A unified mixed-model method for association mapping that accounts for multiple levels of relatedness

As population structure can result in spurious associations, it has constrained the use of association studies in human and plant genetics. Association mapping, however, holds great promise if true signals of functional association can be separated from the vast number of false signals generated by population structure. We have developed a unified mixed-model approach to account for multiple levels of relatedness simultaneously as detected by random genetic markers. We applied this new approach to two samples: a family-based sample of 14 human families, for quantitative gene expression dissection, and a sample of 277 diverse maize inbred lines with complex familial relationships and population structure, for quantitative trait dissection. Our method demonstrates improved control of both type I and type II error rates over other methods. As this new method crosses the boundary between family-based and structured association samples, it provides a powerful complement to currently available methods for association mapping.     

Genomic screening and replication using the same data set in family-based association testing

The Human Genome Project and its spin-offs are making it increasingly feasible to determine the genetic basis of complex traits using genome-wide association studies. The statistical challenge of analyzing such studies stems from the severe multiple-comparison problem resulting from the analysis of thousands of SNPs. Our methodology for genome-wide family-based association studies, using single SNPs or haplotypes, can identify associations that achieve genome-wide significance. In relation to developing guidelines for our screening tools, we determined lower bounds for the estimated power to detect the gene underlying the disease-susceptibility locus, which hold regardless of the linkage disequilibrium structure present in the data. We also assessed the power of our approach in the presence of multiple disease-susceptibility loci. Our screening tools accommodate genomic control and use the concept of haplotype-tagging SNPs. Our methods use the entire sample and do not require separate screening and validation samples to establish genome-wide significance, as population-based designs do.     

Interpreting principal component analyses of spatial population genetic variation

Nearly 30 years ago, Cavalli-Sforza et al. pioneered the use of principal component analysis (PCA) in population genetics and used PCA to produce maps summarizing human genetic variation across continental regions. They interpreted gradient and wave patterns in these maps as signatures of specific migration events. These interpretations have been controversial, but influential, and the use of PCA has become widespread in analysis of population genetics data. However, the behavior of PCA for genetic data showing continuous spatial variation, such as might exist within human continental groups, has been less well characterized. Here, we find that gradients and waves observed in Cavalli-Sforza et al.'s maps resemble sinusoidal mathematical artifacts that arise generally when PCA is applied to spatial data, implying that the patterns do not necessarily reflect specific migration events. Our findings aid interpretation of PCA results and suggest how PCA can help correct for continuous population structure in association studies.     

Dwarf8 polymorphisms associate with variation in flowering time.

Historically, association tests have been used extensively in medical genetics, but have had virtually no application in plant genetics. One obstacle to their application is the structured populations often found in crop plants, which may lead to nonfunctional, spurious associations. In this study, statistical methods to account for population structure were extended for use with quantitative variation and applied to our evaluation of maize flowering time. Mutagenesis and quantitative trait locus (QTL) studies suggested that the maize gene Dwarf8 might affect the quantitative variation of maize flowering time and plant height. The wheat orthologs of this gene contributed to the increased yields seen in the 'Green Revolution' varieties. We used association approaches to evaluate Dwarf8 sequence polymorphisms from 92 maize inbred lines. Population structure was estimated using a Bayesian analysis of 141 simple sequence repeat (SSR) loci. Our results indicate that a suite of polymorphisms associate with differences in flowering time, which include a deletion that may alter a key domain in the coding region. The distribution of nonsynonymous polymorphisms suggests that Dwarf8 has been a target of selection.     

Targeted breakage of a human chromosome mediated by cloned human telomeric DNA.

Novel approaches to the structural and functional analysis of mammalian chromosomes would be possible if the gross structure of the chromosomes in living cells could be engineered. Controlled modifications can be engineered by conventional targeting techniques based on homologous recombination. Large but uncontrolled modifications can be made by the integration of cloned human telomeric DNA. We describe here the combined use of gene targeting and telomere-mediated chromosome breakage to generate a defined truncation of a human chromosome. Telomeric DNA was targeted to the 6-16 gene on the short arm of chromosome 1 in a human cell line. Molecular and cytogenetic analyses showed that, of eight targeted clones that were isolated, one clone had the predicted truncation of chromosome 1.     

Highly effective SNP-based association mapping and management of recessive defects in livestock

The widespread use of elite sires by means of artificial insemination in livestock breeding leads to the frequent emergence of recessive genetic defects, which cause significant economic and animal welfare concerns. Here we show that the availability of genome-wide, high-density SNP panels, combined with the typical structure of livestock populations, markedly accelerates the positional identification of genes and mutations that cause inherited defects. We report the fine-scale mapping of five recessive disorders in cattle and the molecular basis for three of these: congenital muscular dystony (CMD) types 1 and 2 in Belgian Blue cattle and ichthyosis fetalis in Italian Chianina cattle. Identification of these causative mutations has an immediate translation into breeding practice, allowing marker assisted selection against the defects through avoidance of at-risk matings.     

Conditional and joint multiple-SNP analysis of GWAS summary statistics identifies additional variants influencing complex traits

We present an approximate conditional and joint association analysis that can use summary-level statistics from a meta-analysis of genome-wide association studies (GWAS) and estimated linkage disequilibrium (LD) from a reference sample with individual-level genotype data. Using this method, we analyzed meta-analysis summary data from the GIANT Consortium for height and body mass index (BMI), with the LD structure estimated from genotype data in two independent cohorts. We identified 36 loci with multiple associated variants for height (38 leading and 49 additional SNPs, 87 in total) via a genome-wide SNP selection procedure. The 49 new SNPs explain approximately 1.3% of variance, nearly doubling the heritability explained at the 36 loci. We did not find any locus showing multiple associated SNPs for BMI. The method we present is computationally fast and is also applicable to case-control data, which we demonstrate in an example from meta-analysis of type 2 diabetes by the DIAGRAM Consortium.     

Fraser syndrome and mouse blebbed phenotype caused by mutations in FRAS1/Fras1 encoding a putative extracellular matrix protein

Fraser syndrome (OMIM 219000) is a multisystem malformation usually comprising cryptophthalmos, syndactyly and renal defects. Here we report autozygosity mapping and show that the locus FS1 at chromosome 4q21 is associated with Fraser syndrome, although the condition is genetically heterogeneous. Mutation analysis identified five frameshift mutations in FRAS1, which encodes one member of a family of novel proteins related to an extracellular matrix (ECM) blastocoelar protein found in sea urchin. The FRAS1 protein contains a series of N-terminal cysteine-rich repeat motifs previously implicated in BMP metabolism, suggesting that it has a role in both structure and signal propagation in the ECM. It has been speculated that Fraser syndrome is a human equivalent of the blebbed phenotype in the mouse, which has been associated with mutations in at least five loci including bl. As mapping data were consistent with homology of FRAS1 and bl, we screened DNA from bl/bl mice and identified a premature termination of mouse Fras1. Thus, the bl mouse is a model for Fraser syndrome in humans, a disorder caused by disrupted epithelial integrity in utero.     

The effects of human population structure on large genetic association studies

Large-scale association studies hold substantial promise for unraveling the genetic basis of common human diseases. A well-known problem with such studies is the presence of undetected population structure, which can lead to both false positive results and failures to detect genuine associations. Here we examine approximately 15,000 genome-wide single-nucleotide polymorphisms typed in three population groups to assess the consequences of population structure on the coming generation of association studies. The consequences of population structure on association outcomes increase markedly with sample size. For the size of study needed to detect typical genetic effects in common diseases, even the modest levels of population structure within population groups cannot safely be ignored. We also examine one method for correcting for population structure (Genomic Control). Although it often performs well, it may not correct for structure if too few loci are used and may overcorrect in other settings, leading to substantial loss of power. The results of our analysis can guide the design of large-scale association studies.     

Population genetic structure of variable drug response.

Geographic patterns of genetic variation, including variation at drug metabolizing enzyme (DME) loci and drug targets, indicate that geographic structuring of inter-individual variation in drug response may occur frequently. This raises two questions: how to represent human population genetic structure in the evaluation of drug safety and efficacy, and how to relate this structure to drug response. We address these by (i) inferring the genetic structure present in a heterogeneous sample and (ii) comparing the distribution of DME variants across the inferred genetic clusters of individuals. We find that commonly used ethnic labels are both insufficient and inaccurate representations of the inferred genetic clusters, and that drug-metabolizing profiles, defined by the distribution of DME variants, differ significantly among the clusters. We note, however, that the complexity of human demographic history means that there is no obvious natural clustering scheme, nor an obvious appropriate degree of resolution. Our comparison of drug-metabolizing profiles across the inferred clusters establishes a framework for assessing the appropriate level of resolution in relating genetic structure to drug response.     

High-resolution haplotype structure in the human genome

Linkage disequilibrium (LD) analysis is traditionally based on individual genetic markers and often yields an erratic, non-monotonic picture, because the power to detect allelic associations depends on specific properties of each marker, such as frequency and population history. Ideally, LD analysis should be based directly on the underlying haplotype structure of the human genome, but this structure has remained poorly understood. Here we report a high-resolution analysis of the haplotype structure across 500 kilobases on chromosome 5q31 using 103 single-nucleotide polymorphisms (SNPs) in a European-derived population. The results show a picture of discrete haplotype blocks (of tens to hundreds of kilobases), each with limited diversity punctuated by apparent sites of recombination. In addition, we develop an analytical model for LD mapping based on such haplotype blocks. If our observed structure is general (and published data suggest that it may be), it offers a coherent framework for creating a haplotype map of the human genome.     

Erosion of the telomeric single-strand overhang at replicative senescence

Cultured primary human cells inevitably enter a state of replicative senescence for which the specific molecular trigger is unknown. We show that the single-strand telomeric overhang, a key component of telomere structure, is eroded at senescence. Expression of telomerase prevents overhang loss, suggesting that this enzyme prevents senescence by maintaining proper telomere structure. In contrast, progressive overhang loss occurs in cells that avoid senescence through the inactivation of p53 and Rb, indicating that overhang erosion is the result of continuous cell division and not a consequence of senescence. We thus provide evidence for a specific molecular alteration in telomere structure at senescence and suggest that this change, rather than overall telomere length, serves to trigger this state.     

An Icelandic example of the impact of population structure on association studies

The impact of population structure on association studies undertaken to identify genetic variants underlying common human diseases is an issue of growing interest. Spurious associations of alleles with disease phenotypes may be obtained or true associations overlooked when allele frequencies differ notably among subpopulations that are not represented equally among cases and controls. Population structure influences even carefully designed studies and can affect the validity of association results. Most study designs address this problem by sampling cases and controls from groups that share the same nationality or self-reported ethnic background, with the implicit assumption that no substructure exists within such groups. We examined population structure in the Icelandic gene pool using extensive genealogical and genetic data. Our results indicate that sampling strategies need to take account of substructure even in a relatively homogenous genetic isolate. This will probably be even more important in larger populations.     

Population structure, differential bias and genomic control in a large-scale, case-control association study

The main problems in drawing causal inferences from epidemiological case-control studies are confounding by unmeasured extraneous factors, selection bias and differential misclassification of exposure. In genetics the first of these, in the form of population structure, has dominated recent debate. Population structure explained part of the significant +11.2% inflation of test statistics we observed in an analysis of 6,322 nonsynonymous SNPs in 816 cases of type 1 diabetes and 877 population-based controls from Great Britain. The remainder of the inflation resulted from differential bias in genotype scoring between case and control DNA samples, which originated from two laboratories, causing false-positive associations. To avoid excluding SNPs and losing valuable information, we extended the genomic control method by applying a variable downweighting to each SNP.     

Robust signals of coevolution of interacting residues in mammalian proteomes identified by phylogeny-aided structural analysis

The structure of a protein depends critically on the complex interactions among its amino acid residues. It has long been hypothesized that interacting residues might tend to coevolve, but it is not known whether such coevolution is a general phenomenon across the proteome. Here, we describe a novel methodology called phylogeny-aided structural analysis, which uncovers robust signals of interacting-residue coevolution in mammalian proteomes. Furthermore, this new method allows the magnitude of coevolution to be quantified. Finally, it facilitates a comprehensive evaluation of various factors that affect interacting-residue coevolution, such as the physicochemical properties of the interactions between residues, solvent accessibility of the residues and their secondary structure context.