A pedigree-based prediction model identifies carriers of deleterious de novo mutations in families with Li-Fraumeni syndrome

系谱图 李-弗劳门尼综合征 生物 遗传学 种系突变 生殖系 一致性 乳腺癌 人类遗传学 突变 癌症 基因
作者
Fan Gao,Xuedong Pan,Elissa B. Dodd-Eaton,Carlos Vera Recio,Matthew D. Montierth,Jasmina Bojadzieva,L. Phuong,Kristin Zelley,Valen E. Johnson,Danielle Braun,Kim E. Nichols,Judy E. Garber,Sharon A. Savage,Louise C. Strong,Wenyi Wang
出处
期刊:Genome Research [Cold Spring Harbor Laboratory]
卷期号:30 (8): 1170-1180 被引量:3
标识
DOI:10.1101/gr.249599.119
摘要

De novo mutations (DNMs) are increasingly recognized as rare disease causal factors. Identifying DNM carriers will allow researchers to study the likely distinct molecular mechanisms of DNMs. We developed Famdenovo to predict DNM status (DNM or familial mutation [FM]) of deleterious autosomal dominant germline mutations for any syndrome. We introduce Famdenovo.TP53 for Li-Fraumeni syndrome (LFS) and analyze 324 LFS family pedigrees from four US cohorts: a validation set of 186 pedigrees and a discovery set of 138 pedigrees. The concordance index for Famdenovo.TP53 prediction was 0.95 (95% CI: [0.92, 0.98]). Forty individuals (95% CI: [30, 50]) were predicted as DNM carriers, increasing the total number from 42 to 82. We compared clinical and biological features of FM versus DNM carriers: (1) cancer and mutation spectra along with parental ages were similarly distributed; (2) ascertainment criteria like early-onset breast cancer (age 20-35 yr) provides a condition for an unbiased estimate of the DNM rate: 48% (23 DNMs vs. 25 FMs); and (3) hotspot mutation R248W was not observed in DNMs, although it was as prevalent as hotspot mutation R248Q in FMs. Furthermore, we introduce Famdenovo.BRCA for hereditary breast and ovarian cancer syndrome and apply it to a small set of family data from the Cancer Genetics Network. In summary, we introduce a novel statistical approach to systematically evaluate deleterious DNMs in inherited cancer syndromes. Our approach may serve as a foundation for future studies evaluating how new deleterious mutations can be established in the germline, such as those in TP53.

科研通智能强力驱动
Strongly Powered by AbleSci AI
更新
PDF的下载单位、IP信息已删除 (2025-6-4)

科研通是完全免费的文献互助平台,具备全网最快的应助速度,最高的求助完成率。 对每一个文献求助,科研通都将尽心尽力,给求助人一个满意的交代。
实时播报
刚刚
又双叒伊发布了新的文献求助10
1秒前
刚睡醒发布了新的文献求助10
3秒前
朱朱朱完成签到,获得积分10
4秒前
4秒前
柚子发布了新的文献求助10
4秒前
赘婿应助零城XL采纳,获得10
6秒前
挽忆逍遥完成签到 ,获得积分10
7秒前
Tsegeen完成签到,获得积分10
7秒前
焜少完成签到,获得积分10
7秒前
隐形曼青应助CCTV采纳,获得10
9秒前
aff发布了新的文献求助10
10秒前
量子星尘发布了新的文献求助10
10秒前
韩寒完成签到 ,获得积分10
11秒前
VVTTWW完成签到 ,获得积分10
16秒前
17秒前
王娇完成签到 ,获得积分10
18秒前
19秒前
lulu完成签到 ,获得积分10
19秒前
黄子芮发布了新的文献求助10
20秒前
王梓磬完成签到,获得积分10
21秒前
在水一方应助适合初七采纳,获得10
21秒前
21秒前
萱萱大王发布了新的文献求助20
21秒前
搜集达人应助柚子采纳,获得10
22秒前
daemon850121完成签到,获得积分10
22秒前
Criminology34应助沐风采纳,获得10
23秒前
23秒前
可可完成签到,获得积分10
23秒前
研友_nPbeR8完成签到,获得积分10
23秒前
24秒前
meethaha完成签到,获得积分10
25秒前
aff发布了新的文献求助10
25秒前
26秒前
脑洞疼应助快乐冬灵采纳,获得10
27秒前
mosisa发布了新的文献求助10
27秒前
秀丽的莹发布了新的文献求助10
29秒前
柚子完成签到,获得积分10
30秒前
cw完成签到,获得积分10
31秒前
sunny完成签到,获得积分10
31秒前
高分求助中
(应助此贴封号)【重要!!请各用户(尤其是新用户)详细阅读】【科研通的精品贴汇总】 10000
Encyclopedia of Agriculture and Food Systems Third Edition 2000
Clinical Microbiology Procedures Handbook, Multi-Volume, 5th Edition 临床微生物学程序手册,多卷,第5版 2000
人脑智能与人工智能 1000
King Tyrant 720
Silicon in Organic, Organometallic, and Polymer Chemistry 500
Principles of Plasma Discharges and Materials Processing, 3rd Edition 400
热门求助领域 (近24小时)
化学 材料科学 生物 医学 工程类 计算机科学 有机化学 物理 生物化学 纳米技术 复合材料 内科学 化学工程 人工智能 催化作用 遗传学 数学 基因 量子力学 物理化学
热门帖子
关注 科研通微信公众号,转发送积分 5600669
求助须知:如何正确求助?哪些是违规求助? 4686274
关于积分的说明 14842599
捐赠科研通 4677373
什么是DOI,文献DOI怎么找? 2538898
邀请新用户注册赠送积分活动 1505853
关于科研通互助平台的介绍 1471229