Iuliana Ionita-Laza

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Recent developments in sequencing technologies have made it possible to uncover both rare and common genetic variants. Genome-wide association studies (GWASs) can test for the effect of common variants, whereas sequence-based association studies can evaluate the cumulative effect of both rare and common variants on disease risk. Many groupwise association(More)
To evaluate evidence for de novo etiologies in schizophrenia, we sequenced at high coverage the exomes of families recruited from two populations with distinct demographic structures and history. We sequenced a total of 795 exomes from 231 parent-proband trios enriched for sporadic schizophrenia cases, as well as 34 unaffected trios. We observed in cases an(More)
Genome-wide association studies have been able to identify disease associations with many common variants; however most of the estimated genetic contribution explained by these variants appears to be very modest. Rare variants are thought to have larger effect sizes compared to common SNPs but effects of rare variants cannot be tested in the GWAS setting.(More)
Rapid advances in sequencing technologies set the stage for the large-scale medical sequencing efforts to be performed in the near future, with the goal of assessing the importance of rare variants in complex diseases. The discovery of new disease susceptibility genes requires powerful statistical methods for rare variant analysis. The low frequency and the(More)
Over the past few years, substantial effort has been put into the functional annotation of variation in human genome sequences. Such annotations can have a critical role in identifying putatively causal variants for a disease or trait among the abundant natural variation that occurs at a locus of interest. The main challenges in using these various(More)
For genomewide association (GWA) studies in family-based designs, we propose a novel two-stage strategy that weighs the association P values with the use of independently estimated weights. The association information contained in the family sample is partitioned into two orthogonal components--namely, the between-family information and the within-family(More)
The different genetic variation discovery projects (The SNP Consortium, the International HapMap Project, the 1000 Genomes Project, etc.) aim to identify as much as possible of the underlying genetic variation in various human populations. The question we address in this article is how many new variants are yet to be found. This is an instance of the(More)
Many sequencing studies are now underway to identify the genetic causes for both Mendelian and complex traits. Via exome-sequencing, genes harboring variants implicated in several Mendelian traits have already been identified. The underlying methodology in these studies is a multistep algorithm based on filtering variants identified in a small number of(More)
Cluster-detection approaches, commonly used in epidemiology and astronomy, can be applied in the context of genetic sequence data for the identification of genetic regions significantly enriched with rare disease-risk variants (DRVs). Unlike existing association tests for sequence data, the goal of cluster-detection methods is to localize significant(More)
Though there is an increasing support for an important contribution of copy number variation (CNV) to the genetic architecture of complex disease, few methods have been developed for the analysis of such variation in the context of genetic association studies. In this paper, we propose a generalization of family-based association tests (FBATs) to allow for(More)