Disease model distortion in association studies

Systems genetics of metabolic health in the BXD mouse genetic reference population

Susceptibility to metabolic syndrome (MetS) is dependent on genetics, environment, and gene-by-environment interactions, rendering the study of underlying mechanisms challenging. The majority of experiments in model organisms do not incorporate genetic variation and lack specific evaluation criteria for MetS. Here, we derived a continuous metric, the metabolic health score (MHS), based on standard clinical parameters and defined its molecular signatures in the liver and circulation. In human UK Biobank, the MHS associated with MetS status and was predictive of future disease incidence, even in individuals without MetS. Using quantitative trait locus analyses in mice, we found two MHS-associated genetic loci and replicated them in unrelated mouse populations. Through a prioritization scheme in mice and human genetic data, we identified TNKS and MCPH1 as candidates mediating differences in the MHS. Our findings provide insights into the molecular mechanisms sustaining metabolic health across species and uncover likely regulators. A record of this paper's transparent peer review process is included in the supplemental information.

Power and Sample Size Calculations for Genetic Association Studies in the Presence of Genetic Model Misspecification

INTRODUCTION

When analyzing data from large-scale genetic association studies, such as targeted or genome-wide resequencing studies, it is common to assume a single genetic model, such as dominant or additive, for all tests of association between a given genetic variant and the phenotype. However, for many variants, the chosen model will result in poor model fit and may lack statistical power due to model misspecification.

OBJECTIVE

We develop power and sample size calculations for tests of gene and gene × environment interaction, allowing for misspecification of the true mode of genetic susceptibility.

METHODS

The power calculations are based on a likelihood ratio test framework and are implemented in an open-source R package ("genpwr").

RESULTS

We use these methods to develop an analysis plan for a resequencing study in idiopathic pulmonary fibrosis and show that using a 2-degree of freedom test can increase power to detect recessive genetic effects while maintaining power to detect dominant and additive effects.

CONCLUSIONS

Understanding the impact of model misspecification can aid in study design and developing analysis plans that maximize power to detect a range of true underlying genetic effects. In particular, these calculations help identify when a multiple degree of freedom test or other robust test of association may be advantageous.

Insights into malaria susceptibility using genome-wide data on 17,000 individuals from Africa, Asia and Oceania

The human genetic factors that affect resistance to infectious disease are poorly understood. Here we report a genome-wide association study in 17,000 severe malaria cases and population controls from 11 countries, informed by sequencing of family trios and by direct typing of candidate loci in an additional 15,000 samples. We identify five replicable associations with genome-wide levels of evidence including a newly implicated variant on chromosome 6. Jointly, these variants account for around one-tenth of the heritability of severe malaria, which we estimate as ~23% using genome-wide genotypes. We interrogate available functional data and discover an erythroid-specific transcription start site underlying the known association in ATP2B4, but are unable to identify a likely causal mechanism at the chromosome 6 locus.  Previously reported HLA associations do not replicate in these samples. This large dataset will provide a foundation for further research on the genetic determinants of malaria resistance in diverse populations.

Four genome-wide associated loci are currently known for malaria susceptibility. Here, the authors expand on earlier work by combining data from 11 malaria-endemic countries and additional population sequencing informing an African-enriched imputation reference panel, with findings including a previously unreported association on chromosome 6.

Evaluating the Potential of Younger Cases and Older Controls Cohorts to Improve Discovery Power in Genome-Wide Association Studies of Late-Onset Diseases

For more than a decade, genome-wide association studies have been making steady progress in discovering the causal gene variants that contribute to late-onset human diseases. Polygenic late-onset diseases in an aging population display a risk allele frequency decrease at older ages, caused by individuals with higher polygenic risk scores becoming ill proportionately earlier and bringing about a change in the distribution of risk alleles between new cases and the as-yet-unaffected population. This phenomenon is most prominent for diseases characterized by high cumulative incidence and high heritability, examples of which include Alzheimer’s disease, coronary artery disease, cerebral stroke, and type 2 diabetes, while for late-onset diseases with relatively lower prevalence and heritability, exemplified by cancers, the effect is significantly lower. In this research, computer simulations have demonstrated that genome-wide association studies of late-onset polygenic diseases showing high cumulative incidence together with high initial heritability will benefit from using the youngest possible age-matched cohorts. Moreover, rather than using age-matched cohorts, study cohorts combining the youngest possible cases with the oldest possible controls may significantly improve the discovery power of genome-wide association studies.

Genome wide association study to identify predictors for severe skin toxicity in colorectal cancer patients treated with cetuximab

EGFR-antibodies are associated with significant skin toxicity, including acneiform rash and folliculitis. It remains impossible to predict the occurrence of severe skin toxicity due to the lack of predictive markers. Here, we present the first genome-wide association study (GWAS) to find single nucleotide polymorphisms (SNPs) associated with EGFR inhibitor-induced skin toxicity using data of the multicentre randomized phase III CAIRO2 trial (clinicaltrials.gov NCT00208546). In this study, advanced or metastatic colorectal cancer patients were treated with capecitabine, oxaliplatin and bevacizumab with or without cetuximab. Germline DNA was available in 282 of the 368 patients in the cetuximab arm. Mild skin toxicity occurred in 195 patients (i.e. CTC grade 1 or 2, respectively 91 and 104 patients) and severe skin toxicity (i.e. grade 3) in 36 patients. Grade 4 skin toxicity did not occur. None of the SNPs reached the formal genome wide threshold for significance of 5x10(-8), though SNPs of at least 8 loci did show moderate association (p-value between 5x10(-7) and 5x10(-5)) with the occurrence of grade 3 (severe) skin toxicity. These SNPs did not overlap with SNPs associated with cetuximab efficacy as found in a previous GWAS in the same CAIRO2 cohort. If formally proven by replication, the SNPs associated with severe EGFR induced skin toxicity may be helpful to predict the occurrence and severity of skin toxicity in patients that will receive cetuximab and allow for adequate information on the risk of skin toxicity and prophylactic measurements.

What has GWAS done for HLA and disease associations?

The major histocompatibility complex (MHC) is located in chromosome 6p21 and contains crucial regulators of immune response, including human leucocyte antigen (HLA) genes, alongside other genes with nonimmunological roles. More recently, a repertoire of noncoding RNA genes, including expressed pseudogenes, has also been identified. The MHC is the most gene dense and most polymorphic part of the human genome. The region exhibits haplotype-specific linkage disequilibrium patterns, contains the strongest cis- and trans-eQTLs/meQTLs in the genome and is known as a hot spot for disease associations. Another layer of complexity is provided to the region by the extreme structural variation and copy number variations. While the HLA-B gene has the highest number of alleles, the HLA-DR/DQ subregion is structurally most variable and shows the highest number of disease associations. Reliance on a single reference sequence has complicated the design, execution and analysis of GWAS for the MHC region and not infrequently, the MHC region has even been excluded from the analysis of GWAS data. Here, we contrast features of the MHC region with the rest of the genome and highlight its complexities, including its functional polymorphisms beyond those determined by single nucleotide polymorphisms or single amino acid residues. One of the several issues with customary GWAS analysis is that it does not address this additional layer of polymorphisms unique to the MHC region. We highlight alternative approaches that may assist with the analysis of GWAS data from the MHC region and unravel associations with all functional polymorphisms beyond single SNPs. We suggest that despite already showing the highest number of disease associations, the true extent of the involvement of the MHC region in disease genetics may not have been uncovered.

Genetic model misspecification in genetic association studies

Objective

The underlying model of the genetic determinant of a trait is generally not known with certainty a priori. Hence, in genetic association studies, a dominant model might be erroneously modelled as additive, an error investigated previously. We explored this question, for candidate gene studies, by evaluating the sample size required to compensate for the misspecification and improve inference at the analysis stage. Power calculations were carried out with (1) the true dominant model and (2) the incorrect additive model. Empirical power, sample size and effect size were compared between scenarios (1) and (2). In each of the scenarios the estimates were evaluated for a rare (minor allele frequency < 0.01), low frequency (0.01 ≤ minor allele frequency < 0.05) and common (minor allele frequency ≥ 0.05) single nucleotide polymorphism.

Results

The results confirm the detrimental effect of the misspecification error on power and effect size for any minor allele frequency. The implications of the error are not negligible; therefore, candidate gene studies should consider the more conservative sample size to compensate for the effect of error. When it is not possible to extend the sample size, methods that help mitigate the impact of the error should be systematically used.

Electronic supplementary material

The online version of this article (10.1186/s13104-017-2911-3) contains supplementary material, which is available to authorized users.

Whole-genome view of the consequences of a population bottleneck using 2926 genome sequences from Finland and United Kingdom

Isolated populations with enrichment of variants due to recent population bottlenecks provide a powerful resource for identifying disease-associated genetic variants and genes. As a model of an isolate population, we sequenced the genomes of 1463 Finnish individuals as part of the Sequencing Initiative Suomi (SISu) Project. We compared the genomic profiles of the 1463 Finns to a sample of 1463 British individuals that were sequenced in parallel as part of the UK10K Project. Whereas there were no major differences in the allele frequency of common variants, a significant depletion of variants in the rare frequency spectrum was observed in Finns when comparing the two populations. On the other hand, we observed >2.1 million variants that were twice as frequent among Finns compared with Britons and 800 000 variants that were more than 10 times more frequent in Finns. Furthermore, in Finns we observed a relative proportional enrichment of variants in the minor allele frequency range between 2 and 5% (P<2.2 × 10⁻¹⁶). When stratified by their functional annotations, loss-of-function variants showed the highest proportional enrichment in Finns (P=0.0291). In the non-coding part of the genome, variants in conserved regions (P=0.002) and promoters (P=0.01) were also significantly enriched in the Finnish samples. These functional categories represent the highest a priori power for downstream association studies of rare variants using population isolates.

Imputation of KIR Types from SNP Variation Data

Large population studies of immune system genes are essential for characterizing their role in diseases, including autoimmune conditions. Of key interest are a group of genes encoding the killer cell immunoglobulin-like receptors (KIRs), which have known and hypothesized roles in autoimmune diseases, resistance to viruses, reproductive conditions, and cancer. These genes are highly polymorphic, which makes typing expensive and time consuming. Consequently, despite their importance, KIRs have been little studied in large cohorts. Statistical imputation methods developed for other complex loci (e.g., human leukocyte antigen [HLA]) on the basis of SNP data provide an inexpensive high-throughput alternative to direct laboratory typing of these loci and have enabled important findings and insights for many diseases. We present KIR^∗IMP, a method for imputation of KIR copy number. We show that KIR^∗IMP is highly accurate and thus allows the study of KIRs in large cohorts and enables detailed investigation of the role of KIRs in human disease.

Nonadditive Effects of Genes in Human Metabolomics

Genome-wide association studies (GWAS) are widely applied to analyze the genetic effects on phenotypes. With the availability of high-throughput technologies for metabolite measurements, GWAS successfully identified loci that affect metabolite concentrations and underlying pathways. In most GWAS, the effect of each SNP on the phenotype is assumed to be additive. Other genetic models such as recessive, dominant, or overdominant were considered only by very few studies. In contrast to this, there are theories that emphasize the relevance of nonadditive effects as a consequence of physiologic mechanisms. This might be especially important for metabolites because these intermediate phenotypes are closer to the underlying pathways than other traits or diseases. In this study we analyzed systematically nonadditive effects on a large panel of serum metabolites and all possible ratios (22,801 total) in a population-based study [Cooperative Health Research in the Region of Augsburg (KORA) F4, N = 1,785]. We applied four different 1-degree-of-freedom (1-df) tests corresponding to an additive, dominant, recessive, and overdominant trait model as well as a genotypic model with two degree-of-freedom (2-df) that allows a more general consideration of genetic effects. Twenty-three loci were found to be genome-wide significantly associated (Bonferroni corrected P ≤ 2.19 × 10(-12)) with at least one metabolite or ratio. For five of them, we show the evidence of nonadditive effects. We replicated 17 loci, including 3 loci with nonadditive effects, in an independent study (TwinsUK, N = 846). In conclusion, we found that most genetic effects on metabolite concentrations and ratios were indeed additive, which verifies the practice of using the additive model for analyzing SNP effects on metabolites.

A novel test for recessive contributions to complex diseases implicates Bardet-Biedl syndrome gene BBS10 in idiopathic type 2 diabetes and obesity

Rare-variant association studies in common, complex diseases are customarily conducted under an additive risk model in both single-variant and burden testing. Here, we describe a method to improve detection of rare recessive variants in complex diseases termed RAFT (recessive-allele-frequency-based test). We found that RAFT outperforms existing approaches when the variant influences disease risk in a recessive manner on simulated data. We then applied our method to 1,791 Finnish individuals with type 2 diabetes (T2D) and 2,657 matched control subjects. In BBS10, we discovered a rare variant (c.1189A>G [p.Ile397Val]; rs202042386) that confers risk of T2D in a recessive state (p = 1.38 × 10(-6)) and would be missed by conventional methods. Testing of this variant in an established in vivo zebrafish model confirmed the variant to be pathogenic. Taken together, these data suggest that RAFT can effectively reveal rare recessive contributions to complex diseases overlooked by conventional association tests.

Estimation of epistatic variance components and heritability in founder populations and crosses

Genetic association studies have explained only a small proportion of the estimated heritability of complex traits, leaving the remaining heritability “missing.” Genetic interactions have been proposed as an explanation for this, because they lead to overestimates of the heritability and are hard to detect. Whether this explanation is true depends on the proportion of variance attributable to genetic interactions, which is difficult to measure in outbred populations. Founder populations exhibit a greater range of kinship than outbred populations, which helps in fitting the epistatic variance. We extend classic theory to founder populations, giving the covariance between individuals due to epistasis of any order. We recover the classic theory as a limit, and we derive a recently proposed estimator of the narrow sense heritability as a corollary. We extend the variance decomposition to include dominance. We show in simulations that it would be possible to estimate the variance from pairwise interactions with samples of a few thousand from strongly bottlenecked human founder populations, and we provide an analytical approximation of the standard error. Applying these methods to 46 traits measured in a yeast (Saccharomyces cerevisiae) cross, we estimate that pairwise interactions explain 10% of the phenotypic variance on average and that third- and higher-order interactions explain 14% of the phenotypic variance on average. We search for third-order interactions, discovering an interaction that is shared between two traits. Our methods will be relevant to future studies of epistatic variance in founder populations and crosses.

Testing for non-linear causal effects using a binary genotype in a Mendelian randomization study: application to alcohol and cardiovascular traits

Background: Mendelian randomization studies have so far restricted attention to linear associations relating the genetic instrument to the exposure, and the exposure to the outcome. In some cases, however, observational data suggest a non-linear association between exposure and outcome. For example, alcohol consumption is consistently reported as having a U-shaped association with cardiovascular events. In principle, Mendelian randomization could address concerns that the apparent protective effect of light-to-moderate drinking might reflect ‘sick-quitters’ and confounding.

Methods: The Alcohol-ADH1B Consortium was established to study the causal effects of alcohol consumption on cardiovascular events and biomarkers, using the single nucleotide polymorphism rs1229984 in ADH1B as a genetic instrument. To assess non-linear causal effects in this study, we propose a novel method based on estimating local average treatment effects for discrete levels of the exposure range, then testing for a linear trend in those effects. Our method requires an assumption that the instrument has the same effect on exposure in all individuals. We conduct simulations examining the robustness of the method to violations of this assumption, and apply the method to the Alcohol-ADH1B Consortium data.

Results: Our method gave a conservative test for non-linearity under realistic violations of the key assumption. We found evidence for a non-linear causal effect of alcohol intake on several cardiovascular traits.

Conclusions: We believe our method is useful for inferring departure from linearity when only a binary instrument is available. We estimated non-linear causal effects of alcohol intake which could not have been estimated through standard instrumental variable approaches.

A note on the efficiencies of sampling strategies in two-stage Bayesian regional fine mapping of a quantitative trait

In focused studies designed to follow up associations detected in a genome-wide association study (GWAS), investigators can proceed to fine-map a genomic region by targeted sequencing or dense genotyping of all variants in the region, aiming to identify a functional sequence variant. For the analysis of a quantitative trait, we consider a Bayesian approach to fine-mapping study design that incorporates stratification according to a promising GWAS tag SNP in the same region. Improved cost-efficiency can be achieved when the fine-mapping phase incorporates a two-stage design, with identification of a smaller set of more promising variants in a subsample taken in stage 1, followed by their evaluation in an independent stage 2 subsample. To avoid the potential negative impact of genetic model misspecification on inference we incorporate genetic model selection based on posterior probabilities for each competing model. Our simulation study shows that, compared to simple random sampling that ignores genetic information from GWAS, tag-SNP-based stratified sample allocation methods reduce the number of variants continuing to stage 2 and are more likely to promote the functional sequence variant into confirmation studies.

The future of genomics for developmentalists

The momentum of genomic science will carry it far into the future and into the heart of research on typical and atypical behavioral development. The purpose of this paper is to focus on a few implications and applications of these advances for understanding behavioral development. Quantitative genetics is genomic and will chart the course for molecular genomic research now that these two worlds of genetics are merging in the search for many genes of small effect. Although current attempts to identify specific genes have had limited success, known as the missing heritability problem, whole-genome sequencing will improve this situation by identifying all DNA sequence variations, including rare variants. Because the heritability of complex traits is caused by many DNA variants of small effect in the population, polygenic scores that are composites of hundreds or thousands of DNA variants will be used by developmentalists to predict children's genetic risk and resilience. The most far-reaching advance will be the widespread availability of whole-genome sequence for children, which means that developmentalists would no longer need to obtain DNA or to genotype children in order to use genomic information in research or in the clinic.

Incorporating parental information into family-based association tests

Assumptions regarding the true underlying genetic model, or mode of inheritance, are necessary when quantifying genetic associations with disease phenotypes. Here we propose new methods to ascertain the underlying genetic model from parental data in family-based association studies. Specifically, for parental mating-type data, we propose a novel statistic to test whether the underlying genetic model is additive, dominant, or recessive; for parental genotype-phenotype data, we propose three strategies to determine the true mode of inheritance. We illustrate how to incorporate the information gleaned from these strategies into family-based association tests. Because family-based association tests are conducted conditional on parental genotypes, the type I error rate of these procedures is not inflated by the information learned from parental data. This result holds even if such information is weak or when the assumption of Hardy-Weinberg equilibrium is violated. Our simulations demonstrate that incorporating parental data into family-based association tests can improve power under common inheritance models. The application of our proposed methods to a candidate-gene study of type 1 diabetes successfully detects a recessive effect in MGAT5 that would otherwise be missed by conventional family-based association tests.

Including known covariates can reduce power to detect genetic effects in case-control studies

Genome-wide association studies (GWAS) search for associations between genetic variants and disease status, typically via logistic regression. Often there are covariates, such as sex or well-established major genetic factors, that are known to affect disease susceptibility and are independent of tested genotypes at the population level. We show theoretically and with data from recent GWAS on multiple sclerosis, psoriasis and ankylosing spondylitis that inclusion of known covariates can substantially reduce power for the identification of associated variants when the disease prevalence is lower than a few percent. Whether the inclusion of such covariates reduces or increases power to detect genetic effects depends on various factors, including the prevalence of the disease studied. When the disease is common (prevalence of >20%), the inclusion of covariates typically increases power, whereas, for rarer diseases, it can often decrease power to detect new genetic associations.

The aggregate effect of dopamine genes on dependence symptoms among cocaine users: cross-validation of a candidate system scoring approach

Genome-wide studies of psychiatric conditions frequently fail to explain a substantial proportion of variance, and replication of individual SNP effects is rare. We demonstrate a selective scoring approach, in which variants from several genes known to directly affect the dopamine system are considered concurrently to explain individual differences in cocaine dependence symptoms. 273 SNPs from eight dopamine-related genes were tested for association with cocaine dependence symptoms in an initial training sample. We identified a four-SNP score that accounted for 0.55% of the variance in a separate testing sample (p = 0.037). These findings suggest that (1) limiting investigated SNPs to those located in genes of theoretical importance improves the chances of identifying replicable effects by reducing statistical penalties for multiple testing, and (2) considering top-associated SNPs in the aggregate can reveal replicable effects that are too small to be identified at the level of individual SNPs.

Estimating causal effects of genetic risk variants for breast cancer using marker data from bilateral and familial cases

BACKGROUND

Cases with a family history are enriched for genetic risk variants, and the power of association studies can be improved by selecting cases with a family history of disease. However, in recent genome-wide association scans utilizing familial sampling, the excess relative risk for familial cases is less than predicted when compared with unselected cases. This can be explained by incomplete linkage disequilibrium between the tested marker and the underlying causal variant.

METHODS

We show that the allele frequency and effect size of the underlying causal variant can be estimated by combining marker data from studies that ascertain cases based on different family histories. This allows us to learn about the genetic architecture of a complex trait, without having identified any causal variants. We consider several validated common marker alleles for breast cancer, using our own study of high risk, predominantly bilateral cases, cases preferentially selected to have at least two affected first- or second-degree relatives, and published estimates of relative risk from standard case-control studies.

RESULTS

To obtain realistic estimates and to accommodate some prior beliefs, we use Bayesian estimation to infer that the causal variants are probably common, with minor allele frequency >5%, and have small effects, with relative risk around 1.2.

CONCLUSION

These results strongly support the common disease common variant hypothesis for these specific loci associated with breast cancer.

IMPACT

Our results agree with recent assertions that synthetic associations of rare variants are unlikely to account for most associations seen in genome-wide studies.