The 1000 Genomes Project set out to provide a comprehensive description of common human genetic variation by applying whole-genome sequencing to a diverse set of individuals from multiple populations. Here we report completion of the project, having reconstructed the genomes of 2,504 individuals from 26 populations using a combination of low-coverage whole-genome sequencing, deep exome sequencing, and dense microarray genotyping. We characterized a broad spectrum of genetic variation, in total over 88 million variants (84.7 million single nucleotide polymorphisms (SNPs), 3.6 million short insertions/deletions (indels), and 60,000 structural variants), all phased onto high-quality haplotypes. This resource includes >99% of SNP variants with a frequency of >1% for a variety of ancestries. We describe the distribution of genetic variation across the global sample, and discuss the implications for common disease studies.

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SummaryMutations and/or overexpression of various transporters are known to confer drug resistance in a variety of organisms. In the malaria parasite Plasmodium falciparum , a homologue of P-glycoprotein, PfMDR1, has been implicated in responses to chloroquine (CQ), quinine (QN) and other drugs, and a putative transporter, PfCRT, was recently demonstrated to be the key molecule in CQ resistance. However, other unknown molecules are probably involved, as different parasite clones carrying the same pfcrt and pfmdr1 alleles show a wide range of quantitative responses to CQ and QN. Such molecules may contribute to increasing incidences of QN treatment failure, the molecular basis of which is not understood. To identify additional genes involved in parasite CQ and QN responses, we assayed the in vitro susceptibilities of 97 culture-adapted cloned isolates to CQ and QN and searched for single nucleotide polymorphisms (SNPs) in DNA encoding 49 putative transporters (total 113 kb) and in 39 housekeeping genes that acted as negative controls. SNPs in 11 of the putative transporter genes, including pfcrt and pfmdr1 , showed significant associations with decreased sensitivity to CQ and/or QN in P. falciparum . Significant linkage disequilibria within and between these genes were also detected, suggesting interactions among the transporter genes. This study provides specific leads for better understanding of complex drug resistances in malaria parasites.

Functional linear models are developed in this paper for testing associations between quantitative traits and genetic variants, which can be rare variants or common variants or the combination of the two. By treating multiple genetic variants of an individual in a human population as a realization of a stochastic process, the genome of an individual in a chromosome region is a continuum of sequence data rather than discrete observations. The genome of an individual is viewed as a stochastic function that contains both linkage and linkage disequilibrium (LD) information of the genetic markers. By using techniques of functional data analysis, both fixed and mixed effect functional linear models are built to test the association between quantitative traits and genetic variants adjusting for covariates. After extensive simulation analysis, it is shown that the F-distributed tests of the proposed fixed effect functional linear models have higher power than that of sequence kernel association test (SKAT) and its optimal unified test (SKAT-O) for three scenarios in most cases: (1) the causal variants are all rare, (2) the causal variants are both rare and common, and (3) the causal variants are common. The superior performance of the fixed effect functional linear models is most likely due to its optimal utilization of both genetic linkage and LD information of multiple genetic variants in a genome and similarity among different individuals, while SKAT and SKAT-O only model the similarities and pairwise LD but do not model linkage and higher order LD information sufficiently. In addition, the proposed fixed effect models generate accurate type I error rates in simulation studies. We also show that the functional kernel score tests of the proposed mixed effect functional linear models are preferable in candidate gene analysis and small sample problems. The methods are applied to analyze three biochemical traits in data from the Trinity Students Study.

Despite the great success of genome-wide association studies (GWAS) in identification of the common genetic variants associated with complex diseases, the current GWAS have focused on single-SNP analysis. However, single-SNP analysis often identifies only a few of the most significant SNPs that account for a small proportion of the genetic variants and offers only a limited understanding of complex diseases. To overcome these limitations, we propose gene and pathway-based association analysis as a new paradigm for GWAS. As a proof of concept, we performed a comprehensive gene and pathway-based association analysis of 13 published GWAS. Our results showed that the proposed new paradigm for GWAS not only identified the genes that include significant SNPs found by single-SNP analysis, but also detected new genes in which each single SNP conferred a small disease risk; however, their joint actions were implicated in the development of diseases. The results also showed that the new paradigm for GWAS was able to identify biologically meaningful pathways associated with the diseases, which were confirmed by a gene-set-rich analysis using gene expression data.

Linkage-disequilibrium mapping (LDM) recently has been hailed as a powerful statistical method for fine-scale mapping of disease genes. After reviewing its historical background and methodological development, we present a general, mathematical, and conceptually coherent framework for LDM that incorporates multilocus and multiallelic markers and mutational processes at the marker and disease loci. With this framework, we address several issues relevant to fine-scale mapping and propose some efficient computational methods for LDM. We implement various LDM methods that incorporate population growth, recurrent mutation, and marker mutations, on the basis of a general framework. We demonstrate these methods by applying them to published data on cystic fibrosis, Huntington disease, Friedreich ataxia, and progressive myoclonus epilepsy. Since the genes responsible for these diseases all have been cloned, we can evaluate the performance of our methods and can compare ours with that of other methods. Using the proposed methods, we successfully and accurately predicted the locations of genes responsible for these diseases, on the basis of published data only.

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Despite the growing consensus on the importance of testing gene-gene interactions in genetic studies of complex diseases, the effect of gene-gene interactions has often been defined as a deviance from genetic additive effects, which is essentially treated as a residual term in genetic analysis and leads to low power in detecting the presence of interacting effects. To what extent the definition of gene-gene interaction at population level reflects the genes' biochemical or physiological interaction remains a mystery. In this article, we introduce a novel definition and a new measure of gene-gene interaction between two unlinked loci (or genes). We developed a general theory for studying linkage disequilibrium (LD) patterns in disease population under two-locus disease models. The properties of using the LD measure in a disease population as a function of the measure of gene-gene interaction between two unlinked loci were also investigated. We examined how interaction between two loci creates LD in a disease population and showed that the mathematical formulation of the new definition for gene-gene interaction between two loci was similar to that of the LD between two loci. This finding motived us to develop an LD-based statistic to detect gene-gene interaction between two unlinked loci. The null distribution and type I error rates of the LD-based statistic for testing gene-gene interaction were validated using extensive simulation studies. We found that the new test statistic was more powerful than the traditional logistic regression under three two-locus disease models and demonstrated that the power of the test statistic depends on the measure of gene-gene interaction. We also investigated the impact of using tagging SNPs for testing interaction on the power to detect interaction between two unlinked loci. Finally, to evaluate the performance of our new method, we applied the LD-based statistic to two published data sets. Our results showed that the P values of the LD-based statistic were smaller than those obtained by other approaches, including logistic regression models.