Developing Statistical Methods for Incorporating Complexity in Association Studies

Genome-wide association studies (GWAS) have identified thousands of genetic variants associated with hundreds of human traits. Yet the common variant model tested by traditional GWAS only provides an incomplete explanation for the known genetic heritability of many traits. Many divergent methods have been proposed to address the shortcomings of GWAS, including most notably the extension of association methods into rarer variants through whole exome and whole genome sequencing. GWAS methods feature numerous simplifications designed for feasibility and ease of use, as opposed to statistical rigor. Furthermore, no systematic quantification of the performance of GWAS across all traits exists. Beyond improving the utility of data that already exist, a more thorough understanding of the performance of GWAS on common variants may elucidate flaws not in the method but rather in its implementation, which may pose a continued or growing threat to the utility of rare variant association studies now underway. This thesis focuses on systematic evaluation and incremental improvement of GWAS modeling. We collect a rich dataset containing standardized association results from all GWAS conducted on quantitative human traits, finding that while the majority of published significant results in the field do not disclose sufficient information to determine whether the results are actually valid, those that do replicate precisely in concordance with their statistical power when conducted in samples of similar ancestry and reporting accurate per-locus sample sizes. We then look to the inability of effectively all existing association methods to handle missingness in genetic data, and show that adapting missingness theory from statistics can both increase power and provide a flexible framework for extending most existing tools with minimal effort. We finally undertake novel variant association in a schizophrenia cohort from a bottleneck population. We find that the study itself is confounded by nonrandom population sampling and identity-by-descent, manifesting as batch effects correlated with outcome that remain in novel variants after all sample-wide quality control. On the whole, these results emphasize both the past and present utility and reliability of the GWAS model, as well as the extent to which lessons from the GWAS era must inform genetic studies moving forward

Project MinE: study design and pilot analyses of a large-scale whole-genome sequencing study in amyotrophic lateral sclerosis [preprint]

The most recent genome-wide association study in amyotrophic lateral sclerosis (ALS) demonstrates a disproportionate contribution from low-frequency variants to genetic susceptibility of disease. We have therefore begun Project MinE, an international collaboration that seeks to analyse whole-genome sequence data of at least 15,000 ALS patients and 7,500 controls. Here, we report on the design of Project MinE and pilot analyses of newly whole-genome sequenced 1,264 ALS patients and 611 controls drawn from the Netherlands. As has become characteristic of sequencing studies, we find an abundance of rare genetic variation (minor allele frequency \u3c 0.1%), the vast majority of which is absent in public data sets. Principal component analysis reveals local geographical clustering of these variants within The Netherlands. We use the whole-genome sequence data to explore the implications of poor geographical matching of cases and controls in a sequence-based disease study and to investigate how ancestry-matched, externally sequenced controls can induce false positive associations. Also, we have publicly released genome-wide minor allele counts in cases and controls, as well as results from genic burden tests

STrengthening the REporting of Genetic Association Studies (STREGA)— An Extension of the STROBE Statement

Julian Little and colleagues present the STREGA recommendations, which are aimed at improving the reporting of genetic association studies

PLoS Med

Making sense of rapidly evolving evidence on genetic associations is crucial to making genuine advances in human genomics and the eventual integration of this information in the practice of medicine and public health. Assessment of the strengths and weaknesses of this evidence, and hence the ability to synthesize it, has been limited by inadequate reporting of results. The STrengthening the REporting of Genetic Association studies (STREGA) initiative builds on the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) Statement and provides additions to 12 of the 22 items on the STROBE checklist. The additions concern population stratification, genotyping errors, modelling haplotype variation, Hardy-Weinberg equilibrium, replication, selection of participants, rationale for choice of genes and variants, treatment effects in studying quantitative traits, statistical methods, relatedness, reporting of descriptive and outcome data, and the volume of data issues that are important to consider in genetic association studies. The STREGA recommendations do not prescribe or dictate how a genetic association study should be designed but seek to enhance the transparency of its reporting, regardless of choices made during design, conduct, or analysis.MC_U105285807/Medical Research Council/United Kingdom19192942PMC2634792OtherSurveillance and InvestigationCurren

Clinical validity assessment of a breast cancer risk model combining genetic and clinical information

_Background:_ The extent to which common genetic variation can assist in breast cancer (BCa) risk assessment is unclear. We assessed the addition of risk information from a panel of BCa-associated single nucleotide polymorphisms (SNPs) on risk stratification offered by the Gail Model.

_Methods:_ We selected 7 validated SNPs from the literature and genotyped them among white women in a nested case-control study within the Women’s Health Initiative Clinical Trial. To model SNP risk, previously published odds ratios were combined multiplicatively. To produce a combined clinical/genetic risk, Gail Model risk estimates were multiplied by combined SNP odds ratios. We assessed classification performance using reclassification tables and receiver operating characteristic (ROC) curves. 

_Results:_ The SNP risk score was well calibrated and nearly independent of Gail risk, and the combined predictor was more predictive than either Gail risk or SNP risk alone. In ROC curve analysis, the combined score had an area under the curve (AUC) of 0.594 compared to 0.557 for Gail risk alone. For reclassification with 5-year risk thresholds at 1.5% and 2%, the net reclassification index (NRI) was 0.085 (Z = 4.3, P = 1.0×10^-5^). Focusing on women with Gail 5-year risk of 1.5-2% results in an NRI of 0.195 (Z = 3.8, P = 8.6×10^−5^).

_Conclusions:_ Combining clinical risk factors and validated common genetic risk factors results in improvement in classification of BCa risks in white, postmenopausal women. This may have implications for informing primary prevention and/or screening strategies. Future research should assess the clinical utility of such strategies.
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Annals of internal medicine

Making sense of rapidly evolving evidence on genetic associations is crucial to making genuine advances in human genomics and the eventual integration of this information into the practice of medicine and public health. Assessment of the strengths and weaknesses of this evidence, and hence the ability to synthesize it, has been limited by inadequate reporting of results. The STrengthening the REporting of Genetic Association studies (STREGA) initiative builds on the STrengthening the Reporting of Observational Studies in Epidemiology (STROBE) Statement and provides additions to 12 of the 22 items on the STROBE checklist. The additions concern population stratification, genotyping errors, modeling haplotype variation, Hardy-Weinberg equilibrium, replication, selection of participants, rationale for choice of genes and variants, treatment effects in studying quantitative traits, statistical methods, relatedness, reporting of descriptive and outcome data, and issues of data volume that are important to consider in genetic association studies. The STREGA recommendations do not prescribe or dictate how a genetic association study should be designed but seek to enhance the transparency of its reporting, regardless of choices made during design, conduct, or analysis.OtherSurveillance and InvestigationCurren

Genome-wide association study for detecting autoimmune-disease-associated genetic pattern differences in specific HLA type carriers

The HLA locus variants are one of the strongest genetic predictors for most, if not all, human autoimmune diseases. The HLA locus genes include the antigen-presenting cell surface peptide encoding genes, which form an essential component in the maturation of the T-cell population in the thymus, and their subsequent activation in the periphery. Leveraging the modern population-wide genotype information that capture even the most polymorphic loci, this work sets the aim to design a case-control genome-wide association study (GWAS), that would result in the detection of non-HLA genetic variants that have a statistically different effect on an autoimmune disease in the carriers of certain HLA types, in comparison to the non-carriers. For the purpose of this aim, study groups are assembled based on specific HLA allele doses, so that for 42 HLA allele typesselected for this study there are 42 HLA-specific groups where every individual is a carrier of at least one copy of the HLA allele type. The effect sizes from the summary statistics of the HLA-specific GWASs are compared to a general population GWAS (which is done on all the participants of the Estonian Biobank in this case). The variants are considered relevant to this aim if their effect size is statisticallt different in the HLA-specific groups than they are in the general population GWAS

Genetics of type 1 diabetes with particular focus on the major histocompatibility complex

Type 1 diabetes (T1D) is a complex, autoimmune disease with a strong heritable component. The single most important genomic region for this and a large number of other diseases is the major histocompatibility complex (MHC), which contains a high density of immune-response genes. In particular, certain variants of the DR and DQ genes are well-known risk factors in T1D, but studies have shown that additional T1D risk loci must exist within the MHC. Their identity has been elusive, however, mainly owing to unusually strong association between variants (linkage disequilibrium) in this complex, which acts as a severe confounding factor. Controlling for these effects is therefore crucial in genetic studies of the MHC. This thesis goes to the centre of this problem, using a dataset generated by the Type 1 Diabetes Genetics Consortium (T1DGC), which includes several thousand characterised genetic markers in over 2300 T1D families. The statistical power and genetic coverage of the MHC in this dataset is unparalleled by any previous study. In addition, a smaller dataset including over 400 Norwegian families were genotyped for a number of candidate markers. Employing tailored statistical methods and complementary approaches, the results show that at least three defined, narrow regions of the MHC outside of the DR and DQ gene region contain genetic risk factors for T1D. Both novel and previously suggested risk loci are identified. In particular, the evidence for HLA-B as a T1D susceptibility gene is now practically beyond doubt. In addition, a number of previously suggested candidate markers, including in the tumor necrosis factor (TNF) gene, can by all probability be excluded as T1D risk factors. The evidence for other identified variants, in or close to the HLA-A, -C, G and -DPB1 genes, is still somewhat controversial, and will demand further inquiries. These results nonetheless represent important advances in the understanding of the genetic contribution of the MHC to T1D