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Article ; Online: Methods for detecting introgressed archaic sequences.

Sankararaman, Sriram

Current opinion in genetics & development

2020 Volume 62, Page(s) 85–90

Abstract: Analysis of genome sequences from archaic and modern humans have revealed multiple episodes of admixture between highly-diverged population groups. Statistical methods that attempt to localize DNA segments introduced by these events offer a powerful tool ...

Abstract	Analysis of genome sequences from archaic and modern humans have revealed multiple episodes of admixture between highly-diverged population groups. Statistical methods that attempt to localize DNA segments introduced by these events offer a powerful tool to investigate recent human evolution. We review recent advances in methods for detecting introgressed sequences.
MeSH term(s)	Animals ; DNA/analysis ; DNA/genetics ; Evolution, Molecular ; Genetic Variation ; Genome, Human ; Hominidae/genetics ; Humans ; Models, Genetic ; Neanderthals/genetics
Chemical Substances	DNA (9007-49-2)
Language	English
Publishing date	2020-07-24
Publishing country	England
Document type	Journal Article ; Research Support, N.I.H., Extramural ; Research Support, Non-U.S. Gov't ; Review
ZDB-ID	1077312-5
ISSN	1879-0380 ; 0959-437X
ISSN (online)	1879-0380
ISSN	0959-437X
DOI	10.1016/j.gde.2020.05.026
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Article: A scalable and robust variance components method reveals insights into the architecture of gene-environment interactions underlying complex traits.

Pazokitoroudi, Ali / Dahl, Andrew / Zaitlen, Noah / Rosset, Saharon / Sankararaman, Sriram

bioRxiv : the preprint server for biology

2023

Abstract: Understanding the contribution of gene-environment interactions (GxE) to complex trait variation can provide insights into mechanisms underlying disease risk, explain sources of heritability, and improve the accuracy of genetic risk prediction. While ... ...

Abstract	Understanding the contribution of gene-environment interactions (GxE) to complex trait variation can provide insights into mechanisms underlying disease risk, explain sources of heritability, and improve the accuracy of genetic risk prediction. While biobanks that collect genetic and deep phenotypic data over large numbers of individuals offer the promise of obtaining novel insights into GxE, our understanding of the architecture of GxE in complex traits remains limited. We introduce a method that can estimate the proportion of trait variance explained by GxE (GxE heritability) and additive genetic effects (additive heritability) across the genome and within specific genomic annotations. We show that our method is accurate in simulations and computationally efficient for biobank-scale datasets. We applied our method to ≈ 500, 000 common array SNPs (MAF ≥ 1%), fifty quantitative traits, and four environmental variables (smoking, sex, age, and statin usage) measured across ≈ 300, 000 unrelated white British individuals in the UK Biobank. We found 69 trait-environmental variable pairs with significant genome-wide GxE heritability (
Language	English
Publishing date	2023-12-13
Publishing country	United States
Document type	Preprint
DOI	10.1101/2023.12.12.571316
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Article ; Online: Recovering signals of ghost archaic introgression in African populations.

Durvasula, Arun / Sankararaman, Sriram

Science advances

2020 Volume 6, Issue 7, Page(s) eaax5097

Abstract: While introgression from Neanderthals and Denisovans has been documented in modern humans outside Africa, the contribution of archaic hominins to the genetic variation of present-day Africans remains poorly understood. We provide complementary lines of ... ...

Abstract	While introgression from Neanderthals and Denisovans has been documented in modern humans outside Africa, the contribution of archaic hominins to the genetic variation of present-day Africans remains poorly understood. We provide complementary lines of evidence for archaic introgression into four West African populations. Our analyses of site frequency spectra indicate that these populations derive 2 to 19% of their genetic ancestry from an archaic population that diverged before the split of Neanderthals and modern humans. Using a method that can identify segments of archaic ancestry without the need for reference archaic genomes, we built genome-wide maps of archaic ancestry in the Yoruba and the Mende populations. Analyses of these maps reveal segments of archaic ancestry at high frequency in these populations that represent potential targets of adaptive introgression. Our results reveal the substantial contribution of archaic ancestry in shaping the gene pool of present-day West African populations.
MeSH term(s)	Black People/genetics ; Ethnicity/genetics ; Gene Frequency ; Genetics, Population ; Humans ; Phylogeny
Language	English
Publishing date	2020-02-12
Publishing country	United States
Document type	Journal Article ; Research Support, N.I.H., Extramural ; Research Support, U.S. Gov't, Non-P.H.S.
ZDB-ID	2810933-8
ISSN	2375-2548 ; 2375-2548
ISSN (online)	2375-2548
ISSN	2375-2548
DOI	10.1126/sciadv.aax5097
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Book ; Online: dotears

Xue, Albert / Rao, Jingyou / Sankararaman, Sriram / Pimentel, Harold

Scalable, consistent DAG estimation using observational and interventional data

2023

Abstract: Learning causal directed acyclic graphs (DAGs) from data is complicated by a lack of identifiability and the combinatorial space of solutions. Recent work has improved tractability of score-based structure learning of DAGs in observational data, but is ... ...

Abstract	Learning causal directed acyclic graphs (DAGs) from data is complicated by a lack of identifiability and the combinatorial space of solutions. Recent work has improved tractability of score-based structure learning of DAGs in observational data, but is sensitive to the structure of the exogenous error variances. On the other hand, learning exogenous variance structure from observational data requires prior knowledge of structure. Motivated by new biological technologies that link highly parallel gene interventions to a high-dimensional observation, we present $\texttt{dotears}$ [doo-tairs], a scalable structure learning framework which leverages observational and interventional data to infer a single causal structure through continuous optimization. $\texttt{dotears}$ exploits predictable structural consequences of interventions to directly estimate the exogenous error structure, bypassing the circular estimation problem. We extend previous work to show, both empirically and analytically, that the inferences of previous methods are driven by exogenous variance structure, but $\texttt{dotears}$ is robust to exogenous variance structure. Across varied simulations of large random DAGs, $\texttt{dotears}$ outperforms state-of-the-art methods in structure estimation. Finally, we show that $\texttt{dotears}$ is a provably consistent estimator of the true DAG under mild assumptions.
Keywords	Statistics - Machine Learning ; Computer Science - Machine Learning
Subject code	006
Publishing date	2023-05-30
Publishing country	us
Document type	Book ; Online
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Article: Tractable and Expressive Generative Models of Genetic Variation Data.

Dang, Meihua / Liu, Anji / Wei, Xinzhu / Sankararaman, Sriram / Van den Broeck, Guy

bioRxiv : the preprint server for biology

2023

Abstract: Population genetic studies often rely on artificial genomes (AGs) simulated by generative models of genetic data. In recent years, unsupervised learning models, based on hidden Markov models, deep generative adversarial networks, restricted Boltzmann ... ...

Abstract	Population genetic studies often rely on artificial genomes (AGs) simulated by generative models of genetic data. In recent years, unsupervised learning models, based on hidden Markov models, deep generative adversarial networks, restricted Boltzmann machines, and variational autoencoders, have gained popularity due to their ability to generate AGs closely resembling empirical data. These models, however, present a tradeoff between expressivity and tractability. Here, we propose to use hidden Chow-Liu trees (HCLTs) and their representation as probabilistic circuits (PCs) as a solution to this tradeoff. We first learn an HCLT structure that captures the long-range dependencies among SNPs in the training data set. We then convert the HCLT to its equivalent PC as a means of supporting tractable and efficient probabilistic inference. The parameters in these PCs are inferred with an expectation-maximization algorithm using the training data. Compared to other models for generating AGs, HCLT obtains the largest log-likelihood on test genomes across SNPs chosen across the genome and from a contiguous genomic region. Moreover, the AGs generated by HCLT more accurately resemble the source data set in their patterns of allele frequencies, linkage disequilibrium, pairwise haplotype distances, and population structure. This work not only presents a new and robust AG simulator but also manifests the potential of PCs in population genetics.
Language	English
Publishing date	2023-05-18
Publishing country	United States
Document type	Preprint
DOI	10.1101/2023.05.16.541036
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Article ; Online: Fast kernel-based association testing of non-linear genetic effects for biobank-scale data.

Fu, Boyang / Pazokitoroudi, Ali / Sudarshan, Mukund / Liu, Zhengtong / Subramanian, Lakshminarayanan / Sankararaman, Sriram

Nature communications

2023 Volume 14, Issue 1, Page(s) 4936

Abstract: Our knowledge of non-linear genetic effects on complex traits remains limited, in part, due to the modest power to detect such effects. While kernel-based tests offer a versatile approach to test for non-linear relationships between sets of genetic ... ...

Abstract	Our knowledge of non-linear genetic effects on complex traits remains limited, in part, due to the modest power to detect such effects. While kernel-based tests offer a versatile approach to test for non-linear relationships between sets of genetic variants and traits, current approaches cannot be applied to Biobank-scale datasets containing hundreds of thousands of individuals. We propose, FastKAST, a kernel-based approach that can test for non-linear effects of a set of variants on a quantitative trait. FastKAST provides calibrated hypothesis tests while enabling analysis of Biobank-scale datasets with hundreds of thousands of unrelated individuals from a homogeneous population. We apply FastKAST to 53 quantitative traits measured across ≈ 300 K unrelated white British individuals in the UK Biobank to detect sets of variants with non-linear effects at genome-wide significance.
MeSH term(s)	Humans ; Biological Specimen Banks ; Phenotype ; Multifactorial Inheritance ; Genome ; Genome-Wide Association Study ; Models, Genetic ; Polymorphism, Single Nucleotide
Language	English
Publishing date	2023-08-15
Publishing country	England
Document type	Journal Article ; Research Support, U.S. Gov't, Non-P.H.S. ; Research Support, N.I.H., Extramural
ZDB-ID	2553671-0
ISSN	2041-1723 ; 2041-1723
ISSN (online)	2041-1723
ISSN	2041-1723
DOI	10.1038/s41467-023-40346-2
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Article ; Online: MaLAdapt Reveals Novel Targets of Adaptive Introgression From Neanderthals and Denisovans in Worldwide Human Populations.

Zhang, Xinjun / Kim, Bernard / Singh, Armaan / Sankararaman, Sriram / Durvasula, Arun / Lohmueller, Kirk E

Molecular biology and evolution

2023 Volume 40, Issue 1

Abstract: Adaptive introgression (AI) facilitates local adaptation in a wide range of species. Many state-of-the-art methods detect AI with ad-hoc approaches that identify summary statistic outliers or intersect scans for positive selection with scans for ... ...

Abstract	Adaptive introgression (AI) facilitates local adaptation in a wide range of species. Many state-of-the-art methods detect AI with ad-hoc approaches that identify summary statistic outliers or intersect scans for positive selection with scans for introgressed genomic regions. Although widely used, approaches intersecting outliers are vulnerable to a high false-negative rate as the power of different methods varies, especially for complex introgression events. Moreover, population genetic processes unrelated to AI, such as background selection or heterosis, may create similar genomic signals to AI, compromising the reliability of methods that rely on neutral null distributions. In recent years, machine learning (ML) methods have been increasingly applied to population genetic questions. Here, we present a ML-based method called MaLAdapt for identifying AI loci from genome-wide sequencing data. Using an Extra-Trees Classifier algorithm, our method combines information from a large number of biologically meaningful summary statistics to capture a powerful composite signature of AI across the genome. In contrast to existing methods, MaLAdapt is especially well-powered to detect AI with mild beneficial effects, including selection on standing archaic variation, and is robust to non-AI selective sweeps, heterosis from deleterious mutations, and demographic misspecification. Furthermore, MaLAdapt outperforms existing methods for detecting AI based on the analysis of simulated data and the validation of empirical signals through visual inspection of haplotype patterns. We apply MaLAdapt to the 1000 Genomes Project human genomic data and discover novel AI candidate regions in non-African populations, including genes that are enriched in functionally important biological pathways regulating metabolism and immune responses.
MeSH term(s)	Humans ; Animals ; Neanderthals/genetics ; Reproducibility of Results ; Genetics, Population ; Adaptation, Physiological ; Selection, Genetic ; Genome, Human
Language	English
Publishing date	2023-01-06
Publishing country	United States
Document type	Journal Article ; Research Support, N.I.H., Extramural ; Research Support, Non-U.S. Gov't
ZDB-ID	998579-7
ISSN	1537-1719 ; 0737-4038
ISSN (online)	1537-1719
ISSN	0737-4038
DOI	10.1093/molbev/msad001
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Article: Leveraging family data to design Mendelian Randomization that is provably robust to population stratification.

LaPierre, Nathan / Fu, Boyang / Turnbull, Steven / Eskin, Eleazar / Sankararaman, Sriram

bioRxiv : the preprint server for biology

2023

Abstract: Mendelian 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 due to weak instruments as ...

Abstract	Mendelian 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 due to weak instruments as well as the confounding effects of population stratification and horizontal pleiotropy. Here, we show that family data can be leveraged to design MR tests that are provably robust to confounding from population stratification, assortative mating, and dynastic effects. We demonstrate in simulations that our approach, MR-Twin, is robust to confounding from population stratification and is not affected by weak instrument bias, while standard MR methods yield inflated false positive rates. We applied MR-Twin to 121 trait pairs in the UK Biobank dataset and found that MR-Twin identifies likely causal trait pairs and does not identify trait pairs that are unlikely to be causal. Our results suggest that confounding from population stratification can lead to false positives for existing MR methods, while MR-Twin is immune to this type of confounding.
Language	English
Publishing date	2023-01-06
Publishing country	United States
Document type	Preprint
DOI	10.1101/2023.01.05.522936
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Article: Phenotypic subtyping via contrastive learning.

Gorla, Aditya / Sankararaman, Sriram / Burchard, Esteban / Flint, Jonathan / Zaitlen, Noah / Rahmani, Elior

bioRxiv : the preprint server for biology

2023

Abstract: Defining and accounting for subphenotypic structure has the potential to increase statistical power and provide a deeper understanding of the heterogeneity in the molecular basis of complex disease. Existing phenotype subtyping methods primarily rely on ... ...

Abstract	Defining and accounting for subphenotypic structure has the potential to increase statistical power and provide a deeper understanding of the heterogeneity in the molecular basis of complex disease. Existing phenotype subtyping methods primarily rely on clinically observed heterogeneity or metadata clustering. However, they generally tend to capture the dominant sources of variation in the data, which often originate from variation that is not descriptive of the mechanistic heterogeneity of the phenotype of interest; in fact, such dominant sources of variation, such as population structure or technical variation, are, in general, expected to be independent of subphenotypic structure. We instead aim to find a subspace with signal that is unique to a group of samples for which we believe that subphenotypic variation exists (e.g., cases of a disease). To that end, we introduce Phenotype Aware Components Analysis (PACA), a contrastive learning approach leveraging canonical correlation analysis to robustly capture weak sources of subphenotypic variation. In the context of disease, PACA learns a gradient of variation unique to cases in a given dataset, while leveraging control samples for accounting for variation and imbalances of biological and technical confounders between cases and controls. We evaluated PACA using an extensive simulation study, as well as on various subtyping tasks using genotypes, transcriptomics, and DNA methylation data. Our results provide multiple strong evidence that PACA allows us to robustly capture weak unknown variation of interest while being calibrated and well-powered, far superseding the performance of alternative methods. This renders PACA as a state-of-the-art tool for defining
Language	English
Publishing date	2023-01-06
Publishing country	United States
Document type	Preprint
DOI	10.1101/2023.01.05.522921
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Article ; Online: Leveraging family data to design Mendelian randomization that is provably robust to population stratification.

LaPierre, Nathan / Fu, Boyang / Turnbull, Steven / Eskin, Eleazar / Sankararaman, Sriram

Genome research

2023 Volume 33, Issue 7, Page(s) 1032–1041

Abstract: Mendelian 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, ...

Abstract	Mendelian 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, as well as the confounding effects of population stratification and horizontal pleiotropy. Here, we show that family data can be leveraged to design MR tests that are provably robust to confounding from population stratification, assortative mating, and dynastic effects. We show in simulations that our approach, MR-Twin, is robust to confounding from population stratification and is not affected by weak instrument bias, whereas standard MR methods yield inflated false positive rates. We then conduct an exploratory analysis of MR-Twin and other MR methods applied to 121 trait pairs in the UK Biobank data set. Our results suggest that confounding from population stratification can lead to false positives for existing MR methods, whereas MR-Twin is immune to this type of confounding, and that MR-Twin can help assess whether traditional approaches may be inflated owing to confounding from population stratification.
MeSH term(s)	Bias ; Genome-Wide Association Study ; Mendelian Randomization Analysis/methods ; Phenotype ; Reproduction ; Humans
Language	English
Publishing date	2023-05-17
Publishing country	United States
Document type	Journal Article ; Research Support, U.S. Gov't, Non-P.H.S. ; Research Support, N.I.H., Extramural
ZDB-ID	1284872-4
ISSN	1549-5469 ; 1088-9051 ; 1054-9803
ISSN (online)	1549-5469
ISSN	1088-9051 ; 1054-9803
DOI	10.1101/gr.277664.123
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