SNP variants as expressed in MAF in three French dairy breeds.

The minor allele frequency of SNP variants in three French dairy breeds.</p

Summary of GWAS significant SNP and breed overlap for 12 traits in three French dairy breeds.

<p>Summary of GWAS significant SNP and breed overlap for 12 traits in three French dairy breeds.</p

Most significant SNP per chromosome associated with udder depth/development and mapping a gene.

<p>Most significant SNP per chromosome associated with udder depth/development and mapping a gene.</p

Top five clusters of transcription factors/genes and associated GO terms associated with udder morphology in three French dairy breeds.

<p>Top five clusters of transcription factors/genes and associated GO terms associated with udder morphology in three French dairy breeds.</p

Heritability (diagonal), genetic¹ (upper-diagonal), and SNP² (lower-diagonal) correlations of udder conformation, milk production and health traits in three French dairy breeds.

<p>Heritability (diagonal), genetic<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0199931#t004fn001" target="_blank">¹</a> (upper-diagonal), and SNP<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0199931#t004fn002" target="_blank">²</a> (lower-diagonal) correlations of udder conformation, milk production and health traits in three French dairy breeds.</p

Characteristics of udder conformation, production and health traits in three French dairy breeds.

Characteristics of udder conformation, production and health traits in three French dairy breeds.</p

Summary of GWAS for udder related traits in three French dairy cattle breeds.

<p>Summary of GWAS for udder related traits in three French dairy cattle breeds.</p

Manhattan plots for the key trait: Udder depth (Holstein and Normande) or development (Montbeliarde breed).

<p>Manhattan plots for the key trait: Udder depth (Holstein and Normande) or development (Montbeliarde breed).</p

Ten candidate gene-GO term interactions with percentage association in three French dairy breeds.

<p>Ten candidate gene-GO term interactions with percentage association in three French dairy breeds.</p

A system-based analysis of the genetic determinism of udder conformation and health phenotypes across three French dairy cattle breeds

<div><p>Using GWAS to identify candidate genes associated with cattle morphology traits at a functional level is challenging. The main difficulty of identifying candidate genes and gene interactions associated with such complex traits is the long-range linkage disequilibrium (LD) phenomenon reported widely in dairy cattle. Systems biology approaches, such as combining the Association Weight Matrix (AWM) with a Partial Correlation in an Information Theory (PCIT) algorithm, can assist in overcoming this LD. Used in a multi-breed and multi-phenotype context, the AWM-PCIT could aid in identifying udder traits candidate genes and gene networks with regulatory and functional significance. This study aims to use the AWM-PCIT algorithm as a post-GWAS analysis tool with the goal of identifying candidate genes underlying udder morphology. We used data from 78,440 dairy cows from three breeds and with own phenotypes for five udder morphology traits, five production traits, somatic cell score and clinical mastitis. Cows were genotyped with medium (50k) or low-density (7 to 10k) chips and imputed to 50k. We performed a within breed and trait GWAS. The GWAS showed 9,830 significant SNP across the genome (p < 0.05). Five thousand and ten SNP did not map a gene, and 4,820 SNP were within 10-kb of a gene. After accounting for 1SNP:1gene, 3,651 SNP were within 10-kb of a gene (set1), and 2,673 significant SNP were further than 10-kb of a gene (set2). The two SNP sets formed 6,324 SNP matrix, which was fitted in an AWM-PCIT considering udder depth/ development as the key trait resulting in 1,013 genes associated with udder morphology, mastitis and production phenotypes. The AWM-PCIT detected ten potential candidate genes for udder related traits: <i>ESR1</i>, <i>FGF2</i>, <i>FGFR2</i>, <i>GLI2</i>, <i>IQGAP3</i>, <i>PGR</i>, <i>PRLR</i>, <i>RREB1</i>, <i>BTRC</i>, and <i>TGFBR2</i>.</p></div