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Most population isolates examined to date were founded from a single ancestral population. Consequently, there is limited knowledge about the demographic history of admixed population isolates. Here we investigate genomic diversity of recently admixed population isolates from Costa Rica and Colombia and compare their diversity to a benchmark population isolate, the Finnish. These Latin American isolates originated during the 16 th century from admixture between a few hundred European males and Amerindian females, with a limited contribution from African founders. We examine whole-genome sequence data from 449 individuals, ascertained as families to build mutigenerational pedigrees, with a mean sequencing depth of coverage of approximately 36×. We find that Latin American isolates have increased genetic diversity relative to the Finnish. However, there is an increase in the amount of identity by descent (IBD) segments in the Latin American isolates relative to the Finnish. The increase in IBD segments is likely a consequence of a very recent and severe population bottleneck during the founding of the admixed population isolates. Furthermore, the proportion of the genome that falls within a long run of homozygosity (ROH) in Costa Rican and Colombian individuals is significantly greater than that in the Finnish, suggesting more recent consanguinity in the Latin American isolates relative to that seen in the Finnish. Lastly, we find that recent consanguinity increased the number of deleterious variants found in the homozygous state, which is relevant if deleterious variants are recessive. Our study suggests that there is no single genetic signature of a population isolate., (Copyright © 2018 American Society of Human Genetics. Published by Elsevier Inc. All rights reserved.)

Tourette syndrome (TS) is a model neuropsychiatric disorder thought to arise from abnormal development and/or maintenance of cortico-striato-thalamo-cortical circuits. TS is highly heritable, but its underlying genetic causes are still elusive, and no genome-wide significant loci have been discovered to date. We analyzed a European ancestry sample of 2,434 TS cases and 4,093 ancestry-matched controls for rare (< 1% frequency) copy-number variants (CNVs) using SNP microarray data. We observed an enrichment of global CNV burden that was prominent for large (> 1 Mb), singleton events (OR = 2.28, 95% CI [1.39-3.79], p = 1.2 × 10 -3 ) and known, pathogenic CNVs (OR = 3.03 [1.85-5.07], p = 1.5 × 10 -5 ). We also identified two individual, genome-wide significant loci, each conferring a substantial increase in TS risk (NRXN1 deletions, OR = 20.3, 95% CI [2.6-156.2]; CNTN6 duplications, OR = 10.1, 95% CI [2.3-45.4]). Approximately 1% of TS cases carry one of these CNVs, indicating that rare structural variation contributes significantly to the genetic architecture of TS., (Copyright © 2017 Elsevier Inc. All rights reserved.)

The vast majority of genome-wide association study (GWAS) risk loci fall in non-coding regions of the genome. One possible hypothesis is that these GWAS risk loci alter the individual's disease risk through their effect on gene expression in different tissues. In order to understand the mechanisms driving a GWAS risk locus, it is helpful to determine which gene is affected in specific tissue types. For example, the relevant gene and tissue could play a role in the disease mechanism if the same variant responsible for a GWAS locus also affects gene expression. Identifying whether or not the same variant is causal in both GWASs and expression quantitative trail locus (eQTL) studies is challenging because of the uncertainty induced by linkage disequilibrium and the fact that some loci harbor multiple causal variants. However, current methods that address this problem assume that each locus contains a single causal variant. In this paper, we present eCAVIAR, a probabilistic method that has several key advantages over existing methods. First, our method can account for more than one causal variant in any given locus. Second, it can leverage summary statistics without accessing the individual genotype data. We use both simulated and real datasets to demonstrate the utility of our method. Using publicly available eQTL data on 45 different tissues, we demonstrate that eCAVIAR can prioritize likely relevant tissues and target genes for a set of glucose- and insulin-related trait loci., (Copyright © 2016 American Society of Human Genetics. Published by Elsevier Inc. All rights reserved.)

Recently, multiple studies have performed whole-exome or whole-genome sequencing to identify groups of rare variants associated with complex traits and diseases. They have primarily utilized case-control study designs that often require thousands of individuals to reach acceptable statistical power. Family-based studies can be more powerful because a rare variant can be enriched in an extended pedigree and segregate with the phenotype. Although many methods have been proposed for using family data to discover rare variants involved in a disease, a majority of them focus on a specific pedigree structure and are designed to analyze either binary or continuously measured outcomes. In this article, we propose RareIBD, a general and powerful approach to identifying rare variants involved in disease susceptibility. Our method can be applied to large extended families of arbitrary structure, including pedigrees with only affected individuals. The method accommodates both binary and quantitative traits. A series of simulation experiments suggest that RareIBD is a powerful test that outperforms existing approaches. In addition, our method accounts for individuals in top generations, which are not usually genotyped in extended families. In contrast to available statistical tests, RareIBD generates accurate p values even when genetic data from these individuals are missing. We applied RareIBD, as well as other methods, to two extended family datasets generated by different genotyping technologies and representing different ethnicities. The analysis of real data confirmed that RareIBD is the only method that properly controls type I error., (Copyright © 2016. Published by Elsevier Inc.)

In studies of expression quantitative trait loci (eQTLs), it is of increasing interest to identify eGenes, the genes whose expression levels are associated with variation at a particular genetic variant. Detecting eGenes is important for follow-up analyses and prioritization because genes are the main entities in biological processes. To detect eGenes, one typically focuses on the genetic variant with the minimum p value among all variants in cis with a gene and corrects for multiple testing to obtain a gene-level p value. For performing multiple-testing correction, a permutation test is widely used. Because of growing sample sizes of eQTL studies, however, the permutation test has become a computational bottleneck in eQTL studies. In this paper, we propose an efficient approach for correcting for multiple testing and assess eGene p values by utilizing a multivariate normal distribution. Our approach properly takes into account the linkage-disequilibrium structure among variants, and its time complexity is independent of sample size. By applying our small-sample correction techniques, our method achieves high accuracy in both small and large studies. We have shown that our method consistently produces extremely accurate p values (accuracy > 98%) for three human eQTL datasets with different sample sizes and SNP densities: the Genotype-Tissue Expression pilot dataset, the multi-region brain dataset, and the HapMap 3 dataset., (Copyright © 2015 The American Society of Human Genetics. Published by Elsevier Inc. All rights reserved.)

We investigated how different subregions of rodent prefrontal cortex contribute to value-based decision making, by comparing neural signals related to animal's choice, its outcome, and action value in orbitofrontal cortex (OFC) and medial prefrontal cortex (mPFC) of rats performing a dynamic two-armed bandit task. Neural signals for upcoming action selection arose in the mPFC, including the anterior cingulate cortex, only immediately before the behavioral manifestation of animal's choice, suggesting that rodent prefrontal cortex is not involved in advanced action planning. Both OFC and mPFC conveyed signals related to the animal's past choices and their outcomes over multiple trials, but neural signals for chosen value and reward prediction error were more prevalent in the OFC. Our results suggest that rodent OFC and mPFC serve distinct roles in value-based decision making and that the OFC plays a prominent role in updating the values of outcomes expected from chosen actions., (Copyright 2010 Elsevier Inc. All rights reserved.)