Category Archives: Genetics

Genetics

Improved functional mapping of complex trait heritability with GSA-MiXeR implicates biologically specific gene sets – Nature.com

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Improved functional mapping of complex trait heritability with GSA-MiXeR implicates biologically specific gene sets - Nature.com

Genetics

Genetic Risk Score Revolutionizes TNBC Prediction in Black Women – Targeted Oncology

Black women in the U.S. often face a higher risk of developing aggressive breast cancer, particularly triple-negative breast cancer (TNBC), which can occur before routine screening is recommended. To address this disparity, accurate risk prediction methods are crucial. A multiple-ancestry polygenic risk score (MA-PRS), developed from genetic data of diverse populations, has shown promise in predicting overall breast cancer risk. In this study, researchers assessed the effectiveness of MA-PRS in predicting TNBC and early-onset TNBC in a large cohort of self-reported Black women.

Analyzing data from over 14,000 eligible participants, predominantly under 50 years old, the study found that MA-PRS significantly improved TNBC risk prediction beyond clinical factors alone. Specifically, women in the top 5% of MA-PRS distribution had roughly twice the risk of TNBC compared to the general population. Importantly, MA-PRS demonstrated comparable impact to mammographic density, a well-established risk factor for breast cancer.

The findings suggest that incorporating MA-PRS into breast cancer risk assessment could enhance early detection and potentially improve survival rates for TNBC among Black women. By accurately identifying those at elevated risk, interventions and screening strategies can be tailored more effectively, addressing a critical need in breast cancer management for this demographic.

Here, Holly Pederson, MD, breast medical oncologist at Cleveland Clinic, and Elisha Hughes, PhD, director of biostatistics at Myriad Genetics, discuss the findings and implications from this study presented at ASCO 2024.

Transcription:

0:05 | The polygenic score was really powerful risk stratifier, or it really explains a lot of the genetic susceptibility that many women have for, you know, overall breast cancer and specifically triple-negative disease. About as powerful as everything else combined with the exception of maybe mammographic density, and the polygenic score and mammographic density are both, I would say equally powerful risk stratifiers.

0:30 | This may change, help to change, screening recommendations even, because it shouldn't just be based on age, but also on ancestry and genetics. I mean, it only makes sense. The other, you know, the other main implication is that we are looking to evaluate young women and identify those families that seem as if they may have a heritable disorder to prevent future cancers. But we'd also love to identify the woman who might be at risk. And and it's, it's about 6% of women who really fall into that high-risk category. But that's an important 6%. So we'd like to make a difference there.

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Genetic Risk Score Revolutionizes TNBC Prediction in Black Women - Targeted Oncology

Genetics

Gene variants and breast cancer risk in Black women – National Institutes of Health (NIH) (.gov)

June 4, 2024

Breast cancer is the most often diagnosed cancer in many parts of the world, including the U.S. More than 310,000 new cases are expected nationwide this year.

Black women tend to develop breast cancer at a younger age than White women. Black women are also more likely than Whites to die from the disease, and they are twice as likely to develop an aggressive subtype called triple-negative breast cancer. But despite the increased risks faced by women of African descent, most large-scale genetic studies of breast cancer to date have focused on women of European ancestry.

To better understand their unique genetic risks, a research team led by Dr. Wei Zheng of Vanderbilt University analyzed genetic data from over 40,000 females of African descent. About 18,000 had been diagnosed with breast cancer. The data were gathered as part of the NIH-funded African Ancestry Breast Cancer Genetic consortium, which combined data from 26 studies. Most participants (85%) were African Americans. The rest were from Barbados or Africa.

The researchers conducted a genome-wide association study (GWAS) to look for genetic variants that are found more often in participants with breast cancer than in those without. This is believed to be the largest GWAS study to date of breast cancer in this population. Results were reported in Nature Genetics on May 13, 2024.

The analysis pinpointed 12 genetic regions, or loci, associated with breast cancer. Three of these loci were linked to the aggressive triple-negative cancer. About 8% of the women carried two genetic copies of risk variants in all three of these loci. Such women, the researchers found, were 4.2 times more likely to be diagnosed with triple-negative breast cancer than women who hadonly one or no copies of the variants.

Because this type of cancer lacks specific cell receptors often seen with breast cancer (like estrogen or HER2 receptors), there are fewer targeted options for treatment. These findings may help researchers identify new treatment targets.

The researchers also confirmed many breast cancer risk variants that were found earlier in other populations.And they identified an uncommon risk variant in the gene ARHGEF38, which had been previously linked to aggressive prostate and lung cancers.

The scientists used their findings to create polygenic risk scores (PRS) for breast cancer risk in females of African descent. PRS use genomic data to gauge the chance that a person will develop a certain medical condition. PRS created previously, using results from other populations, tend to perform poorly at predicting breast cancer risk for Black women. The new PRS, based on genomic data from African descendants, outperformed previous PRS at predicting breast cancer risk in this population.

The findings and data could lead to improved detection of breast cancer in this at-risk population and provide clues for potential treatment targets. Studies with even larger, more diverse populations will be needed to further improve the prediction of breast cancer risk.

We have worked with researchers from more than 15 institutions in the U.S. and Africa to establish this large genetic consortium, Zheng says. Data put together in this consortium have been and will continue to be used by researchers around the world.

by Vicki Contie

References:Genome-wide association analyses of breast cancer in women of African ancestry identify new susceptibility loci and improve risk prediction. Jia G, Ping J, Guo X, Yang Y, Tao R, Li B, Ambs S, Barnard ME, Chen Y, Garcia-Closas M, Gu J, Hu JJ, Huo D, John EM, Li CI, Li JL, Nathanson KL, Nemesure B, Olopade OI, Pal T, Press MF, Sanderson M, Sandler DP, Shu XO, Troester MA, Yao S, Adejumo PO, Ahearn T, Brewster AM, Hennis AJM, Makumbi T, Ndom P, O'Brien KM, Olshan AF, Oluwasanu MM, Reid S, Butler EN, Huang M, Ntekim A, Qian H, Zhang H, Ambrosone CB, Cai Q, Long J, Palmer JR, Haiman CA,Zheng W. Nat Genet. 2024 May;56(5):819-826. doi: 10.1038/s41588-024-01736-4. Epub 2024 May 13. PMID:38741014.

Funding:NIHs National Cancer Institute (NCI).

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Gene variants and breast cancer risk in Black women - National Institutes of Health (NIH) (.gov)

Genetics

GSA-MiXeR: A powerful tool to improve our understanding of heritable traits and diseases – News-Medical.Net

Researchers from the University of Oslo have developed an innovative tool that promises to improve our understanding of heritable human traits and diseases. Published in Nature Genetics, the analytical tool is designed to make sense of genetic data by focusing on the role of individual genes, and how groups of genes contribute to the risk of developing a disease. With GSA-MiXeR, researchers now have a powerful new way to translate genetic research into practical insights that could lead to better treatments for a range of complex diseases.

More than 970 million people worldwide are living with a mental illness, according to WHO. The global burden of these diseases is considerable. While researchers have been successful in identifying genetic factors associated with conditions like schizophrenia through genome-wide association studies (GWAS), figuring out what these discoveries mean for our health is still a big challenge.

GWAS, which are often produced by large international consortia, analyze the genomes of many individuals to find genetic variations associated with specific diseases. Our tool, called GSA-MiXeR, is designed to analyze the genetic data collected from these large-scale studies, aiming to identify how groups of genes contribute to the risk of developing a disease."

Oleksandr Frei, Researcher, Center for Precision Psychiatry, University of Oslo

Complex polygenic traits, which are influenced by many genetic factors, have been particularly difficult to interpret. "GSA-MiXeR addresses this by providing a clearer picture of how different genes work together", he explains.

When applied to a variety of complex traits and diseases, including schizophrenia, GSA-MiXeR has been able to highlight specific gene groups that are more closely related to the disease than traditional methods have been able to. One example is how GSA-MiXeR identified that genes involved in controlling calcium channels in our cells and those involved in dopamine signaling, play a significant role in schizophrenia. "These findings are not just important for understanding the diseasethey also may point to potential targets for developing new treatments", Frei says.

Better understanding of complex traits and disorders can lead to precision medicine, where treatments are tailored to the genetic makeup of individual patients. This approach can improve the effectiveness of treatments and reduce side effects. By translating genetic research into practical insights, GSA-MiXeR can contribute to more personalized and effective healthcare, ultimately leading to better health outcomes for patients, Frei says.

With GSA-MiXeR, scientists now have a powerful new way to translate genetic research into practical insights that could lead to better treatments for a range of complex diseases.

This research is done in a collaboration with the group of Professor Anders M. Dale at the Center for Multimodal Imaging and Genetics, the University of California in San Diego, USA.

Source:

Journal reference:

Frei, O., et al. (2024). Improved functional mapping of complex trait heritability with GSA-MiXeR implicates biologically specific gene sets.Nature Genetics. doi.org/10.1038/s41588-024-01771-1.

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GSA-MiXeR: A powerful tool to improve our understanding of heritable traits and diseases - News-Medical.Net

Genetics

Understanding the effect genetics have on Alzheimer’s – Buffalo News

Alzheimer's disease is a neurological condition that worsens with time and mainly affects the elderly. It causes changes in personality, memory loss, and cognitive impairment. It is the most frequent form of dementia, a collection of brain abnormalities that impair social and intellectual abilities to the point that they become too great to be useful for day-to-day living. The buildup of tau tangles and amyloid plaques in the brain, which impair neuronal transmission and cause cell death, is the hallmark of the illness.

Alzheimer's disease comes in two primary forms: early-onset and late-onset. While the symptoms of both kinds are similar, there are notable differences between them in terms of the age at which symptoms initially manifest and their underlying genetic makeup. So, this leads to the question:is Alzheimer's genetic?

Late-onset is the most prevalent kind of Alzheimer's disease, which usually first appears after age 65. This kind affects a large population; around 10% of Americans 65 years of age and older have a diagnosis. With age comes a huge rise in danger. There are both hereditary and non-hereditary variables that lead to late-onset Alzheimer's disease.

An important genetic component of Alzheimer's disease with a late start is the e4 variation of the APOE gene. Apolipoprotein E, a protein involved in the body's metabolism of fats, is encoded by the APOE gene. This gene has three common variations, e2, e3, and e4. Alzheimer's disease risk is increased in those with the e4 variation. A single copy of the e4 allele increases risk by three times, whereas two copies raise risk by eight to twelve times. It is crucial to remember that not everyone who carries the e4 variation will have Alzheimer's; some people may still have the illness. This implies that there may be other genetic, environmental, and behavioral variables involved.

Less than 1% of instances of Alzheimer's disease are early-onset, making it a far more uncommon condition. Typically, people in their 30s, 40s, or 50s have symptoms. The genetics of this kind of Alzheimer's frequently play a significant role. Early-onset Alzheimer's disease is mostly linked to three genes: APP, PSEN1, and PSEN2. Amyloid plaques can build up in the brain as a result of aberrant proteins produced by mutations in these genes.

Families with a history of Alzheimer's disease that developed slowly frequently show signs of autosomal dominant inheritance. This indicates that there is a 50% possibility that an offspring will inherit a mutant gene and might acquire the disease if one parent possesses the mutation.

Non-genetic variables increase an individual's risk of Alzheimer's disease, even if hereditary factors account for a large portion of the illness's development. The biggest risk factor is becoming older, especially if you have late-onset Alzheimer's. Lifestyle, general brain health, and cardiovascular health are additional risk factors. Alzheimer's disease risk can also be raised by cardiovascular health conditions such asdiabetes, hypertension, and high cholesterol. Lifestyle elements like nutrition, physical activity, and intellectual pursuits are also crucial. Research indicates that participating in cognitively stimulating activities, maintaining a nutritious diet, and getting regular exercise can lower the risk of cognitive decline.

The answer to the question, "Is Alzheimer's genetic?" is not simple. The likelihood of developing the illness is influenced by both hereditary and non-genetic variables. Although some genetic variations, such as APOE e4, greatly raise risk, they do not control outcome. Important roles are also played by lifestyle and environmental variables. Having a thorough understanding of these variables can aid in the development of management and preventative plans. Our understanding of the interaction between genetics and other risk factors is changing as research advances, which gives us hope for improved treatments and preventive actions in the future.

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Understanding the effect genetics have on Alzheimer's - Buffalo News

Genetics

‘Fossil viruses’ embedded in the human genome linked to psychiatric disorders – Livescience.com

Ancient viral DNA embedded in the human genome may boost people's susceptibility to neuropsychiatric disorders, such as depression, bipolar disorder and schizophrenia.

A study published in May in the journal Nature Communications zoomed in on human endogenous retroviruses (HERVs) snippets of DNA that form approximately 8% of the modern human genome.

Psychiatric disorders tend to run in families, and studies of twins have also hinted that genetics plays a role in whether people develop them. Estimates suggest that schizophrenia and bipolar disorder may have a heritability as high as 80%, meaning most of the variability seen in these disorders comes down to differences in people's genetics.

Specific versions of genes, or gene variants, have been tied to these disorders, but not much is known about the influence of HERVs.

Related: Common cold virus may predate modern humans, ancient DNA hints

"We were fascinated by the concept that [HERVs] existed in the human genome and so much was not known about them," study co-author Timothy Powell, a neuroscientist and molecular geneticist at King's College London, told Live Science.

HERVs are bits of viruses that have been woven into the human genome over evolutionary time, with the oldest examples introduced to our ancestors over 1.2 million years ago. Some HERVs are known to be switched on in cancer cells, and they may contribute to the disease; others are active in healthy tissues or play important roles in early development, so they're not necessarily all bad. Some HERVs are even active in the brain, but it's not yet clear what they're up to.

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Previously, scientists have studied the role of HERVs in psychiatric disorders by comparing the genetic material of individuals without such disorders with that of people affected by a given disorder. A drawback of this method, however, is that it doesn't account for the influence of environmental factors or other conditions a person may have. This makes it difficult to say with certainty that a given stretch of DNA, in isolation, is strongly associated with the disorder.

The new study used a different approach to weigh the effects of thousands of HERVs. The researchers accessed genetic data from previous studies that involved tens of thousands of people, as well as from postmortem brain tissue samples collected from nearly 800 patients with and without psychiatric disorders. They then studied which gene variants different individuals carried, noting whether they seemed to affect nearby HERVs.

They found that specific gene variants were associated with a higher risk of three psychiatric disorders schizophrenia, depression and bipolar disorder. These variants also affected whether HERVs in the brain were "switched on" and to what degree.

"This [association] gives us much more certainty that the genetic differences we're seeing between cases and controls are more likely to be a true reflection of the biology of the disorder," Rodrigo Duarte, a research fellow at King's College London, told Live Science.

The team is the first to identify five new HERVs strongly tied to psychiatric disorders. Two were associated with schizophrenia, one was common to schizophrenia and bipolar disorder, and one was specific to major depressive disorder. These five HERVs are distinct from any previously linked with each of the conditions.

"It is a major advancement," said Dr. Avindra Nath, clinical director at the National Institute of Neurological Disorders and Stroke who was not involved in the study. "The way that we've been studying all these other neurological diseases, we need to look at them again using their technique," Nath told Live Science.

The study suggests that these HERVs enhance the chances of developing the disorders, but at this point, not much can be said for how much these genetic snippets boost an individual person's risk. Carrying one of the HERVs doesn't necessarily guarantee a person will be affected by the linked disorder.

Going forward, the group plans to manipulate HERV activity in brain cells in lab dishes to see whether they affect the way the neurons grow and form connections.

"From a genetic standpoint, it's an advancement of the field," Nath said. "But from a pathogenesis standpoint, much remains to be answered" about how the HERVs actually contribute to disease.

Ever wonder why some people build muscle more easily than others or why freckles come out in the sun? Send us your questions about how the human body works to community@livescience.com with the subject line "Health Desk Q," and you may see your question answered on the website!

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Genetics

Genes Link Sleep Patterns to Autism and Bipolar Disorder – Neuroscience News

Summary: Researchers found genetic associations between sleep patterns and neuropsychiatric conditions like autism, ADHD, and bipolar disorder. Polygenic risk score analysis revealed that autism and schizophrenia link to evening chronotype, while ADHD, schizophrenia, and bipolar disorder link to insomnia. These insights may lead to new therapies for sleep disturbances in these patients.

Key Facts:

Source: European Society of Human Genetics

Disturbed sleep is very common in almost all neuropsychiatric and neurodevelopmental conditions (NDPCs), such as autism, attention deficit and hyperactivity disorder, schizophrenia, and bipolar disorder.

While it is understandable that the symptoms of such conditions would lead to sleep disruption and also that sleep disruption would worsen symptoms in these conditions, Irish researchers have now found new genetic associations between some of these conditions and chronotype, the behavioural manifestations of an individuals circadian rhythm (night owl or early bird).

These findings may point the way to the development of new therapies for patients.

Presenting the results of the study to the annual conference of the European Society of Human Genetics today (Tuesday), Dr Laura Fahey, a postdoctoral researcher in the Family Genomics Research Group, Maynooth University, Republic of Ireland, will say that sleep disturbances are known to predate the onset of major depressive disorder and bipolar disorder, and that polygenic score analysis can identity whether these conditions and sleep traits share genetic variation.

The researchers therefore used polygenic risk score analysis on large-scale genetic studies of NDPCs to test their ability to predict chronotype and insomnia in over 409,000 participants in the UK Biobank.

Their findings strengthen known genetic correlation results in that they show that polygenic scores for autism and schizophrenia are associated with an evening chronotype, while polygenic scores for attention-deficit/hyperactivity disorder, schizophrenia, and bipolar disorder are associated with insomnia.

We also identified novel associations between bipolar disorder and chronotype, as well as insomnia and autism, says Dr Fahey.

These are interesting insights into the genetic basis of sleep disruption, and may open new research avenues for the treatment of sleep and circadian rhythm disturbances in these patients.

The finding that shared genetic variation between bipolar disorder and chronotype was enriched (overrepresented) in a pathway* called NRF2-KEAP1 was interesting to us, as the NRF2 pathway was previously linked to the pathology of bipolar disorder and schizophrenia.

Additionally, NRF2 has previously been shown to be rhythmically regulated by circadian clock genes.

However, it was surprising that there was no enrichment of shared genetic variation in any biological pathway for the other sleep-NDPC phenotype pairs investigated. This was particularly surprising for ADHD and insomnia, as we found these two phenotypes to have the strongest genome-wide correlation.

A reason for this could be that the shared genetic variation is highly polygenic, affecting all biological pathways somewhat equally. It could also be that this shared genetic variation is enriched in cell- or tissue-specific pathways, which we did not explore, Dr Fahey says.

The researchers also intend to test polygenic scores from more diverse populations, the UK Biobank data used in their study being on individuals of white British ancestry.

We need to know whether this work can be applied to other population groups, says Dr Fahey, since we hope that our work may contribute to the development of predictive and preventive interventions in the future..

Further research could also investigate the impact of the genetic variation found in the biological pathways identified by the scientists as influencing circadian rhythm; for example, whether there are specific subsets of patients with these changes where it would be useful to look for differences in gene expression.

However, the next stage of my research project will take a broader perspective and aim to better understand the genetic architecture using different methods and investigating both rare and common genetic variations underlying sleep and circadian rhythm disturbances in NDPCs, Dr Fahey says.

Professor Alexandre Reymond, from the Center for Integrative Genomics, University of Lausanne, Lausanne, Switzerland, and chair of the conference, said: It is interesting to see that perturbations of the same molecular pathways are associated with distinct phenotypes (bipolar disorder/schizophrenia and chronotype), a phenomenon called pleiotropy.

It is tantalising to think that, if we are in presence of direct pleiotropy where one trait influences the other trait, we may have some hints about possible treatments.

Note:

* Gene-regulation pathways turn genes on and off. Such action is vital because genes provide the recipe by which cells produce proteins, which are the key components needed to carry out nearly every task in the body.

Author: Mary Rice Source: European Society of Human Genetics Contact: Mary Rice European Society of Human Genetics Image: The image is credited to Neuroscience News

Original Research: The findings will be presented at the European Society of Human Genetics annual conference

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Genes Link Sleep Patterns to Autism and Bipolar Disorder - Neuroscience News

Genetics

Tilapia genetics: a comparative trial between two strains in Brazil – The Fish Site

GenoMar's premium genetic tilapia line - GenoMar 1000 - and a market leading local strain were compared in both pond and cage production systems under commercial conditions in Brazil, during a recent study. The results showed that GenoMar 1000 is superior for growth, survival, and uniformity, growing approximately 30 percent faster than the local strain during the test. With economic analysis suggesting a 31-36 percent annual increase in profits, the results demonstrate GenoMar 1000s potential to enhance productivity, profitability, and sustainability in the Brazilian tilapia industry.

Nile tilapia (Oreochromis niloticus) is one of the most important farmed fish species in the world, and Brazil is one of the leading producers of tilapia globally. In recent years, the Brazilian tilapia industry has experienced a significant increase in production volumes, with annual production surpassing 579,000 tonnes in 2023.

As Brazil continues to develop its tilapia production capabilities whilst maintaining high-quality standards, the country is well-positioned to further increase its share in the global tilapia export market. Despite having vast potential, the comparatively young Brazilian tilapia farming industry continues to encounter significant challenges, with one major hurdle being the limited availability of highly productive and efficient genetic strains. Available local strains of tilapia typically exhibit lower productivity compared to strains that have undergone selective breeding for better growth, survival, and uniformity.

GenoMar Genetics is a leading producer of genetically improved tilapia fingerlings and juveniles under the brands GenoMar and Aquabel. Following 33 generations of selective breeding using state-of-the-art technologies, the GenoMar tilapia strain has been globally recognised for its superior growth, survival, uniformity, fillet yield and carcass quality. Widely regarded as one of the most sought-after tilapia strains in production, the GenoMar strain contributes significantly to enhancing productivity and profitability in the global tilapia industry. Now, GenoMar 1000 - the premium genetic line of the GenoMar strain - previously unavailable in Brazil, has been accessible to farmers since early 2024.

In this article, we have summarised the results of the three major key performance indicators (growth, survival, and uniformity) of GenoMar 1000 compared to a market leading strain of Nile tilapia under commercial pond and cage farming systems in Brazil.

Growth development, final survival and uniformity of GenoMar 1000 compared to a local commercial strain in Brazil. The weights presented are an average of two cages with a 50/50 mix of the two strains (common garden design) GenoMar

We have summarised the growth performances in the trial using three different measurement units:

The fish were harvested 140 days after stocking in both ponds and cages, with GenoMar 1000 outperforming the local strain in terms of growth in both ponds and cages under commercial rearing conditions (Figure 1). In ponds, GenoMar 1000 grew 33 percent faster than the local strain (1,196 g compared to 902 g). In cages, GenoMar 1000 grew 29 percent faster than the local strain (1,350 g compared to 1,046 g).

Growth curves for GenoMar 1000 and the local commercial strain across pond and cage production systems. The x-axis represents the days of culture (DOC), indicating the number of days after stocking in the production systems for common grow-out. GenoMar

The mean average daily gain (ADG) post stocking was higher for GenoMar 1000 compared to the local strain in both ponds and cage production systems (Figure 2). Grown in ponds, GenoMar 1000 grew at a mean daily rate of 8.37 grams, whilst the local strain of tilapia gained an average of 6.35 grams per day during the 140 day grow-out period. When reared in cages, ADG increased slightly for both strains, with GenoMar 1000 growing at an average of 9.49 grams per day, and the local strain gaining 7.37 grams per day, on average.

Simplified, this means that, compared with the local tilapia strain, GenoMar 1000 grew approximately 33 percent faster when reared in ponds, and 29 percent faster when cage reared.

Comparative analysis of average daily gain (ADG) at various body weights for GenoMar 1000 and the local commercial strain in different pond and cage production systems in Brazil GenoMar

GenoMar 1000 reached 1 kg faster than the local strain of tilapia in both pond and cage production systems. GenoMar 1000 reached 1 kg an average of 31.5 and 21.5 days faster than the local strain of tilapia in ponds and cages respectively (Figure 3).

Comparative analysis of number of days to reach 1 kg for GenoMar 1000 and the local commercial strain of tilapia in different ponds and cage production systems. Number of days is calculated from the day of stocking in the production system for common grow-out GenoMar

These findings, from three different growth metrics, demonstrate the significant growth advantages offered by GenoMar 1000, highlighting its potential for enhancing tilapia farming productivity and sustainability.

In addition to growth rate, another key performance indicator for tilapia producers is survival rate, which holds paramount importance for both tilapia hatcheries and grow-out farmers. Thus, we have compared the survival rate of GenoMar 1000 and the local strain for two different time intervals.

Higher survival rates allow hatcheries to produce more tilapia fry and fingerlings without increasing their consumption of resources.GenoMar 1000 tilapia were found to have significantly higher survival in the hatchery stage: both from 0.016 g to 1 g (sex reversal stage) and from 1 g to 10 g (pre-grow-out stage).

During the sex reversal stage, hatchery survival rates for GenoMar 1000 fish and the local tilapia strain were 56 percent and 36 percent, respectively. For the pre-grow-out stage, survival rates increased for both strains, with GenoMar 1000 tilapia surviving at a rate of 87 percent, compared to 78 percent for the local strain. Overall, the total hatchery survival rate for GenoMar 1000 fish was 72 percent higher than that for the local strain of fish.

The fish in the experiment were not vaccinated so that the effect of their genetics on their survival could be observed. Thus, the difference in survival between the two strains is likely due to their different adaptations and abilities to tolerate diseases and general stress during grow-out.

GenoMar 1000 tilapia survived better in cages (p<0.001), while there was no significant difference in survival between the two strains in ponds (Figure 4). This is likely because the ponds were located in a biosecure facility with low disease pressure, whilst the cages were placed in the Lake Palmas, an open environment with less biosecurity.

Survival during grow-out for GenoMar 1000 and the local commercial strain in pond and cage production systems (left), and the mean of pond and cage production systems (right) GenoMar

Streptococcus agalactiae, a common fish pathogen, was detected at the cage site during the experiment, but no treatment was applied. The higher survival of GenoMar 1000 at this site suggests improved resistance to Streptococcosis infection due to specific selection for this trait in every generation since 2016.

Despite being selected in an Asian environment; the trial demonstrates no negative effect on the adaptability and survivability of GenoMar 1000 in Brazilian conditions.On the contrary, the survival of GenoMar 1000 in the cage environment was significantly better.

Cage environments typically carry higher pathogenic pressure and stress levels. GenoMar 1000 has been bred to be more resistant to various economically important pathogens and production environments for a longer period.

Out of ten yolk sac fry from GenoMar 1000, six survived until 1 g (survival rate = 56 percent), compared to four from the local strain (survival rate = 36 percent). The number of fish is rounded to the nearest whole number.

From these survived fingerlings, five from GenoMar 1000 survived the pre-grow-out stage and became juveniles (total survival rate = 48 percent), whilst three from the local commercial strain survived (total survival rate = 28 percent).

Extending this comparison to the grow-out in cages, all five juveniles from GenoMar 1000 survived until harvest (survival rate = 95.2 percent), compared to only two from the local strain (survival rate = 88.9 percent). Further hazard analysis showed that the risk of death during the grow-out stage is significantly decreased by 49 percent in GenoMar 1000, compared to the local strain of tilapia.

To summarise the entire experimental period: Out of ten yolk sac fry from GenoMar 1000, five survived until harvest, compared to two from the local strain (Figure 5). These figures highlight GenoMars commitment to advancing disease resistance and increasing tilapia survival via genetics. These findings validate the efficacy of our innovations in the tilapia breeding programme and demonstrate the potential for sustainable fish farming practices using GenoMar 1000 fingerlings.

Out of ten yolk sac fry of GenoMar 1000, five survived until harvest, compared to two of the local commercial strain GenoMar

Uniformity was measured by calculating the coefficient of variation (CV) of body weight at harvest (BWH). A higher CV means lower uniformity. GenoMar 1000 was found to be more uniform than the local strain of tilapia in both cages and ponds, exhibiting, 23 percent more uniformity than the local strain of tilapia on average (p<0.001).

Figure 6 describes the size variance of simulated populations of Genomar 1000 tilapia and the local strain for their respective CV values, the target weight being 1 kg. A significantly higher proportion of the GenoMar 1000 fish reached the desired harvest weight in both ponds and cage production systems, underscoring its suitability as genetic material for large-scale grow-out operations.

Simulation of a population of 20,000 from each of GenoMar 1000 and the local commercial strain of tilapia to show the size distribution of the fish populations when the targeted average harvest body weight was 1 kg GenoMar

In the preceding sections, we evaluated the growth and survival of the two strains independently. However, to comprehensively assess the effectiveness of stocked juveniles in terms of both growth and biomass conversion into harvestable fish - a vital consideration for farm profitability - we required an index comprising both these traits. In the following sections we have summarised the performance and yield of juveniles using two different metrics:

Performance is calculated as:

This metric essentially calculates the average biomass production (in g) of each stocked juvenile (corrected to the initial stocking weight of the juvenile) per day in the production system.

In the cage production system, the GenoMar 1000 juveniles exhibited an average performance 38 percent higher than the juveniles of the local strain (Figure 7). Each stocked juvenile of the local commercial strain yielded on an average 6.5 g of harvestable biomass per day. Each stocked GenoMar 1000 juvenile surpassed this, producing approximately 2.5 g more, amounting to 9 g of harvested biomass per day.

Similarly, in the pond production system the GenoMar 1000 juveniles showed an average performance that was 33 percent higher than the juveniles of the local strain (Figure 7). Each stocked juvenile of the local strain on average yielded 5.9 g of harvestable biomass per day, whilst the GenoMar 1000 juveniles produced 7.8 g of harvestable biomass per day.

2. Yield (kg harvested/unit of production area)

The yield is calculated as:

This metric essentially calculates the average biomass production (in kg) per unit area (m2 of the surface area of the pond or m3 of the cages in one harvest). The difference in the values for the yield between GenoMar 1000 and the local strain of tilapia is similar to that for the performance (Figure 7). In the pond-based production system, the local commercial strain yielded 1 kg/m2 whilst GenoMar 1000 yielded an additional 0.4 kg for the same unit area. Similarly, in the cage production system the local strain yielded 25.6 kg/m3, whilst the GenoMar 1000 yield exceeded this, yielding 35.4 kg/m3.

Comparison of performance of juveniles (g per fish per day) and yield of juveniles (kg harvested per unit area) in the pond and cage production systems. A higher value for performance and yield indicates better growth, survival, and productivity of the juveniles in the production system GenoMar

Choosing the right genetics is a strategic decision that significantly influences the long-term success of a tilapia production venture. For this, an economic analysis is imperative, providing insights into the monetary benefits for farmers and aiding them in making informed decisions.

Based on the outcomes of this trial, we conducted an economic analysis for both cage and pond production systems, focusing on harvesting tilapia at 1 kg. Both scenarios simulated a stocking of 200,000 tilapia.

Due to the fact that the ponds and cages were shared by fish of both strains, the feed conversion could not be directly measured and compared. Consequently, we used the same feed conversion ratios (FCRs) for both strains within each production system. However, in practice, we have observed that superior growth and increased survival typically correlate with enhanced feed efficiency, ultimately increasing profit margins.

Considering a higher investment in genetically improved fingerlings from GenoMar 1000, a fish farm stocking 200,000 fish in a cage can potentially achieve an annual increase in harvest of 128,895 kg and a profitability increase of R$ 360,834 (equivalent to US $72,167), representing a 36 percent increase in profits. Similarly, in the pond production system, the potential increase in harvest is 122,625 kg, with a profitability increase of R$ 312,664 (equivalent to US $62,533), reflecting a 31 percent increase in profits.

To establish the experimental populations, broodstocks from GenoMar 1000 and the local strain were reproduced within the same facility in Tocantins. This approach aimed to synchronise spawning and minimise environmental variations.

The experimental populations were created using a batch of eggs collected within the same week to ensure similar developmental stages, thus reducing the hatching interval of the larvae to a maximum of five days, mirroring commercial practices (Figure 8).

Experimental design from hatching till individual tagging (pre-grow-out) before stocking for grow-out in ponds and cages GenoMar

At one day post-hatching, 48,180 yolk sac fry from each strain were stocked in two separate hapas within the same pond, receiving identical treatment. This nursery stage lasted 40 days, with sexual reversal during the first 21 days. Subsequently, in the pre-growth stage, 10,000 fingerlings of each strain were randomly collected and stocked in four hapas, two for each strain (with two additional hapas for backup), within the same pond to minimise environmental effects.

Over the next 30 days, these fish were raised under identical water quality conditions and received the same feeding programme until they reached tagging size. Subsequently, 3,000 juveniles from each strain were randomly collected from all hapas, tagged, and transferred to the grow-out stage for a common garden experiment.

GenoMar

Following individual tagging, an equal number of fish from GenoMar 1000 and the local strain of tilapia (selected randomly) were stocked together in two different ponds and two different cages for a blind grow-out. This design ensures that the two groups of fish were reared under the same environmental conditions in each pond and cage (Figure 9).

The 32 m3 cages were stocked to a density of 55 juveniles per m3, with an expected harvest of 70 kg/m3. Comparatively, the 500 m2 ponds were stocked to a density of 2.48 juveniles per m2, with an expected harvest of 3 kg/m2.

Experimental design of the individually tagged fish for blinded common garden grow-out in cages and ponds GenoMar

The cages were positioned in Lake Palmas, where commercial tilapia farming is already in operation. The ponds were located within GenoMars Tocantins facilities. Activities at both locations were synchronised, and a strict feeding programme was followed to simulate commercial production conditions. The grow-out phase was closely monitored until harvest, with individual biometry measurements taken approximately every 25 days.

The grow-out phase of the trial was conducted in ponds at Serra da Tilapia, Monte do Carmo in Tocantins, Brazil (left) and in cages at Lago de Palmas, Lajeado in Tocantins, Brazil (right). GenoMar

A commercial feeding programme was followed during the trial. For the initial six weeks post-hatching, the tilapia was provided with commercial feed comprising 45 percent protein, followed by a switch to a feed containing 36 percent protein from week seven to nine. As the tilapia continued to grow, this diet was further adjusted to one containing 32 percent protein, starting from week ten.

Due to the nature of the trial, it was not possible to differentiate the FCR for GenoMar 1000 and the local strain of tilapia separately. Therefore, the FCR for the combined group is presented in Figure 10 to demonstrate that the FCR rate in the trial was within normal parameters.

Combined feed conversion ratio (FCR) of the production systems at different average body weight of the fish in each cage and pond. The values listed on the right-hand side with inverted triangles represent the predicted FCR when the fish are around 1 kg in each production environment GenoMar

During the common garden grow-out the temperature and dissolved oxygen levels in all cages and ponds were monitored and recorded throughout the experiment and are presented in Figure 11.

The temperature and dissolved oxygen in different ponds and cages during the grow-out period of the experiment GenoMar

A switch to GenoMar 1000 tilapia can be a very beneficial decision for Brazilian tilapia farmers, impacting productivity, profitability, sustainability, and animal welfare:

How does GenoMar 1000 achieve such improved production performance? The answer lies in selective breeding and the use of advanced scientific technology. GenoMar 1000 was developed over many generations by carefully selecting fish with the best growth, survival, and fillet yield traits, resulting in a product that is genetically predisposed to grow quickly and efficiently.

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Tilapia genetics: a comparative trial between two strains in Brazil - The Fish Site

Genetics

New genetic cause of intellectual disability potentially uncovered in ‘junk DNA’ – Livescience.com

Scientists have uncovered a rare genetic cause of intellectual disability in a historically overlooked part of the human genome: so-called junk DNA.

This knowledge could someday help to diagnose some patients with these disorders, the researchers say.

An intellectual disability is a neurodevelopmental disorder that appears during childhood and is characterized by intellectual difficulties that impact people's learning, practical skills and ability to live independently. Such conditions affect approximately 6.5 million Americans.

Factors such as complications during birth can trigger intellectual disabilities. However, in most cases, the disorders have an underlying genetic cause. So far, around 1,500 genes have been linked with various intellectual disabilities but clinicians are still not always able to identify the specific cause of every patient's condition.

Related: Rates of autism diagnosis in children are at an all time high, CDC report suggests

One possible explanation for this gap in knowledge is that previous approaches for reading DNA have only focused on a tiny portion of it. Specifically, they've looked at the roughly 2% of the genome that codes for proteins, known as coding DNA. About 98% of the genome contains DNA that doesn't code for proteins. This DNA was once considered "junk DNA," but scientists are now discovering that it actually performs critical biological functions.

In a new study, published Friday (May 31) in the journal Nature Medicine, scientists used whole-genome sequencing technology to identify a rare genetic mutation within non-coding DNA that seems to contribute to intellectual disability.

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The team compared the whole genomes of nearly 5,530 people who have a diagnosed intellectual disability to those of about 46,400 people without the conditions. These data were gathered from the U.K.-based 100,000 Genomes Project.

The researchers discovered that 47 of the people with intellectual disabilities about 0.85% carried mutations in a gene called RNU4-2. They then validated this finding in three additional large, independent genetic databases, bringing the total number of cases to 73.

RNU4-2 doesn't code for proteins but rather for an RNA molecule, a cousin of DNA; RNA's code can either be translated into proteins or stand on its own as a functional molecule. The RNA made by RNU4-2 makes up part of a molecular complex called the spliceosome. The spliceosome helps to refine RNA molecules after their codes are copied down from DNA by "splicing" out certain snippets of the code.

Related: 'Look at all this we don't understand': Study unravels whole new layer of Alzheimer's disease

To further determine the prevalence of this new disorder, the team then launched a separate analysis where they looked at the genomes of another 5,000 people in the U.K. who'd been diagnosed with "neurodevelopmental abnormality." This is a term that refers to any deviation from "normal" in the neurodevelopment of a child.

The team's analysis revealed that, out of those 5,000 people, 21 carried mutations in RNU4-2. That made the mutations the second most common type seen in the overall group, following mutations on the X chromosome known to cause a disorder called Rett syndrome. If changes in RNU4-2 can be confirmed as a cause of intellectual disability, this finding hints that the mutations may contribute significantly to a variety of conditions.

The new study joins a second that also linked RNU4-2 to intellectual disabilities. The research has opened up "an exciting new avenue in ID [intellectual disability] research," Catherine Abbott, a professor of molecular genetics at the University of Edinburgh in the U.K. who was not involved in either study, told Live Science in an email.

"These findings reinforce the idea that ID can often result from mutations that have a cumulative downstream effect on the expression of hundreds of other genes," Abbott said. RNA molecules that don't make proteins often help control the activity of genes, turning them on or off. The findings also stress the importance of sequencing the whole genome rather than just coding DNA, she said.

The scientists behind the new study say the findings could be used to diagnose certain types of intellectual disability.

The team now plans to investigate the precise mechanism by which RNU4-2 causes intellectual disabilities for now, they've only uncovered a strong correlation.

Ever wonder why some people build muscle more easily than others or why freckles come out in the sun? Send us your questions about how the human body works to community@livescience.com with the subject line "Health Desk Q," and you may see your question answered on the website!

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New genetic cause of intellectual disability potentially uncovered in 'junk DNA' - Livescience.com