Exome sequencing in one family with gastric- and rectal cancer

Thutkawkorapin, Jessada; Picelli, Simone; Kontham, Vinaykumar; Liu, Tao; Nilsson, Daniel; Lindblom, Annika

doi:10.1186/s12863-016-0351-z

Cited by 31 publications

(27 citation statements)

References 46 publications

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“… 20 It may be very well possible that performing whole-exome sequencing in a more homogeneous patient cohort may allow for improved detection of candidate genes, although other studies also did not identify promising new GC predisposition genes. 16 , 44 , 45 Also, we performed exome sequencing, whereas predisposing variants may also be in non-protein coding parts of the genome, currently not analyzed. Since we collected only one family member for each family and affected family members are often deceased due to the cancer, we were not able to follow-up on potential candidate genes.…”

Section: Discussionmentioning

confidence: 99%

Unraveling genetic predisposition to familial or early onset gastric cancer using germline whole-exome sequencing

et al. 2017

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Recognition of individuals with a genetic predisposition to gastric cancer (GC) enables preventive measures. However, the underlying cause of genetic susceptibility to gastric cancer remains largely unexplained. We performed germline whole-exome sequencing on leukocyte DNA of 54 patients from 53 families with genetically unexplained diffuse-type and intestinal-type GC to identify novel GC-predisposing candidate genes. As young age at diagnosis and familial clustering are hallmarks of genetic tumor susceptibility, we selected patients that were diagnosed below the age of 35, patients from families with two cases of GC at or below age 60 and patients from families with three GC cases at or below age 70. All included individuals were tested negative for germline CDH1 mutations before or during the study. Variants that were possibly deleterious according to in silico predictions were filtered using several independent approaches that were based on gene function and gene mutation burden in controls. Despite a rigorous search, no obvious candidate GC predisposition genes were identified. This negative result stresses the importance of future research studies in large, homogeneous cohorts.

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Section: Discussionmentioning

confidence: 99%

Unraveling genetic predisposition to familial or early onset gastric cancer using germline whole-exome sequencing

et al. 2017

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“…Overall, the targeting of Sec16A by the above listed pathways, would regulate its activities as a primer protein that nucleates the coat inner layer and regulates primer displacement and ER exit. Physiologically, this regulation is clearly manifested by the emerging relations between Sec16 polymorphism (A and B isoforms) and multiple diseases ranging from weight regulation and obesity to cell proliferation and cancer …”

Section: Physiological Regulation Of Traffic At Eresmentioning

confidence: 99%

COPII gets in shape: Lessons derived from morphological aspects of early secretion

Aridor

2018

Traffic

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Our view of the secretory pathway has evolved from a morphological one to one that includes molecular mechanistic understanding of basic traffic components. These components include coat complexes involved in cargo sorting and budding and proteins that mediate targeting, tethering and fusion. The expanding repertoire of regulators that control basic traffic activities begins to paint a unified morphological-molecular view of secretion. The emerging picture provides key insights into the coupling of secretion with physiology. This review examines aspects of morphological-molecular relations that are derived from studies on traffic from the endoplasmic reticulum carried by the coat protein complex II.

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“…Few GWAS for uniformity traits have been performed in livestock and there is no consensus about the most appropriate phenotype to be used in such studies. Phenotypic standard deviation and coefficient of variation was used to address uniformity of egg weight in chickens by Wolc et al [ 7 ] and birth weight in pigs by Wang et al [ 8 , 9 ]. Residual variance per individual from double hierarchical generalized linear model (DHGLM; obtained according to Rönnegård et al [ 10 ]) were used as response variable by Mulder et al [ 11 ] to identify genomic regions related to residual variance of somatic cell score in dairy cattle.…”

Section: Introductionmentioning

confidence: 99%

Genomic regions underlying uniformity of yearling weight in Nellore cattle evaluated under different response variables

Iung

Mulder

Neves³

et al. 2018

BMC Genomics

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BackgroundIn livestock, residual variance has been studied because of the interest to improve uniformity of production. Several studies have provided evidence that residual variance is partially under genetic control; however, few investigations have elucidated genes that control it. The aim of this study was to identify genomic regions associated with within-family residual variance of yearling weight (YW; N = 423) in Nellore bulls with high density SNP data, using different response variables. For this, solutions from double hierarchical generalized linear models (DHGLM) were used to provide the response variables, as follows: a DGHLM assuming non-null genetic correlation between mean and residual variance (rmv ≠ 0) to obtain deregressed EBV for mean (dEBVm) and residual variance (dEBVv); and a DHGLM assuming rmv = 0 to obtain two alternative response variables for residual variance, dEBVv_r0 and log-transformed variance of estimated residuals (ln_).ResultsThe dEBVm and dEBVv were highly correlated, resulting in common regions associated with mean and residual variance of YW. However, higher effects on variance than the mean showed that these regions had effects on the variance beyond scale effects. More independent association results between mean and residual variance were obtained when null rmv was assumed. While 13 and 4 single nucleotide polymorphisms (SNPs) showed a strong association (Bayes Factor > 20) with dEBVv and ln_, respectively, only suggestive signals were found for dEBVv_r0. All overlapping 1-Mb windows among top 20 between dEBVm and dEBVv were previously associated with growth traits. The potential candidate genes for uniformity are involved in metabolism, stress, inflammatory and immune responses, mineralization, neuronal activity and bone formation.ConclusionsIt is necessary to use a strategy like assuming null rmv to obtain genomic regions associated with uniformity that are not associated with the mean. Genes involved not only in metabolism, but also stress, inflammatory and immune responses, mineralization, neuronal activity and bone formation were the most promising biological candidates for uniformity of YW. Although no clear evidence of using a specific response variable was found, we recommend consider different response variables to study uniformity to increase evidence on candidate regions and biological mechanisms behind it.Electronic supplementary materialThe online version of this article (10.1186/s12864-018-5003-4) contains supplementary material, which is available to authorized users.

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Exome sequencing in one family with gastric- and rectal cancer

Cited by 31 publications

References 46 publications

Unraveling genetic predisposition to familial or early onset gastric cancer using germline whole-exome sequencing

Unraveling genetic predisposition to familial or early onset gastric cancer using germline whole-exome sequencing

COPII gets in shape: Lessons derived from morphological aspects of early secretion

Genomic regions underlying uniformity of yearling weight in Nellore cattle evaluated under different response variables

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