Monthly Archives: October 2022

Establishing a new field of science to answer the question of what makes humans unique from our extinct relatives has earned Svante Pbo the Nobel Prize in physiology or medicine.

Humanity has always been intrigued by its origins. Where did we come from and how are we related to those who came before us? What makes us different from hominins that went extinct? said Anna Wedell, a member of the Nobel Assembly at the Karolinska Institute in Stockholm that announced the prize on October 3.

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Before Pbos work, archaeologists and paleontologists studied bones and artifacts to learn about human evolution. But the surface study of those relics couldnt answer some fundamental questions about the genetic changes that led humans to thrive while other ancient hominids went extinct. Pbo, a geneticist at the Max Planck Institute for Evolutionary Anthropology in Leipzig, Germany, worked out a way to extract and analyze DNA from ancient bones (SN: 11/15/06). That led to uncovering small genetic differences between humans and extinct human relatives.

Getting DNA from ancient bones was once considered impossible, says Leslie Vosshall, a neuroscientist at the Rockefeller University in New York City, who is the vice president and chief scientific officer at the Howard Hughes Medical Institute. DNA breaks down over time, so many scientists thought that there would be none remaining in fossils tens of thousands of years old. Not to mention that DNA from bacteria and other microbes and from living people contaminate the ancient genetic material. Yet Pbo managed to stitch together tiny fragments of Neandertal DNA into readable sequences. He started with DNA from mitochondria, the energy-generating organelles inside cells. Then, he assembled a complete genetic instruction book, or genome, for a Neandertal.

Over the years Vosshall watched as Pbo presented snippets of DNA from old bones at scientific meetings. Nobody believed him. Everyone thought it was contamination or broken stuff from living people. Just the mere fact that he did it was so improbable. That he was able to get the complete genome sequence of a Neandertal was viewed, even up until he did it, as an absolutely impossible feat.

On a technical basis, the prize is also richly deserved, she says.

Noted Nils-Gran Larsson, vice chairman of the Nobel committee: This is a very fundamental, big discovery Over the years to come, [this] will give huge insights into human physiology.

Pbos work established the field of paleogenomics. He always pushed the frontiers of evolutionary anthropology, says Ludovic Orlando, a molecular archaeologist at the Centre for Anthropobiology and Genomics of Toulouse in France.

Pbo said that when he got the news of his win, he thought at first it was an elaborate prank by the people in his research group, but soon realized it was the real deal. The thing that is amazing to me is that we now have some ability to go back in time and actually follow genetic history and genetic changes over time, he said in a news conference several hours after the prize was announced.

Pbo and colleagues have made surprising discoveries about human evolution from studying ancient DNA. For instance, they learned that humans and our extinct cousins, Neandertals, had children together. That discovery came as a shock to even people who had been looking for signs of interbreeding (SN: 5/6/10). Evidence of that mixing can still be found in many humans today (SN: 10/10/17).

Pbos study of a finger bone revealed a previously undiscovered extinct human relative called Denisovans (SN: 8/30/12). Like Neandertals, Denisovans interbred with humans.

DNA passed down from those extinct ancestors has influenced human health and physiology for better or worse. For instance, genetic variants inherited from Denisovans helped humans adapt to high altitude in Tibet (SN: 7/2/14). But some Neandertal DNA has been linked to a higher risk of developing some diseases, including severe COVID-19 (SN: 2/11/16; SN: 10/2/20).

His work has also delved into tiny genetic changes that may have influenced the evolution of the human brain (SN: 2/26/15). Other researchers have also applied techniques Pbo developed to study evolution and domestication of animals (SN: 7/6/17), and to learn about how ancient humans moved around the world.

Hes a singular scientist, Vosshall says.

Hes not the only one in his family to win a Nobel Prize, though. Pbos father, Sune Bergstrm, shared the medicine Nobel Prize in 1982 (SN: 10/16/82).

Pbo will take home prize money of 10 million Swedish kronor, roughly $895,000 as of October 3.

Link:
Genetics of human evolution wins 2022 Nobel Prize in physiology or medicine - Science News Magazine

Data collection age groupings

We downloaded gene expression data for multiple individuals and tissues from GTEx V810, which were previously aligned and processed against the hg19 human genome. Tissues were included in the analysis if they had >100 individuals in both the age 55 and <55 cohorts (Supplementary Fig.2). For a given tissue, genes were included if they had >0.1 TPM in 20% of samples and 6 reads in 20% of samples, following GTExs eQTL analysis pipeline. To compare gene expression heritability across individuals of different ages, for some analyses we split the GTEx data for each tissue into two age groups, "young" and "old," based on the median age of individuals in the full dataset, which was 55 (Supplementary Fig.1). Within each tissue dataset, we then equalized the number of individuals in the young and old groups by randomly downsampling the larger group, to ensure that our models were equally powered for the two age groups.

We analyzed existing precomputed PEER factors available from GTEx to check for correlations between these hidden covariates and age. In particular, we fit a linear regression between age and each hidden covariate and identified significant age correlations using an F-statistic (Supplementary Fig.3). Because some of the covariates were correlated with age, we generated age-independent hidden covariates of gene expression to remove batch and other confounding effects on gene expression while retaining age related variation. In particular, we first removed age contributions to gene expression by regressing gene expression on age and then ran PEER on the age-independent residual gene expression to generate 15 age-independent hidden PEER factors.

Using the binary age groups defined above, we assessed the relative significance of eQTLs in old and young individuals by carrying out separate assessment of eQTLs identified by GTEx. We report the number of genes included in analysis for each tissue (Supplementary Table1). For each gene in each tissue and each age group, we regressed the GTEx pre-normalized expression levels on the genotype of the lead SNP (identified by GTEx, MAF>0.01) using 5 PCs, 15 PEER factors, sex, PCR protocol and sequencing platform as covariates, following the GTEx best practices. We confirmed our results using both our recomputed PEER factors as well as the PEER factors provided by GTEx (Supplementary Fig.5). To test for significant differences in genetic associations with gene expression between the old and young age groups, we compared the p-value distributions between these groups for all genes and all SNPs in a given tissue using Welchs t-test. To investigate the validity of the age cutoff used for these binary age groups, we replicated the eQTL analysis using two additional age cutoffs of 45 and 65 years old. We observed the same trends in both cases; however, statistical power decreased due to smaller sample sizes in the resulting age bins, leading to a non-significant result for age cutoff 45 (Supplementary Fig.40).

To quantify differences in gene expression between individuals, we computed the pairwise distance for all pairs of individuals in an age group using the square root of Jensen-Shannon Divergence (JSD) distance metric, which measures the similarity of two probability distributions. Here we applied JSD between pairs of individuals transcriptome vectors containing the gene expression values for each gene, which we converted to a distribution by normalizing by the sum of the entries in the vector. For two individuals transcriptome distributions, the JSD can be calculated as:

$${{{{{{{rm{JSD}}}}}}}}({P}_{1},;{P}_{2})=Hleft(frac{1}{2}{P}_{1}+frac{1}{2}{P}_{2}right)-frac{1}{2}(H({P}_{1})+H({P}_{2}))$$

(1)

where Pi is the distribution for individual i and H is the Shannon entropy function:

$$H(X)=-mathop{sum }limits_{i=1}^{n}P({x}_{i}){log }_{2}(P({x}_{i}))$$

(2)

JSD is known to be a robust metric that is less sensitive to noise when calculating distance compared to traditional metrics such as Euclidean distance and correlation. It has been shown that JSD metrics and other approaches yield similar results but that JSD is more robust to outliers12. The square root of the raw JSD value follows the triangle inequality, enabling us to treat it as a distance metric.

In addition to comparing JSD between the two age groups defined above, "young" and "old", we also binned all GTEx individuals into 6 age groups, from 20 to 80 years old with an increment of 10 years. We then computed pairwise distance and average age for each pair of individuals within each bin using the square root of JSD as the distance metric. We applied a linear regression model of JSD versus age to obtain slopes, confidence intervals, and p-values.

To analyze whether cell type composition affects age-associated expression changes, we utilized the tool CIBERSORTx16 to estimate cell type composition and individual cell type expression levels in GTEx whole blood. Cell type composition estimates were computed using CIBERSORTx regular mode. Individual cell type expression level estimates were computed using CIBERSORTx high resolution mode. We then repeated our JSD and eQTL analyses on each cell type independently (see JSD and eQTL sections for details). In addition, to analyze tissue-specific differences in cell type composition, we referred to a previous study36 that computed cell type composition for different GTEx tissues using CIBERSORTx. We applied the JSD metric to each tissue, using the cell type composition vector as the distribution. Additionally, we applied the Breusch-Pagan test to compute heteroskedasicity coefficients and p-values with respect to age, after inverse logit transformation to give an approximately Gaussian distribution (Supplementary Fig.44) (see section on heteroskedastic gene expression).

We used the Breusch-Pagan test to call heteroskedastic gene expression with age. For each gene and tissue, we computed gene expression residuals by regressing out age-correlated PEER factors, other GTEx covariates, and age. To test for age-related heteroskedasticity, we squared these residuals and divided by the mean, regressed them against age, and looked at the age effect size (het). We called significantly heteroskedastic genes using a two-sided t-test with the null hypothesis that the het is zero. The Benjamini-Hochberg procedure was used to control for false positives. To determine which tissues have more genes with increasing gene expression heterogeneity with age, we compare the number of genes with positive heteroskedasticity (het > 0 and FDR<0.2) to the total of all heteroskedastic genes (FDR < 0.2). We compare this metric to the per-tissue 2-bin JSD (Supplementary Fig.41) and 6-bin JSD slope (Supplementary Fig.15).

We used a multi-SNP gene expression prediction model based on PrediXcan14 to corroborate our findings from the eQTL and JSD analyses on the two age groups, "young" and "old". For each gene in each tissue, we trained a multi-SNP model separately within each age group to predict individual-level gene expression.

$${Y}_{g,t}=mathop{sum}limits_{i}{beta }_{i,g,t}{X}_{i}+epsilon$$

(3)

Where i,g,t is the coefficient or effect size for SNP Xi in gene g and tissue t and includes all other noise and environmental effects. The regularized linear model for each gene considers dosages of all common SNPs within 1 megabase of the genes TSS as input, where common SNPs are defined as MAF > 0.05 and Hardy-Weinberg equilibrium P>0.05. We removed covariate effects on gene expression prior to model training by regressing out both GTEx covariates and age-independent PEER factors (described above). Coefficients were fit using an elastic net model which solves the problem37:

$${min }_{beta_{0},;beta }frac{1}{2N}mathop{sum }limits_{j=1}^{N}{left({Y}_{j}-{beta }_{0}-{X}_{j}^{T}beta right)}^{2}+lambda left(frac{1-alpha }{2}||beta|{|}_{2}^{2}+alpha||beta|{|}_{1}right)$$

(4)

The minimization problem contains both the error of our model predictions ({({Y}_{j}-{beta }_{0}-{X}_{j}^{T}beta )}^{2}) and a regularization term (lambda (frac{1-alpha }{2}||beta|{|}_{2}^{2}+alpha||beta|{|}_{1})) to prevent model overfitting. The elastic net regularization term incorporates both L1 (1)) and L2 ((||beta|{|}_{2}^{2})) penalties. Following PrediXcan, we weighted the L1 and L2 penalties equally using =0.514. For each model, the regularization parameter was chosen via 10-fold cross validation. The elastic net models were fit using Pythons glmnet package and R2 was evaluated using scikit-learn. From the trained models for each gene, we evaluated training set genetic R2 (or h2) for the two age groups and subtracted ({h}_{{{{{young}}}}}^{2}-{h}_{{{{{old}}}}}^{2}) to get the difference in gene expression heritability between the groups. We compared this average difference in heritability to the mean JSDoldJSDyoung and (log ({P}_{old})-log ({P}_{young})) using P-values from the eQTL analyses across genes.

To uncover linear relationships between gene expression and both age and genetics, we built a set of gene expression prediction models using both common SNPs and standardized age as input. An individuals gene expression level Y for a gene g and tissue t is modeled as:

$${Y}_{g,t}=mathop{sum}limits_{i}{beta }_{i,g,t}{X}_{i}+{beta }_{{{{{{{{rm{age}}}}}}}},g,t}A+epsilon$$

(5)

Where A is the normalized age of an individual. Coefficients were fit using elastic net regularization, as above, which sets coefficients for non-informative predictors to zero. The sign of the fitted age coefficient (age,g,t), when nonzero, reflects whether the gene in that tissue is expressed more in young (negative coefficient) or old (positive coefficient) individuals. We also evaluated the training set R2 using the fit model coefficients separately for genetics (across all SNPs in the model) and age:

$${R}_{genetics}^{2}={h}^{2}={R}^{2}({Y}_{g,t},mathop{sum}limits_{i}{beta }_{i,g,t}{X}_{i})$$

(6)

$${R}_{age}^{2}={R}^{2}({Y}_{g,t},;{beta }_{{{{{{{{rm{age}}}}}}}},g,t}A)$$

(7)

We also tested whether the age-related gene expression relationship was sex-specific by rerunning the joint model with an additional age-sex interaction term as follows:

$${Y}_{g,t}=mathop{sum}limits_{i}{beta }_{i,g,t}{X}_{i}+{beta }_{{{{{{{{rm{age}}}}}}}},g,t}A+{beta }_{{{{{{{{rm{age}}}}}}}} * {{{{{{{rm{sex}}}}}}}},g,t}A * S+epsilon$$

(8)

Where agesex,g,t is the additional model weight for the age-sex interaction term and S is the binary sex of the GTEx individual. The R2 of age, genetics, and the age-sex interaction term are evaluated as before by determining the variance explained by each term in the model. We compared the ({R}_{age}^{2}) between the models including or excluding the age-sex interaction term (Supplementary Fig.26). We also compared the tissue-averaged variance explained by age and the age-sex interaction term. Finally, to check the consistency of tissue-specific gene expression heritability estimates from our model and the original PrediXcan model trained on GTEx data, we evaluate Pearsons r between our heritability estimates and those of PrediXcan (Supplementary Fig.20), using heritability estimates from the original PrediXcan model available in PredictDB.

We evaluated the variability of age and genetic associations across tissues using a measure of tissue specificity for age and genetic R238. We measured the tissue-specificity of a gene gs variance explained ({R}_{g}^{2}) using the following metric:

$${S}_{g}=frac{mathop{sum }nolimits_{t=1}^{n}left(1-frac{{R}_{g,t}^{2}}{{R}_{g,max }^{2}}right)}{n-1}$$

(9)

Where n is the total number of tissues, ({R}_{g,t}^{2}) is the variance explained by either age or genetics for the gene g in tissue t and ({R}_{g,max }^{2}) is the maximum variance explained for g over all tissues. This metric can be thought of as the average reduction in variance explained relative to the maximum variance explained across tissues for a given gene. The metric ranges from 0 to 1, with 0 representing ubiquitously high genetic or age R2 and 1 representing only one tissue with nonzero genetic or age R2 for a given gene. We calculate Sg separately for ({R}_{{{{{{{{rm{age}}}}}}}}}^{2}) and ({R}_{{{{{{{{rm{genetics}}}}}}}}}^{2}) across all genes.

We quantified gene constraint using the probability of loss of function intolerance (pLI) from gnomAD 2.1.122. We analyzed the relationships between pLI vs age and pLI vs heritability across genes. For these analyses, genes were only included if age or genetics were predictive of gene expression (R2>0) for that gene. For genes with R2>0, we used linear regression to determine the direction of the relationship between pLI and age or heritability for each tissue. The F-statistic was used to determine whether pLI was significantly related to these two model outputs. For pLI vs age, a significant negative slope was considered a Medawarian trend (consistent with Medawars hypothesis) and a significant positive slope a non-Medawarian trend. To test whether the non-Medawarian trends were driven by genes with higher expression, we excluded genes in the top quartile of median gene expression and repeated the analysis between pLI and age (Supplementary Fig.42). We also analyzed the evolutionary constraint metric dN/dS23 and its tissue-specific relationship with age by determining the slope and significance of the linear regression, as above.

We quantified the per-gene and per-tissue cancer somatic mutation frequency using data from the COSMIC cancer browser26. For each tissue, we selected the closest cancer type as noted in Supplementary Data5 and downloaded the number of mutated samples (tumor samples with at least one somatic mutation within the gene) and the total number of samples for all genes. We computed the cancer somatic mutation frequency by dividing the number of mutated samples by the total number of samples. For each tissue, we plotted the genes age vs its cancer somatic mutation frequency for all genes with>200 tumor samples. We report the slope and significance of the relationship between age and cancer somatic mutation frequency for each tissue. To determine whether age-dependent gene expression heteroskedasticity is related to a genes involvement in cancer (Supplementary Fig.43), we also plotted each genes heteroskedasticity effect size vs the cancer somatic mutation frequency for all genes with >200 tumor samples and moderately significant heteroskedasticity (FDR<0.2). Tissues with5 genes meeting these criteria are not plotted.

To explore the non-Medawarian trend in some tissues, we assessed the distribution of age across Medawarian and non-Medawarian tissues for genes within each of the 50 MSigDB hallmark pathways24. Significant differences between the distributions were called using a t-test, and p-values were adjusted for multiple hypothesis testing using a Benjamini-Hochberg correction.

Further information on research design is available in theNature Research Reporting Summary linked to this article.

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Tissue-specific impacts of aging and genetics on gene expression patterns in humans - Nature.com

Bordeaux Population Health Research Center, University of Bordeaux, Inserm, UMR 1219, Bordeaux, France

Aniket Mishra,Quentin Le Grand,Ilana Caro,Constance Bordes,David-Alexandre Trgout,Marine Germain,Christophe Tzourio,Jean-Franois Dartigues,Sara Kaffashian,Quentin Le Grand,Florence Saillour-Glenisson&Stephanie Debette

Institute for Stroke and Dementia Research (ISD), University Hospital, LMU Munich, Munich, Germany

Rainer Malik,Marios K. Georgakis,Steffen Tiedt&Martin Dichgans

Iwate Tohoku Medical Megabank Organization, Iwate Medical University, Iwate, Japan

Tsuyoshi Hachiya,Makoto Sasaki,Atsushi Shimizu,Yoichi Sutoh,Kozo Tanno&Kenji Sobue

Estonian Genome Centre, Institute of Genomics, University of Tartu, Tartu, Estonia

Tuuli Jrgenson,Kristi Krebs,Kaido Lepik,Tnu Esko,Andres Metspalu,Reedik Mgi,Mari Nelis&Lili Milani

Institute of Mathematics and Statistics, University of Tartu, Tartu, Estonia

Tuuli Jrgenson

Department of Statistical Genetics, Osaka University Graduate School of Medicine, Suita, Japan

Shinichi Namba,Takahiro Konuma&Yukinori Okada

Massachusetts Veterans Epidemiology Research and Information Center (MAVERIC), VA Boston Healthcare System, Boston, MA, USA

Daniel C. Posner,Kelly Cho,Yuk-Lam Ho&Jennifer E. Huffman

TIMI Study Group, Boston, MA, USA

Frederick K. Kamanu,Nicholas A. Marston,Marc S. Sabatine&Christian T. Ruff

Division of Cardiovascular Medicine, Brigham and Womens Hospital, Harvard Medical School, Boston, MA, USA

Frederick K. Kamanu,Nicholas A. Marston,Marc S. Sabatine&Christian T. Ruff

Division of Molecular Pathology, Institute of Medical Sciences, The University of Tokyo, Tokyo, Japan

Masaru Koido,Takayuki Morisaki&Yoishinori Murakami

Laboratory of Complex Trait Genomics, Graduate School of Frontier Sciences, The University of Tokyo, Tokyo, Japan

Masaru Koido,Mingyang Shi,Yunye He&Yoichiro Kamatani

Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA

Marios K. Georgakis,Livia Parodi,Jonathan Rosand,Christopher D. Anderson,Ernst Mayerhofer&Christopher D. Anderson

Program in Medical and Population Genetics, Broad Institute of Harvard and the Massachusetts Institute of Technology, Cambridge, MA, USA

Marios K. Georgakis,Livia Parodi,Phil L. de Jager,Jonathan Rosand,Christopher D. Anderson,Guido J. Falcone,Phil L. de Jager,Ernst Mayerhofer&Christopher D. Anderson

Laboratory of Clinical Genome Sequencing, Department of Computational Biology and Medical Sciences, Graduate School of Frontier Sciences, The University of Tokyo, Tokyo, Japan

Yi-Ching Liaw&Koichi Matsuda

Department of Public Health and Institute of Public Health, Chung Shan Medical University, Taichung, Taiwan

Yi-Ching Liaw,Pei-Hsin Chen&Yung-Po Liaw

Department of Internal Medicine, University of Turku, Turku, Finland

Felix C. Vaura&Teemu J. Niiranen

Department of Public Health and Welfare, Finnish Institute for Health and Welfare, Turku, Finland

Felix C. Vaura&Teemu J. Niiranen

Nuffield Department of Population Health, University of Oxford, Oxford, UK

Kuang Lin,Zhengming Chen,Cornelia M. van Duijn,Robert Clarke,Rory Collins,Richard Peto,Yiping Chen,Zammy Fairhurst-Hunter,Michael Hill,Alfred Pozarickij,Dan Schmidt,Becky Stevens,Iain Turnbull,Iona Y. Millwood,Keum Ji Jung&Robin G. Walters

Department of Research and Innovation, Division of Clinical Neuroscience, Oslo University Hospital, Oslo, Norway

Bendik Slagsvold Winsvold,Ingrid Heuch,Linda M. Pedersen,Amy E. Martinsen,Espen S. Kristoffersen&John-Anker Zwart

K. G. Jebsen Center for Genetic Epidemiology, Department of Public Health and Nursing, Faculty of Medicine and Health Sciences, Norwegian University of Science and Technology (NTNU), Trondheim, Norway

Bendik Slagsvold Winsvold,Sigrid Brte,Kristian Hveem,Ben M. Brumpton,Jonas B. Nielsen,Maiken E. Gabrielsen,Anne H. Skogholt,Ben M. Brumpton,Maiken E. Gabrielsen,Amy E. Martinsen,Jonas B. Nielsen,Kristian Hveem,Laurent F. Thomas&John-Anker Zwart

Department of Neurology, Oslo University Hospital, Oslo, Norway

Bendik Slagsvold Winsvold&Anne H. Aamodt

Department of Biostatistics, School of Public Health, University of Alabama at Birmingham, Birmingham, AL, USA

Vinodh Srinivasasainagendra,Hemant K. Tiwari&George Howard

Department of Neurology and Cerebrovascular Disease Center, Seoul National University Bundang Hospital, Seoul National University College of Medicine, Seongnam, Republic of Korea

Hee-Joon Bae

Rajendra Institute of Medical Sciences, Ranchi, India

Ganesh Chauhan,Amit Kumar&Kameshwar Prasad

Thrombosis and Atherosclerosis Research Institute, David Braley Cardiac, Vascular and Stroke Research Institute, Hamilton, Ontario, Canada

Michael R. Chong&Guillaume Par

Department of Pathology and Molecular Medicine, Michael G. DeGroote School of Medicine, McMaster University, Hamilton, Ontario, Canada

Michael R. Chong&Guillaume Par

Department of Neurology, Helsinki University Hospital and University of Helsinki, Helsinki, Finland

Liisa Tomppo,Jukka Putaala,Gerli Sibolt,Nicolas Martinez-Majander,Sami Curtze,Marjaana Tiainen,Janne Kinnunen&Daniel Strbian

Center for Genomic and Precision Medicine, College of Medicine, University of Ibadan, Ibadan, Nigeria

Rufus Akinyemi,Abiodun M. Adeoye&Mayowa O. Owolabi

Neuroscience and Ageing Research Unit Institute for Advanced Medical Research and Training, College of Medicine, University of Ibadan, Ibadan, Nigeria

Rufus Akinyemi

Department of Epidemiology, Erasmus MC University Medical Center Rotterdam, Rotterdam, The Netherlands

Gennady V. Roshchupkin,Maria J. Knol,Cornelia M. van Duijn,Najaf Amin,Sven J. van der Lee,Mohsen Ghanbari,Mohammad K. Ikram&Mohammad A. Ikram

Department of Radiology and Nuclear Medicine, Erasmus MC University Medical Center Rotterdam, Rotterdam, The Netherlands

Gennady V. Roshchupkin

The Edmond and Lily Safra Center for Brain Sciences, The Hebrew University of Jerusalem, Jerusalem, Israel

Naomi Habib&Anael Cain

Department of Epidemiology, Harvard T. H. Chan School of Public Health, Boston, MA, USA

Yon Ho Jee

Department of Clinical Biochemistry, Copenhagen University HospitalRigshospitalet, Copenhagen, Denmark

Jesper Qvist Thomassen,Anne Tybjrg-Hansen,Marianne Benn&Ruth Frikke-Schmidt

Department of Molecular and Functional Genomics, Weis Center for Research, Geisinger Health System, Danville, VA, USA

Vida Abedi&Jiang Li

Department of Public Health Sciences, College of Medicine, The Pennsylvania State University, State College, PA, USA

Vida Abedi

Stroke Pharmacogenomics and Genetics Laboratory, Biomedical Research Institute Sant Pau (IIB Sant Pau), Barcelona, Spain

Jara Crcel-Mrquez,Nuria P. Torres-Aguila,Natalia Cullell,Elena Muio,Cristina Gallego-Fabrega,Miquel Lleds,Laia Lluci-Carol&Israel Fernndez-Cadenas

Departament de Medicina, Universitat Autnoma de Barcelona, Barcelona, Spain

Jara Crcel-Mrquez

The Danish Twin Registry, Department of Public Health, University of Southern Denmark, Odense, Denmark

Marianne Nygaard&Kaare Christensen

Department of Clinical Genetics, Odense University Hospital, Odense, Denmark

Marianne Nygaard&Kaare Christensen

Center for Alzheimers and Related Dementias, National Institutes of Health, Bethesda, MD, USA

Hampton L. Leonard&Mike A. Nalls

Laboratory of Neurogenetics, National Institute on Aging, National Institutes of Health, Bethesda, MD, USA

Hampton L. Leonard&Mike A. Nalls

Data Tecnica International, Glen Echo, MD, USA

Hampton L. Leonard&Mike A. Nalls

Center for Public Health Genomics, University of Virginia, Charlottesville, VA, USA

Chaojie Yang,Ani Manichaikul,Stephen S. Rich,Wei Min Chen,Michle M. Sale&Wei-Min Chen

Department of Biochemistry and Molecular Genetics, University of Virginia, Charlottesville, VA, USA

Chaojie Yang

British Heart Foundation Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK

Ekaterina Yonova-Doing,Michael Inouye&Joanna M. M. Howson

Department of Genetics, Novo Nordisk Research Centre Oxford, Oxford, UK

Ekaterina Yonova-Doing&Joanna M. M. Howson

Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN, USA

Adam J. Lewis,Jing He,Seung Hoan Choi&Lisa Bastarache

Department of Surgery, University of Pennsylvania, Philadelphia, PA, USA

Renae L. Judy

Department of Medicine and Clinical Science, Graduate School of Medical Sciences, Kyushu University, Fukuoka, Japan

Tetsuro Ago&Takanari Kitazono

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Stroke genetics informs drug discovery and risk prediction across ancestries - Nature.com

Wondering if bad chest genes are real?

The answer is yes, sort of. But it depends on what you consider bad genes. What one person considers bad another person might consider good.

Your genes are units of genetic information that you inherit from your parents. They determine all your inherited traits from your eye color to your bone structure. Environmental factors such as nutrition, exposure to chemicals, and exercise habits can change the way some genes are expressed.

You can build muscle by engaging in resistance training. But genetic factors can influence how easily you add mass. Likewise, genetics can influence how easily you build muscle in a particular area such as your chest.

Keep reading as we take a look at how genetics affect your ability to build muscle in your chest.

Bad chest genes are subjective. Many people use the term to refer to having difficulty building muscle in their chest or difficulty building muscle with the aesthetics they want.

The bulk of your chest is made up of the bellies of your pectoralis major muscles, commonly referred to as your pecs. These muscles originate from your sternum and collar bone and insert into your upper arm.

Some people consider bad chest genes as having a large gap between their pectoralis major muscles or having an asymmetry between each side of their chest.

Do some people have better chest genetics than others? It depends on what your goals are and what you consider bad genetics.

Some people can build more muscle or build muscle at a faster rate in their chests than others. Genes play a role in the following factors:

Researchers are continuing to examine genes that play a role in building muscle mass. In one rodent study, researchers identified 47 genes linked to muscle growth.

Twin studies suggest that more than 50% of muscle fiber composition is estimated to be inherited from your parents.

Body dysmorphia is a mental health condition characterized by preoccupation with your bodys flaws. Muscle dysmorphia is a specific type of body dysmorphia characterized by a persistent worry that youre not muscular or lean.

Becoming preoccupied with the size of your chest could be a symptom of muscle dysmorphia. The Muscle Dysmorphic Disorder Inventory is often used as a testing tool with 13 questions that are scored from never to always. Some of the statements on this inventory include:

In a 2018 study, researchers compared rates of muscular dysmorphia between bodybuilders, strength athletes, and people engaged in general fitness. They found that bodybuilders reported more beliefs about being smaller and weaker than the other groups.

Learn more about how muscle dysmorphia is diagnosed and treated.

A chest gap is the separation of your pectoralis major muscles. Its normal to have a chest gap since theres no muscle body over your sternum. Some people have wider gaps than others as part of their natural anatomy, which is largely predetermined by genetics.

Its important to remember that the idea of bad genetics is subjective. If your goal is to build as much muscle as possible, you might consider bad genetics as having more trouble building muscle than other people around you.

But for some people, not adding muscle mass with training might be considered good genetics. For example, athletes in weight-class sports such as boxing or relative strength sports such as long jump need to build a large amount of strength without adding much extra weight.

You cant change your genetics, but you can change the way your genes are expressed by changing your training program. Consistently training your chest muscles can help you maximize your muscle size and strength. Some people find it helpful to work with a personal trainer who can build them a custom program to help them achieve their goals.

Some men opt to get pectoral implants, but these are primarily meant for people with birth deformities, such as pectus excavatum. People with muscle or body dysmorphia are not candidates for pectoral implants.

The best way to grow your chest is by training your chest muscle regularly. Many different exercises can target your chest. Here are some ideas:

Your genetics influence your ability to build muscle. The idea of bad genetics is subjective. If your goal is to build muscle, your genes might make it easier or harder than most other people to build muscle in general or specifically in your chest.

The best way to maximize your chest growth is to train your chest regularly. You may find it helpful to work with a personal trainer who can build you a custom program.

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About Bad Chest Genetics, and Whether You Can Fix Them - Healthline

Illumina Inc. says it can read a persons entire genetic code for as little as $200 with its new sequencing machine, bringing the company within reach of its long-promised goal of the $100 genome.

Illumina on Thursday unveiled a new line of DNA sequencing machines it says are twice as fast and accurate as its earlier models. Together, those upgrades will bring the cost per genome down two-thirds from its current technology, Chief Executive Officer Francis deSouza said.

Many consumers have been introduced to their DNA through relatively low-cost tests like those marketed by 23andMe Holding Co. that analyze small snippets of the genome for clues to disease risk and ancestry. Whole-genome sequencing can provide a far clearer, more accurate view of patients genetic makeup that doctors can use to precisely identify some diseases, including certain forms of cancer and heart disease. However, the price of performing the tests, along with their interpretation, has been a barrier for many patients that companies have been trying to bridge.

More efficient machinery and materials reduce customer cost to sequencing one genome, or the complete set of genetic material, Illumina said, adding that costs would range from less than $200 per genome, with discounts for bulk use, to $240 for a higher-quality analysis. Slashing the price of reading DNA could allow the practice to move into the mainstream, where it might be used to better tailor medications or treatments to people or have other health benefits.

This will be a huge force in terms of significantly increasing accessibility to genomics in a number of ways, deSouza said in an interview ahead of the announcement. It will democratize access to genomics by allowing sequencing to be offered to hospitals and researchers at much lower prices.

Despite promises of personalized medical care for the masses, genetic data has mostly been confined to research settings in the 21 years since an international group of scientists published the first analysis of the human genome sequence, Eric Topol, founder and director of Scripps Research Translational Institute, recently wrote. Illumina sees its new sequencing machine as a way to change that. Every meaningful price drop has rapidly led to an increase in the number of people whose genes have been analyzed, deSouza said.

Illuminas new NovaSeq X series comes in two models, with the base machine costing $985,000 and a more advanced one at $1.25 million. The new sequencers also come with new features like a simpler interface that could allow people without advanced degrees to use the machines, deSouza said.

This is a crucial test for San Diego-based Illumina at a time of increased scrutiny from Wall Street. The company cut its full-year sales outlook last month, raising questions about demand. New competitors are cropping up and threatening Illuminas dominance of the sequencing market. Moreover, the companys years-long quest to acquire early-cancer detection company Grail is in limbo and facing regulatory challenges in Europe. Shares of Illumina have lost nearly half their value this year.

Already under a microscope, the company is hosting a splashy conference in its hometown this week to unveil the technology.

Investors are closely following the event for signs Illumina can change its story. Customers, mostly drug companies and research institutions, will be paying attention to price. Before the launch, nearly three dozen sequencing customers had estimated Illumina would set its prices at $280 per genome, according to a survey from Cowen analysts.

The new machines could have real financial implications for researchers who sequence large numbers of people, said Aris Baras, who leads Regeneron Pharmaceuticals Inc.s Genetics Center. Regeneron scours genetic data to discover new drug targets. Baras praised Illumina for continuously decreasing the price of sequencing, allowing Regeneron to screen about 2 million people.

Its a testament to Illuminas innovation pushing down costs and increasing output especially when they havent historically had too many competitors being able to match them, Baras said. Still, the price isnt low enough for Regeneron to switch to exclusively whole genome sequencing. The drugmaker mostly scans only genes of key interest, which costs between one-fifth and one-tenth the price of reading all of a persons genetic material.

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Illumina aims to push genetics beyond the lab with $200 genome - The Spokesman Review

A University of Maryland researcher was awarded $2.6 million by the U.S. Department of Energy to investigate the genetics underlying how poplar trees sense nutrients and regulate their metabolisminformation that could help farmers maximize yields of this and other plants used in biofuel production.

Dedicated biomass crops like poplar, switchgrass, miscanthus and bamboo are grown on marginal lands that are not well suited to traditional crops like corn and wheat. It pays to understand how crops grown in such conditions use the nutrients available, how they metabolize and grow tissue, and how they respond to stressful conditions like drought.

Were interested in getting more information about how biomass crops like poplar sense and utilize nutrients so we can develop more informed strategies for manipulating this system and making it more efficient, said Gary Coleman, an associate professor in the Department of Plant Science and Landscape Architecture who is leading the research.

Coleman is looking at the genes that encode for the TOR protein, one of the central components of the TOR complex. Its job is to receive signals from the molecules that sense a wide range of nutrients like carbon and nitrogen, and then relay that information to the cellular machinery that activates growth and inhibits cell death.

Mutating the TOR gene is lethal, which is why its function is not well understood. Poplar is rare in that it has two copies of the TOR gene. Coleman and his colleagues previously demonstrated that they could manipulate one copy or the other without killing the plant, and the team intends to take advantage of the duplicates to investigate how the gene works.

Colemans collaborators include Yiping Qi, an associate professor of plant science and landscape architecture at UMD, Edward Eisenstein, an associate professor at the Institute for Bioscience and Biotechnology Research at UMD, and researchers at the Michigan Technological University.

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$2.6M DOE Grant Supports UMD-led Study on Genetics of Plant Growth - Maryland Today

That said, its important to remember that our erotic interests are the product of many factors. On the biological side, those factors can include our genetic predispositions, unique brain chemistry, and the way our bodies are laid out.

For some people, nipples are extraordinarily sensitive, Dr. Lehmiller says. For other people, theres just no sensation whatsoever. And if your body just happens to have that heightened level of sensitivity, you might be very drawn to various forms of nipple play including more intense BDSM versions of it with nipple clamps and so forth. So I think part of it is that general sensitivity in different parts of our body. That could also have a genetic component to it.

Psychological factors such as our personalities, previous experiences, and general attitudes toward sex represent another piece of the puzzle. And there are environmental factors to considerthe cultural context that, in part, determines the partners we choose and the opportunities available to us.

Whenever were talking about sexual interests, we need to talk about it from a biopsychosocial perspective, Dr. Lehmiller says. Two people can develop the same sexual interest for very different reasons, depending on the confluence of all of these factors.

Many people can pinpoint a specific childhood experience as the source of their kink or fetish. For some, it feels like a fact of life from birth. Others find their kinks later in life through solo or partnered exploration. In Dr. Brames experience, younger generations are becoming aware of their kinks earlier in life thanks to the internet. But in some cases, the culture of silence and shame around sexual kinks can delay the discovery process by decades.

You dont necessarily realize who you are until youre in your teens or maybe even your 20s, Dr. Brame says. Or maybe even your 50s, not because its totally out of the blue. But you dont realize what kink is or what it is to be kinky. Or that some of your private sexual fantasies actually align with kink.

Often the kinks emotional and sexual resonance is reinforced through masturbation.

We know that the connection between the smell centers of the brain and the memory centers of the brain and the emotional centers of the brain are very close, Gates says. And so things that we would consider to be classic kinks, like a foot fetishor rubber or leather or things that are sensorially evocative, especially through smellcan become connected with emotional content and memories to form a kind of cycle where you smell it and you have this stimulus in this memory thats very emotional. You might reinforce that through, say, masturbation to the point where it becomes a very firm pathway in your brain.

But Gates believes some people are primed to develop a kink or fetish under the right conditions.

I interviewed this wonderful guy who considered himself a macrophile, Gates says. He liked to fantasize about giant women. And he said, Nature loads the gun and nurture pulls the trigger. I like that metaphor because it sort of explains how that worksthat you can be primed biologically and neurologically to be ready for it to happen.

Dr. Brame feels strongly that kink isnt a hobbyits a legitimate sexual identity. Throughout her life, relationships that didnt align with her kinks would inevitably fail. The kink was never explicitly discussed or cited as the reason for the breakupthat discovery would come later. But in retrospect, it makes sense that certain power dynamics werent tenable for her.

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Are Kinks Hereditary? What Science Says About the Genetics of Desire - Glamour