Wei Huang

Bio: Wei Huang is an academic researcher from Chinese National Human Genome Center. The author has contributed to research in topics: Human genome & International HapMap Project. The author has an hindex of 8, co-authored 9 publications receiving 11555 citations. Previous affiliations of Wei Huang include Zhejiang University & Chinese Academy of Sciences.

The International HapMap Project

Abstract: The goal of the International HapMap Project is to determine the common patterns of DNA sequence variation in the human genome and to make this information freely available in the public domain. An international consortium is developing a map of these patterns across the genome by determining the genotypes of one million or more sequence variants, their frequencies and the degree of association between them, in DNA samples from populations with ancestry from parts of Africa, Asia and Europe. The HapMap will allow the discovery of sequence variants that affect common disease, will facilitate development of diagnostic tools, and will enhance our ability to choose targets for therapeutic intervention.

A haplotype map of the human genome

Abstract: Inherited genetic variation has a critical but as yet largely uncharacterized role in human disease. Here we report a public database of common variation in the human genome: more than one million single nucleotide polymorphisms (SNPs) for which accurate and complete genotypes have been obtained in 269 DNA samples from four populations, including ten 500-kilobase regions in which essentially all information about common DNA variation has been extracted. These data document the generality of recombination hotspots, a block-like structure of linkage disequilibrium and low haplotype diversity, leading to substantial correlations of SNPs with many of their neighbours. We show how the HapMap resource can guide the design and analysis of genetic association studies, shed light on structural variation and recombination, and identify loci that may have been subject to natural selection during human evolution.

Genetic evidence supports demic diffusion of Han culture

Abstract: The spread of culture and language in human populations is explained by two alternative models: the demic diffusion model, which involves mass movement of people; and the cultural diffusion model, which refers to cultural impact between populations and involves limited genetic exchange between them. The mechanism of the peopling of Europe has long been debated, a key issue being whether the diffusion of agriculture and language from the Near East was concomitant with a large movement of farmers. Here we show, by systematically analysing Y-chromosome and mitochondrial DNA variation in Han populations, that the pattern of the southward expansion of Han culture is consistent with the demic diffusion model, and that males played a larger role than females in this expansion. The Han people, who all share the same culture and language, exceed 1.16 billion (2000 census), and are by far the largest ethnic group in the world. The expansion process of Han culture is thus of great interest to researchers in many fields.

The DNA sequence, annotation and analysis of human chromosome 3

Abstract: After the completion of a draft human genome sequence1, the International Human Genome Sequencing Consortium has proceeded to finish2 and annotate each of the 24 chromosomes comprising the human genome. Here we describe the sequencing and analysis of human chromosome 3, one of the largest human chromosomes. Chromosome 3 comprises just four contigs, one of which currently represents the longest unbroken stretch of finished DNA sequence known so far. The chromosome is remarkable in having the lowest rate of segmental duplication in the genome. It also includes a chemokine receptor gene cluster as well as numerous loci involved in multiple human cancers such as the gene encoding FHIT, which contains the most common constitutive fragile site in the genome, FRA3B3. Using genomic sequence from chimpanzee and rhesus macaque, we were able to characterize the breakpoints defining a large pericentric inversion that occurred some time after the split of Homininae from Ponginae, and propose an evolutionary history of the inversion.

Genetic variants in MGMT and risk of lung cancer in Southeastern Chinese: a haplotype‐based analysis

Abstract: O6-alkylguanine-DNA alkyltransferase (MGMT) is a universal DNA repair protein involved in the DNA direct reversal repair pathway that copes with alkylating carcinogens. Reduced MGMT expression as well as enzyme activity may result in an increased susceptibility to cancers. To elucidate the role of sequence variation in MGMT in the etiology of lung cancer, we conducted a comprehensive association study focusing on linkage disequilibrium (LD) structure of common variations across the MGMT sequence and its modification effect on smoking-related lung cancer risk. We rebuilt the LD block of MGMT by genotyping 39 SNPs and selected a subset of 10 haplotype-tagging SNPs (htSNP) and three pre- and interblock SNPs to capture variation across MGMT. By using a haplotype-based multifactor dimensionality reduction (MDR) analysis, we found that there were significant more-than-multiplicative interactions between diplotypes in block 5 and cumulative smoking and additive interaction between genotypes of preblock SNP rs1625649:C>A and smoking status in relation on lung cancer risk. Diplotypes in block 3 and block 5, genotypes of rs1625649:C>A, and trichotomized cumulative smoking are the four factors included in the MDR-defined best model on lung cancer risk. When these variables were combined and dichotomized, we found that subjects carrying the combined risk stratum had a significantly increased risk for lung cancer of 4.10-fold (odds ratio [OR]=4.10, 95% confidence interval [CI]=3.12-5.37, P=2.09 x 10(-24)). These findings suggest that genetic variants in MGMT may modulate the risk of smoking-related lung cancer. This haplotype-based interaction analysis might provide a "proof-of-principle" approach for studying candidate genes in cancer susceptibility.

The Genome Analysis Toolkit: A MapReduce framework for analyzing next-generation DNA sequencing data

Abstract: Next-generation DNA sequencing (NGS) projects, such as the 1000 Genomes Project, are already revolutionizing our understanding of genetic variation among individuals. However, the massive data sets generated by NGS—the 1000 Genome pilot alone includes nearly five terabases—make writing feature-rich, efficient, and robust analysis tools difficult for even computationally sophisticated individuals. Indeed, many professionals are limited in the scope and the ease with which they can answer scientific questions by the complexity of accessing and manipulating the data produced by these machines. Here, we discuss our Genome Analysis Toolkit (GATK), a structured programming framework designed to ease the development of efficient and robust analysis tools for next-generation DNA sequencers using the functional programming philosophy of MapReduce. The GATK provides a small but rich set of data access patterns that encompass the majority of analysis tool needs. Separating specific analysis calculations from common data management infrastructure enables us to optimize the GATK framework for correctness, stability, and CPU and memory efficiency and to enable distributed and shared memory parallelization. We highlight the capabilities of the GATK by describing the implementation and application of robust, scale-tolerant tools like coverage calculators and single nucleotide polymorphism (SNP) calling. We conclude that the GATK programming framework enables developers and analysts to quickly and easily write efficient and robust NGS tools, many of which have already been incorporated into large-scale sequencing projects like the 1000 Genomes Project and The Cancer Genome Atlas.

Haploview: analysis and visualization of LD and haplotype maps

Abstract: Summary: Research over the last few years has revealed significant haplotype structure in the human genome. The characterization of these patterns, particularly in the context of medical genetic association studies, is becoming a routine research activity. Haploview is a software package that provides computation of linkage disequilibrium statistics and population haplotype patterns from primary genotype data in a visually appealing and interactive interface. Availability: http://www.broad.mit.edu/mpg/haploview/ Contact: jcbarret@broad.mit.edu

DnaSP v5

Abstract: Motivation: DnaSP is a software package for a comprehensive analysis of DNA polymorphism data. Version 5 implements a number of new features and analytical methods allowing extensive DNA polymorphism analyses on large datasets. Among other features, the newly implemented methods allow for: (i) analyses on multiple data files; (ii) haplotype phasing; (iii) analyses on insertion/deletion polymorphism data; (iv) visualizing sliding window results integrated with available genome annotations in the UCSC browser. Availability: Freely available to academic users from: http://www.ub.edu/dnasp Contact: [email protected]

Principal components analysis corrects for stratification in genome-wide association studies

Abstract: Population stratification—allele frequency differences between cases and controls due to systematic ancestry differences—can cause spurious associations in disease studies. We describe a method that enables explicit detection and correction of population stratification on a genome-wide scale. Our method uses principal components analysis to explicitly model ancestry differences between cases and controls. The resulting correction is specific to a candidate marker’s variation in frequency across ancestral populations, minimizing spurious associations while maximizing power to detect true associations. Our simple, efficient approach can easily be applied to disease studies with hundreds of thousands of markers. Population stratification—allele frequency differences between cases and controls due to systematic ancestry differences—can cause spurious associations in disease studies 1‐8 . Because the effects of stratification vary in proportion to the number of samples 9 , stratification will be an increasing problem in the large-scale association studies of the future, which will analyze thousands of samples in an effort to detect common genetic variants of weak effect. The two prevailing methods for dealing with stratification are genomic control and structured association 9‐14 . Although genomic control and structured association have proven useful in a variety of contexts, they have limitations. Genomic control corrects for stratification by adjusting association statistics at each marker by a uniform overall inflation factor. However, some markers differ in their allele frequencies across ancestral populations more than others. Thus, the uniform adjustment applied by genomic control may be insufficient at markers having unusually strong differentiation across ancestral populations and may be superfluous at markers devoid of such differentiation, leading to a loss in power. Structured association uses a program such as STRUCTURE 15 to assign the samples to discrete subpopulation clusters and then aggregates evidence of association within each cluster. If fractional membership in more than one cluster is allowed, the method cannot currently be applied to genome-wide association studies because of its intensive computational cost on large data sets. Furthermore, assignments of individuals to clusters are highly sensitive to the number of clusters, which is not well defined 14,16 .

Genome-wide association study of 14,000 cases of seven common diseases and 3,000 shared controls

Abstract: There is increasing evidence that genome-wide association ( GWA) studies represent a powerful approach to the identification of genes involved in common human diseases. We describe a joint GWA study ( using the Affymetrix GeneChip 500K Mapping Array Set) undertaken in the British population, which has examined similar to 2,000 individuals for each of 7 major diseases and a shared set of similar to 3,000 controls. Case-control comparisons identified 24 independent association signals at P < 5 X 10(-7): 1 in bipolar disorder, 1 in coronary artery disease, 9 in Crohn's disease, 3 in rheumatoid arthritis, 7 in type 1 diabetes and 3 in type 2 diabetes. On the basis of prior findings and replication studies thus-far completed, almost all of these signals reflect genuine susceptibility effects. We observed association at many previously identified loci, and found compelling evidence that some loci confer risk for more than one of the diseases studied. Across all diseases, we identified a large number of further signals ( including 58 loci with single-point P values between 10(-5) and 5 X 10(-7)) likely to yield additional susceptibility loci. The importance of appropriately large samples was confirmed by the modest effect sizes observed at most loci identified. This study thus represents a thorough validation of the GWA approach. It has also demonstrated that careful use of a shared control group represents a safe and effective approach to GWA analyses of multiple disease phenotypes; has generated a genome-wide genotype database for future studies of common diseases in the British population; and shown that, provided individuals with non-European ancestry are excluded, the extent of population stratification in the British population is generally modest. Our findings offer new avenues for exploring the pathophysiology of these important disorders. We anticipate that our data, results and software, which will be widely available to other investigators, will provide a powerful resource for human genetics research.