@article {842, title = {Cell type-specific genetic regulation of gene expression across human tissues.}, journal = {Science}, volume = {369}, year = {2020}, month = {2020 09 11}, abstract = {

The Genotype-Tissue Expression (GTEx) project has identified expression and splicing quantitative trait loci in cis (QTLs) for the majority of genes across a wide range of human tissues. However, the functional characterization of these QTLs has been limited by the heterogeneous cellular composition of GTEx tissue samples. We mapped interactions between computational estimates of cell type abundance and genotype to identify cell type-interaction QTLs for seven cell types and show that cell type-interaction expression QTLs (eQTLs) provide finer resolution to tissue specificity than bulk tissue cis-eQTLs. Analyses of genetic associations with 87 complex traits show a contribution from cell type-interaction QTLs and enables the discovery of hundreds of previously unidentified colocalized loci that are masked in bulk tissue.

}, keywords = {Cells, Gene Expression Regulation, Humans, Organ Specificity, Quantitative Trait Loci, RNA, Long Noncoding, Transcriptome}, issn = {1095-9203}, doi = {10.1126/science.aaz8528}, author = {Kim-Hellmuth, Sarah and Aguet, Francois and Oliva, Meritxell and Mu{\~n}oz-Aguirre, Manuel and Kasela, Silva and Wucher, Valentin and Castel, Stephane E and Hamel, Andrew R and Vi{\~n}uela, Ana and Roberts, Amy L and Mangul, Serghei and Wen, Xiaoquan and Wang, Gao and Barbeira, Alvaro N and Garrido-Mart{\'\i}n, Diego and Nadel, Brian B and Zou, Yuxin and Bonazzola, Rodrigo and Quan, Jie and Brown, Andrew and Martinez-Perez, Angel and Soria, Jos{\'e} Manuel and Getz, Gad and Dermitzakis, Emmanouil T and Small, Kerrin S and Stephens, Matthew and Xi, Hualin S and Im, Hae Kyung and Guig{\'o}, Roderic and Segr{\`e}, Ayellet V and Stranger, Barbara E and Ardlie, Kristin G and Lappalainen, Tuuli} } @article {279, title = {Rare-variant extensions of the transmission disequilibrium test: application to autism exome sequence data.}, journal = {Am J Hum Genet}, volume = {94}, year = {2014}, month = {2014 Jan 02}, pages = {33-46}, abstract = {

Many population-based rare-variant (RV) association tests, which aggregate variants across a region, have been developed to analyze sequence data. A drawback of analyzing population-based data is that it is difficult to adequately control for population substructure and admixture, and spurious associations can occur. For RVs, this problem can be substantial, because the spectrum of rare variation can differ greatly between populations. A solution is to analyze parent-child trio data, by using the transmission disequilibrium test (TDT), which is robust to population substructure and admixture. We extended the TDT to test for RV associations using four commonly used methods. We demonstrate that for all RV-TDT methods, using proper analysis strategies, type I error is well-controlled even when there are high levels of population substructure or admixture. For trio data, unlike for population-based data, RV allele-counting association methods will lead to inflated type I errors. However type I errors can be properly controlled by obtaining p values empirically through haplotype permutation. The power of the RV-TDT methods was evaluated and compared to the analysis of case-control data with a number of genetic and disease models. The RV-TDT was also used to analyze exome data from 199 Simons Simplex Collection autism trios and an association was observed with variants in ABCA7. Given the problem of adequately controlling for population substructure and admixture in RV association studies and the growing number of sequence-based trio studies, the RV-TDT is extremely beneficial to elucidate the involvement of RVs in the etiology of complex traits.

}, keywords = {Alleles, Autistic Disorder, Computer Simulation, Exome, Gene Frequency, Genetic Association Studies, Genetic Predisposition to Disease, Genetic Variation, Haplotypes, Humans, Linkage Disequilibrium, Models, Genetic, Phenotype, Sequence Analysis, DNA}, issn = {1537-6605}, doi = {10.1016/j.ajhg.2013.11.021}, author = {He, Zongxiao and O{\textquoteright}Roak, Brian J and Smith, Joshua D and Wang, Gao and Hooker, Stanley and Santos-Cortez, Regie Lyn P and Li, Biao and Kan, Mengyuan and Krumm, Nik and Nickerson, Deborah A and Shendure, Jay and Eichler, Evan E and Leal, Suzanne M} } @article {297, title = {TM4SF20 ancestral deletion and susceptibility to a pediatric disorder of early language delay and cerebral white matter hyperintensities.}, journal = {Am J Hum Genet}, volume = {93}, year = {2013}, month = {2013 Aug 08}, pages = {197-210}, abstract = {

White matter hyperintensities (WMHs) of the brain are important markers of aging and small-vessel disease. WMHs are rare in healthy children and, when observed, often occur with comorbid neuroinflammatory or vasculitic processes. Here, we describe a complex 4 kb deletion in 2q36.3 that segregates with early childhood communication disorders and WMH in 15 unrelated families predominantly from Southeast Asia. The premature brain aging phenotype with punctate and multifocal WMHs was observed in ~70\% of young carrier parents who underwent brain MRI. The complex deletion removes the penultimate exon 3 of TM4SF20, a gene encoding a transmembrane protein of unknown function. Minigene analysis showed that the resultant net loss of an exon introduces a premature stop codon, which, in turn, leads to the generation of a stable protein that fails to target to the plasma membrane and accumulates in the cytoplasm. Finally, we report this deletion to be enriched in individuals of Vietnamese Kinh descent, with an allele frequency of about 1\%, embedded in an ancestral haplotype. Our data point to a constellation of early language delay and WMH phenotypes, driven by a likely toxic mechanism of TM4SF20 truncation, and highlight the importance of understanding and managing population-specific low-frequency pathogenic alleles.

}, keywords = {Age of Onset, Aging, Premature, Asian Continental Ancestry Group, Base Sequence, Brain, Child, Child, Preschool, Chromosomes, Human, Pair 2, Exons, Female, Genetic Predisposition to Disease, Humans, Language Development Disorders, Leukoencephalopathies, Magnetic Resonance Imaging, Male, Molecular Sequence Data, Pedigree, Sequence Analysis, DNA, Sequence Deletion, Tetraspanins}, issn = {1537-6605}, doi = {10.1016/j.ajhg.2013.05.027}, author = {Wiszniewski, Wojciech and Hunter, Jill V and Hanchard, Neil A and Willer, Jason R and Shaw, Chad and Tian, Qi and Illner, Anna and Wang, Xueqing and Cheung, Sau W and Patel, Ankita and Campbell, Ian M and Gelowani, Violet and Hixson, Patricia and Ester, Audrey R and Azamian, Mahshid S and Potocki, Lorraine and Zapata, Gladys and Hernandez, Patricia P and Ramocki, Melissa B and Santos-Cortez, Regie L P and Wang, Gao and York, Michele K and Justice, Monica J and Chu, Zili D and Bader, Patricia I and Omo-Griffith, Lisa and Madduri, Nirupama S and Scharer, Gunter and Crawford, Heather P and Yanatatsaneejit, Pattamawadee and Eifert, Anna and Kerr, Jeffery and Bacino, Carlos A and Franklin, Adiaha I A and Goin-Kochel, Robin P and Simpson, Gayle and Immken, LaDonna and Haque, Muhammad E and Stosic, Marija and Williams, Misti D and Morgan, Thomas M and Pruthi, Sumit and Omary, Reed and Boyadjiev, Simeon A and Win, Kay K and Thida, Aye and Hurles, Matthew and Hibberd, Martin Lloyd and Khor, Chiea Chuen and Van Vinh Chau, Nguyen and Gallagher, Thomas E and Mutirangura, Apiwat and Stankiewicz, Pawel and Beaudet, Arthur L and Maletic-Savatic, Mirjana and Rosenfeld, Jill A and Shaffer, Lisa G and Davis, Erica E and Belmont, John W and Dunstan, Sarah and Simmons, Cameron P and Bonnen, Penelope E and Leal, Suzanne M and Katsanis, Nicholas and Lupski, James R and Lalani, Seema R} } @article {643, title = {A fast and noise-resilient approach to detect rare-variant associations with deep sequencing data for complex disorders.}, journal = {Genet Epidemiol}, volume = {36}, year = {2012}, month = {2012 Nov}, pages = {675-85}, abstract = {

Next generation sequencing technology has enabled the paradigm shift in genetic association studies from the common disease/common variant to common disease/rare-variant hypothesis. Analyzing individual rare variants is known to be underpowered; therefore association methods have been developed that aggregate variants across a genetic region, which for exome sequencing is usually a gene. The foreseeable widespread use of whole genome sequencing poses new challenges in statistical analysis. It calls for new rare-variant association methods that are statistically powerful, robust against high levels of noise due to inclusion of noncausal variants, and yet computationally efficient. We propose a simple and powerful statistic that combines the disease-associated P-values of individual variants using a weight that is the inverse of the expected standard deviation of the allele frequencies under the null. This approach, dubbed as Sigma-P method, is extremely robust to the inclusion of a high proportion of noncausal variants and is also powerful when both detrimental and protective variants are present within a genetic region. The performance of the Sigma-P method was tested using simulated data based on realistic population demographic and disease models and its power was compared to several previously published methods. The results demonstrate that this method generally outperforms other rare-variant association methods over a wide range of models. Additionally, sequence data on the ANGPTL family of genes from the Dallas Heart Study were tested for associations with nine metabolic traits and both known and novel putative associations were uncovered using the Sigma-P method.

}, keywords = {Angiopoietin-like 4 Protein, Angiopoietin-like Proteins, Angiopoietins, Case-Control Studies, Data Interpretation, Statistical, Gene Frequency, Genetic Association Studies, Genetic Predisposition to Disease, Genetic Variation, High-Throughput Nucleotide Sequencing, Humans, Metabolism, Sequence Analysis, DNA, Texas, Triglycerides}, issn = {1098-2272}, doi = {10.1002/gepi.21662}, author = {Cheung, Yee Him and Wang, Gao and Leal, Suzanne M and Wang, Shuang} } @article {321, title = {SimRare: a program to generate and analyze sequence-based data for association studies of quantitative and qualitative traits.}, journal = {Bioinformatics}, volume = {28}, year = {2012}, month = {2012 Oct 15}, pages = {2703-4}, abstract = {

MOTIVATION: Currently, there is great interest in detecting complex trait rare variant associations using next-generation sequence data. On a monthly basis, new rare variant association methods are published. It is difficult to evaluate these methods because there is no standard to generate data and often comparisons are biased. In order to fairly compare rare variant association methods, it is necessary to generate data using realistic population demographic and phenotypic models.

RESULT: SimRare is an interactive program that integrates generation of rare variant genotype/phenotype data and evaluation of association methods using a unified platform. Variant data are generated for gene regions using forward-time simulation that incorporates realistic population demographic and evolutionary scenarios. Phenotype data can be obtained for both case-control and quantitative traits. SimRare has a user-friendly interface that allows for easy entry of genetic and phenotypic parameters. Novel rare variant association methods implemented in R can also be imported into SimRare, to evaluate their performance and compare results, e.g. power and Type I error, with other currently available methods both numerically and graphically.

}, keywords = {Genetic Association Studies, Genetic Variation, Humans, Phenotype, Quantitative Trait, Heritable, Software}, issn = {1367-4811}, doi = {10.1093/bioinformatics/bts499}, author = {Li, Biao and Wang, Gao and Leal, Suzanne M} }