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Method: Leveraging family data to design Mendelian randomization that is provably robust to population stratification
- Nathan LaPierre,
- Boyang Fu,
- Steven Turnbull,
- Eleazar Eskin,
- and Sriram Sankararaman
Genome Res. July 2023 33: 1032-1041; Published in Advance May 17, 2023, doi:10.1101/gr.277664.123
...: nathanl2012@gmail.com, sriram@cs.ucla.eduAbstractMendelian randomization (MR) has emerged as a powerful approach to leverage genetic instruments to infer causality between pairs of traits in observational studies. However, the results of such studies are susceptible to biases owing to weak instruments...
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Method: A spectral component approach leveraging identity-by-descent graphs to address recent population structure in genomic analysis
- Ruhollah Shemirani,
- Gillian M. Belbin,
- Sinead Cullina,
- Christa Caggiano,
- Christopher R. Gignoux,
- Noah Zaitlen,
- and Eimear E. Kenny
Genome Res. March 2026 36: 534-546; Published in Advance December 23, 2025, doi:10.1101/gr.280659.125
...cohorts with sparser graphs, phenotypes with lower heritability, and dichotomous outcomes in which collinearity may be more pronounced. Third, further work could extend SPCs to additional analytic frameworks, like burden testing, generalized additive models, and polygenic risk score calculation, to assess...
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Method: spRefine denoises and imputes spatial transcriptomic data with a reference-free framework powered by genomic language model
- Tianyu Liu,
- Tinglin Huang,
- Wengong Jin,
- Tinyi Chu,
- Rex Ying,
- and Hongyu Zhao
Genome Res. April 2026 36: 754-768; Published in Advance February 3, 2026, doi:10.1101/gr.281001.125
...transcriptomic data is hindered by high noise levels and missing gene measurements, challenges that are further compounded by the higher cost of spatial data compared to traditional single-cell data. To overcome this challenge, we introduce spRefine, a deep learning framework that leverages genomic language...
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Method: The oligogenic inheritance test GCOD detects risk genes and their interactions in congenital heart defects
- Maureen Pittman,
- Kihyun Lee,
- Franco Felix,
- Yu Huang,
- Adrienne Lam,
- Mauro W. Costa,
- Deepak Srivastava,
- and Katherine S. Pollard
Genome Res. February 2026 36: 330-347; Published in Advance December 12, 2025, doi:10.1101/gr.281141.125
...heterozygous Gata6−/+ Por−/+ mice were born below Mendelian ratios (five observed, eight expected), although this is not a statistically significant finding (Supplemental Table S16). Notably, Gata6−/+ Por−/+ fetus or P0 mice exhibited ASDs or ventricular septal defects (VSDs) at a rate of 53% (nine out of 17...
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Method: A scalable computational framework for predicting gene expression from candidate cis-regulatory elements
- Qinhu Zhang,
- Siguo Wang,
- Zhipeng Li,
- Wenzheng Bao,
- Wenjian Liu,
- and De-Shuang Huang
Genome Res. February 2026 36: 361-374; Published in Advance January 16, 2026, doi:10.1101/gr.281219.125
...CREs in open chromatin regions together with ChIP-seqs of transcription factors and histone modifications to predict the expression level of target genes. EPInformer (Lin et al. 2024) introduced an efficient deep-learning framework based on the transformer architecture to predict gene expression by integrating...
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Review: Leveraging the power of long reads for targeted sequencing
- Shruti V. Iyer,
- Sara Goodwin,
- and William Richard McCombie
Genome Res. November 2024 34: 1701-1718; doi:10.1101/gr.279168.124
...-frequency variants in heterogeneous populations. Here, we review currently used and recently developed targeted sequencing strategies that leverage existing long-read technologies to increase the resolution with which we can look at nucleic acids in a variety of biological contexts.There are several applications...
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Method: A novel multislice framework for precision 3D spatial domain reconstruction and disease pathology analysis
- Daijun Zhang,
- Ren Qi,
- Xun Lan,
- and Bin Liu
Genome Res. August 2025 35: 1794-1808; Published in Advance July 14, 2025, doi:10.1101/gr.280281.124
...in identifying 3D spatial domains. This framework advances the field by incorporating H&E image data to aid in reconstructing 3D tissue structures. It leverages the reconstructed cellular landscape to fill gaps in microlevel information. Additionally, it introduces a precorrection mechanism combined with a GAE...
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Method: EnDeep4mC predicts DNA N⁴-methylcytosine sites using a dual-adaptive feature encoding framework in deep ensembles
- Shuyu Zhang,
- Quan Zou,
- Mengting Niu,
- Zhibin Lv,
- Antony Stalin,
- and Ximei Luo
Genome Res. March 2026 36: 589-599; Published in Advance February 17, 2026, doi:10.1101/gr.280977.125
...2020) to leverage existing models through transfer learning and combined multiple ensemble learning techniques to build DeepSF-4mC, which achieved enhanced prediction performance on three specific species.However, the above methods still exhibit two critical constraints. First, traditional methods...
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Method: Geometric deep learning framework for de novo genome assembly
- Lovro Vrček,
- Xavier Bresson,
- Thomas Laurent,
- Martin Schmitz,
- Kenji Kawaguchi,
- and Mile Šikić
Genome Res. April 2025 35: 839-849; Published in Advance October 29, 2024, doi:10.1101/gr.279307.124
...findings on training on directed graphs were later more formally confirmed (Rossi et al. 2024).First, we test the effectiveness of SymGatedGCN against a baseline—a model that predicts the same score for every edge, resulting in random walks during the decoding. We demonstrate that random walks vastly...
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Research: Mendelian randomization analyses implicate biogenesis of translation machinery in human aging
- Sara Javidnia,
- Stephen Cranwell,
- Stefanie H. Mueller,
- Colin Selman,
- Jennifer M.A. Tullet,
- Karoline Kuchenbaecker,
- and Nazif Alic
Genome Res. February 2022 32: 258-265; Published in Advance January 25, 2022, doi:10.1101/gr.275636.121
...ribosomal and transfer RNAs, namely Pol I and III, and the variation in expression of ribosomal protein (RP) genes, using Mendelian randomization. We find each causally associated with human longevity (β = −0.15 ± 0.047, P = 9.6 × 10−4, q = 0.015; β = −0.13 ± 0.040, P = 1.4 × 10−3, q = 0.023; β = −0.048 ± 0...
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