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      Genome-wide associations of human gut microbiome variation and implications for causal inference analyses

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          Abstract

          Recent population-based 14 and clinical studies 5 have identified a range of factors associated with human gut microbiome variation. Murine quantitative trait loci 6 , human twin studies 7 and microbiome genome-wide association studies (mGWAS) 1, 3, 812 have provided evidence for genetic contributions to microbiome composition. Despite this, there is still poor overlap in genetic association across human studies. Using appropriate taxon-specific models along with support from independent cohorts, we show association between human host genotype and gut microbiome variation. We also suggest that interpretation of applied analyses using genetic associations is complicated by the likely overlap between genetic contributions and heritable components of host environment. Using fecal derived 16S rRNA gene sequences and host genotype data from the Flemish Gut Flora Project (FGFP, n=2223) and two German cohorts (FoCus, n=950, PopGen n=717), we identify genetic associations involving multiple microbial traits (MTs). Two of these associations achieved a study-level p-value threshold of 1.57×10−10; an association between Ruminococcus and rs150018970 near RAPGEF1 on chromosome 9, and between Coprococcus and rs561177583 within LINC01787 on chromosome 1. Exploratory analysis was undertaken using 11 other genome-wide associations with strong evidence for association (p-value < 2.5×10 −08) and a previously reported signal of association between rs4988235 ( MCM6/ LCT) and Bifidobacterium. Across these 14 SNPs there was evidence of signal overlap with other GWAS including those for age at menarche and cardiometabolic traits. Mendelian randomization (MR) analysis was able to estimate associations between MTs and disease (including Bifidobacterium and body composition), however in the absence of clear microbiome driven effects, caution is needed in interpretation. Overall, this work marks a growing catalog of genetic associations which will provide insight into the contribution of host genotype to gut microbiome. Despite this, the uncertain origin of association signals will likely complicate future work looking to dissect function or use associations for causal inference analysis.

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          Most cited references57

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          DADA2: High resolution sample inference from Illumina amplicon data

          We present DADA2, a software package that models and corrects Illumina-sequenced amplicon errors. DADA2 infers sample sequences exactly, without coarse-graining into OTUs, and resolves differences of as little as one nucleotide. In several mock communities DADA2 identified more real variants and output fewer spurious sequences than other methods. We applied DADA2 to vaginal samples from a cohort of pregnant women, revealing a diversity of previously undetected Lactobacillus crispatus variants.
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            MUSCLE: multiple sequence alignment with high accuracy and high throughput.

            We describe MUSCLE, a new computer program for creating multiple alignments of protein sequences. Elements of the algorithm include fast distance estimation using kmer counting, progressive alignment using a new profile function we call the log-expectation score, and refinement using tree-dependent restricted partitioning. The speed and accuracy of MUSCLE are compared with T-Coffee, MAFFT and CLUSTALW on four test sets of reference alignments: BAliBASE, SABmark, SMART and a new benchmark, PREFAB. MUSCLE achieves the highest, or joint highest, rank in accuracy on each of these sets. Without refinement, MUSCLE achieves average accuracy statistically indistinguishable from T-Coffee and MAFFT, and is the fastest of the tested methods for large numbers of sequences, aligning 5000 sequences of average length 350 in 7 min on a current desktop computer. The MUSCLE program, source code and PREFAB test data are freely available at http://www.drive5. com/muscle.
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              PLINK: a tool set for whole-genome association and population-based linkage analyses.

              Whole-genome association studies (WGAS) bring new computational, as well as analytic, challenges to researchers. Many existing genetic-analysis tools are not designed to handle such large data sets in a convenient manner and do not necessarily exploit the new opportunities that whole-genome data bring. To address these issues, we developed PLINK, an open-source C/C++ WGAS tool set. With PLINK, large data sets comprising hundreds of thousands of markers genotyped for thousands of individuals can be rapidly manipulated and analyzed in their entirety. As well as providing tools to make the basic analytic steps computationally efficient, PLINK also supports some novel approaches to whole-genome data that take advantage of whole-genome coverage. We introduce PLINK and describe the five main domains of function: data management, summary statistics, population stratification, association analysis, and identity-by-descent estimation. In particular, we focus on the estimation and use of identity-by-state and identity-by-descent information in the context of population-based whole-genome studies. This information can be used to detect and correct for population stratification and to identify extended chromosomal segments that are shared identical by descent between very distantly related individuals. Analysis of the patterns of segmental sharing has the potential to map disease loci that contain multiple rare variants in a population-based linkage analysis.
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                Author and article information

                Journal
                101674869
                Nat Microbiol
                Nat Microbiol
                Nature microbiology
                2058-5276
                04 March 2021
                01 September 2020
                22 June 2020
                29 March 2021
                : 5
                : 9
                : 1079-1087
                Affiliations
                [1 ]MRC Integrative Epidemiology Unit at University of Bristol, Bristol, BS8 2BN, UK
                [2 ]Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, BS8 2BN, UK
                [3 ]Department of Microbiology and Immunology, Rega Instituut, KU Leuven–University of Leuven, Leuven, Belgium
                [4 ]Center for Microbiology, VIB, Leuven, Belgium
                [5 ]Institute of Microbiology, Chinese Academy of Sciences, Chaoyang District, 100101 Beijing, China
                [6 ]Institute of Clinical Molecular Biology, Christian Albrechts University of Kiel, Kiel, Germany
                [7 ]KU Leuven-University of Leuven, Department of Microbiology, Immunology and Transplantation, Leuven, Belgium
                [8 ]KU Leuven-University Hospitals Leuven, Department of General Internal Medicine, Leuven, Belgium
                [9 ]Bristol Bioresource Laboratories (BBL), University of Bristol, UK
                Author notes
                [** ]co-corresponding authors
                [*]

                co-first authors

                Article
                EMS118385
                10.1038/s41564-020-0743-8
                7610462
                32572223
                d61b6501-7eaf-4e2d-b218-73a8c4f7db38

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