Association study designs for complex diseases
TL;DR: With the discovery of massive numbers of genetic markers and the development of better tools for genotyping, association studies will inevitably proliferate and now is the time to consider critically the design of such studies to avoid the mistakes of the past and to maximize their potential to identify new components of disease.
Abstract: Assessing the association between DNA variants and disease has been used widely to identify regions of the genome and candidate genes that contribute to disease. However, there are numerous examples of associations that cannot be replicated, which has led to skepticism about the utility of the approach for common conditions. With the discovery of massive numbers of genetic markers and the development of better tools for genotyping, association studies will inevitably proliferate. Now is the time to consider critically the design of such studies, to avoid the mistakes of the past and to maximize their potential to identify new components of disease.
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TL;DR: Mendelian randomization provides new opportunities to test causality and demonstrates how investment in the human genome project may contribute to understanding and preventing the adverse effects on human health of modifiable exposures.
Abstract: Associations between modifiable exposures and disease seen in observational epidemiology are sometimes confounded and thus misleading, despite our best efforts to improve the design and analysis of studies. Mendelian randomization-the random assortment of genes from parents to offspring that occurs during gamete formation and conception-provides one method for assessing the causal nature of some environmental exposures. The association between a disease and a polymorphism that mimics the biological link between a proposed exposure and disease is not generally susceptible to the reverse causation or confounding that may distort interpretations of conventional observational studies. Several examples where the phenotypic effects of polymorphisms are well documented provide encouraging evidence of the explanatory power of Mendelian randomization and are described. The limitations of the approach include confounding by polymorphisms in linkage disequilibrium with the polymorphism under study, that polymorphisms may have several phenotypic effects associated with disease, the lack of suitable polymorphisms for studying modifiable exposures of interest, and canalization-the buffering of the effects of genetic variation during development. Nevertheless, Mendelian randomization provides new opportunities to test causality and demonstrates how investment in the human genome project may contribute to understanding and preventing the adverse effects on human health of modifiable exposures.
3,646 citations
TL;DR: Genome-wide association studies will soon become possible, and could open new frontiers in the understanding and treatment of disease, however, the execution and analysis of such studies will require great care.
Abstract: Genetic factors strongly affect susceptibility to common diseases and also influence disease-related quantitative traits. Identifying the relevant genes has been difficult, in part because each causal gene only makes a small contribution to overall heritability. Genetic association studies offer a potentially powerful approach for mapping causal genes with modest effects, but are limited because only a small number of genes can be studied at a time. Genome-wide association studies will soon become possible, and could open new frontiers in our understanding and treatment of disease. However, the execution and analysis of such studies will require great care.
2,912 citations
TL;DR: The use of germline genetic variants that proxy for environmentally modifiable exposures as instruments for these exposures is one form of IV analysis that can be implemented within observational epidemiological studies and can be considered as analogous to randomized controlled trials.
Abstract: Observational epidemiological studies suffer from many potential biases, from confounding and from reverse causation, and this limits their ability to robustly identify causal associations. Several high-profile situations exist in which randomized controlled trials of precisely the same intervention that has been examined in observational studies have produced markedly different findings. In other observational sciences, the use of instrumental variable (IV) approaches has been one approach to strengthening causal inferences in non-experimental situations. The use of germline genetic variants that proxy for environmentally modifiable exposures as instruments for these exposures is one form of IV analysis that can be implemented within observational epidemiological studies. The method has been referred to as 'Mendelian randomization', and can be considered as analogous to randomized controlled trials. This paper outlines Mendelian randomization, draws parallels with IV methods, provides examples of implementation of the approach and discusses limitations of the approach and some methods for dealing with these.
2,364 citations
Cites background from "Association study designs for compl..."
...The Mendelian randomization approach requires reliable estimation of the associations between genotype and the modifiable exposure of interest and between genotype and the outcome of interest, but it is well recognized that genetic association studies often fail to replicate [81–83]....
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TL;DR: It is concluded that there are probably many common variants in the human genome with modest but real effects on common disease risk, and that studies using large samples will convincingly identify such variants.
Abstract: Association studies offer a potentially powerful approach to identify genetic variants that influence susceptibility to common disease1,2,3,4, but are plagued by the impression that they are not consistently reproducible5,6. In principle, the inconsistency may be due to false positive studies, false negative studies or true variability in association among different populations4,5,6,7,8. The critical question is whether false positives overwhelmingly explain the inconsistency. We analyzed 301 published studies covering 25 different reported associations. There was a large excess of studies replicating the first positive reports, inconsistent with the hypothesis of no true positive associations (P < 10−14). This excess of replications could not be reasonably explained by publication bias and was concentrated among 11 of the 25 associations. For 8 of these 11 associations, pooled analysis of follow-up studies yielded statistically significant replication of the first report, with modest estimated genetic effects. Thus, a sizable fraction (but under half) of reported associations have strong evidence of replication; for these, false negative, underpowered studies probably contribute to inconsistent replication. We conclude that there are probably many common variants in the human genome with modest but real effects on common disease risk, and that studies using large samples will convincingly identify such variants.
1,928 citations
Cites background from "Association study designs for compl..."
..., but are plagued by the impression that they are not consistently reproducibl...
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TL;DR: New methods of testing the statistical association between haplotypes and a wide variety of traits, including binary, ordinal, and quantitative traits are developed, which allow adjustment for nongenetic covariates, which may be critical when analyzing genetically complex traits.
Abstract: A key step toward the discovery of a gene related to a trait is the finding of an association between the trait and one or more haplotypes. Haplotype analyses can also provide critical information regarding the function of a gene; however, when unrelated subjects are sampled, haplotypes are often ambiguous because of unknown linkage phase of the measured sites along a chromosome. A popular method of accounting for this ambiguity in case-control studies uses a likelihood that depends on haplotype frequencies, so that the haplotype frequencies can be compared between the cases and controls; however, this traditional method is limited to a binary trait (case vs. control), and it does not provide a method of testing the statistical significance of specific haplotypes. To address these limitations, we developed new methods of testing the statistical association between haplotypes and a wide variety of traits, including binary, ordinal, and quantitative traits. Our methods allow adjustment for nongenetic covariates, which may be critical when analyzing genetically complex traits. Furthermore, our methods provide several different global tests for association, as well as haplotype-specific tests, which give a meaningful advantage in attempts to understand the roles of many different haplotypes. The statistics can be computed rapidly, making it feasible to evaluate the associations between many haplotypes and a trait. To illustrate the use of our new methods, they are applied to a study of the association of haplotypes (composed of genes from the human-leukocyte-antigen complex) with humoral immune response to measles vaccination. Limited simulations are also presented to demonstrate the validity of our methods, as well as to provide guidelines on how our methods could be used.
1,813 citations
Cites background from "Association study designs for compl..."
...…genetic-marker maps, particularly those with single-nucleotide polymorphisms (SNPs) (The International SNP Map Working Group 2001), association studies will improve our ability to detect susceptibility alleles for common complex diseases (Risch 2000; Cardon and Bell 2001; Schork et al. 2001)....
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...It is anticipated that, by use of improved genetic-marker maps, particularly those with single-nucleotide polymorphisms (SNPs) (The International SNP Map Working Group 2001), association studies will improve our ability to detect susceptibility alleles for common complex diseases (Risch 2000; Cardon and Bell 2001; Schork et al. 2001)....
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References
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TL;DR: Pritch et al. as discussed by the authors proposed a model-based clustering method for using multilocus genotype data to infer population structure and assign individuals to populations, which can be applied to most of the commonly used genetic markers, provided that they are not closely linked.
Abstract: We describe a model-based clustering method for using multilocus genotype data to infer population structure and assign individuals to populations. We assume a model in which there are K populations (where K may be unknown), each of which is characterized by a set of allele frequencies at each locus. Individuals in the sample are assigned (probabilistically) to populations, or jointly to two or more populations if their genotypes indicate that they are admixed. Our model does not assume a particular mutation process, and it can be applied to most of the commonly used genetic markers, provided that they are not closely linked. Applications of our method include demonstrating the presence of population structure, assigning individuals to populations, studying hybrid zones, and identifying migrants and admixed individuals. We show that the method can produce highly accurate assignments using modest numbers of loci— e.g. , seven microsatellite loci in an example using genotype data from an endangered bird species. The software used for this article is available from http://www.stats.ox.ac.uk/~pritch/home.html.
27,454 citations
TL;DR: In this article, the authors used haplotype analysis of linkage disequilibrium to spotlight a small segment of 4p16.3 as the likely location of the defect, which is expanded and unstable on HD chromosomes.
7,224 citations
Journal Article•
TL;DR: The Huntington's disease mutation involves an unstable DNA segment, similar to those described in fragile X syndrome, spino-bulbar muscular atrophy, and myotonic dystrophy, acting in the context of a novel 4p16.3 gene to produce a dominant phenotype.
6,992 citations
TL;DR: A deletion of three base pairs that results in the omission of a phenylalanine residue at the center of the first predicted nucleotide-binding domain was detected in CF patients.
Abstract: Overlapping complementary DNA clones were isolated from epithelial cell libraries with a genomic DNA segment containing a portion of the putative cystic fibrosis (CF) locus, which is on chromosome 7 Transcripts, approximately 6500 nucleotides in size, were detectable in the tissues affected in patients with CF The predicted protein consists of two similar motifs, each with (i) a domain having properties consistent with membrane association and (ii) a domain believed to be involved in ATP (adenosine triphosphate) binding A deletion of three base pairs that results in the omission of a phenylalanine residue at the center of the first predicted nucleotide-binding domain was detected in CF patients
6,731 citations