Fast, scalable prediction of deleterious noncoding variants from functional and population genomic data

By Yi-Fei Huang, Brad Gulko, Adam C. Siepel. 2016

318 citations recorded by the source corpus.

Many genetic variants that influence phenotypes of interest are located outside of protein-coding genes, yet existing methods for identifying such variants have poor predictive power. Here we introduce a new computational method, called LINSIGHT, that substantially improves the prediction of noncoding nucleotide sites at which mutations are likely to have deleterious fitness consequences, and which, therefore, are likely to be phenotypically important. LINSIGHT combines a generalized linear model for functional genomic data with a probabilistic model of molecular evolution. The method is fast and highly scalable, enabling it to exploit the 'big data' available in modern genomics. We show that LINSIGHT outperforms the best available methods in identifying human noncoding variants associated with inherited diseases. In addition, we apply LINSIGHT to an atlas of human enhancers and show that the fitness consequences at enhancers depend on cell type, tissue specificity, and constraints at associated promoters.

Subfields: Molecular Biology, Plant Science

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Molecular Biology

1. Amir Hussain
2. Mufti Mahmud
3. Mark Yandell
4. Yicheng Tu
5. M. S. Kaiser
6. Karen Eilbeck
7. Xiaoming Liu
8. Chang Li

In Plant Science

1. Mark Yandell
2. Karen Eilbeck
3. Aaron R. Quinlan

Citations in the corpus

• CADD: predicting the deleteriousness of variants throughout the human genome (2018, neutral): “CADD shares this strength with only a few other scores, such as Eigen (59), LINSIGHT (60) and CDTS (61).”
• Applications of Deep Learning and Reinforcement Learning to Biological Data (2017, methodology): “al. employed CNN to predict the binding between DNA and protein [95]. Zhou et al. proposed a CNN based approach to identify noncoding GV [96] which was also used by Huang et al. for a similar purpose [97]. Park et al. proposed a LSTM based tool to automatically predict 7/33 1. Data App. ose Dom. Dat”
• Settling the score: variant prioritization and Mendelian disease (2017, background): “Although many new approaches are being developed 39,94,95 , there is simply insufficient understanding of the regulatory machinery encrypted in non-coding DNA to prioritize non-coding variants with similar accuracy to that of coding variants 94 .”
• dbNSFP v4: a comprehensive database of transcript-specific functional predictions and annotations for human nonsynonymous and splice-site SNVs (2020, background): “1 added 18 deleteriousness prediction scores (BayesDel_addAF and BayesDel_noAF [15], CADD_hg19 [16], ClinPred [17], DEOGEN2 [18], Eigen and Eigen PC [19], FATHMM-XF [20], GenoCanyon [21], LINSIGHT [22], LIST-S2 [23], M-CAP [24], MPC [25], MutPred [26], MVP [27], PrimateAI [28], REVEL [29], SIFT4G [3”