Identifying a High Fraction of the Human Genome to be under Selective Constraint Using GERP++
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Computational efforts to identify functional elements within genomes leverage comparative sequence information by looking for regions that exhibit evidence of selective constraint. One way of detecting constrained elements is to follow a bottom-up approach by computing constraint scores for individual positions of a multiple alignment and then defining constrained elements as segments of contiguous, highly scoring nucleotide positions. Here we present GERP++, a new tool that uses maximum likelihood evolutionary rate estimation for position-specific scoring and, in contrast to previous bottom-up methods, a novel dynamic programming approach to subsequently define constrained elements. GERP++ evaluates a richer set of candidate element breakpoints and ranks them based on statistical significance, eliminating the need for biased heuristic extension techniques. Using GERP++ we identify over 1.3 million constrained elements spanning over 7% of the human genome. We predict a higher fraction than earlier estimates largely due to the annotation of longer constrained elements, which improves one to one correspondence between predicted elements with known functional sequences. GERP++ is an efficient and effective tool to provide both nucleotide- and element-level constraint scores within deep multiple sequence alignments.
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Top loci had similar properties across comparative genomics resources regardless of the extent of pleiotropy.
“Various comparative genomics resources, including PhyloP (Pollard et al., 2010), PhastCons (Siepel et al., 2005), and GERP++ (Davydov et al., 2010), showed our top loci to have similar properties regardless of the extent of pleiotropy.”
From Genomic Relationships, Novel Loci, and Pleiotropic Mechanisms across Eight Psychiatric Disorders (2019) · cites this paper as Davydov et al., 2010
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- Authors
- Davydov, E. V., Goode, D. L., Sirota, M., Cooper, G. M., Sidow, A., & Batzoglou, S.
- Affiliations
- Stanford University (US)
- Stanford Medicine (US)
- Howard Hughes Medical Institute (US)
- Corresponding author
- Sidow, Arend
- Venue
- PLoS Computational Biology
- Publisher
- Public Library of Science (PLoS)
- Published
- 2010-12-02
- Volume
- 6
- Issue
- 12
- Pages
- e1001025
- ISSN
- 1553-734X (ISSN-L)
- 1553-7358
- PMC
- PMC2996323
- OpenAlex
- W2038473742
- Cited by
- 1,757
- References
- 28
- Citations by year
- 2026: 105 · 2025: 116 · 2024: 128 · 2023: 134 · 2022: 142 · 2021: 174 · 2020: 156 · 2019: 164 · 2018: 134 · 2017: 107 · 2016: 118 · 2015: 100 · 2014: 69 · 2013: 71 · 2012: 25
- FWCI
- 14.08
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Locations
- PLoS Computational Biology (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Figshare (submittedVersion, cc-by)
- Minerva Access (University of Melbourne) (submittedVersion, cc-by)
- Funders
- National Science Foundation (0347952)
- National Institutes of Health (LM007033)
- National Human Genome Research Institute
- U.S. National Library of Medicine (K22LM008261, LM007033, T15LM007033)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Genomics and Phylogenetic Studies
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Genomics and Chromatin Dynamics
- Life Sciences › Agricultural and Biological Sciences › Plant Science › Chromosomal and Genetic Variations
- Keywords
- GERP, selective constraint, functional elements, human genome, sequence comparison, multiple sequence alignment, dynamic programming
- MeSH terms
- Algorithms, Animals, Humans, Mammals, Models, Genetic, Phylogeny, Software, User-Computer Interface, Genome, Human, Sequence Alignment, Sequence Analysis, DNA, Genomics
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