Post-GWAS: where next? More samples, more SNPs or more biology?

生物 全基因组关联研究 单核苷酸多态性 计算生物学 遗传学 进化生物学 基因 基因型
作者
Paul Marjoram,Asif Zubair,Sergey V. Nuzhdin
出处
期刊:Heredity [Springer Nature]
卷期号:112 (1): 79-88 被引量:75
标识
DOI:10.1038/hdy.2013.52
摘要

The power of genome-wide association studies (GWAS) rests on several foundations: (i) there is a significant amount of additive genetic variation, (ii) individual causal polymorphisms often have sizable effects and (iii) they segregate at moderate-to-intermediate frequencies, or will be effectively 'tagged' by polymorphisms that do. Each of these assumptions has recently been questioned. (i) Why should genetic variation appear additive given that the underlying molecular networks are highly nonlinear? (ii) A new generation of relatedness-based analyses directs us back to the nearly infinitesimal model for effect sizes that quantitative genetics was long based upon. (iii) Larger effect causal polymorphisms are often low frequency, as selection might lead us to expect. Here, we review these issues and other findings that appear to question many of the foundations of the optimism GWAS prompted. We then present a roadmap emerging as one possible future for quantitative genetics. We argue that in future GWAS should move beyond purely statistical grounds. One promising approach is to build upon the combination of population genetic models and molecular biological knowledge. This combined treatment, however, requires fitting experimental data to models that are very complex, as well as accurate capturing of the uncertainty of resulting inference. This problem can be resolved through Bayesian analysis and tools such as approximate Bayesian computation—a method growing in popularity in population genetic analysis. We show a case example of anterior–posterior segmentation in Drosophila, and argue that similar approaches will be helpful as a GWAS augmentation, in human and agricultural research.
最长约 10秒,即可获得该文献文件

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
张希伦完成签到 ,获得积分10
刚刚
Emma发布了新的文献求助80
5秒前
7秒前
科目三应助Ilyas0525采纳,获得10
15秒前
DeepLearning发布了新的文献求助10
20秒前
Jasmineyfz完成签到 ,获得积分10
20秒前
22秒前
Ilyas0525完成签到,获得积分10
31秒前
Emma完成签到,获得积分10
34秒前
小木林完成签到 ,获得积分10
43秒前
朴素羊完成签到 ,获得积分10
45秒前
辛勤的喉完成签到 ,获得积分10
48秒前
yunt完成签到 ,获得积分10
48秒前
林利芳完成签到 ,获得积分0
50秒前
神勇的翠丝完成签到,获得积分10
50秒前
xiangwang完成签到 ,获得积分10
57秒前
Joan_89完成签到,获得积分10
1分钟前
juliar完成签到 ,获得积分10
1分钟前
Akim应助DeepLearning采纳,获得10
1分钟前
迈克老狼完成签到 ,获得积分10
1分钟前
Kelevator完成签到,获得积分10
1分钟前
DeepLearning完成签到,获得积分20
1分钟前
1分钟前
DeepLearning发布了新的文献求助10
1分钟前
SOL完成签到 ,获得积分10
1分钟前
丝丢皮的完成签到 ,获得积分10
1分钟前
t铁核桃1985完成签到 ,获得积分10
1分钟前
丝丢皮得完成签到 ,获得积分10
1分钟前
爆米花应助DeepLearning采纳,获得10
1分钟前
铜豌豆完成签到 ,获得积分10
1分钟前
cheng完成签到,获得积分10
2分钟前
君看一叶舟完成签到 ,获得积分10
2分钟前
在水一方应助科研通管家采纳,获得10
2分钟前
2分钟前
Chuang完成签到 ,获得积分10
2分钟前
热心市民完成签到 ,获得积分10
2分钟前
吃不胖的魔芋丝完成签到 ,获得积分10
2分钟前
2分钟前
李东东完成签到 ,获得积分10
2分钟前
fsznc1完成签到 ,获得积分0
2分钟前
高分求助中
The Mother of All Tableaux: Order, Equivalence, and Geometry in the Large-scale Structure of Optimality Theory 3000
A new approach to the extrapolation of accelerated life test data 1000
ACSM’s Guidelines for Exercise Testing and Prescription, 12th edition 500
Indomethacinのヒトにおける経皮吸収 400
Phylogenetic study of the order Polydesmida (Myriapoda: Diplopoda) 370
基于可调谐半导体激光吸收光谱技术泄漏气体检测系统的研究 350
Robot-supported joining of reinforcement textiles with one-sided sewing heads 320
热门求助领域 (近24小时)
化学 材料科学 医学 生物 工程类 有机化学 生物化学 物理 内科学 纳米技术 计算机科学 化学工程 复合材料 遗传学 基因 物理化学 催化作用 冶金 细胞生物学 免疫学
热门帖子
关注 科研通微信公众号,转发送积分 3990836
求助须知:如何正确求助?哪些是违规求助? 3532241
关于积分的说明 11256631
捐赠科研通 3271100
什么是DOI,文献DOI怎么找? 1805313
邀请新用户注册赠送积分活动 882302
科研通“疑难数据库(出版商)”最低求助积分说明 809236