{"id":38310,"date":"2023-02-10T05:59:11","date_gmt":"2023-02-10T10:59:11","guid":{"rendered":"https:\/\/nebula.org\/blog\/?p=38310"},"modified":"2023-05-30T09:20:19","modified_gmt":"2023-05-30T13:20:19","slug":"unlocking-new-obesity-genetics","status":"publish","type":"post","link":"https:\/\/nebula.org\/blog\/unlocking-new-obesity-genetics\/","title":{"rendered":"Unlocking new obesity genetics"},"content":{"rendered":"\n

Obesity is both a national and global health problem. Overall, more than 1.9 billion adults<\/a> were obese in 2016 and worldwide obesity has nearly tripled since 1975. According to the Centers for Disease Control and Prevention (CDC), the U.S. obesity prevalence was 41.9% from 2017 \u2013 March 2020<\/a>. Obesity genetics is a field of research seeking to evaluate the extent to which our predispositions contribute to the condition.
Being obese has health complications in itself and is related to other conditions including heart disease<\/a>, stroke<\/a>, type 2 diabetes<\/a>, and certain types of cancer (e.g., ovarian<\/a>, endometrial, breast, prostate<\/a>, liver, gallbladder, colon<\/a>, and kidney). These are among some of the most common causes of preventable, premature death. It can also lead to issues affecting the quality of life including sleep problems, mental health conditions, and low self-esteem.<\/p>\n\n\n\n

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Photo by i yunmai<\/a> on Unsplash<\/a><\/figcaption><\/figure>\n\n\n\n

obesity based on Body Mass Index (BMI) plus other measurements such as abdominal circumference and the waist-hip ratio.
While lifestyle and culture play the largest role in predicting whether someone is at risk for obesity, did you know that experts are discovering certain genes can also play a role? Notably, there are certain rare variants that, when present, work together to protect from obesity. The majority of the population does not have them, which is normal. However, for the very few cases that do, those folks get some extra protection through the truncation of the gene GPR75<\/em>.<\/p>\n\n\n\n

You can use the Nebula Gene Analysis Tool to find this gene and see if you have these functional variants. Keep reading to learn what these newly discovered mutations are and how you can check your status.<\/strong><\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n\n

What Causes Obesity?<\/strong><\/h2>\n\n\n\n

Obesity is incredibly complex and it is typically impossible to pinpoint just one cause. Experts believe environmental, lifestyle choices, and genetics all play a role to various extents. <\/p>\n\n\n\n

For most people, the key to preventing obesity is eating a healthy diet and regular physical exercise. However, things in one\u2019s environment that can contribute to the condition include a lack of safe places to exercise, food deserts that make healthy foods inaccessible, and a lack of proper education tools in schools. Unfortunately, these issues tend to affect vulnerable areas, including black and economically disadvantaged communities. <\/p>\n\n\n\n

Certain medical conditions (Cushing\u2019s disease and polycystic ovary syndrome) and medications (steroids and antidepressants) can also cause weight gain.<\/p>\n\n\n\n

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Photo by Diana Polekhina<\/a> on Unsplash<\/a><\/figcaption><\/figure>\n\n\n\n

Most genetic research suggests that polygenic contributions each play a small role in predisposing an individual<\/a> to obesity and that exposure to lifestyle risks prompts the condition to occur. Inherited genes that affect energy balance also play a role. Weight management typically reduces this risk in most cases. However, new research published in Science<\/em><\/a> identifies variants in the GPR75 <\/em>gene that may protect against obesity.<\/p>\n\n\n\n

The Study<\/strong><\/h2>\n\n\n\n

The authors of this publication performed high-coverage whole-exome-sequencing for BMI in 645,626 individuals across three distinct cohorts and regions. This included 428,719 individuals of European ancestry (UK Biobank cohort), 121,061 individuals of European ancestry (MyCode Community Health Initiative cohort from the US-based Geisinger Health System), and 95,846 individuals of admixed American ancestry (Mexico City Prospective Study).<\/p>\n\n\n\n

Results<\/strong><\/h2>\n\n\n\n

Overall, they found 16 genes with rare variants associated with BMI. Out of these 16 genes, two had rare mutations known to cause monogenic obesity (MC4R<\/em> and PCSK1<\/em>), two had rare coding variants that have been associated with BMI (GPR151<\/em> and GIPR<\/em>), and 12 linked rare coding variations to BMI and obesity-related phenotypes. <\/p>\n\n\n\n

Out of the 16 discovered, the authors choose to further analyze GPR75<\/em> as it is a G protein\u2013coupled receptor (GPCRs – the largest class of drug targets in the human genome). Other favorable attributes are that it is highly expressed in the brain across species and had the largest effect size with lower BMI in the exome analysis.<\/p>\n\n\n\n

Specifically, they explored 46 predicted loss-of-function (pLOF) variants which occurred in ~4 out of every 10,000 people sequenced. They found a strong association between these variants and lower BMI as well as general protection against obesity. Specifically, heterozygous carriers of GPR75<\/em> pLOF variants had 54% lower odds of obesity compared with noncarriers in all three cohorts. Through testing the association with nearby variants and genes, the authors concluded that multiple independent rare pLOF variants predicted to truncate GPR75<\/em> at different locations led to the association with lower BMI. 
They even saw differences based on where the gene was truncated. Variants resulting in truncation of GPR75<\/em> before the final intracellular domain was associated with a \u22122.1 kg\/m2<\/sup> lower BMI while those resulting in truncation in the final domain were associated with only a \u22121.4 kg\/m2<\/sup> lower BMI.<\/p>\n\n\n\n

Conclusions<\/strong><\/h2>\n\n\n\n

There are some limitations to this study. For example, due to the rarity of these mutations, the authors did not find associations with the common complications like type 2 diabetes that they expected.<\/p>\n\n\n\n

However, they point out the value of exome sequencing in population studies in discovering rare variants that affect complex diseases such as obesity genetics. They believe that overall understanding the molecular and genetic background of body fat can one day lead to more effective therapeutics. Their focus on GPR75<\/em> as a promising candidate is largely due to its notable effect on obesity when knocked out, a concrete avenue towards drug discovery.<\/p>\n\n\n\n

These variants were present in less than 0.1% of sequenced individuals across three multiethnic cohorts, suggesting they are incredibly rare in the general population. Therefore, it\u2019s completely normal not to have variants truncating this protein and not to have this additional protective effect.<\/p>\n\n\n\n

Explore your Genome!<\/strong><\/h2>\n\n\n\n
Did you know you can use the Nebula Gene Analysis Tool (available with Deep and Ultra Deep WGS) to check whether you have these protective variants?<\/strong><\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n\n

This tool empowers you to examine any gene in your genome and identify important genetic variants and mutations. <\/p>\n\n\n\n

    \n
  1. When you click on the \u201cGet Started\u201d button your VCF file will be loaded into the Gene Analysis tool in a new tab.<\/li>\n\n\n\n
  2. Type \u201cGPR75\u201d into the search bar at the top. <\/li>\n\n\n\n
  3. The Gene Analysis tool will extract genetic variants in the GPR75 gene from your VCF file and display them to you using symbols that have different colors. The colors denote the potential importance of variants. The Gene Analysis tool determines this by referencing the ClinVar database and other resources.<\/li>\n\n\n\n
  4. Red and orange variants could potentially be important. Click on them to check if any of them are truncating variants like the ones described in the study described above.<\/li>\n<\/ol>\n\n\n\n

    46 variants discovered to truncate GPR75<\/h3>\n\n\n\n
      \n
    1. c.-110+1G>A<\/li>\n\n\n\n
    2. Met1? (2:53854755:A:G)<\/li>\n\n\n\n
    3. His6fs (2:53854740:TG:T)<\/li>\n\n\n\n
    4. Ser21* (2:53854695:G:T)<\/li>\n\n\n\n
    5. Gly24fs (2:53854685:TC:T)<\/li>\n\n\n\n
    6. His38fs (2:53854644:TG:T)<\/li>\n\n\n\n
    7. Leu91fs (2:53854485:AG:A)<\/li>\n\n\n\n
    8. Cys94fs (2:53854476:C:CA)<\/li>\n\n\n\n
    9. Gly95* (2:53854474:C:A)<\/li>\n\n\n\n
    10. Ala110fs (2:53854421:ACTACTGG:A)<\/li>\n\n\n\n
    11. Cys118fs (2:53854409:A:AG)<\/li>\n\n\n\n
    12. Ser126* (2:53854380:G:C)<\/li>\n\n\n\n
    13. Gln151* (2:53854306:G:A)<\/li>\n\n\n\n
    14. Leu206fs (2:53854137:TAGAG:T)<\/li>\n\n\n\n
    15. Tyr207fs (2:53854135:CAT:C)<\/li>\n\n\n\n
    16. Ser219fs (2:53854099:CAG:C)<\/li>\n\n\n\n
    17. Gln227* (2:53854078:G:A)<\/li>\n\n\n\n
    18. Gln234* (2:53854057:G:A)<\/li>\n\n\n\n
    19. Arg236* (2:53854051:T:A)<\/li>\n\n\n\n
    20. Arg236fs (2:53854045:ACTTT:A)<\/li>\n\n\n\n
    21. Val241fs (2:53854037:A:AG)<\/li>\n\n\n\n
    22. Gln250* (2:53854009:G:A)<\/li>\n\n\n\n
    23. Pro236fs (2:53853967:TGG:T)<\/li>\n\n\n\n
    24. Leu271fs (2:53853946:G:GT)<\/li>\n\n\n\n
    25. Tyr277fs (2:53853927:T:TA)<\/li>\n\n\n\n
    26. Tyr277* (2:53853926:G:T)<\/li>\n\n\n\n
    27. Gln294* (2:53853877:G:A)<\/li>\n\n\n\n
    28. Arg302* (2:53853853:G:A)<\/li>\n\n\n\n
    29. Ser329* (2:53853771:G:C)<\/li>\n\n\n\n
    30. Gln343* (2:53853730:G:A)<\/li>\n\n\n\n
    31. Tyr355* (2:53853692:G:T)<\/li>\n\n\n\n
    32. Tyr355fs (2:53853680:CAATTCAAACTGGT:C)<\/li>\n\n\n\n
    33. Asn372fs (2:53853641:GTT:G)<\/li>\n\n\n\n
    34. Cys400fs (2:53853560:G:GA)<\/li>\n\n\n\n
    35. Lys404* (2:53853547:T:A)<\/li>\n\n\n\n
    36. Arg408* (2:53853535:G:A)<\/li>\n\n\n\n
    37. Arg419* (2:53853502:T:A)<\/li>\n\n\n\n
    38. Lys459fs (2:53853382:TG:T)<\/li>\n\n\n\n
    39. Cys467fs (2:53853354:CCA:C)<\/li>\n\n\n\n
    40. Gln469* (2:53853352:G:A)<\/li>\n\n\n\n
    41. Gln501* (2:53853256:G:A)<\/li>\n\n\n\n
    42. Asn504fs (2:53853245:GT:G)<\/li>\n\n\n\n
    43. Thr519fs (2:53853200:GGT:G)<\/li>\n\n\n\n
    44. Ter541Gluext*? (2:53853136:A:C)<\/li>\n\n\n\n
    45. Ter541Serext*? (2:53853135:T:G)<\/li>\n\n\n\n
    46. Ter541Tyrext*? (2:53853134:T:G)<\/li>\n<\/ol>\n\n\n\n

      Citation<\/h2>\n\n\n\n

      Akbari P, Gilani A, Sosina O, Kosmicki JA, Khrimian L, Fang YY, Persaud T, Garcia V, Sun D, Li A, Mbatchou J, Locke AE, Benner C, Verweij N, Lin N, Hossain S, Agostinucci K, Pascale JV, Dirice E, Dunn M; Regeneron Genetics Center; DiscovEHR Collaboration; Kraus WE, Shah SH, Chen YI, Rotter JI, Rader DJ, Melander O, Still CD, Mirshahi T, Carey DJ, Berumen-Campos J, Kuri-Morales P, Alegre-D\u00edaz J, Torres JM, Emberson JR, Collins R, Balasubramanian S, Hawes A, Jones M, Zambrowicz B, Murphy AJ, Paulding C, Coppola G, Overton JD, Reid JG, Shuldiner AR, Cantor M, Kang HM, Abecasis GR, Karalis K, Economides AN, Marchini J, Yancopoulos GD, Sleeman MW, Altarejos J, Della Gatta G, Tapia-Conyer R, Schwartzman ML, Baras A, Ferreira MAR, Lotta LA. Sequencing of 640,000 exomes identifies GPR75 variants associated with protection from obesity. Science. 2021 Jul 2;373(6550):eabf8683. doi: 10.1126\/science.abf8683. PMID: 34210852.<\/em><\/p>\n","protected":false},"excerpt":{"rendered":"

      Obesity is both a national and global health problem. Overall, more than 1.9 billion adults were obese in 2016 and worldwide obesity has nearly tripled since 1975. According to the Centers for Disease Control and Prevention (CDC), the U.S. obesity …<\/p>\n

      Unlocking new obesity genetics<\/span> Read More \u00bb<\/a><\/p>\n","protected":false},"author":18,"featured_media":38335,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"om_disable_all_campaigns":false,"site-sidebar-layout":"default","site-content-layout":"default","ast-global-header-display":"","ast-banner-title-visibility":"","ast-main-header-display":"","ast-hfb-above-header-display":"","ast-hfb-below-header-display":"","ast-hfb-mobile-header-display":"","site-post-title":"","ast-breadcrumbs-content":"","ast-featured-img":"","footer-sml-layout":"","theme-transparent-header-meta":"","adv-header-id-meta":"","stick-header-meta":"","header-above-stick-meta":"","header-main-stick-meta":"","header-below-stick-meta":"","_FSMCFIC_featured_image_caption":"Source: Shutterstock","_FSMCFIC_featured_image_nocaption":"","_FSMCFIC_featured_image_hide":"","footnotes":""},"categories":[2350],"tags":[],"class_list":["post-38310","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-science"],"yoast_head":"\nUnlocking New Obesity Genetics<\/title>\n<meta name=\"description\" content=\"New research is exploring obesity genetics. See how you can check out your genome for certain genetic changes.\" \/>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/nebula.org\/blog\/unlocking-new-obesity-genetics\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Unlocking New Obesity Genetics\" \/>\n<meta property=\"og:description\" content=\"New research is exploring obesity genetics. See how you can check out your genome for certain genetic changes.\" \/>\n<meta property=\"og:url\" content=\"https:\/\/nebula.org\/blog\/unlocking-new-obesity-genetics\/\" \/>\n<meta property=\"og:site_name\" content=\"Nebula Genomics Blog\" \/>\n<meta property=\"article:publisher\" content=\"https:\/\/facebook.com\/nebulagenomics\" \/>\n<meta property=\"article:published_time\" content=\"2023-02-10T10:59:11+00:00\" \/>\n<meta property=\"article:modified_time\" content=\"2023-05-30T13:20:19+00:00\" \/>\n<meta property=\"og:image\" content=\"https:\/\/nebula.org\/blog\/wp-content\/uploads\/2023\/02\/BMI-Measurement-1.jpg\" \/>\n\t<meta property=\"og:image:width\" content=\"924\" \/>\n\t<meta property=\"og:image:height\" content=\"616\" \/>\n\t<meta property=\"og:image:type\" content=\"image\/jpeg\" \/>\n<meta name=\"author\" content=\"Christina Swords, Ph.D.\" \/>\n<meta name=\"twitter:card\" content=\"summary_large_image\" \/>\n<meta name=\"twitter:creator\" content=\"@nebulagenomics\" \/>\n<meta name=\"twitter:site\" content=\"@nebulagenomics\" \/>\n<meta name=\"twitter:label1\" content=\"Written by\" \/>\n\t<meta name=\"twitter:data1\" content=\"Christina Swords, Ph.D.\" \/>\n\t<meta name=\"twitter:label2\" content=\"Est. reading time\" \/>\n\t<meta name=\"twitter:data2\" content=\"7 minutes\" \/>\n<script type=\"application\/ld+json\" class=\"yoast-schema-graph\">{\"@context\":\"https:\/\/schema.org\",\"@graph\":[{\"@type\":\"Article\",\"@id\":\"https:\/\/nebula.org\/blog\/unlocking-new-obesity-genetics\/#article\",\"isPartOf\":{\"@id\":\"https:\/\/nebula.org\/blog\/unlocking-new-obesity-genetics\/\"},\"author\":{\"name\":\"Christina Swords, Ph.D.\",\"@id\":\"https:\/\/nebula.org\/blog\/#\/schema\/person\/97db973a52b62c2cb68bcf104374a772\"},\"headline\":\"Unlocking new obesity genetics\",\"datePublished\":\"2023-02-10T10:59:11+00:00\",\"dateModified\":\"2023-05-30T13:20:19+00:00\",\"mainEntityOfPage\":{\"@id\":\"https:\/\/nebula.org\/blog\/unlocking-new-obesity-genetics\/\"},\"wordCount\":1386,\"publisher\":{\"@id\":\"https:\/\/nebula.org\/blog\/#organization\"},\"articleSection\":[\"Science\"],\"inLanguage\":\"en-US\"},{\"@type\":\"WebPage\",\"@id\":\"https:\/\/nebula.org\/blog\/unlocking-new-obesity-genetics\/\",\"url\":\"https:\/\/nebula.org\/blog\/unlocking-new-obesity-genetics\/\",\"name\":\"Unlocking New Obesity Genetics\",\"isPartOf\":{\"@id\":\"https:\/\/nebula.org\/blog\/#website\"},\"datePublished\":\"2023-02-10T10:59:11+00:00\",\"dateModified\":\"2023-05-30T13:20:19+00:00\",\"description\":\"New research is exploring obesity genetics. 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