Rare variant discovery by deep whole-genome sequencing of 1,070 Japanese individuals

ABSTRACTEssential tremor (ET) is one of the most common movement disorders. The etiology of ET remains largely unexplained. Whole genome sequencing (WGS) is likely to be of value in understanding a large proportion of ET with Mendelian and complex disease inheritance patterns. In ET families with Mendelian inheritance patterns, WGS may lead to gene identification where WES analysis failed to identify the causative variant due to incomplete coverage of the entire coding region of the genome. Alternatively, in ET families with complex disease inheritance patterns with gene x gene and gene x environment interactions enrichment of functional rare coding and non-coding variants may explain the heritability of ET. We performed WGS in eight ET families (n=40 individuals) enrolled in the Family Study of Essential Tremor. The analysis included filtering WGS data based on allele frequency in population databases, rare variant classification and association testing using the Mixed-Model Kernel Based Adaptive Cluster (MM-KBAC) test and prioritization of candidate genes identified within families using phenolyzer. WGS analysis identified candidate genes for ET in 5/8 (62.5%) of the families analyzed. WES analysis in a subset of these families in our previously published study failed to identify candidate genes. In one family, we identified a deleterious and damaging variant (c.1367G>A, p.(Arg456Gln)) in the candidate gene, CACNA1G, which encodes the pore forming subunit of T-type Ca(2+) channels, CaV3.1, and is expressed in various motor pathways and has been previously implicated in neuronal autorhythmicity and ET. Other candidate genes identified include SLIT3 (family D), which encodes an axon guidance molecule and in three families, phenolyzer prioritized genes that are associated with hereditary neuropathies (family A, KARS, family B, KIF5A and family F, NTRK1). This work has identified candidate genes and pathways for ET that can now be prioritized for functional studies.

Dynamic Scan Procedure for Detecting Rare-Variant Association Regions in Whole Genome Sequencing Studies

10.1101/552950 ◽

2019 ◽

Author(s):

Zilin Li ◽

Xihao Li ◽

Yaowu Liu ◽

Jincheng Shen ◽

Han Chen ◽

...

Keyword(s):

Whole Genome Sequencing ◽

Genome Sequencing ◽

Rare Variant ◽

Type I Error ◽

Rare Variants ◽

Error Rates ◽

Type I ◽

Whole Genome ◽

Rare Variant Association ◽

Dynamic Scan

AbstractWhole genome sequencing (WGS) studies are being widely conducted to identify rare variants associated with human diseases and disease-related traits. Classical single-marker association analyses for rare variants have limited power, and variant-set based analyses are commonly used to analyze rare variants. However, existing variant-set based approaches need to pre-specify genetic regions for analysis, and hence are not directly applicable to WGS data due to the large number of intergenic and intron regions that consist of a massive number of non-coding variants. The commonly used sliding window method requires pre-specifying fixed window sizes, which are often unknown as a priori, are difficult to specify in practice and are subject to limitations given genetic association region sizes are likely to vary across the genome and phenotypes. We propose a computationally-efficient and dynamic scan statistic method (Scan the Genome (SCANG)) for analyzing WGS data that flexibly detects the sizes and the locations of rare-variants association regions without the need of specifying a prior fixed window size. The proposed method controls the genome-wise type I error rate and accounts for the linkage disequilibrium among genetic variants. It allows the detected rare variants association region sizes to vary across the genome. Through extensive simulated studies that consider a wide variety of scenarios, we show that SCANG substantially outperforms several alternative rare-variant association detection methods while controlling for the genome-wise type I error rates. We illustrate SCANG by analyzing the WGS lipids data from the Atherosclerosis Risk in Communities (ARIC) study.

Whole genome sequencing and rare variant analysis in essential tremor families

PLoS ONE ◽

10.1371/journal.pone.0220512 ◽

2019 ◽

Vol 14 (8) ◽

pp. e0220512 ◽

Cited By ~ 6

Author(s):

Zagaa Odgerel ◽

Shilpa Sonti ◽

Nora Hernandez ◽

Jemin Park ◽

Ruth Ottman ◽

...

Keyword(s):

Whole Genome Sequencing ◽

Essential Tremor ◽

Genome Sequencing ◽

Rare Variant ◽

Whole Genome ◽

Rare Variant Analysis ◽

Variant Analysis

Dynamic incorporation of multiple in silico functional annotations empowers rare variant association analysis of large whole-genome sequencing studies at scale

Nature Genetics ◽

10.1038/s41588-020-0676-4 ◽

2020 ◽

Vol 52 (9) ◽

pp. 969-983 ◽

Cited By ~ 5

Author(s):

Xihao Li ◽

◽

Zilin Li ◽

Hufeng Zhou ◽

Sheila M. Gaynor ◽

...

Keyword(s):

Whole Genome Sequencing ◽

Association Analysis ◽

Genome Sequencing ◽

Rare Variant ◽

In Silico ◽

Whole Genome ◽

Rare Variant Association ◽

Functional Annotations ◽

Sequencing Studies

Variant discovery and breakpoint region prediction for studying the human 22q11.2 deletion using BAC clone and whole genome sequencing analysis

Human Molecular Genetics ◽

10.1093/hmg/ddw221 ◽

2016 ◽

Vol 25 (17) ◽

pp. 3754-3767 ◽

Cited By ~ 8

Author(s):

Xingyi Guo ◽

Maria Delio ◽

Nousin Haque ◽

Raquel Castellanos ◽

Matthew S. Hestand ◽

...

Keyword(s):

Whole Genome Sequencing ◽

Genome Sequencing ◽

Breakpoint Region ◽

Whole Genome ◽

Sequencing Analysis ◽

22Q11.2 Deletion ◽

Bac Clone ◽

Variant Discovery

Rare and common variant discovery by whole-genome sequencing of 101 Thoroughbred racehorses

Scientific Reports ◽

10.1038/s41598-021-95669-1 ◽

2021 ◽

Vol 11 (1) ◽

Author(s):

Teruaki Tozaki ◽

Aoi Ohnuma ◽

Mio Kikuchi ◽

Taichiro Ishige ◽

Hironaga Kakoi ◽

...

Keyword(s):

Whole Genome Sequencing ◽

Genome Sequencing ◽

Rare Variants ◽

Industrial Applications ◽

Whole Genome ◽

X Chromosomes ◽

Variant Discovery ◽

Functional Variants ◽

Thoroughbred Racehorses ◽

Using Data

AbstractThe Thoroughbred breed was formed by crossing Oriental horse breeds and British native horses and is currently used in horseracing worldwide. In this study, we constructed a single-nucleotide variant (SNV) database using data from 101 Thoroughbred racehorses. Whole genome sequencing (WGS) revealed 11,570,312 and 602,756 SNVs in autosomal (1–31) and X chromosomes, respectively, yielding a total of 12,173,068 SNVs. About 6.9% of identified SNVs were rare variants observed only in one allele in 101 horses. The number of SNVs detected in individual horses ranged from 4.8 to 5.3 million. Individual horses had a maximum of 25,554 rare variants; several of these were functional variants, such as non-synonymous substitutions, start-gained, start-lost, stop-gained, and stop-lost variants. Therefore, these rare variants may affect differences in traits and phenotypes among individuals. When observing the distribution of rare variants among horses, one breeding stallion had a smaller number of rare variants compared to other horses, suggesting that the frequency of rare variants in the Japanese Thoroughbred population increases through breeding. In addition, our variant database may provide useful basic information for industrial applications, such as the detection of genetically modified racehorses in gene-doping control and pedigree-registration of racehorses using SNVs as markers.