A Whole Genome Scan for Quantitative Trait Loci Affecting Milk Protein Percentage in Israeli-Holstein Cattle, by Means of Selective Milk DNA Pooling in a Daughter Design, Using an Adjusted False Discovery Rate Criterion

Abstract Selective DNA pooling was employed in a daughter design to screen all bovine autosomes for quantitative trait loci (QTL) affecting estimated breeding value for milk protein percentage (EBVP%). Milk pools prepared from high and low daughters of each of seven sires were genotyped for 138 dinucleotide microsatellites. Shadow-corrected estimates of sire allele frequencies were compared between high and low pools. An adjusted false discovery rate (FDR) method was employed to calculate experimentwise significance levels and empirical power. Significant associations with milk protein percentage were found for 61 of the markers (adjusted FDR = 0.10; estimated power, 0.68). The significant markers appear to be linked to 19–28 QTL. Mean allele substitution effects of the putative QTL averaged 0.016 (0.009–0.028) in units of the within-sire family standard deviation of EBVP% and summed to 0.460 EBVP%. Overall QTL heterozygosity was 0.40. The identified QTL appear to account for all of the variation in EBVP% in the population. Through use of selective DNA pooling, 4400 pool data points provided the statistical power of 600,000 individual data points.

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Quantitative Trait Locus Mapping in Dairy Cattle by Means of Selective Milk DNA Pooling Using Dinucleotide Microsatellite Markers: Analysis of Milk Protein Percentage

Genetics ◽

10.1093/genetics/149.3.1557 ◽

1998 ◽

Vol 149 (3) ◽

pp. 1557-1567 ◽

Cited By ~ 6

Author(s):

Ehud Lipkin ◽

Mathias O Mosig ◽

Ariel Darvasi ◽

Ephraim Ezra ◽

A Shalom ◽

...

Keyword(s):

Quantitative Trait Locus ◽

Dairy Cattle ◽

Quantitative Trait ◽

Statistical Power ◽

Milk Protein ◽

Dna Pooling ◽

Marker Allele ◽

Protein Percentage ◽

Trait Locus ◽

Selective Dna Pooling

Abstract “Selective DNA pooling” accomplishes quantitative trait locus (QTL) mapping through densitometric estimates of marker allele frequencies in pooled DNA samples of phenotypically extreme individuals. With poly(TG) microsatellites, such estimates are confounded by “shadow” (“stutter”) bands. A correction procedure was developed on the basis of an observed linear regression between shadow band intensity and allele TG repeat number. Using this procedure, a selective DNA pooling study with respect to milk protein percentage was implemented in Israel-Holstein dairy cattle. Pools were prepared from milk samples of high and low daughters of each of seven sires and genotyped with respect to 11 markers. Highly significant associations with milk protein percentage were found for 5 of the markers; 4 of these markers confirmed previous reports. Selective DNA pooling accessed 80.6 and 48.3%, respectively, of the information that would have been available through individual selective genotyping or total population genotyping. In effect, the statistical power of 45,600 individual genotypings was obtained from 328 pool genotypings. This methodology can make genome-wide mapping of QTL accessible to moderately sized breeding organizations.

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Application of the False Discovery Rate to Quantitative Trait Loci Interval Mapping With Multiple Traits

Genetics ◽

10.1093/genetics/161.2.905 ◽

2002 ◽

Vol 161 (2) ◽

pp. 905-914 ◽

Cited By ~ 1

Author(s):

Hakkyo Lee ◽

Jack C M Dekkers ◽

M Soller ◽

Massoud Malek ◽

Rohan L Fernando ◽

...

Keyword(s):

Quantitative Trait Loci ◽

False Discovery Rate ◽

Error Rate ◽

Quantitative Trait ◽

Interval Mapping ◽

Error Rates ◽

Multiple Traits ◽

Test Statistic ◽

False Discovery ◽

Trait Loci

Abstract Controlling the false discovery rate (FDR) has been proposed as an alternative to controlling the genomewise error rate (GWER) for detecting quantitative trait loci (QTL) in genome scans. The objective here was to implement FDR in the context of regression interval mapping for multiple traits. Data on five traits from an F2 swine breed cross were used. FDR was implemented using tests at every 1 cM (FDR1) and using tests with the highest test statistic for each marker interval (FDRm). For the latter, a method was developed to predict comparison-wise error rates. At low error rates, FDR1 behaved erratically; FDRm was more stable but gave similar significance thresholds and number of QTL detected. At the same error rate, methods to control FDR gave less stringent significance thresholds and more QTL detected than methods to control GWER. Although testing across traits had limited impact on FDR, single-trait testing was recommended because there is no theoretical reason to pool tests across traits for FDR. FDR based on FDRm was recommended for QTL detection in interval mapping because it provides significance tests that are meaningful, yet not overly stringent, such that a more complete picture of QTL is revealed.

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