Levy Library Blog

Mount Sinai Health System Libraries answered over 2,700 reference inquiries in 2017! The Education & Research Services team provided nearly 400 consultations and led 167 education sessions for users across the system. 

Congratulations to all and we look forward to continued growth in 2018!

Earlier this month, Levy Library set up shop at a Graduate School holiday event to promote the platforms ORCID and PlumX.

ORCID is a tool that provides each user with a unique personal identifier to be used when engaging in research, scholarship and other professional activities. This identifier helps create transparency and accuracy in research contribution. PlumX, developed by Plum Analytics, is a platform that aggregates an article's metrics into one dashboard. These metrics include usage, mentions, captures, social media and citations. PlumX lets authors gain insight into not just the article's usage by other researchers but also the impact among casual readers. 

Representing Levy Library were Gali Halevi, Peter Huang, and Chelsea Gizzi. They attended the event to encourage graduate students to sign up for these platforms early and get a head start on planning their publishing careers. 

For information and education on academic publishing, contact refdesk@mssm.edu.

ORCID webpage

Plum Analytics webpage

On Tuesday, January 12th, Gali Halevi, Chief Director of Mount Sinai Health System Libraries, welcomed a full roster to “Submission, Promotion and Impact,” the final class in the Scientific Writing and Publishing series. Attendees showed up in search of learning more information on submitting for journal publication and the tools and practices for maximizing an article's online impact.

Topics covered in the class included:

• How to select the right journal for your needs and available tools to help you do so

• Open access publishing model and how it differs from traditional subscription-based publishing

• What predatory publishing journals are and how to identify them

• A breakdown of different citation metrics used in publishing

• What defines “altmetrics” and how they change the impact landscape

• Suggestions of how to get your article noticed, including social media and online profiles

Missed the class and looking for publication support? Levy Library offers group and one-on-one instruction. For more information, please contact refdesk@mssm.edu.

The end of a term is a very stressful time for medical students with preparing for exams and wrapping up academic work. On Monday, December 11th, Levy Library offered students a small token of appreciation in the form of make-your-own End of Term Survival Kits. Students eagerly lined up early to get first dibs at the available goodies. 

These kits were put together with relaxation and stress relief in mind. Items included: 

• Granola bars/trail mix
• Tea
• Candy
• Earpulgs
• Bookmarks
• Color pens

Thank you to all the students who came out and we wish you the best of luck!

Each month the Levy Library showcases the achievements of Mount Sinai faculty and researchers by highlighting an article and its altmetrics. Altmetrics are alternative measures of impact that capture non-traditional data like abstract views, article downloads, and social media activity.

This month we highlight the article written by a team of researchers including Mount Sinai’s Yingchang Lu, Icahn School of Medicine at Mount Sinai, Charles Bronfman Institute for Personalized Medicine.

Citation: Marouli E, Graff M, Medina-Gomez C, et al. Rare and low-frequency coding variants alter human adult height. Nature. 2017;542(7640):186-190. doi:10.1038/nature21039.

Summary: 

Height is a highly heritable, classic polygenic trait with ∼700 common associated variants identified so far through genome-wide association studies. Here, we report 83 height-associated coding variants with lower minor allele frequencies (range of 0.1-4.8%) and effects of up to 2 cm/allele (e.g. in IHH, STC2, AR and CRISPLD2), >10 times the average effect of common variants. In functional follow-up studies, rare height-increasing alleles of STC2 (+1-2 cm/allele) compromised proteolytic inhibition of PAPP-A and increased cleavage of IGFBP-4 in vitro, resulting in higher bioavailability of insulin-like growth factors. These 83 height-associated variants overlap genes mutated in monogenic growth disorders and highlight new biological candidates (e.g. ADAMTS3, IL11RA, NOX4) and pathways (e.g.proteoglycan/glycosaminoglycan synthesis) involved in growth. Our results demonstrate that sufficiently large sample sizes can uncover rare and low-frequency variants of moderate to large effect associated with polygenic human phenotypes, and that these variants implicate relevant genes and pathways.

Main

Human height is a highly heritable, polygenic trait1,2. The contribution of common DNA sequence variation to inter-individual differences in adult height has been systematically evaluated through genome-wide association studies (GWAS). This approach has thus far identified 697 independent variants located within 423 loci that together explain around 20% of the heritability of height3. As is typical of complex traits and diseases, most of the alleles that affect height that have been discovered so far are common (with a minor allele frequency (MAF) > 5%) and are mainly located outside coding regions, complicating the identification of the relevant genes or functional variants. Identifying coding variants associated with a complex trait in new or known loci has the potential to help pinpoint causal genes. Furthermore, the extent to which rare (MAF < 1%) and low-frequency (1% < MAF ≤ 5%) coding variants also influence complex traits and diseases remains an open question. Many recent DNA sequencing studies have identified only a few of these variants4,5,6,7,8, but this limited success could be due to their modest sample size9. Some studies have suggested that common sequence variants may explain the majority of the heritable variation in adult height10. It is therefore timely to assess whether and to what extent rare and low-frequency coding variations contribute to the genetic landscape of this model polygenic trait.

In this study, we used an ExomeChip11 to test the association between 241,453 variants (of which 83% are coding variants with a MAF ≤ 5%) and adult height variation in 711,428 individuals (discovery and validation sample sizes were 458,927 and 252,501, respectively). The ExomeChip is a genotyping array designed to query in very large sample sizes coding variants identified by whole-exome DNA sequencing of approximately 12,000 participants. The main goals of our project were to determine whether rare and low-frequency coding variants influence the architecture of a model complex human trait (in this case, adult height) and to discover and characterize new genes and biological pathways implicated in human growth.

Results

32 rare and 51 low-frequency coding variants associated with adult height

We conducted single-variant meta-analyses in a discovery sample of 458,927 individuals, of whom 381,625 were of European ancestry. We validated our association results in an independent set of 252,501 participants. We first performed standard single-variant association analyses; technical details of the discovery and validation steps are in Methods (Extended Data Figs 1-​-3,3, Supplementary Tables 1-11). In total, we found 606 independent ExomeChip variants at array-wide significance (P<2×10-7), including 252 non-synonymous or splice site variants (Methods and Supplementary Table 11). Focusing on non-synonymous or splice site variants with MAF <5%, our single-variant analyses identified 32 rare and 51 low-frequency height-associated variants.

To date, these 83 height variants (MAF range 0.1-4.8%) represent the largest set of validated rare and low-frequency coding variants associated with any complex human trait or disease. Among these 83 variants, there are 81 missense, one nonsense (in CCND3), and one essential acceptor splice site (in ARMC5) variants.

We observed a strong inverse relationship between MAF and effect size (Fig. 1). Although power limits our capacity to find rare variants of small effects, we know that common variants with effect sizes comparable to the largest seen in our study would have been easily discovered by prior GWAS, but were not detected. Our results agree with a model based on accumulating theoretical and empirical evidences that suggest that variants with strong phenotypic effects are more likely to be deleterious, and therefore rarer12,13. The largest effect sizes were observed for four rare missense variants, located in the androgen receptor gene AR (rs137852591, MAF=0.21%, Pcombined=2.7×10-14), in CRISPLD2 (rs148934412, MAF=0.08%, Pcombined=2.4×10-20), in IHH (rs142036701, MAF=0.08%, Pcombined=1.9×10-23), and in STC2 (rs148833559, MAF=0.1%, Pcombined=1.2×10-30). Carriers of the rare STC2 missense variant are ∼2.1 cm taller than non-carriers, whereas carriers of the remaining three variants (or hemizygous men that carry the X-linked AR-rs137852591 rare allele) are ∼2 cm shorter than non-carriers. In comparison, the mean effect size of common height alleles is ten times smaller in the same dataset. Across all 83 rare and low-frequency non-synonymous variants, the minor alleles were evenly distributed between height-increasing and -decreasing effects (48% vs. 52%, respectively)

View this article on PlumX