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  1. Evaluation Summary:

    This manuscript is of broad interest to geneticists and bone biologists because it describes a method to filter candidate genes, identified from GWAS, and pinpoint the gene that affects bone biology. This method identified a gene with a previously unknown role in bone biology and the authors showed that its loss reduces bone mineral density, supporting the key claims in the paper.

    (This preprint has been reviewed by eLife. We include the public reviews from the reviewers here; the authors also receive private feedback with suggested changes to the manuscript. Reviewer #2 agreed to share their name with the authors.)

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  2. Reviewer #1 (Public Review):

    In this manuscript, the authors conduct research that is intended to overcome a major challenge of human genetic association studies - the movement from locus to causal polymorphism and causal gene. This is particularly challenging for polymorphisms that affect non-coding regions that affect transcription or RNA stability. Here the authors integrate genome-wide association study (GWAS) phenotype and eQTL data with two independent approaches, TWAS (which integrates gene and GWAS data in a single analysis) and eQTL-GWAS loci co-localization. They use publicly available data from a large UK Biobank GWAS study on bone mineral density (BMD) and transcriptomic data from 49 tissues prepared as part of GTex. The authors use state of the art methods for this analysis, use creative filters to reduce their candidate gene list (e.g. a novel curated list of "known" bone genes, comparison to data on the impact of gene deletion on bone from mice) then conduct a follow-up study for one candidate gene in genetically modified mice. The value of this study is that it clearly shows the value of the integrative approaches but also presents a realistic picture of the strengths, weaknesses, and barriers that influence the quality of candidate gene identification from human GWAS, especially for a phenotype with no eQTL data like bone. The work represents the current state of the art. As such, this work will be a valuable resource for both researchers interested in bone biology as well as those more generally interested in candidate gene selection from human GWAS.

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  3. Reviewer #2 (Public Review):

    The present study used summary statistics from the largest GWAS for bone mineral density, the strongest predictor for osteoporosis, and prioritized genes for further studies using Transcriptome-wide association studies and eQTL data available from 49 GTEx tissues. This resulted in the prioritization of 521 genes, from which they chose the strongest signal (PPP6R3R) to carry on functional studies in mice knockouts. Mice mutants showed reduced trabecular bone and volumetric BMD, providing support as causal gene BMD.

    This study is important because it is one of the first studies to take such a complex bioinformatics approach for gene prioritization and to exemplify how multifaceted are the association found in BMD loci. The gene selected for functional evaluation in mice is located next to LRP5 a known gene to play a role in osteoporosis, therefore suggesting that multiple genes are casual within this locus. One limitation is that bone is not enriched in GTEx data.

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  4. Reviewer #3 (Public Review):

    This well-written manuscript includes a carefully performed bioinformatic analysis, accompanied by an analysis of a candidate gene prioritized for its potential role in bone mineral density (BMD). Authors clearly indicated that although GWAS successfully identified many variants associated with clinically relevant traits, such as BMD, gene-level discoveries are limited without biological context. Therefore, this study nicely illustrates the application of existing bioinformatic strategies to improve our understanding of the genetics of BMD, specifically by using TWAS and eQTL colocalization using GTEx data to identify potentially causal BMD genes. Supplemental files, including a list of 'known bone genes', provide a valuable resource for the field.

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