genetic data as well as data generated by additional new-omics technologies. quantitative genetics the study of population genetics and statistical genetics in order to link variation in genotype with a variation in phenotype. in other words, it is desirable to discover the genetic basis of a measurable trait, a quantitative trait, that is under polygenic control. a genome region that is responsible for a continuous trait is called quantitative trait locus (qtl). the study of qtls become feasible by using molecular markers and measuring traits in populations, but their mapping needs the obtaining of a population from an experimental crossing, like an f2 or recombinant inbred strains/lines (rils). to scan for qtls regions in a genome, a gene map based on linkage have to be built. some of the best-known qtl mapping algorithms are interval mapping, composite interval mapping, and multiple interval mapping. however, qtl mapping resolution is impaired by the amount of recombination assayed, a problem for species in which it is difficult to obtain large offspring. furthermore, allele diversity is restricted to individuals originated from contrasting parents, which limit studies of allele diversity when we have a panel of individuals representing a natural population. for this reason, the genome-wide association study was proposed in order to identify qtls based on linkage disequilibrium, that is the non-random association between traits and molecular markers. it was leveraged by the development of high-throughput snp genotyping. in animal and plant breeding, the use of markers in selection aiming for breeding, mainly the molecular ones, collaborated to the development of marker-assisted selection. while qtl mapping is limited due resolution, gwas does not have enough power when rare variants of small effect that are also influenced by environment. so, the concept of genomic selection (gs) arises in order to use all molecular markers in the selection and allow the prediction of the performance of candidates in this selection. the proposal is to genotype and phenotype a training population, develop a model that can obtain the genomic estimated breeding values (gebvs) of individuals belonging to a genotype and but not phenotype population, called testing population. this kind of study could also include a validation population, thinking in the concept of cross-validation, in which the real phenotype results measured in this population are compared with the phenotype results based on the prediction, what used to check the accuracy of the model. as a summary, some points about the application of quantitative genetics are: this has been used in agriculture to improve crops (plant breeding) and livestock (animal breeding). in biomedical research, this work can assist in finding candidates gene alleles that can cause or influence predisposition to diseases in human genetics expression data studies for differential expression of genes from rna-seq data, as for rt-qpcr and microarrays, demands comparison of conditions. the goal is to identify genes which have a significant change in abundance between different conditions. then, experiments are designed appropriately, with replicates for each condition/treatment, randomization and blocking, when necessary. in rna-seq, the quantification of expression uses the information of mapped reads that are summarized in some genetic unit, as exons that are part of a gene sequence. as microarray results can be approximated by a normal distribution, rna-seq counts data are better explained by other distributions. the first used distribution was the poisson one, but it underestimate the sample error, leading to false positives. currently, biological variation is considered by methods that estimate a dispersion parameter of a negative binomial distribution. generalized linear models are used to perform the tests for statistical significance and as the number of genes is high, multiple tests correction have to be considered. some examples of other analysis on genomics data comes from microarray or proteomics experiments. often concerning diseases or disease stages. other studies ecology, ecological forecasting biological sequence analysis systems biology for gene network inference or pathways analysis. clinical research and pharmaceutical development population dynamics, especially in regards to fisheries science. phylogenetics and evolution pharmacodynamics pharmacokinetics neuroimaging tools there are a lot of tools that can be used to do statistical analysis in biological data. most of them are useful in other areas of knowledge, covering a large number of applications (alphabetical). here are brief descriptions of some of them: asreml: another software developed by vsni that can be used also in r environment as a package. it is developed to estimate variance components under a general linear mixed model using restricted maximum likelihood (reml). models with fixed effects and random effects and nested or crossed ones are allowed. gives the possibility to investigate different variance-covariance matrix structures. cycdesign: a computer package developed by vsni that helps the researchers create experimental designs and analyze data coming from a design present in one of three classes handled by cycdesign. these classes are resolvable, non-resolvable, partially replicated and crossover designs. it includes less used designs the latinized ones, as t-latinized design. orange: a programming interface for high-level data processing, data mining and data visualization. include tools for gene expression and genomics. r: an open source environment and programming language dedicated to statistical computing and graphics. it is an implementation of s language maintained by cran. in addition to its functions to read data tables, take descriptive statistics, develop and evaluate models, its repository contains packages developed by researchers around the world. this allows the development of functions written to deal with the statistical analysis of data that comes from specific applications. in the case of bioinformatics, for example, there are packages located in the main repository (cran) and in others, as bioconductor. it is also possible to use packages under development that are shared in hosting-services as github. sas: a data analysis software widely used, going through universities, services and industry. developed by a company with the same name (sas institute), it uses sas language for programming. pla 3.0: is a biostatistical analysis software for regulated environments (e.g. drug testing) which supports quantitative response assays (parallel-line, parallel-logistics, slope-ratio) and dichotomous assays (quantal response, binary assays). it also supports weighting methods for combination calculations and the automatic data aggregation of independent assay data. weka: a java software for machine learning and data mining, including tools and methods for visualization, clustering, regression, association rule, and classification. there are tools for cross-validation, bootstrapping and a module of algorithm comparison. weka also can be run in other programming languages as perl or r. python (programming language) image analysis, deep-learning, machine-learning sql databases nosql numpy numerical python scipy sagemath lapack linear algebra matlab apache hadoop apache spark amazon web services scope and training programs almost all educational programmes in biostatistics are at postgraduate level. they are most often found in schools of public health, affiliated with schools of medicine, forestry, or agriculture, or as a focus of application in departments of statistics. in the united states, where several universities have dedicated biostatistics departments, many other top-tier universities integrate biostatistics faculty into statistics or other departments, such as epidemiology. thus, departments carrying the name "biostatistics" may exist under quite different structures. for instance, relatively new biostatistics departments have been founded with a focus on bioinformatics and computational biology, whereas older departments, typically affiliated with schools of public health, will have more traditional lines of research involving epidemiological studies and clinical trials as well as bioinformatics. in larger universities around the world, where both a statistics and a biostatistics department exist, the degree of integration between the two departments may range from the bare minimum to very close collaboration. in general, the difference between a statistics program and a biostatistics program is twofold: (i) statistics departments will often host theoretical/methodological research which are less common in biostatistics programs and (ii) statistics departments have lines of research that may include biomedical applications but also other areas such as industry (quality control), business and economics and biological areas other than medicine. specialized journals biostatistics international journal of biostatistics journal of epidemiology and biostatistics biostatistics and public health biometrics biometrika biometrical journal communications in biometry and crop science statistical applications in genetics and molecular biology statistical methods in medical research pharmaceutical statistics statistics in medicine