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This article begins with a brief overview of the history of Brazil and that of Brazilian science, from the European discovery of the country in 1500 up to the early 21st century. The history of the fields of genetics and genomics, from the 1930s, is then first examined from the focal point of the lives and publications of the three persons who are generally considered to be the founders of genetics in Brazil (C. A. Krug, F. G. Brieger, and A. Dreyfus), and then by 12 other researchers up to 1999. The area of molecular genetics and genomics from 2000 to present is then described. Despite the problems of underdevelopment and the periodical political and economic crises that have affected life in Brazil, the fields of genetics and genomics in Brazil can be regarded as having developed at an appropriate pace, and have contributed in several major ways to world science.

We use a genome-wide association of 1 million parental lifespans of genotyped subjects and data on mortality risk factors to validate previously unreplicated findings near CDKN2B-AS1, ATXN2/BRAP, FURIN/FES, ZW10, PSORS1C3, and 13q21.31, and identify and replicate novel findings near ABO, ZC3HC1, and IGF2R. We also validate previous findings near 5q33.3/EBF1 and FOXO3, whilst finding contradictory evidence at other loci. Gene set and cell-specific analyses show that expression in foetal brain cells and adult dorsolateral prefrontal cortex is enriched for lifespan variation, as are gene pathways involving lipid proteins and homeostasis, vesicle-mediated transport, and synaptic function. Individual genetic variants that increase dementia, cardiovascular disease, and lung cancer – but not other cancers – explain the most variance. Resulting polygenic scores show a mean lifespan difference of around five years of life across the deciles. Editorial note: This article has been through an editorial process in which the authors decide how to respond to the issues raised during peer review. The Reviewing Editor's assessment is that all the issues have been addressed (see decision letter ). Ageing happens to us all, and as the cabaret singer Maurice Chevalier pointed out, \"old age is not that bad when you consider the alternative\". Yet, the growing ageing population of most developed countries presents challenges to healthcare systems and government finances. For many older people, long periods of ill health are part of the end of life, and so a better understanding of ageing could offer the opportunity to prolong healthy living into old age. Ageing is complex and takes a long time to study – a lifetime in fact. This makes it difficult to discern its causes, among the countless possibilities based on an individual’s genes, behaviour or environment. While thousands of regions in an individual’s genetic makeup are known to influence their risk of different diseases, those that affect how long they will live have proved harder to disentangle. Timmers et al. sought to pinpoint such regions, and then use this information to predict, based on their DNA, whether someone had a better or worse chance of living longer than average. The DNA of over 500,000 people was read to reveal the specific ‘genetic fingerprints’ of each participant. Then, after asking each of the participants how long both of their parents had lived, Timmers et al. pinpointed 12 DNA regions that affect lifespan. Five of these regions were new and had not been linked to lifespan before. Across the twelve as a whole several were known to be involved in Alzheimer’s disease, smoking-related cancer or heart disease. Looking at the entire genome, Timmers et al. could then predict a lifespan score for each individual, and when they sorted participants into ten groups based on these scores they found that top group lived five years longer than the bottom, on average. Many factors beside genetics influence how long a person will live and our lifespan cannot be read from our DNA alone. Nevertheless, Timmers et al. had hoped to narrow down their search and discover specific genes that directly influence how quickly people age, beyond diseases. If such genes exist, their effects were too small to be detected in this study. The next step will be to expand the study to include more participants, which will hopefully pinpoint further genomic regions and help disentangle the biology of ageing and disease.

The Δ32 mutation at the CCR5 locus is a well-studied example of natural selection acting in humans. The mutation is found principally in Europe and western Asia, with higher frequencies generally in the north. Homozygous carriers of the Δ32 mutation are resistant to HIV-1 infection because the mutation prevents functional expression of the CCR5 chemokine receptor normally used by HIV-1 to enter CD4+ T cells. HIV has emerged only recently, but population genetic data strongly suggest Δ32 has been under intense selection for much of its evolutionary history. To understand how selection and dispersal have interacted during the history of the Δ32 allele, we implemented a spatially explicit model of the spread of Δ32. The model includes the effects of sampling, which we show can give rise to local peaks in observed allele frequencies. In addition, we show that with modest gradients in selection intensity, the origin of the Δ32 allele may be relatively far from the current areas of highest allele frequency. The geographic distribution of the Δ32 allele is consistent with previous reports of a strong selective advantage (>10%) for Δ32 carriers and of dispersal over relatively long distances (>100 km/generation). When selection is assumed to be uniform across Europe and western Asia, we find support for a northern European origin and long-range dispersal consistent with the Viking-mediated dispersal of Δ32 proposed by G. Lucotte and G. Mercier. However, when we allow for gradients in selection intensity, we estimate the origin to be outside of northern Europe and selection intensities to be strongest in the northwest. Our results describe the evolutionary history of the Δ32 allele and establish a general methodology for studying the geographic distribution of selected alleles. A spatially explicit model of the Δ32 mutation, which confers resistance to HIV-1 infection, reveals its spread across Europe and provides a general method for tracking the geographic spread of selected alleles.

Recent advances in -omics technologies such as transcriptomics, metabolomics, and proteomics along with genotypic profiling have permitted dissection of the genetics of complex traits represented by molecular phenotypes in nonmodel species. To identify the genetic factors underlying variation in primary metabolism in potato (Solarium tuberosum), we have profiled primary metabolite content in a diploid potato mapping population, derived from crosses between S. tuberosum and wild relatives, using gas chromatography-time of flight-mass spectrometry. In total, 139 polar metabolites were detected, of which we identified metabolite quantitative trait loci for approximately 72% of the detected compounds. In order to obtain an insight into the relationships between metabolic traits and classical phenotypic traits, we also analyzed statistical associations between them. The combined analysis of genetic information through quantitative trait locus coincidence and the application of statistical learning methods provide information on putative indicators associated with the alterations in metabolic networks that affect complex phenotypic traits.

In the summer of 1991, population geneticists and evolutionary biologists proposed to archive human genetic diversity by collecting the genomes of \"isolated indigenous populations.\" Their initiative, which became known as the Human Genome Diversity Project, generated early enthusiasm from those who believed it would enable huge advances in our understanding of human evolution. However, vocal criticism soon emerged. Physical anthropologists accused Project organizers of reimporting racist categories into science. Indigenous-rights leaders saw a \"Vampire Project\" that sought the blood of indigenous people but not their well-being. More than a decade later, the effort is barely off the ground. How did an initiative whose leaders included some of biology's most respected, socially conscious scientists become so stigmatized? How did these model citizen-scientists come to be viewed as potential racists, even vampires? This book argues that the long abeyance of the Diversity Project points to larger, fundamental questions about how to understand knowledge, democracy, and racism in an age when expert claims about genomes increasingly shape the possibilities for being human. Jenny Reardon demonstrates that far from being innocent tools for fighting racism, scientific ideas and practices embed consequential social and political decisions about who can define race, racism, and democracy, and for what ends. She calls for the adoption of novel conceptual tools that do not oppose science and power, truth and racist ideologies, but rather draw into focus their mutual constitution.

A powerful way to discover key genes with causal roles in oncogenesis is to identify genomic regions that undergo frequent alteration in human cancers. Here we present high-resolution analyses of somatic copy-number alterations (SCNAs) from 3,131 cancer specimens, belonging largely to 26 histological types. We identify 158 regions of focal SCNA that are altered at significant frequency across several cancer types, of which 122 cannot be explained by the presence of a known cancer target gene located within these regions. Several gene families are enriched among these regions of focal SCNA, including the BCL2 family of apoptosis regulators and the NF-κΒ pathway. We show that cancer cells containing amplifications surrounding the MCL1 and BCL2L1 anti-apoptotic genes depend on the expression of these genes for survival. Finally, we demonstrate that a large majority of SCNAs identified in individual cancer types are present in several cancer types. Cancer genomics refined Two Articles in this issue add major data sets to the growing picture of the cancer genome. Bignell et al . analysed a large number of homozygous gene deletions in a collection of 746 publicly available cancer cell lines. Combined with information about hemizygous deletions of the same genes, the data suggest that many deletions found in cancer reflect the position of a gene at a fragile site in the genome, rather than as a recessive cancer gene whose loss confers a selective growth advantage. Beroukhim et al . present the largest data set to date on somatic copy-number variations across more than 3,000 specimens of human primary cancers. Many alterations are shared between multiple tumour types. Functional experiments demonstrate an oncogenic role for the apoptosis genes MCL1 and BCL2L1 that are associated with amplifications found in many cancers. One way of discovering genes with key roles in cancer development is to identify genomic regions that are frequently altered in human cancers. Here, high-resolution analyses of somatic copy-number alterations (SCNAs) in numerous cancer specimens provide an overview of regions of focal SCNA that are altered at significant frequency across several cancer types. An oncogenic function is also found for the anti-apoptosis genes MCL1 and BCL2L1 , which reside in amplified genome regions in many cancers.

The gene encoding the transcriptional coactivator peroxisome proliferator- activated receptor- coactivator-1 alpha (PGC-1 alpha ) was targeted in mice. PGC-1 alpha null (PGC-1 alpha super(-/-)) mice were viable. However, extensive phenotyping revealed multi-system abnormalities indicative of an abnormal energy metabolic phenotype. The postnatal growth of heart and slow-twitch skeletal muscle, organs with high mitochondrial energy demands, is blunted in PGC-1 alpha super(-/-) mice. With age, the PGC-1 alpha super(-/-) mice develop abnormally increased body fat, a phenotype that is more severe in females. Mitochondrial number and respiratory capacity is diminished in slow-twitch skeletal muscle of PGC-1 alpha super(-/-) mice, leading to reduced muscle performance and exercise capacity. PGC-1 alpha super(-/-) mice exhibit a modest diminution in cardiac function related largely to abnormal control of heart rate. The PGC-1 alpha super(-/-) mice were unable to maintain core body temperature following exposure to cold, consistent with an altered thermogenic response. Following short-term starvation, PGC-1 alpha super(-/-) mice develop hepatic steatosis due to a combination of reduced mitochondrial respiratory capacity and an increased expression of lipogenic genes. Surprisingly, PGC-1 alpha super(-/-) mice were less susceptible to diet-induced insulin resistance than wild-type controls. Lastly, vacuolar lesions were detected in the central nervous system of PGC-1 alpha super(-/-) mice. These results demonstrate that PGC-1 alpha is necessary for appropriate adaptation to the metabolic and physiologic stressors of postnatal life.

Genomic selection (GS) emerged as a key part of the solution to ensure the food supply for the growing human population thanks to advances in genotyping and other enabling technologies and improved understanding of the genotype–phenotype relationship in quantitative genetics. GS is a breeding strategy to predict the genotypic values of individuals for selection using their genotypic data and a trained model. It includes four major steps: training population design, model building, prediction, and selection. GS revises the traditional breeding process by assigning phenotyping a new role of generating data for the building of prediction models. The increased capacity of GS to evaluate more individuals, in combination with shorter breeding cycle times, has led to wide adoption in plant breeding. Research studies have been conducted to implement GS with different emphases in crop‐ and trait‐specific applications, prediction models, design of training populations, and identifying factors influencing prediction accuracy. GS plays different roles in plant breeding such as turbocharging of gene banks, parental selection, and candidate selection at different stages of the breeding cycle. It can be enhanced by additional data types such as phenomics, transcriptomics, metabolomics, and enviromics. In light of the rapid development of artificial intelligence, GS can be further improved by either upgrading the entire framework or individual components. Technological advances, research innovations, and emerging challenges in agriculture will continue to shape the role of GS in plant breeding. Core Ideas Genomic selection (GS) is a breeding strategy to predict the genotypic merits of individuals for selection. GS helps to shorten breeding cycle times, increase genetic gains, and facilitate better resource allocation. GS can be enhanced by phenomics, metabolomics, transcriptomics, and enviromics data to improve prediction accuracy. Deep learning (DL)‐based GS is expected to facilitate the integration of multiple layers of information in prediction models. Emerging technologies, research, and agricultural challenges continuously shape the role of GS in plant breeding. Plain Language Summary Genomic selection (GS) is a strategy used in plant breeding to predict measurable traits of plants. GS exploits the relationship between a plant's genetic makeup and a measurable phenotype to build a prediction model. With the prediction model, the molecular marker information of untested plants is used to predict their potential performance for selection. GS increases the evaluation capacity of breeding programs and reduces the time needed to develop new cultivars, leading to faster crop improvement. GS can be used for exploring genetic diversity, selecting breeding parents, or selecting individuals at different stages of the breeding cycle. GS can incorporate other data types such as environmental variables, gene expression, and metabolite abundance. Advances in artificial intelligence are expected to facilitate the integration of multiple data types in prediction models. GS is continuously evolving due to technological advances, research innovations, and emerging challenges in agriculture.

The maize (Zea mays) kernel plays a critical role in feeding humans and livestock around the world and in a wide array of industrial applications. An understanding of the regulation of kernel starch, protein, and oil is needed in order to manipulate composition to meet future needs. We conducted joint-linkage quantitative trait locus mapping and genome-wide association studies (GWAS) for kernel starch, protein, and oil in the maize nested association mapping population, composed of 25 recombinant inbred line families derived from diverse inbred lines. Joint-linkage mapping revealed that the genetic architecture of kernel composition traits is controlled by 21–26 quantitative trait loci. Numerous GWAS associations were detected, including several oil and starch associations in acyl-CoA:diacylglycerol acyltransferase1-2, a gene that regulates oil composition and quantity. Results from nested association mapping were verified in a 282 inbred association panel using both GWAS and candidate gene association approaches. We identified many beneficial alleles that will be useful for improving kernel starch, protein, and oil content.

Massively parallel pyrosequencing of amplicons from the V6 hypervariable regions of small-subunit (SSU) ribosomal RNA (rRNA) genes is commonly used to assess diversity and richness in bacterial and archaeal populations. Recent advances in pyrosequencing technology provide read lengths of up to 240 nucleotides. Amplicon pyrosequencing can now be applied to longer variable regions of the SSU rRNA gene including the V9 region in eukaryotes. We present a protocol for the amplicon pyrosequencing of V9 regions for eukaryotic environmental samples for biodiversity inventories and species richness estimation. The International Census of Marine Microbes (ICoMM) and the Microbial Inventory Research Across Diverse Aquatic Long Term Ecological Research Sites (MIRADA-LTERs) projects are already employing this protocol for tag sequencing of eukaryotic samples in a wide diversity of both marine and freshwater environments. Massively parallel pyrosequencing of eukaryotic V9 hypervariable regions of SSU rRNA genes provides a means of estimating species richness from deeply-sampled populations and for discovering novel species from the environment.