METAL: fast and efficient meta-analysis of genomewide association scans

Summary: METAL provides a computationally efficient tool for meta-analysis of genome-wide association scans, which is a commonly used approach for improving power complex traits gene mapping studies. METAL provides a rich scripting interface and implements efficient memory management to allow analyses of very large data sets and to support a variety of input file formats

The Sequence Alignment/Map format and SAMtools

Summary: The Sequence Alignment/Map (SAM) format is a generic alignment format for storing read alignments against reference sequences, supporting short and long reads (up to 128 Mbp) produced by different sequencing platforms. It is flexible in style, compact in size, efficient in random access and is the format in which alignments from the 1000 Genomes Project are released. SAMtools implements various utilities for post-processing alignments in the SAM format, such as indexing, variant caller and alignment viewer, and thus provides universal tools for processing read alignments

Path: a tool to facilitate pathway-based genetic association analysis

Summary: Traditional methods of genetic study design and analysis work well under the scenario that a handful of single nucleotide polymorphisms (SNPs) independently contribute to the risk of disease. For complex diseases, susceptibility may be determined not by a single SNP, but rather a complex interplay between SNPs. For large studies involving hundreds of thousands of SNPs, a brute force search of all possible combinations of SNPs associated with disease is not only inefficient, but also results in a multiple testing paradigm, whereby larger and larger sample sizes are needed to maintain statistical power. Pathway-based methods are an example of one of the many approaches in identifying a subset of SNPs to test for interaction. To help determine which SNP–SNP interactions to test, we developed Path, a software application designed to help researchers interface their data with biological information from several bioinformatics resources. To this end, our application brings together currently available information from nine online bioinformatics resources including the National Center for Biotechnology Information (NCBI), Online Mendelian Inheritance in Man (OMIM), Kyoto Encyclopedia of Genes and Genomes (KEGG), UCSC Genome Browser, Seattle SNPs, PharmGKB, Genetic Association Database, the Single Nucleotide Polymorphism database (dbSNP) and the Innate Immune Database (IIDB)

Combined Linkage and Association Analyses of the 124-bp Allele of Marker D2S2944 with Anxiety, Depression, Neuroticism and Major Depression

A central issue in psychiatric genetics is whether positive findings replicate. Zubenko et al. (2002b, Mol. Psychiatry 7:460-467) reported an association of the 124-bp allele of D2S2944 with recurrent early-onset major depression for females. We tested for association of this allele to continuous measures of anxiety, depression and neuroticism in a Dutch sample of 347 males and 448 females, and to DSM-IV major depression in a subsample of 210 males and 295 females. The association of the 124-bp allele to depression in females was not replicated, but there were significant associations (not significant after correction for multiple testing) with anxiety and anxious depression in males. However, the association occurred in the absence of evidence for linkage in this region on chromosome 2. © 2006 Springer Science+Business Media, Inc

Metabolic and cardiovascular traits: an abundance of recently identified common genetic variants

Genome-wide association studies are providing new insights into the genetic basis of metabolic and cardiovascular traits. In the past 3 years, common variants in ∼50 loci have been strongly associated with metabolic and cardiovascular traits. Several of these loci have implicated genes without a previously known connection with metabolism. Further studies will be required to characterize the full impact of these loci on metabolism. Many of the identified loci include multiple independent variants that influence the same metabolic or cardiovascular trait and a few loci harbor independent variants that each influence distinct traits. The total proportion of trait heritability explained by variants identified so far is still modest (typically <10%). Future studies will build on these successes by identifying additional common and rare variants and by determining the functional impact of the underlying alleles and genes

Purifying Selection in Deeply Conserved Human Enhancers Is More Consistent than in Coding Sequences

(c) 2014 De Silva et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited

Comparing variant calling algorithms for target-exon sequencing in a large sample

Abstract Background Sequencing studies of exonic regions aim to identify rare variants contributing to complex traits. With high coverage and large sample size, these studies tend to apply simple variant calling algorithms. However, coverage is often heterogeneous; sites with insufficient coverage may benefit from sophisticated calling algorithms used in low-coverage sequencing studies. We evaluate the potential benefits of different calling strategies by performing a comparative analysis of variant calling methods on exonic data from 202 genes sequenced at 24x in 7,842 individuals. We call variants using individual-based, population-based and linkage disequilibrium (LD)-aware methods with stringent quality control. We measure genotype accuracy by the concordance with on-target GWAS genotypes and between 80 pairs of sequencing replicates. We validate selected singleton variants using capillary sequencing. Results Using these calling methods, we detected over 27,500 variants at the targeted exons; >57% were singletons. The singletons identified by individual-based analyses were of the highest quality. However, individual-based analyses generated more missing genotypes (4.72%) than population-based (0.47%) and LD-aware (0.17%) analyses. Moreover, individual-based genotypes were the least concordant with array-based genotypes and replicates. Population-based genotypes were less concordant than genotypes from LD-aware analyses with extended haplotypes. We reanalyzed the same dataset with a second set of callers and showed again that the individual-based caller identified more high-quality singletons than the population-based caller. We also replicated this result in a second dataset of 57 genes sequenced at 127.5x in 3,124 individuals. Conclusions We recommend population-based analyses for high quality variant calls with few missing genotypes. With extended haplotypes, LD-aware methods generate the most accurate and complete genotypes. In addition, individual-based analyses should complement the above methods to obtain the most singleton variants.http://deepblue.lib.umich.edu/bitstream/2027.42/110906/1/12859_2015_Article_489.pd

The variant call format and VCFtools

Summary: The variant call format (VCF) is a generic format for storing DNA polymorphism data such as SNPs, insertions, deletions and structural variants, together with rich annotations. VCF is usually stored in a compressed manner and can be indexed for fast data retrieval of variants from a range of positions on the reference genome. The format was developed for the 1000 Genomes Project, and has also been adopted by other projects such as UK10K, dbSNP and the NHLBI Exome Project. VCFtools is a software suite that implements various utilities for processing VCF files, including validation, merging, comparing and also provides a general Perl API

Identifying variants that contribute to linkage for dichotomous and quantitative traits in extended pedigrees

Compared to genome-wide association analysis, linkage analysis is less influenced by allelic heterogeneity. The use of linkage information in large families should provide a great opportunity to identify less frequent variants. We perform a linkage scan for both dichotomous and quantitative traits in eight extended families. For the dichotomous trait, we identified one linkage region on chromosome 4q. For quantitative traits, we identified two regions on chromosomes 4q and 6p for Q1 and one region on chromosome 6q for Q2. To identify variants that contribute to these linkage signals, we performed standard association analysis in genomic regions of interest. We also screened less frequent variants in the linkage region based on the risk ratio and phenotypic distribution among carriers. Two rare variants at VEGFC and one common variant on chromosome 4q conferred the greatest risk for the dichotomous trait. We identified two rare variants on chromosomes 4q (VEGFC) and 6p (VEGFA) that explain 12.4% of the total phenotypic variance of trait Q1. We also identified four variants (including one at VNN3) on chromosome 6q that are able to drop the linkage LOD from 3.7 to 1.0. These results suggest that the use of classical linkage and association methods in large families can provide a useful approach to identifying variants that are responsible for diseases and complex traits in families

Toward a Microencapsulated 3D hiPSC-Derived in vitro Cardiac Microtissue for Recapitulation of Human Heart Microenvironment Features

SAICTPAC/0047/2015 PTDC/BTMSAL/32566/ 2017 PTDC/MEC-CAR/29590/2017 UIDB/04462/2020 UIDP/04462/2020 H2020, ID:874827 SFRH/BD/52475/2013 SFRH/BPD/120595/2016The combination of cardiomyocytes (CM) and non-myocyte cardiac populations, such as endothelial cells (EC), and mesenchymal cells (MC), has been shown to be critical for recapitulation of the human heart tissue for in vitro cell-based modeling. However, most of the current engineered cardiac microtissues still rely on either (i) murine/human limited primary cell sources, (ii) animal-derived and undefined hydrogels/matrices with batch-to-batch variability, or (iii) culture systems with low compliance with pharmacological high-throughput screenings. In this work, we explored a culture platform based on alginate microencapsulation and suspension culture systems to develop three-dimensional (3D) human cardiac microtissues, which entails the co-culture of human induced pluripotent stem cell (hiPSC) cardiac derivatives including aggregates of hiPSC–CM and single cells of hiPSC–derived EC and MC (hiPSC–EC+MC). We demonstrate that the 3D human cardiac microtissues can be cultured for 15 days in dynamic conditions while maintaining the viability and phenotype of all cell populations. Noteworthy, we show that hiPSC–EC+MC survival was promoted by the co-culture with hiPSC–CM as compared to the control single-cell culture. Additionally, the presence of the hiPSC–EC+MC induced changes in the physical properties of the biomaterial, as observed by an increase in the elastic modulus of the cardiac microtissue when compared to the hiPSC–CM control culture. Detailed characterization of the 3D cardiac microtissues revealed that the crosstalk between hiPSC–CM, hiPSC–EC+MC, and extracellular matrix induced the maturation of hiPSC–CM. The cardiac microtissues displayed functional calcium signaling and respond to known cardiotoxins in a dose-dependent manner. This study is a step forward on the development of novel 3D cardiac microtissues that recapitulate features of the human cardiac microenvironment and is compliant with the larger numbers needed in preclinical research for toxicity assessment and disease modeling.publishersversionpublishe