

Whole-genome sequencing harbors unprecedented potential for characterization of individual and family genetic variation. Here, we develop a novel synthetic human reference sequence that is ethnically concordant and use it for the analysis of genomes from a nuclear family with history of familial thrombophilia. We demonstrate that the use of the major allele reference sequence results in improved genotype accuracy for disease-associated variant loci. We infer recombination sites to the lowest median resolution demonstrated to date (< 1,000 base pairs). We use family inheritance state analysis to control sequencing error and inform family-wide haplotype phasing, allowing quantification of genome-wide compound heterozygosity. We develop a sequence-based methodology for Human Leukocyte Antigen typing that contributes to disease risk prediction. Finally, we advance methods for analysis of disease and pharmacogenomic risk across the coding and non-coding genome that incorporate phased variant data. We show these methods are capable of identifying multigenic risk for inherited thrombophilia and informing the appropriate pharmacological therapy. These ethnicity-specific, family-based approaches to interpretation of genetic variation are emblematic of the next generation of genetic risk assessment using whole-genome sequencing.     

Understanding genetic variation and function- the applications of next generation sequencing

Next generation sequencing (NGS) technology has had a transformatory effect upon population-level studies linking genetic variation to gene function. In this review, I briefly describe recent studies that have used top-down genome scanning and population genetic approaches to identify loci under recent selection, as well as some examples of how large NGS datasets can be deployed to detect the total level of deleterious, neutral and advantageous variation present in standing genetic variation. I then explore studies that have used some of these approaches to study gene function along with advances in sequencing populations under selection, QTL mapping techniques and emerging methodologies utilising targeted capture and NGS.     

SNP discovery in associating genetic variation with human disease phenotypes

With the completion of the human genome project, attention is now rapidly shifting towards the study of individual genetic variation. The most abundant source of genetic variation in the human genome is represented by single nucleotide polymorphisms (SNPs), which can account for heritable inter-individual differences in complex phenotypes. Identification of SNPs that contribute to susceptibility to common diseases will provide highly accurate diagnostic information that will facilitate early diagnosis, prevention, and treatment of human diseases. Over the past several years, the advancement of increasingly high-throughput and cost-effective methods to discover and measure SNPs has begun to open the door towards this endeavor. Genetic association studies are considered to be an effective approach towards the detection of SNPs with moderate effects, as in most common diseases with complex phenotypes. This requires careful study design, analysis and interpretation. In this review, we discuss genetic association studies and address the prospect for candidate gene association studies, comparing the strengths and weaknesses of indirect and direct study designs. Our focus is on the continuous need for SNP discovery methods and the use of currently available prescreening methods for large-scale genetic epidemiological research until more advanced sequencing methods currently under development will become available.     

Relating human genetic variation to variation in drug responses

Although sequencing a single human genome was a monumental effort a decade ago, more than 1000 genomes have now been sequenced. The task ahead lies in transforming this information into personalized treatment strategies that are tailored to the unique genetics of each individual. One important aspect of personalized medicine is patient-to-patient variation in drug response. Pharmacogenomics addresses this issue by seeking to identify genetic contributors to human variation in drug efficacy and toxicity. Here, we present a summary of the current status of this field, which has evolved from studies of single candidate genes to comprehensive genome-wide analyses. Additionally, we discuss the major challenges in translating this knowledge into a systems-level understanding of drug physiology, with the ultimate goal of developing more effective personalized clinical treatment strategies.     

Host genetic determinants of the gut microbiota of wild mice

Identifying a common set of genes that mediate host–microbial interactions across populations and species of mammals has broad relevance for human health and animal biology. However, the genetic basis of the gut microbial composition in natural populations remains largely unknown outside of humans. Here, we used wild house mouse populations as a model system to ask three major questions: (a) Does host genetic relatedness explain interindividual variation in gut microbial composition? (b) Do population differences in the microbiota persist in a common environment? (c) What are the host genes associated with microbial richness and the relative abundance of bacterial genera? We found that host genetic distance is a strong predictor of the gut microbial composition as characterized by 16S amplicon sequencing. Using a common garden approach, we then identified differences in microbial composition between populations that persisted in a shared laboratory environment. Finally, we used exome sequencing to associate host genetic variants with microbial diversity and relative abundance of microbial taxa in wild mice. We identified 20 genes that were associated with microbial diversity or abundance including a macrophage‐derived cytokine (IL12a) that contained three nonsynonymous mutations. Surprisingly, we found a significant overrepresentation of candidate genes that were previously associated with microbial measurements in humans. The homologous genes that overlapped between wild mice and humans included genes that have been associated with traits related to host immunity and obesity in humans. Gene–bacteria associations identified in both humans and wild mice suggest some commonality to the host genetic determinants of gut microbial composition across mammals.     

人类疾病遗传易感性研究方法进展

遗传易感性是指基于个人遗传背景的多基因遗传病发病风险,即来源于父母一方或双方的特定遗传变异在某些情况下会诱发疾病。在特定疾病的发病机制中某些高外显率的遗传变异发挥重要作用,此类疾病通过患病家系分析即可定位疾病相关遗传变异;但另一些低外显率变异的作用则不明显,需要大规模患病人群分析来解析遗传机制。近年来,随着二代测序和多组学分析技术的发展和基因组数据的大量积累,癌症、代谢性疾病、心脑血管疾病和精神疾病等疾病遗传易感性研究中取得了显著进展,为疾病的早期筛查和诊断治疗提供了参考。     

Small‐ and large‐scale heterogeneity in genetic variation across the collard flycatcher genome: implications for estimating genetic diversity in nonmodel organisms

Population genetic studies in nonmodel organisms are often hampered by a lack of reference genomes that are essential for whole‐genome resequencing. In the light of this, genotyping methods have been developed to effectively eliminate the need for a reference genome, such as genotyping by sequencing or restriction site‐associated DNA sequencing (RAD‐seq). However, what remains relatively poorly studied is how accurately these methods capture both average and variation in genetic diversity across an organism's genome. In this issue of Molecular Ecology Resources, Dutoit et al. (2016) use whole‐genome resequencing data from the collard flycatcher to assess what factors drive heterogeneity in nucleotide diversity across the genome. Using these data, they then simulate how well different sequencing designs, including RAD sequencing, could capture most of the variation in genetic diversity. They conclude that for evolutionary and conservation‐related studies focused on the estimating genomic diversity, researchers should emphasize the number of loci analysed over the number of individuals sequenced.     

Discovering genetic polymorphisms in next-generation sequencing data

The ongoing revolution in DNA sequencing technology now enables the reading of thousands of millions of nucleotide bases in a single instrument run. However, this data quantity is often compromised by poor confidence in the read quality. The identification of genetic polymorphisms from this data is therefore problematic and, combined with the vast quantity of data, poses a major bioinformatics challenge. However, once these difficulties have been addressed, next-generation sequencing will offer a means to identify and characterize the wealth of genetic polymorphisms underlying the vast phenotypic variation in biological systems. We describe the recent advances in next-generation sequencing technology, together with preliminary approaches that can be applied for single nucleotide polymorphism discovery in plant species.     

Global patterns of isolation by distance based on genetic and morphological data

The isolation-by-distance model predicts that genetic similarity between populations will decrease exponentially as the geographic distance between them increases, because of the limiting effect of geographic distance on rates of gene flow. Many studies of human populations have applied the isolation-by-distance model to genetic variation between local populations in a limited geographic area, but few have done so on a global level, and these few used different models and analytical methods. I assess genetic variation between human populations across the world using data on red blood cell polymorphisms, microsatellite DNA markers, and craniometric traits. The isolation-by-distance model provides an excellent fit to average levels of genetic similarity within geographic distance classes for all three data sets, and the rate of distance decay is the same in all three. These results suggest that a common pattern of global gene flow mediated by geographic distance is detectable in diverse genetic and morphological data. An alternative explanation is that the correspondence between genetic similarity and geographic distance reflects the history of dispersal of the human species out of Africa.     

Sometimes hidden but always there: the assumptions underlying genetic inference of demographic histories

Demographic processes directly affect patterns of genetic variation within contemporary populations as well as future generations, allowing for demographic inference from patterns of both present-day and past genetic variation. Advances in laboratory procedures, sequencing and genotyping technologies in the past decades have resulted in massive increases in high-quality genome-wide genetic data from present-day populations and allowed retrieval of genetic data from archaeological material, also known as ancient DNA. This has resulted in an explosion of work exploring past changes in population size, structure, continuity and movement. However, as genetic processes are highly stochastic, patterns of genetic variation only indirectly reflect demographic histories. As a result, past demographic processes need to be reconstructed using an inferential approach. This usually involves comparing observed patterns of variation with model expectations from theoretical population genetics. A large number of approaches have been developed based on different population genetic models that each come with assumptions about the data and underlying demography. In this article I review some of the key models and assumptions underlying the most commonly used approaches for past demographic inference and their consequences for our ability to link the inferred demographic processes to the archaeological and climate records.This article is part of the theme issue ‘Cross-disciplinary approaches to prehistoric demography’.     

Isolation by distance, linguistic similarity, and the genetic structure on Bougainville Island

Previous research has revealed extensive genetic variation among villages on Bougainville, in the Solomon Islands. Using previously published gene frequency data for seven loci, the role of isolation by distance in structuring genetic variation on Bougainville was reanalyzed. Newer methods of kinship estimation show that earlier estimates of the isolation by distance parameters were low. The fit of the model is highly significant (R2 = 0.409; P less than 0.001), and the parameter estimates indicate high isolation: a = 0.0538, b = 0.1978, L = -0.0057. Several methods of residual analysis were applied in order to determine factors affecting the fit of the model. Linguistic similarity has a significant effect on genetic variation once the effects of geographic distance are taken into account. Population-specific deviations from the expected model may be explained, in part, in terms of population history. Compared to other human populations, Bougainville Island shows an even greater among-group variation than has been suggested previously.     

Prediction of personalized drugs based on genetic variations provided by DNA sequencing technologies

Recent reports of death and illness caused by adverse drug reactions have boosted rational drug design research. It has been shown through sequencing of the entire human genome that human genetic variations play a key role in adverse reactions to drugs as well as in differences in the effectiveness of drug treatments. The advent of high-throughput DNA sequencing technologies with bioinformatics of system biology have allowed the easy identification of genetic variations and all other pharmacogenetic variants in a single assay, thus permitting truly personalized drug treatment. This would be particularly valuable for many patients with chronic diseases who must take many medications concurrently. In this review, we have focused on pharmacogenomics for the prediction of variable drug responses between individuals with relevant genetic variations through new DNA sequencing technologies and provided directions for personalized drug therapy in the future.     

Interpreting human genetic variation with in vivo zebrafish assays

Rapid advances and cost erosion in exome and genome analysis of patients with both rare and common genetic disorders have accelerated gene discovery and illuminated fundamental biological mechanisms. The thrill of discovery has been accompanied, however, with the sobering appreciation that human genomes are burdened with a large number of rare and ultra rare variants, thereby posing a significant challenge in dissecting both the effect of such alleles on protein function and also the biological relevance of these events to patient pathology. In an effort to develop model systems that are able to generate surrogates of human pathologies, a powerful suite of tools have been developed in zebrafish, taking advantage of the relatively small (compared to invertebrate models) evolutionary distance of that genome to humans, the orthology of several organs and signaling processes, and the suitability of this organism for medium and high throughput phenotypic screening. Here we will review the use of this model organism in dissecting human genetic disorders; we will highlight how diverse strategies have informed disease causality and genetic architecture; and we will discuss relative strengths and limitations of these approaches in the context of medical genome sequencing. This article is part of a Special Issue entitled: From Genome to Function.     

Determination of genetic relatedness from low‐coverage human genome sequences using pedigree simulations

We develop and evaluate methods for inferring relatedness among individuals from low‐coverage DNA sequences of their genomes, with particular emphasis on sequences obtained from fossil remains. We suggest the major factors complicating the determination of relatedness among ancient individuals are sequencing depth, the number of overlapping sites, the sequencing error rate and the presence of contamination from present‐day genetic sources. We develop a theoretical model that facilitates the exploration of these factors and their relative effects, via measurement of pairwise genetic distances, without calling genotypes, and determine the power to infer relatedness under various scenarios of varying sequencing depth, present‐day contamination and sequencing error. The model is validated by a simulation study as well as the analysis of aligned sequences from present‐day human genomes. We then apply the method to the recently published genome sequences of ancient Europeans, developing a statistical treatment to determine confidence in assigned relatedness that is, in some cases, more precise than previously reported. As the majority of ancient specimens are from animals, this method would be applicable to investigate kinship in nonhuman remains. The developed software grups (Genetic Relatedness Using Pedigree Simulations) is implemented in Python and freely available.     

Host genetic variation impacts microbiome composition across human body sites

BackgroundThe composition of bacteria in and on the human body varies widely across human individuals, and has been associated with multiple health conditions. While microbial communities are influenced by environmental factors, some degree of genetic influence of the host on the microbiome is also expected. This study is part of an expanding effort to comprehensively profile the interactions between human genetic variation and the composition of this microbial ecosystem on a genome- and microbiome-wide scale.ResultsHere, we jointly analyze the composition of the human microbiome and host genetic variation. By mining the shotgun metagenomic data from the Human Microbiome Project for host DNA reads, we gathered information on host genetic variation for 93 individuals for whom bacterial abundance data are also available. Using this dataset, we identify significant associations between host genetic variation and microbiome composition in 10 of the 15 body sites tested. These associations are driven by host genetic variation in immunity-related pathways, and are especially enriched in host genes that have been previously associated with microbiome-related complex diseases, such as inflammatory bowel disease and obesity-related disorders. Lastly, we show that host genomic regions associated with the microbiome have high levels of genetic differentiation among human populations, possibly indicating host genomic adaptation to environment-specific microbiomes.ConclusionsOur results highlight the role of host genetic variation in shaping the composition of the human microbiome, and provide a starting point toward understanding the complex interaction between human genetics and the microbiome in the context of human evolution and disease.

Electronic supplementary material

The online version of this article (doi:10.1186/s13059-015-0759-1) contains supplementary material, which is available to authorized users.     

Parallel reverse genetic screening in mutant human cells using transcriptomics

Reverse genetic screens have driven gene annotation and target discovery in model organisms. However, many disease‐relevant genotypes and phenotypes cannot be studied in lower organisms. It is therefore essential to overcome technical hurdles associated with large‐scale reverse genetics in human cells. Here, we establish a reverse genetic approach based on highly robust and sensitive multiplexed RNA sequencing of mutant human cells. We conduct 10 parallel screens using a collection of engineered haploid isogenic cell lines with knockouts covering tyrosine kinases and identify known and unexpected effects on signaling pathways. Our study provides proof of concept for a scalable approach to link genotype to phenotype in human cells, which has broad applications. In particular, it clears the way for systematic phenotyping of still poorly characterized human genes and for systematic study of uncharacterized genomic features associated with human disease.     

Phylogenetic analysis of polyomavirus simian virus 40 from monkeys and humans reveals genetic variation

A phylogenetic analysis of 14 complete simian virus 40 (SV40) genomes was conducted in order to determine strain relatedness and the extent of genetic variation. This analysis included infectious isolates recovered between 1960 and 1999 from primary cultures of monkey kidney cells, from contaminated poliovaccines and an adenovirus seed stock, from human malignancies, and from transformed human cells. Maximum-parsimony and distance methods revealed distinct SV40 clades. However, no clear patterns of association between genotype and viral source were apparent. One clade (clade A) is derived from strain 776, the reference strain of SV40. Clade B contains isolates from poliovaccines (strains 777 and Baylor), from monkeys (strains N128, Rh911, and K661), and from human tumors (strains SVCPC and SVMEN). Thus, adaptation is not essential for SV40 survival in humans. The C terminus of the T-antigen (T-ag-C) gene contains the highest proportion of variable sites in the SV40 genome. An analysis based on just the T-ag-C region was highly congruent with the whole-genome analysis; hence, sequencing of just this one region is useful in strain identification. Analysis of an additional 16 strains for which only the T-ag-C gene was sequenced indicated that further SV40 genetic diversity is likely, resulting in a provisional clade (clade C) that currently contains strains associated with human tumors and human strain PML-1. Four other polymorphic regions in the genome were also identified. If these regions were analyzed in conjunction with the T-ag-C region, most of the phylogenetic signal could be captured without complete genome sequencing. This report represents the first whole-genome approach to establishing phylogenetic relatedness among different strains of SV40. It will be important in the future to develop a more complete catalog of SV40 variation in its natural monkey host, to determine if SV40 strains from different clades vary in biological or pathogenic properties, and to identify which SV40 strains are transmissible among humans.