Bootstrap Distillation: Non-parametric Internal Validation of GWAS Results by Subgroup Resampling

David A. Eccles, Rodney A. Lea and Geoffrey K. Chambers

Abstract

Genome-wide Association Studies are carried out on a large number of genetic variants in a large number of people, allowing the detection of small genetic effects that are associated with a trait. Natural variation of genotypes within populations means that any particular sample from the population may not represent the true genotype frequencies within that population. This may lead to the observation of marker-disease associations when no such association exists.

A bootstrap population sub-sampling technique can reduce the influence of allele frequency variation in producing false-positive results for particular samplings of the population. In order to utilise bioinformatics in the service of a serious disease, this sub-sampling method has been applied to the Type 1 Diabetes dataset from the Wellcome Trust Case Control Consortium in order to evaluate its effectiveness.

While previous literature on Type 1 Diabetes has identified some DNA variants that are associated with the disease, these variants are not informative for distinguishing between disease cases and controls using genetic information alone (AUC=0.7284). Population sub-sampling filtered out noise from genome-wide association data, and increased the chance of finding useful associative signals. Subsequent filtering based on marker linkage and testing of marker sets of different sizes produced a 5-SNP signature set of markers for Type 1 Diabetes. The group-specific markers used in this set, primarily from the HLA region on chromosome 6, are considerably more informative than previously known associated variants for predicting T1D phenotype from genetic data (AUC=0.8395). Given this predictive quality, the signature set may be useful alone as a screening test, and would be particularly useful in combination with other clinical cofactors for Type 1 Diabetes risk.

Background

\label{sec:sig-thy-background}

Personalised medical treatment based on genome profiles is a major goal of genetic research in the \(21^{st}\) century (see Avery et al., 2009; Province et al., 2008). However, complex genotype-environment interactions for common diseases make it difficult to determine which specific genetic features should be used to construct such profiles. Hence the prediction of genetic risk is a major challenge of the \(21^{st}\) century.

The introduction of large-scale Single Nucleotide Polymorphism (SNP) genotyping systems has enabled genetic variants to be typed en-masse, shifting the main effort required in a genetic risk study from genotyping to data analysis (or bioinformatics). Here we investigate genetic markers for Type 1 Diabetes (T1D), demonstrating how a population sub-sampling method may assist in the identification of risk markers for a complex disease.

Type 1 Diabetes

\label{sec:sig-thy-t1dintro}

Type 1 Diabetes mellitus (T1D) is a disorder typically characterised by an absence of insulin-producing beta cells in the pancreas, either through loss of the cells themselves, or through the reduction in capacity of the cells to produce insulin (see Atkinson et al., 2014). This disorder shares with the more common Type 2 Diabetes mellitus (T2D) a characteristic symptom of high blood glucose levels. In some cases, this glucose also passes through to the urine, creating a sticky/sweet substance that attracts ants (see Ekoé et al., 2002, pp. 7,11). In T2D, this high blood glucose is caused by cells not responding to insulin (insulin resistance), while in T1D the excess is caused by a reduction in insulin production (insulin dependence).

The incidence of T1D varies throughout the world, with rates of incidence as low as 0.0006% per year in China, 0.02% in the UK, up to nearly 0.05% per year in Finland. About 50-60% of cases of T1D manifest in childhood (younger than 18 years), and the disease is believed to be caused by an abnormal immune response after exposure to environmental triggers such as viruses, toxins or food (see Daneman, 2006). While a spring birth is correlated with T1D risk, the diagnosis of Type 1 Diabetes is more common in autumn and winter (see Atkinson et al., 2014).

Symptoms, Diagnosis and Management of T1D

\label{sec:sig-thy-t1d-symp-management}

Typical symptoms of T1D include excess urine output (polyuria), thirst and increased fluid intake (polydypsia),blurred vision, and weight loss. When left untreated, this form of diabetes can lead to a build-up of ketone bodies and a reduction of blood pH (ketoacidosis), reducing mental faculties and causing a loss of consciousness (see Ekoé et al., 2002, p. 7).

Diabetes can be diagnosed by a single random11i.e. taken at any time of the day, as opposed to a fasting glucose test taken at least 8 hours after the last meal blood glucose test, as long as symptoms are present and blood glucose levels are found to be in excess (typically \(>11.1~{}mmol~{}l^{-1}\)) of those normally observed. In situations where symptoms are less obvious and/or glucose levels are at the high end of the normal range, a glucose tolerance test (GTT) is used for diagnosis. In this test, fasting patients have their blood glucose level tested, patients then consume a measured dose of oral glucose, and blood glucose levels are measured 2 hours later. A fasting glucose level in excess of \(6.1~{}mmol~{}l^{-1}\), or post-load level in excess of \(11.1~{}mmol~{}l^{-1}\) is considered diagnostic for both forms of Diabetes Mellitus. Type 1 Diabetes (as distinct from T2D) encompasses a range of diseases that involve autoimmunity. It can be diagnosed by the presence of antibodies to glutamic acid decarboxylase, islet cells, insulin, or ICA512 (see Ekoé et al., 2002, p. 19).

As the symptoms of T1D are caused by high blood glucose levels (hyperglycaemia) due to a lack of insulin, these symptoms can be relieved by the introduction of insulin into the blood. This is typically carried out by supplying measured doses of insulin via intramuscular injections or by the use of insulin pumps (see Daneman, 2006). Individuals with T1D need a constant supply of insulin for survival, together with occasional insulin bursts to control variable blood glucose levels throughout the day (e.g. after meals). In contrast, individuals with T2D only require insulin for survival in rare cases (see Ekoé et al., 2002, p. 16). Slow-release insulin and consumption of foods with a low glycaemic index can help to reduce the extremes of T1D symptoms.

Improperly managed treatment can cause further medical complications in a diabetic patient. Too much insulin, excessive physical activity, or not enough dietary sugar can result in low blood glucose levels (hypoglycaemia), which produce short-term autonomic and neurological problems such as trembling, dizziness, blurred vision, and difficulty concentrating. Hypoglycaemia is treated either by ingestion of sugar, or by intravenous glucose in severe cases (see Daneman, 2006).

Complications of T1D

\label{sec:sig-thy-t1d-symptoms}

The initial symptoms of T1D are not usually severe, and the disease may progress for a few years before a diagnosis is made and treatment is given. However, long-term complications can appear when the disease is not managed appropriately (see Ekoé 2002, p. 8). Retinal damage progresses in about 20-25% of individuals with T1D, with later stages causing retinal detachment and consequent loss of sight. Renal failure is also a problem in diabetic individuals, which is indicated by high urinary protein levels. When individuals have these high levels, progression to end-stage renal disease occurs in about 50% of cases. Neural defects are also a potential complication of T1