#quantitative genetics

[differential]

The bottom line is that evolutionary theory sheds light on how empirically successful methods in statistical genetics operate. It further simplifies those models to achieve better interpretability and scalability by leaving only biologically relevant parts of the model.

— Hanbin Lee

[owlservice]

[differential]

Why have we encountered so much apparent misunderstanding of the methods and results in the human genetics community? The core of our method is heavily steeped in the tradition of prediction of random effects and the estimation of variance due to random (latent) effects. While estimation and partitioning of variance has a long history in human genetics, in particular in twin research, the prediction of random effects is alien to many human geneticists and, surprisingly, also to statisticians.

Visscher, Yang, Goddard. A Commentary on ‘Common SNPs Explain a Large Proportion of the Heritability for Human Height’ by Yang et al. (2010).

[awkwardtypo]

Someone come explain quantitative genetics to me in the next 12 hours

How do I estimate Var(G) when given only variance of inbred (homozygous) and natural populations??? And how does that relate to broad-sense heritability???

I Should Have Taken Statistics

This Textbook Has A Lot of Math In It Please Help

[silverbulletsama]

sorry to be nosy, I was just wondering which field of genetics you specialise in? the subject is so complex and seems very fascinating!

The field of genetics has 4 broad areas/fields of study:

Mendelian Genetics

Molecular Genetics

Population Genetics

Quantitative Genetics

# 3 and # 4 mostly deal with numbers, algorithms, complex mathematical concepts, software and programming. They aim to trace back certain alleles, mutations and behaviors using numbers. They can predict when a certain genetic disease came into being… up to a specific year and month.

I took courses about these 2 fields, mainly Population Genetics, as I also worked as a programmer during high school and as a freelancer.

#1 is basically historical concepts in genetics and stuff people learn at school. The field is not very engaged at the moment.

#2 is the field I am following the most. It uses aspects of fields 1,3, and 4. However, there are tons of topics within this field. It is the most engaged genetics field out of the 4, as well as the busiest as well.

My current research project is about HIV - the virus that causes AIDs. The molecular aspects of its RNA-DNA-Protein interactions and mutants… how the virus can develop new strategies to invade the system (Humans) in spite of having different genetic mutations that render it, in theory, inactive.. yet it still functions regardless.

You can read about the field as a whole, in a basic language here [x]You can read more about Population Genetics, which is a field I am greatly interested in on here [x]

If you are interested in specific topics within any of these fields, I can share more important links, diagrams, videos, illustrations, and animations ;w;

[differential]

there are delicate inferential problems when _n_ < _p_ that animal breeders often ignore in a somewhat naïve quest to unravel genetic architecture via statistical modeling

"A contradiction is that the basic model of quantitative genetics assumes random genotypes and fixed substitution effects, but whole-genome prediction models use realized genotypes (thus fixed) and random marker effects. This contradiction must be kept in mind for properly interpreting concepts such as genomic heritability and genomic correlations between traits"

Gianola, D., & Rosa, G. J. M. (2015). One Hundred Years of Statistical Developments in Animal Breeding. Annual Review of Animal Biosciences, 3(1), 19–56.

[romannzx-blog]

Francis Galton: Pioneer of Heredity and Biometry

Francis Galton: Pioneer of Heredity and Biometry If not for the work of his half cousin Francis Galton, Charles Darwin's evolutionary theory might have met a somewhat different fate. In particular, with no direct evidence of natural selection and no convincing theory of heredity to explain it, Darwin needed a mathematical explanation of variability and heredity. Galton's work in biometry -- the application of statistical methods to the biological sciences -- laid the foundations for precisely that. This book offers readers a compelling portrait of Galton as the "father of biometry," tracing the development of his ideas and his accomplishments, and placing them in their scientific context.Though Michael Bulmer introduces readers to the curious facts of Galton's life -- as an explorer, as a polymath and member of the Victorian intellectual aristocracy, and as a proponent of eugenics -- his chief concern is with Galton's pioneering studies of heredity, in the course of which he invented the statistical tools of regression and correlation. Bulmer describes Galton's early ambitions and experiments -- his investigations of problems of evolutionary importance (such as the evolution of gregariousness and the function of sex), and his movement from the development of a physiological theory to a purely statistical theory of heredity, based on the properties of the normal distribution. This work, culminating in the law of ancestral heredity, also put Galton at the heart of the bitter conflict between the "ancestrians" and the "Mendelians" after the rediscovery of Mendelism in 1900. A graceful writer and an expert biometrician, Bulmer details the eventual triumph of biometrical methods in the history of quantitative genetics based on Mendelian principles, which underpins our understanding of evolution today.