# BIGFAM - variance components analysis from relatives without genotype

## TL;DR

BIGFAM is a new method that estimates genetic and environmental influences on traits using only phenotype data from relatives, without needing genotype information.

## Contribution

BIGFAM introduces a genotype-free approach to estimate variance components, including X chromosome effects, using only relative pairs' phenotypes.

## Key findings

- BIGFAM estimates show high correlation with genotype-based methods (r = 0.85 for heritability and 0.64 for X chromosome components).
- Strong shared environmental effects in dietary-related phenotypes were identified using BIGFAM.

## Abstract

Estimating variance components of phenotypes provides a fundamental basis for understanding complex traits. However, most existing methods require genotype data, which is costly to obtain and often unavailable, limiting their scalability. To address this limitation, we developed BIGFAM, a genotype-free framework that estimates variance components by genetic, shared environmental, and X chromosome effects using only phenotype data from relative pairs. We analyze variance components in Generation Scotland and UK Biobank datasets and demonstrate that BIGFAM’s estimates show high correlation with genotype-based methods (\documentclass[12pt]{minimal}
\usepackage{amsmath}
\usepackage{wasysym}
\usepackage{amsfonts}
\usepackage{amssymb}
\usepackage{amsbsy}
\usepackage{mathrsfs}
\usepackage{upgreek}
\setlength{\oddsidemargin}{-69pt}
\begin{document}$$r$$\end{document}r = 0.85 for heritability and 0.64 for X chromosome components). We identify strong nuclear-family-specific shared environmental effects in dietary-related phenotypes. These results establish a new approach for analyzing variance components across diverse populations without the need for genetic data.

Here the authors reveal a genotype-free framework termed BIGFAM that estimates variance components by genetic, shared environmental, and X chromosome effects using only phenotype data from relative pairs thus providing a practical option for variance-component analysis in cohorts where dense genotype data are unavailable.