Association statistics under the PPL framework

Abstract

In this dissertation, the posterior probability of linkage (PPL) framework is extended to the analysis of case-control (CC) data and three new linkage disequilibrium (LD) statistics are introduced. These statistics measure the evidence for or against LD, rather than testing the null hypothesis of no LD, and they therefore avoid the need for multiple testing corrections. They are suitable not only for CC designs but also can be used in application to family data, ranging from trios to complex pedigrees, all under the same statistical framework, allowing for the unified analysis of these disparate data structures. They also provide the other core advantages of the PPL framework, including the use of sequential updating to accumulate LD evidence across potentially heterogeneous sets of subsets of data; parameterization in terms of a very general trait likelihood, which simultaneously considers dominant, recessive, and additive models; and a straightforward mechanism for modeling two-locus epistasis. Finally, being implemented within the PPL framework, the new statistics readily allow linkage information obtained from distinct data, to be incorporated into LD analyses in the form of a prior probability distribution. Performance of the proposed LD statistics is examined using simulated data. In addition, the effects of key modeling violations on performance are assessed. These statistics are also applied to a previously published type 1 diabetes (T1D) family dataset with a few candidate genes with previously reported weak associations, and another T1D CC dataset also previously published as a genome-wide association (GWA) study with some strong associations reported. The new LD statistics under the PPLD framework confirm most of the findings in the published work and also find some new SNPs suspected of being associated with T1D. Sequential updating between the family dataset and the CC dataset dramatically increased the association signal strength for a CTLA4 SNP genotyped in both studies. Linkage information gleaned from the family dataset is also combined into the LD analysis of the CC dataset 2 to demonstrate the utility of this unique feature of the PPL framework, and specifically for the new LD statistics.Abstract Approved: ____________________________________ Thesis Supervisor

A Genome Scan for Loci Shared by Autism Spectrum Disorder and Language Impairment

“…K elvin implements the posterior probability of linkage (PPL) metric to measure the probability that a genetic location is linked with the trait of interest and the combined posterior probability of linkage disequilibrium (cPPLD) metric to measure the probability that a single nucleotide polymorphism (SNP) is in linkage disequilibrium (LD) with the trait of interest conditional upon the evidence for linkage at a given locus. It is important to note that the PPLD uses a pedigree likelihood that explicitly accounts for family structure while assessing the evidence that LD is present between the SNP and the disease (27, 28). For quantitative trait analysis where some ASD subjects lacked data due to inability to participate in some cognitive tests, the measures for those individuals were treated as censored data, meaning the “true scores” were unknown but known to be below a threshold.…”

Revisiting Schizophrenia Linkage Data in the NIMH Repository: Reanalysis of Regularized Data Across Multiple Studies

“…As is well known, failure to replicate true loci is to be expected for even moderately complex disorders (46). What is perhaps less widely appreciated is that traditional meta-analysis will tend to fail in precisely those same circumstances where independent replication cannot be relied upon for confirmation of results (17, 18). At the same time, we are appropriately skeptical of weak findings that fail to replicate.…”

“…In the presence of appreciable heterogeneity, sequential updating is far more robust in retaining true signals originating from individual subsamples than analyses that simply combine subsets for a single analysis (10, 15, 16). It is also substantially more sensitive in detecting true genetic effects across heterogeneous disorders than standard meta-analyses (17, 18). Moreover, the PPL accumulates evidence against linkage as well as in favor of linkage.…”

KELVIN: A Software Package for Rigorous Measurement of Statistical Evidence in Human Genetics

“…It can also be used to accumulate evidence within data sets across data subsets, say, divided by ancestry or other demographic features or by clinical features. It is beneficial even when the basis for subdivision is not a perfect classifier of homogeneous subsets, and it is only mildly detrimental when data are (inadvertently) subdivided on random (genetically irrelevant) variables [31, 32]. However, in the presence of relatively homogeneous genetic effects across data sets, ‘pooling’ families together for a single, combined analysis is always more powerful than sequentially updating across families.…”