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1.  Copy number variation prevalence in known asthma genes and their impact on asthma susceptibility 
Background
Genetic studies have identified numerous genes reproducibly associated with asthma, yet these studies have focused almost entirely on single nucleotide polymorphisms (SNPs), and virtually ignored another highly prevalent form of genetic variation: Copy Number Variants (CNVs).
Objective
To survey the prevalence of CNVs in genes previously associated with asthma, and to assess whether CNVs represent the functional asthma-susceptibility variants at these loci.
Methods
We genotyped 383 asthmatic trios participating in the Childhood Asthma Management Program (CAMP) using a competitive genomic hybridization (CGH) array designed to interrogate 20,092 CNVs. To ensure comprehensive assessment of all potential asthma candidate genes, we purposely used liberal asthma gene inclusion criteria, resulting in consideration of 270 candidate genes previously implicated in asthma. We performed statistical testing using FBAT-CNV.
Results
Copy number variation in asthma candidate genes was prevalent, with 21% of tested genes residing near or within one of 69 CNVs. In 6 instances, the complete candidate gene sequence resides within the CNV boundaries. On average, asthmatic probands carried 6 asthma-candidate CNVs (range 1–29). However, the vast majority of identified CNVs were of rare frequency (< 5%), and were not statistically associated with asthma. Modest evidence for association with asthma was observed for 2 CNVs near NOS1 and SERPINA3. Linkage disequilibrium analysis suggests that CNV effects are unlikely to explain previously detected SNP associations with asthma.
Conclusions
Although a substantial proportion of asthma-susceptibility genes harbor polymorphic CNVs, the majority of these variants do not confer increased asthma risk. The lack of linkage disequilibrium (LD) between CNVs and asthma-associated SNPs suggests that these CNVs are unlikely to represent the functional variant responsible for most known asthma associations.
doi:10.1111/cea.12060
PMCID: PMC3609036  PMID: 23517041
2.  Does Rate of Progression Run in Essential Tremor Families? Slower vs. Faster Progressors 
Parkinsonism & related disorders  2012;19(3):363-366.
Background
Essential tremor (ET) is a progressive disorder, worsening gradually with time in most patients. Yet there are few data on the factors that influence rate of progression. ET is a highly familial disorder, and physicians often care for patients who have other affected family members. Do ET families differ from one another with respect to rate of progression? Are some families slower progressors and other families faster progressors? We are unaware of published data.
Methods
ET probands and relatives were enrolled in a cross-sectional genetic study at Columbia University. Rate of progression was calculated as total tremor score ÷ log disease duration.
Results
There were 100 enrollees (28 probands, 72 relatives). Data from 78 enrollees (23 probands, 55 relatives) were selected for final analysis. The mean familial rate of progression ranged from as little as 8.4 to as much as 34.3, a >4-fold difference. In an analysis of variance, we found significant evidence of heterogeneity in the log rate of progression across families (p <0.001), with more than one-half (i.e., 55.4%) of the total variance in the log rate of progression explained by the family grouping.
Conclusions
Familial factors seem to affect rate of tremor progression in ET. There was a 4-fold difference across families in observed mean rate of progression; thus, some families seemed to be more rapid progressors than others. We hope these data may be used by clinicians to provide basic prognostic and family guidance information to their patients and families with ET.
doi:10.1016/j.parkreldis.2012.10.005
PMCID: PMC3578031  PMID: 23121728
essential tremor; genetics; familial; clinical; rate of progression
3.  Essential Tremor in a Charcot-Marie-Tooth Type 2C Kindred Does Not Segregate with the TRPV4 R269H Mutation 
Case Reports in Neurology  2014;6(1):1-6.
Background
We investigated 4 members of a family with type 2C Charcot-Marie-Tooth (CMT) and self-reported essential tremor (ET). A heterozygous missense mutation, R269H, in the TRPV4 gene was previously reported in this family. Our genotypic data provided a rare opportunity to determine the etiology of the tremor.
Methods
Family study; the 4 tremor cases underwent a detailed neurological assessment.
Results
The clinical diagnosis of ET was confirmed in all 4 tremor cases based on stringent published research criteria. Two of these also had CMT. We genotyped all 4 family members for the TRPV4 R269H mutation. We confirmed the presence of the TRPV4 R269H mutation in the 2 family members with ET and CMT; however, the TRPV4 R269H mutation did not segregate with ET in the same family.
Conclusions
In this particular CMT family, the tremor was clinically attributed to ET. Furthermore, genotype data indicated that the tremor was unlikely to be caused by incomplete penetrance or variable expressivity of the TRPV4 R269H mutation. Hence, the tremor likely represents ET. This establishes that in some CMT families the tremor diathesis likely represents a second disorder, namely ET.
doi:10.1159/000357665
PMCID: PMC3934698  PMID: 24575025
Essential tremor; Charcot-Marie-Tooth; Neuropathy; Genetics
4.  De novo gene mutations highlight patterns of genetic and neural complexity in schizophrenia 
Nature genetics  2012;44(12):1365-1369.
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 structure 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 excess of non-synonymous single nucleotide variants as well as a higher prevalence of gene-disruptive de novo mutations. We found four genes (LAMA2, DPYD, TRRAP and VPS39) affected by recurrent de novo events within or across the two populations, a finding unlikely to have occurred by chance. We show that de novo mutations affect genes with diverse functions and developmental profiles but we also find a substantial contribution of mutations in genes with higher expression in early fetal life. Our results help define the pattern of genomic and neural architecture of schizophrenia.
doi:10.1038/ng.2446
PMCID: PMC3556813  PMID: 23042115
5.  Copy number variation genotyping using family information 
BMC Bioinformatics  2013;14:157.
Background
In recent years there has been a growing interest in the role of copy number variations (CNV) in genetic diseases. Though there has been rapid development of technologies and statistical methods devoted to detection in CNVs from array data, the inherent challenges in data quality associated with most hybridization techniques remains a challenging problem in CNV association studies.
Results
To help address these data quality issues in the context of family-based association studies, we introduce a statistical framework for the intensity-based array data that takes into account the family information for copy-number assignment. The method is an adaptation of traditional methods for modeling SNP genotype data that assume Gaussian mixture model, whereby CNV calling is performed for all family members simultaneously and leveraging within family-data to reduce CNV calls that are incompatible with Mendelian inheritance while still allowing de-novo CNVs. Applying this method to simulation studies and a genome-wide association study in asthma, we find that our approach significantly improves CNV calls accuracy, and reduces the Mendelian inconsistency rates and false positive genotype calls. The results were validated using qPCR experiments.
Conclusions
In conclusion, we have demonstrated that the use of family information can improve the quality of CNV calling and hopefully give more powerful association test of CNVs.
doi:10.1186/1471-2105-14-157
PMCID: PMC3668900  PMID: 23656838
6.  Domain-dependent clustering and genotype-phenotype analysis of LGI1 mutations in ADPEAF 
Neurology  2012;78(8):563-568.
Objective:
In families with autosomal dominant partial epilepsy with auditory features (ADPEAF) with mutations in the LGI1 gene, we evaluated clustering of mutations within the gene and associations of penetrance and phenotypic features with mutation location and predicted effect (truncation or missense).
Methods:
We abstracted clinical and molecular information from the literature for all 36 previously published ADPEAF families with LGI1 mutations. We used a sliding window approach to analyze mutation clustering within the gene. Each mutation was mapped to one of the gene's 2 major functional domains, N-terminal leucine-rich repeats (LRRs) and C-terminal epitempin (EPTP) repeats, and classified according to predicted effect on the encoded protein (truncation vs missense). Analyses of phenotypic features (age at onset and occurrence of auditory symptoms) in relation to mutation site and predicted effect included 160 patients with idiopathic focal unprovoked seizures from the 36 families.
Results:
ADPEAF-causing mutations clustered significantly in the LRR domain (exons 3–5) of LGI1 (p = 0.026). Auditory symptoms were less frequent in individuals with truncation mutations in the EPTP domain than in those with other mutation type/domain combinations (58% vs 80%, p = 0.018).
Conclusion:
The LRR region of the LGI1 gene is likely to play a major role in pathogenesis of ADPEAF.
doi:10.1212/WNL.0b013e318247ccbf
PMCID: PMC3280014  PMID: 22323750
7.  Rare Variant Analysis for Family-Based Design 
PLoS ONE  2013;8(1):e48495.
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. Here we propose a novel method to test for association of rare variants obtained by sequencing in family-based samples by collapsing the standard family-based association test (FBAT) statistic over a region of interest. We also propose a suitable weighting scheme so that low frequency SNPs that may be enriched in functional variants can be upweighted compared to common variants. Using simulations we show that the family-based methods perform at par with the population-based methods under no population stratification. By construction, family-based tests are completely robust to population stratification; we show that our proposed methods remain valid even when population stratification is present.
doi:10.1371/journal.pone.0048495
PMCID: PMC3546113  PMID: 23341868
8.  Refinement of primate copy number variation hotspots identifies candidate genomic regions evolving under positive selection 
Genome Biology  2011;12(5):R52.
Background
Copy number variants (CNVs), defined as losses and gains of segments of genomic DNA, are a major source of genomic variation.
Results
In this study, we identified over 2,000 human CNVs that overlap with orthologous chimpanzee or orthologous macaque CNVs. Of these, 170 CNVs overlap with both chimpanzee and macaque CNVs, and these were collapsed into 34 hotspot regions of CNV formation. Many of these hotspot regions of CNV formation are functionally relevant, with a bias toward genes involved in immune function, some of which were previously shown to evolve under balancing selection in humans. The genes in these primate CNV formation hotspots have significant differential expression levels between species and show evidence for positive selection, indicating that they have evolved under species-specific, directional selection.
Conclusions
These hotspots of primate CNV formation provide a novel perspective on divergence and selective pressures acting on these genomic regions.
doi:10.1186/gb-2011-12-5-r52
PMCID: PMC3219974  PMID: 21627829
9.  Identifying rare disease variants in the Genetic Analysis Workshop 17 simulated data: a comparison of several statistical approaches 
BMC Proceedings  2011;5(Suppl 9):S17.
Genome-wide association studies have been successful at identifying common disease variants associated with complex diseases, but the common variants identified have small effect sizes and account for only a small fraction of the estimated heritability for common diseases. Theoretical and empirical studies suggest that rare variants, which are much less frequent in populations and are poorly captured by single-nucleotide polymorphism chips, could play a significant role in complex diseases. Several new statistical methods have been developed for the analysis of rare variants, for example, the combined multivariate and collapsing method, the weighted-sum method and a replication-based method. Here, we apply and compare these methods to the simulated data sets of Genetic Analysis Workshop 17 and thereby explore the contribution of rare variants to disease risk. In addition, we investigate the usefulness of extreme phenotypes in identifying rare risk variants when dealing with quantitative traits. Finally, we perform a pathway analysis and show the importance of the vascular endothelial growth factor pathway in explaining different phenotypes.
doi:10.1186/1753-6561-5-S9-S17
PMCID: PMC3287851  PMID: 22373071
10.  GENETIC ASSOCIATION ANALYSIS OF COPY NUMBER VARIATION (CNVs) IN HUMAN DISEASE PATHOGENESIS 
Genomics  2008;93(1):22-26.
Structural genetic variation, including copy number variation (CNV), constitutes a substantial fraction of total genetic variability and the importance of structural genetic variants in modulating human disease is increasingly being recognized. Early successes in identifying disease-associated CNVs via a candidate gene approach mandate that future disease association studies need to include structural genetic variation. Such analyses should not rely on previously developed methodologies that were designed to evaluate single nucleotide polymorphisms (SNPs). Instead, development of novel technical, statistical, and epidemiologic methods will be necessary to optimally capture this newly-appreciated form of genetic variation in a meaningful manner.
doi:10.1016/j.ygeno.2008.08.012
PMCID: PMC2631358  PMID: 18822366
Copy number variation; CNV; structural genetic variation; disease association study; complex trait
11.  On the frequency of copy number variants 
Bioinformatics  2008;24(20):2350-2355.
Motivation: Estimating the frequency distribution of copy number variants (CNVs) is an important aspect of the effort to characterize this new type of genetic variation. Currently, most studies report a strong skew toward low-frequency CNVs. In this article, our goal is to investigate the frequencies of CNVs. We employ a two-step procedure for the CNV frequency estimation process. We use family information a posteriori to select only the most reliable CNV regions, i.e. those showing high rates of Mendelian transmission.
Results: Our results suggest that the current skew toward low-frequency CNVs may not be representative of the true frequency distribution, but may be due, among other reasons, to the non-negligible false negative rates that characterize CNV detection methods. Moreover, false positives are also likely, as low-frequency CNVs are hard to detect with small sample sizes and technologies that are not ideally suited for their detection. Without appropriate validation methods, such as incorporation of biologically relevant information (for example, in our case, the transmission of heritable CNVs from parents to offspring), it is difficult to assess the validity of specific CNVs, and even harder to obtain reliable frequency estimates.
Availability: Software implementing the methods described in this article is available for download at the following address: http://www.isites.harvard.edu/icb/icb.do?keyword=k36162
Contact: iionita@hsph.harvard.edu
Supplementary informantion: Supplementary data are available at Bioinformatics online.
doi:10.1093/bioinformatics/btn421
PMCID: PMC2562008  PMID: 18689430
12.  On Quality Control Measures in Genome-Wide Association Studies: A Test to Assess the Genotyping Quality of Individual Probands in Family-Based Association Studies and an Application to the HapMap Data 
PLoS Genetics  2009;5(7):e1000572.
Allele transmissions in pedigrees provide a natural way of evaluating the genotyping quality of a particular proband in a family-based, genome-wide association study. We propose a transmission test that is based on this feature and that can be used for quality control filtering of genome-wide genotype data for individual probands. The test has one degree of freedom and assesses the average genotyping error rate of the genotyped SNPs for a particular proband. As we show in simulation studies, the test is sufficiently powerful to identify probands with an unreliable genotyping quality that cannot be detected with standard quality control filters. This feature of the test is further exemplified by an application to the third release of the HapMap data. The test is ideally suited as the final layer of quality control filters in the cleaning process of genome-wide association studies. It identifies probands with insufficient genotyping quality that were not removed by standard quality control filtering.
Author Summary
Genome-wide association studies have led to the discovery of many novel, reproducible associations between genetic loci and disease phenotypes. An important step in the analysis of genome-wide association studies is the data cleaning/QC filtering step. The statistical analysis tools that are applied as QC filters typically include testing for Hardy-Weinberg equilibrium, testing for Mendelian inconsistencies, evaluating quality scores, etc. We propose a new genome-wide transmission test for family-based designs that is applied to the dataset after the QC filtering. It allows for the assessment of the genotyping error rate that is caused by miscalled genotypes that could not be detected by the QC filters. By applying the test to individual probands, probands with insufficient genotyping quality can be identified and removed from the dataset before the analysis.
doi:10.1371/journal.pgen.1000572
PMCID: PMC2706974  PMID: 19629167
13.  Joint study of genetic regulators for expression traits related to breast cancer 
BMC Proceedings  2007;1(Suppl 1):S10.
Background
The mRNA expression levels of genes have been shown to have discriminating power for the classification of breast cancer. Studying the heritability of gene expression levels on breast cancer related transcripts can lead to the identification of shared common regulators and inter-regulation patterns, which would be important for dissecting the etiology of breast cancer.
Results
We applied multilocus association genome-wide scans to 18 breast cancer related transcripts and combined the results with traditional linkage scans. Regulatory hotspots for these transcripts were identified and some inter-regulation patterns were observed. We also derived evidence on interacting genetic regulatory loci shared by a number of these transcripts.
Conclusion
In this paper, by restricting to a set of related genes, we were able to employ a more detailed multilocus approach that evaluates both marginal and interaction association signals at each single-nucleotide polymorphism. Interesting inter-regulation patterns and significant overlaps of genetic regulators between transcripts were observed. Interaction association results returned more expression quantitative trait locus hotspots that are significant.
PMCID: PMC2367474  PMID: 18466439
14.  Constructing gene association networks for rheumatoid arthritis using the backward genotype-trait association (BGTA) algorithm 
BMC Proceedings  2007;1(Suppl 1):S13.
Background
Rheumatoid arthritis (RA, MIM 180300) is a common and complex inflammatory disorder. The North American Rheumatoid Arthritis Consortium (NARAC) data, as part of the Genetic Analysis Workshop 15 data, consists of both genome scan and candidate gene studies on RA patients.
Results
We applied the backward genotype-trait association (BGTA) algorithm to capture marginal and gene × gene interaction effects of multiple susceptibility loci on RA disease status. A two-stage screening approach was used for the genome scan, whereas a comprehensive study of all possible subsets was conducted for the candidate genes. For the genome scan, we constructed an association network among 39 genetic loci that demonstrated strong signals, 19 of which have been reported in the RA literature. For the candidate genes, we found strong signals for PTPN22 and SUMO4. Based on significant association evidence, we built an association network among the loci of PTPN22, PADI4, DLG5, SLC22A4, SUMO4, and CARD15. To control for false positives, we used permutation tests to constrain the family-wise type I error rate to 1%.
Conclusion
Using the BGTA algorithm, we identified genetic loci and candidate genes that were associated with RA susceptibility and association networks among them. For the first time, we report possible interactions between single-nucleotide polymorphisms/genes, which may be useful for biological interpretation.
PMCID: PMC2367461  PMID: 18466472

Results 1-14 (14)