The future of genomics for developmentalists

AbstractThe momentum of genomic science will carry it far into the future and into the heart of research on typical and atypical behavioral development. The purpose of this paper is to focus on a few implications and applications of these advances for understanding behavioral development. Quantitative genetics is genomic and will chart the course for molecular genomic research now that these two worlds of genetics are merging in the search for many genes of small effect. Although current attempts to identify specific genes have had limited success, known as the missing heritability problem, whole-genome sequencing will improve this situation by identifying all DNA sequence variations, including rare variants. Because the heritability of complex traits is caused by many DNA variants of small effect in the population, polygenic scores that are composites of hundreds or thousands of DNA variants will be used by developmentalists to predict children's genetic risk and resilience. The most far-reaching advance will be the widespread availability of whole-genome sequence for children, which means that developmentalists would no longer need to obtain DNA or to genotype children in order to use genomic information in research or in the clinic.

Download Full-text

Family-based gene-environment interaction using sequence kernel association test (FGE-SKAT) for complex quantitative traits

Scientific Reports ◽

10.1038/s41598-021-86871-2 ◽

2021 ◽

Vol 11 (1) ◽

Author(s):

Chao-Yu Guo ◽

Reng-Hong Wang ◽

Hsin-Chou Yang

Keyword(s):

Complex Traits ◽

Association Studies ◽

Association Test ◽

Whole Genome Sequence ◽

Environment Interaction ◽

Genome Wide Association Studies ◽

Whole Genome ◽

Sequence Kernel Association Test ◽

Gene Environment ◽

Family Based

AbstractAfter the genome-wide association studies (GWAS) era, whole-genome sequencing is highly engaged in identifying the association of complex traits with rare variations. A score-based variance-component test has been proposed to identify common and rare genetic variants associated with complex traits while quickly adjusting for covariates. Such kernel score statistic allows for familial dependencies and adjusts for random confounding effects. However, the etiology of complex traits may involve the effects of genetic and environmental factors and the complex interactions between genes and the environment. Therefore, in this research, a novel method is proposed to detect gene and gene-environment interactions in a complex family-based association study with various correlated structures. We also developed an R function for the Fast Gene-Environment Sequence Kernel Association Test (FGE-SKAT), which is freely available as supplementary material for easy GWAS implementation to unveil such family-based joint effects. Simulation studies confirmed the validity of the new strategy and the superior statistical power. The FGE-SKAT was applied to the whole genome sequence data provided by Genetic Analysis Workshop 18 (GAW18) and discovered concordant and discordant regions compared to the methods without considering gene by environment interactions.

Download Full-text

Large-Scale Whole-Genome Sequencing Reveals the Genetic Architecture of Primary Membranoproliferative GN and C3 Glomerulopathy

Journal of the American Society of Nephrology ◽

10.1681/asn.2019040433 ◽

2020 ◽

Vol 31 (2) ◽

pp. 365-373 ◽

Cited By ~ 7

Author(s):

Adam P. Levine ◽

Melanie M.Y. Chan ◽

Omid Sadeghi-Alavijeh ◽

Edwin K.S. Wong ◽

H. Terence Cook ◽

...

Keyword(s):

Large Scale ◽

Rare Variants ◽

Alternative Pathway ◽

Atypical Hemolytic Uremic Syndrome ◽

Gene Mutations ◽

Whole Genome Sequence ◽

European Ancestry ◽

Whole Genome ◽

C3 Glomerulopathy ◽

Complement Gene

BackgroundPrimary membranoproliferative GN, including complement 3 (C3) glomerulopathy, is a rare, untreatable kidney disease characterized by glomerular complement deposition. Complement gene mutations can cause familial C3 glomerulopathy, and studies have reported rare variants in complement genes in nonfamilial primary membranoproliferative GN.MethodsWe analyzed whole-genome sequence data from 165 primary membranoproliferative GN cases and 10,250 individuals without the condition (controls) as part of the National Institutes of Health Research BioResource–Rare Diseases Study. We examined copy number, rare, and common variants.ResultsOur analysis included 146 primary membranoproliferative GN cases and 6442 controls who were unrelated and of European ancestry. We observed no significant enrichment of rare variants in candidate genes (genes encoding components of the complement alternative pathway and other genes associated with the related disease atypical hemolytic uremic syndrome; 6.8% in cases versus 5.9% in controls) or exome-wide. However, a significant common variant locus was identified at 6p21.32 (rs35406322) (P=3.29×10−8; odds ratio [OR], 1.93; 95% confidence interval [95% CI], 1.53 to 2.44), overlapping the HLA locus. Imputation of HLA types mapped this signal to a haplotype incorporating DQA1*05:01, DQB1*02:01, and DRB1*03:01 (P=1.21×10−8; OR, 2.19; 95% CI, 1.66 to 2.89). This finding was replicated by analysis of HLA serotypes in 338 individuals with membranoproliferative GN and 15,614 individuals with nonimmune renal failure.ConclusionsWe found that HLA type, but not rare complement gene variation, is associated with primary membranoproliferative GN. These findings challenge the paradigm of complement gene mutations typically causing primary membranoproliferative GN and implicate an underlying autoimmune mechanism in most cases.

Download Full-text

Predicting causal variants affecting expression using whole genome sequence and RNA-seq from multiple human tissues

10.1101/088872 ◽

2016 ◽

Cited By ~ 2

Author(s):

Andrew Anand Brown ◽

Ana Viñuela ◽

Olivier Delaneau ◽

Tim Spector ◽

Kerrin Small ◽

...

Keyword(s):

Genome Sequence ◽

Complex Traits ◽

Causal Variant ◽

Whole Genome Sequence ◽

Open Chromatin ◽

Whole Genome ◽

Rna Seq ◽

Derived Properties ◽

Causal Variants ◽

Genomic Regions

Genetic association mapping produces statistical links between phenotypes and genomic regions, but identifying the causal variants themselves remains difficult. Complete knowledge of all genetic variants, as provided by whole genome sequence (WGS), will help, but is currently financially prohibitive for well powered GWAS studies. To explore the advantages of WGS in a well powered setting, we performed eQTL mapping using WGS and RNA-seq, and showed that the lead eQTL variants called using WGS are more likely to be causal. We derived properties of the causal variant from simulation studies, and used these to propose a method for implicating likely causal SNPs. This method predicts that 25% - 70% of the causal variants lie in open chromatin regions, depending on tissue and experiment. Finally, we identify a set of high confidence causal variants and show that they are more enriched in GWAS associations than other eQTL. Of these, we find 65 associations with GWAS traits and show examples where the gene implicated by expression has been functionally validated as relevant for complex traits.

Download Full-text

Methods for Collapsing Multiple Rare Variants in Whole-Genome Sequence Data

Genetic Epidemiology ◽

10.1002/gepi.21820 ◽

2014 ◽

Vol 38 (S1) ◽

pp. S13-S20 ◽

Cited By ~ 14

Author(s):

Yun Ju Sung ◽

Keegan D. Korthauer ◽

Michael D. Swartz ◽

Corinne D. Engelman

Keyword(s):

Genome Sequence ◽

Rare Variants ◽

Sequence Data ◽

Whole Genome Sequence ◽

Whole Genome ◽

Genome Sequence Data

Download Full-text

Rare variants analysis using penalization methods for whole genome sequence data

BMC Bioinformatics ◽

10.1186/s12859-015-0825-4 ◽

2015 ◽

Vol 16 (1) ◽

Cited By ~ 9

Author(s):

Akram Yazdani ◽

Azam Yazdani ◽

Eric Boerwinkle

Keyword(s):

Genome Sequence ◽

Rare Variants ◽

Sequence Data ◽

Whole Genome Sequence ◽

Whole Genome ◽

Genome Sequence Data ◽

Penalization Methods

Download Full-text

Fungal Genomic Resources for Strain Identification and Diversity Analysis of 1900 Fungal Species

Journal of Fungi ◽

10.3390/jof7040288 ◽

2021 ◽

Vol 7 (4) ◽

pp. 288

Author(s):

Mir Asif Iquebal ◽

Sarika Jaiswal ◽

Vineet Kumar Mishra ◽

Rahul Singh Jasrotia ◽

Ulavappa B. Angadi ◽

...

Keyword(s):

Genome Sequence ◽

In Silico ◽

Sequence Data ◽

Fungal Species ◽

Genomic Research ◽

Whole Genome Sequence ◽

Strain Identification ◽

Diversity Analysis ◽

Whole Genome

Identification and diversity analysis of fungi is greatly challenging. Though internal transcribed spacer (ITS), region-based DNA fingerprinting works as a “gold standard” for most of the fungal species group, it cannot differentiate between all the groups and cryptic species. Therefore, it is of paramount importance to find an alternative approach for strain differentiation. Availability of whole genome sequence data of nearly 2000 fungal species are a promising solution to such requirement. We present whole genome sequence-based world’s largest microsatellite database, FungSatDB having >19M loci obtained from >1900 fungal species/strains using >4000 assemblies across globe. Genotyping efficacy of FungSatDB has been evaluated by both in-silico and in-vitro PCR. By in silico PCR, 66 strains of 8 countries representing four continents were successfully differentiated. Genotyping efficacy was also evaluated by in vitro PCR in four fungal species. This approach overcomes limitation of ITS in species, strain signature, and diversity analysis. It can accelerate fungal genomic research endeavors in agriculture, industrial, and environmental management.

Download Full-text

Comparison of methods that use whole genome data to estimate the heritability and genetic architecture of complex traits

10.1101/115527 ◽

2017 ◽

Cited By ~ 10

Author(s):

Luke M. Evans ◽

Rasool Tahmasbi ◽

Scott I. Vrieze ◽

Gonçalo R. Abecasis ◽

Sayantan Das ◽

...

Keyword(s):

Complex Traits ◽

Snp Array ◽

Causal Variant ◽

Whole Genome Sequence ◽

Whole Genome ◽

Narrow Sense Heritability ◽

Frequency Spectra ◽

Genome Wide ◽

Variant Frequency ◽

Causal Variants

ABSTRACTHeritability, h2, is a foundational concept in genetics, critical to understanding the genetic basis of complex traits. Recently-developed methods that estimate heritability from genotyped SNPs, h2SNP, explain substantially more genetic variance than genome-wide significant loci, but less than classical estimates from twins and families. However, h2SNP estimates have yet to be comprehensively compared under a range of genetic architectures, making it difficult to draw conclusions from sometimes conflicting published estimates. Here, we used thousands of real whole genome sequences to simulate realistic phenotypes under a variety of genetic architectures, including those from very rare causal variants. We compared the performance of ten methods across different types of genotypic data (commercial SNP array positions, whole genome sequence variants, and imputed variants) and under differing causal variant frequencies, levels of stratification, and relatedness thresholds. These results provide guidance in interpreting past results and choosing optimal approaches for future studies. We then chose two methods (GREML-MS and GREML-LDMS) that best estimated overall h2SNP and the causal variant frequency spectra to six phenotypes in the UK Biobank using imputed genome-wide variants. Our results suggest that as imputation reference panels become larger and more diverse, estimates of the frequency distribution of causal variants will become increasingly unbiased and the vast majority of trait narrow-sense heritability will be accounted for.

Download Full-text