A WHOLE GENOME SEQUENCING STUDY IDENTIFIES A RARE VARIANT IN ANK3 THAT MAY CONTRIBUTE TO BIPOLAR DISORDER

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.

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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.

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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

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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

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

Nature Communications ◽

10.1038/ncomms9018 ◽

2015 ◽

Vol 6 (1) ◽

Cited By ~ 213

Author(s):

Masao Nagasaki ◽

◽

Jun Yasuda ◽

Fumiki Katsuoka ◽

Naoki Nariai ◽

...

Keyword(s):

Whole Genome Sequencing ◽

Genome Sequencing ◽

Rare Variant ◽

Whole Genome ◽

Variant Discovery

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

The American Journal of Human Genetics ◽

10.1016/j.ajhg.2019.03.002 ◽

2019 ◽

Vol 104 (5) ◽

pp. 802-814 ◽

Cited By ~ 10

Author(s):

Zilin Li ◽

Xihao Li ◽

Yaowu Liu ◽

Jincheng Shen ◽

Han Chen ◽

...

Keyword(s):

Whole Genome Sequencing ◽

Genome Sequencing ◽

Rare Variant ◽

Whole Genome ◽

Rare Variant Association ◽

Sequencing Studies ◽

Dynamic Scan

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Whole Genome Rare-Variant Association Study of HIV-1 Progression in a Southern African Population

10.1101/2020.12.16.20248307 ◽

2020 ◽

Author(s):

Prisca K. Thami ◽

Wonderful Choga ◽

Delesa D. Mulisa ◽

Collet Dandara ◽

Andrey K. Shevchenko ◽

...

Keyword(s):

Functional Analysis ◽

Whole Genome Sequencing ◽

Genome Sequencing ◽

Rare Variant ◽

Rare Variants ◽

Variant Calling ◽

Analysis Tool ◽

Whole Genome ◽

Host Genetics ◽

Hiv 1

ABSTRACTDespite the high burden of HIV-1 in Botswana, the population of Botswana is significantly underrepresentation in host genetics studies of HIV-1. Furthermore, the bulk of previous genomics studies evaluated common human genetic variations, however, there is increasing evidence of the influence of rare variants in the outcome of diseases which may be uncovered by comprehensive complete and deep genome sequencing. This research aimed to evaluate the role of rare-variants in susceptibility to HIV-1 and progression through whole genome sequencing. Whole genome sequences (WGS) of 265 HIV-1 positive and 125 were HIV-1 negative unrelated individuals from Botswana were mapped to the human reference genome GRCh38. Population joint variant calling was performed using Genome Analysis Tool Kit (GATK) and BCFTools. Cumulative effects of rare variant sets on susceptibility to HIV-1 and progression (CD4+ T-cell decline) were determined with optimized Sequence Kernel Association Test (SKAT-O). In silico functional analysis of the prioritized variants was performed through gene-set enrichment using databases in GeneMANIA and Enrichr. Novel rare-variants within the ANKRD39 (8.48 × 10−8), LOC105378523 (7.45 × 10−7) and GTF3C3 (1.36 × 10−6) genes were significantly associated with HIV-1 progression. Functional analysis revealed that these genes are involved in viral translation and transcription. These findings highlight the significance of whole genome sequencing in pinpointing rare-variants of clinical relevance. The research contributes towards a deeper understanding of the host genetics HIV-1 and offers promise of population specific interventions against HIV-1.

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