Genotype imputation is a computational method where probable genotypes are inferred to a lower-density dataset (target) based on a higher-density reference panel and it is considered to be a cost-effective approach to prepare data for various purposes, including genome-wide association studies (GWAS). In order for the final outcomes of any downstream analysis to be trustworthy, it is important to choose the most optimal method available for the imputation step. The main focus of this study is to compare and evaluate different available genotype imputation algorithms and their overall performance together with their respective quality metrics using the data obtained through SISu and FinMetSeq projects. Of special interest is the accuracy of low-frequency and rare variants.

Numerous novel biomarkers are being considered as tools to enhance cardiovascular risk estimation. Statistical models can indicate how a biomarker influences movements between health states, and clinicians are interested in the impact they may have compared to traditional risk factors alone. Net reclassification indices (NRIs) have recently become popular statistics for measuring the prediction increment of new biomarkers. The msm package within R can fit multi-state models to longitudinal data, giving output for all permitted state-to-state transitions within the one calculation. However its usefulness could be further enhanced through its extension to include NRIs. This paper focuses on extending the msm package to enable NRIs to be calculated. The results are demonstrated for cardiovascular data, with the inclusion of a panel of novel biomarkers (CRP, NT-pro BNP, and troponin I) giving rise to a NRI of 0.2089 for males and 0.1678 for females.

MOTIVATION: The goal of fine-mapping in genomic regions associated with complex diseases and traits is to identify causal variants that point to molecular mechanisms behind the associations. Recent fine-mapping methods using summary data from genome-wide association studies rely on exhaustive search through all possible causal configurations, which is computationally expensive. RESULTS: We introduce FINEMAP, a software package to efficiently explore a set of the most important causal configurations of the region via a shotgun stochastic search algorithm. We show that FINEMAP produces accurate results in a fraction of processing time of existing approaches and is therefore a promising tool for analyzing growing amounts of data produced in genome-wide association studies and emerging sequencing projects. AVAILABILITY AND IMPLEMENTATION: FINEMAP v1.0 is freely available for Mac OS X and Linux at http://www.christianbenner.com CONTACT: : [email protected] or [email protected].

MOTIVATION: A dominant approach to genetic association studies is to perform univariate tests between genotype-phenotype pairs. However, analyzing related traits together increases statistical power, and certain complex associations become detectable only when several variants are tested jointly. Currently, modest sample sizes of individual cohorts, and restricted availability of individual-level genotype-phenotype data across the cohorts limit conducting multivariate tests. RESULTS: We introduce metaCCA, a computational framework for summary statistics-based analysis of a single or multiple studies that allows multivariate representation of both genotype and phenotype. It extends the statistical technique of canonical correlation analysis to the setting where original individual-level records are not available, and employs a covariance shrinkage algorithm to achieve robustness.Multivariate meta-analysis of two Finnish studies of nuclear magnetic resonance metabolomics by metaCCA, using standard univariate output from the program SNPTEST, shows an excellent agreement with the pooled individual-level analysis of original data. Motivated by strong multivariate signals in the lipid genes tested, we envision that multivariate association testing using metaCCA has a great potential to provide novel insights from already published summary statistics from high-throughput phenotyping technologies. AVAILABILITY AND IMPLEMENTATION: Code is available at https://github.com/aalto-ics-kepaco CONTACTS: [email protected] or [email protected] SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

MOTIVATION: A typical genome-wide association study searches for associations between single nucleotide polymorphisms (SNPs) and a univariate phenotype. However, there is a growing interest to investigate associations between genomics data and multivariate phenotypes, for example, in gene expression or metabolomics studies. A common approach is to perform a univariate test between each genotype-phenotype pair, and then to apply a stringent significance cutoff to account for the large number of tests performed. However, this approach has limited ability to uncover dependencies involving multiple variables. Another trend in the current genetics is the investigation of the impact of rare variants on the phenotype, where the standard methods often fail owing to lack of power when the minor allele is present in only a limited number of individuals. RESULTS: We propose a new statistical approach based on Bayesian reduced rank regression to assess the impact of multiple SNPs on a high-dimensional phenotype. Because of the method's ability to combine information over multiple SNPs and phenotypes, it is particularly suitable for detecting associations involving rare variants. We demonstrate the potential of our method and compare it with alternatives using the Northern Finland Birth Cohort with 4702 individuals, for whom genome-wide SNP data along with lipoprotein profiles comprising 74 traits are available. We discovered two genes (XRCC4 and MTHFD2L) without previously reported associations, which replicated in a combined analysis of two additional cohorts: 2390 individuals from the Cardiovascular Risk in Young Finns study and 3659 individuals from the FINRISK study. AVAILABILITY AND IMPLEMENTATION: R-code freely available for download at http://users.ics.aalto.fi/pemartti/gene_metabolome/.